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Investigating the Clostridium botulinum neurotoxin
production process using a genome-scale metabolic network
enhanced surrogate system
Daniel Christopher Griffin
Thesis submitted August 2015 to the University of Surrey for the
degree of Doctor of Philosophy
University of Surrey, Microbial Sciences, Guildford, Surrey, United Kingdom
© Daniel Griffin, 2015
“Strength without determination means nothing, and determination without strength is
equally useless”
- Godo Kisaragi
Acknowledgements
First and foremost, I would like to thank my principal supervisor Professor Michael Bushell
for his guidance, mentoring, expertise and friendship throughout the entirety of this
research project and I wish him a long and happy retirement.
I am very grateful to Dr Noel Wardell, Dr Jane Newcombe, Dr Douglas Hodgson, Dr Claudio
Avignone-Rossa and Jonathan Ridgeon for their ever willingness to offer technical advice,
support, training and valuable scientific opinion. My appreciations to Sonal Dahale for
constructing the genome-scale metabolic networks utilised in my research.
Thank you to the project supervisors at Ipsen, Dr Martin Mewies and Dr Mike Burns, for
your professional input and support. I am also grateful to Ipsen for funding this PhD
research project.
Lastly, a massive thank you to my beautiful partner Jessica, for her encouragement and
support throughout.
i
Abstract
Clostridium botulinum (C. botulinum) produces a neurotoxin which can be
used in a clinical environment to treat diseases and disorders characterised by muscle
hypertension or spasm. However, previous research has mostly focused on the biochemical
mode of action of the toxin and the disease it manifests. In order to increase our
understanding of the process further, this study aimed to investigate the metabolism of
various biomarkers, thought to be correlated with neurotoxin biosynthesis. The objective
was to increase our understanding of the metabolism which drives the production of C.
botulinum toxin using a Genome-Scale Metabolic Network (GSMN) enhanced surrogate
system. A linear correlation was established between the accumulation of intracellular
Poly-β-hydroxybutyrate (PHB) and neurotoxin in silico (R2 = 0.988). This correlation was
confirmed by chemostat experiments in C. sporogenes demonstrating that increased supply
of gaseous carbon dioxide (CO2) to the culture results in increased accumulation of PHB and
in silico neurotoxin in C. botulinum. Experiments revealed the correlation is a result of
modulation of carbon flux partitioning between glycolysis and the TCA cycle, ultimately
increasing the availability of carbon for storage as PHB. Phosphate limitation and
supplementation with Homoserine and other oxaloacetate derived amino acids, gave rise to
increased PHB, owing to reduced activity and/or demand of the TCA cycle increasing the
availability of acetyl CoA, the energy storage polymer’s precursor. Altering the growth
medium to decrease TCA activity also resulted in decreased flagellin biosynthesis. The
results of this study can be used to design a C. botulinum production process based on
experimentally proven correlations and pathway analysis to yield a process which promotes
neurotoxin biosynthesis over competing pathways, such as flagellin biosynthesis.
ii
Abbreviations
BDM – Basal defined medium
CMM – Cooked meat medium
EMP - Embden-Meyerhoff-Parnas pathway
FBA – Flux balance analysis
FVA – Flux variance analysis
G6PD – Glucose-6-Phospate Dehydrogenase
GMP – Good Manufacturing Practice
GSMN – Genome-Scale Metabolic Network
HPLC - High-Performance Liquid Chromatography
MSC – Microbial Safety Cabinet
NCBI - National Centre for Biotechnology Information
PBD – Plackett-Burman Design
PBS – Phosphate Buffer Solution
PEPc – Phosphoenolpyruvate Carboxylase
PHB - Poly-β-hydroxybutyrate
SNAP - Synaptosomal-associated protein
TCA – tricarboxylic acid cycle
iii
Table of Contents
Prologue
Acknowledgments
i
Abstract
ii
Abbreviations
iii
Table of Contents
iv
Chapter 1: Introduction
1.1 The significance of Clostridium
1
1.1.1 Clinical significance
1
1.2 Clostridium botulinum and disease
4
1.3 Botulinum Toxin: Structure & Mechanism of Action
6
1.4 Botulinum Toxin: Commercial Production
10
1.5 Methodologies in Process Development & Optimisation
12
1.5.1 Availability of Metabolites in the Bacterial Growth Medium
12
1.5.2 Statistical Methods for Process Optimisation: Plackett-Burman
Experimental Design
15
1.5.3 Strain Selection & Bioengineering
17
1.5.4 Exploiting Biomarkers of Product Biosynthesis
18
1.5.4.1 Sporulation
18
1.5.4.2 Poly-β-hydroxybutyrate Metabolism
19
1.5.4.3 Flagellin Biosynthesis
21
1.5.5 Surrogate Research Approach: Clostridium sporogenes
23
1.6 Research Tools: Genome-Scale Metabolic Networks
25
1.6.1 Flux Balance Analysis
27
1.6.2 Flux Variability Analysis
28
1.7 Research Tools: Using Chemostat Culture in Physiological Investigations
29
iv
1.8 Impact, Aims & Objectives
31
Chapter 2: Methods & Materials
2.1 Clostridium sporogenes strains and working stock preparation
33
2.2 Culture Medium
35
2.3 Determination of Culture Growth by Optical Density
37
2.4 Determination of Biomass by Dry Cell Weight Measurement
37
2.5 Determination of Intracellular PHB Accumulation
38
2.6 Determination of Supernatant Flagellin
40
2.6.1 Bis/Polyacrylamide Gels & SDS-PAGE Reagents
42
2.7 Determination of Sporulation
43
2.8 Plackett-Burman Design
44
2.10 Determination of Nutrient Concentration
46
2.10.1 Glucose Assay
46
2.10.2 Ammonium Assay
46
2.10.3 Phosphate Assay
47
2.11 Determination of Enzyme Activity
48
2.11.1 Glucose-6-Phosphate Dehydrogenase Activity Assay
48
2.11.2 Citrate Synthase Activity Assay
49
2.11.3 Phosphoenolpyruvate Carboxylase Activity Assay
50
2.12 Chemostat Culture
52
2.13 Determination of Protein Concentration
54
2.14 RNA Assay
54
2.15 Amino Acid Determination
56
2.16 Genome-Scale Metabolic Network Analysis
57
2.16.1 Construction of C. sporogenes & C. botulinum GSMN
57
2.16.2 Flux Balance Analysis
57
2.16.3 Flux Variability Analysis
58
v
Chapter 3: Validation of the surrogate system – Investigation of metabolism
and biomarkers of neurotoxin biosynthesis in Clostridium sporogenes.
3.1 Strain Selection
59
3.2 Bioinformatic Comparison of C. sporogenes & C. botulinum
65
3.3 The Effect of Carbon, Nitrogen and Phosphate limitation on Growth
& Biomarker Metabolism in Cultures of C. Sporogenes
67
3.3.1 The Effect of Phosphate Concentration on the Growth of C. sporogenes
69
3.3.2 The Effect of Nitrogen Concentration on the Growth of C. sporogenes
73
3.3.3 The Effect of Carbon Concentration on Growth of C. sporogenes
76
3.3.4 The Effects of Nutrient Limitation on Sporulation, PHB and
Flagellin Production in Cultures of C. sporogenes
80
3.3.4.1 The Effect of Phosphate Concentration on Biomarker
Production in Cultures of C. sporogenes
81
3.3.4.2 The Effect of Nitrogen Concentration on Biomarker Production in
Cultures of C. sporogenes
85
3.3.4.3 The Effect of Carbon Concentration on Biomarker Production
in Cultures of C. sporogenes
89
3.3.5 Summary of the Effects of Nutrient Limitation on Sporulation, PHB
and Flagellin Production in Cultures of C. sporogenes
94
3.4 Plackett-Burman Design Experimental Approach to Test the Effects of
Amino Acid Metabolism on Flagellin Production, PHB Accumulation
and Sporulation in Cultures of C. sporogenes
97
3.5 In silico Analysis Investigating the Effects of Nitrogen, Glucose and
Phosphate Concentration on Neurotoxin Production by C. botulinum
109
3.6 In silico Analysis of the Correlation between PHB accumulation and
Neurotoxin Production by C. botulinum
114
3.7 Chapter Conclusions
120
vi
Chapter 4: Assimilating Computational and Experimental Research Tools to Investigate the
Correlation between Poly-β-hydroxybutyrate and Botulinum Neurotoxin
4.1 Extrapolating PBD results using Flux Variability Analysis
121
4.2 Increasing PHB yields using a targeted amino acid supplementation
approach
127
4.2.1 The relationship between TCA cycle - derived amino acids
and PHB accumulation
135
4.3 Investigating the Relationship between PHB Accumulation
and Pathways of Central Metabolism using Enzymatic Assay
138
4.4 Plackett-Burman Design Experimental Approach to Test the Effects
of Amino Acid Metabolism on Enzymatic Activity in relationship
to PHB Accumulation in Cultures of C. sporogenes
147
4.5 Investigating Anaplerotic Reactions as a Process Development
Target using Genome Scale Metabolic Modelling
155
4.6 Chapter Conclusions
159
Chapter 5: Investigating the Correlation between Carbon Dioxide Uptake, PHB
Accumulation and Neurotoxin Biosynthesis Using Chemostat culture
5.1 The effect of Growth Rate & Increased Carbon Dioxide Concentration
on PHB Accumulation in Continuous Cultures of C. sporogenes
162
5.2 Validation of Bicarbonate as an Alternative Supplementation
Approach to Altering Carbon Dioxide Concentration
171
5.3 The Effect of Growth Rate & Increased Carbon Dioxide Concentration
on Flagellin Biosynthesis in Continuous Cultures of C. sporogenes
173
5.4 The Effect of Growth Rate & Increased Carbon Dioxide Concentration
on Sporulation in Continuous Cultures of C. sporogenes
181
vii
5.5 The Effect of Increased Carbon Dioxide Concentration on
Nutrient Metabolism in Continuous Cultures of C. sporogenes
185
5.6 Validation of Genome-Scale Metabolic Network using Flux Data
obtained by Chemostat Culture
191
5.7 Chapter Conclusions
193
Chapter 6: Conclusions
6.1 Project Conclusions & Achievements
195
6.2 Research Impact
198
6.3 Process Recommendations for the Botulinum Neurotoxin
Production Process
198
6.4 Recommendations for Future Studies
200
Appendix
202
Bibliography
203
viii
Chapter 1: Introduction
1.1 The Significance of Clostridium
Clostridium is a genus of bacteria whose products support a diverse range of industries
including medicine, biofuels, synthetic chemicals and cosmetics (Arnon, 2002; Hallett, 1999).
Clostridial products have attained significance in disease and even biological warfare; driving
scientific research of the bacteria and their related biosynthetic products. The complex
metabolism, toxins and products synthesised by the genus, combined with our ability to
utilise and wield Clostridia as an industrial tool has yielded a genus of bacteria which is both
harmful and beneficial to modern man.
1.1.1 Clinical Significance
The clinical significance of Clostridia is far greater than is generally recognised. Whilst
Clostridium botulinum (C. botulinum) and Clostridium tetani (C. tetani) are widely known,
due to the lethality of their respective neurotoxins, clostridia related disease is both more
diverse among the species and clinically widespread. Clostridium perfringens (C. perfringens)
is a ubiquitous member of the species with natural habitats including marine sediment, soil,
vegetation and the commensal population of humans and other vertebrates (Hendrix et al,
2011). Despite only ~5% of C. perfringens strains being considered pathogenic to humans
(Decker & Hall, 1966), the species is one of the most significant causes of foodborne illness
in the developed world with an estimated one million cases every year in the United States
and is the third most common causative agent of foodborne illness in the United Kingdom
(Grass et al, 2013; Scallan et al, 2011).
1
In common with other disease-causing Clostridium species, C. perfringens produces several
toxins (over 20 exotoxins in certain strains) which are responsible for illness in humans. The
most clinically significant of C. perfringens toxins, α-toxin, is the most common cause of gas
gangrene; a life-threatening disease characterised by fever, edema, myonecrosis, pain and
gas production (Sakurai et al, 2004). C. perfringens thrives in the anaerobic environment of
necropsied tissue (Hendrix et al, 2011). The α-toxin protein biosynthesised during
fermentation consists of two domains; a binding domain (C-domain) and action domain (Ndomain) which requires zinc for activation, similar to Botulinum neurotoxin (Lacy et al, 1998;
Sakurai et al 2004). The toxin’s action on cells results in the cleavage of Phosphatidylcholine
(major component of membrane phospholipids), disrupting the plasma membrane which
ultimately results in cellular death and thus the tissue degradation which characterises
infection (Ochi et al, 2002). C. perfringens, among other members of the clostridia species, is
also a major cause of septic abortion (Rello et al, 2007).
Clostridium difficile (C. difficile) is another member of the genus which has attained clinical
importance. Despite being a very common gut commensal in humans, the opportunistic
pathogen is the most common cause of antibiotic associated diarrhoea (Noren, 2010). In
common with many Clostridia, C. difficile is abundant in soils and water and asymptomatic
colonisation is frequent. Following antibiotic treatment and disruption of the normal gut
microflora, C. difficile can proliferate considerably owing to diminished microbial
competition (Novogrudsky & Plaut, 2003). Two primary toxins (Type A & B), produced at
significant quantities to cause disease by the increased C. difficile population, target the Ras
superfamily of small GTPases for modification via glycosylation, inducing irreversible
2
modification of the colon cells which results in the symptoms of disease (Voth & Ballard,
2005). The family of GTPases is also the target of bacterial toxins produced by Clostridium
sordelli and Clostridium novyi; categorised with C. difficile type A & B toxins amongst the
largest toxins discovered to date (~300kDa) (Voth & Ballard, 2005). C. difficile toxins have
also been correlated with pseudomembranous colitis and paralytic ileus disease (George et
al, 1982).
C. tetani is the causative agent of tetanus; a disease which has been documented
throughout history with records dating back to early Greek physicians over 2000 years ago
(Humeau et al, 2000). The characteristics of the disease are very recognisable; severe and
debilitating spastic paralysis as a result of motor neuron disinhibition (Curtis & Groat, 1968).
Despite the disease manifesting opposite symptoms to the flaccid paralysis induced by C.
botulinum, both conditions are the result of the remarkably similar (in terms of structure
and function) neurotoxins produced by the respective organisms (Humeau et al, 2000).
Tetanospasmin is produced and enters the body via a wound which is colonised by C. tetani.
The neurotoxin is produced as a single 150kDa protein which is proteolytically cleaved into a
light chain (50kDa) and a heavy chain (100kDa) linked by both a disulphide bridge and noncovalent interactions. During intoxication process, the interchain bridge is reduced; a
necessary prerequisite for the intracellular action of the toxin (Hilger et al, 1989; Humeau et
al, 2000). Tetanospasmin targets synaptobrevin, a protein also targeted by Botulinum toxin
produced by serotypes B, D, F & G (Turton et al, 2002; Wictome & Shone, 1998). However,
whilst botulinum toxin affects the neuromuscular junction resulting in inhibition of
neurotransmitter release, tetanospasmin acts on the spinal inhibitory interneurons
3
(Montecucco & Schiavo, 1994). This blocking of central nervous system neurons interferes
with the release of inhibitory neurotransmitter release and therefore results in the spastic
paralysis characteristic of the disease (Bhum et al, 2012).
In more recent years, the heavy chain of tetanospasmin has become an interesting
medicinal target in the treatment of psychological disorders, such as Alzheimer’s and
Parkinson’s disease, owing to its ability to penetrate the central nervous system (Toivonan
et al, 2010). As the protein is proteolytically cleaved from the light chain which is
responsible for the neurotoxic affects resulting in disease, safe neuronal targeting using the
heavy chain of the tetanus toxin shows promise (Toivonan et al, 2010). However, it is
Botulinum toxin which has become established as a clostridial medicine of substantial
clinical importance (Hallett, 1999; Sifton 2003).
1.2 Clostridium botulinum and Disease
Clostridium botulinum (C. botulinum) is a Gram positive, spore-forming, anaerobic bacterium
which has attained its significance in the field of microbiology due to its ability to produce
Botulinum neurotoxin; the causative agent of botulism and the most potent neurotoxin
discovered to date. C. botulinum has been subject to medical research owing to the severity
of botulism disease, but it is the mode of action of botulinum toxin which has also led to
interest and research in utilising the neurotoxin for clinical medicine, cosmesis and even
biological warfare (Arnon, 2002; Hallett, 1999).
4
Records of botulism disease date back to the late 1700s and details of the bacterial agent
and toxicological mechanism of action responsible for botulism disease were published by
Emile van Ermengem at the end of the 19th century (van Ermengem, 1897) after thorough
investigation of an outbreak of botulism in Ellezelles, Belgium. Today, the disease is
categorised into 5 clinical forms; foodborne botulism, wound botulism, infant botulism,
hidden botulism and inadvertent botulism (Smith, 1979), all of which are serious lifethreatening diseases. The ability of C. botulinum to produce resilient spores is a major factor
contributing to the organism’s virulence. The spores are widespread throughout the world,
inhabiting soil as well as both fresh and salt water. The spores are capable of surviving for
up to 2 hours at temperatures of 100oC. However, the neurotoxin responsible for disease is
heat labile and is deactivated by holding for 5 minutes at 85 oC or higher (Dunbar et al,
1990).
C. botulinum is phylogenetically categorised based on the distinct antigens of the
neurotoxins it produces. Seven original serotypes; A, B, C-α, D, E, F, G have been described,
as well as a more recent 8th serotype, C-β. However, this serotype produces C2-toxin which
is neither a neurotoxin nor a causative agent of botulism (Hatheway, 1998). As well as being
categorised on the distinct neurotoxin antigens, the serotypes of C. botulinum can be
grouped by genetic differences. Toxin types A, B and F are coded for by genes located on
clostridial chromosome material. The genes responsible for production of neurotoxins C, D
and E are carried by bacteriophages and the gene which codes for the production of type G
toxin is present on a plasmid (Hatheway, 1998). The majority (98%) of human botulism cases
are caused by serotypes A, B and E (Dolman & Lida, 1963; Popoff, 1995). Types C, D and F
are predominantly associated with botulism in animals (especially birds), although rare
5
cases have been described in humans (Green et al, 1983; Moller & Scheibel, 1960; Prevot et
al, 1955). It is still unclear whether type G toxin is able to cause disease in humans due to a
lack of evidence, although it has been reported previously in a case which caused the
sudden death of 5 humans (Sonnabend et al, 1981). In addition to the focus on the
significant pathology of botulinum toxin, modern medicine is increasingly making positive
use of botulinum toxin for treatment of disorders characterised by muscle hyperactivity
and/or spasm, including the treatment of blepharospasm, strabismus, cervical dystonia,
glabellar lines, spastic dysphonia, limb spasticity associated with underlying neurological
problems, tremors, hyperhidrosis and many others currently under development (Hallett,
1999; Sifton 2003). It is the neurotoxic mechanism of botulinum toxin which enables it to be
utilised for such clinical applications.
1.3 Botulinum Toxin: Structure & Mechanism of Action
Regardless of serotype, the structure of botulinum toxin is a di-chain peptide molecule with
a molecular mass of approximately 150kDa (Crane et al, 1999). The neurotoxin is produced
and secreted (Wang et al, 2010) as a single inactive polypeptide which is proteolytically
cleaved by a clostridial trypsin protease forming the di-chain peptide consisting of a heavy
chain (100kDa) and a light chain (50kDa) linked by a disulphide bond (Figure 1). The heavy
chain is responsible for binding target neural tissue and the light chain is directly responsible
for the neurotoxic effects of the toxin (Lacy et al, 1998; Wictome & Shone, 1998). Botulinum
toxin is a metalloproteinase, a class of proteases for which the catalytic mechanism of the
enzyme requires a metal ion. Remarkably similar in terms of structure and function to
tetanospasmin, the toxin produced by C. tetani, botulinum toxin enters nerve cells and
6
blocks neurotransmitter release by zinc-dependent cleavage of proteins required by the
neuroexocytosis apparatus (Lacy et al, 1998; Wictome & Shone, 1998).
Figure 1: The structure of Botulinum neurotoxin. Botulinum neurotoxin is composed of a
heavy chain (Hc) attached to a light chain (Lc) by a non-covalent disulfide bridge. The heavy
chain is divided in an amino region (Hn) and a carboxyl region (Hc). Hc is the binding
domain, Hn is the translocation domain and the Lc is the catalytic domain responsible for
the neurotoxin effects of the protein (Image adapted from Zhongxing et al, 2012)
The target protein affected differs between the different serotypes of toxins. Botulinum
toxin type A and E target the synaptosomal-associated protein 25 (SNAP-25), a t-SNARE
protein which is associated with the fusion of the synaptic vesicle to the plasma membrane
of the neuron. Toxin types B, D, F and G target the vesicle associated membrane protein
(VAMP) synaptobrevin, another SNARE protein involved in neuronal exocytosis. Toxin type C
targets both SNAP-25 and syntaxin, a membrane protein which forms the core SNARE
7
complex together with SNAP-25 and synaptobrevin (Wictome & Shone, 1998). Regardless of
target protein, toxin associated cleavage significantly disrupts the release of the
neurotransmitter acetylcholine (Figure 2).
Figure 2: The action of botulinum toxin at the axon terminal resulting in the prevention of
acetylcholine release. Following heavy chain (Hc) mediated absorption of the neurotoxin,
the light chain (Lc) targets neuronal proteins Synaptobrevin/VAMP, SNAP-25 and Syntaxin,
dependant on the serotype of toxin. Independent of the target protein, the assembly of the
SNARE complex required for acetylcholine exocytosis is disrupted (Image adapted from
Turton et al, 2002).
8
Botulinum toxin that has entered the body (most frequently via the gastrointestinal tract
after ingestion) is able to travel passively in the bloodstream owing to the biophysical
properties of the toxin (Hatheway, 1998). In its native state, botulinum toxin is bound to
chaperone proteins which greatly enhance the stability of the toxin, allowing it to reach
target tissues without being denatured (Hatheway, 1998). Although not fully characterised,
it is likely the heavy chain of the toxin functions as a chaperone to the light chain during
translocation (Brunger et al, 2007). Toxin is carried around the body via the bloodstream
binds to nerve-ending receptors, becomes internalised within the neuron, and causes an
irreversible blockade of cholinergic transmission of ganglionic synapses, post ganglionic
parasympathetic synapses and neuromuscular junctions; resulting in widespread flaccid
paralysis and potentially fatal autonomic nervous system disruptions (Maselli, 1998; Smith,
1979). It is the ability to exercise control of these symptoms, by controlled dosage and
administration, which has led to the neurotoxin becoming the interesting and valuable
clinical medicine it has become today.
9
1.4 Botulinum Toxin: Commercial Production
Alan Scott, a researcher at the Smith-Kettlewell Institute in California, first had the idea to
inject small doses of botulinum toxin into overactive muscles as a means of treating patients
with strabismus; a common condition characterised by a lack of coordination between the
extraocular muscles (Scott, 1980). The treatment proved successful and investigators began
testing the neurotoxin’s ability to treat other diseases and disorders characterised by
overactive contraction of muscle tissue. Early results were excellent for the treatment of
blepharospasm and hemifacial spasm, and the U.S Food and Drug administration (FDA)
approved botulinum toxin for treatment of the three conditions in 1989. Since its
introduction into the clinical market, the toxin is now approved for use in over 60 countries
and is used to treat over 50 medical conditions; as well as being widely used for cosmetic
purposes (Hallett, 1999; Münchau & Bhatia, 2000). This increase in demand led to the
requirement for industrial scale production of botulinum toxin.
Industrial production of botulinum toxin generally consists of a fermentation process in
which C. botulinum is cultured in a nutrient rich growth medium followed by various protein
harvesting and purification processes to ensure toxin samples attain the stringent quality
standards required by international regulatory agencies. The commercially produced
neurotoxin which is utilised in medical practices is actually a very dilute suspension of toxin,
containing only 0.44-0.73ng (depending on the product supplier/brand) of toxin per 100
dose vial (Frevert, 2010). Due to such low quantities of neurotoxin being effective for clinical
applications, there is currently no overbearing demand to increase production within the
botulinum toxin industry, however, due to the potency and lethality of the toxin, as well as
10
the fact the product must meet strict clinical specifications, the production process must be
predictable, reproducible, well characterised and comply with strict international good
manufacturing practice (GMP) standards. Current published processes for the production of
neurotoxin from C. botulinum rely on empirically developed media and procedures, rather
than an a priori appreciation of the underlying physiology of regulation of biosynthesis.
Several biomarkers have been identified as being associated with botulinum toxin
production which could provide valuable insight and increase our understanding of both C.
botulinum and the industrial production process. Research has also been published on the
effects of various metabolites on the production of botulinum toxin, however metabolically
defined biosynthesis physiology of its production remains incomplete and much remains to
be gained from process development focused research regarding industrial scale neurotoxin
production.
11
1.5 Methodologies in Process Development & Optimisation
Commercial process development and optimisation of microbial product synthesis can
encompass a wide spectrum of approaches, ranging from simple alterations in culture
conditions (such as the effect of pH) to complex production strain bioengineering, offering
potential improvements with regard to yield, process robustness, reduced costs and other
factors.
1.5.1 Availability of Metabolites in the Bacterial Growth Medium
Controlling the availability of metabolites by supplementing the growth medium of the
bacterial culture is a common process development approach. Adjustment of medium
formulation, and therefore the microbial environment, can have significant impacts on both
the quantity and diversity of secondary metabolites produced in culture (Van der Molen et
al, 2013). In the commercial environment, establishing and optimising growth conditions
can significantly increase the production of the compound of interest (Van der Molen et al,
2013). One of most researched groups of microbial products are the antibiotics; a group of
compounds which are typically produced in nature as a method to gain an advantage over
other organisms competing for the same environmental nutrient sources (Chater, 2006).
Secondary metabolism is triggered by nutrient limitations which activate biochemical
pathways resulting in the biosynthesis of secondary metabolites such as antibiotics and
toxins (Chater & Horinouchi, 2003).
Therefore, the determination and subsequent
replication of the conditions which trigger these events in nature by control of the available
nutrients in the growth medium can be an effective method of microbial product process
12
development and optimisation. In Streptomyces, a bacterial genus which is responsible for
the production of almost two thirds of known antibiotics (Butler et al, 2002), specific
macronutrient limitations have been correlated with gene regulation resulting in product
biosynthesis, with limitations in carbon, phosphate or nitrogen being the most common
controls (Bibb, 2005). Such understanding can offer an invaluable advantage in terms of
optimising product yield to an industrial process.
Other components of the growth medium, such as amino acids and vitamins, can also have
a considerable effect on a process. A substantial fraction of the energy budget of bacteria is
devoted to biosynthesis of amino acids (Akashi & Gojobori, 2002). The fuelling reactions of
central metabolism provide precursor metabolites for synthesis of the 20 amino acids
incorporated into proteins. Thus, synthesis of an amino acid entails a dual cost; energy is
lost by diverting chemical intermediates from fuelling reactions and additional energy is
required to convert precursor metabolites to amino acids. The range in amino acid
biosynthesis cost varies from 11 ATP equivalents per molecule of Glycine, Alanine, and
Serine to over 70 ATP per molecule of Tryptophan (Akashi & Gojobori, 2002).
Simultaneously, metabolism of amino acids is likely to effect the biosynthetic pathways of
microbial products and in many bacteria, including Clostridia, are essential for growth
(Karasawa et al, 1995). Therefore, the concentration and diversity of amino acids in the
growth medium can have a significant effect on the growth kinetics of a microbial process,
which in turn affects the biosynthesis of metabolism dependant compounds. Establishing
events which are associated with secondary metabolite production, such as the correlation
between sporulation and toxin production in C. perfringens and C. difficile (Kamiya et al,
13
1992; Mitchell, 2001) is also advantageous, as the process can be engineered to promote a
biological state to increase product yields.
