Purdue University
Purdue e-Pubs
Open Access Dissertations
Theses and Dissertations
Fall 2014
Dietary carbohydrates influence the structure and
function of the intestinal alpha-glucosidases
Mohammad Chegeni
Follow this and additional works at: http://docs.lib.purdue.edu/open_access_dissertations
Part of the Biochemistry Commons, Biology Commons, Food Science Commons, and the
Molecular Biology Commons
Recommended Citation
Chegeni, Mohammad, "Dietary carbohydrates influence the structure and function of the intestinal alpha-glucosidases" (2014). Open
Access Dissertations. 433.
http://docs.lib.purdue.edu/open_access_dissertations/433
This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for
additional information.
DIETARY CARBOHYDRATES INFLUENCE THE STRUCTURE AND FUNCTION
OF THE INTESTINAL α-GLUCOSIDASES
A Dissertation
Submitted to the Faculty
of
Purdue University
by
Mohammad Chegeni
In Partial Fulfillment of the
Requirements of the Degree
of
Doctor of Philosophy
May 2015
Purdue University
West Lafayette, Indiana
ii
I dedicate this work to my wife to honor her love, patience, and support.
iii
ACKNOWLEDGMENTS
I would like to express my sincere gratitude to my advisor, Dr. Bruce Hamaker,
who has supported me throughout my thesis with his inspiring and delicate guidance,
advice, and expertise. I sincerely thank my advisory committee members, Dr. Mario
Ferruzzi, Dr. Buford Nichols and Dr. Kee-Hong Kim. I am much grateful to them for
their valuable advice and using their precious times to read this thesis. I would like also
to thank our collaborator Dr. Hassan Naim. Without his expertise, this research would not
have been as developed as it is.
I would also like to acknowledge the help and support of my labmates, for always
providing a positive work atmosphere. Much appreciation goes to my special friends Dr.
Deepak Bhopatkar, Dr. Amandeep Kaur, Dr. Madhuvanti Kale, Dr. Choon Young Kim,
Dr. Chih-Yu Chen, and Beth Pletsch. Their wisdom and endless help in research and
Ph.D life were much appreciated. My very special and warm recognition goes to my very
best friend Dr. Daniel Erickson. He made my Ph.D. life enjoyable at Purdue University. I
really appreciated all his time helping me. The time we have shared together is
unforgettable.
My family and my in-laws have provided invaluable support to me through this
journey, and for that and much more, I express my deepest appreciation. And last, but not
the least, I owe my loving thanks to my dear wife, Fariba Tayyari. Words fail me to
iv
convey my gratitude to her dedication and love. I owe her for generously letting her
intelligence, passions, and ambitions collide with mine.
v
TABLE OF CONTENTS
Page
LIST OF TABLES ........................................................................................................... viii
LIST OF FIGURES ........................................................................................................... ix
ABSTRACT...................................................................................................................... xii
CHAPTER 1. INTRODUCTION ........................................................................................1
1.1 Research Hypothesis and Specific Aims .................................................................4
1.2 Significance of the Study .........................................................................................5
1.3 Thesis Organization .................................................................................................6
1.4 References ................................................................................................................8
CHAPTER 2. REVIEW OF LITERATURE .....................................................................10
2.1 Carbohydrates ........................................................................................................10
2.2 Carbohydrate Digestion .........................................................................................12
2.2.1 α-Amylase .....................................................................................................13
2.2.2 Intestinal α-Glucosidases ..............................................................................14
2.2.3 Sucrase-Isomaltase ........................................................................................16
2.3 Carbohydrate Absorption .......................................................................................18
2.3.1 Monosaccharide Absorption .........................................................................18
2.3.1.1 Movement of Monosaccharides Across the Brush Border
Membrane ..................................................................................................18
2.3.1.2 Movement of Monosaccharides Across the Basolateral Membrane....19
2.4 Carbohydrate Sensing ............................................................................................20
2.4.1 Sweet Taste Receptor ....................................................................................21
2.4.1.1 Carbohydrate Sensing by the Sweet Taste Receptor ...........................22
2.4.2 Dietary Carbohydrate Sensing and α-Glucosidase Enzymes .......................23
2.5 References ..............................................................................................................32
vi
Page
CHAPTER 3. DIETARY CARBOHYDRATES INFLUENCE THE STRUCTURE AND
FUNCTION OF SUCRASE-ISOMALTASE ...................................................................41
3.1 Introduction ............................................................................................................41
3.2 Materials and Methods ...........................................................................................45
3.2.1 Immunochemical Reagents ...........................................................................45
3.2.2 Cell Culture Procedure ..................................................................................46
3.2.3 Cell Treatment ..............................................................................................46
3.2.4 Total Protein Extraction ................................................................................47
3.2.5 Western Blot Analysis ..................................................................................47
3.2.6 Membrane Protein Extraction .......................................................................48
3.2.7 Preparation of Brush Border Membranes .....................................................49
3.2.8 Pulse-chase and Metabolic Labeling ............................................................49
3.2.9 Immunoprecipitation and SDS-PAGE ..........................................................50
3.2.10 Isolation of Detergent Resistant Membranes (DRMs)................................51
3.2.11 Immunoassays .............................................................................................52
3.2.12 Enzyme Activity Measurement...................................................................52
3.2.13 Total RNA Isolation....................................................................................53
3.2.14 cDNA Synthesis and Real-Time PCR Analysis .........................................53
3.2.15 Quantification of Band Intensities ..............................................................55
3.2.16 Statistical Analysis ......................................................................................55
3.3 Result .....................................................................................................................55
3.3.1 Intracellular Distribution and Compartmentalization of SI ..........................55
3.3.2 The Kinetics of Glycosylation Alterations ...................................................56
3.3.3 Association of SI with Detergent-Resistant Microdomains..........................57
3.3.4 Sucrase-Isomaltase Enzyme Activity ...........................................................58
3.3.5 Deglycosylation Treatment of Treated Cells ................................................59
3.3.6 Gene Expression of the Sweet Taste Receptor with Maltose Exposure .......60
3.4 Discussion ..............................................................................................................62
3.3 References ..............................................................................................................98
CHAPTER 4. INDUCTION OF DIFFERENTIATION OF SMALL INTESTINAL
ENTEROCYTE CELLS BY MALTOOLIGOSACCHARIDE TREATMENT USING A
METABOLOMICS APPROACH ...................................................................................103
4.1 Introduction ..........................................................................................................103
4.1.1 Sample Preparation .....................................................................................104
4.1.1.1 Cell Samples ......................................................................................105
4.1.1.2 Cell Sample Preparation ....................................................................105
4.1.2 Analytical Instrument Platforms .................................................................106
4.1.3 Data Analysis ..............................................................................................107
4.1.3.1 Pre-Data Processing ...........................................................................107
4.1.3.2 Statistical Methods .............................................................................109
4.1.4 Metabolomic Application in the Present Study ..........................................111
vii
Page
4.2 Materials and Methods .........................................................................................113
4.2.1 Chemicals and Samples ..............................................................................113
4.2.1.1 Chemicals...........................................................................................113
4.2.1.2 Cell Culture Procedure .......................................................................114
4.2.1.3 Cell Treatment ...................................................................................114
4.2.1.4 Measurement of Epithelial Monolayer Electrical Resistance ............115
4.2.1.5 Determination of Paracellular Permeability.......................................115
4.2.1.6 Sample Preparation for NMR Experiments .......................................116
4.3 NMR Experiments and Data Processing..............................................................117
4.3.1 NMR Experiments ......................................................................................117
4.3.2 Data Processing...........................................................................................118
4.3.2.1 Statistical Analysis .............................................................................119
4.4 Results ..................................................................................................................119
4.4.1 Different Source of Carbohydrates Alters Intestinal Epithelial Tight
Junction Barrier........................................................................................119
4.4.2. Different Source of carbohydrate Alters Metabolite Profiles in
Caco-2 Monolayers ..................................................................................120
4.5 Discussion ............................................................................................................122
4.6 References ............................................................................................................143
CHAPTER 5. OVERALL CONCLUSIONS AND FUTURE
RECOMMENDATIONS .................................................................................................150
5.1 References ............................................................................................................154
VITA ................................................................................................................................155
viii
LIST OF TABLES
Tables
Page
2.1 Classification of monosaccharides (Fennema, 2005). ............................................ 25
2.2 Substrates and products of small intestinal glycosidase enzyme. ........................... 26
3.1 Relative protein amount (P2/P1) of intercellular distribution of SI in present of
different carbohydrate ............................................................................................. 72
3.2 Association of SI with detergent-resistant microdomains in present of glucose
and maltose ............................................................................................................. 73
3.3 Sucrase activity of SI in cells fed with different carbohydrates... .......................... 74
3.4 Maltase activity of SI in cells fed with different carbohydrates ............................. 75
3.5 The kinetic parameters of sucrase and maltase in the cells fed with glucose or
maltose .................................................................................................................... 76
4.1 Metabolite assignments and chemical shifts of identified peaks .......................... 127
ix
LIST OF FIGURES
Figure
Page
2.1 The structures of common dietary disaccharids lactose (a), sucrose (b), and
maltose (c) (Fennema, 2005). ................................................................................. 27
2.2 Schematic diagram of the action of salivary and pancreatic α-amylases on
starch ....................................................................................................................... 28
2.3 Position and protein organization of the MGAM and SI complex. TMD:
transmembrane domain; O-link: O-glycosylated linker (adapted from Sim et
al., 2008). ................................................................................................................ 29
2.4 The structure of the sucrase-isomaltase complex (Lieberman, 2013) .................... 30
2.5 The absorption and transfer out of sugars by the enterocyte. G, glucose; Ga,
galactose; F, fructose (adapted from Paulev, 1999) ................................................ 31
3.1 Biochemical analysis of the distribution of SI. Brush border membranes were
isolated by the calcium chloride precipitation method. .......................................... 77
3.2 Band intensities from P1 and P2 fractions .............................................................. 78
3.3 Trafficking kinetics of SI to the cell surface ........................................................... 79
3.4 Graphical representation of the pulse-chase experiment ........................................ 80
3.5 Transport kinetics of SI to the cell surface ............................................................. 81
3.6 Distribution of SI in Tween-20 detergent resistant membranes ............................. 82
3.7 Graphical representation of the distribution of SI in Tween-20 detergent
resistant membrane ................................................................................................. 83
3.8 Comparison of sucrase activity of SI at the brush border membrane of Caco-2
cells treated with different concentrations of glucose and maltose ........................ 84
3.9 Comparison of maltase activity of SI at the brush border membrane of Caco-2
cells treated with different concentrations of glucose and maltose ........................ 85
x
Figure
Page
3.10 Michaelis-Menten plots for SI. The enzymatic activity of sucrase was
measured in the presence of different concentrations of substrate at the brush
border membrane of Caco-2 cells treated with glucose and maltose (means
±SEM, n=3) ............................................................................................................ 86
3.11 Lineweaver-Burk plots for SI. The enzymatic activity of sucrase was
measured in the presence of different concentrations of substrate at the brush
border membrane of Caco-2 cells treated with glucose and maltose (means
±SEM, n=3) ............................................................................................................ 87
3.12 Michaelis-Menten plots for SI. The enzymatic activity of maltase was
measured in the presence of different concentrations of substrate at the brush
border membrane of Caco-2 cells treated with glucose and maltose (means
±SEM, n=3) ............................................................................................................ 88
3.13 Lineweaver-Burk plots for SI. The enzymatic activity of maltase was
measured in the presence of different concentrations of substrate at the brush
border membrane of Caco-2 cells treated with glucose and maltose (means
±SEM, n=3) ............................................................................................................ 89
3.14 Deglycosylation (Endo F) of SI in cells treated with glucose and maltose .......... 90
3.15 Deglycosylation (Endo H) of SI in cells treated with glucose and maltose.......... 91
3.16 T1R3 gene transcription in Caco-2 cells in the presence of glucose and
maltose. ................................................................................................................... 92
3.17 T1R2 gene transcription in Caco-2 cells in the presence of glucose and
maltose. ................................................................................................................... 93
3.18 α-Gustducin gene transcription in Caco-2 cells in the presence of glucose and
maltose. ................................................................................................................... 94
3.19 SI gene transcription in Caco-2 cells in the presence of glucose and maltose ..... 95
3.20 GLUT5 gene transcription in Caco-2 cells in the presence of glucose and
maltose .................................................................................................................... 96
3.21 GLUT2 gene transcription in Caco-2 cells in the presence of glucose and
maltose .................................................................................................................... 97
4.1 Intestinal epithelial tight junction barrier function in Caco-2 cells in presence
of different types of carbohydrates ....................................................................... 128
xi
Figure
Page
4.2 Intestinal epithelial tight junction barrier function in Caco-2 cells in presence
of different types of carbohydrates. ...................................................................... 129
4.3 Epithelial paracellular permeability in Caco-2 cells in presence of different
types of carbohydrates... ....................................................................................... 130
4.4 Epithelial paracellular permeability in Caco-2 cells in presence of different
types of carbohydrates. ......................................................................................... 131
4.5 Representative 1D 1H NMR noesygppr1d spectrum of metabolites extracted
from Caco-2 cells fed with glucose. ..................................................................... 132
4.6 Aliphatic region of a representative 1D 1H NMR noesygppr1d spectrum of
metabolites extracted from Caco-2 cells fed with glucose. Identified
metabolites have been labeled with numbers, according to Table 4.1.................. 133
4.7 Aromatic region of a representative 1D 1H NMR noesygppr1d spectrum of
metabolites extracted from Caco-2 cells fed with glucose. Identified
metabolites have been labeled with numbers, according to Table 4.1.................. 134
4.8 Relative population of taurine in Caco-2 cells in presence of different types of
carbohydrates. ....................................................................................................... 135
4.9 Relative population of taurine in Caco-2 cells in presence of different types of
carbohydrates. ....................................................................................................... 136
4.10 Relative population of taurine in Caco-2 cells in presence of different types
of carbohydrates. ................................................................................................... 137
4.11 Relative population of myo-inositol in Caco-2 cells in presence of different
types of carbohydrates. ......................................................................................... 138
4.12 Relative population of phosphorylcholine in Caco-2 cells in presence of
different types of carbohydrates............................................................................ 139
4.13 Relative population of phosphorylcholine in Caco-2 cells in presence of
different types of carbohydrates............................................................................ 140
4.14 Relative population of glycerophosphocholine in Caco-2 cells in presence of
different types of carbohydrates............................................................................ 141
4.15 Relative population of glycerophosphocholine in Caco-2 cells in presence of
different types of carbohydrates............................................................................ 142
xii
ABSTRACT
Chegeni, Mohammad. Ph.D., Purdue University, May 2015. Dietary Carbohydrates
Influence the Structure and Function of the Intestinal α-Glucosidases. Major Professor:
Bruce R. Hamaker.
As the primary products of starch digestion by pancreatic α-amylase,
maltooligosaccharides (including maltose) are the main substrates for the α-glucosidases
at the intestinal brush border. Here, maltose was shown to induce the formation of a
higher molecular weight (HMW) sucrase-isomaltase (SI) species in Caco-2 cells that
sorts more quickly to the enterocyte surface to act as a digestive enzyme. As this finding
suggested a maltose sensing ability of small intestinal enterocytes, molecular mechanisms
associated with the maturation and trafficking of HMW SI were further investigated. A
pulse-chase experiment using [35S]-methionine revealed a higher rate of early trafficking
and maturation of the HMW SI species in cells treated with maltose. Endoglycosidase
treatment of immunoprecipitated SI showed that increased molecular weight is a
consequence of additional N- and O-glycosylation of the enzyme. In comparison to the
control, the HMW SI was found to be more associated with lipid rafts at the membrane
surface which was related to the higher apical sorting of these species. Thus, maltose
sensing of small intestinal enterocytes triggers the intracellular processing of HMW SI,
speculatively to enhance its digestive property. Study on the sweet taste receptor
subunits (T1R2 and T1R3) by qRT-PCR showed that the expression of T1R2 and T1R3
increased in presence of maltose compared to glucose. It appeared also that
xiii
maltooligosaccharides may signal other events in small intestine enterocytes. Culturerelated conditions (e.g. glucose concentration) are known to alter physical barrier
properties of Caco-2 cell monolayers, which affect transepithelial transport of solutes
permeating the cell monolayer barrier. A transepithelial electrical resistance experiment
was conducted to measure the integrity of the Caco-2 monolayer in response to
maltooligosaccharides. Maltose promoted higher tight junction formation and
permeability compared to glucose and sucrose at 12 hours. Contrary to this finding,
paracellular permeability increased with maltose. In addition to barrier function of small
intestinal enterocytes, a metabolomics study was conducted using high-resolution 1H
NMR. Results showed that concentration of metabolites (taurine, phosphorycholine, and
glycerophosphocholine) which are known to be markers for cell differentiation increased
in Caco-2 cells treated with different types of maltooligosaccharides compared to glucose
and sucrose. Overall, these findings are indicative of a maltooligosaccharide sensing
ability by enterocytes that trigger a number of important events in the cell including a
higher level of SI processing and enzyme activation for digestion purpose, and an
increased cell differentiation and tight junction barrier function. From the broader
perspective of a desire to control of postprandial glycemic response, as well as to elicit
the gut-brain axis and ileal brake mechanisms for appetite control, this work suggests a
new potential point of controlling glucose release and absorption in the small intestine
(i.e., a putative maltooligosaccharide enterocyte receptor).
1
CHAPTER 1. INTRODUCTION
Food intake provides essential nutrients for growth, repair, and maintenance of
the body. Glycemic carbohydrates are a main source of energy for many animal species.
Different homeostatic systems (e.g., brain and gut hormones) effectively regulate the
digestion and absorption of dietary carbohydrates through various physiological
responses (e.g., expression/secretion of hydrolytic enzymes and transporters, gastric
emptying).
Dietary carbohydrates can be absorbed only in the form of monosaccharides
(glucose, fructose, and galactose), with the major source being glucose from starch, and
glucose and fructose from sucrose and glucose and galactose from lactose. The glycemic
response of starchy foods is heavily dependent on the rate and amount of glucose released
during starch digestion. A slow glucose release has been shown to be beneficial for
maintaining glucose homeostasis and is linked to reduction of the incidence of certain
diet-related metabolic diseases (Atkinson et al., 2008). Low glycemic index foods and
slowly digestible starches are two possible ways to alleviate the detrimental effect caused
by long-term exposure of postprandial glycemic spikes from consumption of foods with
high glycemic index (Jenkins et al., 2002). However, more mechanistic studies on the
digestion and absorption of carbohydrates in the gastrointestinal tract are needed to fully
understand the role of postprandial glucose release rate and health. Investigation of the
2
nutrition property of glycemic carbohydrates and metabolism is important for human
health.
Starch, the main dietary carbohydrate in foods, is digested first by α-amylase,
consisting of salivary and pancreatic isoforms, that cleave the α-1,4 glycosidic linkages
between internal glucose units that are the basic building blocks of starch molecules. The
degradation products, small linear oligosaccharides and branched α-limit dextrins, are
generally composed of maltose and somewhat larger oligosaccharides with different
chemical structures (size and linkages), termed together “maltooligosaccharides”. The αlimit dextrins are then hydrolyzed by brush border-anchored α-glucosidases, maltaseglucoamylase (MGAM) and sucrase-isomaltase (SI) to liberate glucose for absorption
(Holmes, 1971; Freeze, 1999). Although pancreatic α-amylase is generally thought to be
the dominant enzyme in carbohydrate digestion (Englyst et al., 1992), SI and MGAM are
the ultimate enzymes for glucose liberation, and their relative activities can, and probably
are, related glycemic control in everyday life. They also are the control point of glucose
generation in the gut lumen, and consequently offer another potential opportunity for
glucose control. After starch is fully digested, glucose is transported into small intestinal
enterocytes through the energy and sodium-dependent glucose transporter (SGLT1) and
transported from enterocytes into the circulation system through the facilitated glucose
transporter type 2 (GLUT2) (Levin, 1994).
