characterization of bt3299: a family gh31 enzyme from a
TRANSCRIPT
Characterization of BT3299: A Family GH31 Enzyme from a Prominent Gut Symbiont, Bacteroides thetaiotaomicron
by
Jenny-Lyn L. I. Jacobs
A thesis submitted in conformity with the requirements for the degree of Master of Science
Department of Medical Biophysics University of Toronto
© Copyright by Jenny-Lyn L. I. Jacobs 2011
ii
Characterization of BT3299: A Family GH31 Enzyme from a
Prominent Gut Symbiont, Bacteroides thetaiotaomicron
Jenny-Lyn L. I. Jacobs
Master of Science
Department of Medical Biophysics
University of Toronto
2011
Abstract
The human gut is host to a vast consortium of microorganisms, collectively referred to as the
microbiota or microflora, which play important roles in health and disease. Current applications
focus only on a single type of bacteria, which are not the most dominant numerically, and
without detailed knowledge of the specific functions of these bacteria. A good indicator of the
function of a bacterial species involves detailed analysis of its enzymes. Bacteroides
thetaiotaomicron is one of the predominant bacterial species with a great representation of the
carbohydrate processing enzymes, glycoside hydrolases in its proteome. This thesis reports the
production and purification of one such enzyme, BT3299, suitable for kinetic and structural
studies. The enzyme displayed a broad substrate specificity with a slight preference for 13 and
16 glycosidic linkages and longer chain saccharides. Future work will focus on structural
analysis as an aid to the understanding of the enzyme function.
iii
Acknowledgments
First of all, I would like to thank the Almighty God, centre of my life and source of all
knowledge and blessings.
I would also like to thank my supervisor, Dr. David Rose for giving me the opportunity to work
with him on this project and for his guidance and continued encouragement. In addition, I would
like to thank my advisory committee: Dr. Emil Pai and Dr. Avi Chakrabartty for their input,
advice and helpful suggestions, as well as the other members of my exam committee: Dr. Peter
Burns, Dr. Lothar Lilge and Dr. Gil Privé for a truly fun and engaging thesis discussion.
A big thank-you to: Dr. Lyann Sim for being instrumental in getting me started on this project,
Dr. Meenakshi Venkatesan for her special way of making everything become clear and to Dr.
Doug Kuntz and Megan Barker for going above and beyond. Thanks to the past and present
members of Dr. Gil Privé’s lab for sharing their valuable knowledge and expertise with me as
well as for all their comments and suggestions during lab meetings.
A special thank-you to Dr. Kathy Singfield at Saint Mary’s University Chemistry Department for
sparking my interest in research and for her continued support, encouragement and belief in my
abilities.
I could not have been where I am today without the love, help and support of my mother, Jenifer
Jacobs and other family members. Special thanks to all my friends for helping to keep me
focused and to Wilston Allen for showing tremendous love and support from the very start.
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Table of Contents
Acknowledgments .......................................................................................................................... iii
Table of Contents ........................................................................................................................... iv
List of Tables ................................................................................................................................ vii
List of Figures .............................................................................................................................. viii
List of Appendices ......................................................................................................................... xi
Chapter 1 Introduction and Background ....................................................................................... 1
1.1 The Human Microbiota .................................................................................................... 2
1.2 The Gut Microbiota in Health and Disease .................................................................... 3
1.2.1 Trophic Role .......................................................................................................... 3
1.2.2 Protective Role ...................................................................................................... 5
1.2.3 Metabolic Role ....................................................................................................... 7
1.3 Applications of Gut Microbial Research ...................................................................... 10
1.3.1 Probiotics ............................................................................................................. 10
1.3.2 Prebiotics ............................................................................................................. 12
1.3.3 Synbiotics ............................................................................................................. 14
1.4 Bacteroides thetaiotaomicron .......................................................................................... 15
1.5 BT3299: A Family 31 Glycoside Hydrolase from Bacteroides thetaiotaomicron ....... 18
1.5.1 CAZy Classification of Glycoside Hydrolases .................................................. 18
1.5.2 Glycoside Hydrolase Family 31 ......................................................................... 19
1.5.3 Bacteroides thetaiotaomicron GH31 Enzymes .................................................. 20
1.6 Experimental Focus of BT3299 Research ..................................................................... 23
Chapter 2 Experimental Design and Methods ............................................................................. 24
2.1 Bacterial Expression system ........................................................................................... 25
2.2 Expression Vector ........................................................................................................... 25
v
2.3 Cloning of bt3299 gene .................................................................................................... 27
2.4 Protein Production .......................................................................................................... 27
2.5 Protein Purification ........................................................................................................ 28
2.6 SDS-PAGE ....................................................................................................................... 29
2.7 pNP-glucose Assay .......................................................................................................... 29
2.8 Enzyme Kinetics .............................................................................................................. 30
2.9 Crystallization Experiments .......................................................................................... 33
Chapter 3 Results ......................................................................................................................... 34
3.1 Cloning of bt3299 Gene ................................................................................................... 35
3.2 Protein Production .......................................................................................................... 36
3.3 Ni-NTA Affinity Chromatography ................................................................................ 38
3.4 Mass Spectrometry ......................................................................................................... 40
3.5 Gel Filtration Chromatography .................................................................................... 41
3.6 Enzymatic Activity .......................................................................................................... 43
3.7 Enzyme Kinetics .............................................................................................................. 46
3.8 Substrate Screening ........................................................................................................ 49
3.9 Crystallization Experiments .......................................................................................... 56
Chapter 4 Discussion ................................................................................................................... 58
4.1 Discussion of Results ....................................................................................................... 59
4.2 Future Directions ............................................................................................................ 64
References ..................................................................................................................................... 66
Appendix A: Supplemental Data .................................................................................................. 71
A1: Trehalose Kinetics Data ................................................................................................. 72
A2: Sucrose Kinetics Data ..................................................................................................... 74
vi
A3: Nigerose Kinetics Data ................................................................................................... 77
A4: Turanose Kinetics Data .................................................................................................. 79
A5: Maltose Kinetics Data ..................................................................................................... 81
A6: Cellobiose Kinetics Data ................................................................................................. 84
A7: Lactose Kinetics Data ..................................................................................................... 86
A8: Palatinose Kinetics Data ................................................................................................. 88
A9: β-Gentiobiose Kinetics Data ........................................................................................... 90
A10: Soluble Starch Kinetics Data ....................................................................................... 92
A11: β-D Glucan Kinetics Data............................................................................................. 94
Copyright Acknowledgements ...................................................................................................... 97
vii
List of Tables
1.1 Four subgroups of GH31 enzymes....................................................................................20
3.1 Table of experimentally derived Ki and BT3299 specific activity values.........................56
viii
List of Figures
1.1 Trophic importance of gut microbiota.................................................................................3
1.2 The three classes of microbes making up the microbiota....................................................6
1.3 Intricate relationship among dietary intake, the gut microbiota and disease risk................9
1.4 The starch-utilization system (sus) of Bacteroides thetaiotaomicron...............................17
1.5 Comparison of BT GH31 enzymes with structurally characterized GH31 enzymes........22
2.1 Vector map of the pET-29a(+) expression vector..............................................................26
2.2 Schematic of pNP-glucose enzyme assay..........................................................................29
2.3 Secondary substrates screened for BT3299 activity..........................................................32
3.1 DNA gel of BT3299 PCR amplification............................................................................35
3.2 SDS-PAGE of BL21 codon plus cell growth and BT3299 induction at different
temperatures.......................................................................................................................36
3.3 SDS-PAGE of BL21 codon plus cell growth and overnight BT3299 induction at
15°C......................................................................................................................................37
3.4 SDS-PAGE of BT3299 purification by Ni-NTA affinity chromatography.......................39
3.5 Mass spectrometry of purified BT3299.............................................................................40
3.6 Gel filtration purification of BT3299.................................................................................42
3.7 Glycoside hydrolase activity with pNP-glucose as a substrate..........................................43
ix
3.8 pH profile of BT3299 activity using pNP-glucose as a substrate......................................44
3.9 p-Nitrophenol standard curve.............................................................................................45
3.10 The effect of various substances on BT3299 activity........................................................46
3.11 Time curve of BT3299 activity with pNP-glucose as a substrate......................................47
3.12 BT3299 kinetics using pNP-glucose as a substrate...........................................................48
3.13 KM,app plot of BT3299 activity with inhibition by the secondary substrate trehalose........50
3.14 KM,app plot of BT3299 activity with inhibition by the secondary substrate sucrose..........50
3.15 KM,app plot of BT3299 activity with inhibition by the secondary substrate nigerose.........51
3.16 KM,app plot of BT3299 activity with inhibition by the secondary substrate turanose.........51
3.17 KM,app plot of BT3299 activity with inhibition by the secondary substrate maltose..........52
3.18 KM,app plot of BT3299 activity with inhibition by the secondary substrate cellobiose......52
3.19 KM,app plot of BT3299 activity with inhibition by the secondary substrate lactose...........53
3.20 KM,app plot of BT3299 activity with inhibition by the secondary substrate palatinose......53
3.21 KM,app plot of BT3299 activity with inhibition by the secondary substrate β-gentiobiose 54
3.22 KM,app plot of BT3299 activity with inhibition by the secondary substrate soluble
starch..............................................................................................................................................54
3.23 KM,app plot of BT3299 activity with inhibition by the secondary substrate β-D glucan....55
3.24 Photograph of microcrystals obtained during BT3299 crystallization screen...................57
x
A1 Michaelis-Menten and Lineweaver-Burk plots for trehalose kinetics...............................74
A2 Michaelis-Menten and Lineweaver-Burk plots for sucrose kinetics.................................76
A3 Michaelis-Menten and Lineweaver-Burk plots for nigerose kinetics................................78
A4 Michaelis-Menten and Lineweaver-Burk plots for turanose kinetics................................81
A5 Michaelis-Menten and Lineweaver-Burk plots for maltose kinetics.................................83
A6 Michaelis-Menten and Lineweaver-Burk plots for cellobiose kinetics.............................85
A7 Michaelis-Menten and Lineweaver-Burk plots for lactose kinetics..................................87
A8 Michaelis-Menten and Lineweaver-Burk plots for palatinose kinetics.............................90
A9 Michaelis-Menten and Lineweaver-Burk plots for β-gentiobiose kinetics........................91
A10 Michaelis-Menten and Lineweaver-Burk plots for soluble starch kinetics.......................94
A11 Michaelis-Menten and Lineweaver-Burk plots for β-D glucan kinetics...........................96
xi
List of Appendices
Appendix A: Supplemental Data...................................................................................................71
1
Chapter 1
Introduction and Background
2
1.1 The Human Microbiota
The human body contains 1014
cells, 90% of which are microbial in nature. This is because the
human body is home to a vast consortium of microorganisms, which are either native (acquired
at birth) or transient (ingested from the environment). Collectively, these microorganisms are
referred to as the microflora or microbiota. The human microbiota comprises species from all
three domains; Bacteria, Archaea and Eukarya and although the microbiota is made up of
predominantly bacterial species, the types and composition vary according to the particular body
niche; oral cavity, skin, vagina, urinary tract and gut (stomach, ileum and colon). The human gut
hosts the greatest concentration of microbes, residing on the intestinal mucosal surface or within
the gut lumen. The numbers increase as a progression is made down the intestinal tract, with
approximately 103
organisms/g in the acidic stomach lumen, to 108 organisms/ml in the distal
small intestine to 1011
-1012
organisms/g of colonic contents.
The normal interaction in the gut between host and microbes is a symbiotic relationship where
the microbiota is given a protected, nutrient-rich niche and the host benefits from a variety of
metabolic, protective and trophic functions performed by the microbiota. Metabolic functions
include the fermentation of endogenous mucus and of a number of dietary substances that are
otherwise indigestible, biotransformation of conjugated bile acids, degradation of dietary
oxalates, synthesis of certain vitamins and production of exogenous enzymes. Protective
functions include colonization resistance to invasion by pathogens, while trophic functions
involve the stimulation of intestinal transit, control of epithelial cell proliferation and
differentiation and the development and homeostatic regulation of the immune system [1-4].
3
1.2 The Gut Microbiota in Health and Disease
1.2.1 Trophic Role
The impact on the immune system is important due to the close interaction between the gut
microbes and Peyer’s patches – large number of organized lymphoid structures – in the small
intestine mucosa. These Peyer’s patches have specialized epithelium for the uptake and sampling
of antigens and contain lymphoid germinal centers for the induction of adaptive immune
responses. There are also lymphoid aggregates in the large intestine as well as diffusely spread
immune cells in the lamina propria of the gastrointestinal tract which are in contact with the rest
of the immune system via local mesenteric lymph nodes.
Figure 1.1: Trophic importance of gut microbiota. The gut microbiota is in close proximity
and can therefore interact with Peyer’s patches important in immune responses and can therefore
exhibit immunomodulatory effects. (Figure reproduced with permission from Elsevier6).