14
1.5.2 Statistical Methods for Process Optimisation: Plackett-Burman Experimental Design
Multifactorial experiments are an effective method to assess the effects of numerous
influential factors on a production process, offering a broad-range approach to target
factors for more intricate extrapolation and optimisation. The Plackett-Burman design (PBD)
fractional experimental approach can prove an effective tool in industrial microbial process
optimisation. Owing to is design, PBD allows the investigation of a number of influential
factors with a smaller number of trials compared to full factorial design experiments
(Plackett & Burman, 1946). For example, to test 10 variables on two levels (210), a full
factorial design would require 1024 trials. Using PBD, investigating 10 variables would
require only 11 trials (n + 1) (Cordenunsi et al, 1985) (Table 1). Another advantage of PBD
over full factorial design is it allows the consideration of possible interactions between
factors (Kalil et al, 2000). When investigating the effect of 11 variables on a production
process, 12 trials with each variable tested at a low and a high value and at least three
dummy variables, also at low and high values, should be employed to estimate the
experimental error. The experimental error is generated by calculating the square root of
the variance of effect (See equation below) and a t test is used to determine the significance
of the data (Greasham & Inamine, 1986).
Calculating the variance of effect: Veff = ∑ (Ed)2 / n
Veff = variance of effect, Ed = the effect determined for the dummy variables, n = number of
dummy variables.
15
Trial
χ1
χ2
χ3
χ4
Variables (χ)
χ5 χ6 χ7 χ8
1
2
3
4
5
6
7
8
9
10
11
12
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
χ9
χ10
χ11
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
Table 1: PBD for 11 variables. Number of trials = n +1 (n = number of variables). + and –
represent input variables at a high (+) and low (-) value. The results are tested against at
least three dummy variables (theoretical data calculated from the predicted effects of
variables at a combination not actually used in the experiment), also input at a high and low
value, to test the experimental error. Statistical significance of the data can be tested using
a t-test (Greasham & Inamine, 1986).
An example of the PBD in practice, with regards to microbial process development, is testing
the observable effects of altering various medium components on microbial metabolism
and products. For example, the effects of 11 different amino acids on protein biosynthesis
or sporulation could be trialled using the design detailed in table 1, where ‘+’ would
represent the supplementation of a particular amino acid into the growth medium and ‘-‘
would represent no addition of the particular amino acid. As depicted in table 1, trial
number 12 would act as a negative control and contain no additional amino acids in this
example.
16
1.5.3 Strain Selection & Bioengineering
Genetic variation between the strains of a bacterial species can have diverse implications on
a microbial process. Members of the clostridia species, beyond the use of C. botulinum in
the pharmaceutical industry, are increasingly being utilised for a number of industrial
applications. Clostridium acetobutyricum (C. acetobutyricum) can synthesise ethanol,
acetone and butanol; all of which are commercially useful products (Lee et al, 2012). A
typical wildtype strain of C. acetobutyricum produces butanol, acetone and ethanol at a
ratio of 6:3:1 (Jones & Woods, 1986). Although fermentation of all three compounds is
feasible (ABE fermentation), ethanol can be produced more efficiently from yeast
fermentation (Pfromm et al, 2010), and if either butanol or acetone is the desired microbial
product, concurrent synthesis of the other two ultimately lowers the respective yield of
desired product per unit of mass substrate utilised. This caveat has become the basis of
acetogenic Clostridia bioengineering in more recent years in order to maximise the yield of
butanol or acetone (Papoutsakis, 2008). Classical strain development has typically relied on
random mutagenesis and subsequent screening to yield improved strains (Parekh et al,
2000), but modern bioengineering approaches can utilise targeted gene and pathway knock
outs, such as disrupting the acetate biosynthesis pathway in C. acetobutyricum to create an
optimal butanol producing strain (Lehmann et al, 2012). Different strains of a bacterial
species can also exhibit random metabolic variation without mutation or strain engineering,
resulting in different growth rates, tolerance and product yields (Aucamp et al, 2014). It is
therefore a vital element of process development to select a production strain which lends
its physiology to a processes advantage, in terms of optimal growth rate and desired
product yields for example.
17
1.5.4 Exploiting Biomarkers of Product Biosynthesis
A biomarker is an analyte which is indicative of a specific biological state or process and is
often measured to assess the metabolic state or pathological changes in an organism which
lead to the ability to cause disease (Aerts et al, 2011). Therefore identifying, assessing and
characterising the biomarkers which correlate with toxin production in C. botulinum is a
means of understanding the physiology of neurotoxin biosynthesis and offers potential for
targeted development of the industrial production process.
1.5.4.1 Sporulation
Clostridium species possess the ability to produce heat labile endospores (Dunbar et al,
1990). Spores are involved in host colonisation and persistence of bacteria in the
environment. In all spore forming bacteria, the first major morphological manifestation of
sporulation is an asymmetric division, which divides the sporulating cell into a larger mother
cell and a smaller forespore, which will eventually become the mature spore. Following
septum formation, the mother cell engulfs the forespore and a layer of peptidoglycan (the
spore cortex) is deposited between the inner and outer forespore membranes. This layer is
then encased by a protective proteinaceous coat followed by the release of the mature
spore (Stephenson & Lewis, 2005). The initiation of sporulation is dependent on the
phosphorylation of the transcription factor Spo0A; the master regulator of sporulation.
Phosphorylation of Spo0A occurs in response to environmental and physiological signals
such as nutrient deficiency, temperature and other stressors such as the presence of oxygen
in anaerobic bacteria (Barketi-Klai et al, 2004). Such environmental and physiological factors
18
are often associated with the production of secondary metabolites and a correlation has
been established between sporulation and toxin production in other members of the
Clostridial species, including C. perfringens and C. difficile (Kamiya et al, 1992; Mitchell,
2001). Previous studies indicate a possible correlation exists between sporulation and
neurotoxin excretion in C. botulinum. Early work (Bonventre and Kempe, 1960) suggests
that C. botulinum initiates autolysis as a mechanism to liberate toxin harbored inside the
cells, but also demonstrated toxin is excreted prior to autolysis or sporulation of the cells.
This is supported by more recent research (Artin et al, 2010), who reported the expression
of genes associated with neurotoxin production before sporulation-associated genes were
expressed in batch cultures of C. botulinum. Other studies (Cooksley et al, 2010) have shown
that C. botulinum and Clostridium sporogenes (C. sporogenes), considered the non-toxigenic
equivalent of C. botulinum, possess two distinct agr gene loci, encoding putative proteins
similar to those of the well-studied Staphylococcus aureus agr quorum sensing system. The
studies of Cooksley et al, 2010 (Cooksley et al, 2010) demonstrated that modulation of agr
gene expression drastically reduced both sporulation and neurotoxin production in
C. botulinum cultures. To summarise, the correlation between neurotoxin production and
sporulation in C. botulinum currently remains unclear. Experimentally establishing a link that
sporulation is a biomarker of toxin production would broaden our understanding of the
metabolism of C. botulinum and neurotoxin production.
1.5.4.2 Poly-β-hydroxybutyrate Metabolism
Poly-β-hydroxybutyrate (PHB) is another metabolite of interest concerning toxin production
in cultures of C. botulinum. PHB is utilised as an energy storage molecule by many bacteria.
19
Glucose is metabolised through the Embden-Meyerhoff-Parnas (EMP) pathway, yielding
pyruvate and acetate as the main products. Once glucose is exhausted, the acids are
oxidised via the tricarboxylic acid (TCA) cycle. Exhaustion of glucose triggers the events that
lead to sporulation in many organisms (Mignone & Avignone-Rossa, 1996).
Biosynthesis of PHB occurs via a three-step pathway by which acetyl CoA is initially
condensed to form acetoacetyl CoA, which is then reduced to D-3-hydroxybutyryl CoA at the
expense of NADPH and finally polymerised (Anderson & Dawes, 1990; Steinbüchel, 1991).
Studies have shown that in Bacillus species and C. botulinum, amongst other Clostridia, PHB
is utilised during sporulation (Benoit et al, 1990; Emeruwa & Hawirko, 1973). In well
characterised organisms, the accumulation of PHB and other energy storage compounds is
promoted in response to physico-chemical stress and nutrient limitations, and it is therefore
often associated with secondary metabolites, including antibiotics and certain toxins.
Furthermore, a direct correlation between PHB and toxin biosynthesis has been established
in Bacillus thuringiensis (Navarro et al, 2006).
In Streptomyces species, glycogen accumulates in response to excess carbon when nitrogen
or phosphate is limited (Lillie & Pringle, 1980). Glycogen is metabolised into glucose-1phosphate; a precursor of deoxysugar antibiotics, such as the antibiotic and secondary
metabolite, avilamycin (Salas & Mendez, 2005). Glycogen is also utilised during sporulation
in Streptomyces species, (Preiss & Romeo, 1989), suggesting PHB may act in an analogous
manner to glycogen regarding toxin production in C. botulinum.
Ralstonia eutropha (R. eutropha) is perhaps the most well studied PHB producing organism.
Studies in R. eutropha have shown that PHB accumulation can be maximised in cultures
subject to nitrogen and/or phosphate limited conditions, with carbon in excess (Lillo &
20
Rodriguez-Valera, 1990; Raberg et al, 2008; Ryu et al, 2007; Shang et al, 2003). By limiting
nitrogen sources in the production media, the metabolic flux through the tricarboxylic acid
(TCA) cycle is limited. This in turn limits growth rate which results in a reduced demand for
acetyl CoA by the TCA cycle. Meanwhile, excess glucose is metabolised via the EMP pathway
yielding acetyl CoA and pyruvate. The increase in glycolysis, driven by excess carbon
availability, combined with the limitation of the TCA cycle, owing to the limited availability
of nitrogen, ultimately results in excess acetyl CoA; the precursor for PHB. Similarly, limiting
phosphate in the production medium results in less available inorganic phosphate to bond
with ADP to form ATP in the TCA cycle and subsequent electron transport chains. This limits
the progression of the TCA cycle, which subsequently effects available acetyl CoA; similar to
the effects of nitrogen limitation. Raberg et al (Raberg et al, 2008) has also shown that
maximising PHB production via nitrogen limitation in cultures of R. eutropha decreases the
production of the protein flagellin, which may compete with toxin biosynthesis for
metabolite availability in C. botulinum (Peplinski et al, 2010; Raberg et al, 2008).
1.5.4.3 Flagellin Biosynthesis
Flagellin is a protein which aligns itself in a hollow cylinder to form the filament of bacterial
flagella; the most widely characterised bacterial motility structure (Bardy et al, 2003). Most
flagella are composed of over 20 distinct structural proteins that assemble to form the
flagellar basal body, hook, and filament, with the filament comprising around 20,000
subunits of the flagellin protein (Paul et al, 2007). In Clostridia spp., flagella synthesis and
assembly is regulated by ~35 genes. Genetic variation of Clostridial flagellin is high and can
be used as a method for identification between species serotypes, including distinguishing
21
between different toxin producing serotypes of C. botulinum (Paul et al, 2007; Woudstra et
al, 2013). Despite being predominantly associated with motility, more recent studies have
revealed numerous other functions of bacterial flagella which suggest the structures
secondary role as a virulence factor. In some pathogenic bacteria, including C. difficile, the
major subunit of flagella, flagellin, has been reported to function as an adhesin (Haiko &
Westerlund, 2013). Furthermore, FliC in shiga-toxigenic E. coli has been associated with
cellular invasion (Claret et al, 2007). Although a potential but not yet characterised virulence
factor, for a currently unknown purpose, cultures of C. botulinum and other genera such as
Salmonella typhimurium (S. typhimurium) produce flagellin excessively, beyond the amount
necessary for flagella assembly (Homma & Iino, 1985). As flagellin has been established as a
virulence factor in many pathogenic bacteria, it is possible that a correlation between
flagellin overproduction and neurotoxin biosynthesis could exist in C. botulinum.
Comparative studies have revealed many similarities in the amino acid composition of
flagellin and botulinum toxin (Appendix Figure 1) and it is thought producing flagellin in such
excess is likely to result in competition for metabolites which may result in decreased toxin
biosynthesis. Therefore a detailed study of the biosynthesis of flagellin in C. botulinum may
not only demonstrate flagellin to be a biomarker for neurotoxin production but may also
lead to the ability to control its production and maximise available metabolites for toxin
biosynthesis.
22
1.5.5 Surrogate Research Approach: Clostridium sporogenes
In the context of microbiology, a surrogate is an organism used to study the physiology of a
different but closely related species (Sinclair et al, 2012). Both pathogenic and nonpathogenic organisms are used as surrogates for a variety of purposes, including behaviour,
physiology, method development and conditional biomarker research (Sinclair et al, 2012).
The greatest benefit of the research approach is often the element of safety provided when
studying pathogenic organisms and therefore is commonly practiced when researching
C. botulinum; producer of the most lethal toxin discovered to date (Bradbury et al, 2012). C.
sporogenes is widely used as a surrogate organism for testing the metabolism of C.
botulinum (Bradbury et al, 2012), owing to the fact C. sporogenes is believed to have
originated from a non-toxigenic species of C. botulinum and therefore exhibits the same
metabolic and behavioural properties, without the hazards associated with C. botulinum
(Brown et al, 2012; Cooksley et al, 2010). C. sporogenes has successfully been utilised as a
surrogate to C. botulinum for many avenues of research, including food safety (Brown et al,
2012), gene expression (Cooksley et al, 2010) and metabolic studies (Taylor et al, 2013).
Owing to the extreme pathogenicity of C. botulinum, the organism can only be cultivated in
specialised facilities, under stringent conditions of microbiological and institutional security
(which are not present at the University of Surrey). C. sporogenes was therefore, employed
as a surrogate species throughout this study to in order to examine the relationship
between flagellin production, PHB accumulation and sporulation. This led to the overall
objective of this study: to develop C. sporogenes as a “safe” surrogate system to investigate
the implied physiology of toxin production in C. botulinum. Because neurotoxin production
cannot be assessed in C. sporogenes, genetic annotation software (RAST prokaryotic
23
genome annotation server, the SEED) was used to create a Genome-Scale Metabolic
Network (GSMN) of C. botulinum (type A) and C. sporogenes (ATCC15579), using genome
sequence data obtained from the National Centre for Biotechnology Information (NCBI) FTP
(Bao et al, 2011). The primary requirement of the models was to assess neurotoxin
production by C. botulinum in silico using conditions and experimental data obtained in C.
sporogenes, in order to evaluate the capabilities of the surrogate system.
24
1.6 Research Tools: Genome-Scale Metabolic Networks
Simplified, genome-scale metabolic networks (GSMN) represent a network of chemical
reactions which can be utilised to simulate an organism’s metabolism. The information
provided by the genome of an organism can be used to obtain information on the metabolic
functioning and pathways utilised by the organism (Price et al, 2003). Obtaining the required
genetic information can be achieved by screening the genome for open reading frames that
code for enzymes utilised in metabolic processes. Identifying the genes coding for metabolic
enzymes results in a genome-scale metabolic network which can be used to obtain a better
understanding of cellular metabolism as well as aid design of media and experimental
processes (Teusink et al, 2005; Xie et al, 1994), analyse culture data and develop metabolic
engineering strategies (Hua et al, 2006; Fong et al, 2005; Smid et al, 2005). Although
screening opening reading frames will offer an understanding of metabolic enzymes, such a
network may still contain gaps due to the incomplete or incorrect annotation of the
genome. Biochemical literature, transcriptome data or direct experimental testing can be
used to extend the knowledge of the metabolic network, determining the presence of
missing enzymatic reactions and metabolic pathways. In the case of a ‘complete’ metabolic
model, there are still underdetermined parts due to presence of parallel or cyclic pathways,
and experimental data may not agree with the model owing to regulation of gene
expression. This means that for certain parts of the genome-scale network, flux values
cannot be determined. However, constraints can be set on certain enzymatic reactions on
the basis of biochemical or thermodynamic information, found in the literature or
determined by experimentation, in order to reduce the possible solutions of metabolic
network. The most populated model to date, the iJO1366 metabolic model of Eschericia coli
25
(E. coli) K-12MG1655, has tested 97% of the genomes open reading frames (ORFs) through
experimentation (Orth et al, 2011). Such models are invaluable as a tool to aid
biotechnological process design, including accurate analysis of substrate metabolism and
uptake, growth rates, primary and secondary metabolite biosynthesis, and testing the
effects of pathway knockouts or mutations (Feist & Palsson, 2008).
26
1.6.1 Flux Balance Analysis
Flux balance analysis (FBA) is a widely used technique to simulate the capabilities of a
genome-scale metabolic network (Durot et al, 2009). Mathematically modelling an
organism’s metabolism relies on a steady-state assumption, in which all metabolites are
produced and consumed at the same rate (Durot et al, 2009). This makes the technique less
intensive in terms of input data into the GSMN compared to other methods of modelling,
whilst still predicting cellular growth rate accurately (Edwards et al, 2001). Flux through the
network is enabled by exchange reactions, such as uptake of nutrients and production of
biomass. Stoichiometric constraints are often the only constraints affecting the matrix of
reactions and optimal production of biomass can be computed by solving a linear program
(Muller & Bockmayr, 2013). However, although an effective and efficient method of biomass
generation simulation, FBA only computes one such solution, despite there being more than
one optimal flux distribution that achieves optimal biomass production. Techniques which
analyse the entire flux network such as elementary flux modes can prove exponentially
complex as the number of reactions in a network increases (Zhangellini et al, 2013), which is
often unnecessary for metabolic networking in which the effect of fluxes is more valuable
than the specific flux data (Driouch et al, 2012). One limitation of FBA, however, is that the
array of fluxes calculated is but one of many possible solutions to the balance objective,
meaning the individual enzyme flux values may have no physiological significance. Such
analyses can only calculate unique values for each individual enzyme reaction if Flux
Variability Analysis (FVA) is computed. For each reaction, the range of fluxes which satisfy
the constraints allowing optimisation of the objective function, are calculated.
27
1.6.2 Flux Variability Analysis
FVA determines the maximum and minimum values of all the fluxes that will satisfy the
constraints and allow for the same optimal objective value (Muller & Bockmayr, 2013).
Whilst classical FBA can predict optimal fluxes, FVA can be utilised to predict ranges of flux
through particular pathways and reactions as well as analyse the reactions which contribute
to the observed ranges of flux. Variations of FVA can also be used to determine blocked or
unessential reactions (Burgard et al, 2004). This dynamic method of analysis is in most cases
more realistic than the steady-state assumption relied on by FBA and flux dynamics can be
assimilated with gene expression data to model the ranges of fluxes observed (Bilu et al,
2006). Therefore the benefits offered by this computational technique are particularly
advantageous for modelling the biosynthesis of products which have many factors
contributing to optimisation, such as secondary metabolites including antibiotics and toxins
(Bushell et al, 2006). Utilising FVA with an objective of determining pathways affecting the
biosynthesis of a specific microbial product can be analysed to produce a secondary
metabolite generating network; a powerful microbial process development tool which has
proven successful in optimising antibiotic yields in Streptomyces spp. (Bushell et al, 2006).
28
1.7 Research Tools: Using Chemostat Culture in Physiological Investigations
Despite genomic analysis offering valuable insight into the constraints of a microbial
process, the kinetics and physiology of microbial growth is fundamental to every discipline
of microbiology (Bull, 2010; Hoskisson & Hobbs, 2005). Microbial culture exists in three
basic platforms; batch culture, fed-batch culture and continuous culture. Continuous
cultures (open systems) receive a constant supply of fresh nutrients whilst spent medium,
biomass and microbial products are removed at the same rate (Bull, 2010). This enables the
dissection of microbial physiology and growth kinetics independent from the effects of the
physiochemical environment (Hoskisson & Hobbs, 2005), including the decreased availability
of nutrients and increased accumulation of by-products over time.
Many variations of continuous cultures exist (Bull, 2010; Drake & Brogden, 2002), but by far
the most widely used is the chemostat culture; cited in around 90% of recent publications
on continuous cultures (Bull, 2010). This technique of bacterial culture was developed
simultaneously by Monod and Novick & Szilard in 1950 (Monod, 1950; Novick & Szilard,
1950). The unique characteristic of chemostat culture which makes the technique a
powerful research method to analyse microbial physiology is the ability to establish a timeindependent steady state. In steady state, growth rate and all parameters affecting the
culture including the availability of nutrients, cell density, microbial product concentration,
pH and culture volume remain invariant (Herbert et al, 1956; Hoskisson & Hobbs, 2005).
Under such conditions, the specific growth rate of an organism is dependent on the rate of
supply of a growth limiting substrate present in the growth medium. Therefore in steady
state, specific growth rate is equal to dilution rate, allowing external control of the cultures
29
specific growth rate (Bull, 2010; Herbert et al, 1956; Hoskisson & Hobbs, 2005) (See Chapter
2: Materials & Methods – 2.7: Chemostat Culture).
As a method to investigate microbial physiology, chemostat culture offers a unique method
to analyse supposed constants that are subject to environmental factors and changes in
microbial population, which can provide reproducible conditions for global regulation (Bull
2010; Ferenci, 2006). The technique offers a substantial advantage when investigating the
effects of a single parameter, for example, on the biosynthesis of a microbial product, which
would otherwise be affected by growth kinetics and environmental factors (Aon & Cortassa,
2001; Avignone-Rossa et al, 2002).
One limitation of genome-scale flux analysis is the requirement to assume steady state
conditions (Durot et al, 2009). Metabolic flux analysis of organisms grown in batch culture
can prove unreliable owing to transient growth effects which alter gene regulation and
metabolic pathway fluxes (Hoskisson & Hobbs, 2005). Applying metabolic flux analysis to
chemostat-grown cultures therefore indirectly eliminates metabolic constraints between
the two research tools. Genome-scale flux analysis and chemostat culture in combination
therefore complement one another, encompassing techniques from different eras of
microbiology research to provide an effective process development and optimisation tool.
30
1.8 Impact, Aims & Objectives
Despite C. botulinum fermentation for the production of its neurotoxin being practiced for
decades, little research about the underlying physiology of the process has been published.
Current methods of commercial botulinum toxin production predominantly rely on legacy
production processes which can be inflexible in terms of process development and can
prove difficult to rectify specific production issues such as reproducibility and yields.
Furthermore, the introduction of modifications into an already licensed and in-manufacture
process poses constraints; with large overhauls of the process such as strain changes or
mutations requiring costly and timely relicensing. It is therefore an emphasis of this research
project to increase our understanding of the botulinum toxin production process and
provide insight into potential process development approaches.
This research project will utilise a genome-scale metabolic network enhanced surrogate
system to assess the physiology, metabolism and behaviour of C. botulinum with regards to
neurotoxin biosynthesis and its industrial production process.
By using a research approach which combines experimental studies in C. sporogenes with in
silico analysis of C. botulinum, the primary aims and objectives of this project were
proposed;

Seek to establish and elaborate on potential correlations with sporulation, flagellin
biosynthesis and PHB metabolism in C. sporogenes cultures.

Investigate the potential metabolic factors affecting neurotoxin biosynthesis in silico,
particularly in relation to the biomarkers investigated.
31

Build upon established correlations and utilise data obtained from a combination of
the process development methodologies covered in sections 1.5, 1.6 & 1.7 to design
a physiologically and biochemically defined process, with an outlook of optimising
neurotoxin production over other metabolites which may compete for bioflux.

Increase knowledge of the botulinum toxin production process, offering potential
avenues for process improvements which could benefit the project sponsor.

Further validate the suitability of C. sporogenes as a surrogate organism for studies
in C. botulinum.

Yield metabolic data which may offer insight into the correlations of biomarkers
with microbial products in other members of the Clostridium genus, potentially
impacting the approach taken for process development in closely related bacteria
exploited for industrial processes, such as the acetogenic Clostridia.
32
Chapter 2: Methods & Materials
2.1 Clostridium sporogenes strains and working stock preparation
Fifteen different strains of C. sporogenes were obtained from NCIMB (NCIMB Ltd, Aberdeen,
UK). The strains were assigned a working reference number (Table 2) before revival on to
glucose broth agar and in cooked meat medium (CMM). The cultures were grown at 37 oC
for 24 hours in an anaerobic jar. Anaerobic conditions were achieved using AnaeroGen TM
anaerobic gas generating sachets (Oxoid Ltd, United Kingdom). Cultures grown in CMM
were plated onto glucose broth agar plates (Oxoid Ltd, United Kingdom) and slopes to
ensure the cultures were free from contaminants. Isolated colonies were used to inoculate
Erlenmeyer flasks containing 100ml of sterile CMM which were incubated at 37 oC in an
anaerobic workstation (Don Whitley Scientific Ltd, United Kingdom) for 24h. These cultures
were then used to prepare frozen seed stocks by dispensing 600µl of culture into a cryotube
containing 400µl sterile glycerol to attain a 40% glycerol stock culture (v/v) and were
subsequently frozen at -80oC. The frozen stocks were used as inocula to generate seed
cultures for experimentation throughout the study.
33
Strain number
Organism
NCIMB532
NCIMB8053
NCIMB8243
NCIMB9381
NCIMB9382
NCIMB9383
NCIMB10196
NCIMB10696
NCIMB12148
NCIMB12343
NCIMB700933
NCIMB701789
NCIMB701791
NCIMB701792
NCIMB701793
Clostridium sporogenes
Clostridium sporogenes
Clostridium sporogenes
Clostridium sporogenes
Clostridium sporogenes
Clostridium sporogenes
Clostridium sporogenes
Clostridium sporogenes
Clostridium sporogenes
Clostridium sporogenes
Clostridium sporogenes
Clostridium sporogenes
Clostridium sporogenes
Clostridium sporogenes
Clostridium sporogenes
Allocated strain
reference number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Table 2: Fifteen NCIMB strains of C. sporogenes and their allocated working reference
numbers. Lyophilised cultures sealed in glass vials were revived under sterile and anaerobic
conditions provided by an anaerobic workstation (Don Whitley Scientific Ltd, United
Kingdom). Cultures recovered from sealed glass vials were suspended in glucose broth liquid
medium and used to inoculate flasks of CMM and glucose broth agar plates as described in
2.1 above.
34
2.2 Culture Medium
Cooked-meat medium: 10g of dry cooked meat medium (Oxoid ltd, United Kingdom) was
added to 100ml of Milli-Q water. The medium was sterilised using an autoclave (121oC for
15 minutes). After sterilisation, the liquid was removed from the cooked-meat and replaced
with the same volume (100ml) of sterile glucose broth liquid medium (See below) aseptically
in a Class II microbial safety cabinet (MSC).
Glucose Broth Liquid Medium: 2.5g glucose and 8.25g nutrient broth no.2 (Oxoid Ltd, United
Kingdom) added to 250ml of Milli-Q water sterilised by autoclaving (121oC for 15 minutes).
Glucose Broth Agar: Glucose Broth Liquid Medium + 1.2% Technical Agar (Oxoid Ltd, United
Kingdom) sterilised by autoclaving (121oC for 15 minutes). Liquid agar medium was poured
into petri-dishes whilst still molten post-autoclave in an MSC as above. Glucose broth agar
which had solidified was warmed in a thermostatic water bath (Fisher scientific Ltd, United
Kingdom) maintained at 50oC until molten and then poured as described above.
Rich Clostridium Growth Medium (USA2): Proteose peptone No.3 20g/L; Bacto yeast extract
10g/L; NZ Amine A 10g/L; Sodium mercaptoacetate 0.5g/L; Glucose 20ml/L (50%w/v) added
post-autoclave to avoid heat decomposition (121oC for 15 minutes) in 1L Milli-Q water.
35
Basal Defined Medium:
Amino Acid
Concentration (mg/L)
Vitamin
Concentration (mg/L)
Histidine
Tryptophan
Glycine
Tyrosine
Arginine
Phenylalanine
Methionine
Threonine
Alanine
Lysine
Serine
Valine
Isoleucine
Aspartic acid
Leucine
Cysteine
Proline
Glutamic acid
100
100
100
100
200
200
200
200
200
300
300
300
300
300
400
500
600
900
Thiamine
Calcium-D-pantothenate
Nicotinamide
Riboflavin
Pyridoxine
p-Aminobenzoic acid
Folic acid
Biotin
B12
1
1
1
1
1
0.05
0.0125
0.0125
0.005
Minerals
NaCl
CaCl,2H2O
MgCl, 6H2O
MnCl, 4H2O
FeSO4 7H2O
CoCl2 6H2O
Distilled water (ml)
NaHCO3
900
26
20
10
4
1
1000
5000
Variable nutrients (see below)
Glucose
Na,HPO4
KH2PO4
(NH4)2S04
2000
1500
300
40
Table 3: Basal Defined Medium (BDM) composition from Karasawa et al, 1995
Carbon-limited media: BDM (Table 3) with glucose altered to 200mg/L, 500mg/L, 1000mg/L,
2000mg/L and 4000mg/L respectively.