As the primary product of starch digestion by pancreatic α-amylase, maltose and
maltooligosaccharides are presented to the α-glucosidases at the apical surface of the
enterocytes. Based on our initial studies with Caco-2 cells as an enterocyte model system,
maltose treatment appeared to induce the maturation of SI, while other sugars including
3
fructose, sucrose, isomaltose, and glucose do not show this effect (Cheng et al., 2014).
This was surprising, as it suggested that a receptor for maltose/maltooligosaccharides
exists that has not yet been reported in literature. So we hypothesized that there is a
maltose sensing mechanism in the enterocytes that triggers the maturation and trafficking
of SI, and that may have other effects related to enterocyte function. Further, an
attenuation of this pathway might be used to control glucose production rate and the
glycemic response.
From what has been known so far, sugar sensing is rather complex process
involving their sensing by receptors on cell membranes, their further digestion, if
necessary, and transport through the small intestinal enterocytes, and metabolism in the
body. GLUT2 is a glucose sensor resulting in high expression of SI, which is termed the
GLUT2-dependent signaling pathway (Le Gall et al., 2007). Another important sugar
sensor is the sweet taste receptor composed of T1R2 and T1R3 subunits on the
enteroendocrine cells of gastrointestinal tract. The receptor can bind sucrose, other sugars
(e.g. glucose and sucrose), and artificial sweeteners (e.g. sucralose) to stimulate
enterocyte SGLT1 expression through the function of hormones like gastric inhibitory
polypeptide (GIP) or glucagon-like peptide-1 (GLP-1) secreted by enteroendocrine cells
(Margolskee et al., 2007). In behavioral studies on rodents that sense starch taste,
including starch-derived maltose and Polycose™ (a maltodextrin), starch taste was found
distinct from sugar (mainly sucrose) taste (Sclafani, 1987). Recent studies (Zukerman et
al., 2009) also showed that the T1R3 subunit of the sweet taste receptor is critical for
sucrose sensing, but that starch-derived maltodextrins showed no stimulus of the
receptor. Dietary glucose and fructose have been shown to up-regulate the transcription
4
of SI by binding of the dimeric nuclear protein (Cdx-2) on the SI promoter, while glucose
can down-regulate SI expression (Goda, 2000) by affecting the binding of hepatocyte
nuclear factors (HNF)-1a to its promoter (Gu et al., 2007).
As mentioned above, a rapid digestion of starch and absorption of glucose is
detrimental to health due to a glycemic spike that is a stress to the glucose homeostasis
regulatory system. If the mechanism of SI maturation triggered by maltose is understood,
there may be ways to manipulate the activity of brush border anchored α-glucosidases so
as to modulate the postprandial glycemic response for glycemic control and improved
health. Perhaps this approach could be used by way of a dietary intervention to manage,
or even prevent, type 2 diabetes.
1.1 Research Hypothesis and Specific Aims
Based on our initial study using Caco-2 cell as the enterocyte model system,
Cheng et al. (2014) showed that maltose treatment induced apparent maturation of SI,
leading to the hypothesis that maltooligosaccharides generated by α-amylase degradation
of dietary starch regulate the maturation, trafficking, and activation of HMW SI to the
apical membrane of intestinal enterocytes.
Our long term goal is to develop ways to control delivery of glucose to the body,
both in amount and rate, for glycemic control and release of certain small intestinal
hormones through the ileal brake and gut-brain axis mechanisms that relate to relevant
metabolic diseases. The aim, based our initial studies, was to characterize SI maturation
5
and activation as regulated by maltose or maltooligosaccharides using the Caco-2 cell
model.
Specific Aim 1: Demonstrate the influence of maltooligosaccharides with
different chemical structures on the maturation of SI by showing SI post-translational
glycosylation event including trafficking, apical sorting, and activation in lipid rafts.
Specific Aim 2: Investigation of the molecular mechanism of the maturation of SI
triggered by maltose and maltooligosaccharides. Genomic and metabolomics techniques
were used to reveal the genes associated with the glycosylation of SI.
Research results showed the influence and mechanism involved in maturation and
activation of the brush border α-glucosidases for glucose generation of dietary glycemic
carbohydrates at the small intestinal enterocyte level, and could bring forth the
development of novel technologies to modulate SI glucogenic enzyme activity and
postprandial glycemia.
1.2 Significance of the Study
The hypothesize of this study was to investigate the mechanism of the maturation,
trafficking, and activation of HMW SI induced by maltose sensing on the apical
membrane of intestinal enterocytes. This hypothesis is indirectly supported by studies on
rodents that “taste” starch and that this sensing (including starch-derived
maltooligosaccharide) is distinct from sugar (mainly sucrose) taste (Sclafani 1987).
Maltose, as an important digestion product of starch by α-amylase, and symbolizes the
presence of glucose-composed starch which is the main glycemic carbohydrate on which
6
humans evolved. On the other hand, sucrose is a source of carbohydrate nutrition from
fruits and there is a known receptor for sucrose, the sweet taste receptor. From the
viewpoint of evolution, there could be different receptors for maltose and sucrose
representing different sources of carbohydrate in human diets.
Rapid digestion of glycemic carbohydrates and absorption of glucose can be detrimental
to health, particularly for pre-diabetics and diabetics, due to glycemic spikes that stress
the glucose homeostasis regulatory system. If the mechanism of recently observed SI
maturation triggered by maltose is understood, an approach may be developed to
manipulate the activity of brush border anchored enzymes so as to modulate postprandial
glycemic response for improved health.
1.3 Thesis Organization
Chapter 1 is a brief introductory part that introduces background of carbohydrate
digestion, and the research purpose and its significance.
Chapter 2 reviews literature on carbohydrates and mucosal α-glucosidases. This
chapter has a review of protein structure, expression at the cell level, enzyme three
dimensional properties, and distribution in the small intestine of the mucosal αglucosidases. In addition, structure and function of the sweet taste receptor is reviewed.
Chapter 3 is the main experimental chapter. It investigates influence of dietary
carbohydrates on structure and function of the intestinal α-glucosidases.
Chapter 4 is the second experimental chapter. It presents the effects of dietary
carbohydrates on the physiological state of small intestinal enterocyte cells.
7
Chapter 5 provides an overall summary of the thesis, the major findings, and
suggestions for future research.
8
1.4 References
Atkinson, F. S., Foster-Powell, K., & Brand-Miller, J.C. (2008). International tables of
glycemic index and glycemic load values: 2008. Diabetes Care, 31(12), 2281-2283.
Cheng, M. W., Chegeni, M., Kim, K. H., Zhang, G. Y., Benmoussa, M., QuesadaCalvillo, R., Nichols, B. L., & Hamaker, B. R. (2014). Different sucrose-isomaltase
response of Caco-2 cells to glucose and maltose suggests dietary maltose sensing.
Journal of Clinical Biochemistry and Nutrition, 54(1), 55-60.
Englyst, H. N., Kingman, S. M., & Cummings, J. H. (1992). Classification and
measurement of nutritionally important starch fractions. European Journal of Clinical
Nutrition, 46, S33-S50.
Freeze, H. H. (1999). Monosaccharide Metabolism. Essentials of Glycobiology, 69-84.
Goda, T. (2000). Regulation of the expression of carbohydrate digestion/absorptionrelated genes. British Journal of Nutrition, 84(S2), S245-S248.
Gu, N., Adachi, T., Matsunaga, T., Tsujimoto, G., Ishihara, A., Yasda, K., & Tsuda, K.
(2007). HNF-1α participates in glucose regulation of sucrase-isomaltase gene expression
in epithelial intestinal cells. Biochemical and Biophysical Research Communications,
353(3), 617-622.
Holmes, R. (1971). Carbohydrate digestion and absorption. Journal of Clinical
Pathology, 5, 10-13.
Jenkins, D. J., & Kendall, C. W. (2002). Glycemic index: overview of implications in
health and disease. American Journal of Clinical Nutrition, 76(1), 266S- 273S.
Le Gall, M. L., Tobin, V., Stolarczyk, E., Dalet, V., Leturque, A., & Brot-Laroche, E.
(2007). Sugar sensing by enterocytes combines polarity, membrane bound detectors and
sugar metabolism. Journal of Cellular Physiology, 213(3), 834-843.
Levin, R. (1994). Digestion and absorption of carbohydrates-from molecules and
membranes to humans. American Journal of Clinical Nutrition, 59(3), 690S-698S.
9
Margolskee, Robert F., Jane Dyer, Zaza Kokrashvili, Kieron SH Salmon, Erwin Ilegems,
Kristian Daly, Emeline L. Maillet, Yuzo Ninomiya, Bedrich Mosinger, and Soraya P.
Shirazi-Beechey. (2007). T1R3 and gustducin in gut sense sugars to regulate expression
of Na+-glucose cotransporter 1. Proceedings of the National Academy of Sciences USA,
104(38), 15075-15080.
Sclafani, A., & Mann, S. (1987). Carbohydrate taste preferences in rats: glucose, sucrose,
maltose, fructose and polycose compared. Physiology & Behavior, 40(5), 563-568.
Zukerman, S., Glendinning, J. I., Margolskee, R. F., & Sclafani, A. (2009). T1R3 taste
receptor is critical for sucrose but not polycose taste. American Journal of PhysiologyRegulatory, Integrative and Comparative Physiology, 296(4), R866-R876.
10
CHAPTER 2. REVIEW OF LITERATURE
2.1 Carbohydrates
In order to understand the regulation of carbohydrate digestion, it is important to
briefly review carbohydrate structure and terminology. Carbohydrates are organic
compounds consisting of carbon, hydrogen, and oxygen with a general composition of
Cx(H2O)y. They constitute more than 90% of the dry matter in nature and are important in
living organisms as structural components of plant cell walls, backbone molecules of
DNA and RNA, linked to some lipids and proteins (Berg, 2002), and as energy sources.
Starch, lactose, and sucrose are the main dietary carbohydrates used by the body for
energy, providing 70-80% of the calories in the human diet worldwide.
Carbohydrates are classified into three major catagories: monosaccharides,
oligosaccharides, and polysaccharides (Fennema, 2005). Carbohydrates also are
classifield based on dietary purpose. Monosaccharides and disacchardies are called
simple sugar and oligosaccharides are called complex carbohydrates. Monosaccharides
are the simplest form of carbohydrate with the formula (CH2O)n. Glucose, for example,
is one of the monosaccharides with n=6. The classification of monosaccharides scheme is
shown in Table 1.1 (Fennema, 2005).
Carbohyrates which contain of two to twenty monosaccharides units, are called
oligosacchardies. Oligosaccharides are named based on number of monosaccharides units
11
such as disaccharides, trisaccharides, tetrasaccharides, and so forth. Sucrose, maltose, and
lactose are simple oligosaccharides woth two monosaccharide units and they are the most
important dietary disaccharides. Figure 1 illustrates examples of common disaccharides.
Sucrose is the most abundant disaccharide found in nature (also called simply
sugar or table sugar) composed of α-D-glucopyranosyl and a β-D-fructofuranosyl units.
Maltose, maltotriose, maltotetraose, and maltopentose are α-1,4-linked glucosecontaining oligosaccharides, or maltooligosaccharides produced in the gastrointestinal
tract from starch hydrolysis by α-amylase. Maltose rarely occurs in nature and only in
plants as a result of partial hydrolysis of starch. It is produced during malting of grains,
such as of barley, and commercially by the specific enzyme-catalyzed hydrolysis of
starch using β-amylase from Bacillus species, although the β-amylases from barley seed,
soybeans, and sweet potatoes may also be used. Maltose is used sparingly as a mild
sweetener in foods and can be reduced to the maltitol, which is used in sugarless
chocolate. Oligosaccharides play several roles in living organisms (e.g. protein posttranslational modification).
Polysaccharides are polymers of monosaccharides composed of glycosyl units,
usually referring to linear or branched arrangements with more than 20 monosaccharide
units. Polysaccharides are categorized in two groups base on their monosaccharides
compositions. Polysaccharide with more than one kind of monosaccharide is called a
heteroglycan or heteropolysaccharide. When a polysaccharide contains of only one type
of monosaccharide, it is called a homoglycan or homopolysaccharide. Examples of
homoglycans are starch amylose and amylopectin. Polysaccharides play two important
functions for living organisms: they form the supporting structures and protection of
12
living organisms and they act as the storage energy source. In living cells, maintaining
proper osmotic pressure is necessary to keep cells functioning well.
Starch is the main dietary available carbohydrate in foods which is present in
grains, tubers, and some fruits and vegetables as storage energy polysaccharides. The
structure and composition of amylose and amylopectin is different among different plant
(Kennedy et al., 1995). Starches contain 10,000 to over 1 million glucosyl units.
Carbohydrates are common components of foods, both naturally and as added
ingredients. They can be chemically, enzymatically, and physically modified to improve
their properties and functions. Sweeteners, in the form of sucrose and high-fructose corn
syrup (hydrolyzed starch partially isomerized to fructose), also appear in the diet as
additives to processed foods. In starch hydrolysates, starch molecules are partly
hydrolyzed with acid and/or enzymes to produce blends containing different amounts of
glucose, maltose, and maltooligosaccharides (Strickler, 1982). Most of these hydrolyzate
products have high amount of sugar and can be used as sweeteners, while others are
designed to have a low sugar content and are used or their bulking property. The variation
in the composition of carbohydrate components and how they are metabolized in the
body makes it important to understand the mechanisms driving their fate in the digestive
system.
2.2 Carbohydrate Digestion
According to the Dietary Guidelines for Americans 2010 (USDA), dietary
carbohydrates should provide 45-65% of total caloric intake. For the available
13
carbohydrates that are digested to glucose in the small intestine and utilized by the body
for energy, there exist variation concerning the rate and extent of carbohydrate digestion
and absorption (Jenkins et al., 1981; Englyst et al., 2003). Chronic consumption of foods
with a fast rate of glucose production and absorption may be detrimental to health
(Ludwig, 2002). Under prolonged hyperglycemic conditions, increased intracellular
glucose levels elevate the production of mitochondrial superoxides. These superoxides
elevated the hexosamine pathway favoring energy storage by lipid synthesis (Brownlee,
2001). On the contrary, glucose released at a slow rate is likely associated with beneficial
health effects by reducing the risk factors for chronic diseases (Jenkins et al, 2002). How
to make glucose release more slowly along the gastrointestinal tract is one of the
important keys to the carbohydrate nutrition and human health. Although there are some
techniques that have been developed to make slowly digestible starches by focusing on
the food itself, the regulation of carbohydrate digestion and absorption into the body at
the small intestine enterocyte is also an important side to consider. A deep understanding
of the carbohydrate digestion process in the gastrointestinal tract and the regulation
mechanisms involved will be another basis for developing novel techniques for glycemic
control.
2.2.1 α-Amylase
Food starches, commercial starch-based products, and the disaccharides of
sucrose and lactose are the important nutritional carbohydrates for mammalian species,
which have to be digested to their respective monosaccharides and absorbed first in order
to be utilized by the body’s cells or tissues. Carbohydrate digestion in the human involves
14
the function of several enzymes. Salivary α-amylase is secreted in the buccal cavity to
start the carbohydrate digestion (Gropper et al., 2004). α-amylase is an endoamylase with
ability to hydrolyze the α-1,4 glucosidic linkages of amylose and amylopectin. The
product of α-amylase is shorter linear chains form, i.e. maltose, maltotriose,
maltotetraose, maltopentose, and α-limit dextrins (Semenza et al., 2001).
The pancreas also produces α-amylase in the small intestine. Pancreatic αamylase plays important role in starch digestion (Lebenthal, 1987). This enzyme
continues to hydrolyze the starches (and glycogen), forming the same collection of linear
maltooligosaccharides and α-limit dextrins (Figure 2).
The branched α-limit dextrins are usually four to nine glucosyl units long and
contain one or more α-1,6 branches. The two glucosyl residues that contain the α-1,6glycosidic bond eventually become in digestion the disaccharide isomaltose, but αamylase itself does not cleave these branched oligosaccharides all the way down to
isomaltose. α-Amylase has no activity toward sugar-containing polymers other than
glucose linked by α-1,4-bonds. α-Amylase also has little activity for the α-1,4-bond at the
non-reducing end of a chain. Digestion of these branched linkage structures, as well as
the linear maltooligosaccharides produced from α-amylase digestion of starch, is at the
level of the intestinal glucosidases in the small intestine.
2.2.2 Intestinal α-Glucosidases
After the initial hydrolysis of starch by α-amylase, the products, and sucrose and
lactose, are further digested by integral glycosidases found in the brush border
15
membranes of small intestinal enterocytes. These enzymes are MGAM, SI, and trehalase.
The substrates for each enzyme and their products are shown in Table 1.2 (Gray et al.,
1979; Gropper et al., 2004; Levin, 1994; Quezada-Calvillo et al., 2007b).
MGAM and SI each contain two catalytic subunits (Figure 1.3), an N-terminal
subunit (maltase and isomaltase) bordering with the membrane and a C-terminal
luminally-oriented subunit (glucoamylase and sucrase).
The MGAM and SI subunits are 60% identical in amino acid sequence. In
addition, MGAM and SI are within complexes 40% identical. It has been suggested that
MGAM and SI evolved through duplication of an ancestral gene (Nichols et al. 2003).
Further, the catalytic site signature sequence WiDMNE is characteristic of GH31
subgroup 4 members (Ernst et al. 2006). Both MGAM and SI comprise a small cytosolic
domain, a transmembrane domain (TMD), an O-glycosylated linker (O-link), and two
homologous catalytic subunits (maltase, glucoamylase, isomaltase, sucrase) (Jones et al.
2011).
Galand (1989) reported that MGAM contributes about 20% of the maltase activity
and SI contributes 80% in the small intestine. He demonstrated that sucrase plays an
important role in hydrolyzing intermediate α-amylolyzed products of starch digestion in
the small intestine. This is because mammals express SI more abundantly than MGAM
(Quezada-Calvillo et al., 2007).
Both MGAM and SI have exo-hydrolytic activity on α-1,4 (Gray et al., 1979;
Heymann et al., 1995; Robayo-Torres et al., 2006) and α-1,6 glucosidic linkages (Sim et
al., 2010). However, isomaltase has much higher hydrolytic activity on the branch
16
linkage than maltase (Lin et al., 2012). The glucoamylase subunit of MGAM has much
higher maltase activity than does maltase itself (Lee at al., 2014). These enzymes also
have different hydrolysis properties on different chain-length oligosaccharides (Heymann
et al., 1994) and α-glucosidic linkages (Lin et al., 2012). Quezada-Calvillo et al. (2007a)
observed that MGAM become the dominant enzymes when substrate concentration is
low.
2.2.3 Sucrase-Isomaltase
Because this thesis focuses on SI processing, and not that of MGAM, more detail
about SI is provided. The SI mRNA in human contains 5481 nucleotide bases which
translates a 1827 amino acid SI polypeptide (Hunziker et al., 1986). The primary
structure of SI contains five different domains, including cytoplasmic segment, a single
membrane-spanning anchor, a glycosylated stalk, isomaltase subunit and sucrase subunit
(Semenza, 1979) (Figure 1.4).