Studies involving the use of germ-free (sterile) mice have shown that these animals suffer
extensive defects in the development of gut-associated lymphoid tissues and in antibody
production and have fewer and smaller Peyer’s patches and mesenteric lymph nodes. These
animals also showed impaired development and maturation of isolated lymphoid follicles as well
4
as low levels of serum immunoglobulins compared with traditionally raised control animals,
housed under specific pathogen free conditions. When these germ free animals were challenged
with an enteric pathogen, they showed decreased immune resistance to infection and increased
mortality. Interestingly, once these sterile animals were exposed to commensal microbes, there
was a rapid expansion in the number of mucosal lymphocytes and an increase in size of germinal
centers in lymphoid follicles [3,5-8].
The normal microflora is able to exert its beneficial role as long as mechanisms are in place to
confine the microbes to a particular site. For example, the gut microbiota is confined by the
maintenance of lotic environments, which include the flow of secretions and digesta, and also by
intact epithelial barriers. Any disruption of this fine balance exposes the indigenous microbiota
which then becomes a potent source of opportunistic infections for example, infections following
bowel surgery and Crohn’s disease.
In the case of Crohn’s disease, genetic susceptibilities, microbial imbalance and leaky epithelia
are thought to be causative factors in the pathogenesis of the disease. Bacterial cells and/or their
antigens are able to pass into the Peyer’s patches, as a result of the leaky epithelia, leading to the
activation of CD4+ T cells which migrate to the lamina propria. In healthy humans, the T cells
die by apoptosis once at the lamina propria, however, in Crohn’s disease T-cell activation is
triggered and this leads to the breakdown of tolerance mediated by immunosuppressive cytokines
and T regulatory cells. The immune cells therefore attempt to assume their normal role of
eradicating threatening bacteria and their components. Since the antigenic sources are from
bacteria residing in the bowel, this role proves impossible to carry out and the result is chronic
inflammation [3,6].
5
1.2.2 Protective Role
Under normal, homeostatic conditions, an inflammatory response is triggered upon the invasion
of a pathogenic species. Interestingly enough, indigenous gut microbiota has been implicated in
the resistance to pathogenic species thereby exhibiting a protective role for the human host. This
is exemplified in gnotobiotic (involving the use of animals with known strains of
microorganisms) experiments where the presence of normal microflora enhances nonspecific
resistance to infection. Although it is not yet quite clear just how competitive exclusion is
achieved, there have been some very interesting findings in this area. For example, the
expression of defensins and other antimicrobial proteins is defective in germ-free animals and in
line with that observation is the finding that the Gram-negative commensal organism Bacteroides
thetaiotaomicron, induces the expression of the antimicrobial peptide REG3γ which has
specificity directed towards certain Gram-positive bacteria [8].
Though the gut microbiota has the ability to detect the presence of foreign, pathogenic organisms
and induce protective mechanisms against the invading species, the normal gut microbial
community is not pathogen-free. As an example, the bacterial species Helicobacter pylori, which
is a member of the normal microbiota, is known to be a key player in the pathogenesis of Peptic
Ulcer Disease. H. pylori produces urease, which hydrolyzes urea into carbon dioxide and
ammonia, thereby allowing it to survive in the acidic environment of the stomach. Studies
indicate that H. pylori produces different molecules which allow it to adhere to the mucosal
surface of the gastrointestinal tract, as well as toxins that allow it to gain nutrients from the
environment. The host reacts to the presence of the bacterium and its toxins by inducing the
inflammatory response. It is this inflammatory response that leads to mucosal atrophy, which
predisposes to the formation of ulcers [9].
6
It is not quite clear how the gut is able to tolerate such a pathogenic species as H. pylori under
normal conditions. However, one proposed model of the microbiota suggests that the community
is made up of three classes of microbes; the symbionts (mutualists), the commensals and the
pathobionts. The symbionts are organisms with known health promoting functions and are
involved in regulation. The commensals are those members of the community that provide no
known benefit or detriment to the host, while the pathobionts are organisms which have the
potential to induce pathology and are involved in inflammation. In conditions where there is an
unnatural shift in the composition of the microbiota whereby the number of symbionts is
decreased and/or the number of pathobionts is increased, the result is non-specific inflammation
which may predispose the host to various gastrointestinal diseases [8].
Figure 1.2: The three classes of microbes making up the microbiota in health and disease
states. a) During periods of health there is thought to be a balance of all three classes of the
microbiota: the symbionts (mutualists), commensals and pathobionts, b) an unnatural shift in the
composition of the three classes where there is either a reduction in symbionts and/or an increase
in pathobionts is thought to lead to disease conditions. (Figure adapted by permission from
Macmillan Publishers Ltd.8).
7
1.2.3 Metabolic Role
The primary role of the colonic microbiota is thought to be the metabolic function— involving
the salvaging of energy from dietary substances that have escaped digestion in the upper
gastrointestinal tract. These substances include starches (referred to as resistant starch), dietary
fibres and other non starch polysaccharides (for eg. cellulose, pectins, gums and nondigestible
oligosaccharides), sugars and non-absorbable sugar alcohols, proteins and amino acids. Dietary
fibre, which comprises plant structural and exudative components, is known for its beneficial
role as described by the ―roughage model‖. In this model, it is thought that the physical presence
of fibre in the intestines leads to the dilution or binding of toxins and carcinogens. In addition to
this, a varying degree of dietary fibre and all of the other indigestible substances undergo
fermentation by the gut microbiota as a means of extracting energy. The principal products of
this fermentation process are short chain fatty acids (SCFA). More than 95% of SCFA are
produced and absorbed within the colon. SCFA are the principal luminal anions in humans and
are relatively weak acids. An increase in the concentration of SCFA through fermentation leads
to a decrease in digesta pH. Lower pH values are thought to prevent the overgrowth of pH-
sensitive pathogenic bacteria. The most common types of SCFA are acetate, propionate and
butyrate. Acetate is metabolized systemically in the brain and muscle tissues, propionate is
cleared by the liver, while butyrate is the major source of fuel for the colonic epithelium. The
latter is thought to be the key player in the contribution of SCFA to the normal functioning of the
large bowel and pathology prevention. More specifically, butyrate is involved in mitosis and
mucosal regeneration and plays an important role in the metabolism and normal development of
colonic epithelial cells [10,11].
8
While fermentation of resistant carbohydrates lead to the production of the beneficial short chain
fatty acids, the end product of proteolytic fermentation include phenolic compounds, amines, N-
nitroso compounds and indoles; all of which can be toxic to the human host. In fact, studies have
suggested that greater protein intakes are associated with an increase in DNA damage in the form
of single and double strand breaks, as well as an increased risk for colorectal cancer. However, in
a study conducted by Topping, D.L et al12
, genetic damage, measured as single strand breaks,
was decreased in rats fed protein followed by high amylose maize starch (a resistant starch)
versus those fed protein only. The effect was even more pronounced when butyrate was added to
the resistant starch before feeding. Here, the important role diet plays in the pathogenesis of
disease is highlighted, but what is also interesting is that this effect is mediated by the gut
microbiota. In another study involving individuals of similar race but from different geographical
locations, O’Keefe, S.J.D. et al13
made observations that shed some light on the intricate role the
gut microbiota may be playing in mediating the effect of diet on disease. In one group, where the
normal diet includes high intakes of maize meal and low consumption of meat and animal fat,
there were higher rates of colonization of Lactobacilli species. Lactobacilli is known to promote
mucosal health. In the other group, where the normal diet includes high intakes of red meat, there
were higher rates of colonization of secondary bile acid-producing bacteria as well as sulphate-
reducing bacteria. Both these types of bacteria are known to produce cytotoxic and genotoxic
agents [11-13].
9
Figure 1.3: Intricate relationship among dietary intake, the gut microbiota and disease
risk. The diet is thought to play an important role in the risk and prevention of disease such as
colon cancer. This effect is mediated by the gut microbiota whose composition and metabolism
can vary according to dietary intakes. These changes in turn influence disease risk. (Figure
reproduced with permission from Elsevier11
).
10
1.3 Applications of Gut Microbial Research [3, 14]
Given the impact of the microbiota on human health and disease, as highlighted above, much
attention has been given to the manipulation of the microflora in attempts to improve disease
conditions. The current applications involve what are known as probiotics, prebiotics and
synbiotics and majority are focused on lactic acid bacteria resident in the gut, mainly those
belonging to the genera Lactobacillus, Bifidobacterium and Streptococcus.
1.3.1 Probiotics
Probiotics are defined by WHO as live microorganisms that when administered in adequate
amounts confer a health benefit on the host14
. Thus, the probiotic microorganisms, Lactobacillus,
Bifidobacterium and Streptococcus were selected from a wide range of lactic acid bacteria and
other microorganisms for their health-promoting qualities. They have also passed the selection
criteria for probiotics, which include, their safety for humans, their origin from the intestinal tract
of healthy persons, their tolerance to gastric and bile acids and digestive enzymes and the
detection of parameters enabling a positive influence on the intestinal microflora.
Probiotic health benefits have been investigated in a variety of diseases. Perhaps the most
thoroughly investigated health-relevant effect is the enhancement of lactose digestion and the
avoidance of intolerance symptoms in lactose malabsorbers. The lactose malabsorption is due to
a lactase deficiency and this leads to such symptoms as diarrhea, flatulence, abdominal bloating
and pain after ingesting lactose, which is found mostly in fermented milk products. Bacteria such
as Streptococcus thermophilus are used as starter cultures in yogurt are able to improve lactose
digestion and eliminate symptoms3. This benefit is thought to be due to the activity of microbial
beta-galactosidases which have the ability to hydrolyze the lactose.
11
Abdominal bloating and flatulence are also symptoms of Irritable Bowel Syndrome (IBS) which
is as a result of the generation of variable gas volumes in the intestine due to colonic
fermentations. Several studies have demonstrated that significant therapeutic gains in the form of
reduction of IBS symptoms were observed upon administration of probiotics3.
Probiotics have also been shown to exhibit protective effects particularly in the treatment of
diarrhea and the eradication of H. pylori. The growth of H. pylori, a known pathogenic resident
of the human stomach, can be inhibited by some strains of lactic acid bacteria in vitro3.
Therefore, probiotics have been tested as a means to eradicate H. pylori during infection.
One of the side effects of antibiotic use is diarrhea, appropriately termed antibiotic-associated
diarrhea which is induced by the overgrowth of C.dificile. Antibiotic-associated diarrhea can be
prevented or at least be less frequent with the use of probiotics, especially if the probiotics are
administered before and during antibiotic treatment. Another type of diarrhea which is common
in hospitalized children and in developing countries, rotavirus-induced diarrhea, can be
prevented with the use of probiotics. In one study, supplementation of infant formula with
Bifidobacterium bifidum and S. thermohlilus prevented the incidence of diarrhea in hospitalized
infants3. Probiotics have also been shown to reduce the incidence and severity of diarrhea that
has been induced by chemotherapy treatment in cancer patients14
. Another protective effect of
probiotics is seen in its use to prevent conditions involving dysfunction of the gut mucosal
barrier. Systemic infections and sepsis that occur in some pathologic conditions as a result of
bacterial translocation have been shown to occur at a lower rate in patients treated with
probiotics. Low birth weight infants have also shown reduced incidence and severity of
necrotizing enterocolitis when given probiotic mixtures3.
12
Probiotics also have trophic applications in inflammatory bowel diseases. Studies involving
patients in remission from ulcerative colitis showed that those patients on standard medication
who received supplements of Bifidobacteria had fewer relapses than those who did not receive
the probiotic supplementation14
. There has been some success as well in probiotic applications to
food allergy. Randomized controlled trials have demonstrated the effectiveness of probiotic
therapy in the prevention of atopic eczema.
There have even been experimental studies that have given evidence that certain strains of
probiotic bacteria may prevent viral respiratory tract infections14
or even colon cancer. Beneficial
bacteria can prevent the establishment, growth and metastasis of transplantable and chemically
induced tumours. [3,14]
1.3.2 Prebiotics
A prebiotic is a selectively fermented ingredient that allows specific changes, both in the
composition and/or activity in the gastrointestinal microflora that confers benefits upon host
well-being and health3,14
. In order to be classified as a prebiotic, the ingredient must be non-
digestible, that is, neither hydrolyzed nor absorbed in the upper gastrointestinal tract; it must be
selectively fermented by one or a limited number of potentially beneficial bacteria commensal to
the colon and it must be able to selectively stimulate the growth and activity of beneficial
intestinal bacteria while reducing putrefactive microorganisms. Prebiotics are used as
alternatives to probiotics for intestinal microbiota modulation. They have the advantages of not
relying on culture viability and of being constituents of the normal human diet thereby not
posing as great a challenge from the aspects of safety and consumer acceptability as do
probiotics. Prebiotic carbohydrates are essentially dietary fibres. They increase biomass, feces
13
weight and frequency, have a positive effect on constipation and on the health of the large
intestine mucosa. The most recognized prebiotics currently are inulin (a mixture of fructooligo-
and polysaccharides), fructooligosaccharides (FOS; 3 to 10 carbohydrate monomers),
galactooligosaccharides (GOS) and lactulose.
There have been applications of prebiotics in intestinal infection and inflammation. In patients
with intestinal infection, treatment usually involves the use of antibiotics. However, this leads to
a loss of Bifidobacteria and an overgrowth of the pathogenic C .dificile. Studies have shown that
administration of oligofructose not only suppressed colonization by C. dificile, but also increased
Bifidobacteria levels14
. In those patients who suffered from an intestinal bowel disease such as
Crohn’s disease, a decrease in the disease activity was observed upon intake of a combination of
inulin and FOS.