Nitrogen-limited media: BDM (Table 3) with ammonium sulphate altered to 0mg/L,
250mg/L, 500mg/L, 750mg/L and 1000mg/L respectively.
Phosphate-limited media: BDM (Table 3) with sodium phosphate/potassium phosphate
altered
to
1500mg/300mg,
1000mg/200mg,
750mg/150mg,
500mg/100mg
and
250mg/50mg respectively.
36
2.3 Determination of Culture Growth by Optical Density
Culture growth was determined by optical density. Samples were mixed using a vortex mixer
(Fisher scientific Ltd, United Kingdom), 1ml transferred into a silicon cuvette and the
absorbance measured at a wavelength of 560nm (Karasawa et al, 1995) against a blank of
sterile growth medium using an Ultaspec2000 spectrophotometer (Pharmacia Biotech, UK).
2.4 Determination of Biomass by Dry Cell Weight Measurement
Biomass was determined by dry weight measurement following lyophilisation. A 1 ml
volume of well-mixed sample was transferred into a pre-weighed micro centrifuge tube and
the biomass pelleted using a bench-top centrifuge (Eppendorf, United Kingdom) set at
8000rpm for 10 minutes. The supernatant was carefully removed and the cells washed by
re-suspending in Milli-Q and then pelleting again. After two repeats of the wash-spin cycle
(described above) to wash the media components from the cells, the pelleted cells were
frozen at -80oC for 1 hour before being transferred to a freeze-dryer (Edwards high vacuum
International, United Kingdom). Lyophilisation was performed overnight (approximately
12h, -40oC) to remove water from the sample and the micro centrifuge tube, containing the
lyophilised cells, was then re-weighed to determine biomass.
37
2.5 Determination of Intracellular PHB Accumulation
Whole cells, isolated from culture samples, were lyophilised and weighed to determine cell
biomass as described in section 2.4. A 1ml of commercial sodium hypochlorite solution (15%
v/v) was added to the lyophilised cells and incubated at 37oC for 1 hour to allow cell lysis to
occur. After incubation, 4ml of Milli-Q water was added, the suspension well mixed using a
vortex (Fisher scientific Ltd, United Kingdom) and centrifuged at 13000rpm for 10 minutes.
The supernatant was removed and the pelleted cells were washed with 5ml of acetone,
followed by 5ml of absolute ethanol using the spin and centrifuge profile described above.
The sedimented lipid granules generated were then extracted using 3ml of chloroform by
submersing submerging the capped tube for 2 minutes in a boiling water bath (Fisher
scientific Ltd, United Kingdom). The tubes were removed, cooled on ice, centrifuged at
13000rpm for 10 minutes and the extract decanted into a graduated tube using a pipette.
This extraction process was repeated twice and the pooled extracts made-up to 10ml with
chloroform. A 1ml volume of the extract was then transferred into a boiling tube and
immersed in heating block (Fisher scientific Ltd, United Kingdom) set at 100 oC inside a fume
cabinet until the chloroform had fully evaporated. 10ml of concentrated (>95%) sulphuric
acid was then added, the tube capped with a glass marble, and heated for a further 10
minutes. After cooling, the sample was transferred into a quartz crystal cuvette and the
absorbance measured at 235nm against a blank of the sulphuric acid used in the assay using
an Ultaspec2000 spectrophotometer (Pharmacia Biotech, UK). Heating PHB in concentrated
sulphuric acid to temperatures of 85-100oC results in the conversion of PHB into crotonic
acid, which, according to Law and Slepecky (1961), has a molecular extinction coefficient
identical to PHB (ε = 1.56 x 104) when measured at 235nm in concentrated sulphuric acid.
38
This step is necessary because PHB does not absorb light, making it difficult to assay without
conversion into crotonic acid. To confirm the correlation and ensure accuracy of the assay, a
standard curve of PHB was constructed (Figure 3).
This was prepared by adding measured pure PHB (Sigma-Aldrich Co., USA) to chloroform
and repeating the method detailed above.
PHB Standard Curve
1.8
1.6
R² = 0.9948
1.4
OD235
1.2
1
0.8
0.6
0.4
0.2
0
2
4
6
8
10
PHB (µg/ml)
Figure 3: Standard curve of PHB achieved by adding measured pure PHB (Sigma-Aldrich Co.,
USA) to chloroform and repeating the assay detailed in section 2.5. Data shown are
averages of triplicate biological and triplicate technical repeats. Error bars represent
standard error of the mean.
39
2.6 Determination of Supernatant Flagellin
Supernatant flagellin content was determined following isolation by sodium dodecyl
polyacrylamide gel electrophoresis (SDS-PAGE) and subsequent band densitometry analysis
(Twine et al, 2009). Samples taken from C. sporogenes cultures were mixed using a vortex
mixer (Fisher scientific Ltd, United Kingdom) and centrifuged at 13000rpm for 10 minutes
using a bench-top centrifuge (Eppendorf, United Kingdom). The supernatant was
transferred to a sterile micro centrifuge tube, frozen at -80oC for >1 hour and lyophilised as
described in section 2.5. Following lyophilisation, the samples were resuspended in 100µl of
Milli-Q water. Samples were then mixed using a vortex (Fisher scientific Ltd, United
Kingdom) with 50µl of Bromophenol Blue loading dye and heated at 100 oC for at least 5
minutes. After cooling, samples were mixed using a vortex mixer (Fisher scientific Ltd,
United Kingdom), loaded into 12% w/v bis/polyacrlyamide gels and separated using SDSPAGE (Biorad Laboratories Ltd., UK) at room temperature and 200V constant voltage for
approximately 30 minutes. A protein marker reagent ranging from 20 to 200kDa (SigmaAldrich Co., USA) was also added to gels and samples of bovine serum albumin (BSA) of
known concentration were added to calculate protein mass from band densities. Gels were
then stained with Coomassie-blue overnight (detailed section 2.6.1) and de-stained using a
solution containing 30% methanol (v/v) and 10% (v/v) acetic acid in Milli-Q water (detailed
section 2.6.1). Flagellin was identified using in-gel tryptic digests of candidate protein bands,
analysed by nano-LC-ESI-MS/MS using a hybrid quadrapole/time of flight mass
spectrometer. The density of flagellin bands were then determined using a multi-image light
cabinet fitted with a high resolution camera (Alpha Innotech, South Africa) combined with
Flurochem QTM image analysis software (ProteinSimple, USA). The protein content of the
40
sample was then calculated based on a calibration of band densities obtained from samples
of known protein quantities present on the gel.
41
2.6.1 Bis/Polyacrylamide Gels & SDS-PAGE Reagents
12% w/v Bis/polyacrylamide gels: 14.4ml 30% w/v Bis/acrylamide; 12 ml Resolving Gel
Buffer; 21.6ml Milli-Q Water; 300µl 10% (w/v) Ammonium Persulphate (Sigma-Aldrich Co.,
USA); 20µl TEMED-30 (Sigma-Aldrich Co., USA) added before pouring.
Bis/polyacrylamide stacking gel (Used for loading section of gel): 4ml 30% w/v
Bis/acrylamide; 6ml Stacking Gel Buffer; 14ml Milli-Q Water; 200µl 10% (w/v) Ammonium
Persulphate; 20µl TEMED-30 added before pouring.
Resolving Gel Buffer: 181.65g 1.5M Tris-HCL; 0.4% (v/v) Sodium dodecyl Sulphate (SigmaAldrich Co., USA); 1L Milli-Q water.
Stacking Gel Buffer: 60.55g 0.5M Tris-HCL; 0.4% (v/v) Sodium dodecyl Sulphate (SigmaAldrich Co., USA); 1L Milli-Q water.
Tank Buffer: 60.55g 0.5M Tris-HCL; 41.28g Glycine; 1% (v/v) Sodium dodecyl Sulphate
(Sigma-Aldrich Co., USA); 1L Milli-Q Water.
Coomassie Blue (gel stain): 0.12g Coomasie Blue; 50ml Methanol; 20ml Glacial Acetic Acid;
50ml Milli-Q water.
Bromophenol Blue Loading Dye: 1ml Stacking Gel Buffer; 25% (v/v) Sodium dodecyl
Sulphate; 0.5ml β-mercaptoethanol; 1ml Glycerol; 0.1g Bromophenol Blue.
42
2.7 Determination of Sporulation
The number of spores present in cultures was determined using a 0.1mm grid, laser etched
haemocytometer (Hawksley Ltd., UK). Samples were vortex-mixed (Fisher scientific Ltd,
United Kingdom) and 50µl transferred onto a haemocytometer and covered with a glass
microscope slide cover to create a chamber of known volume. Samples were then examined
using phase-contrast light microscopy (x1000 magnification with immersion oil) and spores
counted against the laser etched grids of the haemocytometer to calculate spores/ml (Burns
et al, 2010). Samples too numerous in spores for accurate determination were diluted in
series using Milli-Q water.
43
2.8 Plackett-Burman Design
The Plackett-Burman design (PBD) experimental approach was used to assess the effects of
amino acid supplementation on growth, sporulation, PHB accumulation, flagellin
biosynthesis (Section 3.4) and enzyme activity (Section 4.4) in C. sporogenes. A series of
Erlenmeyer flasks containing BDM (Karasawa et al, 1995) were supplemented with various
amino acids following the PBD approach and principles (Plackett & Burman, 1946). All flasks
were inoculated using the same seed culture of C. sporogenes, generated using the methods
detailed in section 2.1. Inoculated flasks were incubated at 37oC in an anaerobic workstation
(Don Whitley Scientific Ltd, United Kingdom) for 24h. Dry cell weight and sporulation was
determined immediately following sampling using the methods detailed in section 2.4 & 2.7
respectively and the remaining culture frozen at -20oC for assay dependant pre-treatments.
Table 4 displays the schematic of supplementation used to assess the effects of, and
interaction between (Kalil et al, 2000), 11 variables. Variables tested using PBD in this study
were amino acids added to the growth medium (Sections 3.4 & 4.4). The experimental error
was generated by calculating the square root of the variance of effect (Section 1.5.2) and a t
test is used to determine the significance of the data (Greasham & Inamine, 1986).
44
Trial
χ1
χ2
χ3
χ4
Variables (χ)
χ5 χ6 χ7 χ8
χ9
χ10
χ11
1
2
3
4
5
6
7
8
9
10
11
12
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
Table 4: PBD for 11 variables. Number of trials = n +1 (n = number of variables). + and –
represent variables at two different values, a high value (+) and low value (-). The results
were tested against at least three dummy variables (theoretical data calculated from the
predicted effects of variables at a combination not actually used in the experiment), also at
high and low values, to generate the experimental error. Statistical significance of the data
was tested using a t test (Greasham & Inamine, 1986).
45
2.10 Determination of Nutrient Concentration
2.10.1 Glucose Assay
Glucose present in the medium of C. sporogenes cultures was assayed using a
Reflectoquant® dipstick glucose assay (Merck & Co., Germany), read using a RQflex2
reflectometer (Merck & Co., Germany). Culture supernatant was separated by
centrifugation (Eppendorf, United Kingdom) for 10 minutes at 8000rpm. The test strip was
submersed in the supernatant for 15 seconds and immediately scanned using the
reflectometer which displayed a value of mg/L glucose. The reaction strip contains glucose
oxidase which converts the glucose present in the supernatant into δ-gluconolactone.
Hydrogen peroxide generated by the reaction reacts with an organic redox indicator,
developing colour that is measured reflectometrically. Samples’ concentrations were
determined from a calibration curve prepared using standards of glucose diluted in Milli-Q
water. Samples which were out of the range of the assay specifications detailed in the assay
kit (Merck & Co., Germany) were diluted in Milli-Q water to obtain a validated glucose
concentration.
2.10.2 Ammonium Assay
Ammonium present in the medium of C. sporogenes cultures was assayed using a
Reflectoquant® dipstick ammonium assay (Merck & Co., Germany), read using a RQflex2
reflectometer (Merck & Co., Germany). Culture supernatant was separated by
centrifugation (Eppendorf, United Kingdom) for 10 minutes at 8000rpm. The test strip was
submersed in the supernatant for 2 seconds and immediately scanned using the
reflectometer which displayed a value of mg/L ammonium. Sample concentrations were
46
determined from a calibration curve prepared using standards of ammonium sulphate
diluted in Milli-Q water. Samples which were out of the range of the assay specifications
detailed in the assay kit (Merck & Co., Germany) were diluted in Milli-Q water to obtain a
validated ammonium concentration.
2.10.3 Phosphate Assay
Phosphate present in the medium of C. sporogenes cultures was assayed using a
Reflectoquant® dipstick phosphate assay (Merck & Co., Germany), read using a RQflex2
reflectometer (Merck & Co., Germany). Culture supernatant was separated by
centrifugation (Eppendorf, United Kingdom) for 10 minutes at 8000rpm. 8ml Milli-Q water
was added to 500µl of supernatant, 500µl of PO4-1 reagent (Dipstick Phosphate Assay) and
the required dose of PO4-2 reagent (Dipstick Phosphate Assay). The mixture was then well
mixed and measured spectrophotometrically at 690nm. Samples were tested against a
calibration curve prepared using standards of sodium phosphate diluted in Milli-Q water.
The reagents of the assay kit react with phosphate ions in the sample to form
molybdophosphoric acid, which is subsequently reduced to phosphomolybdenum blue
providing the photometric change. Samples which were out of the range of the assay
specifications detailed in the assay kit (Merck & Co., Germany) were diluted in Milli-Q water
to obtain a validated phosphate concentration.
47
2.11 Determination of Enzyme Activity
2.11.1 Glucose-6-Phosphate Dehydrogenase Activity Assay
C. sporogenes cells were pelleted using a bench-top centrifuge (Eppendorf, United Kingdom)
at 8000rpm for 10 minutes. The supernatant was carefully removed and the cell pellet
washed with phosphate buffer solution (PBS), remixed and the centrifugation repeated.
After two repeats to wash media components from the cells, 500µl CellLytic Cell lysis
reagent (Sigma-Aldrich Co., USA) was added and the cells incubated at 30oC on a platform
shaker for 15 minutes. The lysed cells were then centrifuged at 13000rpm for 20 minutes to
pellet cellular debris. 50µl of the enzyme-containing supernatant was added to a 96 well
plate; 50µl of the ’reaction mix’ was then added to the wells and immediately measured
spectrophotometrically at 450nm. The ‘reaction mix’ was a mixture of reagents supplied in
the Glucose-6-phosphate dehydrogenase (G6PD) kit, containing 46µl Assay buffer (G6PD Kit
Sigma-Aldrich Co., USA), 2µl Substrate mix (G6PD Kit Sigma-Aldrich Co., USA) and 2µl
Developer (G6PD Kit Sigma-Aldrich Co., USA) per 50µl. Following the first OD450
measurement, plates were incubated at 37oC for 30 minutes and measured
spectrophotometrically at 450nm for a second time. The results were compared against a
standard curve prepared using a 1.25mM NADH standard (G6PD Kit Sigma-Aldrich Co., USA)
to determine G6PD activity in mU/ml (enzyme activity/ml) using the following equation:
G6PD activity = (B / (T2-T1) x V) x sample dilution = nmol/min/ml = mU/ml
B = NADH generated between T1 & T2 (compare to standard curve); T1 = time at first
reading (min); T2 = time at second reading (min); V = Volume of sample added to well (ml)
48
2.11.2 Citrate Synthase Activity Assay
Whole cells of C. sporogenes were pelleted as previously using a bench top centrifuge
(Eppendorf, United Kingdom). The supernatant was carefully removed and the cells washed
with PBS. After two repeats to wash media components from the cells, 500µl CellLytic Cell
lysis reagent (Sigma-Aldrich Co., USA) was added and the cells incubated at 30oC on a
platform shaker for at least 15 minutes. The lysed cells were then centrifuged at 13000rpm
for 20 minutes to pellet cellular debris. 8µl of the enzyme-containing supernatant was
added to a 96 well plate in wells containing 186µl assay buffer (Citrate Synthase Assay Kit,
Sigma-Aldrich Co., USA), 3µl 30mM Acetyl CoA solution (Citrate Synthase Assay Kit, SigmaAldrich Co., USA) and 10mM DTNB solution (Citrate Synthase Assay Kit, Sigma-Aldrich Co.,
USA). 10µl 10mM Oxaloacetate solution was added immediately before reading
spectrophotometrically at 412nm wavelength. The sample was measured every 10 seconds
for a duration of 90 seconds using a spectrophotometric plate reader (MultiSkan-FC,
Thermofisher scientific Ltd, United Kingdom). Citrate synthase (Citrate Synthase Assay Kit,
Sigma-Aldrich Co., USA) was added to separate wells in place of sample as a positive control.
Citrate synthase activity was then calculated using the equation below:
Citrate Synthase Activity =
Units (µmole/ml/min) = (ΔA412/min x V(ml) x dil) / ɛmM x L(cm) x Venz(ml)
dil = dilution factor of original sample; V(ml) reaction volume (ml); V enz Sample volume (ml);
ɛmM = extinction coefficient of 5-thio-2-nitrobenzoic acid (412nm = 13.6); L(cm) = pathway
length of absorbance measurement (96 well plate reader = 0.552cm).
49
2.11.3 Phosphoenolpyruvate Carboxylase Activity Assay
Whole cells of C. sporogenes were pelleted as previously using a bench top centrifuge
(Eppendorf, United Kingdom). The supernatant was carefully removed and the cells washed
with PBS. After two repeats to wash media components from the cells, 500µl CellLytic Cell
lysis reagent (Sigma-Aldrich Co., USA) was added and the cells incubated at 30oC on a
platform shaker for at least 15 minutes. The lysed cells were then centrifuged at 13000rpm
for 20 minutes to pellet cellular debris. 10 µl of the enzyme-containing supernatant was
added to a 1ml cuvette containing 0.9ml 110mM Tris Sulfate Buffer (pH 8.5 at 25°C), 50µl
300mM Magnesium Sulphate, 50µl 6mM ß-Nicotinamide Adenine Dinucleotide, 300µl
100mM sodium bicarbonate, 300µl Dioxane, 100µl 300mM Dithioerythritol solution and 1µl
Malic dehydrogenase enzyme solution (6x103 mU/ml). A blank was prepared by substituting
10µl of 5mM MgSO4 in place of the enzyme containing supernatant. The mixture was mixed
by inversion and 100µl of 30mM Phosphoenolpyruvate solution added immediately before
measuring spectrophotometrically at 340nm. The decrease in OD340 was measured for a
period of 5 minutes to achieve a value of ΔOD340/min. Phosphoenolpyruvate carboxylase
activity was then calculated using the following equation (Wohl & Markus, 1972):
Phosphoenolpyruvate carboxylase activity =
Units/mg enzyme = (ΔOD340/min sample - ΔOD340/min blank) / (6.22) (mg enzyme/ml RM)
6.22 = Millimolar extinction coefficient of ß-NADH at 340nm; RM = Reaction Mix
The results were compared with a standard curve prepared by substituting the sample with
standards of known Phosphoenolpyruvate carboxylase concentration (Figure 4).
50
Standard Curve of Phosphoenolpyruvate Carboxylase Activity
Total Absorbance Decrease (OD340)
0.6
0.5
R² = 0.9482
0.4
0.3
0.2
0.1
0
0
10
20
30
Phosphoenolpyruvate (U/ml)
40
50
Figure 4: Standard curve of Phosphoenolpyruvate Carboxylase (PEPc) Activity achieved by
testing prepared samples of known PEPc concentration. Data shown are averages of
triplicate biological and triplicate technical repeats. Error bars represent standard error of
the mean.
51
2.12 Chemostat Culture
A 2 litre fermentation vessel (Adaptive Biosciences, USA) equipped with a temperature
probe, pH probe, dissolved oxygen probe, heating element and sampling line was used for
chemostat culture of C. sporogenes. Flow-rate of BDM medium into the fermentation vessel
was externally controlled using pump calibrated for accurate media flow-rate control. pH
was controlled using computer software (BioDirector v1.2, Adaptive Biosciences, USA)
attuned to the pH probe which introduced 1mM HCl or 1mM NaOH to maintain culture pH.
Filtered (0.22 micron) input gas (varied depending on conditions, see below) was controlled
using a flow meter and sparged through the culture and was released from the top of the
fermentation vessel before passing through a desiccant cylinder packed with silica gel. A
weir overflow, set to maintain a working culture-volume of 1.5L facilitated the removal of
spent medium and fermentation products into a pre-sterilised waste bottle. All tubing
connected to the fermentation vessel was oxygen-impermeable neoprene to maintain
anaerobic conditions. The fermentation vessel was sterilised by autoclaving at 121oC for 30
minutes. The inoculum was prepared by emptying 1ml frozen stock culture into Erlenmeyer
flasks containing 100ml BDM medium and incubating at 37 oC for 24h in an anaerobic
workstation (Don Whitley Scientific Ltd, United Kingdom). Cultures were well mixed before
being transferred into the fermentation vessel using a sterile 50ml syringe. Feed media flow
rate was set to 0ml/min to allow initial growth in batch culture for 24h (timing based on
previous experiments in study). Following batch growth, the contents of the fermentation
vessel were tested to ensure culture of axenic C. sporogenes cultures. Growth-rate was then
controlled by altering the flow-rate of growth medium into the fermentation vessel (ml/h)
to give dilution rates of 500, 750 and 1L per hour in a working volume of 1.5L (0.33, 0.5 &
52
0.66 dilution rate). At least four volume changes were required before sampling following
culture parameter changes, such as gas composition or growth rate, to ensure true
representation of steady-state culture conditions. Biological repeats were sampled at least
one volume change apart (working volume (ml) / dilution rate (ml/h) = volume change (h)).
Experiments designed to test the effects of growth rate on C. sporogenes were supplied
with 100% nitrogen gas (oxygen-free) in order to maintain strict anaerobic conditions in the
culture vessel. Experiments testing the effects of CO2 were supplied with gas mixtures
containing 10%, 25% or 50% CO2 and balance nitrogen at a controlled rate of 0.2L/h-1.
Samples were collected when required in 30ml Universals sterilised by autoclaving. Sample
turbidity (OD560) and biomass (Dry cell weight) were determined immediately following
sampling and further sample was stored at -20oC for assay dependant pre-treatments. Every
precaution was taken to ensure axenic cultures were maintained throughout
experimentation, in particular when sampling and changing medium supply or waste
containers. Following sampling and experimental condition changes, the contents of the
fermentation vessel were tested to ensure culture of axenic C. sporogenes cultures.
53
2.13 Determination of Protein Concentration
Supernatant protein concentration was assayed using the Bradford protein assay (Ernst &
Zor, 2010). Samples were diluted with 0.15M NaCl to a final volume of 100µl to achieve a
protein content within the range of the standard curve. The standard curve was generated
by assaying samples of known protein content, achieved by preparing samples containing 0,
50, 100, 200 & 300mg/L of Bovine Serum Albumin (Sigma-Aldrich Co., USA). 5ml of Bradford
reagent (Sigma-Aldrich Co., USA) was then added, vortex-mixed (Fisher scientific Ltd, United
Kingdom) and incubated at room temperature for 30 minutes. The optical density was then
measured spectrophotometrically at 595nm and compared to the standard curve to
determine protein content (mg/L).
2.14 RNA Assay
Whole cells of C. sporogenes were pelleted using a bench-top centrifuge (Eppendorf, United
Kingdom) at 8000rpm for 10 minutes. The supernatant was carefully removed and the cells
washed with PBS, resuspended and the process repeated. The washed pellet was then
transferred to a 1ml ‘Fastprep’ tube containing 1ml of sodium phosphate/MT Buffer
(RNApro fast direct kit, MPbio Llc, United Kingdom). The tube was then processed to release
intracellular content using a Fastprep instrument (MP Bio Llc, United Kingdom) for 40
seconds at setting 6.0. The tube was then centrifuged at 14,000rpm for 5 minutes and the
liquid transferred to a new microcentrifuge tube. 750µl of Phenol:Chloroform solution (MP
Bio Llc, United Kingdom) was added and the sample vortexed (Fisher scientific Ltd, United
Kingdom) for 10 seconds before being incubated at room temperature for 5 minutes. The
sample was then centrifuged at 14,000rpm for another 5 minutes at 4 oC. The upper
aqueous phase of the sample was transferred to a new centrifuge tube containing 200µl of
54
inhibitor removal solution (MP Bio Llc, United Kingdom) and inverted 5 times to mix
thoroughly. The sample was then centrifuged at 14,000rpm for another 5 minutes at room
temperature and the supernatant transferred to a new microcentrifuge tube containing
660µl of cold 100% isopropanol. The sample was inverted 5 times to mix thoroughly and
centrifuged at 14,000rpm 15 minutes at 4oC. The supernatant was then discarded and the
RNA containing pellet washed gently with 500µl of cold 70% ethanol (made with DEPC-H20
(MP Bio Llc, United Kingdom). The ethanol was then carefully removed and the pellet
allowed to air dry for 5 minutes at room temperature. The pellet was resuspended in 200µl
DEPC-H20 (MP Bio Llc, United Kingdom) and 600µl of RNAMATRIX Binding Solution (MP Bio
Llc, United Kingdom) and 10µl RNAMATRIX Slurry (MP Bio Llc, United Kingdom) was added
before being incubated at room temperature on a shaker table for 5 minutes. The sample
was pelleted using a microcentrifuge (pulse spin) for 10 seconds and the supernatant
removed carefully. The pellet was then washed in 500µl of RNAMATRIX wash solution (MP
Bio Llc, United Kingdom) and allowed to air dry for 5 minutes at room temperature before
being resuspended in 50µl DEPC-H H20 (MP Bio Llc, United Kingdom). RNA content was then
analysed using a NanoDrop Spectrophotometer (Fisher scientific Ltd, United Kingdom) to
provide RNA content in ng/ml.
55
2.15 Amino Acid Determination
Whole cells of C. sporogenes were pelleted using a bench top centrifuge (Eppendorf, United
Kingdom) set at 8000rpm for 10 minutes. The supernatant was carefully removed and the
cells washed with PBS, remixed and repeated. The resulting sample supernatants were
submitted to an external laboratory for their amino acid content (Alta Biosciencess Ltd, Uk).
In brief, the supernatants were assayed for amino acid content by ion exchange
chromatography using a series of sodium citrate or lithium citrate buffers. After separation
via chromatography, the amino acids were reacted post column with a stream of Ninhydrin
for photometric detection. Amino acid content of chemostat culture samples were
compared with the analysis of sterile BDM medium (medium used for chemostat culture
before inoculation) at the different growth rates detailed in the chemostat experiment
methods to evaluate amino acid consumption by C. sporogenes cultures.
56
2.16 Genome-Scale Metabolic Network Analysis
2.16.1 Construction of C. sporogenes & C. botulinum GSMN
The GSMN employed in this study was constructed by submitting the genomes of both
C. botulinum (type A) and C. sporogenes (ATCC15579) to the RAST (Rapid Annotation using
Subsystem Technology) server of the SEED. The genome sequence data of both organisms
was obtained from the National Centre for Biotechnology Information (NCBI) FTP (Bao et al,
2011). RAST prokaryotic genome annotation server is a fully automated service for
annotating bacterial genomes using genome sequence data (Aziz et al, 2008). The metabolic
models were downloaded from ‘The Model SEED’ in SBML format and imported into Jymet2
for metabolic network based analysis. Jymet (and Jymet2) are graphics interface software
written in Python programming language. The software allows models to be presented in a
spreadsheet based format and provides the necessary data format for GSMN analysis.
2.16.2 Flux Balance Analysis
Flux balance analysis (FBA) was utilised as a tool to achieve in silico analysis of C. botulinum
metabolism throughout this study. FBA values were obtained by testing optimal biomass,
PHB and toxin fluxes as individual objective functions using the GSMN of C. botulinum type
A imported into Jymet2. Available metabolites were input into the GSMN representative of
the available nutrients in BDM growth medium used throughout experimentation, including
glucose, phosphate, ammonium and various amino acids and vitamins. Data on the effect of
glucose, nitrogen, phosphate and amino acid fluxes (Section 3.6) on both PHB and Toxin flux
57
was achieved by FBA testing optimal PHB and toxin flux as individual objective functions at
varied maximum nutrient fluxes.