SI is synthesized in a single chain precursor form. The N-terminal subunit of this
precursor form includes the isomaltase domain and the C-terminal subunit includes the
sucrase subunit. After SI precursor is anchored to the brush border membrane with its Nterminal subunit, SI will be cleaved into the two catalytic subunits denoting the mature
form enzyme complex (Hauri et al., 1982; Naim et al., 1988a; Naim et al., 1988b). The
isomaltase subunits of SI represent the brush border membrane-bound ends. Figure 1.4
shows the way SI are anchored to the brush border (Sim et al., 2008).
17
Both subunits of SI are heavily N- and O-glycosylated with the N-glycosylation
occurring at 16 potential N- and O-glycosylation sites (Naim et al., 1988). In addition, the
stalk domain contains more Ser/Thr-rich sites for glycosylation in the vicinity of the
membrane (Hauri et al., 1982; Naim et al., 1988b; Gorvel et al., 1991). The fully
glycosylated precursor (pro-Slc, 245 kDa) is transported to the microvillus apical
membrane where it is cleaved by trypsin (Naim et al., 1988) into sucrase and isomaltase
which remain strongly associated with each other through non-covalent ionic interactions
(Semenza, 1979).
A high mannose intermediate (O-linked glycon) of SI or pro-SIh has a molecular
weight of 210 kDa produced by rapid processing by rough endoplasmic reticulum
membrane-bound glycosidases. SI is then glycosylated in the Golgi apparatus by
additional O-linked and N-linked glycons to its mature form, or pro-SIc, (Mw=245 kDa)
(Kornfeld and Kornfeld, 1985). A study on individual subunits of SI shows that the
glycosylated isomaltase subunit has a molecular weight of 145 kDa and glycosylated
sucrase subunit of 130 kDa. Both sucrase and isomaltase contain eight N-linked glycan
units. Moreover, the mature form of sucrase comprises at least four populations varying
in their content of O-linked glycans (Naim et al., 1988).
N- and O-Glycosylation are essential co- and posttranslational modifications that
are associated with the folding, trafficking, and biological function of SI (Varki, 1993).
N- and O-linked glycosylation are involved in trafficking and polarized sorting events
beyond the endoplasmic reticulum. SI is associated with membrane micro-domains at the
enterocyte apical membrane (detergent-resistant membranes or lipid rafts) and
18
glycosylation is a key factor in this pathway mechanism (Scheiffele et al., 1995; Hein et
al., 2009). In addition to their role in trafficking mechanisms,
O-glycosylation is also implicated in enhancement of the enzymatic activity of SI in lipid
rafts (Wetzel et al., 2009).
The catalytic centers of isomaltase and sucrase are the aspartate residues 505 and
1394 within the motif WDMNE (Hunziker et al., 1986). These residues and the entire
catalytic regions are surrounded by several potential N- and O-glycosylation sites that
likely affect the final conformation of Sl and, thus, would play a role in modulating the
glycosyl hydrolase activities (Hoefsloot et al., 1988).
2.3 Carbohydrate Absorption
The absorption of monosaccharides in the small intestines can be devided into two
phases, the movement of monosaccharides across the brush border membrane and the
movement of monosaccharides across the basolateral membrane.
2.3.1 Monosaccharide Absorption
2.3.1.1 Movement of Monosaccharides Across the Brush Border Membrane
There are three mechanisms for monosaccharide absorption from the lumen
across the brush border membrane of the small intestinal enterocytes: active transport,
facilitated diffusion, and passive diffusion (Levin, 1994).
19
In active transport, molecules transfer across a membrane against a concentration
gradient using a carrier protein. The active transport requires adenosine triphosphate as
energy source. Glucose and galactose use sodium-dependent glucose transporter
(SGLT1) for transportation across intestinal enterocytes membrane.
Facilitated diffusion requires a carrier to transport molecules across a membrane
without energy expenditure. This diffusion is triggered by concentration difference of the
transported molecules. Glucose transporter 5 (GLUT5) is an example of facilated
diffusion which transport fructose from the lumen of the small intestine into the small
intestinal enterocyte cells (Dehnam and Levin, 1975; Dawson et al., 1987).
Passive diffusion is another transport system which is also caused by a
concentration difference. These concentration gradients in molecules permeate the
movement from high concentration to low concentration side of membrane. Passive
diffusion does not require carrier proteins (Levin, 1994).
2.3.1.2 Movement of Monosaccharides Across the Basolateral Membrane
After monosaccharides transport into small intestinal enterocytes cells, another
facilitative glucose transporter, GLUT2, transports these sugars across the basolateral
membrane into the circulation system going to the liver. GLUT2 transfers all available
glucose, fructose, and galactose out of the enterocyte into the intracellular space and into
circulation (Thorens et al., 1992). The classical model of monosaccharide absorption is
shown in Figure 1.5.
20
Another potential diffusive mechanism of glucose has been suggested. This
involves intercellular movment of nutrients and ions in the small intestine where
diffusion occurs into circulation system through tight junctions between enterocytes
(Kellett et al., 2008). The mechanism is that glucose transport via SGLT1 which induces
a cytoskeletal rearrangement of the enterocyte involving restructuring tight junction and
openning of the intercellular spaces. It is believed that this mechanism of glucose
absorption could contribute 75% of all glucose absorption, but it is not able to be detected
in vivo (Pappenheimer and Reiss, 1987). In this model, GLUT2 can be inserted into
apical membrane of the enterocyte when there is high luminal sugar concentration, and
has a role in sugar absorption (AU et al., 2002; Kellett and Brot-Laroche, 2005).
2.4 Carbohydrate Sensing
The sense of taste is important in assessing foods’ nutritive quality. Mammals can
detect and differentiate between five taste qualities: sweet, bitter, sour, salty, and umami.
While all tastes are important, sweet and bitter tastes are particularly significance in
obtaining the carbohydrates, and avoiding potentially hurmful substrates like alkaloids
and other bitter tasting toxins (Breslin, 2006).
The sweet taste receptor is present throughout GI tract and has the ability to sense
carbohydrates by a single heterodimeric receptor, T1R2/T1R3 (Hoon et al., 1999; Adler
et al., 2000). Another protein which has the sugar sensing ability is GLUT2. As a basallateral transporter for monosaccharide to the blood stream, GLUT2 is a sugar sensor
which causes increase in the expression of SI in the presence of glucose and fructose (Le
Gall et al., 2007). Recently, there has been work on the sweet taste receptor and its
21
importance in regulation of carbohydrate digestion and absorption (Treesukoso et al.,
2011). In the next section this receptor will be described in more detail.
2.4.1 Sweet Taste Receptor
The sweet taste receptor belongs to the G protein-coupled receptors (GPCR)
family characterized by a large extracellular amino terminal domain, which is linked by a
cysteine-rich domain to the 7TM domain (Xu et al., 2004).
Sweet taste receptor has two subunits (T1R2 and T1R3) which form a
heterodimer and can detect wide range of compounds that humans describe as “sweet”,
including natural and artificial sweeteners and some proteins (Li et al., 2002). Thus, the
T1R2/T1R3 receptor is assumed to be involved in sweet sensing. Key components of the
lingual sweet taste transduction mechanism are also expressed in the small intestinal
epithelium and, thereby, constitute a putative molecular mechanism for detection of
intestinal carbohydrates (Nelson et al., 2001). These findings postulate a potential
mechanism through which carbohydrates trigger vagal and hormonal feedback to alter
gastric motor function.
There have been different mechanisms suggested for sugar sensing by the sweet
taste receptor. Margolskee et al. (2007) proposed a mechanism by which sweet taste
receptors in the small intestine act through T1R2 and T1R3 subunits. The main
components of the sweet taste receptor pathways are the α, β, and γ subunits of gustducin
(Damak et al., 2003), phospholipase Cβ2 (PLCβ2), and transient receptor potential
channel type M5, a Ca2+-activated cation channel (Mace et al., 2007). Sweet compounds
22
likely bind to heterodimer T1R2/T1R3 and activate one or more GPCR. α-Gustducin
activates a specific second messenger cascades phospholipase C (PLCβ2) which
increases intracellular Ca2+ concentration. Increased Ca2+ triggers the transient receptor
potential (TRP) family TRPM5 and causes significant expression of gastrointestinal
peptides (i.e., GLP-1 and GIP) into the basolateral portion of the cell (Damak et al.,
2003).
2.4.1.1 Carbohydrate Sensing by the Sweet Taste Receptor
In appetite studies, maltose sensing was recently reported at the site of the sweet
taste receptor T1R2 and T1R3 subunits. This suggestion is based on the observation that
T1R2 and/or T1R3 knockout mice show no preference for maltose compared to wild type
mice (Treesukoso et al., 2011). In addition, rodents showed preferences for flavored
solutions that were paired with concurrent intragastric infusions of sugars or
maltodextrins (Allen et al., 1966; Catonguay et al., 1985; Collier et al., 1968).
The sweet taste receptor can bind to different carbohydrates and this, in turn, has
some effect on regulation of carbohydrate digestion and absorption. The expression of
glucose transporters SGLT1 and GLUT2 in the small intestine increase sugar absorption
during a meal (Booth et al., 1985; Cabanac and Johnson, 1983) Importantly, the upregulation of sugar transporters can be increased by intragastric infusions of both
nutritive and non-nutritive sugars (Cangan and Maller, 1974; Cantor and Eichler, 1977).
Gut sweet receptors also cause the release of incretin hormones (i.e., GLP-1 and GIP) in
mice. This is based on a study that T1R3 knockout mice show decrease in GLP-1 release
(Kokrashvili et al., 2009).
23
Sugar sensing by the sweet taste receptor is supported by behavioral studies of
rodents showing that starch taste (including starch-derived maltooligosaccharide) is
distinct from sugar (mainly sucrose) taste (Sclafani, 1987). Recent studies (Zukerman et
al., 2009) showed that T1R3, one of the subunits of the sweet taste receptor, is critical for
sucrose sensing, but was not related to maltodextrin (Polycose™) sensing. The sensing
receptor candidates are GPCRs composed of T1R2 and T1R3 (Bachmanov et al., 2001;
Max et al., 2001; Nelson et al., 2001). Additionally, genetically engineered mice missing
one of the two subunits of the sweet taste receptor in the gut (either T1R2 or T1R3) have
displayed very weak concentration-dependent responses to maltose, maltotriose, and
maltotetraose, as measured by brief-access taste tests and electrophysiological methods
(Treesukoso et al., 2011).
2.4.2 Dietary Carbohydrate Sensing and α-Glucosidase Enzymes
Recent studies have been focused on the effects of different dietary carbohydrates
on α-glucosidase expression and activity. Among the monosaccharides, fructose
influenced expression and rate of small intestine enterocyte enzymes and transporters
(Goda, 2000). Sugar sensing is a complex process involving sugar transport, nutrient
metabolism, and receptors on cell membranes throughout gastrointestinal track. Dietary
sucrose and fructose have been shown to up-regulate the transcription of SI, while
glucose down-regulates SI expression (Goda, 2000) by affecting the binding of
hepatocyte nuclear factors (HNF)-1a to its promoter (Gu et al., 2007). The transcription
factor Cdx-2 is also important for the expression of the SI gene, and a change of histone
H3 modification for Cdx-2 binding is observed during the transition of enterocytes from
24
the crypt to the villi (Suzuki et al., 2008). The expression of the SI gene induced by a
high carbohydrate, low fat diet was related to acetylation of histone H3 and H4 as well as
binding of HNF-1 and Cdx-2 (Honma et al., 2007).
Recently, maltose was shown to induce the formation of a higher molecular
weight (HMW) SI species in Caco-2 cells (Cheng et al., 2014). Immunoblotting showed
that most of the sugar treatments had a positive result on SI protein synthesis, but only
maltose treatment showed synthesis of a second higher Mw protein band that was
concentration-dependent. Protein sequencing of HMW SI using MALDI-TOF confirm
that the HMW SI has the same sequence as SI in other carbohydrate treatments. Since SI
is a glycoprotein that requires post-translational modification to add glycans in the Golgi
apparatus for full processing and trafficking of the enzyme complex (with a higher
molecular weight), the second band is speculated from this work to be the mature SI
enzyme, to coincide with the digestion of the disaccharide of maltose.
25
Table 2.1 Classification of monosaccharides (Fennema, 2005).
Number of
Kind of carbonyl group
carbon atoms
Aldehyde
Ketone
3
Triose
Triulose
4
Tetrose
Tetulose
5
Pentose
Pentulose
6
Hexose
Hexulose
7
Heptose
Heptulose
8
Octose
Octulose
9
Nonose
Nonulose
26
Table 2.2 Substrates and products of small intestinal glycosidase enzyme.
Hydrolysis
Enzyme
Substrate(s)
Lactase-phlorizin
Lactose
β-1,4 linkage
Glucose, Galactose
Trehalase
Trehalose
α-1,1 linkage
Glucose
Sucrase-isomaltase
Sucrose
α-1,2 linkage of
Glucose, Fructose
linkage(s)
Product(s)
hydrolase
sucrose
Linear and branched
α-1,4 & α-1,6
oligosaccharides
linkages of starch
Maltase-
Linear or branched
Mainly α-1,4
glucoamylase
oligosaccharides
linkage
Glucose
Glucose
27
(a)
(b)
(c)
Figure 1.1. The disaccharides lactose (a), sucrose (b), and
Figure 2.1 The structuresmaltose
of common
dietary disaccharids
(c) (Fennema,
2005) lactose (a), sucrose (b), and
maltose (c) (Fennema, 2005).
28
!
Starch
Linear Oligosaccharides
α-amylase
Maltase
α-1,4
Branched
Oligosaccharides
Isomaltase
Glucoamylase
α-1,4
Sucrase
α-1,4
α-1,4
α-1,6
α-1,2
Glucose
!
Figure'1)1:'Schematic'diagram'of'the'process'of'starch'digestion'by'α)amylase,'maltase)
Figure 2.2 Schematic diagram of the
glucoamylase,'and'sucrase'isomatase.'
action of salivary and pancreatic α-amylases on
starch.
SI!demonstrates!hydrolytic!activity!on!branched!α@1,6!linkages,!which!is!complemented!by!
the!hydrolytic!activity!of!both!SI!and!MGAM!on!α@1,4!linkages!(Quezada@Calvillo!et!al.!2008);!
however,!the!main!substrate!of!SI!is!the!α@1,4!linkage!(Nichols!et!al.!2003).!The!complementary!
activities!of!the!human!enzymes!allow!for!digestion!of!the!spectrum!of!food!starches!from!more!
than!400!plants!that!comprise!two@thirds!of!most!human!diets;!SI!is!more!abundant!but!MGAM!
displays!higher!hydrolytic!activities!(Heymann!and!Günther!1994;!Heymann,!Breitmeier,!and!
Günther!1995;!Robayo!Torres,!Quezada!Calvillo,!and!Nichols!2006).!In!studies!of!severely!
malnourished!infants,!it!has!been!demonstrated!that!the!terminal!steps!of!MGAM!α@1,4!and!SI!α@
1,6!starch!digestion!are!decreased.!The!loss!of!MGAM!message!parallels!the!reduction!in!villin!
message!and!degree!of!villous!atrophy,!and!also!parallels!reduction!in!the!SI!message.!It!has!also!
been!shown!that!the!correlations!between!the!two!enzyme!activities!(SI!and!MGAM)!exist!at!the!
mRNA!level!(Nichols!et!al.!2000).!Recent!studies!demonstrate!that!feeding!rats!a!high!
fat/carbohydrate!ratio!diet!reduces!the!jejunal!sucrase/isomaltase!(S/I)!activity!ratio.!In!
addition,!the!SI@linked!oligosaccharides!show!modifications!of!unsialylated!galactose!as!well!as!
the!mannose@linked!form!when!the!S/I!activity!ratio!changes,!and!thus!is!thought!to!be!
regulated!by!altering!the!glycosylation!of!SI!(Samulitis@Dos!Santos,!Goda,!and!Koldovsky!1992;!
!
2!
29
TMD
O-link
Maltase
Glucoamylase
Small intestinal
lumen
Cytosol
TMD
O-link
Isomaltase
Sucrase
Figure 2.3 Position and protein organization of the MGAM and SI complex. TMD:
transmembrane domain; O-link: O-glycosylated linker (adapted from Sim et al., 2008).
30
CHAPTER 27 ■ DIGESTION, ABSORPTION, AND TRANSPORT O
Can
struc
lyzed
C
Sucrase
CH2OH
O
C
OH
N
OH
Isomaltase
Connecting
segment (stalk)
Transmembrane
segment
N
Sucrase–
isomaltase
Cytoplasmic
domain
FIG. 27.5. The major portion of the sucrase–isomaltase complex, containing the catalytic
sites,2.4
protrudes
from theofabsorptive
cells into the lumen
of the intestine.
Other2013).
domains of
Figure
The structure
the sucrase-isomaltase
complex
(Lieberman,
the protein form a connecting segment (stalk) and an anchoring segment that extends through
the membrane into the cell. The complex is synthesized as a single polypeptide chain that is
split into its two enzyme subunits extracellularly. Each subunit is a domain with a catalytic
site (distinct sucrase–maltase and isomaltase–maltase sites). In spite of their maltase activity,
these catalytic sites are often called just sucrase and isomaltase.
polypeptide chain forms two globular domains, each with a catalytic site. In glucoamylase, the two catalytic sites have similar activities, with only small differences
in substrate specificity. The protein is heavily glycosylated with oligosaccharides
that protect it from digestive proteases.
Glucoamylase is an exoglucosidase that is specific for the !-1,4-bonds between
glucosyl residues (Fig. 27.6). It begins at the nonreducing end of a polysaccharide or limit dextrin and sequentially hydrolyzes the bonds to release glucose
monosaccharides. It will digest a limit dextrin down to isomaltose, the glucosyl
disaccharide with an !-1,6-branch, which is subsequently hydrolyzed principally
by the isomaltase activity in the sucrase–isomaltase complex.
2. SUCRASE–ISOMALTASE COMPLEX
The structure of the sucrase–isomaltase complex is very similar to that of
Acar
that
atic
!-glucosidases
The drug is pr
and is a uniqu
given to patien
purpose of redu
carbohydrate r
a meal. This is
blood glucose
loss has not
this drug, but f
colonic bacteri
side effects of
α
HO
1
O
31
G
Ga
SGLT1
G
GLUT5
Ga
GLUT2
G
F
Ga
F
GLUT2
F
Blood
Figure 2.5 The absorption and transfer out of sugars by the enterocyte. G, glucose; Ga,
galactose; F, fructose (adapted from Paulev, 1999).
32
2.5 References
Adler, E., Hoon, M. A., Mueller, K. L., Chandrashekar, J., Ryba, N. J., & Zuker, C. S.
(2000). A novel family of mammalian taste receptors. Cell, 100(6), 693-702.
Allen, R. J. L., & Leahy J. S. (1966). Some effects of dietary dextrose, fructose, liquid
glucose and sucrose in the adult male rat. British Journal of Nutrition, 20(02), 339-347.
Atkinson, F. S., Foster-Powell, K., & Brand-Miller, J.C. (2008). International tables of
glycemic index and glycemic load values: 2008. Diabetes Care, 31(12), 2281-2283.
Au, A., Gupta, A., Schembri, P., & Chesseman, C. I. (2002). Rapid insertion of GLUT2
into the rat jejunal brush-border membrane promoted by glucagon-like peptide 2.
Biochemical Journal, 367, 247-254.