In a study where oligofructose was supplemented to the cereal of infants attending daycare,
prebiotics appeared to be playing a role in the modulation of immune function. The infants
receiving the prebiotic showed a decrease in the severity of diarrheal disease, decreased bowel
movement discomfort, decreased vomiting and regurgitation. They also displayed adequate
growth, reduction in cold symptoms and day care absenteeism. Their levels of Bifidobacteria
were higher and their levels of pathogenic bacteria were lower than controls3.
There have also been studies into the application of prebiotics to mineral absorption and bone
metabolism and lipid metabolism. In the former, animal and human studies indicated that inulin-
type fructans led to an increase in calcium absorption from the diet. Inulin-type fructans have
also been shown to lower triglyceride and cholesterol levels in experimental models as well as
lower the body weight and fat mass of genetically obese rats3.
14
In animal chemoprevention models, inulin-type fructans exhibited the ability to inhibit
carcinogen-induced colonic DNA damage and to suppress the development of colonic
preneoplastic lesions and tumours3,14
.
1.3.3 Synbiotics
In some instances the effect of a probiotic or prebiotic may be enhanced by considering the use
of a symbiotic. A synbiotic is a mixture of probiotics and prebiotics that beneficially affects the
host, by improving the survival and implantation of live microbial dietary supplements in the
gastrointestinal tract, by selectively stimulating the growth and/or activating the metabolism of
one or a limited number of health promoting bacteria and thus improving host welfare [3].
In patients with liver failure, proteolytic activities of gut bacteria contribute to the induction of
hepatic encephalopathy. In a clinical trial with chronic liver disease patients, fifty percent of the
patients treated with a synbiotic preparation had minimal encephalopathy reversed. In other
clinical studies, involving the eradication of the pathobiont H. pylori, supplementation of anti-H.
pylori with probiotics have enhanced the eradication rates. Patients suffering from an intestinal
infection or intestinal inflammation, who have experienced loss of Bifidobacteria, had the
numbers restored upon administration of oligofructose as a synbiotic or oligofructose-enriched
inulin together with a probiotic respectively. Synbiotics can also play a role in colon cancer
prevention. In animal models, the genotoxicity of fecal water was reduced and the number of
chemically induced pre-cancerogenic lesions was decreased by feeding inulin and/or
oligofructose14
. In polyp and cancer patients, synbiotics helped to reduce the risk factors for
colon cancer by increasing Bifidobacteria and Lactobacilli levels and decreasing numbers of
Clostridia3.
15
1.4 Bacteroides thetaiotaomicron
Given the impact of the microbiota on human health and disease, as highlighted above, the
importance of understanding the normal functioning, including optimal conditions to facilitate
such functions, becomes quite clear. The functions and benefits attributed to the microbiota are
applied to the whole as the contributions of individual species are not well known. Current
applications are limited to only a few of the approximately 1000 bacterial species in the gut and
do not include members of the more dominant phyla. Therefore, a good starting point into the
deeper understanding of the microbiota involves investigating individual species, specifically
those present in large numbers in the normal microflora.
Bacteroides thetaiotaomicron is one such species, a predominant member of the normal adult gut
microbiota. It is a genetically manipulatable organism whose entire genome has been sequenced.
This makes it an ideal candidate for investigation and it has in fact been used as a model for
understanding the impact of the microbial constituents on gene expression. However, one of the
indicators of the function of a bacterial species involves detailed analysis of its enzymes.
In the proteome of B. thetaiotaomicron, there is a large number of paralogous groups for
environmental sensing and signal transduction, DNA mobilization, capsular polysaccharide
biosynthesis and polysaccharide uptake and degradation. In fact, the latter group comprises many
outer membrane proteins which are thought to be involved in the acquisition of oligo- and
polysaccharides and glycoside hydrolases – a group of carbohydrate processing enzymes. The
number of glycoside hydrolases in the proteome of B.thetaiotaomicron not only greatly exceeds
that in any other sequenced bacteria but is more than 2.5 times that in the human proteome. Sixty
one percent of these glycoside hydrolases are predicted to be in the periplasm or outer membrane
16
or extracellular. Therefore they may be important not just for B. thetaiotaomicron but perhaps for
maintenance of its environment as well [15].
In addition to the large number of glycoside hydrolases encoded in its genome, B.
thetaiotaomicron possesses several polysaccharide utilization loci as well. One such is the well
studied starch-utilization system (sus). This system includes eight starch utilization genes, susA-
susG and susR. The proteins SusC, SusD, SusE and SusF are outer membrane proteins which
form a complex which facilitates the binding of incoming starch molecules to the bacterial cell
surface. This sets the stage for the glycoside hydrolases to process the polysaccharide. The α-
amylase SusG, begins digestion of the bound starch, breaking it down into smaller fragments that
can now pass into the bacterial periplasmic space. Another α-amylase, SusA, further breaks
down the starch and an α-glucosidase, SusB, completes the digestion process by releasing
glucose which is then imported across the cytoplasmic membrane. The presence of maltose or
larger oligosaccharide acts as a signal for SusR, which then increases transcription of susA-susG
(Figure 1.4), [2,16-17].
The B. thetaiotaomicron genome also encodes paralogous groups with 106 members having
homology to SusC and 57 members with homology to SusD. The proteins, SusC and SusD as
well as a few of their homologs have been structurally characterized. So also have SusB and
SusG [15-23].
This starch utilization system which is one of many polysaccharide utilization loci along
with the large number of glycoside hydrolases encoded in the genome leads to the
hypothesis that Bacteroides thetaiotaomicron is a major player in the metabolic role of
carbohydrate processing.
17
Figure 1.4: The starch-utilization system (sus) of Bacteroides thetaiotaomicron. 1) Incoming
starch molecules, 2) SusC-SusF are outer membrane proteins which form a starch-binding
complex (SusD bound to β-cyclodextrin shown) 3) SusG, an α-amylase begins starch
degradation, 4) SusA, another α-amylase and SusB an α-glucosidase continues degradation to di-
and monosaccharide units, 5) and 6) released oligosaccharides serve as signals for SusR, a
transcriptional activator that increases transcription of susA-susG, 7) depolymerised sugars are
imported across the cytoplasmic membrane. (―This figure was originally published in The
Journal of Biological Chemistry16
. © the American Society for Biochemistry and Molecular
Biology.‖).
18
1.5 BT3299: A Family 31 Glycoside Hydrolase from Bacteroides thetaiotaomicron
1.5.1 CAZy Classification of Glycoside Hydrolases
The CAZy (Carbohydrate Active EnZyme) database is an online, specialist database that is
dedicated to the display and analysis of genomic, structural and biochemical information on
Carbohydrate-Active Enzymes (http://www.cazy.org/).24
These Carbohydrate-Active Enzymes
are enzymes that catalyze the breakdown, biosynthesis or modification of carbohydrates and
glycoconjugates. There are four classes of enzymes covered in this database: Carbohydrate
Esterases (CEs), Polysaccharide Lyases (PLs), GlycosylTransferases (GTs) and Glycoside
Hydrolases (GHs).
Glycoside Hydrolases, also referred to as glycosidases or glycosyl hydrolases are enzymes that
catalyze the hydrolysis of the glycosidic linkages of glycosides leading to the formation of a
sugar hemiacetal or hemiketal and the corresponding free aglycon.25
Glycoside Hydrolases can
be classified in many different ways. In the endo/exo based classification, the enzymes are
grouped according to the manner in which the enzyme cleaves the substrate. Exo-acting enzymes
cleave the substrate at the end while endo-acting enzymes cleave within the middle of the chain.
Enzyme Commission (EC) numbers represent another way in which glycoside hydrolases can be
classified. This system is based on a numerical classification scheme for enzymes, based on the
chemical reactions they catalyze, hence, this particular classification system is only applicable to
those enzymes for which a function has been biochemically identified. It also implies that
different enzymes, even those from different organisms can receive the same EC number.
Glycoside hydrolases can also be classified mechanistically based on whether the enzyme
catalyzes the reaction via an inverting or retaining mechanism. Inverting enzymes hydrolyze
19
glycosides with a net inversion of the anomeric configuration via a one-step single displacement
mechanism, while retaining enzymes hydrolyze with a net retention of the anomeric
configuration via a two-step double displacement mechanism. The classification system
employed by the CAZy database is a sequence based one whereby glycoside hydrolases are
grouped into families based on amino acid sequence. There are over a hundred families
containing proteins that are related by sequence and by corollary fold. Given this direct
relationship between sequence and folding similarities, the CAZy classification of GHs into
families: i) reflects the structural features of these enzymes better than their sole substrate
specificity, ii) helps to reveal the revolutionary relationships between these enzymes, iii)
provides a convenient tool to derive mechanistic information, as usually the mechanism used is
conserved within a GH family and iv) illustrates the difficulty of deriving relationships between
family membership and substrate specificity. As a result, a single family may exhibit a variety of
substrate specificities among its members [24, 25].
1.5.2 Glycoside Hydrolase Family 31
The CAZy Family GH31 and Family GH13 are two major families containing glycoside
hydrolases that play a role in the digestion of starch in humans owing to the presence of α-
glucosidase family members. Alpha-glucosidases are involved in the hydrolysis of terminal, non-
reducing (14) linked α-D-glucose residues. Two such enzymes are maltase-glucoamylase
(MGAM) and sucrase-isomaltase (SI; a target for diabetic drugs such as miglitol): two small-
intestinal brush-border enzymes that are involved in the final glucose-releasing step in starch
digestion. In addition to α-glucosidase activity, sucrase-isomaltase also hydrolyses (16)-α-D-
glucosidic linkages. Not all of the α-glucosidases within family GH31 are involved in starch
digestion; human lysosomal α-glucosidase plays a role in catabolism and ER glucosidase II is
20
involved in glycoprotein processing. The latter hydrolyzes terminal (13)-α-D-glucosidic
linkages. Family GH31 also comprises α-xylosidases (hydrolysis of terminal, non-reducing α-
xylose residues) isomaltosyltransferases and α-glucan lyases (catalysis of sequential degradation
of (14)-α-D-glucans from the non-reducing end with the release of 1,5-anhydro-D-fructose).
The family GH31 enzymes are not only found in humans but in other animals, plants, archaea
and bacteria as well. Mechanistically, family GH31 enzymes are retaining and all are believed to
follow the double displacement mechanism of retaining enzymes except for α-glucan lyases
which are believed to involve a displacement followed by an elimination step.
There are currently five family GH31 crystal structures that have helped to elaborate on the
retaining mechanism of these enzymes. An Asp residue acts as the catalytic nucleophile while
the acid/base is another Asp residue [24, 26-30]. Sequence comparisons of the region
surrounding the catalytic nucleophile of glycoside hydrolase family 31 enzymes can further
divide them into four subgroups. Each subgroup has a characteristic sequence motif surrounding
the catalytic nucleoplile and these signatures are thought to correlate to some extent with the
function or specificity of the enzymes (Table 1) [28].
Subgroup Taxonomic groups Signature in nucleophile
(*) region
Enzymatic activities
1 Plants, Animals, Fungi,
Bacteria, Archaea
W(ILN)D*MNE α-glucosidase,
glucoamylase, sucrose-
isomaltase, α-xylosidase
2 Algae, Fungi,
Cyanobacteria
WQD*MT α-1,4 glucan lyase
3 Archaea, Bacteria W(LM)D*A α-xylosidase
4 Bacteria, Fungi KTD*FGE α-xylosidase
Table 1.1: Four subgroups of GH31 enzymes according to Larsen et al. (Table reproduced
with permission from Elsevier28
).
21
1.5.3 Bacteroides thetaiotaomicron GH31 Enzymes
There are six predicted glycoside hydrolases found in family 31 from B. thetaiotaomicron none
of which have been characterized. However, three of these, BT3659, BT3085 and BT3169 are
predicted to have α-xylosidase activity, while BT0339 is predicted to have α-glucosidase activity
and both BT3086 and BT3299 α-glucosidase II activity. As mechanism is usually conserved
within a family, all are expected to exhibit a retaining mechanism with their respective
substrates.
Of the BT GH31 enzymes, BT3299 and BT3086 both have a signature sequence around the
catalytic Asp residue that places them in subgroup 1. This is the same subgroup that contains the
two human GH31 enzymes, MGAM and SI with known biological roles. BT3299 further
displays the highest percent protein identity to these enzymes; 31% with MGAM and 30% with
SI. These sequence comparison results suggest that BT3299 may be involved in a role similar to
that of MGAM and SI. However, since the CAZy system reflects structural features rather than
substrate specificity, detailed biochemical analyses are needed to determine substrate specificity
and the potential role of BT3299.
The aim of this project is to determine the role of the predicted BT GH31 enzyme, BT3299.