2.16.3 Flux Variability Analysis
Flux variability analysis (FVA) was achieved by simulating optimal PHB flux as an objective
function using the GSMN of C. botulinum type A imported into Jymet2. The analysis was
then repeated with sub-optimal PHB flux (50% of the maximum flux of PHB used to achieve
optimal flux) as an objective function. The values of the 1114 individual reactions which
calculated maximum PHB flux were arranged for both analysis and compared to evaluate
which reactions demonstrated the greatest flux between the analysis achieved at optimal
and sub-optimal PHB flux. This highlighted which reactions in the GSMN exhibited the
greatest differences between achieving optimal and sub-optimal PHB flux and therefore
which reactions were most important for achieving optimising of the objective function,
PHB. The analysis was repeated with neurotoxin flux as an objective function and the results
of both FVA arranged into a network of reactions fundamental to both PHB and neurotoxin
(Section 4. 1).
58
Chapter 3: Validation of the surrogate system – Investigation of metabolism
and biomarkers of neurotoxin biosynthesis in Clostridium sporogenes.
3.1 Strain Selection
Selecting a strain of bacteria which lends its physiology to a processes advantage is an
important consideration in industrial microbiology (Aucamp et al, 2014). Fifteen strains of
C. sporogenes were obtained (See Chapter 2, Table 2) and evaluated in terms of cell growth
rate and biomarker expression in order to assess their suitability to fulfil the primary
objectives of the research project. Maximum cell density was obtained from incubation and
turbidity measurement in CMM medium (Figures 5 & 6) for 24 & 72 hours at 37oC in an
anaerobic cabinet.
59
Growth of fifteen strains of C. sporogenes in CMM for 24h
3
Growth (OD560)
2.5
2
1.5
1
0.5
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Strain number
Figure 5: The growth (OD560) of fifteen different strains of C. sporogenes grown in CMM for
24 hours in an anaerobic cabinet maintained at 37 oC. As shown by the data, many strains
had grown significantly in comparison with others, emphasising the metabolic variation
between strains. Results displayed are averages of triplicate biological and technical repeats.
Error bars are representative of the standard error of the mean.
60
Growth of fifteen strains of C. sporogenes in CMM for 72h
3
Growth (OD560)
2.5
2
1.5
1
0.5
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Strain number
Figure 6: The growth (OD560) of fifteen different strains of C. sporogenes grown in CMM for
72 hours in an anaerobic cabinet maintained at 37oC. Growth at 72 hours was fairly
analogous among the strains when compared with the data obtained at 24 hours,
suggesting strain differences in lag phase and growth rates. Results displayed are averages
of triplicate biological and technical repeats. Error bars are representative of the standard
error of the mean.
Selecting a strain of C. sporogenes which exhibited a significant growth rate was an
important consideration owing to the time constraints of the research project. Furthermore,
commercially used research strains of both C. sporogenes and C. botulinum typically
complete a growth cycle within 24 hours (Bonventre and Kempe, 1960; Emeruwa &
Hawirko, 1974) and PHB accumulation, flagellin biosynthesis and the initiation of
sporulation are events observed in the first 24 hours of growth (Artin et al, 2010; Benoit et
al, 1990; Bonventre and Kempe, 1960; Cooksley et al, 2010; Emeruwa & Hawirko, 1974).
Sufficient growth after 24 hours incubation was therefore a necessary requirement when
61
selecting a strain suitable for the research project. The vast majority of physiological events
concerning this research project occur within the first 24 hours of growth in both C.
sporogenes and C. botulinum (Artin et al, 2010; Benoit et al, 1990; Bonventre and Kempe,
1960; Cooksley et al, 2010; Emeruwa & Hawirko, 1974), therefore cultures were grown for
72 hours (Figure 6) to confirm which strains had reached stationary phase by 24 hours
incubation. The difference between cell density at 24 & 72 hours was then considered
during strain selection (see equation below).
The fifteen strains of C. sporogenes were also tested following 24 hours incubation for the
accumulation of the energy storage polymer, PHB (Figure 7); a hypothesised biomarker of
neurotoxin biosynthesis that has been directly correlated with toxin production in other
species (Navarro et al, 2006). The presence of spores in the cultures was confirmed by
microscopic observation in a haemocytometer and the presence of the protein flagellin in
the culture supernatant was determined by SDS-PAGE. The fifteen strains were also tested
on their ability to grow in a typical neurotoxin production medium and a defined clostridium
growth medium (Karasawa et al, 1995).
62
PHB accumulation in fifteen strains of C. sporogenes following
24h incubation in CMM
1.8
PhB (mg/g biomass)
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Strain number
Figure 7: PHB accumulation in fifteen different strains of C. sporogenes grown in CMM for
24 hours in an anaerobic cabinet maintained at 37 oC. PHB accumulation in the cells ranged
from 0.61-1.29 which was less diverse than the growth observed at 24 hours between
strains. As the data is expressed per gram of biomass, total PHB is dependent on biomass.
Results displayed are averages of triplicate biological and technical repeats. Error bars are
representative of the standard error of the mean.
Many studies have investigated and confirmed the diversity of strains within a species
(Davis & Nixon, 1992; Fernández-Espinar et al, 2001). The ranges of cell densities and PHB
accumulation between the strains of C. sporogenes tested were indicative of the variation
between phenotypes in a single species and emphasised the importance of strain selection
in microbial processes. An algorithm (shown below) was applied to the data displayed in
figure 5, 6 and 7 to mathematically select the strain which best lends its physiology to the
research project. Supernatant flagellin, sporulation and growth in defined and selective
63
medium were satisfactory in all strains and therefore were not deciding factors in strain
selection.
Determining optimal strain selection =
∑(a/b-a) × c
a = Growth (OD560) at 24h, b = growth (OD560) at 72h, c = PHB accumulation (mg/g biomass)
Using the equation above, Strain 10 (NCIMB12343) was determined as the most suitable
strain to experimentally achieve the primary objectives of the research project in
comparison to the other strains tested.
64
3.2 Bioinformatic Comparison of C. sporogenes & C. botulinum
C. sporogenes is widely used as a surrogate for studies in C. botulinum owing to the genetic
similarity between the two species and the safety concerns associated with C. botulinum
(Bradbury et al, 2012; Brown et al, 2012; Cooksley et al, 2010). Published comparisons have
identified a gene homology of >85% between C. sporogenes and C. botulinum (Bradbury et
al, 2012), with genome nucleotide similarities between 99-100% (Bradbury et al, 2012). In
comparison, genetic homology is ~90% between different serotypes of C. botulinum
(Virginia et al, 2014) and with the influence of the genetic variation between different
strains of the same species, genetic taxonomy between C. sporogenes and C. botulinum can
prove untenable (Kalia et al, 2011).
Genetic annotation software (RAST prokaryotic genome annotation server, the SEED) was
used to compare the genomes of C. botulinum (type A) and C. sporogenes (ATCC15579),
which were obtained from NCBI FTP and used to develop the GSMNs employed in this
study. A dot plot (Figure 8) was obtained of the ‘bidirectional hits’ arising from a comparison
of the genomes. The plot represents genetic matches or regions of strong similarities
(identical genes or function) between the C. sporogenes and C. botulinum genomes tested.
The majority of genes compared were identical (>86%), confirming that C. sporogenes is
genetically a suitable surrogate organism to study the metabolism and behaviour of C.
botulinum.
65
Genomic comparison of C. botulinum and
C.sporogenes
Identical Genes
Genetic Difference
Figure 8: Genomic comparison of C. botulinum (type A) and C. sporogenes (ATCC15579)
using RAST - Prokaryotic genome annotation server (the SEED). The chart represents the
‘bidirectional hits’ between the genomes which is representative of the ‘identical genes’.
The comparison confirmed a strong genomic homology between the species reinforcing the
use of C. sporogenes as a surrogate organism for studies of C. botulinum.
66
3.3 The Effect of Carbon, Nitrogen and Phosphate limitation on Growth & Biomarker
Metabolism in Cultures of C. Sporogenes
Secondary metabolism is triggered by nutrient limitations which activate biochemical
pathways, resulting in the biosynthesis of secondary metabolites such as antibiotics and
toxins (Chater & Horinouchi, 2003). Therefore alterations in culture growth medium, and
the microbial environment, can have impact on both the quantity and diversity of microbial
products produced in culture (VanderMolen et al, 2013). The biosynthesis of many different
types of antibiotics and other secondary metabolites is regulated by phosphate (Martin,
2004). Production of these microbial products, including streptomycin, oxytetracycline,
clavulanic acid, tylosin, echinomycin, cephalosporin, cephamycin C and thienamycin, among
many other valuable compounds, occurs only under phosphate-limiting conditions (Liras et
al, 1990; Martin, 1989; Masuma et al, 1986).
The quality and quantity of the nitrogen source in the microbial environment has also been
correlated with the biosynthesis of many secondary metabolites (Tudzynski, 2014). These
include sterigmatocystin, aflatoxin, fusaric acid, patulin, gibberellin and penicillin (Calvo et
al, 2002; Ehrlich and Cotty, 2002; Niehaus et al, 2014; Wiemann et al, 2009).
Many secondary metabolites have been associated with carbon limitation. Glucose is the
preferred microbial carbon source, and as a consequence, excess glucose availability can
interfere with the production of many secondary metabolites (Demain, 1989; Ruiz et al,
2010). Carbon regulation is essential to the growth of most organisms and given the
association between secondary metabolites and lower levels of growth, altering the carbon
source and concentration is an effective method to control secondary metabolite
biosynthesis (Demain, 1989; Ruiz et al, 2010). Important secondary metabolites including
67
streptomycin, kanamycin, istamycin, neomycin, gentamicin and β-lactam antibiotics are all
suppressed by the presence of a sufficient carbon source (Demain, 1989; Ruiz et al, 2010;
Piepersberg & Distler, 1997).
A series of experiments were designed to explore the effects of carbon, nitrogen and
phosphate limitations on both the growth and the production of the various biomarkers of
interest with regard to neurotoxin biosynthesis in cultures of C. sporogenes (strain
NCIMB12343). Cell density, dry cell weight, sporulation, PHB production and supernatant
flagellin were experimentally assessed. The basal defined medium (BDM) detailed by
Karasawa et al, 1995 (Section 2.2) (Karasawa et al, 1995) was adapted to meet the demands
of the experiment, altering the concentration of glucose as a carbon source, ammonium
sulphate as a nitrogen source and both sodium phosphate and potassium phosphate
simultaneously as phosphate sources.
A series of flasks containing a range of nutrient concentrations were prepared to test the
effects of different growth - limiting nutrient concentrations on the metabolic behaviour of
C. sporogenes (Section 2.2). All cultures were incubated in an anaerobic cabinet (Don
Whitley Scientific, UK) at 37oC and inoculated with the same seed culture of C. sporogenes
which was prepared from 24 hours growth at 37oC on Sheep blood agar in an anaerobic jar.
Harvested cells were washed, resuspended in sterile Milli-Q water and measured in 100µl
aliquots to inoculate the flasks. Biological triplicates were prepared for all flasks and strict
axenic culture conditions were maintained throughout experimentation.
68
3.3.1 The Effect of Phosphate Concentration on the Growth of C. sporogenes
Cultures grown in BDM containing a range of phosphate concentrations (125mg/L –
1500mg/L) were prepared to identify the concentration of phosphate which limited the
growth of C. sporogenes. Sodium phosphate and potassium phosphate were altered
simultaneously to maintain the original ratio of the nutrients detailed in the BDM medium
(Karasawa et al, 1995) (Appendix 8.4).
The effect of phosphate concentration on growth of C. sporogenes in
flask culture
0.7
0.6
Growth (OD560)
0.5
1500mg/L
1000mg/L
0.4
500mg/L
0.3
250mg/L
125mg/L
0.2
0.1
0
0
3
6
9
12
18
24
Time (h)
Figure 9: The effect of phosphate concentration on the growth of C. sporogenes in flask
culture. All flasks were inoculated from the same seed culture and incubated at 37 oC in an
anaerobic cabinet for 24h. Data displayed are averages of triplicate biological samples and
triplicate technical repeats. Error bars represent the standard error of the mean.
69
Effect of phosphate concentration on growth at early stationary
phase
0.6
R² = 0.9356
0.5
OD560
0.4
0.3
0.2
0.1
0
125mg
250mg
500mg
1000mg
1500mg
Na2PO4/L
Figure 10: The effect of phosphate concentration on the growth of C. sporogenes in flask
culture. Data shown are cell density values at early stationary phase of the culture (12h).
Phosphate concentration had a linear effect on growth (R2 = 0.9356) up to a concentration
of 1000mg/L, demonstrated by no significant difference in growth being observed between
cultures grown in 1000mg/L & 1500mg/L (P = 0.3877). Data displayed are averages of
triplicate biological samples and triplicate technical repeats. Error bars represent the
standard error of the mean.
70
The effect of phosphate concentration on growth rate (y = ln x) of
C.sporogenes
Growth rate (slope gradient/y = ln x)
1.1
1.05
1
0.95
0.9
0.85
125mg
250mg
500mg
1000mg
1500mg
Na2PO4/L
Figure 11: The effect of phosphate concentration on growth rate of C. sporogenes cultures.
Values displayed are the slope gradient of the exponential phase of growth (calculated using
the equation; y = ln x. Applies to all growth rates in section). Data used to calculate values
were averages of triplicate biological samples and triplicate technical repeats.
Final cell density (Figures 9 and 10) and growth rate (Figure 11) were affected by phosphate
concentration. The concentration of phosphate present in the medium had a linear effect
on growth (R2= 0.9356). This indicates that phosphate was limiting growth in cultures
containing less than 1000mg/L. No statistical difference was found between the growth of C.
sporogenes in flasks containing phosphate concentrations of 1000mg/L and 1500mg/L (P =
0.3877) using Student’s t-test. Despite demonstrating both reduced cell density and growth
rate, cultures grown in medium containing 125mg/L phosphate were able to complete a full
growth cycle, reaching stationary phase by 24h, several hours later than cultures provided
with sufficient phosphate. According to literature, conditions in which growth is limited by
the availability of phosphate in the growth medium encourage the biosynthesis of
71
secondary metabolites (Liras et al, 1990; Martin, 1989; Masuma et al, 1986). The significant
effect on growth observed in these cultures (Figure 9, 10 & 11) advocated the requirement
to analyse the metabolic effects of limiting phosphate on the potential biomarkers of
neurotoxin biosynthesis.
72
3.3.2 The Effect of Nitrogen Concentration on the Growth of C. sporogenes
The effect of ammonium sulphate concentration on the growth of C. sporogenes was
assessed at five different concentrations, including flasks containing BDM with the exclusion
of ammonium sulphate. Owing to the fact C. sporogenes requires amino acids for growth
(Karasawa et al, 1995), ammonium sulphate was not the sole nitrogen source in the growth
medium and therefore this experiment aimed to assess the effect of additional nitrogen on
the growth of C. sporogenes cultures.
The effect of Ammonium Sulphate concentration on growth of
C.sporogenes in flask culture
0.6
Growth (OD560)
0.5
0.4
1000mg/L
750mg/L
0.3
500mg/L
250mg/L
0.2
0mg/L
0.1
0
0
3
6
9
12
18
24
Time (h)
Figure 12: The effect of ammonium sulphate concentration on the growth of C. sporogenes
in flask culture. All flasks were inoculated from the same seed culture and incubated at 37 oC
in an anaerobic cabinet for 24h. Data displayed are averages of triplicate biological samples
and triplicate technical repeats. Error bars represent the standard error of the mean.
73
Effect of ammonium concentration on growth at early stationary
phase
0.6
Growth (OD560)
0.5
0.4
0.3
0.2
0.1
0
0mg
250mg
500mg
750mg
1000mg
(NH4)2SO4/L
Figure 13: The effect of ammonium concentration on the growth of C. sporogenes in flask
culture. Data shown are cell density values at early stationary phase of the culture.
Ammonium concentration had no significant effect on cell density at any of the
concentrations tested. Data displayed are averages of triplicate biological samples and
triplicate technical repeats. Error bars represent the standard error of the mean.
74
Growth rate (slope gradient/y = ln x)
The effect of ammonium concentration on growth rate (y = ln x)
of C.sporogenes
0.8
0.75
0.7
0.65
0.6
0.55
0.5
0mg
250mg
500mg
750mg
1000mg
(NH4)2SO4/L
Figure 14: The effect of ammonium concentration on growth rate of C. sporogenes cultures.
Values displayed are the slope gradient of the exponential phase of growth (y = ln x). Data
used to calculate values were averages of triplicate biological samples and triplicate
technical repeats.
Ammonium levels in the medium did not have a significant effect on the growth yield or
growth rate (Figures 12, 13 and 14) implying that the presence of amino acids in the
medium is confounding effects due to varying ammonium concentration. This suggests
another nutrient is governing growth rate and that the amino acids present are the
preferred nitrogen source in the media; meaning the cultures tested were never nitrogen
limited. Despite this finding, it is possible that ammonia is being metabolised without having
significant effects on growth, which may lead to differences in biomarker production
between cultures. Therefore cultures containing 1000mg/L and 0mg/L (the highest and
lowest concentrations tested) were used for further experimentation.
75
3.3.3 The Effect of Carbon Concentration on Growth of C. sporogenes
Glucose is the preferred carbon source of many organisms, and as a consequence of this,
glucose availability can interfere with the production of many secondary metabolites
(Demain, 1989; Ruiz et al, 2010). In order to investigate the effects of altering the available
carbon in cultures of C. sporogenes, glucose concentration was altered in flasks of BDM
medium (Karasawa et al, 1995). Five different concentrations ranging from 200mg/L to
4000mg/L of glucose were tested.
The effect of Glucose concentration on growth of C.sporogenes in flask
culture
0.6
Growth (OD560)
0.5
0.4
4000mg/L
2000mg/L
0.3
1000mg/L
0.2
500mg/L
200mg/L
0.1
0
0
3
6
9
12
18
24
Time (h)
Figure 15: The effect of glucose concentration on the growth of C. sporogenes in flask
culture. All flasks were inoculated from the same seed culture and incubated at 37 oC in an
anaerobic cabinet for 24h. Data displayed are averages of triplicate biological samples and
triplicate technical repeats. Error bars represent the standard error of the mean.
76
Effect of glucose concentration on growth at early stationary phase
0.6
Growth (OD560)
0.5
R² = 0.8189
0.4
0.3
0.2
0.1
0
200mg
500mg
1000mg
2000mg
4000mg
Glucose/L
Figure 16: The effect of glucose concentration on the growth of C. sporogenes in flask
culture. Data shown are cell density values at early stationary phase of the culture. Glucose
concentration had a linear effect on growth from 200mg to 2000mg (R2 = 0.9256). No
significant difference was observed between cultures grown in glucose concentrations of
2000mg/L & 4000mg/L (P = 0.7114), proving glucose was no longer limiting growth at
concentrations of 2000mg/L and above. This affected the linearity of the correlation when
cultures grown in 4000mg/L glucose were included (R2 = 0.8189). Data displayed are
averages of triplicate biological samples and triplicate technical repeats. Error bars
represent the standard error of the mean.
77
The effect of Glucose concentration on growth rate (y = ln x) of C.
sporogenes
Growth rate (slope gradient/ y = ln x)
0.8
0.75
0.7
0.65
0.6
0.55
0.5
200mg
500mg
1000mg
2000mg
4000mg
Glucose/L
Figure 17: The effect of glucose concentration on growth rate of C. sporogenes cultures.
Values displayed are the slope gradient of the exponential phase of growth (y = ln x). Data
used to calculate values were averages of triplicate biological samples and triplicate
technical repeats.
The data shows that glucose concentration is limiting growth in cultures grown in media
containing less than 2000mg/L. No significant difference in growth was calculated in
cultures grown in media containing 2000mg/L and 4000mg/L using a student’s t test
(P = 0.7114). The effect of glucose concentration in the medium was linear below 2000mg/L
(R2 = 0.9256). Growth rate was noticeably decreased in cultures containing 200mg/L and
500mg/L, however the highest growth rate was observed in cultures containing 1000mg/L,
despite these cultures demonstrating lower total growth. This could be due to the lower
78
concentration of environmental glucose leading to a switch in uptake mechanism, resulting
in a higher affinity for glucose, meaning it is metabolised faster during the exponential
phase of growth. If this is the case, the glucose in the production medium will therefore be
fully utilised earlier than in cultures with a higher concentration, leading to the lower total
growth but higher growth rate observed in these cultures.
79
3.3.4 The Effects of Nutrient Limitation on Sporulation, PHB and Flagellin Production in
Cultures of C. sporogenes
Cultures grown at various concentrations of nitrogen, carbon and phosphate were selected
to assay the production of the biomarkers significant to the research project; PHB,
supernatant flagellin and sporulation. Samples taken from cultures grown in previously
determined concentrations (Sections 3.3.1, 3.3.2 & 3.3.3) of nitrogen, carbon and phosphate
were used to represent the effect of high concentrations of the nutrients on biomarker
production. Cultures grown in the lowest concentration of nitrogen, carbon and phosphate
which demonstrated sufficient growth were selected to represented nutrient limited
conditions and examine the effects that these conditions had on the potential biomarkers of
neurotoxin biosynthesis. Sporulation was assayed using a haemocytometer and phase
contrast microscopy (Valdez & Piccolo, 2006); flagellin was assayed by SDS-PAGE with band
densitometry (Twine et al, 2009) and PHB production by UV spectrophotometry following
conversion into crotonic acid (Law & Slepecky, 1961).
80
3.3.4.1 The Effect of Phosphate Concentration on Biomarker Production in Cultures of
C. sporogenes.
The effects of phosphate concentration on bacterial metabolism and subsequent
biosynthesis of microbial products has been demonstrated extensively (Liras et al, 1990;
Martin, 1989; Martin, 2004; Masuma et al, 1986). Using concentrations known to establish
metabolic differences in cultures of C. sporogenes (Figures 9, 10 & 11), flasks containing
BDM (Karasawa et al, 1995) were prepared with sodium phosphate concentrations of
1500mg/L and 125mg/L. The experiment was used to test the effects of phosphate
concentration on the potential biomarkers of neurotoxin biosynthesis, which were
identified from reviewing the literature regarding this project in respective to the objectives
of the research. Growth was assessed by both optical density and dry cell weight
measurement; phosphate metabolism was analysed using reflectometry; sporulation was
assayed using a haemocytometer and phase contrast microscopy (Valdez & Piccolo, 2006);
flagellin was assayed by SDS-PAGE with band densitometry (Twine et al, 2009) and PHB
production by UV spectrophotometry following conversion into crotonic acid (Law &
Slepecky, 1961).
81
Growth, phosphate metabolism and biomarker production over time
1500mg/L Phosphate
20
0.7
18
0.6
Flagellin
0.5
14
12
0.4
10
0.3
8
6
0.2
4
Growth (OD560)
Flagellin (µg/ml-2);
PhB (% cell weight);
Spores (spores/µl);
Phosphate (mg x 102/L)
16
PhB
Spores
Phosphate
Growth
0.1
2
0
0
0
3
6
9
12
18
24
Time (h)
Figure 18: Growth, phosphate metabolism, sporulation, flagellin production and PHB
accumulation over time in cultures of C. sporogenes grown in BDM medium (Karasawa et al,
1995) supplemented with 1500mg/L sodium phosphate (data shown represents phosphate
concentration from sodium salt). This experiment was designed to analyse cultures grown
with sufficient levels of phosphate for comparison with phosphate limited cultures.
Phosphate concentrations of 1500mg/L were deemed sufficient by previous experiments
which observed no significant effect on biomass or growth rate when decreasing phosphate
to 1000mg/L (P = 0.3877). PHB and flagellin are displayed from 9h onwards as cellular
density was too low for accurate determination before this stage of the culture. Data
displayed are averages of triplicate biological samples and triplicate technical repeats. Error
bars represent the standard error of the mean.
82
Growth, phosphate metabolism and biomarker production over time
125mg/L Phosphate
24
0.5
20
Flagellin
16
0.3
12
0.2
8
Growth (OD560)
Flagellin (µg/ml-2);
PhB (% cell weight);
Spores (spores/ml x 3);
Phosphate (mgx10/L)
0.4
PhB
Spores
Phosphate
Growth
0.1
4
0
0
0
3
6
9
12
18
24
Time (h)
Figure 19: Growth, phosphate metabolism, sporulation, flagellin production and PHB
accumulation over time in cultures of C. sporogenes grown in BDM medium (Karasawa et al,
1995) supplemented with 125mg/L sodium phosphate. This experiment was designed to
analyse cultures grown under phosphate limited conditions. Limitation was confirmed by
previous experiments which demonstrated a significant effect on culture growth when
phosphate concentration was 125mg/L (Figures 9, 10 & 11). PHB and flagellin are displayed
from 9h onwards as cellular density was too low for accurate determination before this
stage of the culture. Data displayed are averages of triplicate biological samples and
triplicate technical repeats. Error bars represent the standard error of the mean.
83
It is well established that reducing phosphate in the growth medium leads to an increase in
the accumulation of PHB (Lillo & Rodriguez-Valera, 1990; Ryu et al, 2007; Shang et al, 2003).
In cultures containing 125mg/L sodium phosphate, PHB was increased, demonstrating the
highest observed PHB accumulation of all conditions tested. Sporulation was also increased
showing the highest number of spores in all flasks tested by 24 hours. Contrary to previous
findings however, flagellin present in the supernatant was also increased in cultures limited
by phosphate in the growth medium when compared with those grown in 1500mg/L sodium
phosphate. These findings may require refining of the hypothesis to include the possibility
that flagellin production and PHB accumulation are not directly correlated, but are being
independently effected by the nutrient limitations imposed. On the other hand, these
findings could be explained by the fact cultures grown in phosphate limited conditions
demonstrated a delayed stationary phase/extended exponential phase. Transciptome
analysis of R. eutropha in previous studies have shown that the gene fliC (H16_B2360) which
encodes flagellin (Macnab, 2003) and flgE (H16_B0264) which encodes the flagellar hook
protein, were significantly repressed in the stationary growth phase (Peplinski et al, 2010).
84
3.3.4.2 The Effect of Nitrogen Concentration on Biomarker Production in Cultures of
C. sporogenes.
Previous analysis demonstrated that altering the ammonium concentration of the growth
medium had no effect on the growth rate or cell density of C. sporogenes cultures (Figures
12, 13 & 14). This suggests the amino acids present are most likely governing growth rate
and are the preferred nitrogen source in the media. Despite this finding, it is possible that
ammonium is being metabolised without having observable effects on growth rate or
biomass, which may affect other areas of metabolism and lead to differences in biomarker
production. Therefore cultures containing 1000mg/L and 0mg/L (the highest and lowest
concentrations tested in previous experiments; section 3.3.2) were tested to assess the
effects of ammonia concentration on the potential biomarkers of neurotoxin biosynthesis.
Growth was assessed by both optical density and dry cell weight, ammonia metabolism was
analysed using reflectometry, sporulation was assayed by haemocytometry using phase
contrast microscopy, flagellin was isolated by SDS-PAGE and quantified using band
densitometry and PHB accumulation was assayed by UV spectrophotometry following
conversion into crotonic acid.
85
20
0.5
18
0.45
16
0.4
14
0.35
12
0.3
10
0.25
8
0.2
6
0.15
4
0.1
2
0.05
0
Growth (OD560); Ammonium (g/L)
Flagellin (µg/ml-2);
PhB (% cell weight);
Spores (spores/ml x 3)
Growth, ammonium metabolism and biomarker production over time
1000mg/L Ammonium Sulphate
Flagellin
PhB
Spores
Growth
Ammonium
0
0
3
6
9
12
18
24
Time (h)
Figure 20: Growth, ammonium metabolism, sporulation, flagellin production and PHB
accumulation over time in cultures of C. sporogenes grown in BDM medium (Karasawa et al,
1995) supplemented with 1000mg/L of ammonium sulphate. This experiment was designed
to analyse cultures grown with the addition of ammonium for comparison with cultures
grown without supplementation (0mg/L ammonium). PHB and flagellin are displayed from
9h onwards as cellular density was too low for accurate determination before this stage of
the culture. Data displayed are averages of triplicate biological samples and triplicate
technical repeats. Error bars represent the standard error of the mean.