Bachmanov, A. A., Li X., Reed, D. R., Ohmen, J. D., Li S, Chen, Z., Tordoff, M. G., de
Jong, P. J, Wu C, West, D. B., Chatterjee, A., Ross, D. A., & Beauchamp, G. K. (2001).
Positional cloning of the mouse saccharin preference (Sac) locus. Chemical Senses,
26(7), 925-933.
Beaulieu, J.-F., Nichols, B., & Quaroni, A. (1989). Posttranslational regulation of
sucrase-isomaltase expression in intestinal crypt and villus cells. Journal of Biological
Chemistry, 264(33), 20000-20011.
Berg, J. M., Tymoczko, J. L., & Stryer, L. (2002). Biochemistry 5th edition. W. H.
Freeman and Company. New York. 295 – 303.
Booth, D. A., Lovett, D., & McSherry, G. M. (1972). Postingestive modulation of the
sweetness preference gradient in the rat. Journal of Comparative and Physiological
Psychology, 78(3), 485-512.
Breslin, P. A., & Huang, L. (2006). Human taste: peripheral anatomy, taste transduction,
and coding. Adv. Otorhinolaryngol, 63,152-90.
Brownlee, M. (2001). Biochemistry and molecular cell biology of diabetic complications.
Nature, 414(6865), 813-20.
Cabanac, M., & Johnson, K. G. (1983). Analysis of a conflict between palatability and
cold exposure in rats. Physiology & Behavior, 31, 249-253.
33
Cagan, R. H., & Mailer, O. (1974). Taste of sugars: Brief exposure single-stimulus
behavioral method. Journal of Comparative and Physiological Psychology, 87(1), 47-55.
Cantor, M. B., & Eichler, R. J. (1977). Sweetness: A supernormal reinforcer. Chemical
Technology, 7, 214-216.
Castonguay, T. W., Philips, S., & Collier, G. H. (1985). Sucrose procurement cost and
dietary selection. Nutrition & Behavior, 2(4), 201- 21.
Cheng, M. W., Chegeni, M., Kim, K. H., Zhang, G. Y., Benmoussa, M., QuesadaCalvillo, R., Nichols, B. L., & Hamaker, B. R. (2014). Different sucrose-isomaltase
response of Caco-2 cells to glucose and maltose suggests dietary maltose sensing.
Journal of Clinical Biochemistry and Nutrition, 54(1), 55-60.
Collier, G., & Bolles, R. (1968). Some determinants of intake of sucrose solutions.
Journal of Comparative and Physiological Psychology, 65(3p1), 379-383.
Conklin, K. A., Yamashiro, K. M., & Gray, G. M. (1975). Human intestinal sucraseisomaltase. Identification of free sucrase and isomaltase and cleavage of the hybrid into
active distinct subunits. Journal of Biological Chemistry, 250(15), 5735-5741.
Dahlqvist, A., & Borgstrom, B. (1959). Characterization of intestinal invertase as a
glucosido-invertase. II: Studies on transglycosylation by intestinal invertase. Acta
Chemica Scandinavica, 13(3), 1659-1667.
Damak, S., Rong, M., Yasumatsu, K., Kokrashvili, Z., Varadarajan, V., Zou, S., Jiang, P.,
Ninomiya, Y., & Margolskee, R. F. (2003). Detection of sweet and umami taste in the
absence of taste receptor T1r3. Science, 301, 850-853.
Dawson, D. J., Burrows, P. C., Lobley, R. W., & Holmes, R. (1987). The kinetics of
monosaccharide absorption by human jejunal biopsies: evidence for active and passive
processes. Digestion, 38(2), 124-132.
Dehnam, E. S., & Levin, R. J. (1975). An experimental method of identifying and
quantifying the active transfer of electrogenic component from the diffusive component
during sugar absorption measured in vivo. Journal of Physiology, 246(1), 181-196.
Englyst, H. N., Kingman, S. M., & Cummings, J. H. (1992). Classification and
measurement of nutritionally important starch fractions. European Journal of Clinical
Nutrition, 46, S33-S50.
34
Englyst, K. N., Vinoy, S., Englyst, H. N., & Lang, V. (2003). Glycaemic index of cereal
products explained by their content of rapidly and slowly available glucose. British
Journal of Nutrition, 89(3), 329-339.
Ernst, H. A., & Lo Leggio L. (2006). Structure of the Sulfolobus solfataricus αGlucosidase: Implications for Domain Conservation and Substrate Recognition in GH31.
Journal of Molecular Biology, 358(4), 1106-1124.
Fennema, O.R. (2007). Fennema’s Food Chemistry, (fourth ed.), CRC Press, 1170p.
ISBN:0849392721.
Freeze, H. H. (1999). Monosaccharide Metabolism. Essentials of Glycobiology, 69-84.
Goda, T. (2000). Regulation of the expression of carbohydrate digestion/absorptionrelated genes. British Journal of Nutrition, 84(S2), S245-S248.
Gorvel, J. P., Ferrero, A., Chambraud, L., Rigal, A., Bonicel, J., & Maroux, S. (1991).
Expression of Sucrase-Isomaltase and Dipeptidylpeptidase-Iv in Human Small-Intestine
and Colon. Gastroenterology, 101(3), 618-625.
Grand, R. J., Watkins, J. B., & Torti, F. M. (1976). Development of the human
gastrointestinal tract. Gastroenterology, 70(5), 790-810.
Gray, G. M., Lally, B. C., & Conklin, K. A. (1979). Action of intestinal sucraseisomaltase and its free monomers on an α-Limit Dextrin. Journal of Biological
Chemistry, 254(13), 6038-6043.
Gropper, S. S., Smith, J. L., & Groff, J. L. (2004). The digestive system: Mechanism for
nourishing the body. In: Advanced Nutrition and Human Metabolism.
Thomson/Wadsworth, California. 4th ed. 24-53.
Gu, N., Adachi, T., Matsunaga, T., Tsujimoto, G., Ishihara, A., Yasda, K., & Tsuda, K.
(2007). HNF-1α participates in glucose regulation of sucrase-isomaltase gene expression
in epithelial intestinal cells. Biochemical and Biophysical Research Communications,
353(3), 617-622.
Hauri, H. P., Wacker, H., Rickli, E. E., Bigler-Meier, B., Quaroni, A., & Semena, G.
(1982). Biosynthesis of sucrase-isomaltase Purification and NH2-terminal amino acid
sequence of the rat sucrase-isomaltase precursor (pro-sucrase-isomaltase) from fetal
intestinal transplants. Journal of Biological Chemistry, 257(8), 4522-4528.
35
Heine, M., Rohwedder, A., & Naim, H. Y. (2009). Impact of glycosylation and detergent
resistant membranes on the function of intestinal sucrase-isomaltase. Biological
Chemistry, 390(7), 545-549.
Heymann, H., & Günther, S. (1994). Calculation of subsite affinities of human small
intestinal glucoamylase–maltase. Biological Chemistry Hoppe-Seyler, 375(7), 451–455.
Heymann, H., Breitmeier, D., & Günther, S. (1995). Human small intestinal sucrase–
isomaltase: different binding patterns for malto- and isomaltooligosaccharides.Biological
Chemistry Hoppe-Seyler, 376(4), 249–253.
Hoefsloot, L. H., Hoogeveenwesterveld, M., Kroos, M. A., Vanbeeumen, J., Reuser, A. J.
J., & Oostra, B. A. (1988). Primary Structure and Processing of Lysosomal AlphaGlucosidase - Homology with the Intestinal Sucrase Isomaltase Complex. Embo Journal
7(6), 1697-1704.
Hoffman, L. R., & Chang, E. B. (1991). Determinants of regional sucrase-isomaltase
expression in adult rat small intestine. Journal of Biological Chemistry, 266(32), 2181521820.
Hoffman, L. R., & Chang, E. B. (1993). Regional Expression and Regulation of Intestinal
Sucrase-Isomaltase. Journal of Nutritional Biochemistry, 4(3), 130-142.
Holmes, R. (1971). Carbohydrate digestion and absorption. Journal of Clinical
Pathology, 5, 10-13.
Honma, K., Mochizuki, K., & Goda, T. (2007). Carbohydrate/fat ratio in the diet alters
histone acetylation on the sucrase-isomaltase gene and its expression in mouse small
intestine. Biochemical and Biophysical Research Communications, 357(4), 1124-1129.
Hoon, M. A., Adler, E., Lindemeier, J., Battey, J. F., Ryba, N. J., & Zuker, C. S. (1999).
Putative mammalian taste receptors: a class of taste-specific GPCRs with distinct
topographic selectivity. Cell, 96(4), 541-551.
Hunziker, W., Spiess, M., Semenza, G., & Lodish, H. (1986). The sucrase-isomaltase
complex: primary structure, membrane-orientation, and evolution of a stalked, intrinsic
brush border protein. Cell, 46, 227-234.
Jenkins, D., & Wolever, T. (1981). Glycemic index of foods: a physiological basis for
carbohydrate exchange. American Journal of Clinical Nutrition, 34(3), 362- 366.
36
Jenkins, D. J., & Kendall, C. W. (2002). Glycemic index: overview of implications in
health and disease. American Journal of Clinical Nutrition, 76(1), 266S- 273S.
Jones, K., & Sim, L. (2011). Mapping the intestinal alpha-glucogenic enzyme
specificities of starch digesting maltase-glucoamylase and sucrase-isomaltase. Bioorganic
& Medicinal Chemistry, 19(13), 3929-3934.
Kellett, G. L., & Brot-Laroche, E. (2005). Apical GLUT2: a major pathway of intestinal
sugar absorption. Diabetes, 54(10), 3056-3062.
Kellett, G. L., Brot-Laroche, E., Mace, O. J., & Leturque, A. (2008). Sugar absorption in
the intestine: the role of GLUT2. Annual Review of Nutrition, 28, 35- 54.
Kennedy, J. F., Knill, C. J., Taylor, D. W. (1995). Handbook of Starch Hydrolysis
Products and their Derivatives. Blackie Academic., London. 65–82.
Kitagawa, M., Kusakabe, Y., Miura, H., Ninomiya, Y., & Hino, A. (2001). Molecular
genetic identification of a candidate receptor gene for sweet taste. Biochemical and
Biophysical Research Communications, 283(1), 236-242.
Kokrashvili, Z., Mosinger, B., & Margolskee, R. F. (2009). T1r3 and alpha-gustducin in
gut regulate secretion of glucagon-like peptide-1. Annals of the New York Academy of
Sciences, 1170(1), 91-94.
Kornfeld, R., & Kornfeld, S. (1985). Assembly of asparagine-linked oligosaccharides.
Annual Review of Biochemistry, 54(1), 631-664.
Le Gall, M. L., Tobin, V., Stolarczyk, E., Dalet, V., Leturque, A., & Brot-Laroche, E.
(2007). Sugar sensing by enterocytes combines polarity, membrane bound detectors and
sugar metabolism. Journal of Cellular Physiology, 213(3), 834-843.
Lebenthal, E. (1987). Role of salivary amylase in gastric and intestinal digestion of
starch. Digestive Disease and Science, 32(10), 1155-1157.
Lee, M. F., Russell, R. M., Montgomery, R. K., & Krasinski, S. D. (1997). Total
intestinal lactase and sucrase activities are reduced in aged rats. Journal of Nutrition,
127(7), 1382-1387.
37
Lee, B. H., Lin, A. H., Nichols, B. L., Jones, K., Rose, D. R., Quezada-Calvillo, R., &
Hamaker, B. R. (2014). Mucosal C-terminal maltase-glucoamylase hydrolyzes large size
starch digestion products that may contribute to rapid postprandial glucose generation.
Molecular Nutrition & Food Research, 58(5), 1111-1121.
Levin, R. (1994). Digestion and absorption of carbohydrates-from molecules and
membranes to humans. American Journal of Clinical Nutrition, 59(3), 690S-698S.
Li, X., Staszewski, L., Xu, H., Durick, K., Zoller, M., & Adler, E. (2002). Human
receptors for sweet and umami taste. Proceedings of the National Academy of Sciences
USA, 99(7), 4692-4696.
Lin, A. H.-M., Lee, B.-H., Nichols, B. L., Quezada-Calvillo, R., & Hamaker, B. R.
(2012). Starch source influences dietary glucose generation at the mucosal α-glucosidase
level. Journal of Biological Chemistry, 287(44), 36917–36921.
Ludwig, D. S. (2002). The glycemic index. Journal of the American Medical Association,
287(18), 2414-2423.
Mace, O. J., Affleck, J., Patel, N., Kellett, G. L. (2007). Sweet taste receptors in rat small
intestine stimulate glucose absorption through apical GLUT2. Journal of Physiology,
582(1), 379-392.
Margolskee, Robert F., Jane Dyer, Zaza Kokrashvili, Kieron SH Salmon, Erwin Ilegems,
Kristian Daly, Emeline L. Maillet, Yuzo Ninomiya, Bedrich Mosinger, and Soraya P.
Shirazi-Beechey. (2007). T1R3 and gustducin in gut sense sugars to regulate expression
of Na+-glucose cotransporter 1. Proceedings of the National Academy of Sciences USA,
104(38), 15075-15080.
Max, M., Shanker, Y. G., Huang, L., Rong, M., Liu, Z., Campagne, F., Weinstein, H.,
Damak, S., & Margolskee, R. F. (2001). Tas1r3, encoding a new candidate taste receptor,
is allelic to the sweet responsiveness locus Sac. Nature Genetics, 28(1), 58-63.
Naim, H. Y., Sterchi, E. E., & Lentze, M. J. (1988a). Biosynthesis of the human sucraseisomaltase complex. Differential O-glycosylation of the sucrase subunit correlates with
its position within the enzyme complex. Journal of Biological Chemistry, 263(15), 72427253.
Naim, H. Y., Sterchi, E. E., & Lentze, M. J. (1988b). Structure, biosynthesis, and
glycosylation of human small intestinal maltase-glucoamylase. Journal of Biological
Chemistry, 263(36), 19709-19717.
38
Nelson, G., Hoon, M. A., Chandrashekar, J., Zhang ,Y., Ryba, N. J., & Zuker, C. S.
(2001). Mammalian sweet taste receptors. Cell, 106(3), 381-390.
Nichols, B. L., Avery, S., Sen, P., Swallow, D. M., Hahn, D., & Sterchi, E. (2003). The
maltase-glucoamylase gene: common ancestry to sucrase-isomaltase with complementary
starch digestion activities. Proceedings of the National Academy of Sciences USA,
100(3), 1432-1437.
Pappenheimer, J.R., & Reiss, K. Z. (1987). Contribution of solvent drag through
intercellular junctions to absorption of nutrients by the small intestine of the rat. Journal
of Membrane Biology, 100(1), 123-136.
Paulev, P. E. (1999-2000). Gastrointestinal function and disorders. In: Medical
physiology and pathophysiology - Essentials and Clinical Problems. Copenhagen
Medical Publishers.
Quezada-Calvillo, R., Robayo-Torres, C. C., Ao, Z., Hamaker, B. R., Quaroni, A.,
Brayer, G. D., Sterchi, E. E., Baker, S. S., & Nichols, B. L. (2007a). Luminal substrate
"brake" on mucosal maltase-glucoamylase activity regulates total rate of starch digestion
to glucose. Journal of Pediatric Gastroenterology and Nutrition, 45(1), 32-43.
Quezada-Calvillo, R. Robayo-Torres, C. C., Opekun, A. R., Sen, P., Ao, Z., Hamaker, B.
R., Quaroni, A., Brayer, G. D., Wattler, S., Nehls, M. C., Sterchi, E. E., & Nichols, B. L.
(2007b). Contribution of mucosal maltase-glucoamylase activities to mouse small
intestinal starch α-glucogenesis. Journal of Nutrition, 137(7), 1725-1733.
Robayo-Torres, C. C., Quezada-Calvillo, R., & Nichols, B. L. (2006). Disaccharide
digestion: clinical and molecular aspects. Clinical Gastroenterology and Hepatology,
4(3), 276-287.
Scheiffele, P., Peranen, J., & Simons, K. (1995). N-Glycans as Apical Sorting Signals in
Epithelial-Cells. Nature, 378(6552), 96-98.
Sclafani, A. (2007). Sweet taste signaling in the gut. Proceedings of the National
Academy of Sciences USA, 104(38), 14887-14888.
Sclafani, A., & Mann, S. (1987). Carbohydrate taste preferences in rats: glucose, sucrose,
maltose, fructose and polycose compared. Physiology & Behavior, 40(5), 563-568.
Semenza, G., Auricchio, S., & Mantei, N. (2001). The Metabolic Basis of Inherited
Disease. McGraw- Hill, New York. 8th ed. 1623-1650.
39
Semenza, G. (1979). Mode of insertion of the sucrase-isomaltase complex in the brush
border membrane: Implications for the biosynthesis of the stalked intrinsic membrane
protein. In Ciba Foundation Symposium 70-Development of Mammalian Absorptive
Processes. John Wiley & Sons, Ltd. 133-146.
Sim, L., Willemsma, C., Mohan, S., Naim, H. Y., & Hamaker, B. R. (2010). Structural
basis for substrate selectivity in human maltase- glucoamylase and sucrase-isomaltase Nterminal domains. Journal of Biological Chemistry, 285(23), 17763-17770.
Sim, L., Quezada-Calvillo, R., Sterchi, E. E., Nichols, B. L., & Rose, D. R. (2008).
Human intestinal maltase–glucoamylase: crystal structure of the N-terminal catalytic
subunit and basis of inhibition and substrate specificity. Journal of Molecular Biology,
375(3), 782-792.
Strickler, A. J. (1982). Corn syrup selections in food applications. In: Food
Carbohydrates. Edited by D. R. Lineback and G. E. Inglett. Westport, CT: Avi
Publishing Co. Inc. 12-24.
Suzuki, T., Mochizuki, K., & Goda, T. (2008). Histone H3 modifications and Cdx-2
binding to the sucrase-isomaltase (SI) gene is involved in induction of the gene in the
transition from the crypt to villus in the small intestine of rats. Biochemical and
Biophysical Research Communications, 369(2), 788-793.
Thorens, B. (1992). Molecular and cellular physiology of GLUT2, a high-Km facilitated
diffusion glucose transporter. International Review of Cytology, 137(A), 209–238.
Treesukosol, Y., & Spector, A. C. (2011). The functional role of the T1R family of
receptors in sweet taste and feeding. Physiology & Behavior, 105(1), 14-26.
Trugnan, G., Rousset, M., Chantret, I., Barbat, A., & Zweibaum, A. (1987). The
posttranslational processing of sucrase-isomaltase in HT-29 cells is a function of their
state of enterocytic differentiation. Journal of Cell Biology, 104(5), 1199-1205.
Varki, A. (1993). Biological Roles of Oligosaccharides: All of the Theories Are Correct.
Glycobiology, 3(2), 97-130.
Wetzel, G., Heine, M., Rohwedder, A., & Naim, H. Y. (2009). Impact of glycosylation
and detergent-resistant membranes on the function of intestinal sucrase-isomaltase.
Biological Chemistry, 390(7), 545-549.
40
Xu, H., Staszewski, L., Tang, H., Adler, E., Zoller, M., & Li, X. (2004). Different
functional roles of T1R subunits in the heteromeric taste recep- tors. Proceedings of the
National Academy of Sciences of the United States of America USA, 101(39), 1425814263.