22
a)
SI WWANECSIFHQEVQYDGLWIDMNEVSSFIQGS--------------TKGCNVNKLNYPPF
MGAM WWTKEFELFHNQVEFDGIWIDMNEVSNFVDGS--------------VSGCSTNNLNNPPF
BT3299 WWRNLYKDFLAQG-VDGVWNDVNEPQ-INDTP--------------NKTMPEDNLHRG--
BT3086 WWGTYQQKPIDDG-ISGFWTDMGEPAWSNEEQ--------------TERLVMK-------
MALA WWAGLISEWLSQG-VDGIWLDMNEPTDFSRAI--------------EIRDVLSSLPVQFR
RUMOBE WFGDKYRFLIDQG-IEGFWNDMNEPAIFYSSEGLAEAKEFAGEFAKDTEGKIHPWAMQAK
BT3659 YWEAMKKNIFDLG-MDAWWLDSTEPDHMDIKD----------------------------
BT0339 WYKGLLKQLLDMG-VTCIKTDFGENIHMDAVY----------------------------
YicI WYADKLKGLVAMG-VDCFKTDFGERIPTDVQW----------------------------
BT3085 IFTDYHRTLIEEG-ISGFKLDECDNSNISFAS----------------------------
BT3169 -----------------VLKTDVAWVGAGYSFGLNGVADVGHIMPYYGNDARPFIISLDG
b)
YicI MALA MGAM SI RUMOBE
BT3659 24 25 24 25 24
BT3085 23 24 27 31 24
BT3299 27 33 31 30 28
BT0339 35 27 23 24 26
BT3169 27 29 26 27 22
BT3086 26 29 27 26 26
Figure 1.5: Comparison of BT GH31 enzymes with structurally characterized GH31
enzymes. a) Sequence alignment of region surrounding catalytic Asp residue. b) Protein blast
results showing % protein identity.
23
1.6 Experimental Focus of BT3299 Research
Research into the gut microbiota is currently an area of interest in the scientific community given
its impact and close involvement in human health and disease. Current applications do not
involve the more dominant species of the gut microbiota and are based solely on the numerical
comparison of only a few beneficial species before and after treatment. A deeper understanding
into the role members of the gut microbiota play may lead to a wider range of- and more efficient
applications for health and disease prevention. Indicators of functions include bacterial
metabolite levels and enzymes.
BT3299 is an enzyme of one of the dominant species in the gut microbiota. In order to
understand its role, detailed biochemical and structural analyses are needed. In this project, the
initial requirements for these analyses are presented. They include successfully cloning the gene
and expressing and purifying the enzyme in large amounts. This will allow for subsequent
studies in substrate screening and crystallization. Analyzing the X-ray crystal structure of
BT3299 can shed some light on the active site architecture and further assist in the substrate
screening process in an effort to determine enzyme function.
24
Chapter 2
Experimental Design and Methods
25
2.1 Bacterial Expression system
The bacterial expression system is a rapid, low cost means of producing large scale recombinant
protein. The pET system is one such system. The pET-29a(+) vector (Novagen, EMD Bioscience
Inc., Gibbstown, NJ, USA) was first established in a non expression host, Escherichia coli (E.
coli) DH5α cells. Upon gene insertion, the vector was transferred to the expression host, E. coli
BL21 (DE3) codon plus cells which contain a copy of the T7RNA polymerase gene under
lacUV5 control.
2.2 Expression Vector
pET-29a(+) contains a T7lac promoter that is induced upon addition of isopropyl β-D-
thiogalactopyranoside (IPTG) to the cell media. It also contains a C-terminal polyhistidine (His)
tag for protein detection and purification (Figure 2.1).
26
Figure 2.1: Vector map of the pET-29a(+) expression vector. The cloning/expression region
is highlighted to indicate the location of the C-terminal His-Tag and the T7lac promoter (which
comprises a lac operator sequence immediately downstream of the promoter region). (Figure
obtained and reprinted with permission from EMD Millipore).
27
2.3 Cloning of bt3299 gene
The gene encoding BT3299 (GenBank accession number AA078405) was amplified by the
polymerase chain reaction (PCR) using the genomic DNA of B. thetaiotaomicron VPI-5482 and
a pair of flanking primers (forward primer: 5’-
GTACGATACATATGATGGTTGGGGACGGAAT-3’ and reverse primer: 5’-
CGGAGCTCGAGTAGTCTTATTTCAATACCTTC-3’), which were designed to contain NdeI
and XhoI recognition sites (underlined) respectively. PCR was performed with initial
denaturation at 98˚C for 90s and 30 cycles using the following conditions: 98˚C, 10s; 73˚C, 20s;
72˚C, 51s. A final extension was then carried out at 72˚C for 61s.
The resulting DNA fragment was sequentially digested with NdeI and XhoI and ligated with the
pET29a(+) vector which, was previously digested with the restriction enzymes and
dephosphorylated, to generate a C-terminal His-tagged BT3299, resulting in pET29-bt3299. The
generated plasmid was then sequenced at ACGT Corp. (Toronto, ON, Canada) using T7 primers
which span the entire BT3299 coding region.
2.4 Protein Production
The constructed expression vector, pET29-bt3299, was then transformed into E. coli BL21 DE3
codon plus cells. A positive colony was selected and inoculated into 50 mL LB media containing
kanemycin (30 µg/mL) and chloramphenicol (34 µg/mL). This starter culture was set to shake at
37°C overnight and 10 mL was diluted into 1000 mL of fresh LB media containing 30 µg/mL
kanemycin and 34 µg/mL chloramphenicol. The culture was allowed to grow at 37°C until an
OD600 of 0.4 was reached, then transferred to a 15°C incubator where it was further grown up to
28
an OD600 of approximately 0.6. At that point, protein production was induced with 0.4 mM IPTG
for 20 h with shaking. The cells were harvested by centrifugation at 10,000 rpm for 20 min and
the pellet was resuspended in 20 mM HEPES buffer (pH 8.0) containing 300 mM Nacl and 10
mM imidazole. Cell lysis was achieved by passing the suspension through an EmulsiFlex-C5
high pressure cell homogenizer (Avestin). The soluble fraction was obtained by centrifugation at
10,000 rpm for 20 min.
2.5 Protein Purification
The cleared lysate was applied to 10 mL of a column containing Ni-chelating agarose,
equilibrated with 20 mM HEPES buffer (pH 8.0) containing 300 mM Nacl and 10 mM
imidazole. The column was then washed with 6 column volumes of the equilibrating buffer
followed by 6 column volumes of 20 mM HEPES buffer (pH 8.0) containing 300 mM Nacl and
20 mM imidazole. Protein was then eluted step-wise with 250 mM and 500 mM imidazole in the
wash buffer. Purification fractions were analyzed using SDS-PAGE to identify fractions
containing BT3299. These fractions were pooled and dialyzed against 20 mM HEPES, 300 mM
Nacl and 1 mM EDTA to remove residual nickel and imidazole. A BioCad HPLC (PerSeptive
Bioystems) was used to further purify a small sample (500 uL) of the BT3299 which was
identified after elution from the Ni-NTA column. A prep grade Superdex 200 column
(Pharmacia) was washed with filtered and degassed 20% ethanol, H2O and starting buffer, 20
mM HEPES, 300 mM Nacl pH 8.0. The latter was then used to equilibrate the column. The
sample, which was diluted with the starting buffer, was then loaded on the column and eluted in
3 mL fractions which were analyzed using SDS-PAGE. The fractions containing BT3299 were
pooled and concentrated to ~5 mg/mL.
29
2.6 SDS-PAGE
All samples analyzed by SDS-PAGE were first prepared by the addition of sample buffer at 4X
concentration which contained 200mM Tris-HCl pH 6.8, 8% SDS, 0.1% bromophenol blue, 40%
glycerol. Samples were then run on a 10% SDS-PAGE gel.
2.7 pNP-glucose Assay
The pNP-glucose assay is a quick method used for the verification of glucosidase activity. It
involves the use of 4-nitrophenyl α-D-glucopyranoside (pNP-glucose, pNPG) as a substrate. The
assay was carried out in a 96-well plate containing 40 mM MES pH 6.0, 10 mM pNP-glucose,
BT3299 and H2O to a final volume of 50 µL. Any glucosidase such as BT3299 hydrolyses the
glycosidic bond to give p-Nitrophenol (pNP) and free glucose. After a specified time period, the
reaction was quenched using 0.5 M Na2CO3 to give the yellow p-nitrophenolate ion.
Figure 2.2: Schematic of pNP-glucose enzyme assay. Any enzyme with glucosidase activity,
such as BT3299, cleaves the glycosidic bond within the substrate pNPG to produce pNP and
glucose. Upon addition of sodium carbonate to quench the reaction, colourless pNP is converted
into its yellow p-nitrophenolate ion and the absorbance can be measured as an indication of
enzyme activity.
N+
O-
O
O
O
OHOH
OH
OH
Glucosidase
Glucose
+
N+
O-
O
OH Base
N+
O-
O
O-
pNP-Glucosep-nitrophenol p-nitrophenolate
OD(405)
30
2.8 Enzyme Kinetics
The pNP-glucose assay was carried out and the reaction quenched at different time points in
order to construct a time course. A suitable reaction time for the assay was then determined. In
order to determine the kinetic parameters of the hydrolysis of pNP-glucose by BT3299 (0.8 µM),
the concentration of pNP-glucose was varied (1-40mM) while all other parameters were kept
constant. The program GraFit 4.05 was used to fit the data to the Michaelis-Menten equation
(Equation 2.1) and to estimate the Michaelis-Menten constant, KM and the maximal reaction
velocity, Vmax.
][
]max[
SKm
SVV
Equation 2.1: The Michaelis-Menten equation. In this equation, V is the rate of conversion (or
velocity), [S] is the substrate concentration, KM is the Michaelis-Menten constant and Vmax is the
maximal reaction velocity.
Secondary substrates, consisting of varying types of glycosidic linkages and monosaccharide
moieties (Figure 2.3), were screened using the pNP-glucose assay according to the KM,app
method31
.
O
HH
H
OH
OH
H OH
H
OH
O
O
HH
H
OH
OH
H OH
H
OH
A
O
HH
H
OH
OH
H OH
H
OH
OHH
OH H
O
O
OH
OH
B
31
O
OH
HH
H
OH
H OH
H
OH
O
HH
H
OH
OH
H OH
H
OH
O
C
O
OH
OH
H
HOH
OH
O
HH
H
OH
OH
H OH
H
OH
O
D
O
HH
H
OH
OH
H OH
H
OH
O
OH
HH
H
OH
H OH
H
OH
O
E
O
H
HH
H
OH
OH
H OH
OH
O
H
HH
H
OH
H OH
OH
OH
O
F
O
OH
HH
H
OH
H OH
H
OH
O
O
H
HH
OH
H
OH
H OH
OH
G
OH
H
H
OH H
O
O
O
HH
H
OH
OH
H OH
H
OH
OH
OH
H
O
H
HH
H
OH
OH
H OH
OH
O
H
HH
H
OH
OH
H OH
OHO
I
32
O
HH
H
OH
H OH
H
OH
O
HH
H
OH
H OH
H
OH
OOH O H
n
J
O
H
HH
H
OH
H OH
OH
O
H
HH
H
OH
H OH
OHO
OH
O
H
n
K
Figure 2.3: Secondary substrates screened for BT3299 activity. A range of di- and
polysaccharides of different substrate linkages and monosaccharide moieties were added to the
BT3299- pNPG kinetic experiment. Generation of the inhibitor constant, Ki served as an
indication of the binding affinity of BT3299 for the substrate. A) trehalose, B) sucrose, C)
nigerose, D) turanose, E) maltose, F) cellobiose, G) lactose, H) palatinose, I) β-gentiobiose, J)
soluble starch, K) β-D glucan.
In this method, the secondary substrates were added as inhibitors to the pNP-glucose kinetic
experiment, containing 0.75 µM BT3299. Each substrate screen included a control of pNP-
glucose only and 3 to 4 different inhibitor concentrations. The apparent KM (KM,app) and Vmax
were determined for each condition. A plot of KM,app/Vmax vs. [I] was then generated in order to
determine the inhibitor constant, Ki. The equation governing this relationship is given below.
Equation 2.2: The Km,app method equation31
. A plot of KM,app/Vmax vs. [I] allows for the
determination of the inhibitor constant, Ki, in two ways, 1) as the negative x-intercept, 2) from
the slope of the plot. The Ki was determined both ways and was reported as the average.
maxmax)(
].[
max V
Km
VKi
IKm
V
Kmapp
33
All experiments were performed in triplicate and the average absorbance reading was either
reported (pNPG assay) or used in further calculations (kinetics and substrate screening).
2.9 Crystallization Experiments
Purified BT3299 at a concentration of 5 mg/mL was used for crystallization. Initial
crystallization trials were carried out in two 24-well hanging drop crystal trays with screw caps
(Nextal) and were performed via the hanging drop method using 48 Crystal Screen (Hampton
Research) conditions. Hits were verified by checking the reproducibility of the experiment using
lab made conditions to match the Crystal Screen conditions.
34
Chapter 3
Results
35
3.1 Cloning of bt3299 Gene
PCR amplification of bt3299 from genomic Bacteroides thetaiotaomicron DNA resulted in a
gene product containing NdeI and XhoI restriction sites, which was between 2000 and 3000 base
pairs (bp) in size, along with a low molecular product (Figure 3.1).
Figure 3.1: DNA gel of BT3299 PCR amplification. Lane (1) DNA ladder, (2) PCR
amplification products.