86
18
0.5
16
0.45
14
0.4
0.35
12
0.3
10
0.25
8
0.2
6
0.15
4
0.1
2
0.05
0
Growth (OD560)
Flagellin (µg/ml-2);
PhB (% cell weight);
Spores (sporesx103:dcw)
Growth, Ammonia metabolism and Biomarker Production over Time
0mg/L Ammonium Sulphate
Flagellin
PhB
Spores
Growth
Ammonium
0
0
3
6
9
12
18
24
Time (h)
Figure 21: Growth, ammonium metabolism, sporulation, flagellin production and PHB
accumulation over time in cultures of C. sporogenes grown in BDM medium (Karasawa et al,
1995) with no ammonium sulphate (0mg/L). Despite previous experiments demonstrating
the absence of ammonium from the growth medium did not affect growth rate or yield, this
experiment was designed to investigate any metabolic effects regarding sporulation,
flagellin production or PHB accumulation influenced by differences in available ammonium.
PHB and flagellin are displayed from 9h onwards as cellular density was too low for accurate
determination before this stage of the culture. Data displayed are averages of triplicate
biological samples and triplicate technical repeats. Error bars represent the standard error
of the mean.
87
As shown in Figure 20, cultures metabolised the majority of the ammonium present in the
growth medium during the first 6 hours in flasks containing 1000mg/L ammonium sulphate,
but was never fully utilised and remained at low concentrations in later stages of the
culture. Despite this, these cultures demonstrated little difference in terms of both growth
and sporulation when compared with cultures grown in the absence of ammonium (0mg/L;
Figure 21). Interestingly however, both PHB and flagellin production were affected by the
nutritional differences despite growth remaining unaffected, suggesting the ammonium was
having metabolic effects on C. sporogenes but also that PHB and flagellin production are not
directly correlated with growth.
Flagellin present in the supernatant was increased (56%) in cultures containing 1000mg/L
ammonium sulphate compared with those containing 0mg/L. Furthermore, PHB in the
cultures with 0mg/L was increased compared to those grown in 1000mg/L ammonium
sulphate. This finding agrees with the work of Raberg et al (Raberg et al, 2008) which
demonstrated that limiting nitrogen increased cellular PHB and limited flagellin in
R. eutropha. The correlation was also demonstrated by Peplinski et al (Peplinski et al, 2010).
Whether the excess nitrogen supplied by the ammonium supplementing the growth
medium is affecting PHB and flagellin as separate metabolites or whether the differences in
flagellin production observed is directly correlated with the accumulation of PHB is difficult
to define. Nevertheless, the prospect that flagellin production and PHB accumulation may
be closely related, combined with the metabolic information on PHB accumulation from
both the literature and demonstrated by this research, may prove to be a powerful tool in
understanding flagellin as a by-product.
88
3.3.4.3 The Effect of Carbon Concentration on Biomarker Production in Cultures of
C. sporogenes.
Glucose is the preferred carbon source of many organisms, and as a consequence of this,
glucose availability can interfere with the production of many secondary metabolites
(Demain, 1989; Ruiz et al, 2010). The effect of lowering glucose concentration, resulting in
the growth of C. sporogenes cultures being limited by the carbon source’s availability, is
demonstrated in Figures 15, 16 and 17 (Section 3.3.3). Following analysis of the results,
cultures grown in BDM (Karasawa et al, 1995) containing 4000mg/L and 500mg/L of Glucose
were assessed to investigate the effect of carbon limitation on the metabolism of C.
sporogenes and the subsequent influence on the potential biomarkers of neurotoxin
biosynthesis. Growth was assessed by both optical density and dry cell weight, glucose
metabolism was analysed using reflectometry, sporulation was assayed by haemocytometry
using phase contrast microscopy, flagellin was isolated by SDS-PAGE and quantified using
band densitometry and PHB accumulation was assayed by UV spectrophotometry following
conversion into crotonic acid.
89
Growth, Glucose metabolism and Biomarker Production over Time
4000mg/L Glucose
0.6
14
0.5
Flagellin (µg/ml-2);
PhB (% cell weight);
Spores (spores/ml x 4)
12
0.4
10
8
0.3
6
0.2
4
0.1
2
0
Growth (OD560); Glucose (mg x 104/L)
16
Flagellin
PhB
Spores
Growth
Glucose
0
0
3
6
9
12
18
24
Time (h)
Figure 22: Growth, glucose metabolism, sporulation, flagellin production and PHB
accumulation over time in cultures of C. sporogenes grown in BDM medium (Karasawa et al,
1995) supplemented with 4000mg/L of glucose. This experiment was designed to analyse
cultures of C. sporogenes grown with sufficient glucose for optimal growth. Concentrations
of glucose which yielded optimal growth and those which resulted in carbon limited cultures
were assessed in section 3.3.3 (Figures 15, 16 & 17). PHB and flagellin are displayed from 9h
onwards as cellular density was too low for accurate determination before this stage of the
culture. Data displayed are averages of triplicate biological samples and triplicate technical
repeats. Error bars represent the standard error of the mean.
90
16
0.45
14
0.4
0.35
Flagellin (µg/ml-2);
PhB (% cell weight);
Spores (spores/ml x 3)
12
0.3
10
0.25
8
0.2
6
0.15
4
0.1
2
Growth (OD560); Glucose (mg x 104/L)
Growth, Glucose metabolism and Biomarker Production over Time
500mg/L Glucose
Flagellin
PhB
Spores
Growth
Glucose
0.05
0
0
0
3
6
9
12
18
24
Time (h)
Figure 23: Growth, glucose metabolism, sporulation, flagellin production and PHB
accumulation over time in cultures of C. sporogenes grown in BDM medium (Karasawa et al,
1995) supplemented with 500mg/L of glucose. This experiment was designed to analyse
cultures of C. sporogenes limited by the available glucose in the growth environment.
Confirmation that cultures of C. sporogenes grown in 500mg/L glucose are carbon limited
was demonstrated by previous experiments (Figures 15, 16 & 17). PHB and flagellin are
displayed from 9h onwards as cellular density was too low for accurate determination
before this stage of the culture. Data displayed are averages of triplicate biological samples
and triplicate technical repeats. Error bars represent the standard error of the mean.
91
The biomass yields in cultures containing 4000mg/L and 500mg/L reflected the two different
glucose concentrations, confirming that cultures containing 500mg/L were glucose limited.
In both cultures, the majority of glucose was metabolised in the first 6h of the culture,
driving an exponential growth phase between 6 and 12h. Interestingly, the cultures grown in
4000mg/L of glucose never fully exhausted the glucose in the media, and it remained
present throughout the growth cycle at low levels, suggesting one of the other nutrients
present in the growth medium had become limiting before the end of the culture. In the
flasks which originally contained 500mg/L glucose however, glucose is fully exhausted by
12h. It is during the time point between 9 and 12h when glucose is exhausted, that changes
in the levels of biomarkers are observed. Perhaps the most interesting finding is the level of
PHB present in the cells. Prior to glucose exhaustion, PHB accumulations to a relatively low
concentration, which is expected in carbon limited cultures owing to the biochemistry of
PHB production (Anderson & Dawes, 1990; Mignone & Avignone-Rossa, 1996; Steinbuchel,
1991). However, at the same time that glucose is exhausted in the cultures, an increase in
PHB accumulation is being driven. This PHB increase is not observed in cultures grown in
4000mg/L glucose, in which glucose is never fully exhausted throughout the culture. Based
on the data that glucose is fully exhausted at this time in cultures grown in 500mg/L glucose
(Figure 23), it is likely that C. sporogenes is switching from metabolising glucose as a primary
carbon source to relying on the carbon in the amino acids present within the production
medium. Furthermore, flagellin in the supernatant was also observed to increase following
glucose exhaustion, although whether these events are linked are not is less easy to define
owing to the fact flagellin was observed to increase over time in other cultures.
Nevertheless, this spurs the hypothesis to whether the metabolism of different amino acids
lead to differences in observed biomarker production.
92
The data also showed lower levels of sporulation present in cultures grown in 500mg/L
glucose, despite the optical density suggesting the cultures had begun death phase as early
as 24h. It is already established that carbon availability (predominantly stored PHB) is
necessary to drive sporulation (Emeruwa & Hawirko, 1973), and it is possible that the level
of sporulation was decreased due to less available carbon in the production medium. There
is a possibility that the growth rate was slowed due to C. sporogenes beginning to
metabolise amino acids as a primary carbon source, and this slower growth rate led to less
cells developing into spores by 24h, despite the decline in growth values (OD 560) observed.
Another interesting observation was the flagellin present in the supernatant of the cultures
grown in medium containing 4000mg/L glucose. The high concentration of glucose
appeared to delay the production/excretion of flagellin into the growth medium. This also
resulted in the lowest overall concentration of flagellin across all the conditions tested. This
finding is similar to the findings of Raberg et al (Raberg et al, 2008) in R. eutropha, which
demonstrated that flagella assembly was decreased in a rich complex medium. Although the
phenomenon as to why flagellin is produced in excess remains unclear, the fact that
providing the cultures with excess energy resources reduces its production advocates the
possibility that excess flagellin production is a reaction to nutrient limitation and may be
affected by secondary metabolism. One possible explanation for this mechanism may be the
result of the organism attempting to express motility as a nutritional stress response,
therefore increasing the organism’s ability to locate a stable nutrient supply. This finding
may also be related to a pathogenic mechanism; coinciding the production of toxin with the
pathogenic properties of flagellin (Claret et al, 2007; Haiko & Westerlund, 2013).
93
3.3.5 Summary of the Effects of Nutrient Limitation on Sporulation, PHB and Flagellin
Production in Cultures of C. sporogenes
The results of the experiments covered in section 3.3 demonstrate that the availability of
nitrogen, carbon and phosphate present in the growth medium is affecting the production
of flagellin, accumulation of PHB and sporulation in C. sporogenes; the potential biomarkers
of neurotoxin biosynthesis which were identified following review of the literature regarding
this research project. Phosphate limitation resulted in the greatest increase in all three
biomarkers tested: Flagellin, PHB and spores (Table 2 & Figure 24). Although the effects of
phosphate on bacterial metabolism are well established (Liras et al, 1990; Martin, 1989;
Martin, 2004; Masuma et al, 1986), the data obtained investigating the effects of carbon
and nitrogen concentration has highlighted the fact that the metabolism of C. sporogenes is
also being controlled by the concentration of different amino acids present in the growth
medium.
Several members the of Clostridia genus are able to ferment amino acids in a mode of
metabolism termed Stickland reactions (Bouillaut et al, 2013; Stickland, 1934; Stickland,
1935). Stickland reactions couple metabolism of pairs of amino acids in which one amino
acid, acting as an electron donor, is oxidatively deaminated or decarboxylated and a second
amino acid, acting as an electron acceptor, is reduced or reductively deaminated (Bouillaut
et al, 2013; Stickland, 1934; Stickland, 1935). This allows certain species of bacteria, such as
C. sporogenes and C. botulinum, to utilise amino acids as a primary carbon and/or nitrogen
source (Bouillaut et al, 2013). This fact, coupled with our observation led hypothesis that
amino acids present in the growth medium are affecting the metabolism of C. sporogenes
which in turn influences the biosynthesis of the biomarkers of neurotoxin production,
94
presents a requirement to experimentally assess the effects of amino acid metabolism with
regard to the primary objectives of the research project.
Biomarker
Nitrogen
Limitation
Carbon
Phosphate
Limitation Limitation
Flagellin
(mg/g)
11.9
10.0
17.2
PHB
(mg/g)
91.7
97.3
131.7
Spores
(spores x103/g)
133.3
48.5
273.8
Table 3: Maximum values of flagellin production, PHB accumulation and sporulation in
cultures of C. sporogenes grown in low concentrations of nitrogen, carbon and phosphate.
Phosphate limitation resulted in the highest values obtained in the experiment for flagellin
production, PHB accumulation and sporulation. Low nitrogen and carbon availability in the
growth medium also resulted in differences in observed biomarker metabolism, albeit less
dramatic than the effects of phosphate limitation. Values shown are relative of culture
biomass to express the effect of the nutrient limitation when compared with other cultures.
Data displayed are averages of triplicate biological samples and triplicate technical repeats.
95
The effect of carbon, nitrogen and phosphate limitation on
biomarker production in C. sporogenes
160
Flagellin (mg/g x 106)
PhB (mg/g)
Spores (spores/500mg x 103)
140
120
Flagellin
100
PhB
80
Spores
60
40
Spores
20
PhB
0
Nitrogen
Flagellin
Carbon
Phosphate
Figure 24: A perspective presentation summarising the effects of limited availability of
phosphate, carbon and nitrogen in the growth medium on flagellin, PHB and spore
production in cultures of C. sporogenes. Values shown are relative of culture biomass to
express the effect of the nutrient limitation when compared with other cultures. Units of
biomarkers have been adjusted to for improved comparison of data. Data shown are
maximum values obtained from cultures grown for 24h. Data displayed are averages of
triplicate biological samples and triplicate technical repeats.
96
3.4 Plackett-Burman Design Experimental Approach to Test the Effects of Amino Acid
Metabolism on Flagellin Production, PHB Accumulation and Sporulation in Cultures of
C. sporogenes
The findings of the experiments detailed in section 3.3, together with the published
physiology of the Clostridium amino acid production process (Bouillaut et al, 2013; Stickland,
1934; Stickland, 1935), led to the conclusion that amino acids are being utilised as a carbon
and/or nitrogen source by C. sporogenes. This finding presented a requirement to
metabolically assess the effects of amino acid consumption with regards to the objectives of
this research project.
The PBD experimental approach and principles (Section 1.5.2) were used to test the
metabolic effects of supplementing the growth medium with amino acids in cultures of
C. sporogenes (Table 4). The experiment was designed to assess the effect of individual
amino acids on growth, PHB accumulation, flagellin production and sporulation in cultures
of C. sporogenes incubated at 37oC for 24h in an anaerobic cabinet. Values displayed in the
results are t-values calculated using the PBD experiment (Plackett & Burman, 1946) and are
representative of the level of variance from flasks containing no additional amino acids and
therefore represent the calculated observed effect of the trialled amino acid. The
calculation used to determine the t-values for each variable (amino acid) also considered
the interaction between the variables (Kalil et al, 2000). Data entered into the calculation
were averages from triplicate biological and technical repeats.
97
Variables (Amino acids)
Trial
HIS
SER
PHE
ALA
ASP
ASN
ILEU
VAL
LEU
GLU
ARG
1
2
3
4
5
6
7
8
9
10
11
12
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
Table 4: PBD experiment to test the effects of amino acid supplemented growth media on
growth, PHB accumulation, flagellin production and sporulation in cultures of C. sporogenes.
‘+’ represents a supplementation of 1mmol of the corresponding amino acid to BDM growth
medium (Karasawa et al). All flasks contained the same total volume and were inoculated
from the same seed culture of C. sporogenes.
98
Effect of amino acid supplementation on growth (OD560) of
C.sporogenes cultures assessed by Plackett-Burman Design
0.6
Significance of effect (t-value)
0.4
0.2
0
PHE
ARG
LEU
GLU
ALA
HIS
SER
VAL
ILEU
ASP
ASN
-0.2
-0.4
-0.6
Figure 25: T-values of the variance of effect calculated following PBD to assess the effect of
amino acid supplementation on the growth of C. sporogenes cultures incubated for 24h.
Data shown is calculated from averages of triplicate biological and technical repeats.
Calculated values were subject to testing against three ‘dummy variables’ which decreases
the experimental error of the calculated t-values displayed.
99
Effect of amino acid supplementation on PHB Accumulation (mg/g) of
C.sporogenes cultures assessed by Plackett-Burman Design
0.6
Significance of effect (t-value)
0.4
0.2
0
ILEU
ASN
ASP
ALA
VAL
HIS
GLU
ARG
SER
LEU
PHE
-0.2
-0.4
-0.6
Figure 26: T-values of the variance of effect calculated following PBD to assess the effect of
amino acid supplementation on PHB accumulation in C. sporogenes cultures incubated for
24h. Data shown is calculated from averages of triplicate biological and technical repeats.
Calculated values were subject to testing against three ‘dummy variables’ which decreases
the experimental error of the calculated t-values displayed.
100
Effect of amino acid supplementation on Supernatant Flagellin (µg/ml)
of C.sporogenes cultures assessed by Plackett-Burman Design
Significance of effect (t-value)
0.6
0.4
0.2
0
ALA
HIS
ASN
GLU
ASP
ILEU
SER
VAL
ARG
PHE
LEU
-0.2
-0.4
-0.6
Figure 27: T-values of the variance of effect calculated following PBD to assess the effect of
amino acid supplementation on flagellin production by C. sporogenes cultures incubated for
24h. Data shown is calculated from averages of triplicate biological and technical repeats.
Calculated values were subject to testing against three ‘dummy variables’ which decreases
the experimental error of the calculated t-values displayed.
101
Effect of amino acid supplementation on Sporulation(spores/ml) of
C.sporogenes cultures assessed by Plackett-Burman Design
0.6
Significance of effect (t-value)
0.4
0.2
0
ASP
SER
GLU
ASN
HIS
ALA
ARG
PHE
VAL
ILEU
LEU
-0.2
-0.4
-0.6
Figure 28: T-values of the variance of effect calculated following PBD to assess the effect of
amino acid supplementation on sporulation in C. sporogenes cultures incubated for 24h.
Data shown is calculated from averages of triplicate biological and technical repeats.
Calculated values were subject to testing against three ‘dummy variables’ which decreases
the experimental error of the calculated t-values displayed.
102
In terms of increased growth, phenylalanine supplementation had the largest observed
effect (Figure 25). The data from the PBD experiment also shows that arginine is the second
highest ranking amino acid in terms of increasing growth. If the assumption is made that C.
sporogenes and C. botulinum share these aspects of metabolic physiology, this finding
corresponds with the various sources in the literature which suggest growth and toxin
production are largely effected by arginine in C. botulinum (Patterson-Curtis & Johnson,
1992). Asparagine had the largest detrimental effect on the growth of the organism and was
the only amino acid tested which was not included in the original BDM formula; although no
indication to why asparagine is excluded from the BDM is mentioned by Karasawa et al
(Karasawa et al, 1995). Supplementation with a number of other amino acids, namely
valine, isoleucine and aspartate resulted in detrimental effects on growth. Interestingly, the
biosynthesis pathways for these three amino acids are all metabolites of pyruvate
(Figure 29). Supplementation of these amino acids may have lowered the demand for
metabolites involved in the TCA cycle, indicating that their biosynthesis may be rate limiting
factors in cell biomass synthesis. Phenylalanine, the amino acid which resulted in the
greatest increase in growth observed, has a biosynthesis pathway early in glycolysis
(Figure 29). Therefore supplementation with phenylalanine may result in more available
carbon and nitrogen for the production of metabolites in the TCA cycle by lowering the
demand for phenylalanine, which is synthesised from precursors of glycolysis, leading to the
conclusion that C. sporogenes grown in BDM is rate-limited by metabolites that feed directly
into the TCA cycle.
Moreover, the differences observed on growth, both positive and negative, may be due to
the metabolic cost associated with the metabolism of amino acids. A substantial fraction of
103
the energy budget of bacteria is devoted to biosynthesis of amino acids (Akashi & Gojobori,
2002). The fuelling reactions of central metabolism provide precursor metabolites for
synthesis of the 20 amino acids incorporated into proteins. Thus, synthesis of an amino acid
entails a dual cost; energy is lost by diverting chemical intermediates from fuelling reactions
and additional energy is required to convert precursor metabolites to amino acids. The
range in amino acid biosynthesis cost varies from 11 ATP equivalents per molecule of
Glycine, Alanine, and Serine to over 70 ATP per molecule of Tryptophan (Akashi & Gojobori,
2002). Observing decreased growth due to the supplementation of a particular amino acid,
may be due to the metabolic demands caused by the supplementation. Many enzymes are
inhibited by products and other metabolites in a feedback cycle (Engasser & Horvath, 1974)
and certain amino acids are precursors for other amino acids (Figures 29a and 29b).
Therefore, if a particular amino acid is added to the growth medium, this may result in a
decreased affinty of the enzymes responsible for the production of other amino acids. Using
Figure 29 and the results (Figure 25) of the experiment as an example, supplementation
with Asparagine may be in turn lowering the the demand for Aspartate, its precursor. Owing
to the fact Aspartate is also the precursor for Methoinine, this may result in the methonine
limitation, which has a negative effect on growth. On the other hand, excess Asparagine
may result in an increase in the biosynthesis of Methonine from Aspartate, which may carry
a greater metabolic cost and thus carries energy expenditure which would otherwise be
utilised in fueling growth dependant reactions.
104
Figure 29a: Generic pathways of fuelling reactions and amino acid biosynthesis pathways
(blue arrows). Image adapted from Akashi & Gojobori, 2002 (Akashi & Gojobori, 2002).
105
Figure 29b: Central metabolic reactions and amino acid biosynthesis in C. botulinum, assuming anaerobic respiration is occurring over
fermentation. (KEGG: Kanehisa Laboratories, 2012).
106
The PBD experiment showed that supplementation with Isoleucine, Asparagine and
Aspartate and to a lesser degree Valine and Alanine, increased the cellular accumulation of
PHB in cultures of C. sporogenes. Examination of the amino acid biosynthesis pathways
(Figure 29) for these amino acids shows that Isoleucine, Asparagine and Aspartate are all
synthesised from oxaloacetate; a key metabolite of the TCA cycle which reacts with acetyl
CoA to form citrate (Pentyala & Benjamin, 1995). Therefore, it is possible that
supplementation with either of these amino acids ultimately lowers the demand for
oxaloacetate or citrate, leading to more available acetyl CoA; the precursor of PHB.
Supplementation with Alanine and Valine also increased PHB accumulation, both of which
are biosynthesised from pyruvate as a precursor. The increased PHB accumulation observed
may therefore be due to the amino acid supplementation resulting in either more available
pyruvate or a lessened demand for pyruvate, which would also result in more available
acetyl CoA for PHB biosynthesis.
The majority of amino acids used to supplement the growth medium resulted in an increase
in flagellin production (Figure 27). This observation agreed with the results obtained in
nitrogen limited cultures (Figure 21) which demonstrated that increasing available nitrogen
in the growth medium increased flagellin production. Interestingly, the only amino acids
which decreased flagellin when added to the growth medium were Arginine, Phenylalanine
and Leucine; all three of which increased maximum growth. This could be due to changes in
growth rate and time spent in exponential phase, owing to the fact it has been
demonstrated that the genes responsible for flagellin production are down regulated during
stationary phase (Peplinski et al, 2010). On the other hand, the presence of the amino acids
could be having a metabolic effect on the production of flagellin precursors.
107
Supplementation with Aspartate, Asparagine and Serine all increased sporulation in the
cultures significantly (Figure 28), as well as decreased growth, suggesting that
supplementation with the amino acids was limiting a key element of central metabolism.
Interestingly, Glutamate increased both growth and sporulation, suggesting the possibility
that glutamate had increased growth rate, resulting in cell death and sporulation faster than
cultures supplemented with other amino acids.
108
3.5 In silico Analysis Investigating the Effects of Nitrogen, Glucose and Phosphate
Concentration on Neurotoxin Production by C. botulinum
The results from the experiments detailed in sections 3.3 & 3.4 demonstrated that
alteration of the growth medium composition is an effective method to alter the metabolic
influences affecting the biomarkers hypothesised to correlate with neurotoxin biosynthesis
in C. botulinum.
In an effort to compare the results obtained in C. sporogenes on growth, PHB accumulation,
flagellin production and sporulation with neurotoxin biosynthesis, we developed a GSMN of
C. botulinum, created by Bioinformatics Research Scientist Sonal Dahale, University of
Surrey. The GSMN was used to assess the effects of nitrogen (represented by ammonium
for comparison with previous experiments in C. sporogenes), glucose and phosphate
concentration on botulinum toxin production in silico. The data presented displays the FBA
values generated when testing nitrogen, glucose or phosphate flux over a minimum to
maximum range with optimal neurotoxin production (flux) as an objective function. The aim
of this analysis was to generate data representative of the effects of supplementing the
growth medium of C. botulinum cultures with these nutrients at a range of concentrations
and the subsequent effects on neurotoxin production for comparison with the data
obtained experimentally in C. sporogenes.
109
Effect of glucose flux on C. botulinum neurotoxin production
(in silico)
14
Toxin Flux (arb. units)
12
10
8
6
4
2
0
0
500
1000
1500
2000
2500
3000
Glucose Flux (arb. units)
Figure 30: The effect of glucose uptake on C. botulinum neurotoxin production (in silico)
generated using FBA to test minimum to maximum range glucose flux with optimal toxin
yield as an objective. GSMN used in analysis developed from C. botulinum type A genome
data obtained from NCBI FTP (Bao et al, 2011).
110
Effect of Ammonium flux on C. botulinum neurotoxin
production (in silico)
35
Toxin Flux (arb. units)
30
25
20
15
10
5
0
0
2000
4000
6000
8000
10000
Ammonium Flux (arb. units)
Figure 31: The effect of ammonium uptake on C. botulinum neurotoxin production (in silico)
generated using FBA to test minimum to maximum range ammonium flux with optimal toxin
yield as an objective. GSMN used in analysis developed from C. botulinum type A genome
data obtained from NCBI FTP (Bao et al, 2011).
111
Effect of Phosphate flux on C. botulinum neurotoxin
production (in silico)
35
Toxin Flux (arb. units)
30
25
20
15
10
5
0
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Phosphate Flux (arb. units)
Figure 32: The effect of phosphate uptake on C. botulinum neurotoxin production (in silico)
generated using FBA to test minimum to maximum range phosphate flux with optimal toxin
yield as an objective. GSMN used in analysis developed from C. botulinum type A genome
data obtained from NCBI FTP (Bao et al, 2011).
The in silico data generated by the GSMN (summarised in Table 5) suggests that high
ammonia, low glucose and low phosphate concentrations are optimal for neurotoxin
production in cultures of C. botulinum. However, owing to the restraints of the model and
FBA, growth of the organism is not accounted for, which is a crucial variable in toxin
production (Artin et al, 2010; Bonventre and Kempe, 1960). Although the FBA generated by
the model demonstrates that minimal glucose is optimal for toxin production, minimal
glucose in the growth medium would experimentally result in drastically reduced growth,
112
demonstrated by the experiments testing glucose limitation in C. sporogenes (Figure 15 &
16). Therefore it is more realistic to assume excess ammonia and limited phosphate are
conditions which would increase toxin production. These findings correlate with increased
sporulation and flagellin production, which were both observed in cultures containing a high
concentration of ammonium and limited phosphate in the data collected from cultures of C.
sporogenes (Figures 19 & 20). This correlation, although yet to be tested experimentally, is
an insight into the possible correlation between these biomarkers and the production of
botulinum neurotoxin; a finding which offers potential progression towards the primary
objectives of the research project.
Biomarker
Low
Nitrogen
Low
Carbon
Low
Phosphate
Flagellin
(mg/g)
11.9
10.0
17.2
91.7
97.3
131.7
Spores
(spores x103/g)
133.3
48.5
273.8
Toxin (in silico, FBA)
-
+
+
PHB
(mg/g)
Table 5: The effects of nitrogen, carbon and phosphate limitation on C. botulinum
neurotoxin production (in silico) compared with the experimental results obtained in
C. sporogenes demonstrating the effects of nutrient limitation on biomarker production. ‘+’
represents an increased production and ‘-‘ represents a decreased flux in botulinum toxin as
a result of the tested nutrient at high or low values, generated by FBA.
113
3.6 In silico Analysis of the Correlation between PHB accumulation and Neurotoxin
Production by C. botulinum
The GSMN was used to test both the suitability of C. sporogenes as a surrogate for the
studies of C. botulinum and whether in silico analysis would reveal any correlation between
the production of PHB and toxin; a correlation which has been established in Bacillus
thuringiensis (Navarro et al, 2006). The effects of glucose, ammonium, phosphate and
amino acid supplementation tested experimentally in cultures of C. sporogenes (Sections 3.3
& 3.4) were compared with data generated using the GSMN of C. botulinum obtained from
testing the flux of the nutrient supplementation on both PHB production and toxin
production as an objective. The results not only confirmed the observed PHB accumulation
in C. sporogenes to be a representation of C. botulinum metabolism, but also highlighted a
linear correlation between PHB accumulation and neurotoxin production; validating the use
of C. sporogenes as a surrogate organism for studies of C. botulinum and PHB as a potential
biomarker of neurotoxin production.