Zukerman, S., Glendinning, J. I., Margolskee, R. F., & Sclafani, A. (2009). T1R3 taste
receptor is critical for sucrose but not polycose taste. American Journal of PhysiologyRegulatory, Integrative and Comparative Physiology, 296(4), R866-R876.
41
CHAPTER 3. DIETARY CARBOHYDRATES INFLUENCE THE STRUCTURE AND
FUNCTION OF SUCRASE-ISOMALTASE
3.1 Introduction
Based on the Dietary Guidelines for Americans 2010 (USDA), dietary
carbohydrates should provide 45-65% of total caloric intake. Available carbohydrates,
however, are not all equal and in particular there exist significant variations in rate and
extent of carbohydrate digestion and absorption that relate to its nutritional quality
(Jenkins et al., 1981; Englyst et al., 2003). Chronic consumption of foods with fast and
high rate of glucose production and absorption may be detrimental to health (Ludwig,
2002) through stresses on glucose homeostasis controls, advanced glycation end-product
formation, and favoring energy storage by hexosamine pathway which leads to lipid
synthesis (Brownlee, 2001). This is particularly relevant for diabetics and pre-diabetics,
where control of blood glucose level is a problem. On the other hand, glucose released at
a slow rate is likely associated with health benefits through reduction of risk factors for
some diet-associated metabolic diseases (Jenkins et al., 2002). How to make glucose
release and absorption slower is one of the keys to improving carbohydrate nutrition and
its relation to human health. Although there are some techniques to make low glycemic
index (GI) foods, such as slowly digestible starches, by focusing on food factors; the
results seem to be variable. Another possible and viable strategy is to control postprandial
42
glycemic excursions through control of the carbohydrate digestion and absorption
processes. This might be through natural inhibitors to the α-glucosidases and SGLT-1, or
through another mechanism that reduces rate of glucose delivery to the body. Thus, a
fundamental and mechanistic understanding of the carbohydrate digestion and absorption
processes could be the basis for developing novel techniques for glycemic control.
Starch is the main dietary glycemic carbohydrate in foods. It is digested first by αamylase which hydrolyzes the α-1,4 glycosidic linkages between glucosyl units of linear
chains. The degradation products are composed of maltose and other oligosaccharides
with different structures features (size and both linear and branched oligomers). These
degradation products are then hydrolyzed by the brush border-anchored α-glucosidases,
MGAM and SI, to glucose for absorption. Although pancreatic α-amylase is generally
thought of as the dominant enzyme in starch digestion (Englyst et al., 1992), MGAM and
SI are the ultimate control point for glucose liberation and their activities and amount are
critical to glycemic control, as evidenced by the study of Nichols et al. (2009) showing a
decrease of 40% glucose absorption for MGAM null mice, that had only SI, compared to
the wild-type control. The mucosal α-glucosidases, and their control, are thus critical for
the health outcome of dietary glycemic carbohydrates.
Sucrase-isomaltase is a common marker for differentiated polarized small
intestinal cell cultured enterocytes. Its biosynthesis and structure have been extensively
studied. Two different subunits of SI, which are associated through ionic interaction after
protease cleavage, are present in the apical membrane of absorptive brush border villus
cells, or enterocytes, and the isomaltase subunit at the N-terminal is the site for
membrane anchoring (Hunziker et al., 1986). SI is synthesized by co-translational
43
glycosylation as a single chain precursor (‘high mannose precursor’, pro-SIh, Mw 210
kDa) that has low enzyme activity. After the precursor is processed by further
glycosylation in the Golgi apparatus, a mature ‘complex precursor’ (pro-SIc , Mw 245
kDa) containing additional N- and O-linked oligosaccharide chains is generated with full
enzyme activity (Beaulieu et al., 1989), and then it is inserted into the apical membrane
of the small intestine enterocytes through apical sorting and trafficking (Hauri, 1979).
The cleavage by trypsin, the small intestine luminal protease, generates two subunits with
different substrate specificities. The sucrase subunit (C-terminal) is known for sucrose
digestion though has other activities, and the N-terminal subunit can cleave both α-1,6
and α-1,4-linked glucans (Gray et al., 1979; Heymann et al., 1995; Robayo-Torres et al.,
2006). The integrity of the SI structure is important for its function as it was shown that
disruption of the detergent resistant membrane (DRM) with Triton X-100, the anchoring
location, substantially reduces its activity, and modification of its glycosylation pattern
decreases its association with DRMs, and also decreases enzyme activity (Wetzel et al.,
2009).
Composition of the diet (carbohydrate) influences the expression of SI, which is
termed dietary adaptation. Dietary sucrose, fructose, and starch all have been shown to
up-regulate the transcription of SI, while glucose down-regulates SI expression (Goda et
al., 2000) by increasing the binding of hepatocyte nuclear factors (HNF)-1a to its
promoter (Gu et al., 2007). In addition to gene transcription regulation, SI glycosylation
occurs at its translational and post-translational stages and is essential to the maturation
of the SI enzyme active form. Beaulieu et al (1989) studied SI expression regulation in
44
small intestine intestinal crypt and villus cells, and showed that post-translational
regulation is critical for the enzyme activity of SI in differentiated villus cells.
Sugar sensing is essential to understand the molecular mechanism of SI
expression. GLUT2, as a basolateral transport for monosaccharides to the blood stream,
is also a sugar sensor for high expression of SI (at the mRNA level). This is termed the
GLUT2-dependent signaling pathway (Le Gall et al., 2007). Another important sugar
sensor is the sweet taste receptor composed of two subunits T1R2, T1R3 and the G
protein gustducin (Gαgust) on the enteroendocrine cells of gastrointestinal tract. The
receptor can bind sucrose, artificial sweeteners (sucralose) and other sugars to stimulate
absorptive enterocyte SGLT1 expression through the function of hormones like GIP or
GLP-1secreated by enteroendocrine cells (Margolskee et al., 2007). Another study
showed the existence of T1R2, T1R3, and G protein gustducin in the enterocytes that can
sense sugar molecules, although no effect on SI expression was reported (Mace et al.,
2007). However, no receptor for maltose, one of the products from starch by α-amylase
digestion, has been reported in literature.
Recently, our laboratory showed that maltose induces the formation of a higher
molecular weight (HMW) SI species in Caco-2 cells (Cheng et al., 2014).
Immunoblotting showed that most of the sugar treatments had a positive result on SI
protein synthesis, but only maltose treatment showed synthesis of a second higher Mw
protein band that was concentration-dependent. Protein sequencing of HMW SI using
MALDI-TOF confirmed that the HMW SI has the same sequence as SI in other
carbohydrate treatments. Since SI is a glycoprotein that requires post-translational
modification to add glycans in the Golgi apparatus for full processing and trafficking of
45
the enzyme complex (with a higher molecular weight), the second band is speculated
from this work to be the mature SI enzyme, and coinciding with the digestion of the
disaccharide of maltose. Thus, the objectivw of this study was to investigate the
mechanism of the maturation and trafficking of HMW SI caused by maltose sensing on
apical membrane of intestinal enterocytes. If the mechanism of SI maturation triggered
by maltose is understood, an approach may be developed to manipulate the activity of
brush border anchored enzymes so as to modulate postprandial glycemic response for
improved health.
3.2 Materials and Methods
3.2.1 Immunochemical Reagents
Monoclonal mouse anti-SI antibodies (mAb) HSI2, HBB1/691/79, HBB2/614/88,
HBB2/219/20, and HBB3/705/60, to recognize different conformations and glycoforms
of SI, were provided by Dr. H.P. Hauri (University of Bern, Bern, Switzerland). A
mixture of these antibodies was used for immunoprecipitation of the different glycoforms
of SI. Horseradish peroxidase-coupled goat anti-rabbit and goat anti-mouse and rabbit
anti-goat antibodies were from Dako (Glostrup, Denmark). HybondTM-P PVDF
membrane was obtained from Amersham Biosciences (Freiburg, Germany), and
enhanced chemiluminescence (ECL) from Pierce (Thermo Fisher Scientific (Bonn,
Germany). Cell culture media, fetal calf serum, and antibiotics were purchased from PAA
(Pasching, Austria). Secondary antibodies coupled to Alexa Fluor dyes were obtained
46
from Invitrogen (Karlsruhe, Germany) and horseradish peroxidase-conjugated secondary
antibodies were from Thermo Fisher Scientific (Bonn, Germany).
3.2.2 Cell Culture Procedure
Caco-2 cells HTB37 (ATCC, Manassas, VA) (passage 30 to 45) were used for
this study. Cells were grown in Dulbecco’s modified Eagle’s medium (DMEM)
(Corning, Lowell, MA) supplied with 10% fetal bovine serum (FBS) (Lonza,
Walkersville, MD), 50 µg/ml gentamycin sulfate (J R Scientific Inc., Woodland, CA), 10
mM HEPES, 100 µg/ml streptomycin and 100 U/ml penicillin (Lonza, Walkersville,
MD), and 100 µM non-essential amino acid (Corning, Lowell, MA). Cells were
incubated at 37°C with 5% CO2, 95% air atmosphere, and at constant humidity. Cells
were grown to confluence for the complete differentiation of the cells according to the
procedures of Mahraoui et al. (1994) and Le Gall et al. (2007).
3.2.3 Cell Treatment
Cells on transwells were washed twice with 1 ml PBS after they reached 100%
confluence and were fully differentiated. Cells were then fed with glucose-free DMEM
(Gibco, Carlsbad, CA). Test carbohydrates of maltose, maltooligosaccharides
(maltotriose), and glucose (as control) were added to the glucose-free DMEM (Gibco,
Carlsbad, CA). Different carbohydrate concentrations were used with mannitol to adjust
the osmolarity of the media and the treatment time points were adjusted according to each
experiment (Cheng, 2009).
47
3.2.4 Total Protein Extraction
Cells were washed twice with 1X PBS and harvested in 1 ml of 1X ice cold PBS
followed by centrifugation at 14,000 rpm for 3-5 min at 4°C. After discarding the PBS
supernatant, cell pellets were lysed on ice for 20 min using 50- 60 µl of lysis buffer
consisting of 1% Nonidet P-40 (NP-40), 0.5% sodium deoxycholate, 150 mM NaCl, 20
mM Tris - HCl pH 8.0, 5% SDS, 10 mM DTT, and 1mM PMSF. During the lysis
process, cells were vigorously shaken every 5 min for 20 min. Cells were then
centrifuged at 14,000 rpm for 10 min at 4oC to remove cell debris. Protein concentration
was measured using the BCA protein assay kit according to the manufacturer’s protocol
(Pierce, Rockford, IL). Cell lysates were denatured and stabilized by adding sample
solvent (final concentration after adding together was 10% glycerol, 0.1% bromophenol
blue, 2% SDS, 50 mM Tris-Cl pH 6.8, and 10 mM DTT) and then placed in a boiling
water bath for 5 min. Samples were then directly used for SDS-PAGE or stored at -80°C
(Cheng, 2009).
3.2.5 Western Blot Analysis
For Western blot analysis, equal amounts of protein-containing lysates (35 µg)
were loaded onto 8% SDS-PAGE mini-gels and separated by electrophoresing with 35
mA/gel constant current for 1 hour. The separated proteins were then electrotransferred at
100 V to PVDF membranes (BioRad, Hercules, CA) at room temperature with ice in the
transfer cassette for 1.5 h. The membranes were blocked with phosphate buffered saline
containing 5% skim milk (BioRad, Hercules, CA) and 0.1% Tween 20 (BioRad,
Hercules, CA) at room temperature for 1-2 hours and then allowed to react with 1/1500
48
dilution of mouse anti-SI antibodies (B. Nichols, Baylor College of Medicine) (mixed
with equal volume of HSI 1/691/79, HIS 3/190, and HIS 3/42/1/2; Quezada-Calvillo et al
2007). After incubation with horseradish peroxidase-conjugated secondary antibody
(1/2000 dilution, anti-mouse and anti-goat, from Santa Cruz Biotechnology, Santa Cruz,
CA; anti-rabbit from Amersham, GE Healthcare Biosciences, Piscataway, NJ), proteins
were visualized using enhanced chemiluminescence (ECL) (Pierce, Rockford, IL or
Amersham of GE Healthcare Biosciences, Piscataway, NJ) followed by exposure to
Amersham Hyperfilm (Cheng, 2009).
3.2.6 Membrane Protein Extraction
Apical (brush border) membranes fraction were obtained as follows. Cells (5x106
cells) were washed 3 times with cold PBS and homogenized using glass/Teflon PotterElvehjem homogenizer in 2 mM-Tris/50mM-mannitol.
Homogenates were centrifuged at 2 700 g for 10 min and supernatants were spun
at 207 000 g for 30 min. Resulting pellets were designated 'crude membrane fractions '. A
mixture of protease inhibitors was added to all buffers and solutions used for
homogenization and membrane purification.
3.2.7 Preparation of Brush Border Membranes
Brush border membranes of Caco-2 cells were isolated by the modified (Sterchi
and Woodley, 1980) divalent cation precipitation method (Schmitz et al., 1973). Briefly,
the cells were homogenized using a Potter-Elvehjem homogenizer in a hypertonic
49
homogenization buffer (2 mM Tris-HCl, 50 mM mannitol, pH 7.1) supplemented with a
mixture of protease inhibitors. The homogenates were passed through a Luer-21 gauge
needle and CaCl2 was added to a final concentration of 10 mM. Following 30 minutes of
gentle agitation at 4°C, the homogenates were centrifuged at 2,000 g for 20 min to obtain
basolateral and intracellular membranes. Finally the supernatant was centrifuged at
25,000 g for 30 min to yield the brush border membranes. Where appropriate,
solubilization of the membranes was performed with 1% Triton X-100 in cold PBS for 2
hours followed by high-speed centrifugation and the supernatant was further used for
biochemical analyses.
3.2.8 Pulse-chase and Metabolic Labeling
In brief, treated cells were starved for 2 h and labeled with [35S]methionine (ICN
Biomedicals, Meckenheim, Germany). The dishes were rinsed once with 80 µCi L[35S]methionine in 5 ml of methionine-free (Met-free) DMEM containing 10% dialyzed
fetal calf serum, 50 units/ml penicillin, and 50 µg/ml streptomycin (Met-free medium).
The cells were incubated in 10 ml of Met-free medium 2 hours at 37oC to deplete their
methionine pool. Thereafter, the cells were labeled with 80 µCi of [35S]methionine in 10
ml of Met-free medium for 30 min, followed by a chase with nonlabeled methionine for
1, 2, 4, 8, 12, 24, and 48 hours. The cells were placed on ice and rinsed three times with
cold PBS, and cells corresponding to one dish were solubilized with 1 ml of cold lysis
buffer containing 25 mM Tris-HCI, pH 8.0, 50 mM NaC1, 0.5% sodium deoxycholate,
0.5% Triton X-100, 1 mM phenylmethanesulfonyl fluoride, 1 g/ml pepstatin, 5 µg/ml
leupeptin, 5 µg/ml antipain, and 1 µg/ml aprotinin. The cell extracts were centrifuged for
50
15 min at 1,000 g to remove nuclei and debris. The supernatant was retained and spun at
100,000 g for 1 hour at 4°C and immunoprecipitated with anti-SI antibodies. SI was also
immunoprecipitated from purified human intestinal brush border membranes. In this case,
1 mg/ml membranes in 25 mM Tris-HC1, pH 8.0, supplemented with 50 mM NaCl were
solubilized with Triton X-100 and sodium deoxycholate (0.5% final concentrations) by
stirring on ice for 30 min. The solubilized material was spun at 100,000 g, and the
supernatant was processed further for immunoprecipitation with anti-SI antibodies as
described below (Naim et al., 1987).
3.2.9 Immunoprecipitation and SDS-PAGE
Metabolically-labeled Caco-2 cells were lysed at 4°C for 1 hour in lysis buffer (25
mM Tris-HCl, pH 8.0, 50 mM NaCl, 0.5% Triton X-100, 0.5% sodium deoxycholate and
a mixture of protease inhibitors containing 1 mM PMSF, 1 µg/ml pepstatin, 5 µg/ml
leupeptin, 80 µg/ml soybean trypsin inhibitor, and 1 µg/ml aprotinin, all from Sigma Co.,
Deisenhofen, Germany). Ice-cold lysis buffer (1 ml) was used for each 100 mm culture
dish (about 2-4×106 cells). Detergent extracts of cells were centrifuged for 1 hour at
100,000 g at 4°C and the supernatants were immunoprecipitated. For
immunoprecipitation, first the lysate was pre-cleared with protein A-Sepharose alone and
then the preconjugated immunobeads were incubated with the sample with gentle mixing
at 4°C for 1 hour. Finally, the immunoprecipitants were washed three times with each
wash buffer I (0.5 % Triton X-100 and 0.05 % deoxycholate in PBS) and wash buffer II
(125 mM Tris, 10 mM EDTA, 500 mM NaCl, 0.5 % Triton X-100, pH 8.0), sequentially.
Usually, 0.2 µl mAb in the form of ascites was used for each immunoprecipitation.
51
Human α-glucosidase was immunoprecipitated using 0.5 µl of a rabbit polyclonal antihuman α-glucosidase serum (Reuser et al., 1985). SDS-PAGE was performed according
to the method of Laemmli (1970) and the apparent molecular masses were assessed by
comparison with high molecular mass markers (Bio-Rad Laboratories GmbH, München,
Germany) run on the same gel. In some experiments, deglycosylation of the
immunoprecipitates with Endo-N-acetylglucosaminidase H (Endo H) or Endo-Nacetylglucosaminidase F/gly- copeptidase F (Endo F/GF, also known as PNGase F) (both
from New England Biolabs GmbH, Schwalbach/Taunus, Germany) was performed prior
to SDS-PAGE analysis (Naim et al., 1987).
3.2.10 Isolation of Detergent Resistant Membranes (DRMs)
Caco-2 cells were collected in PBS and solubilized with 1 % Triton X-100 on ice,
briefly homogenized by pipetting, and incubated at 4°C for 2 hours with gentle shaking.
The lysates were then centrifuged at 5,000 g for 10 min at 4°C and the supernatants were
subjected to stepwise sucrose gradient ultracentrifugation at 100,000 g for 18 hours at
4°C. Finally, 1 ml fractions were collected from the top and examined by immunoblotting
for the target proteins.
3.2.11 Immunoassays
In immunoprecipitation experiments, Caco-2 cells were solubilized with Triton X100 (1% w/v in PBS) for 2 hours at 4°C and a post-nuclear supernatant (lysate) was
obtained by a short high speed centrifugation. The supernatant was subjected to
52
ultracentrifugation (100,000 g for 1 hour). Supernatant (S1) was used in
immunoprecipitation and the pellet (P1) (DRM) was solubilized further with
deoxycholate (0.5 % in PBS) at 4°C for 1 hour. This lysate was centrifuged briefly to
separate insoluble particles (P2) and the antibody for immunoprecipitation was added to
its supernatant (S2).
3.2.12 Enzyme Activity Measurement
After cells were cultured and treated with glucose (25 mM) and maltose (12.5
mM) as described above, brush border membrane (BBM) containing mostly the mature
form of SI was prepared as described in Section 3.2.7. The enzyme assay was performed
with 20 µl of BBM in PBS, pH 6.5, and incubated with 20 µl of different substrate
concentrations of sucrose (0, 10, 15, 20, 30, 50, 100 mM) or maltose (0, 2, 4, 6, 8, 10, 20,
40 mM) for 1 hour at 37°C. Activities were calculated based on detection of the released
glucose by adding 200 µl GOPOD mono-reagent (Megazyme, Ireland) using a microplate
reader at 550 nm and glucose as a standard. Each sample was compared to a similar zero
time sample for the mean activity. The reported unit of activity was µM.h-1.mg-1.