Excision of the band near 2000 bp followed by gel purification led to the isolation of the gene
product of interest. Sequential double digestion of the amplified gene and of the pET29a(+)
vector gave rise to sticky ends, which facilitated the insertion of the gene into the vector via
ligation experiments. The formation of positive colonies after transformation of the ligation
product into the non-transforming E. coli DH5α cells, along with sequence alignment of the
theoretical pET29-bt3299 construct with the experimental data, confirmed a successful ligation.
36
3.2 Protein Production
To determine the optimal conditions for BT3299 expression, several small scale expression tests
were done. The constructed pET29-bt3299 expression vector was transformed into different E.
coli expression cells; BL21 (DE3), BL21 (DE3) pLysS, BL21 codon plus and Rosetta (DE3)
cells. There were positive colonies for all of the cells except BL21 DE3. These cells were then
setup for growth and protein induction. Cell growth was monitored by measuring the absorbance
at OD600. BL21 codon plus took the shortest time to attain a specified OD600 reading. BL21
growth was then performed and monitored at several different temperatures; 15°C, 30°C and
37°C of which 15°C gave the optimal results (Figure 3.2).
Figure 3.2: SDS-PAGE of BL21 codon plus cell growth and BT3299 induction at different
temperatures. Lane (1) Unstained protein ladder, (2) whole cell lysate at 15°C, (3) whole cell
lysate at 30°C, (4) whole cell lysate at 37°C, (5) supernatant at 15°C, (6) supernatant at 30°C, (7)
supernatant at 37°C, (8-10) supernatant after treatment with Ni-NTA beads at 15°C, 30°C and
37°C respectively.
37
At the time of harvesting, BT3299 was present in higher levels than other proteins in the cell
lysate at 15 and 30°C. However, much of the protein was lost at 30 and 37°C after separation of
the soluble and insoluble contents of the cell and only the cell kept at 15°C retained BT3299 in
the soluble fraction. This soluble fraction was then batch-bound onto Ni-NTA beads to
distinguish BT3299 containing the hexahistidine tag from other contaminants. Only the cell
incubated at 15°C displayed an intense band near 70kDa corresponding to the size of BT3299.
Given the low cell growth temperature of 15°C, BT3299 induction was allowed to continue
overnight and the cells were harvested the following day (Figure 3.3). This slow expression
condition allowed for the production of a large quantity of soluble protein.
Figure 3.3: SDS-PAGE of BL21 codon plus cell growth and overnight BT3299 induction at
15°C. Lane (1) Unstained protein ladder, (2) before BT3299 induction, (3) protein expression 1
hour after induction, (4) 2 hours after induction, (5) 3 hours after induction, (6) 4 hours after
induction, (7) 5 hours after induction, (8-9) overnight BT3299 expression levels at 19.5 and 20.5
hours respectively.
38
Before protein induction, the SDS-PAGE gel shows that the band near 70kDa corresponding to
BT3299 is minimally expressed, at levels similar to other proteins in the cell. One hour after
induction, this band indicates a marked increase in BT3299 expression. After which point there
is a steady increase in protein levels shown by the small change in band intensity at each hour
point. After overnight induction, BT3299 achieved maximal expression levels illustrated by the
19.5 and 20.5 hour time points.
3.3 Ni-NTA Affinity Chromatography
The amount of protein present in the supernatant was estimated by measuring the absorbance at
280nm. This estimate was used to determine the volume of Ni-NTA slurry to add to a
purification column such that upon addition of the protein mixture there was between 5-10 mg of
protein per mL of resin. The resin was first washed using ultrapure H2O, followed by lysis buffer
to equilibrate the column and activate the nickel beads for binding of BT3299 via its
hexahistidine tag. After the resin settled, the protein mixture was added to the column and the
desired protein was eluted by increasing the imidazole concentration which resulted in
displacement of nickel bound BT3299. A sample of each purification fraction was analyzed by
SDS-PAGE (Figure 3.4).
39
Figure 3.4: SDS-PAGE of BT3299 purification by Ni-NTA affinity chromatography. Lane
(1) Unstained protein ladder, (2) Cleared lysate (3) Unbound proteins, (4) Wash buffer 1 (10 mM
imidazole), (5) Wash buffer 2 (20 mM imidazole), (6) 250 mM imidazole, (7) 500 mM
imidazole.
As shown in Figure 3.4, there was a large amount of expressed BT3299 present in the cleared
lysate as seen by the intense band between 70 and 85 kDa. Upon addition of the protein mixture
to the column, the intensity of this band is decreased which signifies the successful binding of
BT3299 to the column. The unbound fraction in lane 3 also shows that many low molecular
weight contaminants did not bind to the column and so were eluted. Addition of the wash buffer,
which contained 10mM imidazole, reduced non-specific binding of contaminants that may
contain histidine residues and therefore may have binded to the column. Most of the
contaminants were eluted after 10mM as only a few bands were detected after increasing the
imidazole concentration to 20mM in the second wash buffer. BT3299, along with a few
contaminants was not eluted until the sharp increase of imidazole concentration to 250 mM. The
column appears clean of all contaminants at 500mM imidazole with the presence of a single
intense band near 70kDa.The 250mM and 500mM imidazole fractions containing the desired
40
protein were pooled and dialyzed against 20 mM HEPES, 300 mM Nacl and 1 mM EDTA to
remove residual nickel and imidazole.
3.4 Mass Spectrometry
Electrospray Ionization mass spectrometry (ESI-MS) analysis was performed on a sample of
BT3299 in order to confirm the purity and size of the protein (Figure 3.5). Results from the mass
spectrometry show a single high intensity peak corresponding to a mass of 77352 Da, along with
a few low intensity peaks. This mass corresponds to the size of the protein detected by SDS-
PAGE and was assigned as a singly-charged species of BT3299.
Figure 3.5: Mass Spectrometry of purified BT3299. ESI mass spectrometry was used to
determine the mass and purity of BT3299 after affinity chromatography. A purified sample of
BT3299 gave a single high intensity peak of a singly-charged species at 77352.00 Da along with
a few low intensity peaks.
41
The predicted mass of recombinant BT3299 fused with a hexa-histidine tag is 77349 Da. This
mass difference of 3 Da is within error for this mass spectrometry experiment and so confirmed
the expressed, purified protein to be His-tagged BT3299. Given that the other peaks from the
mass spectrometry experiment were significantly lower than the peak assigned as a singly-
charged species of BT3299, the purified sample was determined to be of sufficient purity for
enzyme activity and kinetic studies.
The total yield of pure BT3299 from an E. coli expression system was approximately 12 mg
from 1000 mL of cells.
3.5 Gel Filtration Chromatography
Due to the presence of a few extra bands in the 250 mM fraction, a further purification step was
performed for crystallization trials. A sample of the pooled, dialyzed fractions was applied to the
BioCad Superdex 200 column. The sample was eluted in 3mL fractions and those fractions
corresponding to the protein peaks on the BioCad output were pooled and concentrated in 20mM
HEPES pH 8 (Figure 3.6).
The gel filtration step was sufficient to remove high molecular weight contaminants which eluted
in the void volume between 40 and 50 mL. The low intensity peak of this void volume suggests
that there were very little high molecular weight contaminants in the purified BT3299 sample.
Analysis of the high intensity peak between 70 and 80 mL by SDS-PAGE showed its
correspondence to a species between 70 and 85kDa present in large quantities, which was
assigned BT3299. These fractions also contained very small quantities of low molecular
contaminants as shown by the very faint bands. Therefore, only the cleanest fractions were
pooled for crystallization trials.
42
A
B
Figure 3.6: Gel filtration purification of BT3299. Gel filtration chromatography was used to
further purify a sample of recombinant BT3299. A) The chromatogram shows a species of high
absorbance eluting between 70 and 80 mL corresponded to a mass of ~70kDa. There was also a
low absorbance species eluting between 40 and 50 mL. B) SDS-PAGE of fractions under the
70kDa peak.
43
3.6 Enzymatic Activity
The pNP-glucose assay was used to verify that purified BT3299 was functional and
appropriately active. Given that the physiological role of BT3299 and its biologically relevant
substrate is unknown, a more specific activity assay was not used. As an initial test of the
glycoside hydrolase activity of BT3299, the release of glucose from the hydrolysis of pNP-
glucose at increasing BT3299 concentrations was monitored (Figure 3.7).
Figure 3.7: Glycoside hydrolase activity of BT3299 with pNP glucose as a substrate.
Varying concentrations of BT3299 (0.08 -0.8 µM) were added to 5mM of pNP-glucose in order
to monitor enzyme activity. The glucose released was measured indirectly by recording
absorbance values of the simultaneously released nitrophenolate moiety.
As the concentration of BT3299 was increased, the amount of glucose released increased
proportionally, as can be seen by the perfectly correlated data (R2=1). This confirmed that
BT3299 was in fact behaving as a glycoside hydrolase by cleaving the glycosidic bond of pNP-
glucose. There were background absorbance values observed in control samples (no enzyme),
hence all values were blank corrected.
44
With the above confirmation of BT3299 glycoside hydrolase activity, the pNP-glucose assay was
used to further investigate those conditions which promoted optimal BT3299 activity. Firstly, a
pH profile was generated by varying the pH of the buffer in the assay and monitoring the activity
of the enzyme (Figure 3.8).
Figure 3.8: pH profile of BT3299 activity using pNP glucose as a substrate. The pNP-glucose
assay was performed under different pH conditions using a variety of buffers (pH 4.0 - 9.0). The
pH at which the optimal activity occurred (pH 6.0) was assigned an activity value of 100% and
all other activity values were assigned relative to this value.
The data produced the classical bell shaped curve that is typical of most enzyme pH profiles. The
activity of BT3299 gradually increased as the pH is increased up to a maximum (optimal) pH.
After which point, the activity of the enzyme gradually decreases with increasing pH. This
optimal pH value, 6.0 in this case, frequently coincides with the physiological pH and so sheds
some light on the environment in which BT3299 carries out its physiological role.
To get a better understanding of the activity of BT3299, a p-Nitrophenol (pNP) standard curve
was generated in order to derive more standard enzyme activity units (Figure 3.9). Several pNP
solutions of varying concentrations were made up and the absorbance value at 405nm was
recorded. The experiment was carried out in conditions similar to those of the pNP-glucose assay
at the point when the absorbance of the generated pNP species is read.
45
Usually Beer’s Law, A=εlc is used to derive the concentration (c) of product formed. With the
absorbance value (A), the length, l, of the plate well and the extinction coefficient, ε, of pNP
known, the concentration is easily calculated. However, the extinction coefficient of pNP varies
and is very specific to the conditions of the assay and so is not documented for every possible
condition. Therefore to ensure a more accurate determination of product formation quantities, a
pNP standard curve under specific conditions was generated.
Figure 3.9: p-Nitrophenol standard curve. The absorbance value of various concentrations of
pNP in MES buffer pH 6.0 and 0.5M Na2CO3 was read at 405 nm to simulate the pNP-glucose
assay conditions.
The activity of BT3299 was then further investigated by monitoring the effect of several
substances of known stabilizing ability, on the enzyme’s activity (Figure 3.10). A variety of
substances were added to the pNP-glucose assay which was carried out using 0.8 µM of BT3299
at the determined physiological pH of 6.0. At the end of the reaction time, the reaction was
quenched and the absorbance values were read. The raw absorbance data was converted to
concentration values, using the experimentally derived p-Nitrophenol standard curve (Figure
3.9), and finally to specific activity of the enzyme. The Activity, U is defined as the
concentration of substrate (nM) hydrolysed by the enzyme per minute.
46
Figure 3.10: The effect of various substances on BT3299 activity. Each test substance was
added to the reaction of 0.8 µM BT3299 with pNP-glucose at a pH of 6.0. The effect on BT3299
was monitored by recording the specific activity (mU/mg) of the enzyme.
Of all the substances tested, the protein bovine serum albumin (BSA) resulted in the greatest
increase in BT3299 activity showing a specific activity close to 600 mU/mg. The reducing agent
dithiothreitol (DTT) also significantly enhanced BT3299 activity while Nacl had very little effect
(specific activity, 1.5 mU/mg, unable to be seen on graph).
3.7 Enzyme Kinetics
A time course following the production of p-Nitrophenol, and therefore glucose, by BT3299
hydrolysis of pNP-glucose over time was carried out. The results were used to construct a time
curve and the optimal time point for the hydrolysis reaction was determined.
47
Figure 3.11: Time curve of BT3299 activity with pNP-glucose as a substrate. The final
concentration of pNP-glucose in the assay was varied (1-40mM) in order to monitor the activity
of BT3299 (0.8µM) over time. The reaction was quenched at specific time points, up to 90
minutes, and the absorbance was read at 405nm as an indication of BT3299 hydrolysis.
As expected, absorbance values, as an indirect indication of glucose production increased with
increasing levels of pNP-glucose as the enzyme has more substrate available for hydrolysis. The
rate of hydrolysis, signified by the increasing slope also increased with increasing substrate
concentration. There appears to be a nonlinear increase in absorbance after 45 minutes, hence 45
minutes was chosen as the optimal reaction time and was used in subsequent analyses.