114
Effect of glucose flux on PHB production in C. botulinum (in silico)
8200
PhB Flux (arb. units)
8000
7800
7600
7400
7200
7000
6800
6600
6400
6200
0
2000
4000
Glucose Flux (arb. units)
Figure 33: The effect of glucose uptake on PHB production in C. botulinum (in silico)
generated using FBA to test minimum to maximum range glucose flux with optimal PHB
yield as an objective. GSMN used in analysis developed from C. botulinum type A genome
data obtained from NCBI FTP (Bao et al, 2011).
115
Effect of Ammonium flux on PHB production in C. botulinum (in silico)
10000
PhB Flux (arb. units)
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
0
2000
4000
6000
Ammonium Flux (arb. units)
8000
10000
Figure 34: The effect of ammonium uptake on PHB production in C. botulinum (in silico)
generated using FBA to test minimum to maximum range ammonium flux with optimal PHB
yield as an objective. GSMN used in analysis developed from C. botulinum type A genome
data obtained from NCBI FTP (Bao et al, 2011).
116
Effect of Phosphate flux on PHB production in C. botulinum (in silico)
9000
PhB Flux (arb. units)
8000
7000
6000
5000
4000
3000
2000
1000
0
0
2000
4000
6000
8000
Phosphate Flux (arb. units)
Figure 35: The effect of phosphate uptake on PHB production in C. botulinum (in silico)
generated using FBA to test minimum to maximum range phosphate flux with optimal PHB
yield as an objective. GSMN used in analysis developed from C. botulinum type A genome
data obtained from NCBI FTP (Bao et al, 2011).
117
10000
40
[1]
9000
60
8500
50
8000
40
7500
30
7000
2000
Alanine Flux (arb. units)
6000
5000
20
4000
3000
10
2000
1000
0
0
0
Toxin
1000
2000
Phenylalanine Flux (arb. units)
PhB
Toxin
PHB
[3]
10500
PhB Flux (arb. units)
7000
30
[4]
9500
10000
9500
9000
R² = 0.988
8500
8000
7500
PhB Flux (arb. units)
0
Toxin Flux (arb. units)
9500
PhB Flux (arb. units)
Toxin Flux (arb. units)
8000
70
[2]
9000
PhB Flux (arb. units)
80
8500
R² = 0.9011
7500
6500
5500
4500
30
50
Toxin Flux (arb. units)
70
0
10
20
Toxin Flux (arb. units)
30
Figure 36: The correlation between PHB and neurotoxin production in C. botulinum
(in silico). Increased Alanine uptake resulted in both increased PHB flux and Toxin flux [1].
Increased Phenylalanine uptake resulted in both decreased PHB flux and Toxin flux [2].
Comparison of the data demonstrated a linear correlation between PHB and neurotoxin
production (R2 = 0.988 & R2 = 0.9011) in relation to both Alanine uptake [3] and
Phenylalanine uptake [4]. Data generated using FBA. GSMN used in analysis developed from
C. botulinum type A genome data obtained from NCBI FTP (Bao et al, 2011).
118
The results generated by the GSMN suggest a strong linear correlation (R 2 = 0.988 & 0.901)
between PHB accumulation and neurotoxin production in C. botulinum, which was
demonstrated by plotting the results obtained from minimum to maximum flux of alanine
and phenylalanine when testing PHB and toxin production as individual objectives (Figure
36). Alanine flux increased both PHB and toxin production (Figure 36). This finding was
consistent with experimental data obtained in C. sporogenes which demonstrated alanine
supplementation increased the accumulation of PHB (Figure 26). Phenylalanine flux
decreased both PHB and toxin production (Figure 36), which was also consistent with the
data on phenylalanine supplementation in cultures of C. sporogenes (Figure 26). In silico
analysis of the effects of glucose, ammonium and phosphate uptake also demonstrated the
correlation between PHB (Figures 33, 34 & 35) and neurotoxin production (Figures 31, 32 &
33) and agreed with experimental findings in C. sporogenes (section 3.3). These results
provide confidence in the use of C. sporogenes as a surrogate organism and PHB as a
biomarker of neurotoxin production in C. botulinum.
119
3.7 Chapter Conclusions
Phosphate limitation in cultures of C. sporogenes gave rise to increased PHB accumulation
(Figure 19), presumably due to the increased availability of the storage compounds
precursor; acetyl CoA, when the progression of the TCA cycle is limited by the availability of
inorganic phosphate for optimal ATP biosynthesis (Lillo & Rodriguez-Valera, 1990; Raberg et
al, 2008; Ryu et al, 2007; Shang et al, 2003). The PBD experiment examining the effects of
amino acids present in the culture medium (Section 3.4), yielded results which were
consistent with the hypothesis that PHB biosynthesis competes with the metabolites of the
TCA cycle for carbon flux and confirmed the hypothesis that amino acid metabolism effects
biomarker production. Extracellular flagellin production and sporulation were also increased
in cultures subject to limited phosphate (Figure 19). High concentrations of glucose and the
addition of phenylalanine, leucine and arginine individually, resulted in decreased flagellin
production (Figures 22 & 27) and addition of several amino acids into the growth medium
resulted in increased sporulation (Figure 28). In silico analysis using a GSMN of C. botulinum
and subsequent comparison with experimental data obtained in cultures of C. sporogenes
has vindicated the use of C. sporogenes as a surrogate organism for studies of C. botulinum.
Furthermore, in silico analysis has demonstrated a linear correlation (R2 = 0.988) between
PHB and neurotoxin production in C. botulinum; a correlation to be explored further in
pursuit of achieving the primary objectives of this research project.
120
Chapter 4: Assimilating Computational and Experimental Research Tools to
Investigate the Correlation between Poly-β-hydroxybutyrate and
Botulinum Neurotoxin
The results of the experiments described in Chapter 3 revealed a potential correlation
between neurotoxin biosynthesis and PHB accumulation (R2 = 0.988) (Section 3.6). PHB has
been directly correlated with toxin production in other species previously (Navarro et al,
2002). Furthermore, the biochemistry of the carbon storage polymers metabolism lends
evidence to support the hypothesis that the relationship may prove analogous to glycogen
and antibiotic production in Streptomyces spp. (Anderson & Dawes, 1990; Lillie & Pringle,
1980; Salas & Mendez, 2005; Steinbuchel, 1991). The data therefore presented an
opportunity to investigate and potentially exploit the correlation in order to achieve the
primary objectives of the project, driving our subsequent research in an effort to increase
our knowledge on the correlation between neurotoxin and PHB.
4.1 Extrapolating PBD results using Flux Variability Analysis
The major challenge implicit in this research project is the inability to culture the production
organism outside of facilities with elevated levels of microbiological and controlled access
containment. The extreme pathogenicity of C. botulinum (Brown et al, 2012; Cooksley et al,
2010), therefore, results in an inability to assay neurotoxin production directly in these
investigations. However, C. sporogenes is widely used as a surrogate organism for testing
the metabolism of C. botulinum (Brown et al, 2012; Cooksley et al, 2010) and in this study,
genome-scale metabolic modelling has been used as a tool to validate the surrogate system
and relate findings to the production organism, including calculating toxin yield in silico.
121
In Chapter 3, PBD was utilised yielding results which demonstrated amino acid
supplementation affects the production of the energy storage polymer PHB in cells of C.
sporogenes (Figure 26). Genome-scale metabolic modelling suggested that this relationship
also exists in C. botulinum and demonstrated a correlation between PHB and toxin
production in C. botulinum (Figures 32-36). In an effort to explore the biochemical pathways
and reactions which effect the correlation between PHB and neurotoxin in C. botulinum,
FVA was utilised together with the results obtained in Chapter 3.
Despite the correlation between PHB and C. botulinum neurotoxin being established using
FBA, FVA can be utilised to predict ranges of flux through particular pathways and reactions
as well as analyse the reactions which contribute to the observed ranges of flux, providing a
more realistic analysis of products which have many factors contributing to maximum
optimisation (Bushell et al, 2006), such as PHB and neurotoxin in this study.
FVA was utilised with an objective of obtaining potential PHB increasing supplementation
targets based on the reactions which demonstrated the greatest fluxes when PHB was
tested as an objective using FBA. The analysis resulted in several reactions which effect the
accumulation of PHB. The reactions were organised into a metabolite-increasing network,
which has been used successfully to optimise antibiotic yields previously (Bushell et al,
2006). This analysis highlights potential supplementation targets, which based on the
biochemistry of the reactions in the network, should theoretically increase PHB
accumulation when availability is increased.
122
Figure 37: Network of high flux reactions - Poly-β-hydroxybutyrate (FVA)
Alanine
Aspartic Acid
L-4-Aspartyl Phosphate
L-Aspartate4-Semialdehyde
Glycerol
Threonine
Acetaldehyde
acetoacetic acid
O-phospho-L-homoserine
Homoserine
Coenzyme A
Succinyl-CoA
3-Acetoacetyl-CoA
Succinate
(S)-3-Hydroxybutyryl-CoA
- Highly significant reactions (FVA >10000)
O-Succinyl-L-Homoserine
Poly-β- hydroxybutyrate
- Significant reactions (2000-10000FVA)
123
The network of reactions displayed in Figure 37 was generated using FVA, comparing the
results of optimal PHB with suboptimal PHB as an objective function. This determines the
reactions which have the greatest effect on PHB yield and therefore highlight reactions
which can be targeted with an objective of increasing PHB experimentally. The analysis was
repeated with optimal and suboptimal neurotoxin yield as an objective for comparison with
the results obtained on PHB metabolism and to further our knowledge on the biosynthetic
pathways which result in the in silico correlation calculated (Figures 32-36).
124
Figure 38: Network of High Flux Reactions - Botulinum Toxin (FVA)
Alanine
Dihydroxy-acetone-phosphate
L-Glycerol-1-phosphate
Aspartic Acid
L-4-Aspartyl Phosphate
L-Aspartate4-Semialdehyde
Iminoaspartate
Glycerol
Threonine
Acetaldehyde
acetoacetic acid
O-phospho-L- Homoserine
Homoserine
Coenzyme A
Succinyl-CoA
3-Acetoacetyl-CoA
Succinate
(S)-3-Hydroxybutyryl-CoA
- Highly significant reactions (FVA >1000)
O-Succinyl-L-homoserine
Poly-β- hydroxybutyrate
- Significant reactions (<1000FVA)
125
The results of the FVA and generated networks displayed in Figures 37 & 38 provide
confidence to the correlation between PHB accumulation and neurotoxin biosynthesis in C.
botulinum, demonstrating multiple pathways which are significant to both metabolites and
the importance of PHB biosynthesis to neurotoxin flux. Interpreting the analysis to assume
increasing flux of the significant reactions to both PHB and toxin will increase yield, the
results of the FVA highlighted three potential PHB and neurotoxin increasing supplements;
Aspartic acid, Threonine and Homoserine. To experimentally test the hypothesised PHB and
neurotoxin increasing supplements, and simultaneously the accuracy of the GSMN, flasks
containing a defined medium (BDM) (Karasawa et al, 1995) were supplemented individually
to assess the differences observed in PHB accumulation and compared with data obtained
from cells grown without supplementation.
126
4.2 Increasing PHB yields using a targeted amino acid supplementation approach
Aspartic acid, Threonine and Homoserine were added to BDM (Karasawa et al, 1995) at
concentrations of 7.5mM and 15mM individually to test the hypothesis that providing
cultures of C. sporogenes with increased availability of the amino acids will increase PHB
accumulation, as demonstrated by the results of the FVA (Figure 37). All flasks were
inoculated from the same seed culture and incubated at 37 oC in an anaerobic cabinet for
24h. The results were compared with cultures grown in BDM under the same conditions
with no additional amino acids as a negative control.
127
Effect of amino acid supplementation on PHB accumulation in cultures of
C.sporogenes
180
170
PHB (% of BDM value)
160
150
140
130
120
110
100
90
80
BDM
Threonine
7.5mM
Threonine
15mM
Homoserine Homoserine Aspartic Acid Aspartic Acid
7.5mM
15mM
7.5mM
15mM
Figure 39: The Effect of Aspartic acid, Threonine and Homoserine on PHB accumulation in
cultures of C. sporogenes. Supplements were added to BDM (Karasawa et al, 1995) at
concentrations of 7.5mM and 15mM individually. Data shown are values obtained following
24h incubation. Data displayed are averages of triplicate biological samples and triplicate
technical repeats. Error bars represent the standard error of the mean.
128
Effect of amino acid supplementation on PHB
accumulation in cultures of C. sporogenes
PHB (% increase from BDM value)
50
***
45
40
35
30
25
20
15
10
*
5
0
Aspartic acid
Threonine
Homoserine
Figure 40: The Effect of Aspartic acid, Threonine and Homoserine on PHB accumulation in
cultures of C. sporogenes. Data shown are averages of supplementation at 7.5mM & 15mM.
Data shown are values obtained following 24h incubation. Data displayed are averages of
triplicate biological samples and triplicate technical repeats. Error bars represent the
standard error of the mean. Statistical significance of the observed PHB increase of cultures
supplemented with Threonine (P = 0.045) Homoserine (P = 0.001) and Aspartic acid
(P = 0.106) were calculated using a student’s t test. ‘*’ represents a statistical significance of
P = ≤0.05 and ‘***’ represents a statistical significance of P = ≤0.001 (applies to all data).
129
Cells grown in medium supplemented with Aspartic acid contained on average 10% more
PHB (Figure 40). Higher concentrations of Aspartic acid (15mM) resulted in 21% more PHB
when compared with cultures grown with no additional amino acids (Figure 39), leading to
the conclusion that Aspartic acid is required at higher concentrations to observe an
appreciable effect on the accumulation of PHB. This is possibly due to the plethora of
cellular functions governed by aspartic acid, which may possibly take priority over PHB
biosynthesis with regard to aspartic acid consumption. The addition of Threonine to the
growth medium on average resulted in a 6% increase in total PHB in cells of C. sporogenes
(Figure 40), which was calculated as statistically significant using a student’s t test
(P = 0.045). PHB was only increased in cultures supplemented with 15mM Threonine
however (Figure 39), with no observed effect when the growth medium was supplemented
with lower concentrations of the amino acid (7.5mM).
The most interesting result of the experiment was observed in cultures supplemented with
Homoserine, which resulted in a 43% increase in cellular PHB (Figure 40) following
incubation for 24h (P = 0.001).
In contrast to the results obtained from cultures
supplemented with Threonine and Aspartic acid, those grown in concentrations of 15mM
Homoserine accumulated less PHB than those supplemented with a lower concentration of
7.5mM, although both concentrations demonstrated a significant increase in PHB when
compared with cultures grown without additional amino acids (Figure 39) (P = 0.001). This is
possible explained by the effect of high concentration of Homoserine (15mM) on the growth
of C. sporogenes, which demonstrated an extended lag phase (Figure 41). Owing to the fact
PHB accumulation is largely affected by the growth cycle (Anderson & Dawes, 1990;
Mignone & Avignone-Rossa, 1996; Steinbuchel, 1991) (Section 3.3.4), this is likely to have
130
affected the yield of PHB observed. The inhibitory effect of Homoserine on growth has been
demonstrated previously in bacteria including E. coli (Kotrea et al, 1973; Sritharan et al,
1987). The most plausible explanation of this effect is the inhibitory effect of Homoserine on
glutamate uptake rate at high concentrations, observed at 15mM Homoserine by Sritharan
et al (Sritharan et al, 1987).
The effect of Homoserine supplemtation on the growth of C. sporogenes
0.45
0.4
Growth (OD560)
0.35
0.3
BDM
0.25
Homoserine
7.5mM
Homoserine
15mM
0.2
0.15
0.1
0.05
0
0
3
6
9
12
15
18
21
24
Time (h)
Figure 41: The effect of Homoserine concentration on the growth of C. sporogenes cultures.
Increasing Homoserine concentration led to an extended lag phase, which may have
affected the total PHB accumulated by 24h. Data displayed are averages of triplicate
biological samples and triplicate technical repeats. Error bars represent the standard error
of the mean.
131
These findings identify Homoserine as a primary nutrient supplement capable of increasing
the accumulation of PHB in cultures of C. sporogenes. In addition, based on the results of
the in silico analysis (Figures 37 & 38), increasing Homoserine concentration in the growth
medium results in increased neurotoxin biosynthesis in C. botulinum. The correlation
between the experimental and in silico data added further confidence to the viability of C.
sporogenes as a suitable surrogate organism for this study and the confirmed the reliability
of the GSMN utilised for analysis of C. botulinum metabolism in this research project.
Furthermore, the findings support the hypothesis proposed in chapter 3; that PHB
accumulation is strongly correlated with neurotoxin biosynthesis in C. botulinum.
To investigate the correlation further, the biosynthetic pathways of both PHB and
neurotoxin were examined. Figure 42 shows the metabolic network of reactions significant
to neurotoxin biosynthesis in C. botulinum (Kanehisa laboratories, 2013). Interestingly, the
reactions include acetyl-CoA, acetaldehyde and glycerol as metabolites; all three of which
were identified as important metabolites with regard to toxin flux using FVA (Figure 38).
Furthermore, acetaldehyde and acetyl-CoA are both break down products of PHB,
highlighting a possible biosynthetic link for the in silico correlation. Figure 43 displays the
possible network of reactions correlating PHB and neurotoxin production based on this
hypothesis. Although it is difficult to prove this hypothesis, it has highlighted metabolites
which are associated with both PHB and neurotoxin, suggesting the possibility of a directly
or indirectly linked correlation.
132
Figure 42: Metabolic reactions essential for neurotoxin biosynthesis in C. botulinum (Kanehisa laboratories, 2013).
133
Dihydroxy-acetone-phosphate
L-Glycerol-1-phosphate
Iminoaspartate
Glycerol
Aspartic Acid
Alanine
Threonine
Acetaldehyde
Acetoacetic acid
Glycerol-3-phosphate
O-phospho-L- homoserine
Coenzyme A
Succinyl-CoA
3-Acetoacetyl-CoA
Succinate
(S)-3-Hydroxybutyryl-CoA
Poly-β- hydroxybutyrate
Acetaldehyde
Neurotoxin Biosynthesis
Acetyl-CoA
Figure 43: Network of the correlation reactions between PHB and neurotoxin biosynthesis. Network generated using a combination of FVA and
genomic pathway examination of C. botulinum using KEGG (Kanehisa laboratories, 2013).
134
4.2.1 The relationship between TCA cycle - derived amino acids and PHB accumulation
Examination of the amino acid biosynthetic pathways of the Threonine, Aspartic Acid and
Homoserine (Akashi & Gojobori, 2002) revealed an interesting association; all three
supplements tested have biosynthetic pathways originating from Oxaloacetate (OAA)
(Figure 44). OAA is a TCA cycle intermediate which reacts with acetyl-CoA, a precursor of
PHB, to form citrate (Anderson & Dawes, 1990; Steinbuchel, 1991). The finding that
increasing the availability of OAA derived amino acids results in an increase in PHB
accumulation, therefore agrees with studies on PHB which have concluded limiting the
carbon requirement of the TCA cycle results in an increased accumulation of the energy
storage polymer (Lillo & Rodriguez-Valera, 1990; Raberg et al, 2008; Ryu et al, 2007; Shang
et al, 2003).
Figure 43a: The biosynthetic pathways of amino acids fuelled via central metabolism.
Supplementation with Aspartic acid, Threonine and Homoserine gave rise to increased
cellular PHB. As displayed, the amino acids have biosynthetic pathways originating from the
same metabolite; Oxaloacetate. Image adapted from Akashi & Gojobori, 2002 (Akashi &
Gojobori, 2002).
136
This suggests that by increasing the availability of threonine, aspartic acid and Homoserine,
less OAA is required for the amino acids biosynthesis and as a consequence of this,
availability of carbon for the TCA cycle is increased, requiring less activity to generate amino
acids and demanding less carbon from glycolysis in the form of acetyl-CoA; the precursor of
PHB (Anderson & Dawes, 1990; Steinbuchel, 1991).
Isoleucine biosynthesis provides a bridge between the metabolites pyruvate and
oxaloacetate owing to the fact it can be biosynthesised from pyruvate or the
supplementation
targets
examined;
aspartic
acid,
threonine
and
Homoserine.
Supplementation with isoleucine also provided the largest increase in PHB in the PBD
experiments (Figure 26), agreeing with this hypothesis. Furthermore, anaplerotic
metabolism provides a direct link between phosphoenolpyruvate (PEP), the intermediate of
pyruvate, and oxaloacetate, therefore the increase in PHB observed may be a result of
increased available pyruvate, decreased requirement of oxaloacetate, or a combination of
both.
137
4.3 Investigating the Relationship between PHB Accumulation and Pathways of Central
Metabolism using Enzymatic Assay
In an effort to increase our understanding of the effects observed in previous experiments
with regards to the effects of supplements on PHB accumulation and to test the hypothesis
that amino acid supplementation affects central metabolic pathway fluxes, enzymatic assays
were used to test the activity of Glucose-6-phosphate dehydrogenase (G6PD), Citrate
synthase and Phosphoenolpyruvate carboxylase (PEPc). This experiment was designed to
analyse the activity of particular areas of central metabolism under conditions permissive
for increased PHB accumulation, to analyse the metabolic differences which contribute to
PHB yield in C. sporogenes.
G6PD catalyses the rate limiting step of the pentose phosphate pathway (Tian et al, 1998); a
series of reactions which provides the majority of cellular NADPH. NADPH is the principle
cellular reductant and plays a vital role in the regulation of energy generating redox
reactions (Tian et al, 1998). As an energy storage polymer, reactions effecting energy
generation and growth kinetics are likely to affect the metabolism of PHB. Pentose
phosphate pathway activity was therefore assessed by assaying G6PD activity in relation to
PHB accumulation.
138
Figure 43b: The biosynthetic pathways of amino acids fuelled via central metabolism and
the reactions catalysed by Glusose-6-Phosphate dehydrogenase (G6PD), Citrate Synthase
and Phosphoenolpyruvate Carboxylase (PEPc). Image adapted from Akashi & Gojobori, 2002
(Akashi & Gojobori, 2002).
139
Citrate synthase is a TCA cycle enzyme which catalyses the reaction between acetyl-CoA and
OAA to form citrate (Wigand & Remington, 1986). Acetyl-CoA is the precursor of PHB
(Anderson & Dawes, 1990; Steinbuchel, 1991) and OAA is a metabolite of the biosynthetic
pathways of amino acids found to increase PHB accumulation by this studies previous
experiments (Figures 40 & 43) (Akashi & Gojobori, 2002). Assessing activity of this reaction
was therefore deemed vital to understanding the relationship between OAA, acetyl-CoA and
PHB accumulation, and simultaneously understanding the findings regarding amino acids
which increase PHB biosynthesis when availability is increased.
Anaplerotic reactions are responsible for replenishing intermediates of the TCA cycle using
metabolites from glycolysis and vice versa (Owen et al, 2002). PEPc is an enzyme which
reacts with the glycolysis and pyruvate intermediate phosphoenolpyruvate (PEP), to refuel
OAA providing carbon to the TCA cycle (Kai et al, 2003; Owen et al, 2002). With the findings
on the effect of OAA derived amino acids on PHB accumulation (Figures 40 & 43) and the
literature on PHB biosynthesis which has demonstrated that limiting the carbon
requirement of the TCA cycle results in an increased accumulation of PHB (Lillo & RodriguezValera, 1990; Raberg et al, 2008; Ryu et al, 2007; Shang et al, 2003), PEPc activity was
assayed to investigate the relationship between TCA cycle anaplerotic reactions and PHB
biosynthesis.
Cultures grown in a defined growth medium (Karasawa et al, 1995) supplemented with
Aspartic acid, Threonine and Homoserine at concentrations of 7.5mM and 15mM
individually were tested for G6PD, Citrate synthase and PEPc activity for comparison with
PHB accumulation. The results were compared with cultures grown in BDM under the same
conditions with no additional amino acids as a negative control.
140
Glucose-6-phosphate dehydrogenase activity in cultures of
C.sporogenes supplemented with PHB increasing Amino Acids
50
45
G6PD ∆ (µmol/mín/mg)
40
35
30
25
20
15
10
5
0
BDM
Threonine
7.5mM
Threonine Homoserine Homoserine Aspartic Acid Aspartic Acid
15mM
7.5mM
15mM
7.5mM
15mM
Figure 44: The Effect of Aspartic acid, Threonine and Homoserine on G6PD activity in
cultures of C. sporogenes. Supplements were added to BDM (Karasawa et al, 1995) at
concentrations of 7.5mM and 15mM individually. Data shown are values obtained following
24h incubation. Data displayed are averages of triplicate biological samples and triplicate
technical repeats. Error bars represent the standard error of the mean.
141
Citrate Synthase activity in cultures of C.sporogenes supplemented
with PHB increasing Amino Acids
Citrate synthase ∆ (µmol/mín/mg)
14
12
10
8
6
4
2
0
BDM
Threonine
7.5mM
Threonine Homoserine Homoserine Aspartic Acid Aspartic Acid
15mM
7.5mM
15mM
7.5mM
15mM
Figure 45: The Effect of Aspartic acid, Threonine and Homoserine on Citrate Synthase
activity in cultures of C. sporogenes. Supplements were added to BDM (Karasawa et al,
1995) at concentrations of 7.5mM and 15mM individually. Data shown are values obtained
following 24h incubation. Data displayed are averages of triplicate biological samples and
triplicate technical repeats. Error bars represent the standard error of the mean.
142
Phosphoenolpyruvate carboxylase activity in cultures of C.sporogenes
supplemented with PHB increasing Amino Acids
PEP carboxylase ∆ (µmol/mín/mg)
25
20
15
10
5
0
BDM
Threonine
7.5mM
Threonine
15mM
Homoserine Homoserine Aspartic Acid Aspartic Acid
7.5mM
15mM
7.5mM
15mM
Figure 46: The Effect of Aspartic acid, Threonine and Homoserine on PEPc activity in cultures
of C. sporogenes. Supplements were added to BDM (Karasawa et al, 1995) at concentrations
of 7.5mM and 15mM individually. Data shown are values obtained following 24h incubation.
Data displayed are averages of triplicate biological samples and triplicate technical repeats.
Error bars represent the standard error of the mean.
143
Citrate synthase activity was reduced in cells which had accumulated more PHB (Figure 45),
agreeing with the hypothesis that PHB biosynthesis competes with the TCA cycle for carbon
flux. Lower activity of Citrate synthase is most likely an indication of less TCA cycle activity in
general, owing to the decreased demand for biosynthesis of TCA derived amino acids. This
results in a lowered requirement of acetyl-CoA from glycolysis to react with OAA to form
citrate, consequentially increasing the available acetyl-CoA available for PHB biosynthesis.
No obvious trends were demonstrated with regards to G6PD activity and increased PHB
(Figure 44), although increasing the availability of aspartic acid in the growth medium
resulted in increased G6P activity. The highest observed G6PD activity was demonstrated in
cultures of C. sporogenes grown with an additional 15mM aspartic acid. These cultures also
demonstrated a 23% increase in intracellular PHB when compared with those grown
without supplementation (Figure 39). This suggests PHB is controlled by 2 key effects;
reduction of competition for carbon from the TCA cycle and increased glycolysis, both
leading to more of PHB’s precursor, acetyl CoA. These findings also agree with the fact high
aspartic acid stimulates synthesis of PEP and pyruvate (Alexander et al, 2000). The PBD
experiments covered in chapter 3 (Section 3.4) also yielded results supporting this
hypothesis. Supplementation with Alanine and Valine increased PHB accumulation, both of
which have biosynthesis pathways from pyruvate (Figure 43). In the previous experiments
(Section 3.3.4.2) which investigated the effects of ammonium on cultures of C. sporogenes,
we observed an increase in PHB when ammonium was absent from the culture media.
Ammonium inhibits enzymes required for desynthesis of Valine and Leucine back to
pyruvate (Zhu et al, 2007), which explains this observation and agrees with the hypothesis
that increasing available pyruvate results in increased PHB biosynthesis.