3.2.13 Total RNA Isolation
Total RNA of cells were stabilized by storing in RNAlater solution (Ambion,
Austin, TX) at 4°C for overnight. Equipment used for RNA extraction was soaked in
DEPC-H2O (diethyl dicarbonate, 0.1% v/v; Sigma Co., St. Louis, MO) for at least 1 hour
at 37°C followed by autoclaving at 121°C for 30 min to inactivate RNases. Total RNA
53
was extracted using the SV Total RNA Isolation System (Promega, Madison, WI)
according to manufacturer’s protocol. RNA samples were diluted and measured at 260
and 280 nm. RNA concentration was calculated by A260 according to the Beer-Lambert
law and RNA purity was determined by the ratio of A260/A280 (Cheng, 2009).
3.2.14 cDNA Synthesis and Real-Time PCR Analysis
Relative levels of mRNA expressed in the treated Caco-2 cells of T1R2, T1R3, αgustducin, SI, GLUT2, GLUT5, and 18S rRNA were quantified by qRT-PCR. Oligo dT
(500 µg/ml, Promega, Madison, WI) was used as primer for first-strand cDNA synthesis
of total RNA. qRT-PCR was performed using two systems. Some samples were reverse
transcripted by using the Access qRT-PCR System (Promega, Madison, WI) according to
manufacturer’s instructions. qRT-PCR of other samples (20 µl) were performed by
heating a mixture [1 µl of Oligo dT, 1-2 µg of total RNA, 1 µl of 10 mM dNTP mix
(Invitrogen, Grand Island, NY) and 16 µl of DEPC-H2O] at 65oC for 5 min and then by
adding M-MuLV Reverse Transcriptase Reaction Buffer (BioLabs, Ipswich, MA) and 25
units of M-Mulv reverse transcriptase (BioLabs, Ipswich, MA) to react at 42°C for 1
hour. The reaction was inactivated by heating at 95°C for 5 min. qRT-PCR analysis was
performed by using the MX3000P QPCR system (Stratagene, La Jolla, CA) in the
presence of SYBR-green (Cheng, 2009). The primers used were:
•
T1R2 primers, 5’- TGG CAT TTA TCA CGG TAC TCA AA-3’ and 5’- AGT
ACG GGT GGT GGG ACT GA-3’
•
T1R3 primers, 5´-CAC ACG CTC CTG CAG CTT CG-3´ and 5´-CCA TGC
CCA GGA CAC AGA G-3´
54
•
α-gustducin primers, 5´- TTG TGC TGC ACT TAG TGC CTA TG -3´ and 5´GAA GGC TTT CAT GCA TTC TAT TCA-3´
•
SI primers, 5´-CAT CCT ACC ATG TCA AGA GCC AG-3´ and 5´-GCT TGT
TAA GGT GGT CTG GTT TAA ATT-3´
•
GLUT2 primers, 5’-GTC CAG AAA GCC CCA GAT ACC-3’ and 5 ́-GTG ACA
TCC TCA GTT CCT CTT AG-3 ́
•
GLUT5 primers, 5’-TCT CCT TGC AAA CGT AGA TGG-3’ and 5’-GAA GAA
GGG CAG CAG AAG G-3’
•
18S rRNA primers, 5´-ATC AGA TAC CGT CGT AGT TC-3 and 5-CCA GAG
TCT CGT TCG TTA T-3´
Partial sequences were matched to corresponding human full-length sequences by
GenBank BLAST searches. The qRT-PCR reaction was performed using Brilliant II
QPCR Master Mix (Stratagene, La Jolla, CA) scaled down to 25 µl per reaction. The
reaction followed standard conditions and the melting temperature was 55°C. qRT-PCR
results were expressed as fold change of relative amount of mRNA (Schmittgen and
Livak, 2008; User Bulletin of ABI Prism 7700 Sequence Detection System, 1997).
3.2.15 Quantification of Band Intensities
Specific bands on PVDF membranes detected by antibodies were visualised using
the ChemiDoc XRS Molecular Imager (BioRad, Hercules, CA) device. Digital images
obtained from the imager were quantified by image processing and analysis software
(ImageJ, 1.37v).
55
3.2.16 Statistical Analysis
Data are shown as means ± S.E.M. Statistical analysis was performed using SAS
9.4 software. T-test was used to determine the significance of treatment effect.
Much of these studies were done with our collaborator, Professor Hassan Naim
and Dr. Mahdi Amiri at the University of Veterinary Medicine in Hannover, Germany.
3.3 Results
3.3.1 Intracellular Distribution and Compartmentalization of SI
Intracellular sorting of SI in Caco-2 cell fed with glucose or maltose was
examined to gain insights into the cell compartment distribution of HMW SI induced by
maltose and to compare it to SI in cells fed with glucose. For this, cell-surface
immunoblotting of pro-SI was performed with Caco-2 cells treated with maltose (12.5
and 25 mM) or glucose (25 and 50 mM). Results showed that more pro-SIc appeared
predominantly at the apical membrane in Caco-2 cells fed with maltose after 12 hours
(Figure 3.1). This increase in sorting of SI into the apical membrane was concentrationdependent. More HMW SI was observed in in cells fed with 25 mM maltose compared
to 12.5 mM maltose. In addition, the results show that HMW SI distributed more to
apical membrane and that higher glycosylation on HMW SI did not impair SI reaching
the cell surface. Relative protein amount (P2/P1 ratio) showed that enrichment of SI in
the brush border fraction increased substantially (about 6 fold) in Caco-2 cells fed with
maltose compared to cells fed with glucose (Table 3.1). The greater intensity of SI bands
56
in cells fed with maltose suggests that maltose induced more and faster trafficking of
HMW SI from intracellular fraction (P1) to brush border membrane (P2) (Figure 3.2).
3.3.2 The Kinetics of Glycosylation Alterations
A pulse-chase experiment was conducted to understand the glycosylation process
of SI and identify and assess the size of the precursor and mature forms of HMW SI.
After differentiation, Caco-2 cells were fed with glucose or maltose, labeled with
[35S]methionine for 15 minutes and were collected in different times (0, 1, 2, 4, 8, 12, 24,
and 48 hours). SI was immunoprecipitated with monoclonal antibodies and SDS-PAGE
used to visualize SI bands (Figure 3.3). The intensity of each band was measured to
compare the amount of SI in different time points (Figure 3.4). A single high mannose
polypeptide MW≅ 210 kDa (SIh) was detected after 15 minutes of labeling. SI in cells fed
with glucose did not show any molecular weight difference from SI in cells fed with
maltose. After 1hour, a second band which indicates the mature form of SI MW≅ 245 kDa
was revealed. SI in Caco-2 cells fed with maltose appeared faster with higher intensity
and higher molecular weight (≅265kDa) indicating faster formation of HMW SI. At 2
hours and 4 hours, a population of SI reached the Golgi apparatus and acquired more Oand N-glycosylation to develop the mature SIc form. In the cells fed with maltose, the
intensity and molecular weight of SIh stayed high compare to cells fed with glucose. After
8 hours to 24 hours, a smear population of SIh with different high molecular weights was
observed only in cells fed with maltose which disappeared and became a single band at
48 hours.
57
To determine the glycosylation nature of the SI population with smear effect after
8 hours, the experiment was conducted using the endo F/GF deglycosylation enzyme.
After feeding differentiated cells with glucose and maltose for 12 hours, cells were
labeled with [35S]methionine for 15 minutes and collected in different times (2, 4, 8, and
24 hours). Then SI was immunoprecipitated with monoclonal antibodies and subjected to
endo F/GF enzyme. SDS-PAGE was used to visualize SI bands (Figure 3.5). With the
endo F/GF enzyme, glycan of some portion of HMW SI was cleaved and reduced in
molecular weight. However, the majority of HMW SI remained of a similar molecular
weight, perhaps indicating the diversity of HMW SI glycan species.
3.3.3 Association of SI with Detergent-Resistant Microdomains
It has been shown that SI is associated with lipid-raft on apical membrane of
intestinal epithelial cells (Danielsen and Van Deurs, 1997). To assess if the higher
molecular weight pro-SIc was associated with lipid raft microdomains at the apical
membrane, a detergent-resistant microdomain experiment (DRM experiment) was
performed with Caco-2 cells fed with DMEM contained 25 mM glucose (as control)
compared to 12.5 mM maltose for 12 hours. Cells then were solubilised with 1% Triton
X-100 and the supernatants and pellets were further processed for immunoblotting.
Pellets contained the SI in DRM portion and the supernatant contained the non-lipid raft
portion of SI. Within 12 hours, in comparison to the control, HMW SI was found to be
more associated with Triton X-100 lipid rafts (Figure 3.6), indicating higher apical
sorting of these species, as further supported by quantification of the brush border
58
membrane of the Caco-2 cells (Table 3.2). The intensity of SI was measured and showed
significantly higher amount of SI in DRM (Figure 3.7).
3.3.4 Sucrase-Isomaltase Enzyme Activity
The activity of SI was measured in Caco-2 cells fed with maltose compared to
cells fed with glucose. For this analysis, brush border membranes were separated from
intracellular and basolateral membrane through the discriminatory effect of divalent ions
(Ca2+ or Mg2+). Brush border membranes do not precipitate with these divalent ions and
can be separated in a relatively pure form from other membranes by centrifugation.
Usually the enrichment factor of the brush border membranes in the P2 fraction (see
Materials and Methods) correlates with a well developed brush border membrane
(Chantret et al., 1988; Schmitz et al., 1973). This factor was assessed by comparison of
the activities of the intestinal differentiation of SI in the P2 fraction versus P1 and the total
cellular homogenates.
Brush border membranes were prepared in Caco-2 cells fed with glucose (0, 25,
and 50 mM) or maltose (12.5 and 25 mM). Sucrose (200 mM) and maltose (50 mM) were
used as substrates for measuring SI activity. Here, the sucrase activity of SI in the P2
fraction versus P1 and the total cellular homogenates was lower in cell fed with maltose
compared to cells fed glucose (Figure 3.8). The activity of SI in different cell fractions
decreased when sucrose was used as a substrate; there were substantially lower SI
activity levels when sucrose was used as the substrate (Table 3.3).
59
The maltase activity of SI in the P2 fraction versus P1 and the total cellular
homogenates was same in cells fed with maltose compared to cells fed with glucose
(Figure 3.9). The maltase activity of SI in different cell fractions decreased, but not
significantly, when maltose was used as the substrate (Table 3.4).
For further investigation of the enzyme kinetics of digestion of the disaccharides,
maltase and sucrase activity of SI was measured using different concentrations of both
substrates and cells fed with glucose or maltose. The data were analyzed using
Michaelis–Menten kinetics (Figures 3.10, 3.12) and presented as Lineweaver-Burk plots
(Figure 3.11, 3.13). Vmax and Km parameters were determined for each sample. There was
no significant change in Vmax and Km of maltase and sucrase activities in cells fed with
maltose or glucose (Table 3.5).
3.3.5 Deglycosylation Treatment of Treated Cells
To determine the size, nature (high mannose/complex type), and type of linkage
(N-linked/O-linked) of the glycan units present on SIh and SIc, enzymic deglycosylation
was performed. For this objective, differentiated cells were fed with glucose or maltose
and then labeled with [35S]methionine. After cells were collected and lysed, the
immunoprecipitated SI was treated with endo F/GF, SI and endo H (Figure 3.15)
deglycosylation enzymes (see Materials and Methods). Endo F/GF removes almost all Nlinked (Asn-linked) glycosylated moieties. Endo H, on the other hand, removes only high
mannose and some hybrid types of N-linked carbohydrate moieties.
60
As shown in Figure 3.14, SI immunoprecipitated from cells fed with glucose and
treated with endo F/GF revealed predominately pro-SIC which converted upon treatment
with endo F/GF to a MW≅205 and 185 kDa species. Endo F/GF treated SI
immunoprecipitated from cells fed with maltose produced the similar digested SI with
both MW≅205 and 185 kDa species showing that HMW SI contains N-link glycan.
The endo H treatment of immunoprecipitated SI to remove high mannose glycans
is shown in Figure 3.15. SI immunoprecipitated from cells fed with glucose and treated
with endo H lowered the MW of pro-SIc to 210 kDa and the high mannose band (SIh) to
134 KD. Endo H-treated SI immunoprecipitated from cells fed with maltose produced the
same digested SIh at 134 kDa, but the complex form of SI (pro-SIc) had a higher
molecular weight band for pro-SIc (MW≅265).
3.3.6 Gene Expression of the Sweet Taste Receptor with Maltose Exposure
In appetite studies, mice and rats were reported to have different carbohydrates
preference (including maltose) (Faller et al., 2004; Treesukosol, 2009). These results
suggest a stimulation of sweet taste receptor subunits (T1R2 and T1R3) by different
carbohydrates. Sweet taste receptor is a G protein-coupled receptors (GPCRs) expressed
through gastrointestinal tract (GI) (Hoon et al. 1999; Adler et al. 2000; Margolskee et al.,
2007). To investigate if maltose induces sweet taste receptor expression, the relative
quantity of mRNA for different genes (SI, T1R2, T1R3, α-gustducin, GLUT2, and
GLUT5) were measured in Caco-2 cells fed with glucose or maltose. Since GLUT5 has
no function in glucose transportation and GLUT2 is positively affected by glucose, these
61
proteins were used as controls. For this study, confluenced Caco-2 cells were fed with
glucose or maltose, and mRNA was extracted. qRT-PCR was used to measure mRNA
expression of the genes encoding these proteins.
Results from qRT-PCR revealed a higher gene expression of both sweet taste
receptor subunits in the presence of maltose compared to Caco-2 cells fed with glucose.
This increase in T1R3 mRNA level in cells fed with maltose was significantly higher
(compared to cells fed with glucose) in the first 2 hours and after 24 h (Figure 3.16). The
increase was greater in the T1R2 subunit in the first 2 hours (Figure 3.17). There was no
significant difference in mRNA level of α-gustducin between treatments (Figure 3.18).
Expression of the SI gene in cells fed with maltose increased over time (Figure 3.19). The
highest transcriptional level of SI mRNA was observed at 12 hours followed by
degradation of SI mRNA after 24 hours suggesting a dynamic change in SI gene
transcription and degradation of SI mRNA.
Cells appeared to accumulate more GLUT5 mRNA initially at 2 hours in the
presence of glucose and maltose (Figure 3.20). However the amount of expression was
greater in cells treated with glucose. In general, cells displayed higher GLUT5 mRNA
levels in the first 6 hours of treatment with glucose and maltose. In the presence of both
glucose and maltose, a constant decreasing trend of GLUT5 mRNA was observed after
12 hours. Both carbohydrates seemed to have a positive effect on GLUT2 gene
expression. Caco-2 cells fed with maltose showed increase in level of GLUT2 mRNA
compared to the glucose (Figure 3.21) over the 24 hours treatment period.
62
3.4 Discussion
It has been known that the presence of monosaccharides (e.g., glucose, fructose)
in small intestinal enterocytes alter intracellular events, including SI gene expression, and
trigger gut hormone responses (Kishi et al., 1999; Goda et al., 2000; Le Gall et al., 2007;
Margolskee et al., 2007). However, sensing of α-amylase starch degradation products
(maltooligosaccharides) by gut enterocytes with effect on SI had not been reported until
recently with the publication of an article by Cheng et al. (2014) from our laboratory
suggesting such a relationship. In appetite studies, carbohydrate sensing (including
maltose) was reported at the site of the sweet taste receptor T1R2 and T1R3 subunits
(Sclafani, 2007; Treesukosol et al., 2009), however it was not related to SI processing or
starch maltooligosaccharide product digestion.
In Cheng et al. (2014), we reported that maltose appears to induce the
transformation of SI to a HMW SI species in Caco-2 cells. Analyzing the HMW SI by a
combination of MALDI-TOF and MS/MS suggested that this maltose-induced event was
due to SI glycosylation. They proposed that maltose, in contrast to other sugars tested
(i.e., glucose, fructose, sucrose, isomaltose), acts as a signal molecule to trigger HMW SI
processing and trafficking to the apical membrane. The data presented in the current
study provide a better understanding of the sensing mechanism, and maturation and
trafficking of this HMW SI species in Caco-2 cells in the presence of maltose.
SI is a highly glycosylated protein with N- and O-glycosylation sites that are
added during co-translational and post-translational processing before the final insertion
63
of the enzyme in the microvillar membrane of the small intestinal enterocytes (Hunziker
et al., 1986). Polypeptides can be modified in the rough endoplasmic reticulum (RER)
either during their synthesis (co-translational) or after the initial synthesis has been
completed (post-translational). N- and O-glycosylation of SI are essential modifications
that are associated with folding and intracellular trafficking (Naim et al., 1988). SI is
sorted to the apical membrane via O-linked glycans that mediate its association with lipid
rafts (Wetzel, 2009) which is associated with increase of SI activity (Heine et al., 2009).
Here, we report on changes in SI glycosylation and sorting as induced by maltose, a
surrogate for the array of maltooligosaccharides that are presented to small intestine
enterocytes from the digestion of starch by α-amylase.
Deglycosylation of HMW SI was done using endo F and endo H enzymes to
investigate the structure of HMW SI. The endo F treatment cleaves the N-linked
glycosylation sites and endo H treatment cleaves most of (but not all) the O-linked
glycosylation sites. Endo F treatment resulted in reduced molecular weight of HMW SI
(induced by maltose) to the original molecular weight of SI associated with normal
feeding of glucose to the cells. Endo H treatment of HMW SI (induced by maltose)
somewhat reduced the molecular weight, but it was still higher than the original SI
molecular weight. The deglycosylation experiments showed that there is a higher degree
of N-glycosylation than O-glycosylation on the HMW SI. N- and O-glycosylation are
involved in processing and trafficking of SI (Naim et al., 1999; Wetzel et al., 2009),
therefore we can conclude that SI in presence of maltose gets both N- and O-linked
glycans, though more N-, which produces faster trafficking of SI.
64
In the DRM experiment, regions of apical membrane which are resistance to
detergent were isolated. These regions, also called the lipid rafts, have a unique structure.
Lipid rafts are enriched in sphingolipids such as sphingomyelin and cholesterol (Pike,
2009). Some proteins such as SI are associated with these regions, meaning that more SI
is found at the enterocyte apical membrane in lipid raft regions (Le Bivic et al., 1990). In
addition, SI has more enzyme activity when they in lipid raft region (Alfalah et al., 1999).
In the DRM experiment, we assessed the association of SI with the lipid raft region on
the enterocyte apical membrane. SI is sorted to the apical membrane via O-linked glycans
that mediate its association with DRMs (Jacob et al., 2000), and the deglycosylation
experiment described above showed that O-glycosylation occurred with maltose
exposure. There was a higher amount of HMW SI in membrane in cells fed with maltose
compare to SI to cells fed with glucose, and HMW SI induced by maltose was more
associated with the DRM fraction. The association of SI with DRMs requires an intact
transmembrane domain and minimal stretch of the Ser/Thr-rich stalk O-glycosylated
domain (Jacob et al., 2000). Jacob et al. (2000) showed that alteration of O-glycosylation
in the stalk region damaged the trafficking and maturation of SI. It is, therefore, likely
that the additional glycosylation caused by the presence of maltose on SI did not occur on
stalk region. Since Jacob et al. (2000) showed that O-glycosylation of SI is the main
factor in trafficking of SI to lipid raft, it is also probable that a different O-glycosylation
pattern on HMW SI could have caused more association of HMW SI with DRMs (Wetzel
et al., 2009), as was found in the present experiment.