As a first step into the deeper understanding of the enzyme activity and characteristics, kinetic
experiments were carried out to determine the Michaelis-Menten constant, KM and the maximal
velocity of the enzymatic reaction, Vmax. A series of experiments were performed in which the
concentration of pNP-glucose in the assay was increased and BT3299 at a concentration of 0.75
µM was added to each for 45 minutes. The rate or velocity of product formed (mM/s) under each
condition was derived from the concentration of product formed (pNP standard curve) and the
reaction time in seconds. The program GraFit 4.05 was then used to generate a plot of rate versus
substrate concentration (mM). The rate of the reaction increases linearly at low substrate
48
concentrations then begins to plateau at high substrate levels indicating that BT3299 patterns the
basic Michaelis-Menten equation of steady-state enzyme kinetics (Figure 3.12).
[Substrate]0 10 20 30
Rate
000
0.000010.000010.000010.000010.000010.000020.000020.000020.000020.000020.000030.00003
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
Figure 3.12: BT3299 kinetics using pNP-glucose as a substrate. A Michaelis-Menten plot of
BT3299 (0.75 µM) activity with varying levels of pNP-glucose for 45 minutes was generated
using the program GraFit version 4.05. A Lineweaver-Burk plot was also generated to determine
the Michaelis-Menten constant KM and the maximal reaction velocity Vmax.
The program also generated a Lineweaver-Burk plot (1/rate vs. 1/[substrate]) for the
determination of the KM and Vmax of the reaction. The Vmax was determined to be 3.8±0.14 x 10-5
mM s-1
and the KM, 11.4 ± 1.1 mM pNP-glucose. The Vmax along with the concentration of
enzyme used in the reaction were then used to calculate the enzyme turnover number, kcat, of
0.0508 s-1
. The enzyme specificity constant, kcat/KM was also calculated and found to be 4.5 s-1
M-1
.
49
3.8 Substrate Screening
Given the low enzyme turnover number, which indicated that pNPG was not the best fit substrate
for this enzyme, pNPG was used as a means to screen other substrates. The idea here is that a
more natural substrate, when added to the pNP-glucose assay would compete with pNP-glucose
in such a way as to inhibit the BT3299 activity on pNP-glucose. Hence, each secondary substrate
was treated as an inhibitor.
The KM,app Method31
was used to investigate the ability of several secondary substrates to inhibit
the activity of BT3299 on the hydrolysis of pNP-glucose. With each secondary substrate, the
pNP-glucose kinetic experiment was carried out as before (varying concentrations of pNP-
glucose and constant BT3299 concentration (0.75µM)) as the control, along with the addition of
3-4 inhibitor (secondary substrate) concentrations to give 4-5 data sets. Each data point was
performed in triplicate and averaged. The Vmax and KM values were determined using the GraFit
program for each data set. Given that the KM value determined in the presence of an inhibitor is
not the true KM value, it is referred to as the apparent KM (KM,app). A linear regression plot of
KM,app/ Vmax as a function of inhibitor concentration, was then generated in order to estimate the
Ki value, as an indication of binding affinity, according to Equation 2.2.
50
Figure 3.13: KM, app plot of BT3299 activity with inhibition by the secondary substrate
trehalose. The pNP-glucose assay was used to measure BT3299 activity with increasing levels
of pNPG and 0, 5, 10, 15mM trehalose.
Figure 3.14: KM,app plot of BT3299 activity with inhibition by the secondary substrate
sucrose. The pNP-glucose assay was used to measure BT3299 activity with increasing levels of
pNPG and 0, 1, 5, 10, 15mM sucrose.
51
Figure 3.15: KM,app plot of BT3299 activity with inhibition by the secondary substrate
nigerose. The pNP-glucose assay was used to measure BT3299 activity with increasing levels of
pNPG and 0, 1, 5, 11mM nigerose.
Figure 3.16: KM,app plot of BT3299 activity with inhibition by the secondary substrate
turanose. The pNP-glucose assay was used to measure BT3299 activity with increasing levels of
pNPG and 0, 1, 5, 10, 15mM turanose.
52
Figure 3.17: KM,app plot of BT3299 activity with inhibition by the secondary substrate
maltose. The pNP-glucose assay was used to measure BT3299 activity with increasing levels of
pNPG and 0, 1, 5, 10, 15mM maltose.
Figure 3.18: KM,app plot of BT3299 activity with inhibition by the secondary substrate
cellobiose. The pNP-glucose assay was used to measure BT3299 activity with increasing levels
of pNPG and 0, 1, 5, 11mM cellobiose.
53
Figure 3.19: KM,app plot of BT3299 activity with inhibition by the secondary substrate
lactose. The pNP-glucose assay was used to measure BT3299 activity with increasing levels of
pNPG and 0, 5, 10, 15mM lactose.
Figure 3.20: KM,app plot of BT3299 activity with inhibition by the secondary substrate
palatinose. The pNP-glucose assay was used to measure BT3299 activity with increasing levels
of pNPG and 0, 1, 5, 10, 15mM palatinose.
54
Figure 3.21: KM,app plot of BT3299 activity with inhibition by the secondary substrate β-
gentiobiose. The pNP-glucose assay was used to measure BT3299 activity with increasing levels
of pNPG and 0, 1, 10mM β-gentiobiose.
Figure 3.22: KM,app plot of BT3299 activity with inhibition by the secondary substrate
soluble starch. The pNP-glucose assay was used to measure BT3299 activity with increasing
levels of pNPG and 0, 0.5, 1, 2, 5mg/mL soluble starch.
55
Figure 3.23: KM,app plot of BT3299 activity with inhibition by the secondary substrate β-D
glucan. The pNP-glucose assay was used to measure BT3299 activity with increasing levels of
pNPG and 0, 0.5, 1, 2mg/mL β-D glucan.
All of the plots generated for the determination of Ki had an R2 value > 0.7, indicating that the
method employed was sufficient for Ki prediction. All of the substrates tested, except soluble
starch, displayed competitive inhibition. This was indicated by the positive slope of the plot as
well as the decrease in the KM value and the unchanged Vmax (Appendix A). For soluble starch,
the plot resulted in a negative slope and there was a decrease in both KM and Vmax values. A
summary of the Ki values obtained for the substrates screened is given in Table 3.1. This table
also shows the specific activity of the enzyme in the absence and presence of the secondary
substrate for further analysis of the effect of the secondary substrate on the affinity of BT3299
for pNPG.
56
Substrate Type of linkage(s) Ki / mM
Specific activity
without inhibitor,
mU/mg
Specific
activity
with inhibitor,
mU/mg
% change in
specific
activity
mU/mg
Trehalose α-1,1 (glu-glu) 29±1.4 570 468 17.9
Sucrose α-1,2 (glu-fru) 49±1.5 598 560 6.4
Nigerose α-1,3 (glu-glu) 23±0.5 721 579 19.7
Turanose α-1,3 (glu-fru) 21±1.3 613 518 15.5
Maltose α-1,4 (glu-glu) 36±3.0 545 509 6.6
Cellobiose β-1,4 (glu-glu) 35±1.3 590 516 12.5
Lactose α-1,4 (gal-glu) 26±0.9 618 564 8.7
Palatinose α-1,6 (glu-fru) 20±3.8 640 514 19.7
β-
Gentiobiose β-1,6 (glu-glu) 64±1.5 621 578 6.9
Soluble
Starch
α-1,4; α-1-6 (glu-
glu) Uncompetitive 596 518 13.1
β-D Glucan β-1,3 (glu-glu) 2.86* 944 876 7.2
Table 3.1: Table of experimentally derived Ki and BT3299 specific activity values. Ki values
were determined for secondary substrates added to the BT3299 kinetic experiment using pNP-
glucose as a substrate. The specific activity was also generated in order to monitor the effect of
the secondary substrate on the enzyme activity with pNP-glucose.
3.9 Crystallization Experiments
Pure recombinant BT3299 was set up in hanging drop crystal trays and screened for
crystallization according to Hampton Research Crystal Screen conditions. Prospective crystal
conditions were repeated for reproducibility. Condition # 16, containing 0.1M HEPES pH 7.5
and 1.5M LithiumSulfate Monohydrate, reproduced the microcrystals obtained in the original
screen (Figure 3.24). These conditions will be further optimized in order to grow larger, data
quality crystals for X-ray diffraction and data collection.
57
Figure 3.24: Photograph of microcrystals obtained during BT3299 crystallization screen.
Of the 48 Crystal Screen conditions set up, condition #16 was the only condition where the
crystal obtained in the original screen were able to be reproduced.
58
Chapter 4
Discussion
59
4.1 Discussion of Results
The bacterial expression system proved to be a successful method for the over-expression of the
family 31 GH enzyme, BT3299, from Bacteroides thetaiotaomicron. Under this method,
subcloning of the gene of interest was achieved by utilising a sequential digest instead of a
double digest as well as a dephosphorylated vector. A sequential overnight restriction digest,
allowed for the optimal performance of each restriction enzyme in its respective, desired
conditions, and facilitated complete cleavage at the recognition site. The vector
dephosphorylation procedure ensured that the phosphate groups available for the formation of
DNA backbone phosphodiester bonds were supplied by the gene of interest only. This helped to
reduce vector self ligation and encouraged the formation of phosphodiester bonds between the
insert and vector, thereby ensuring successful ligation of the gene into the vector. Attempts to
over-express the corresponding protein of the inserted gene using conventional E. coli BL21
(DE3) cells were unsuccessful perhaps as a result of the protein being toxic or codon bias.
Inducing the expression of a foreign protein in large amounts in a host cell, results in the
recombinant protein being present at levels significantly higher than even the physiological level
in its native cell. The protein is also expressed at a time where it is often not needed by the host
cell and may even be detrimental to the proliferation and differentiation of the cell. The BL21
(DE3) pLysS strain has the ability of exerting tight control of the expression of toxic proteins by
providing a small amount of T7 lysozyme, which is a natural inhibitor of T7 RNA polymerase.
Another consequence of recombinant protein over-expression is the demand for one or more
tRNAs that may be rare or lacking in the E. coli host cell. The BL21 codon plus (DE3) cells,
which resulted in optimal expression levels, as well as the Rosetta (DE3), provided a means to
resolve this codon bias problem by supplying extra copies of the tRNA genes rarely used in E.
coli 32
.
60
Sufficient purification of BT3299 can be achieved by a single purification step using Ni-affinity
chromatography. An SDS-PAGE showed that this method resulted in a single intense band
between 70 and 85 kDa after elution with 500 mM imidazole. A large quantity of the desired
protein was also eluted using 250 mM imidazole but this fraction also had a few additional faint
bands as well. However, after combining both fractions for gel filtration chromatography, the
resulting chromatogram showed a very small intensity peak corresponding to the void volume
and a single high intensity peak, approximately 70 kDa in size, indicating a highly pure sample.
ESI mass spectrometry analysis determined the exact size of the protein to be 77352 Da, which,
is only 3 Da more than the predicted recombinant His-tagged BT3299 mass of 77349 Da. The 3
Da difference lies within the mass error range for this method and so serves as confirmation that
the desired protein, BT3299 was successfully expressed and purified.
The activity of the expressed protein was investigated using the general pNP-glucose assay for
glycoside hydrolase behaviour. Increasing the enzyme concentration in the assay resulted in a
proportional increase in product formation indicating that the activity detected at lower enzyme
concentrations was not just due to background product formation. This therefore served as
confirmation that BT3299 was in fact a glycoside hydrolase.
The characteristics of the enzyme were then investigated further; first by generating a pH profile
to determine the pH at which the enzyme displays optimal activity. A variety of buffer solutions
ranging in pH values from 4.0-9.0 were added to the pNPG assay and the classical bell-shaped
pH profile was observed, indicating at least two ionizable groups in the free enzyme. The
estimated pH optimum of 6.0 suggests that the enzyme prefers a slightly acidic to neutral
environment. This value is also just a few pH units below that of other B. thetaiotaomicron and
family 31 glycoside hydrolases, which also have reported slightly acidic to neutral pH optimum
61
values of 6.4 and 6.5 19, 27, 30
. More importantly, this pH optimum value is in line with the acidic
to neutral pH (pH 5-7)33
of the colonic environment in which these symbiotic microbes are
found.
Another indicator as to the native environment of the enzyme is its behaviour in the presence of
certain compounds of known enzyme stability. Of the various compounds tested, the protein
BSA resulted in the greatest increase in BT3299 activity. BSA is a known and well used enzyme
stability protein which prevents components of the enzyme assay from adhering to the walls of
the reaction vessel, thereby increasing the likelihood of a reaction which results in optimal
activity. The reducing agent dithiothreitol (DTT) also resulted in a significant increase in
BT3299 activity. This may be attributed to the fact that the purification process subjects the
protein to oxidative conditions and the addition DTT leads to a more reducing environment. DTT
is known to affect proteins by reducing the disulfide bridge that may have formed between two
cysteine residues. Disulfide bridge formation is one of the interactions that drives protein folding
and structure stability in proteins. However, since the activity of BT3299 increased in conditions
not favouring disulfide bridge formation, it is unlikely that this particular interaction is an
important one in BT3299. A more likely explanation for the observed effect is that DTT
provided an environment that is similar to that of the protein’s native environment. One such
environment is found on the inside of cells; in fact disulfide bridges are extremely rare in
intracellular proteins but are more common in secreted proteins. Given the effect DTT has on its
activity, it is very likely that BT3299 is an intracellular, non-secreted cytoplasmic enzyme.