144
The most interesting results of the analysis were demonstrated by testing PEPc activity in
cultures of C. sporogenes permissive for increased PHB biosynthesis (Figure 46). PEPc
activity was decreased in cultures which accumulated more PHB (Figures 39 & 46) and
demonstrated the strongest correlation with PHB accumulation of the enzyme activities
tested (Figure 47) (R2 = 0.745). Supplementing the growth medium with amino acids which
have biosynthetic pathways originating from OAA, resulted in a decrease in the reaction
which refuels the TCA cycle metabolite, presumably owing to a decreased demand of OAA
for the biosynthesis of amino acids which are already available. This results in a decreased
demand for carbon by the TCA cycle, increasing the available carbon for storage in the form
of PHB. Owing to the fact the reaction catalyses by PEPc is responsible for refuelling the TCA
cycle, this correlation agrees with previous data which suggests PHB is increased as a result
of increased available acetyl-CoA, either as a result of decreased activity and/or demand for
carbon by the TCA cycle or increased availability from glycolysis in respect to the
requirement acetyl-CoA by the TCA cycle.
This data suggests the flux of PHB biosynthesis reactions is dictated by the reaction
balancing between glycolysis and the TCA cycle; affecting available pyruvate and acetyl-CoA
and affecting the demand for acetyl-CoA by the TCA cycle. In C. sporogenes and
C. botulinum, bacterial metabolism exists as either fermentation, a mode of metabolism
which relies on the reactions of glycolysis, or anaerobic metabolism, which utilises the
reactions of the TCA cycle (although incomplete in many Clostridia) (Amador-Noguez et al,
2010; Hasan & Hall, 1974). The physiology of Clostridial metabolism (Amador-Noguez et al,
2010; Hasan & Hall, 1974), the biochemistry of PHB synthesis (Anderson & Dawes, 1990;
Steinbuchel, 1991) and the data from these experiments (Figures 39, 44, 45 & 46) therefore
145
suggest PHB biosynthesis, and consequentially neurotoxin biosynthesis, may be affected by
the ratio of fermentation and anaerobic respiration exhibited by the growing bacterial
culture.
146
4.4 Plackett-Burman Design Experimental Approach to Test the Effects of Amino Acid
Metabolism on Enzymatic Activity in relationship to PHB Accumulation in Cultures of
C. sporogenes
The enzyme assay experiments investigating the effect of amino acid supplementation on
central metabolic pathway fluxes yielded results confirming PHB is increased as a result of
decreased demand for carbon and/or activity of the TCA cycle and to a lesser degree, when
acetyl-CoA availability is increased owing to increased glycolysis activity. However, this
correlation was established in cultures permissive for increased PHB biosynthesis, by
targeting PHB increasing pathways identified using FVA (Figure 37). The PBD experimental
approach was used successfully to assess the effects of and interactions between amino
acids when added to the growth medium of C. sporogenes cultures in previous experiments
(Section 3.4). In an effort to increase our understanding of the relationship between PHB
biosynthesis, amino acid metabolism and central metabolic pathway flux, the PBD approach
and principles (Section 1.5.2) were used to investigate PHB accumulation, G6PD, Citrate
synthase and PEPc activity in cultures of C. sporogenes supplemented with amino acids
(Table 6) which were not selected to target increased PHB accumulation, allowing us to test
this correlation further.
Values displayed in the results are t-values calculated using the PBD experiment (Plackett &
Burman, 1946) and are representative of the level of variance from flasks containing no
additional amino acids and therefore represent the calculated observed effect of the trialled
amino acid. The calculation used to determine the t-values for each variable (amino acid)
also consider the interaction between the variables (Kalil et al, 2000). Data entered into the
calculation were averages from triplicate biological and technical repeats.
147
Variables (Amino acids)
Trial
PRO
TYR
PHE
ALA
ASP
THE
ILEU
LYS
LEU
GLY
ARG
1
2
3
4
5
6
7
8
9
10
11
12
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
-
Table 6: PBD experiment to test the effects of amino acid supplemented growth media on
PHB accumulation, G6PD, Citrate synthase and PEPc activity in cultures of C. sporogenes. ‘+’
represents a supplementation of 1mmol of the corresponding amino acid to BDM growth
medium (Karasawa et al, 1995). All flasks contained the same total volume and were
inoculated from the same seed culture of C. sporogenes.
148
Effect of amino acid supplementation on PHB Accumulation (mg/g) of
C.sporogenes cultures assessed by Plackett-Burman Design
0.6
Significance of effect (t-value)
0.4
0.2
0
THE
ASP
ILEU
PRO
LYS
LEU
ARG
ALA
PHE
TYR
GLY
-0.2
-0.4
-0.6
Figure 47: T-values of the variance of effect calculated following PBD to assess the effect of
amino acid supplementation on PHB accumulation in C. sporogenes cultures incubated for
24h. Data shown is calculated from averages of triplicate biological and technical repeats.
Calculated values were subject to testing against three ‘dummy variables’ which decreases
the experimental error of the calculated t-values displayed.
149
Effect of amino acid supplementation on G6PD activity (µmol/mín/mg)
of C.sporogenes cultures assessed by Plackett-Burman Design
0.6
Significance of effect (t-value)
0.4
0.2
0
LEU
PRO
ARG
THE
ALA
LYS
ASP
GLY
TYR
PHE
ILEU
-0.2
-0.4
-0.6
Figure 48: T-values of the variance of effect calculated following PBD to assess the effect of
amino acid supplementation on G6PD activity in C. sporogenes cultures incubated for 24h.
Data shown is calculated from averages of triplicate biological and technical repeats.
Calculated values were subject to testing against three ‘dummy variables’ which decreases
the experimental error of the calculated t-values displayed.
150
Effect of amino acid supplementation on Citrate Synthase activity
(µmol/mín/mg)
of C.sporogenes cultures assessed by Plackett-Burman Design
0.6
Significance of effect (t-value)
0.4
0.2
0
LEU
LYS
ARG
PHE
PRO
TYR
ALA
GLY
ILEU
ASP
THE
-0.2
-0.4
-0.6
Figure 49: T-values of the variance of effect calculated following PBD to assess the effect of
amino acid supplementation on Citrate Synthase activity in C. sporogenes cultures incubated
for 24h. Data shown is calculated from averages of triplicate biological and technical
repeats. Calculated values were subject to testing against three ‘dummy variables’ which
decreases the experimental error of the calculated t-values displayed.
151
Effect of amino acid supplementation on PEPc activity (µmol/mín/mg)
of C.sporogenes cultures assessed by Plackett-Burman Design
0.6
Significance of effect (t-value)
0.4
0.2
0
ALA
PHE
PRO
LYS
ARG
ASP
LEU
TYR
GLY
ILEU
THE
-0.2
-0.4
-0.6
Figure 50: T-values of the variance of effect calculated following PBD to assess the effect of
amino acid supplementation on PEPc activity in C. sporogenes cultures incubated for 24h.
Data shown is calculated from averages of triplicate biological and technical repeats.
Calculated values were subject to testing against three ‘dummy variables’ which decreases
the experimental error of the calculated t-values displayed.
152
The results of the PBD investigating the effects of increasing amino acid availability in the
growth medium on PHB accumulation, G6PD, Citrate synthase and PEPc activity in cultures
of C. sporogenes were consistent with our previous findings. Conditions permissive for
increased PHB accumulation resulted in decreased activity of PEPc (Figures 47 & 50).
Threonine, Isoleucine and aspartic acid supplementation resulted in the largest increase in
PHB accumulation (Figure 47), agreeing with our hypothesis that increasing the availability
of amino acids with biosynthetic pathways originating from OAA results in increased PHB
accumulation. Both citrate synthase and PEPc activity were decreased in cultures which
demonstrated increased PHB accumulation (Figures 47, 49 & 50), agreeing with our
hypothesis that the increased PHB accumulation observed is a result of decreased demand
for carbon by and/or activity of the TCA cycle. Supplementation with Phenylalanine and
Alanine increased PEPc activity (Figure 50). These amino acids have biosynthetic pathways
originating from Phosphoenolpyruvate and pyruvate (Figure 43), two key metabolites of
Glycolysis. Together with the results obtained on OAA derived amino acid supplementation
and PHB accumulation, this highlights the effect of increasing the availability of amino acids
on the demand of for carbon compounds of central metabolism. Supplementation of the
growth medium with an amino acid results in a decreased biosynthesis requirement to fuel
bacterial metabolism; resulting in an increased availability of the amino acid’s precursor.
The finding that this correlation can be exploited to increase pathway fluxes resulting in
increased PHB accumulation confirms our hypothesis that the observed effects on
intracellular PHB is owing to carbon demand and availability between glycolysis and the TCA
cycle.
153
The data also highlights the importance of the reaction catalysed by PEPc with regard to
PHB accumulation, which is responsible for carbon balance/refuelling between glycolysis
and the TCA cycle (Kai et al, 2003; Owen et al, 2002), and presents the reaction as an
interesting process development target, allowing control of PHB accumulation and,
presuming our correlation is proved accurate, neurotoxin production.
154
4.5 Investigating Anaplerotic Reactions as a Process Development Target using
Genome-Scale Metabolic Modelling
Our results confirmed that increasing the availability of amino acids with biosynthetic
pathways originating from OAA resulted in increased PHB accumulation in cells of
C. sporogenes (Figures 39 & 47) and suggested this would result in increased PHB and
neurotoxin biosynthesis in C. botulinum (Figures 37 & 38). The experimentation also
highlighted the importance of the anaplerotic reaction catalysed by PEPc, demonstrating a
correlation between decreased PEPc activity and increased PHB accumulation (Figure 47) (R2
= 0.745). OAA is refuelled by the reaction catalysed by PEPc (Kai et al, 2003; Owen et al,
2002) which incorporates the carbon from carbon dioxide (CO2) into the TCA cycle (Figure
51). With the data from the previous experiments suggesting PHB accumulation and
neurotoxin biosynthesis are effected by carbon balancing and availability of glycolysis and
the TCA cycle, together with the finding that increasing available OAA results in increased
PHB biosynthesis, increasing the introduction of carbon into central metabolism via
anaplerotic reaction incorporating CO2 , attained potential as a process development target.
155
Figure 51: Anaplerotic reactions of central metabolism. CO2 is incorporated during the
reaction
catalysed
by
Phosphoenolpyruvate
carboxylase
(PEPc)
in
which
Phosphoenolpyruvate is metabolised to oxaloacetate (OAA) refuelling the TCA cycle. Image
from Eggeling & Bott, 2005 (Eggeling & Bott, 2005).
It has been established that increasing atmospheric CO2 increases CO2 fixation by
anaplerotic metabolism and increases PHB accumulation in other PHB producing organisms
(Miyake et al, 2000; Tanaka et al, 2011), presumably owing the decreased demand of OAA
for amino acid biosynthesis. In an effort to assess whether this correlation exists in
C. botulinum, FBA was used to assess the effect of CO2 flux on PHB yield as an objective.
156
FBA assessing the effect of CO2 flux on PHB accumulation in
C.botulinum (in silico)
9000
PhB Flux (arb. units)
8000
R² = 0.995
7000
6000
5000
4000
0
700
1400
2100
2800
3500
4200
4900
5600
6300
7000
7700
8400
9100
9800
10500
11200
11900
12600
13300
14000
14700
15400
16100
3000
CO2 flux (arb. units)
Figure 52: The effect of CO2 flux on PHB biosynthesis in C. botulinum (in silico) generated
using FBA to test minimum to maximum range CO2 flux with optimal PHB yield as an
objective. PHB flux appears to fall beyond 14000 arbitrary units CO2 flux owing to the
polynomial trend line used to increase graph resolution. GSMN used in analysis developed
from C. botulinum type A genome data obtained from NCBI FTP (Bao et al, 2011),
constructed by Sonal Dahale, University of Surrey.
The results of the FBA to investigate the correlation between CO2 uptake and PHB
biosynthesis demonstrated a linear correlation (Figure 52) (R2 = 0.995), agreeing with
studies in other PHB producing organisms which demonstrated an increase in atmospheric
CO2 during culture resulted in increased PHB accumulation (Miyake et al, 2000; Tanaka et al,
157
2011). It has been reported that increasing the concentration of atmospheric CO2 in the
culture vessel of C. botulinum cultures results in increased neurotoxin biosynthesis (Artin et
al, 2008). The work of Artin et al, demonstrated a two-fold increase in neurotoxin yield
when 70% CO2 was supplied to the fermenter (Artin et al, 2008). This relationship has also
been established in other toxin producing organisms, including Bacillus anthracis,
Staphylococcus aureus and Vibrio cholerae (Drysdale et al, 2004; Ross & Ouderdonk, 2000;
Shimamura et al, 1985).
This finding, together with the relevant literature (Artin et al, 2008; Drysdale et al, 2004;
Miyake et al, 2000; Ross & Ouderdonk, 2000; Shimamura et al, 1985; Tanaka et al, 2011),
adds merit to the hypothesised correlation between PHB accumulation and neurotoxin
biosynthesis in C. botulinum and suggests increasing atmospheric CO2 as a process
development approach, offering the ability to control PHB and therefore neurotoxin yield;
presenting an opportunity to achieve the primary goals of this research project.
158
4.6 Chapter Conclusions
FVA provided further evidence to the correlation hypothesised in Chapter 3; that increasing
PHB accumulation results in increased neurotoxin biosynthesis is C. botulinum (Figures 36 &
38). The analysis also revealed several metabolic pathways significant to the biosynthesis of
both neurotoxin and PHB (Figures 37 & 38). Increasing the flux of the pathways, by
increasing availability of the metabolites Threonine, Aspartic acid and Homoserine, resulted
in increased PHB accumulation in cultures of C. sporogenes (Figure 40). Enzymatic assay of
cultures which demonstrated increased PHB accumulation owing to supplementation with
OAA derived amino acids and assessment of the relationship using PBD demonstrated a
correlation between increased PHB accumulation and decrease PEPc and Citrate synthase
activity (Sections 4.3 & 4.4). Review of the results and the relevant literature led to the
conclusion that decreased demand for carbon and/or activity of the TCA cycle resulted in
increased availability of Acetyl-CoA and consequentially, increased PHB accumulation (Kai et
al, 2003; Lillo & Rodriguez-Valera, 1990; Owen et al, 2002; Raberg et al, 2008; Ryu et al,
2007; Shang et al, 2003). These results, combined with the knowledge that the anaplerotic
reaction catalysed by PEPc introduces the carbon from CO2 into the TCA cycle (Figure 51),
increased atmospheric CO2 increases PHB accumulation in other organisms (Miyake et al,
2000; Tanaka et al, 2011) and increased CO2 has been positively correlated with neurotoxin
biosynthesis in cultures of C. botulinum previously (Artin et al, 2008) presented a valuable
hypothesis; that increasing the concentration of CO2 available to cultures of C. sporogenes
would result in an increase in cellular PHB accumulation and the effect of CO2 would
increase both PHB accumulation and neurotoxin biosynthesis in C. botulinum.
159
Chapter 5: Investigating the Correlation between Carbon Dioxide Uptake,
PHB Accumulation and Neurotoxin Biosynthesis Using Chemostat culture
In Chapter 4, the hypothesis that increasing the concentration of CO2 supplied to cultures of
C. sporogenes would result in an increase in intracellular PHB accumulation and the effect of
CO2 would increase both PHB accumulation and neurotoxin biosynthesis in C. botulinum was
proposed.
This hypothesis was formulated based on the data obtained on the correlation between CO2
and PHB in silico (Figure 52), the literature regarding the correlation between CO2 and
neurotoxin in C. botulinum (Artin et al, 2008) and other toxin producing species (Drysdale et
al, 2004; Ross & Ouderdonk, 2000; Shimamura et al, 1985) and data obtained during this
study on factors effecting the accumulation of PHB, including enzyme activity and amino
acid supplementation data which suggests a net reduction in TCA cycle activity/demand for
carbon results in increased PHB accumulation (Sections 4.3 & 4.4).
Chemostat culture enables the dissection of microbial physiology and growth kinetics
independent from the effects of the physiochemical environment (Bull, 2010; Hoskisson &
Hobbs, 2005), including the decreased availability of nutrients and increased accumulation
of by-products over time. Chemostat culture was therefore deemed an appropriate
experimental approach in order to test the hypothesis, eliminating changes in growth rate
which are likely to affect PHB accumulation. Chemostat culture also provides a method to
analyse the effects of growth rate on biomarker production with an outlook of assessing the
160
proposed hypothesis that altering growth conditions can affect the ratio of fermentation
and anaerobic metabolism; ultimately effecting PHB and the biosynthesis of other
metabolites. Most importantly, it provides steady state conditions, which are directly
comparable to flux model predictions. Experiments designed to test the effects of growth
rate on C. sporogenes were sparged with 100% nitrogen gas (oxygen-free) in order to
maintain strict anaerobic conditions in the Chemostat vessel and experiments testing the
effects of CO2 were sparged with gas mixtures containing 10%, 25% or 50% CO2 and the
remaining concentration nitrogen at a controlled rate of 0.2L/h -1. Stirrer speed,
temperature, pH, dissolved O2 (ensured zero), working culture volume and axenic conditions
were controlled and maintained throughout experimentation.
161
5.1 The effect of Growth Rate & Increased Carbon Dioxide Concentration on PHB
Accumulation in Continuous Cultures of C. sporogenes
The effect of growth rate and CO2 gas concentration on PHB accumulation were investigated
in chemostat cultures of C. sporogenes. Assays were performed and experimental
conditions were maintained using the methods described previously (section 2.12).
Intracellular PHB Accumulation at specific growth rates in
continuous cultures of C. sporogenes
130
120
PHB (mg/g)
110
100
90
80
70
60
50
0.25
0.5
Dilution (Growth) Rate
0.75
(h-1)
Figure 53: The effect of growth rate on PHB accumulation in continuous cultures of
C. sporogenes. Growth rate was controlled by dilution rate of growth medium into the
fermentation vessel (ml/h-1) maintained at a working volume of 1.5L. Data shown are
averages of triplicate biological samples taken at least one volume change apart and
triplicate technical repeats. Error bars represent standard error of the mean. At least four
volume changes were required before sampling following change of culture parameter
changes such as dilution rate.
162
The effect of CO2 concentration on PHB accumulation in continuous
cultures of C. sporogenes
180
**
160
PHB (mg/g)
140
120
100
80
60
10% CO2
25% CO2
50% CO2
Figure 54: The effect of CO2 concentration on PHB accumulation. Growth rate was
controlled by maintaining a dilution rate of 0.5 h -1 throughout experimentation. Statistical
analysis using a student’s t-test showed no significant increase in PHB at 10% & 25% CO2
when compared at the same growth rate supplied with 100% nitrogen (P = 0.3569 & P =
0.2332 respectively), however the increase in PHB observed at 50% was calculated to be
highly significant (P = 0.01). Data shown are averages of triplicate biological samples taken
at least one volume change apart and triplicate technical repeats. Error bars represent
standard error of the mean. ‘**’ represents a statistical significance of P = ≤0.01 (applies to
all data).
163
The effects of increased CO2 concentration on Biomass and PHB
accumulation in chemostat culture of C. sporogenes
0.8
R² = 0.975
140
0.7
120
0.6
100
0.5
80
0.4
R² = 0.9843
60
0.3
40
0.2
20
0.1
0
Biomass (g/L)
PHB (mg/g)
160
PhB
Biomass
0
10
25
50
Fermentation Vessel CO2 Concentration (%)
Figure 55: The contrasting effects of increased atmospheric CO2 on PHB accumulation and
Biomass in continuous cultures of C. sporogenes. Biomass was negatively correlated (R2 =
0.9843) with CO2 and PHB positively correlated (R2 = 0.975). Data shown are averages of
triplicate biological samples taken at least one volume change apart and triplicate technical
repeats.
164
The effects of increased CO2 flux on Biomass and PHB accumulation in
C. botulinum (in silico)
8000
3500
R² = 0.9753
7000
3000
2500
5000
R² = 1
2000
4000
1500
3000
Biomass flux
PhB Flux
6000
PhB
Biomass
1000
2000
500
1000
0
0
1
50
CO2 flux
Figure 56: The contrasting effects of increased CO2 flux on PHB and Biomass flux in
C. botulinum (in silico). Data was attained by FBA testing the effects on CO2 flux on PHB and
Biomass flux as individual objectives. GSMN used in analysis developed from C. botulinum
type A genome data obtained from NCBI FTP (Bao et al, 2011), constructed by Sonal Dahale,
University of Surrey.
165
Increasing the concentration of CO2 supplied to the fermentation vessel of C. sporogenes
cultures had a positive linear correlation on PHB accumulation (R2 = 0.975) and a negative
linear correlation on biomass (R2 = 0.9843). When introduced into the chemostat culture at
a concentration of 50%, the difference in PHB observed was highly significant (P = 0.01),
confirming the hypothesis that CO2 can be used as a process development approach to
increase PHB accumulation in cultures of C. sporogenes. FBA on the effects of CO2 flux
(Figure 56) suggested the correlations demonstrated with both PHB and biomass also exist
in C. botulinum. Artin et al (Artin et al, 2008) tested the effects of increased CO2
concentration in cultures of C. botulinum, demonstrating increased CO2 resulted in an
increase in neurotoxin biosynthesis and a decrease in biomass; agreeing with our findings in
C. sporogenes and the proposed correlation between PHB and botulinum neurotoxin. The
inhibitory effect of CO2 on the growth of C. botulinum has been described previously (Artin
et al, 2008; Gibson et al, 2000; Fernandez et al, 2001; Lo¨venklev et al, 2004) and associated
with a decrease in culture growth rate (Artin et al, 2008). This agrees with our findings on
the reduction of TCA cycle activity/demand for carbon during conditions permissive for
increased PHB biosynthesis and suggests increased CO2 concentration promotes
fermentative respiration, which although is less energy efficient and results in lower growth
rate, provides more available carbon for storage as PHB. This hypothesis is supported by the
work of Dixon et al (Dixon et al, 2008) on chemostat culture of C. sporogenes, who
demonstrated increased CO2 resulted in increased yields of acetate; a precursor of PHB.
Figure 57 represents a compilation graph displaying a comparison of Artin et al’s (Artin et al,
2008) observations on the effects of CO2 on neurotoxin biosynthesis with our own data
obtained in C. sporogenes displaying the effect of CO2 on PHB accumulation (Artin et al,
2008). This data comparison, together with the validation of the GSMN enhanced surrogate
166
organism approach used throughout this research project, confirms the hypothesis that PHB
is linearly correlated with neurotoxin production in C. botulinum and validates our work on
PHB metabolism as a viable process development approach to control the biosynthesis of
botulinum neurotoxin.
167
700
220
600
200
180
500
160
400
140
300
120
200
PHB (mg/g)
Botulinum Neurotoxin (ng/g)
The effect of CO2 on PHB and Botulinum neurotoxin in C.botulinum
(Artin et al, 2008)
BoNT
PhB
100
100
80
0
60
10
25/35
50/70
CO2 gas composition (%)
Figure 57: The effect of increased CO2 concentration on PHB accumulation and botulinum
neurotoxin biosynthesis. As displayed, both PHB and neurotoxin are increased as a result of
increased CO2 concentration in the culture vessel. Correlation plot of botulinum neurotoxin
and PHB was linear (R2 = 0.9637). Botulinum neurotoxin data taken from Artin et al, 2008.
CO2 gas compositions tested by Artin et al were 10, 35 and 70%. PHB data shown are
averages of triplicate biological samples taken at least one volume change apart and
triplicate technical repeats. No significant difference was observed at 10% CO 2 compared
with 0% CO2 (100% oxygen-free nitrogen) with regards to either PHB or neurotoxin. R2
(0.9637) was calculated by plotting 0 to 50% CO2 for both PHB and neurotoxin and therefore
takes into the consideration the non-linear x-axis depicted.
168
The combination of our data obtained in continuous cultures of C. sporogenes and the work
of Artin et al in C. botulinum (Artin et al, 2008) demonstrate increased CO2 results in
increased PHB accumulation and toxin biosynthesis at the cost of decreased biomass.
Owing to the fact cell population will have a direct impact on the total yield of PHB and
neurotoxin, the effect of CO2 on PHB and toxin in a given culture volume is not linear. Figure
58 was generated using a combination of in silico data achieved by FBA in C. sporogenes.
The graph represents the effect of CO2 concentration on the PHB:Biomass ratio,
demonstrating the point at which the increase in PHB/neurotoxin yield influenced by
increased CO2 concentration becomes detrimental to the maximum yield of the culture,
owing to the negative effects of CO2 on biomass. The optimal CO2 concentration supplied to
the culture vessel, with regards to maximum PHB and/or neurotoxin yield in a given culture
volume, was calculated at 36% (Figure 58).
169
Optimal CO2 concentration (%) to achieve maximum PHB &
Neurotoxin yield in a given culture volume
Maximum PHB (Biomass x PHB flux)
1800
1750
1700
1650
1600
1550
1500
1450
0
10
20
30
40
50
60
70
CO2 Flux (% composition)
Figure 58: Optimal CO2 concentration (%) with an objective of acquiring maximal PHB/toxin
yield in a given culture volume. Data represents Biomass × PHB flux with increasing CO2 flux.
As demonstrated, although cellular PHB percentage increases up to at least 50% CO2
concentration, the negative effect on biomass results in a decrease in total yield after 36%
CO2. Data used to calculate PHB:Biomass ratio were attained by FBA testing the effects of
CO2 flux on PHB and Biomass flux as individual objectives. CO2 flux values were adjusted to
represent a CO2 percentage of the atmospheric gas available to the culture. GSMN used in
analysis developed from C. botulinum type A genome data obtained from NCBI FTP (Bao et
al, 2011), constructed by Sonal Dahale, University of Surrey.
170
5.2 Validation of Bicarbonate as an Alternative Supplementation Approach to Altering
Carbon Dioxide Concentration
To further our findings on the use of increased CO2 as a process development approach
when pursuing increased PHB/neurotoxin yield, a series of experiments were design to
investigate the substitute of bicarbonate; offering the advantages of increased CO 2
described previously as an addition to the growth medium, as an alternative to input gas
alteration. The addition of bicarbonate to the growth medium provides a direct alternative
mechanism to increase available CO2 (Ueda et al, 2008), as shown by the equation below;
CO2 + H2O
↔
H2CO3
↔
H+ + HCO3- ↔
H+ +
CO32-
However, the alternative is not without limitations, as additional buffering capacity is
required to maintain pH owing to the hydrogen ions formed in the reactions. In an effort to
investigate the effect of bicarbonate on PHB accumulation, flask cultures of C. sporogenes
were grown in USA2 medium (Chapter 2, Section 2) supplemented with bicarbonate at 5 &
10g/L. Flasks of USA2 medium were also prepared with 10mM Homoserine; a supplement
demonstrated to increase PHB yield in this studies previous experiments (Chapter 4, Section
2), for comparison with the effect of bicarbonate on PHB accumulation. Intracellular PHB of
the C. sporogenes cultures was assed following 24h incubation and compared with the
values obtained in USA2 medium without additional supplementation.
171
The effect of bicarbonate on PHB accumulation in continuous
cultures of C. sporogenes
160
***
140
**
PHB (mg/g)
120
100
80
60
40
BDM
5g/L Bicarbonate
10g/L Bicarbonate
10mM Homoserine
Figure 59: The effect of bicarbonate on PHB accumulation. Statistical analysis using a
student’s t–test calculated no significant difference in PHB accumulation between USA2
media and flasks supplemented with 5g/L bicarbonate (P = 0.07), however when
supplemented at 10g/L, bicarbonate resulted in a highly significant increase in PHB
(P = 0.001). Homoserine, which increases PHB by reducing the carbon requirement of the
TCA cycle, demonstrated the greatest increase in PHB when compared with cultures grown
without additional supplementation (P = 0.0001), confirming the supplement is effective
with regards to increasing PHB in both defined (BDM) and rich medium (USA2).
172
5.3 The Effect of Growth Rate & Increased Carbon Dioxide Concentration on Flagellin
Biosynthesis in Continuous Cultures of C. sporogenes
Studies have indicated flagellin biosynthesis is negatively correlated with neurotoxin
production in C. botulinum. As a consequence of this, one of the primary objectives of this
research project was to develop a process under which flagellin biosynthesis could be
reduced; minimising competition with neurotoxin for metabolite bioflux. The data obtained
previously (Chapter 3, Section 3) demonstrated that flagellin biosynthesis is affected by
nutrient alterations to the growth medium, particularly altering the available carbon
(Figures 22 & 23), in cultures of C. sporogenes, suggesting the metabolic changes induced
by increased CO2 are likely to have an impact flagellin biosynthesis.