The pulse-chase experiment clearly shows that the SI in cells fed with maltose
was processed at a faster rate compared to SI in cells fed with glucose. We observed the
65
formation of pro-SIc with greater band intensity after 1 h. The molecular weight of the
pro-SIc induced by maltose was also greater (≅ 265 kDa) than SI in cells induced with
glucose (245 kDa). After 8 h, SI in cells with maltose treatment began to become more
glycosylated and formed a smear; this population existed until 24 h then was reduced to
one single HMW SI. When this smeared higher molecular weight population of SI was
treated with endo F enzyme, the molecular weight of HMW SI population decreased but
the smear still existed. Since endo F only cleaves N-linked glycans, the smear effect
might be due to a variety of O-glycosylation types on SI in presence of maltose. The
reason for higher O-glycosylation on HMW SI is not completely clear to us. Base on
DRMs and pulse-chase results, it seems these additional N- and O-glycosylations have a
direct impact on maturation and trafficking of HMW SI, and that there may be various
forms that could have different trafficking or other (e.g., enzyme activity) functions.
This finding was confirmed by biochemical analysis of the distribution of SI in
cells fed with glucose or maltose. The results from this experiment showed that a greater
proportion of SI appeared at the apical membrane when maltose was fed to the cells. This
shows that the trafficking of SI in cells fed with maltose increased with the new N- and
O-glycosylations on HMW SI. A remarkable characteristic of intestinal SI is its
heterogeneous O-glycosylation pattern revealed by the expression of multiple differently
O-glycosylated molecules (Naim et al., 1988). This higher N- and O-glycosylation of SI
in the presence of maltose correlates with the high amount of SI at apical membrane.
We asked the question of whether the new glycosylation pattern of SI induced by
maltose, affected its enzyme activity. It has been shown that disruption of the DRM with
Triton X-100 substantially reduces its activity (Wetzel et al., 2009). The results from
66
enzyme activity experiment showed that sucrase activity significantly decreased HMW SI
of cells induced by maltose, however, the total maltase activity was similar to SI of cell
induced by glucose. Naim et al. (1988) showed that the sucrase subunit in SI contains at
least four populations varying in their content of O-linked glycans. Taken together with
the higher O-glycosylation of HMW SI noted above, this may be due to different Oglycosylation events on the sucrase subunit. There was no change in maltase activity in
HMW SI. The next step in evaluating enzyme activity of HMW SI was to check if the
enzyme followed Michealis-Menten kinetics. The Michaelis–Menten and LineweaverBurk shows that Vmax and Km decreases slightly in HMW SI induced by maltose. Since
the maltase activity was not changed, we conclude that HMW SI form induced with
maltose has better binding to maltose with similar enzyme activity for digesting maltose.
Another question posed in the current study was related to the investigation of a
potential receptor for maltose sensing which could trigger the processing of HMW SI. In
an appetite study, maltose preference was reported in mice and rats (Faller et al., 2004;
Treesukosol, 2009). These studies suggested a stimulation of sweet taste receptor
subunits (T1R2 and T1R3) by maltose related to appetite. Since these studies showed
potential binding of maltose to sweet taste receptor, we tested the gene expression of
sweet taste receptor subunits (T1R2/T1R3) and SI in presence of maltose.
The sweet taste receptor signaling pathway might be involved in maltose sensing
which induces HMW SI expression. Results from qRT-PCR of sweet taste subunits
(T1R2 andT1R3) revealed a significant increase in gene expression of both sweet taste
receptor subunits in cells fed by maltose compared to glucose. mRNA expression of
T1R2 subunit increased in the first two hours in the presence of maltose but not in
67
presence of glucose. Also, mRNA expression of T1R3 increased at 2 and 24 hours.
However, no significant change was observed in α-gustducin expression level. Measuring
mRNA expression of the SI gene showed significant increase of SI mRNA level in the
first 12 hours following by decrease in SI expression after 24 hours. This suggests a
dynamic change in SI gene transcription and degradation of SI mRNA. Cheng et al.
(2012) showed that maltose increases expression of SI after 6 hours, but here the results
indicated that this higher expression starts even at one hour of feeding the cells with
maltose.
Gene expression of monosaccharide transporters (GLUT5 and GLUT2) were
measured to monitor their change in presence of maltose. GLUT5 is facilitative fructose
transporter (Bell et al., 1993; Corpe et al., 2002; Wasserman et al., 1996). In the small
intestine, fructose was shown to significantly increase levels of GLUT5 mRNA and
enhance the expression of GLUT5 protein (Cui et al., 2003; David et al., 1995; Jiang et
al., 2001; Jiang and Ferraris, 2001; Kirchner et al., 2008; Kishi et al., 1999; Miyamoto et
al., 1993). This may explain the high expression of GLUT5 in both glucose and maltose
fed cells. GLUT2 is known to be enhanced in high glucose diet (Miyamoto et al., 1993),
but the expression of GLUT2 was greater in cells fed with maltose. Cui et al. (2003)
presented evidence that glucose enhance GLUT2 mRNA levels by transcriptional
regulation. It possible that maltose may enhance the expression of GLUT2 more than
glucose.
Maltooligosaccharides, important digestion products of starch by α-amylase,
symbolize the presence of starch, which is the main glycemic carbohydrate on which
humans evolved. Rapid digestion of glycemic carbohydrates and absorption of glucose
68
can be detrimental to health, particularly for diabetics and pre-diabetics, due to glycemic
spikes that stress the glucose homeostasis regulatory system. This study showed that the
sweet taste receptor subunits sense the existence of maltooligosaccharides via
gastrointestinal tract and signal the cell to express higher molecular weight species of SI.
This increase in molecular weight in SI is due to post-translational events which are
critical to the maturation of the SI enzyme active form. These new N- and O-linked
glycans promoted different events of maturation and trafficking of SI. The change in
glycosylation of SI induced by maltose also altered the distribution of SI in cell
compartments through higher apical sorting (intercellular/apical membrane).
The pulse-chase study clearly revealed that maltose not only induces the
formation of higher N- and O-glycosylated SI, but also that the mature form of SI traffics
faster to the apical membrane for digestion of maltooligosaccharides in the small
intestine. This novel finding led us to postulate that the sweet taste receptor on the small
intestine enterocytes senses maltose and triggers signaling events leading to the
processing and trafficking of SI. This expression of HMW SI promotes faster digestion of
maltooligosaccharides. One might postulate that the function of the receptor for
maltooligosaccharides could be manipulated through receptor blocking or through
a
signaling pathway to modulate glucose production for glycemic control and improved
health.
72
Table 3.1 Relative protein amount (P2/P1) of intercellular distribution of SI in present of
different carbohydrate.
Carbohydrate treatments
Relative protein amount
P2/P1
Without glucose
1.72±1.098
Glucose 25mM
1.00
Glucose 50mM
1.80±0.41
Maltose 12.5mM
6.12±0.73
Maltose 25mM
8.35±1.65
73
Table 3.2 Association of SI with detergent-resistant microdomains in present of
glucose and maltose.
Carbohydrate treatments
Lysate
Supernatant
Pellet
Glucose
1.28±0.014
0.67± 0.011
1±0.013
Maltose
1.35±0.018
0.65±0.015
1.18± 0.013
74
Table 3.3 Sucrase activity of SI in cells fed with different carbohydrates.
Sucrase activity
Homogenate
P1
P2
Without glucose
0.85±0.12
0.62±0.09
0.78±0.05
Glucose 25mM
1
1
1
Glucose 50mM
1.33±0.06
1.23±0.16
1.11±0.16
Maltose 12.5mM
0.76±0.10
0.72±0.04
0.68±0.07
Maltose 25mM
0.70±0.10
0.63±0.03
0.57±0.06
75
Table 3.4 Maltase activity of SI in cells fed with different carbohydrates.
Maltase activity
Homogenate
P1
P2
Without glucose
1.01±0.1
0.94±0.05
0.9±0.06
Glucose 25mM
1
1
1
Glucose 50mM
0.96±0.06
1±0.07
0.96±0.08
Maltose 12.5mM
1.05±0.08
0.98±0.03
0.85±0.05
Maltose 25mM
1.02±0.07
0.96±0.06
0.88±0.05
76
Table 3.5 The kinetic parameters of sucrase and maltase in the cells fed with glucose or
maltose.
Sucrase Activity
Glucose-treated
Maltose-treated
Km
14.949 ± 1.771
13.050 ± 1.667
Vmax
0.0194 ± 0.001
0.0168 ± 0.001
Km
1.4554 ± 0.241
1.3898 ± 0.114
Vmax
0.0582 ± 0.002
0.0545 ± 0.001
Maltase Activity
77
Protein Fraction
P2
P1
Treatments: Glc(mM)
0
25
50
0
0
0
25
50
0
0
Mal (mM)
0
0
0
12.5
25
0
0
0
12.5
25
Figure 3.1 Biochemical analysis of the distribution of SI. Brush border membranes were
isolated by the calcium chloride precipitation method. Protein amounts in homogenate
(H) (total membranes and cytosol, microsomal, internal membranes (P1) (membrane
bound intracellular components and basolateral membranes) and brush border
membranes (P2) were determined and equal amounts were loaded on SDS-PAGE gels for
each protein investigated (SI, 75µg).
78
14
Relative protein amount P2/P1
12
10
8
6
4
2
0
-Glc
+Glc 25mM
+Glc 50mM
+Mal 12.5mM
+Mal 25mM
Figure 3.2 Band intensities from P1 and P2 fractions were quantified and mean intensity
of P2 was divided by mean intensity of P1 for SI (means ± SEM, n=3).
79
Treatment Glc Mal Glc Mal Glc Mal Glc Mal Glc Mal Glc Mal Glc Mal Glc Mal
Time
0h
0h
1h
1h
2h
2h
4h
4h
8h
8h 12h 12h 24h 24h 48h 48h
Figure 3.3 Trafficking kinetics of SI to the cell surface. Caco-2 cells were labelled with
[35S]-methionine for 30 minutes and chased for the indicated times. SI was immunoisolated from cell lysate. After cells were lysed with Triton X-100, SI was detected by
immunoprecipitation. Samples were resolved on SDS-PAGE and visualised by
phosphorimaging.
80
3
Intensity of SI bands (arbitrary unit)
SIh
2.5
SIc
2
1.5
1
0.5
0
Glc 0 Mal 0 Glc 1 Mal 1 Glc 2 Mal 2 Glc 4 Mal 4 Glc 8 Mal 8 Glc 12 Mal 12 Glc 24 Mal 24 Glc 48 Mal 48
Figure 3.4 Graphical representation of the pulse-chase experiment of Figure 3.3 by
quantification of the SDS-PAGE band intensities from 3 independent experiments (means
±SEM, n=3). SIh is high mannose form of SI and SIc is complex form of SI.
81
Endo F treatment
Treatment
Time
Untreated
Treated
Glc Mal Glc Mal Glc Mal Glc Mal Glc Mal Glc Mal Glc Mal Glc Mal
2h
2h
4h
4h
8h
8h 24h 24h
2h
2h
4h 4h
8h
8h
24 24h
Figure 3.5 Transport kinetics of SI to the cell surface. Caco-2 cells were labelled with
[35S]-methionine for 30 minutes and pulse chased for the indicated times. SI was
immunoisolated at the cell surface. After cells were lysed with Triton X-100, SI was
detected by immunoprecipitation. Enzymatic deglycosylation was carried out using Endo
F/GF for 1 hour at 37 °C. Samples were resolved on SDS-PAGE and visualised by
phosphorimaging.
82
Treatment
Tween-20 DRMs
Glc (25mM)
Lys
Sup
Mal (12.5mM)
Pellet
Lys
Sup
Pellet
Figure 3.6 Distribution of SI in Tween-20 detergent resistant membranes. Cells were
treated with glucose 25 mM and maltose 12.5 mM for 12 hours. Lysis was performed in
1% Tween-20 in PBS for 2 hours at 4 °C. Detergent resistant membrane were pelleted by
ultracentrifugation.
83
1.6
Lys
Intensity of SI bands
(arbitrary unit)
1.2
Sup
Pellet
0.8
0.4
0
Glucose
Maltose
Figure 3.7 Graphical representation of the distribution of SI in Tween-20 detergent
resistant membrane as shown in Figure 3.6, by quantification of the SDS-PAGE band
intensities from 3 independent experiments (means ±SEM, n=3).
84
2
1.8
1.6
Specific Activity
1.4
Homogenate
P1
1.2
P2
1
0.8
0.6
0.4
0.2
0
-Glu
+Glu
+50 Glu
+12.5Malt.
+25Mlat.
Figure 3.8 Comparison of sucrase activity of SI at the brush border membrane of Caco-2
cells treated with different concentrations of glucose and maltose. Brush border
membranes were isolated by the calcium chloride precipitation method. Protein amount
in homogenate (H) (total membranes and cytosol, microsomal, internal membranes (P1)
(membrane bound intracellular components and basolateral membranes) and brush border
membranes (P2) were determined. Results represent the mean ± SEM n=6 (*, p < 0.05;
**, p < 0.01; ***, p < 0.001).
85
1.4
Homogenate
1.2
P1
P2
Specific Activity
1
0.8
0.6
0.4
0.2
0
-Glu
+Glu
+50 Glu
+12.5Malt.
+25Mlat.
Figure 3.9 Comparison of maltase activity of SI at the brush border membrane of Caco-2
cells treated with different concentrations of glucose and maltose. Brush border
membranes were isolated by the calcium chloride precipitation method. Protein amounts
in homogenate (H)(total membranes and cytosol, microsomal, internal membranes (P1)
(membrane bound intracellular components and basolateral membranes) and brush border
membranes (P2) were determined. Results represent the mean ± SEM n=6 (*, p < 0.05;
**, p < 0.01; ***, p < 0.001).
86
V (unit)
Michaelis-Menten
Glucose treated cells
Maltose treated cells
[Sucrose] (mM)
Figure 3.10 Michaelis-Menten plots for SI. The enzymatic activity of sucrase was
measured in the presence of different concentrations of substrate at the brush border
membrane of Caco-2 cells treated with glucose and maltose (means ±SEM, n=3).
87
Lineweaver-Burk
1/V
Glucose treated cells
Maltose treated cells
1/[Sucrose] (mM)
Figure 3.11 Lineweaver-Burk plots for SI. The enzymatic activity of sucrase was
measured in the presence of different concentrations of substrate at the brush border
membrane of Caco-2 cells treated with glucose and maltose (means ±SEM, n=3).
88
V (unit)
Michaelis-Menten
Glucose treated cells
Maltose treated cells
[Maltose] (mM)
Figure 3.12 Michaelis-Menten plots for SI. The enzymatic activity of maltase was
measured in the presence of different concentrations of substrate at the brush border
membrane of Caco-2 cells treated with glucose and maltose (means ±SEM, n=3).
89
Lineweaver-Burk
1/V
Glucose treated cells
Maltose treated cells
1/[Maltose] (mM)
Figure 3.13 Lineweaver-Burk plots for SI. The enzymatic activity of maltase was
measured in the presence of different concentrations of substrate at the brush border
membrane of Caco-2 cells treated with glucose and maltose (means ±SEM, n=3).
90
Figure 3.14 Deglycosylation (Endo F) of SI in cells treated with glucose or maltose.
Caco-2 cells were biosynthetically labeled with [35S]methionine for 1.5 hours. The cells
were homogenized and solubilized. Homogenates and medium were immunoprecipitated
with SI antibodies. The immunoprecipitates were treated with Endo F/GF or left
untreated. They were subjected to SDS-PAGE using 7% slab gels. Gels were analyzed by
fluorography.
91
Endo H
Untreated
Treated
Treatments Glc (mM)
0
25
0
0
0
25
0
0
Mal (mM)
0
0
12.5
25
0
0
12.5
25
Figure 3.15 Deglycosylation (Endo H ) of SI in cells treated with glucose or maltose.
Caco-2 cells were biosynthetically labeled with [35S]methionine for 1.5 hours. The cells
were homogenized and solubilized. Homogenates and medium were immunoprecipitated
with SI antibodies. The immunoprecipitates were treated with Endo H or left untreated.
They were subjected to SDS-PAGE using 7% slab gels. Gels were analyzed by
fluorography.
92
12
T1R3 mRNA
(Relative amount)
10
**
***
8
6
Glc
Mal
4
2
0
1
2
6
Hours
12
24
Figure 3.16 T1R3 gene transcription in Caco-2 cells in the presence of glucose or
maltose. Cells were fed with 12.5 and 25 mM glucose (Glc) for 1, 2, 6, 12, and 24 hours.
The data are presented as relative amount of T1R3 mRNA levels. Results represent the
mean ± SEM of at least three wells of treated Caco-2 cells (*, p < 0.05; **, p < 0.01; ***,
p < 0.001).
93
50
***
T1R2 mRNA
(Relative amount)
40
30
Glc
20
10
Mal
***
0
1
2
6
Hours
12
24
Figure 3.17 T1R2 gene transcription in Caco-2 cells in the presence of glucose or
maltose. Cells were fed with 12.5 and 25 mM glucose (Glc) for 1, 2, 6, 12, or 24 hours.
The data are presented as relative amount of T1R2 mRNA levels. Results represent the
mean ± SEM of at least three wells of treated Caco-2 cells (*, p < 0.05; **, p < 0.01; ***,
p < 0.001).
94
10
α-gustducin mRNA
(Relative amount)
8
6
Glc
4
Mal
2
0
1
2
6
Hours
12
24
Figure 3.18 α-Gustducin gene transcription in Caco-2 cells in the presence of glucose or
maltose. Cells were fed with 12.5 and 25 mM glucose (Glc) for 1, 2, 6, 12, or 24 hours.
The data are presented as relative amount of α-Gustducin mRNA levels. Results represent
the mean ± SEM of at least three wells of treated Caco-2 cells (*, p < 0.05; **, p < 0.01;
***, p < 0.001).
95
120
**
SI mRNA
(Relative amount)
100
80
**
60
40
*
**
Glc
Mal
***
20
0
1
2
6
Hours
12
24
Figure 3.19 SI gene transcription in Caco-2 cells in the presence of glucose or maltose.
Cells were fed with 12.5 and 25 mM glucose (Glc) for 1, 2, 6, 12, or 24 hours. The data
are presented as relative amount of SI mRNA levels. Results represent the mean ± SEM
of at least three wells of treated Caco-2 cells (*, p < 0.05; **, p < 0.01; ***, p < 0.001).
96
60
***
GLUT5 mRNA
(Relative amount)
50
**
40
30
Glc
Mal
20
10
0
1
2
6
Hours
12
24
Figure 3.20 GLUT5 gene transcription in Caco-2 cells in the presence of glucose or
maltose. Cells were fed with 12.5 and 25 mM glucose (Glc) for 1, 2, 6, 12, or 24 hours.
The data are presented as of GLUT5 mRNA levels. Results represent the mean ± SEM of
at least three wells of treated Caco-2 cells (*, p < 0.05; **, p < 0.01; ***, p < 0.001).
97
50
**
GLUT2 mRNA
(Relative amount)
40
*
30
***
20
Glc
Mal
*
10
0
1
2
6
Hours
12
24
Figure 3.21 GLUT2 gene transcription in Caco-2 cells in the presence of glucose or
maltose. Cells were fed with 12.5 and 25 mM glucose (Glc) for 1, 2, 6, 12, or 24 hours.