Having determined the optimal conditions for BT3299 activity, kinetic studies were carried out
using the pNPG assay at pH 6.0 and with DTT as an additive. Analysis of the time course
generated by varying the concentration of pNPG in the assay and measuring the absorbance at
62
set time points determined 45 minutes as a sufficient reaction time for the kinetic studies. The
program GraFit 4.0 was used to analyse the data obtained and generate the kinetic parameters.
The Michaelis-Menten constant, KM, was determined to be 11.4 ± 1.1 mM pNP-glucose. This
value is most similar to that obtained for the GH31 human enzyme, NtMGAM which has a
reported value of 12.1± 1.0 mM30
. The GH31 E. coli enzyme, YicI reports a KM value of 7.7±0.4
mM27
for pNPG, which is just slightly below that for BT3299, however, the fellow B.
thetaiotaomicron GH enzyme, SusB reports a value of 0.16±0.01 mM19
which is well below that
obtained for BT3299. This is not surprising as SusB is a GH97 enzyme, while NtMGAM, YicI
and BT3299 are all GH31 enzymes and are expected to display the same mechanism, according
to the CAZy system. The Vmax, for the reaction was determined to be 3.8 ± 0.14 x 10-5
mM s-1
however, this value was not reported for the published structures. A more useful value in enzyme
kinetic studies is the enzyme specificity constant, kcat/KM, which was determined, in this case, to
be 4.5 s-1
M-1
. This value is most in line with that obtained for YicI (6.0 s-1
M-1
)27
. The other
reported values were greater by at least two orders of magnitude. The difference here may be due
to the fact that both BT3299 and YicI are GH31 enzymes of bacterial origin.
However, even within a family, substrate specificity can be quite varied and so substrate
screening kinetic studies were carried out. A range of secondary substrates of varying glycosidic
linkages and monosaccharide units were added to the pNPG assay as inhibitors and the
experimentally determined Ki values served as indicators of binding affinity and BT3299
substrate preference. Competitive inhibition was observed for all of the secondary substrates
tested except soluble starch which displayed uncompetitive inhibition behaviour (decreased KM
and Vmax). Of the remaining substrates, the polysaccharide β-D glucan (β-1,3) resulted in the
smallest Ki value (2.86 mM), indicating the greatest binding affinity. However, the disaccharides
palatinose (α-1,6), turanose (α-1,3) and nigerose (α-1,3) had not only the smallest Ki values
63
among the disaccharides but the greatest change in specific activity (~20%). β-D glucan
however, only changed the enzyme’s specific activity by ~7%. These results indicate that
BT3299 may prefer 13 and 16 linkages as the enzyme was bound more tightly and active in
these instances. In the case of β-D glucan, the high binding affinity suggests that there is some,
perhaps very important, characteristic of the substrate that is similar to the physiological
substrate of BT3299. Given that turanose and nigerose comprise 13 linkages as does β-D
glucan, one can look at the major difference between β-D glucan and the disaccharides, which is
the chain length. It can therefore be speculated, that perhaps BT3299 prefers substrates
comprising longer chains. The enzyme displayed greater activity with α- versus β-linkages and
so the small change in specific activity with β-D glucan was possibly as a result of this substrate
comprising β-1,3 instead of α-1,3 units.
Although the enzyme did show greater binding affinity for β-D glucan by an order of magnitude
and displayed slight preferences of substrate linkage, overall none of the tested substrates
resulted in a change in activity of more than 20%. This presents two scenarios; either the right
combination of linkage and saccharide is yet to be explored or the enzyme is less specific and is
acting as a broad specificity enzyme. The latter is actually uncommon for glycoside hydrolases,
however, the fully characterized glycoside hydrolase from Bacteroides thetaiotaomicron, SusB,
also displayed a wide specificity on various types of α-glucosidic linkages19
. The enzyme also
showed a higher specificity on trisaccharides which is in agreement with its physiological role.
This suggests that a third scenario, which is a combination of both scenarios presented earlier,
may be at play whereby these microbial enzymes may display a certain substrate specificity
while at the same time possessing the ability to hydrolyse a wide range of substrates to a lesser
extent. This may possibly be as a result of an evolutionary process in which the symbiotic gut
64
bacteria is adapting to the ever changing human diet which comprises a wide variety
carbohydrates of various linkages.
This project reports the successful expression of the Bacteroides thetaiotaomicron enzyme,
BT3299. The enzyme displayed glycoside hydrolase activity by hydrolysing the glycosidic bond
present in pNP-glucose. BT3299 functions optimally in conditions near pH 6.0, which is in line
with the colonic environment, and in reducing environments. The Michaelis-Menten constant,
KM (11.4 ± 1.1 mM) and the enzyme specificity constant, kcat/KM (4.5 s-1
M-1
) as determined using
pNP-glucose as a substrate, were most in line with those obtained for YicI, another GH31
enzyme of bacterial origin. BT3299 appears to be a broad specificity enzyme, which showed a
slight preference for α-1,3 and α-1,6 glycosidic linkages as well as longer chained saccharides.
This behaviour of broad substrate specificity with a slight preference for a particular substrate
type was observed in another Bacteroides thetaiotaomicron glycoside hydrolase. This leads to
the idea that this behaviour may be an evolutionary consequence as the gut microbial symbionts
adjust to changes in the human diet.
4.2 Future Directions
In order to determine if BT3299 displays single substrate or broad specificity, additional
substrates have to be screened. In addition to di- and polysaccharides, oligosaccharides can also
be screened, as chain length is a good indicator of enzyme specificity. In particular,
oligosaccharides containing α-1,3 and α-1,6 glycosidic linkages can be focused on since the
enzyme displayed a slight preference for these types of linkages.
65
To further characterize the enzyme, attempts can be made to elucidate its crystal structure. An
important step leading to structure determination is the optimization of crystallization conditions
to obtain protein crystals that are suitable for X-ray diffraction. The crystals obtained and
reproduced in the BT3299 crystallization screens are not large enough to verify as protein
crystals using X-ray diffraction. Therefore, the conditions that led to crystal formation, such as
buffer pH, salt and precipitant concentrations, will be varied. Once data quality crystals are
available, eventual structure elucidation may even assist in the substrate screening process by
shedding some light on the active site architecture.
The work reported in this thesis is a necessary first step towards the further characterization and
determination of the role of BT3299. The KM,app method, provided a means to efficiently screen
a variety of substrates, utilizing the well-known, single step pNP-glucose assay. Determination
of the enzyme specificity using this method and future expansion to other microbial enzymes,
will shed some light on the physiological role these enzymes play. It will also be a useful tool in
the clinical applications of gut microbial research particularly to prebiotics. One of the selection
criteria for prebiotics requires them to be selectively fermented by one or a limited number of
potentially beneficial bacteria. With enzyme specificity determined, this selection criterion will
be more feasible.
66
References
67
References
1. McFarland, L.V. (2000) Normal Flora: diversity and functions. Microb Ecol Health Dis
12, 193-207.
2. Hooper, L.V., Midtvedt, T., and Gordon, J.I. (2002) How Host-Microbial Interactions
Shape the Nutrient Environment of the Mammalian Intestine. Annu Rev Nutr 22, 283-
307.
3. Versalovic, J., and Wilson, M., eds. Therapeutic Microbiology: Probiotics and Related
Strategies. Washington DC: ASM Press, 2008.
4. Xu, J., and Gordon, J.I. (2003) Honor thy symbionts. Proc Natl Acad Sci USA. 100(18),
10452-10459.
5. Bruzzese, E., et al. (2006) Impact of prebiotics on human health. Digestive and Liver
Disease. 38(Suppl. 2), S283-S287.
6. ―Reprinted from Cell 140, Garrett, W.S., Gordon, J.I., and Glimcher, L.H., Homeostasis
and Inflammation in the intestine, 859-870, Copyright (2010), with permission from
Elsevier.‖
7. Seikov, I., et al. (2010) Gut Microbiota in Health and Disease. Physiol Rev 90, 859-904.
8. Reprinted by permission from Macmillan Publishers Ltd: [Nature Rev Immunol, 9],
(Round, J.L., and Mazmanian, K., The gut microbiota shapes intestinal responses during
health and disease, 313-323), Copyright (2009).
9. Gustafson, J., and Welling, D. (2010) ―No Acid, No Ulcer‖—100 Years Later: A Review
of the History of Peptic Ulcer Disease. J Am Coll Surg 210(1), 110-116.
10. Topping, D.L., and Clifton, P.M. (2001) Short Chain Fatty Acids and Human Colonic
Function: Roles of Resistant Starch and Nonstarch Polysaccharides. Physiol Rev 81(3),
1031-1064.
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11. ―Reprinted from J Nutr Biochem 20, Davis, C., and Milner, J.A., Gastrointestinal
microflora, food components and colon cancer prevention., 743-752, Copyright (2009),
with permission from Elsevier.‖
12. Bajka, B.H., Clarke, J.M., Cobiac, L., and Topping, D.L. (2008) Butyrylated starch
protects colonocyte DNA against dietary protein-induced damage in rats. Carcin 29(11),
2169-2174.
13. O’Keefe, S. J. D., et al. (2009) Products of the Colonic Microbiota Mediate the Effects of
the Diet on Colon Cancer Risk. J Nutr 139, 2044-2048.
14. de Vrese, M., and Schrezenmeir, J. (2008) Probiotics, Prebiotics, and Synbiotics. Adv
Biochem Engin/Biotechnol 111, 1-66.
15. Xu, J., et al. (2003) A Genomic View of the Human—Bacteroides thetaiotaomicron
Symbiosis. Science 299, 2074-2076.
16. Martens, E.C., Koropatkin, N.M., Smith, T.J and Gordon, J.I., Complex Glycan
Catabolism by the Human Gut Microbiota: The Bacteroidetes Sus-like Paradigm, J Biol
Chem, 2009; 284(37), 24673-24677.
17. Bakolista, C. (2010) Structure of BT_3984, a member of the SusD/RagB family of
nutrient binding molecules. Acta Cryst F66, 1274-1280.
18. Gloster, T.M., et al. (2008) Divergence of Catalytic Mechanism within a Glycosidase
Family Provides Insight into Evolution of Carbohydrate Metabolism by Human Gut
Flora. Chem Biol 15, 1058-1067.
19. Kitamura, M., et al. (2008) Structural and Functional Analysis of a Glycoside Hydrolase
Family 97 Enzyme from Bacteroides thetaiotaomicron. J Biol Chem 283(52), 36328-
36337.
20. Yeh, A.P., et al. (2010) Structure of Bacteroides thetaiotaomicron BT2081 at 2.05 Å
resolution: the first structural representative of a new protein family that may play a role
in carbohydrate metabolism. Acta Cryst F66, 1287-1296.
21. Koropatkin, N.M., Martens, E.C., Gordon, J.I., and Smith, T.J. (2008) Starch Catabolism
by a Prominent Human Gut Symbiont Is Directed by the Recognition of Amylose
Helices. Structure 16, 1105-1115.
69
22. Koropatkin, N.M., Martens, E.C., Gordon, J.I., and Smith, T.J. (2009) Structure of a
SusD Homologue, BT1043, Involved in Mucin O-Glycan Utilization in a Prominent
Human Gut Symbiont. Biochem 48(7), 1532-1542.
23. Koropatkin, N.M., and Smith, T.J. (2010) SusG: A Unique Cell-Membrane-Associated α-
Amylase from a Prominent Human Gut Symbiont Targets Complex Starch Molecules.
Structure 18, 200-215.
24. Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B (2009) The
Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics.
Nucleic Acids Res 37:D233-238 [PMID: 18838391].
25. Withers, S., and Williams, S. ―Glycoside hydrolases‖ in CAZypedia, available at URL
http://www.cazypedia.org/index.php/Glycoside_Hydrolases, accessed 13 December
2010.
26. Zhang, R. ―Glycoside Hydrolase Family 31‖ in CAZypedia available at URL
http://www.cazypedia.org/index.php/Glycoside_Hydrolase_Family_31, accessed 13
December 2010.
27. Lovering, A.L., et al. (2005) Mechanistic and Structural Analysis of a Family 31 α-
Glycosidase and Its Glycosyl-enzyme Intermediate. J Biol Chem 280(3), 2105-2115.
28. ―Reprinted from J Mol Biol 358(4), Ernst, H.A., et al., Structure of the Sulfolobus
solfataricus α-Glucosidase: Implications for Domain Conservation and Substrate
Recognition in GH31., 1106-1124, Copyright (2006), with permission from Elsevier.‖
29. Sim, L., et al. (2008) Human Intestinal Maltase—Glucoamylase: Crystal Structure of the
N-Terminal Catalytic Subunit and Basis of Inhibition and Substrate Specificity. J Mol
Biol 375, 782-792.
30. Sim, L., et al. (2010) Structural Basis for Substrate Selectivity in Human Maltase-
Glucoamylase and Sucrase-Isomaltase N-terminal Domains. J Biol Chem 285(23),
17763-17770.
31. Kakkar, T., Boxenbaum, H., and Mayersohn, M. (1999) Estimation of Ki in a
Competitive Enzyme-Inhibition Model: Comparisons Among Three Methods of Data
Analysis. Drug Metab Disp 27(6), 756-762.
70
32. Novagen. Tenth Edition pET System Manual. Madison, WI: Novagen, Inc., 2002.
33. Guarner, F., and Malagelada, J-R. (2003) Gut flora in health and disease. The Lancet 360,
512-519.