173
Flagellin biosynthesis at specific growth rates in continuous cultures
of C. sporogenes
120
Flagellin (µg/ml)
100
80
60
40
20
0
0.25
0.5
0.75
Dilution (Growth) Rate (h-1)
Figure 60: The effect of growth rate on flagellin biosynthesis in continuous cultures of
C. sporogenes. Growth rate is controlled by dilution rate of growth medium into the
fermentation vessel (h-1). Data shown are averages of triplicate biological samples taken at
least one volume change apart and triplicate technical repeats. Error bars represent
standard error of the mean. At least four volume changes were required before sampling
following change of culture parameter changes such as dilution rate.
174
120
The effect of CO2 concentration on flagellin biosynthesis in
continuous cultures of C. sporogenes
Flagellin (µg/ml)
100
*
80
R² = 0.9696
60
***
40
20
0
10%
25%
50%
CO2 Concentration
Figure 61: The effect of CO2 concentration on flagellin biosynthesis in chemostat cultures of
C. sporogenes. Flagellin production decreased linearly with increasing CO2 concentration
(R2 = 0.9696). There was no significant decrease in flagellin at 10% CO2 (P = 0.296) when
compared with cultures grown in 100% Nitrogen (oxygen-free) at the same growth rate
(0.5 h-1). The decrease in flagellin biosynthesis at CO2 concentrations of 25 & 50% were
calculated as statistically significant using a student’s t-test (P = 0.01 & P = 0.0001
respectively). Data shown are averages of triplicate biological samples taken at least one
volume change apart and triplicate technical repeats. Error bars represent standard error of
the mean. At least four volume changes were required before sampling following change of
culture parameter changes such as CO2 concentration. R2 (0.9696) was calculated by plotting
flagellin biosynthesis from 0 to 50% CO2 and therefore takes into the consideration the nonlinear x-axis depicted.
175
Flagellin (30kDa) –
Figure 62: The effect of CO2 concentration on flagellin biosynthesis. Image displays flagellin
bands separated using SDS-PAGE, stained with Coomassie-Blue and visualised using
Flurochem QTM image analysis software. Samples displayed were triplicate samples taken at
least one volume change apart at 10, 25 and 50% CO2 concentration. Growth rate was
maintained at 0.5 h-1.
Increasing the concentration of CO2 supplied to the fermentation vessel of C. sporogenes
cultures had a negative linear correlation on flagellin biosynthesis (R2 = 0.9696). The effect
of CO2 concentration on flagellin biosynthesis observed can be explained by our conclusion
that the effect of lowering the demand for carbon by the TCA cycle ultimately results in
C. sporogenes respiring predominantly by fermentation over anaerobic metabolism. Our
previous work on nutrient limitation demonstrated that high glucose cultures drastically
reduced flagellin biosynthesis (Figure 63 & 64). Greater availability of carbon in relation to
other nutrients is likely to result in fermentation being utilised as a primary mode of
metabolism. Furthermore, studies have demonstrated flagellin biosynthesis genes are
greatly expressed during early stationary phase (Bergara et al, 2003), whilst fermentation
predominates during exponential phase when nutrient availability drives rapid growth.
176
Studies in Bacillus subtlis, a close relative of the Clostridia species (Brown et al, 1994), have
demonstrated that flagellin gene expression is repressed in nutrient rich environments
under the effect of the gene repressor CodY (Bergara et al, 2003). The gene is also present in
C. botulinum (Ecogene, NCBI library). Flagellin production and increased motility have been
directly correlated to TCA cycle activity under anaerobic respiration in Proteus morabilis
(Alteri et al, 2012), adding merit to our findings.
177
Growth, Glucose metabolism and Flagellin biosynthesis over Time
4000mg/L Glucose
0.6
14
0.5
Flagellin (µg/ml-2)
12
0.4
10
8
0.3
6
0.2
4
0.1
2
0
Growth (OD560); Glucose (mg x 104/L)
16
Flagellin
Growth
Glucose
0
0
3
6
9
12
18
24
Time (h)
Figure 63: Growth, glucose metabolism and flagellin production over time in cultures of
C. sporogenes (Chapter 3, Section 3). High glucose concentration drastically reduced flagellin
biosynthesis in flask cultures of C. sporogenes. Interestingly, flagellin biosynthesis increased
following glucose exhaustion, which may suggest a metabolic change from rapid glucose
metabolism (fermentation) to amino acid respiration (anaerobic metabolism). PHB and
flagellin are displayed from 9h onwards as cellular density was too low for accurate
determination before this stage of the culture. Data displayed are averages of triplicate
biological samples and triplicate technical repeats. Error bars represent the standard error
of the mean.
178
16
0.45
14
0.4
0.35
Flagellin (mg/ml-2)
12
0.3
10
0.25
8
0.2
6
0.15
4
0.1
2
Growth (OD560); Glucose (mg x 104/L)
Growth, Glucose metabolism and Flagellin biosynthesis over Time
500mg/L Glucose
Flagellin
Growth
Glucose
0.05
0
0
0
3
6
9
12
18
24
Time (h)
Figure 64: Growth, glucose metabolism and flagellin production over time in cultures of C.
sporogenes (Chapter 3, Section 3). Cultures of C. sporogenes grown under glucose limited
conditions exert a drastically reduced glucose consumption rate and increased flagellin
biosynthesis. Interestingly, glucose is not fully exhausted until ~12h, when the culture
reaches stationary phase. At this time, a further increase in flagellin biosynthesis occurs,
which may be due to a switch to a primarily amino acid based metabolism. PHB and flagellin
are displayed from 9h onwards as cellular density was too low for accurate determination
before this stage of the culture. Data displayed are averages of triplicate biological samples
and triplicate technical repeats. Error bars represent the standard error of the mean.
179
Growth rate also had a notable effect on flagellin biosynthesis, with the lowest and highest
dilution rates tested (0.25 h-1 & 0.75 h-1) demonstrating lower flagellin biosynthesis than
cultures maintained at a dilution rate of 0.5 h-1. These observations may also be due to
fermentation and anaerobic metabolism ratio differences that occur throughout the
different phases of the bacterial growth cycle, such as flagellin biosynthesis genes being
greatly expressed during early stationary phase, for example (Bergara et al, 2003). These
observations suggest controlling growth rate in cultures of C. botulinum, in addition to using
increased CO2 concentrations, as potential process development approaches which could be
used to limit flagellin biosynthesis; limiting competition with botulinum neurotoxin
biosynthesis for metabolite bioflux.
180
5.4 The Effect of Growth Rate & Increased Carbon Dioxide Concentration on Sporulation in
Continuous Cultures of C. sporogenes
Many studies have demonstrated a correlation between sporulation and toxin biosynthesis
(Artin et al, 2010; Kamiya et al, 1992; Mitchell, 2001). Studies in C. botulinum have also
demonstrated that the carbon in PHB is utilised to drive sporulation (Benoit et al, 1990;
Emeruwa & Hawirko, 1973). Following the observations on the effects of increased CO 2
concentration on both PHB and biomass, assessing the effect of CO2 on sporulation was
deemed an important consideration. Owing to the fact chemostat culture requires medium
and culture turnover to maintain growth rate, spores would not accumulate (as observed in
batch culture) unless the spore production rate were to equal or exceed the culture
throughput. All cultures tested in the experimentation had controlled drain rates equal to
their dilution rate and therefore spore counts in continuous culture are representative of
the rate of sporulation in the cellular population maintained by the culture conditions.
181
Sporulation at specific growth rates in continuous cultures of
C.sporogenes
1400
Sporulation (No. spores/ml)
1200
1000
800
600
400
200
0
0.25
0.5
0.75
Dilution (Growth) Rate (h-1)
Figure 65: The effect of growth rate on sporulation in continuous cultures of C. sporogenes.
Growth rate is controlled by dilution rate of growth medium into the fermentation vessel
( h-1). Data shown are averages of triplicate biological samples taken at least one volume
change apart and triplicate technical repeats. Error bars represent standard error of the
mean. At least four volume changes were required before sampling following change of
culture parameter changes such as dilution rate.
182
The effect of CO2 concentration on sporulation in continuous
cultures of C. sporogenes
2000
1800
Sporulation (No. spores/ml)
1600
1400
1200
1000
800
600
400
200
0
10% CO2
25% CO2
50% CO2
CO2 Concentration (%)
Figure 66: The effect of CO2 concentration on sporulation in chemostat cultures of C.
sporogenes. Sporulation increased with increasing CO2 concentration (R2 = 0.9481).
Interestingly, sporulation was increased in cultures grown in 100% Nitrogen (oxygen-free) at
the same growth rate (0.5 h-1) when compared with cultures subject to 10% CO2. Data
shown are averages of triplicate biological samples taken at least one volume change apart
and triplicate technical repeats. Error bars represent standard error of the mean. At least
four volume changes were required before sampling following change of culture parameter
changes such as CO2 concentration. R2 (0.9481) was calculated by plotting sporulation from
0 to 50% CO2 therefore takes into the consideration the non-linear x-axis depicted.
183
Not surprisingly, owing to the detrimental effects observed on biomass, increased CO2
concentration increased sporulation in cultures of C. sporogenes. The correlation was linear
(R2 = 0.9481), although cultures grown in 10% CO2 demonstrated lower sporulation than
those grown in the absence of CO2 (Figure 65), possibly owing to low concentrations of CO2
having limited effects on culture biomass whilst refuelling carbon. If our hypothesis is
correct, it is possible sporulation is increased due to fermentation operating as the primary
mode of respiration in cultures subject to increased CO2. The increase in PHB influenced by
increased CO2 concentration may also contribute to the increase in sporulation observed, as
the carbon storage compound is utilised during sporulation in C. botulinum and other
organisms (Benoit et al, 1990; Emeruwa & Hawirko, 1973). Many studies have
demonstrated a correlation between sporulation and toxin biosynthesis (Artin et al, 2010;
Kamiya et al, 1992; Mitchell, 2001). This correlates with our work and suggests PHB and
fermentation/anaerobic respiration ratio play a part in both sporulation and toxin
biosynthesis.
184
5.5 The Effect of Increased Carbon Dioxide Concentration on Nutrient Metabolism in
Continuous Cultures of C. sporogenes
Our experiments detailed in Chapter 3 demonstrated the effects of both nutrient availability
and consumption on biomarker metabolism in cultures of C. sporogenes. Owing to our
findings on increasing the availability of amino acids on the growth medium’s effect on PHB
and neurotoxin biosynthesis (Chapter 4), assessing the metabolic influences of increased
CO2 concentration, and thus refuelling the TCA cycle with carbon, was vital to understanding
the demonstrated correlations. Analysis of all steady states and CO2 concentrations tested
in chemostat culture were assessed for amino acid content using high-performance liquid
chromatography (HPLC). Phosphate and Glucose consumption rates were also analysed.
185
Effect of CO2 on amino acid consumption - aspartate group (oxaloacetate
biosythetic pathways)
Consumption rate (mg/L)
300
250
200
Aspartic acid
150
Isoleucine
100
Threonine
50
0
10
25
50
CO2 concentration (%)
Figure 67: The consumption of amino acids with biosynthetic pathways originating from TCA
cycle intermediates (a group which increase PHB when supplemented in the growth
medium) are increased under the metabolic changes induced by increased CO 2
concentration. This finding offers evidence to our hypothesis that TCA cycle activity is
reduced under conditions permissive for PHB accumulation and therefore a greater quantity
of TCA cycle derived amino acids are required from the growth medium. The supplied
growth medium contained 300mg/L Aspartic acid, 300mg/L Isoleucine and 200mg/L
Threonine. Data shown are averages of triplicate biological samples taken at least one
volume change apart and triplicate technical repeats. Error bars represent standard error of
the mean.
186
The effect of CO2 on total amino acid consumption
in continuous culutres of C. sporogenes
3500
Amino acid consuption (mg/L)
3000
2500
2000
1500
1000
500
0
10
25
50
CO2 concentration (%)
Figure 68: The effect of increased CO2 concentration on total amino acid consumption,
respective of biomass, in continuous cultures of C. sporogenes. Individual amino acid
concentration was analysed in samples and combined to calculate total amino acid
consumption when compared with unspent growth medium. The supplied growth medium
contained 3800mg/L total amino acids. Data shown are averages of triplicate biological
samples taken at least one volume change apart and triplicate technical repeats. Error bars
represent standard error of the mean.
187
The effect of CO2 on glucose and phosphate
consumption in continuous cultures of C.sporogenes
Nutrient Consumption (µg/g)
3500
3000
2500
Glucose
consumption
2000
1500
Phosphate
consumption
1000
500
0
10
25
50
CO2 concentration (%)
Figure 69: The effect of CO2 on glucose and phosphate consumption in continuous cultures
of C. sporogenes. Glucose and phosphate consumption are affected by the metabolic
changes induced by increased CO2 concentration. The greatest consumption rates of
Glucose, Phosphate and Amino acids (Figure 68) were observed in cultures subject to 50%
CO2, suggesting a less efficient primary mode of metabolism, which results in excess
available carbon for storage at a given biomass. Data shown are averages of triplicate
biological samples taken at least one volume change apart and triplicate technical repeats.
Error bars represent standard error of the mean.
188
The consumption rate of amino acids biosynthesised from TCA cycle intermediates, which
we have previously correlated with increased PHB accumulation when supplemented to the
growth medium, increased with CO2 concentration (Figure 67). The observation that total
amino acid consumption was not increased under conditions of increased CO 2 concentration
(Figure 68) adds merit to the fact only amino acids with biosynthetic pathways from TCA
intermediates which compete for carbon flux with PHB are consumed at a greater rate. This
data agrees with our hypothesis that under conditions permissive for increased PHB
accumulation, influenced by increased CO2 concentration, TCA activity is reduced, as higher
consumption of amino acids refuelled via this pathway were required. This is most likely
owing to a reduction in the amino acid precursors generated from TCA cycle progression,
therefore requiring a greater quantify of amino acids from the growth medium. Another
interesting observation is that in cultures subject to 25 & 50% CO 2, amino acid analysis
detected appreciable quantities of Norleucine, which is generated by the metabolism of
acetyl-CoA; the precursor of PHB (Anderson & Dawes, 1990; Carvalho & Blanchard, 2006;
Steinbuchel, 1991).
Glucose consumption increased with increasing CO2 concentration (Figure 69) and
therefore, owing to our previously established correlations (Figure 54), with increased PHB,
adding justification to our hypothesis that the metabolic observations are due to an increase
in fermentation rate, which is primarily fuelled by glucose. Overall nutrient consumption
rates were increased at higher concentrations of CO2, which provides evidence of a less
efficient mode of metabolism, such as fermentation, which produces less ATP generation
per mole of glucose compared to anaerobic metabolism (Berg et al, 2002). Phosphate
consumption rate remained relatively unchanged with increasing CO2 concentration. Owing
189
to the fact the majority of inorganic phosphate is consumed by the TCA cycle (Berg et al,
2002), this adds merit to our hypothesis that TCA cycle activity or the demand of the TCA
cycle is reduced under the conditions influenced by increased CO2 concentration, as glucose
consumption rate increases driving fermentation.
190
5.6 Validation of Genome-Scale Metabolic Network using Flux Data obtained by
Chemostat Culture
Experimental values obtained from chemostat culture samples for total RNA, DNA and
Protein were used to enhance the GSMN used throughout the research project. The
experimental values were used to replace the theoretical values used in the biomass
equation of C. botulinum model. Given that all fluxes determined using the metabolic
network must balance with the biomass equation, the use of experimentally determined
values lowers the calculation error of the model, resulting in more accurate in silico analysis
(Beste et al, 2007). The chemostat-data enhanced model was then compared with the
original GSMN to determine any significant difference in fluxes with respect to Biomass, PHB
and toxin as objectives (Figure 70). As shown below, no significant difference was calculated
using Student’s t-test between the two GSMNs when testing biomass, PHB and toxin flux as
individual objective functions, validating and adding merit to the in silico data obtained
throughout this research project.
191
Flux comparison calculated by C. botulinum GSMN vs
chemostat enhanced GSMN
9000
Flux (MoleMetabolite-1gBiomass-1)
8000
7000
6000
Original C. botulinum
Model
5000
Chemostat data enhanced
model
4000
3000
2000
1000
0
Biomass
PhB
Toxin
Objective Function
Figure 70: Determining the flux differences between the original C. botulinum GSMN
constructed at the University of Surrey and used throughout this study and the chemostat
data enhanced GSMN, constructed by altering our C. botulinum GSMN with experimentally
obtained values. As shown, no significant difference was calculated between the two
GSMN’s when testing biomass, PHB and toxin flux as individual objective functions,
validating and adding merit to the in silico data obtained throughout this research project.
192
5.7 Chapter Conclusions
The results covered in this chapter demonstrate that increased CO2 concentration
introduces carbon into the TCA cycle via anaplerotic reactions, thus limiting the demand
and/or requirement of the TCA cycle. This results in a linear effect on PHB accumulation
(R2 = 0.975) and Botulinum neurotoxin (R2 = 0.9637) (Artin et al, 2008). Our findings suggest
the metabolic effects of introducing the carbon in CO2 into central metabolism results in
fermentation being utilised as the primary mode of respiration in C. sporogenes and our
data comparison with the studies on CO2 in C. botulinum (Artin et al, 2008) suggest this
effect is homologous in this species. The net result of decreased TCA activity and demand
for carbon provides additional acetyl-CoA; the precursor of PHB, which ultimately results in
increased neurotoxin biosynthesis. Sporulation increased linearly with increased CO 2
concentration (R2 = 0.9975), perhaps owing to the negative linear effect on biomass induced
by elevated concentrations of CO2 (R2 = 0.9843). Although sporulation has been associated
with PHB in C. botulinum and other species previously (Benoit et al, 1990; Emeruwa &
Hawirko, 1973), it is difficult to directly correlate increased PHB with sporulation or vice
versa, although the effects of increased CO2 concentration affect both linearly. Increased
CO2 concentration had a negative linear correlation on flagellin biosynthesis (R2 = 0.9696),
offering a strategy to achieve a primary objective of this research project; reducing
competition for metabolite bioflux with neurotoxin. The effect is likely owing to flagellin
gene downregulation influenced by the metabolic effects of increased CO 2 concentration
(Alteri et al, 2012; Bergara et al, 2003; Brown et al, 1994). Comparison with findings in C.
perfringens that demonstrated increased acetate-derived product yield during fermentation
when compared with anaerobic metabolism (Hasan & Hall, 1974) and studies in E. coli which
have revealed anaplerotic metabolism regulated fermentation/respiration switch proteins
193
(Koo et al, 2004) support the conclusion that both PHB and neurotoxin are largely affected
by the primary respiration mode of the growing culture.
194
Chapter 6: Conclusions
6.1 Project Conclusions & Achievements
For the first time, a linear correlation was established between the energy-storage polymer
PHB accumulation and Botulinum neurotoxin (R2 = 0.964). This was established by GSMN
analysis including FBA (Figure 36), FVA and subsequent testing of PHB metabolism (Figure
40) and confirmed by direct comparison of the effects of CO 2 on PHB accumulation (Figure
57) and neurotoxin (Artin et al, 2008). The work has provided extensive knowledge on PHB
and botulinum neurotoxin metabolism, providing possible approaches for process
development. Based on a combination of the literature (Lillo & Rodriguez-Valera, 1990;
Raberg et al, 2008; Ryu et al, 2007; Shang et al, 2003) and our research on central metabolic
pathway fluxes (Chapter 4, Section 3) which influence PHB accumulation, it can be
concluded that limited availability of inorganic phosphate for optimal ATP biosynthesis limits
the progression of the TCA cycle. This limitation of TCA cycle activity reduces the
requirement of carbon required by the TCA cycle from glycolysis in the form of Acetyl-CoA;
the precursor of PHB. A combination of data obtained from multi-factorial experiments
(Chapter 3, Section 4 & Chapter 4, Section 4), FVA and experiments to assess PHB increasing
metabolism (Chapter 4) yielded results consistent with the hypothesis that PHB competes
for carbon flux with the TCA cycle. Reducing the biosynthesis requirement of amino acids
originating from oxaloacetate, which reacts with the precursor of PHB, acetyl-CoA, to form
citrate, decreased the demand for carbon from glycolysis, resulting in an increased
availability for storage as PHB. This was confirmed by analysis demonstrating TCA cycle
enzymatic activity is reduced (Figures 45, 46, 49 & 50) and consumption of amino acids
195
refuelled by the TCA cycle is increased (Figure 67) under conditions permissive for increased
PHB biosynthesis. Furthermore, the demand for carbon by the TCA cycle, which coincidently
results in increased availability of carbon from glycolysis for storage as PHB, was further
reduced by increasing the introduction of carbon to the TCA cycle via anaplerotic
metabolism. This was concluded by observing increased PEPc activity under conditions
which resulted in increased PHB biosynthesis (Figures 46 & 50). It can be concluded that the
findings on the effect of increased available CO2 on PHB accumulation, and the effect
observed on neurotoxin by Artin et al (Artin et al, 2008), can be attributed to a decreased
demand for carbon by the TCA cycle, owing to the refuelling effect supplied of CO 2. This
increases the availability of Acetyl-CoA which is metabolised by the PHB forming pathway
(Anderson & Dawes, 1990; Steinbuchel, 1991), proven by our analysis of anaplerotic
metabolism (Figures 46 & 50) and an increase in Norleucine, a biomarker of acetly-CoA
metabolism (Carvalho & Blanchard, 2006), under conditions permissive for increased PHB
biosynthesis. Although further study would be required to validate our hypothesis, our
findings suggest PHB and neurotoxin biosynthesis are largely affected by the ratio of
fermentative and anaerobic respiration of the growing culture; akin to the relationship
demonstrated in C. perfringens affecting acetate-derived product yield (Hasan & Hall, 1974).
By reviewing our results and the relevant literature we can conclude that decreased
demand for carbon and/or activity of the TCA cycle resulted in increased availability of
Acetyl-CoA, increasing PHB accumulation and consequently, neurotoxin biosynthesis (Artin
et al, 2008; Kai et al, 2003; Lillo & Rodriguez-Valera, 1990; Owen et al, 2002; Raberg et al,
2008; Ryu et al, 2007; Shang et al, 2003).
196
Sporulation increased linearly with increased CO2 concentration (R2 = 0.9975), perhaps
owing to the negative linear effect on biomass induced by elevated concentrations of CO 2
(R2 = 0.9843). Although sporulation has been associated with PHB in C. botulinum and other
species previously (Benoit et al, 1990; Emeruwa & Hawirko, 1973), it is difficult to directly
correlate increased PHB with sporulation or vice versa, although the effects of increased CO2
concentration affect both linearly. Increased CO2 concentration had a negative linear
correlation on flagellin biosynthesis (R2 = 0.9696), offering a strategy to achieve a primary
objective of this research project; reducing competition for metabolite bioflux with
neurotoxin. The effect is likely owing to flagellin gene downregulation influenced by the
metabolic effects of increased CO2 concentration (Alteri et al, 2012; Bergara et al, 2003;
Brown et al, 1994). High glucose concentration in cultures of C. sporogenes drastically
reduced flagellin biosynthesis (Figure 63 & 64). As greater availability of carbon in relation to
other nutrients is likely to result in fermentation being utilised as a primary mode of
metabolism and previous studies have demonstrated flagellin biosynthesis genes are greatly
expressed during early stationary phase (Bergara et al, 2003), our findings suggest the
reduction in flagellin biosynthesis observed from high glucose and/or CO2 concentration is
owing to a downregulation of flagellin biosynthesis genes, influences by the metabolic
effects of these process alterations.
By investigating biomarkers of neurotoxin biosynthesis, within the constraints of the
surrogate system, this research project has proved successful in establishing a strong
correlation with PHB which can provide extensive information to further develop a
production orientated process. The findings on flagellin metabolism can also be used to
197
reduce competition for metabolite bioflux with botulinum neurotoxin, achieving both
primary objectives of our research.
6.2 Research Impact
The findings and results revealed in this research project offer substantial insight into what
drives PHB metabolism in C. sporogenes and C. botulinum, which combined with the impact
of the linear correlation between PHB and botulinum neurotoxin synthesis established by
this study, offers unique insight into potential process development approaches. The
findings on the metabolism and conditions which result in increased PHB accumulation can
also offer guidance for study of PHB in other PHB producing organisms, including offering
potential approaches to increasing PHB yield in industrial PHB production processes.
Importantly, this research encourages investigations into the possibility of a correlation
between PHB and toxin biosynthesis in other toxin producing members of the Clostridia
genus, such as C. perfringens and C. difficile. This research could also form the basis of
investigation into the correlation between PHB and other secondary metabolites, such as
butanol production in C. Acetobutyricum, which shares biosynthetic pathway metabolites
with PHB.
Our work has also highlighted the use of CO2 as a supplementation approach to refuel TCA
cycle intermediates and offered insight into metabolic manipulation of flagellin biosynthesis
and sporulation, which may relate to organisms beyond C. sporogenes and C. botulinum.
198
6.3 Process Recommendations for the Botulinum Neurotoxin Production Process
The results of this study demonstrate the process yield benefits attainable by controlling
growth medium composition in C. botulinum cultures with respect to the botulinum
neurotoxin production process. Altering the growth medium to achieve conditions
permissive for PHB, and by association, neurotoxin biosynthesis offer a control strategy with
experimentally determined biochemical and metabolic effects, offering potential increased
and controlled neurotoxin yield. Based on these findings, supplementation with 10mM
Homoserine may be an effective method of increasing PHB and neurotoxin biosynthesis by
C. botulinum. Growth medium should also be phosphate limited with respect to carbon
content and the culture sparged with 36% CO2 gas (remaining composition oxygen-free
nitrogen), limiting the flux of competing pathways such as flagellin biosynthesis and
increasing toxin yield. If supplementation to the growth medium is the preferred approach
owing to process constraints, 10mM Bicarbonate can be substituted in place of increased
CO2 concentration, demonstrated by the experiments covered in chapter 5 (Figure 59).
199
6.4 Recommendations for Future Studies
Our findings on the conditions which result in increased PHB and neurotoxin biosynthesis
and coincidentally decrease flagellin biosynthesis suggest the ratio of fermentative and
anaerobic respiration utilised by the growing cultures plays an important role in influencing
metabolism and affecting both biomarker and toxin yield. As this was not directly assessed,
this work could be progressed by analysing culture respiration modes for comparison with
both PHB and botulinum neurotoxin.
Another recommendation to further this research is assessing the interplay between
glycogen storage, PHB accumulation and neurotoxin biosynthesis. In Streptomyces spp.,
glycogen accumulates in response to excess carbon (Lillie & Pringle, 1980) and has been
correlated with the biosynthesis of secondary metabolites, such as avilamycin (Salas &
Mendez, 2005). Glycogen is stored in a pathway originating from glucose. Therefore
comparatively, if excess carbon is available prior to being metabolised via glycolysis, the
carbon is stored as glycogen and if excess carbon is available at the interaction between
glycolysis and the TCA cycle, the carbon is stored as PHB. Based on our findings, this
relationship is likely to affect PHB metabolism and coincidently, neurotoxin biosynthesis.
Glycogen metabolism therefore may offer a further strategy to control neurotoxin
biosynthesis and a further process development approach with regards to the botulinum
neurotoxin production process.
Increasing the concentration of CO2 offers a process development approach which increases
both PHB and neurotoxin; however sporulation is also linearly increased. Owing to the fact
200
our hypothesis were tested in chemostat culture, it is difficult to estimate the effects of
increased sporulation on culture population in batch culture, where spores will accumulate
as opposed to being removed at the set dilution rate in continuous culture. It would
therefore prove advantageous to assess the effects of increased CO2 on sporulation in batch
cultures of C. botulinum.
201
Appendix
Appendix I: Amino acid composition comparison of Botulinum toxin and flagellin
Amino acid composition comparison of Botulinum toxin and flagellin
15
10
Toxin
5
cystiene
Histidine
Methionine
Tryptophan
Arginine
Proline
Alanine
Glycine
Phenylalan…
Valine
Lysine
Threonine
Tyrosine
Serine
Leucine
Isoleucine
Glutamic…
Flagellin
0
Aspartic acid
Amino acid composition (%)
20
Appendix Figure 1: A comparative display of amino acid composition between botulinum
neurotoxin and flagellin protein. Values of botulinum toxin amino acid composition were
obtained from Stefanye et al (Stefanye et al, 1967) and values of flagellin amino acid
composition were obtained from Chang et al (Chang et al, 1976).
202
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