71
Appendix A: Supplemental Data
72
Before generation of the plots used to determine Ki using the KM,app method31
(Figures 3.13-
3.23), a series of kinetic experiments were carried out to first determine Vmax and KM values.
With each secondary substrate, a control pNP-glucose kinetic experiment was carried out with
varying concentrations of pNP-glucose, a constant BT3299 concentration and without the
secondary substrate. This experiment was then repeated with 3-4 secondary substrate
concentrations to give 4-5 data sets. The Vmax (mM s-1
) and KM (mM) values were determined
using the GraFit program for each data set. The results are shown below.
A1: Trehalose Kinetics Data
A
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
000
0.000010.000010.000010.000010.000010.000020.000020.000020.000020.000020.000030.00003
Parameter Value Std. Error
Vmax 4.07686e-005 4.95117e-006
Km 10.9236 2.9327
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
73
B
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
Parameter Value Std. Error
Vmax 4.08403e-005 1.49212e-006
Km 14.2250 1.0239
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
C
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
0.00003
Parameter Value Std. Error
Vmax 5.14782e-005 1.16373e-006
Km 20.6678 0.7944
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
74
D
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
Parameter Value Std. Error
Vmax 4.23662e-005 5.54478e-006
Km 17.1370 4.0853
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
1.2e5
Figure A1: Michaelis-Menten and Lineweaver-Burk plots for trehalose kinetics. The rate
was measured in units of mM s-1
while substrate concentrations are in units of mM. A) 0mM
trehalose, B) 5mM trehalose, C) 10mM trehalose, D) 15 mM trehalose.
A2: Sucrose Kinetics Data
A
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0.00001
0.00002
0.00003
Parameter Value Std. Error
Vmax 4.89080e-005 3.41610e-006
Km 14.9116 2.0105
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
75
B
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
000
0.000010.000010.000010.000010.000010.000020.000020.000020.000020.000020.000030.00003
Parameter Value Std. Error
Vmax 4.95039e-005 3.52710e-006
Km 16.1315 2.1483
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
C
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
000
0.000010.000010.000010.000010.000010.000020.000020.000020.000020.000020.000030.00003
Parameter Value Std. Error
Vmax 5.24659e-005 2.35794e-006
Km 19.1404 1.5044
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
76
D
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
000
0.000010.000010.000010.000010.000010.000020.000020.000020.000020.000020.000030.00003
Parameter Value Std. Error
Vmax 5.46340e-005 7.00098e-006
Km 21.3089 4.5900
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
E
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
000
0.000010.000010.000010.000010.000010.000020.000020.000020.000020.000020.000030.00003
Parameter Value Std. Error
Vmax 5.65346e-005 2.85748e-006
Km 22.7721 1.8899
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
Figure A2: Michaelis-Menten and Lineweaver-Burk plots for sucrose kinetics. The rate was
measured in units of mM s-1
while substrate concentrations are in units of mM. A) 0mM sucrose,
B) 1mM sucrose, C) 5mM sucrose, D) 10 mM sucrose, E) 15mM sucrose.
77
A3: Nigerose Kinetics Data
A
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0.00001
0.00002
0.00003
Parameter Value Std. Error
Vmax 5.18335e-005 6.10788e-006
Km 11.1945 2.8869
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
B
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0.00001
0.00002
0.00003
Parameter Value Std. Error
Vmax 5.01901e-005 3.56862e-006
Km 12.4174 1.8434
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
78
C
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0.00001
0.00002
0.00003
Parameter Value Std. Error
Vmax 4.62832e-005 5.02188e-006
Km 11.7852 2.7350
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
D
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
000
0.000010.000010.000010.000010.000010.000020.000020.000020.000020.000020.000030.00003
Parameter Value Std. Error
Vmax 5.20012e-005 6.97548e-006
Km 17.2614 4.2151
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
Figure A3: Michaelis-Menten and Lineweaver-Burk plots for nigerose kinetics. The rate
was measured in units of mM s-1
while substrate concentrations are in units of mM. A) 0mM
nigerose, B) 1mM nigerose, C) 5mM nigerose, D) 11 mM nigerose.
79
A4: Turanose Kinetics Data
A
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0.00001
0.00002
0.00003
Parameter Value Std. Error
Vmax 4.31397e-005 3.94374e-006
Km 10.1542 2.1243
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
B
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
0.00003
Parameter Value Std. Error
Vmax 3.81637e-005 1.60146e-006
Km 9.7000 0.9530
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
80
C
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
0.00003
Parameter Value Std. Error
Vmax 4.99288e-005 2.41210e-006
Km 18.0296 1.5588
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
D
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
0.00003
Parameter Value Std. Error
Vmax 4.89427e-005 3.91220e-006
Km 18.3038 2.6014
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
81
E
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
0.00003
Parameter Value Std. Error
Vmax 5.02586e-005 2.40246e-006
Km 20.6913 1.6809
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
Figure A4: Michaelis-Menten and Lineweaver-Burk plots for turanose kinetics. The rate
was measured in units of mM s-1
while substrate concentrations are in units of mM. A) 0mM
turanose, B) 1mM turanose, C) 5mM turanose, D) 10 mM turanose, E) 15 mM turanose.
A5: Maltose Kinetics Data
A
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
0.00003
Parameter Value Std. Error
Vmax 3.99988e-005 2.05158e-006
Km 10.8218 1.2341
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
82
B
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
0.00003
Parameter Value Std. Error
Vmax 4.63260e-005 1.75780e-006
Km 15.9109 1.1352
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
C
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
0.00003
Parameter Value Std. Error
Vmax 4.83229e-005 5.35712e-006
Km 17.1958 3.4735
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
83
D
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
0.00003
Parameter Value Std. Error
Vmax 4.59380e-005 3.93173e-006
Km 17.3245 2.6927
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
E
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
Parameter Value Std. Error
Vmax 4.61200e-005 9.68339e-006
Km 19.5030 7.1031
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
Figure A5: Michaelis-Menten and Lineweaver-Burk plots for maltose kinetics. The rate was
measured in units of mM s-1
while substrate concentrations are in units of mM. A) 0mM maltose,
B) 1mM maltose, C) 5mM maltose, D) 10 mM maltose, E) 15 mM maltose.
84
A6: Cellobiose Kinetics Data
A
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
000
0.000010.000010.000010.000010.000010.000020.000020.000020.000020.000020.000030.00003
Parameter Value Std. Error
Vmax 5.19781e-005 1.29103e-005
Km 18.7103 8.1762
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
B
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
0.00003
Parameter Value Std. Error
Vmax 4.07042e-005 4.23555e-007
Km 11.5407 0.2593
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
85
C
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
0.00003
Parameter Value Std. Error
Vmax 4.57448e-005 6.26033e-006
Km 14.4709 3.8640
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
D
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
0.00003
Parameter Value Std. Error
Vmax 4.86606e-005 1.32934e-005
Km 18.5787 8.9821
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
Figure A6: Michaelis-Menten and Lineweaver-Burk plots for cellobiose kinetics. The rate
was measured in units of mM s-1
while substrate concentrations are in units of mM. A) 0mM
cellobiose, B) 1mM cellobiose, C) 5mM cellobiose, D) 11 mM cellobiose.
86
A7: Lactose Kinetics Data
A
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0.00001
0.00002
0.00003
Parameter Value Std. Error
Vmax 4.33090e-005 3.09551e-006
Km 9.8818 1.6378
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
B
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
000
0.000010.000010.000010.000010.000010.000020.000020.000020.000020.000020.000030.00003
Parameter Value Std. Error
Vmax 4.57436e-005 2.69832e-006
Km 13.7534 1.6209
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
87
C
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
000
0.000010.000010.000010.000010.000010.000020.000020.000020.000020.000020.000030.00003
Parameter Value Std. Error
Vmax 5.18094e-005 5.47585e-007
Km 16.8279 0.3271
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
D
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
000
0.000010.000010.000010.000010.000010.000020.000020.000020.000020.000020.000030.00003
Parameter Value Std. Error
Vmax 5.35394e-005 2.23376e-006
Km 19.6871 1.4219
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
Figure A7: Michaelis-Menten and Lineweaver-Burk plots for lactose kinetics. The rate was
measured in units of mM s-1
while substrate concentrations are in units of mM. A) 0mM lactose,
B) 5mM lactose, C) 10mM lactose, D) 15 mM lactose.
88
A8: Palatinose Kinetics Data
A
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0.00001
0.00002
0.00003
Parameter Value Std. Error
Vmax 3.64278e-005 7.17544e-006
Km 5.2910 3.3570
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
B
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0.00001
0.00002
0.00003
Parameter Value Std. Error
Vmax 4.26963e-005 1.89874e-006
Km 9.8821 1.0197
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
89
C
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
000
0.000010.000010.000010.000010.000010.000020.000020.000020.000020.000020.000030.00003
Parameter Value Std. Error
Vmax 4.65475e-005 9.28377e-006
Km 12.3688 5.1638
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
D
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
000
0.000010.000010.000010.000010.000010.000020.000020.000020.000020.000020.000030.00003
Parameter Value Std. Error
Vmax 4.69097e-005 5.77547e-006
Km 14.1835 3.4391
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
90
E
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
0.00003
Parameter Value Std. Error
Vmax 3.91658e-005 1.25023e-006
Km 11.8558 0.8073
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
Figure A8: Michaelis-Menten and Lineweaver-Burk plots for palatinose kinetics. The rate
was measured in units of mM s-1
while substrate concentrations are in units of mM. A) 0mM
palatinose, B) 1mM palatinose, C) 5mM palatinose, D) 10 mM palatinose, E) 15 mM palatinose.
A9: β-Gentiobiose Kinetics Data
A
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0.00001
0.00002
0.00003
Parameter Value Std. Error
Vmax 6.44773e-005 2.06015e-005
Km 24.0493 12.3515
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
91
B
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0.00001
0.00002
0.00003
Parameter Value Std. Error
Vmax 6.70948e-005 2.38563e-005
Km 27.1626 14.9193
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
C
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0.00001
0.00002
0.00003
Parameter Value Std. Error
Vmax 7.71168e-005 2.08436e-005
Km 34.1587 13.3217
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
Figure A9: Michaelis-Menten and Lineweaver-Burk plots for β-gentiobiose kinetics. The
rate was measured in units of mM s-1
while substrate concentrations are in units of mM. A) 0mM
β-gentiobiose, B) 1mM β-gentiobiose, C) 10mM β-gentiobiose.
92
A10: Soluble Starch Kinetics Data
A
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0.00001
0.00002
0.00003
Parameter Value Std. Error
Vmax 4.51603e-005 4.41170e-006
Km 12.6217 2.5571
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
B
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
000
0.000010.000010.000010.000010.000010.000020.000020.000020.000020.000020.000030.00003
Parameter Value Std. Error
Vmax 4.44482e-005 2.55873e-006
Km 12.7655 1.5165
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
93
C
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
Parameter Value Std. Error
Vmax 3.58417e-005 2.19757e-006
Km 9.0304 1.3413
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
D
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
0.00003
Parameter Value Std. Error
Vmax 3.85204e-005 6.54019e-006
Km 9.6503 3.8471
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
94
E
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0.00001
0.00002
0.00003
Parameter Value Std. Error
Vmax 4.22143e-005 5.34522e-006
Km 5.6464 2.2133
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
Figure A10: Michaelis-Menten and Lineweaver-Burk plots for soluble starch kinetics. The
rate was measured in units of mM s-1
while substrate concentrations are in units of mM. A) 0
mg/mL soluble starch, B) 0.5 mg/mL soluble starch, C) 1 mg/mL soluble starch, D) 2 mg/mL
soluble starch, E) 5 mg/mL soluble starch.
A11: β-D Glucan Kinetics Data
A
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
0.00003
Parameter Value Std. Error
Vmax 3.77440e-005 3.88867e-006
Km 9.7024 2.3313
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
95
B
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
Parameter Value Std. Error
Vmax 4.62208e-005 1.15629e-005
Km 22.5495 9.2928
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
C
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
Parameter Value Std. Error
Vmax 6.95343e-005 2.72248e-005
Km 41.4019 22.2339
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
1.2e5
96
D
[Substrate]0 2 4 6 8 10 12 14 16 18 20
Rate
0
0
0
0.00001
0.00001
0.00001
0.00001
0.00001
0.00002
0.00002
0.00002
0.00002
0.00002
Parameter Value Std. Error
Vmax 0.0001 0.0002
Km 84.7609 133.0998
Enzyme Kinetics Data
1 / [Substrate]
00.020.040.060.080.10.120.140.160.180.2
1 /
Rate
0
20000
40000
60000
80000
1e5
1.2e5
Figure A11: Michaelis-Menten and Lineweaver-Burk plots for β-D glucan kinetics. The rate
was measured in units of mM s-1
while substrate concentrations are in units of mM. A) 0 mg/mL
β-D glucan, B) 0.5 mg/mL β-D glucan, C) 1 mg/mL β-D glucan, D) 2 mg/mL β-D glucan.
97
Copyright Acknowledgements
I would like to recognise and thank the following organisations for rights and permissions to
reuse published material in this thesis:
Elsevier
Macmillan Publishers
The American Society for Biochemistry and Molecular Biology
EMD Millipore