The data are presented as relative amount of GLUT2 mRNA levels. Results represent the
mean ± SEM of at least three wells of treated Caco-2 cells (*, p < 0.05; **, p < 0.01; ***,
p < 0.001).
98
3.5 References
Alfalah, M., Jacob, R., Preuss, U., Zimmer, K. P., Naim, H., & Naim, H. Y. (1999). Olinked glycans mediate apical sorting of human intestinal sucrase-isomaltase through
association with lipid rafts. Current Biology, 9(11), 593-S2.
Beaulieu, J. F., Nichols, B., & Quaroni, A. (1989). Posttranslational regulation of
sucrase-isomaltase expression in intestinal crypt and villus cells. The Journal of
Biological Chemistry, 264: 20000-20011.
Bell, G. I., Burant, C. F., Takeda, J., & Gould, G. W. (1993). Structure and function of
mammalian facilitative sugar transporters. Journal of Biological Chemistry, 268(26),
19161-19164.
Brownlee M. (2001). Biochemistry and molecular cell biology of diabetic complications.
Nature, 414(6865): 813-20.
Chantret, I., Rodolosse, A., Barbat, A., Dussaulx, E., Brotlaroche, E., Zweibaum, A., &
Rousset, M. (1994). Differential expression of sucrase-isomaltase in clones isolated from
early and late passages of the cell line Caco-2: evidence for glucose-dependent negative
regulation. Journal of Cell Science, 107, 213-225.
Cheng, M. W., Chegeni, M., Kim, K. H., Zhang, G. Y., Benmoussa, M., QuesadaCalvillo, R., Nichols, B. L., & Hamaker, B. R. (2014). Different sucrose-isomaltase
response of Caco-2 cells to glucose and maltose suggests dietary maltose sensing.
Journal of Clinical Biochemistry and Nutrition, 54(1): 55-60.
Corpe, C. P., Bovelander, F. J., Munoz, C. M., Hoekstra, J. H., Simpson, I. A., Kwon, O.,
Levine, M., & Burant, C. F. (2002). Cloning and functional characterization of the mouse
fructose transporter, GLUT5. Biochimica Et Biophysica Acta-Gene Structure and
Expression, 1576(1-2), 191-197.
Cui, X. L., Jiang, L., & Ferraris, R. P. (2003). Regulation of rat intestinal GLUT2 mRNA
abundance by luminal and systemic factors. Biochimica Et Biophysica ActaBiomembranes, 1612(2), 178-185.
David, E. S., Cingari, D. S., & Ferraris, R. P. (1995). Dietary induction of intestinal
fructose absorption in weaning rats. Pediatric Research, 37(6), 777-782.
99
Dudley, M.A., Nichols B.L., Rosenberger J., Perkinson J.S., & Reeds P.J. (1992).
Feeding status affects in vivo prosucrase.isomaltase processing in rat jejunum. Journal of
Nutrition, 122:528-34.
Dudley M.A., Hachey D.L., Quaroni A., Hutchens T.W., Nichols B.L., Rosenberger J.,
Perkinson J.S., Cook G., & Reeds P.J. (1993). In vivo sucrase-isomaltase and lactasephlorizin hydrolase turnover in the fed adult rats. Journal of Biological Chemistry,
268:13609-13616.
Englyst, H. N., Kingman, S. M., & Cummings, J. H. (1992). Classification and
measurement of nutritionally important starch fractions. European Journal of Clinical
Nutrition, 46: S33-S50.
Englyst, K. N., Vinoy, S., Englyst, H. N., & Lang, V. (2003). Glycaemic index of cereal
products explained by their content of rapidly and slowly available glucose. British
Journal of Nutrition, 89(3): 329-339.
Faller, G., Dimmler, A., Rau, T., Spaderna, M., Hlubek, F., Jung, A., Kirchner, T., &
Brabletz, T. (2004). Evidence for acid-induced loss of Cdx2 expression in duodenal
gastric metaplasia. Journal of Pathology, 203(4), 904-908.
Goda, T. (2000). Regulation of the expression of carbohydrate digestion/absorptionrelated genes. British Journal of Nutrition, 84: S245-8.
Gu, N., Adachi, T., Matsunaga, T., Tsujimoto, G., Ishihara, A., Yasda, K., & Tsuda, K.
(2007). HNF-1α participates in glucose regulation of sucrase-isomaltase gene expression
in epithelial intestinal cells. Biochemical and Biophysical Research Communications,
353: 617-622.
Hauri, H. P., Quaroni, A., & Isselbacher, K. J. (1979). Biogenesis of intestinal plasma
membrane: Posttranslational route and cleavage of sucrase-isomaltase. Proceedings of the
National Academy of Sciences USA, 76(10), 5183-5186.
Heine, M., Rohwedder, A., & Naim, H. Y. (2009). Impact of glycosylation and detergent
resistant membranes on the function of intestinal sucrase-isomaltase. Journal of
Biological Chemistry, 390: 545-549.
Hunziker W, Spiess M, Semenza G, & Lodish H. (1985). The sucrase-isomaltase
complex: primary structure, membrane-orientation, and evolution of a stalked, intrinsic
brush border protein. Cell, 46: 227-234.
100
Jacob, R., Weiner, J. R., Stadge, R., & Naim, H. Y. (2000). Additional N-glycosylation
and its impact on the folding of intestinal lactase-phlorizin hydrolase, Journal of
Biological Chemistry, 275: 10630–10637.
Jenkins, D., & Wolever, T. (1981). Glycemic index of foods: a physiological basis for
carbohydrate exchange. The American Journal of Clinical Nutrition, 34(3): 362- 366.
Jenkins, D. J., & Kendall, C. W. (2002). Glycemic index: overview of implications in
health and disease. The American Journal of Clinical Nutrition, 76(1): 266S- 273S.
Jiang, L., & Ferraris, R. P. (2001). Developmental reprogramming of rat GLUT-5
requires de novo mRNA and protein synthesis. American Journal of PhysiologyGastrointestinal and Liver Physiology, 280(1), G113-G120.
Jiang, L., David, E. S., Espina, N., & Ferraris, R. P. (2001). GLUT-5 expression in
neonatal rats: crypt-villus location and age-dependent regulation. American Journal of
Physiology-Gastrointestinal and Liver Physiology, 281(3), G666-G674.
Kirchner, S., Muduli, A., Casirola, D., Prum, K., Douard, V., & Ferraris, R. P. (2008).
Luminal fructose inhibits rat intestinal sodium-phosphate cotransporter gene expression
and phosphate uptake. American Journal of Clinical Nutrition, 87(4), 1028-1038.
Kishi, K., Tanaka, T., Igawa, M., Takase, S., & Goda, T. (1999). Sucraseisomaltase and
hexose transporter gene expressions are coordinately enhanced by dietary fructose in rat
jejunum. Journal of Nutrition, 129(5), 953-956.
Le Bivic, A., Quaroni, A., Nichols, B., & Rodriguez-Boulan, E. (1990). Biogenetic
pathways of plasma membrane proteins in Caco-2, a human intestinal epithelial cell line.
The Journal of Cell Biology, 111(4), 1351-1361.
Laemmli, U. K. (1970). Cleavage of structural proteins during the assembly of the head
of bacteriophage T4. Nature, 227: 680–685.
Le Gall, M. L., Tobin, V., Stolarczyk, E., Dalet, V., Leturque, A., & Brot-Laroche, E.
(2007). Sugar sensing by enterocytes combines polarity, membrane bound detectors and
sugar metabolism. Journal of Cellular Physiology, 213(3): 834-843.
Ludwig, D. S. (2002). The glycemic index. The Journal of the American Medical
Association, 287(18): 2414-2423.
101
Mace, O. J., Affleck, J., Patel, N., & Kellett, G. L. (2007). Sweet taste receptors in rat
small intestine stimulate glucose absorption through apical GLUT2. The Journal of
Physiology, 582: 379–92.
Mahraoui, L., Rodolosse, A., Barbat, A., Dussaulx, E., Zweibaum, A., Rousset, M., &
Brotlaroche, E. (1994). Presence and differential expression of SGLT1, GLUT1, GLUT2,
GLUT3 and GLUT5 hexose-Transporter messenger-RNAs in Caco-2 cell clones in
relation to cell growth and glucose consumption. Biochemical Journal, 298, 629-633.
Margolskee, R. F., Dyer, J., Kokrashvili, Z., & Salmon, K. S. H. (2007). T1R3 and
gustducin in gut sense sugars to regulate expression of Na+-glucose cotransporter 1.
Proceedings of the National Academy of Sciences USA. 104:15075-15080.
Miyamoto, K., Hase, K., Takagi, T., Fujii, T., Taketani, Y., Minami, H., Oka, T., &
Nakabou, Y. (1993). Differential responses of intestinal glucose-transporter messengerRNA transcripts to levels of dietary sugars. Biochemical Journal, 295, 211-215.
Naim, H. Y., Sterchi, E. E., & Lentze, M. J. (1987). Biosynthesis and maturation of
lactase-phlorizin hydrolase in the human small intestinal epithelial cells, Biochemistry
Journal, 241; 427–434.
Naim, H. Y., Sterchi, E. E., & Lentze, M. J. (1988). Biosynthesis of the human sucraseisomaltase complex. Differential O-glycosylation of the sucrase subunit correlates with
its position within the enzyme complex. Journal of Biological Chemistry, 263: 72427253.
Naim, H. Y., Joberty, G., Alfalah, M., & Jacob, R. (1999). Temporal Association of the
N-andO-Linked Glycosylation Events and Their Implication in the Polarized Sorting of
Intestinal Brush Border Sucrase-Isomaltase, Aminopeptidase N, and Dipeptidyl Peptidase
IV. Journal of Biological Chemistry, 274(25), 17961-17967.
Nichols, B. L., Quezada-Calvillo, R., Robayo-Torres, C. C., Ao, Z., Hamaker, B. R.,
Butte, N., Marini, J., Jahoor, F., & Sterchi, E. E. (2009). Mucosal maltase-glucoamylase
plays a crucial role in starch digestion and prandial glucose homeostasis of mice. Journal
of Nutrition, 139:684–690.
Pike, L. J. (2009). The challenge of lipid rafts. Journal of Lipid Research, 50, S323-S328.
102
Quezada-Calvillo, R., Robayo-Torres, C. C., Ao, Z., Hamaker, B. R., Quaroni, A.,
Brayer, G. D., Sterchi, E. E., Baker, S. S., & Nichols, B. L. (2007). Luminal substrate
"brake" on mucosal maltase-glucoamylase activity regulates total rate of starch digestion
to glucose. Journal of Pediatric Gastroenterology and Nutrition, 45: 32-43.
Reuser, A. J., Kroos, M., Oude Elferink, R. P., & Tager, J. M. (1985). Defects in
synthesis, phosphorylation, and maturation of acid alpha- glucosidase in glycogenosis
type II. Journal of Biological Chemistry, 260: 8336-8341.
Schmittgen, T. D., & Livak, K. J. (2008). Analyzing real-time PCR data by the
comparative C-T method. Nature Protocols, 3(6), 1101-1108.
Schmitz, J., Preiseer, H., Maeshacci, D., Ghosh, B. K., Cerda, J. J., & Crane, R. K.
(1973). Purification of the human intestinal brush border membrane. Biochimica et
Biophysica Acta, 323: 98-112.
Sclafani, A. (2007). Fat and sugar flavor preference and acceptance in C57BL/6J and 129
mice: Experience attenuates strain differences. Physiology & Behavior, 90: 602–611.
Sim L., Willemsma C., Mohan S., Naim H. Y., Pinto B.M., & Rose D. R. (2010).
Structural basis for substrate selectivity in human maltase-glucoamylase and sucraseisomaltase N-terminal domains. Journal of Biological Chemistry, 285: 17763–17770.
Sterchi, E. E., & Woodley, J. F. (1980). Peptide hydrolases of the human small intestinal
mucosa: distribution of activities between brush border membranes and cytosol. Clinica
Chimica Acta, 102: 49-56.
Treesukosol Y., Blonde G. D., & Spector A. C. (2009). T1R2 and T1R3 subunits are
individually unnecessary for normal affective licking response to polycose: implication
for saccharide taste receptors in mice. American Journal of Physiology - Regulatory,
Integrative and Comparative Physiology, 296: R855–R865.
Wasserman, D., Hoekstra, J. H., Tolia, V., Taylor, C. J., Kirschner, B. S., Takeda, J.,
Bell, G. I., Taub, R., & Rand, E. B. (1996). Molecular analysis of the fructose transporter
gene (GLUT5) in isolated fructose malabsorption. Journal of Clinical Investigation,
98(10); 2398-2402.
Wetzel, G., Heine, M., Rohwedder, A., & Naim, H. Y. (2009). Impact of glycosylation
and detergent-resistant membranes on the function of intestinal sucrase-isomaltase.
Journal of Biological Chemistry, 390: 545-9.
103
CHAPTER 4. INDUCTION OF DIFFERENTIATION OF SMALL INTESTINAL
ENTEROCYTE CELLS BY MALTOOLIGOSACCHARIDE TREATMENT USING A
METABOLOMICS APPROACH
4.1 Introduction
While genomic and proteomic techniques have been greatly improved for
studying biological system, an additional high-throughput method is desirable to
investigate the end-products of cellular processes. In 1998 for the first time, the term
”metabolome” was introduced to convey the concept of the complete pool of metabolites
synthesized by an organism (Oliver, et al., 1998). Although the metabolite profiling
concept has been used for decades for human disease diagnosis (Gates, et al., 1978), the
term “metabolic profile” was introduced by Horning et al. (1971). The term of
metabonomics is defined as “the quantitative measurement of the dynamic
multiparametric metabolic response of living systems to pathophysiological stimuli or
genetic modification” (Nicholson, et al., 1999) and metabolomics as defined by Fiehn
(2002) is “a comprehensive and quantitative analysis of all the metabolites”. However,
often these two terms are used interchangeably, metabolomics aim to measure the whole
metabolome, whereas metabonomics intend to measure the changes across the
metabolome which is a smaller set of metabolites.
Metabolites are small molecules produced during metabolism including
intermediates and products. These small molecules are the final downstream products of
104
gene and protein expression, therefore, they can provide a bigger picture of what is
happening in biological systems than other “omics” techniques. Studying the metabolome
provides valuable information to monitor alterations of metabolites caused by diseases,
drug treatments, or any other environmental stress. The information from metabolomics
can be combined with genomic and proteomic data to open up new perspectives for
understanding biological systems.
Metabolomic data are classified into two categorizes, non-targeted (also known as
global metabolite profiling) and targeted metabolomics. Untargeted metabolomics
includes a comprehensive analysis of all the measurable metabolites in a biological
system which requires extensive data analysis. On the other hand, targeted metabolomics
considers a subset of metabolites with chemically similar characteristics.
In the following sections, steps involved in performing a metabolomics study will
be discussed. Sample preparation, analytical instrument platform, and data analysis are
the main steps in performing a metabolomics study.
4.1.1 Sample Preparation
Variety of matrices has been used for metabolomics studies including biofluids,
tissues, cells, plants, and foods. It is important to choose an appropriate sample
preparation method to avoid inaccurate data interpretation.
Methods are designed based on purpose of the studies, type of the samples (e.g.
blood, tissue, cell), and the analytical techniques for data collection. In general, the
sample preparation step should be reliable, reproducible, and time efficient. If the nontargeted metabolomics is the aim of the study, the extraction method should be as non-
105
selective as possible. Appropriate storage at cold temperature should be applied between
sample collection and preparation. Depending on the experimental design and strategy to
be employed, additional preparation steps such as separation, purification, and
derivatization may be applied.
4.1.1.1 Cell Samples
Numerous metabolomics studies have been conducted on different cell lines
which provide valuable information to quantitatively measure molecular biomarkers and
physiological consequences in response to a treatment (Tate et al., 1996; Tate et al., 1998,
31; Florian et .al, 1995). Cell culture media also can be used to assess the impact of the
medium on the cell culture performance. However, they are not as common as direct
analysis on the cells.
4.1.1.2 Cell Sample Preparation
Cell sample collection for a metabolomics study is a very sensitive procedure. It is
important to maintain the integrity of the cells and to limit leakage of intracellular
metabolites, particularly for cell lines that are highly sensitive to osmotic changes of the
medium (Faijes et al., 2007; Bolten et al., 2008). The medium has to be removed and
cells should be collected as quickly as possible. Then, cells and/or medium have to be
quenched and stored, or the metabolites extracted right after quenching. Quenching is the
process used to inhibit enzyme activity and stop metabolic reactions by freezing samples.
Therefore, quenching gives a snapshot of a metabolic state at a given time point. The
106
common strategies to quench cell metabolism are immersion of cells in liquid nitrogen, or
a dry ice-ethanol or methanol slurry (Viant et al., 2005; Castrillo et al., 2003; Koning et
al., 1992).
The extraction of metabolites is an essential step in sample preparation. Several
protocols have been developed for extracting of intracellular metabolites such as
methanol/water solution, chloroform/methanol/water solution, and a freeze-thaw
technique (Le Belle et al., 2002; Dettmer et al., 2011). Different extracting methods can
be selected based on targeted or non-targeted metabolomics analysis. The non-targeted
metabolomics extraction method should be non-selective to collect all metabolites in cell,
whereas targeting metabolomics is limited to a group of metabolites with known
chemical properties.
4.1.2 Analytical Instrument Platforms
The two most common techniques in metabolomics studies are nuclear magnetic
resonance spectroscopy (NMR) and mass spectrometry (MS). NMR is highly
reproducible, non-destructive, quantitative, and non-selective which gives the ability to
collect the data of all the detectible metabolites in a one snap-shot, with minimal sample
preparation, and the ability to detect isomers. However, it has limitations including
sensitivity, complexity, and spectral overlap in NMR spectra. Although MS is more
sensitive than NMR, it requires a pre-separation of the metabolic components using
liquid chromatography or gas chromatography (GC), and to use GC metabolites have to
be first derivatized.
107
4.1.3 Data Analysis
4.1.3.1 Pre-Data Processing
1
H NMR spectra are recorded on high field NMR spectrometers and run on NMR
processing softwares packages such as Topspin (BrukerBiospin, Billerica, MA), VnmrJ
(Varian, Palo Alto, CA), NMRPipe (Delaglio et al., 1995), and MestReNova (Mestrelab
Research, Santiago de Compostela, Spain). The collected NMR data are processed by
Fourier transformation following by apodization and zero filling of the free induction
decay. After that, the phase of each spectrum has to be corrected to obtain absorption line
shapes followed by a subsequent baseline correction, if it is necessary. Spectra are
referenced to chemical shift of an internal standard peak such as the singlet of
trimethylsilyl propanoic acid or 4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS), or one
of the metabolites such as alanine. This step is also known as global alignment. Phasing,
baseline correcting, and referencing correction are done using one of the available NMR
processing software packages mentioned above.
Most of the time global alignment is not sufficient and has to be followed by
alignments of individual peaks performed manually or using particular algorithms such as
the recursive segment-wise peak alignment (Veselkov et al., 2009).
Other processing steps are binning, normalization, and scaling. Binning, also
known as bucketing, is often used after spectral alignment to perform multivariate data
analysis. The spectra are divided into buckets or segments. The intensities within each
bucket are summed and this results in data reduction. However, the drawback for this
108
method is the spectral complexity of peaks with overlapping data. Binning can introduce
errors from inaccurate spectral intensities when neighbor peaks contribute to a bucket. To
avoid or eliminate these types of errors, analysis of full resolution spectra (Cloarec et al.,
2005) or using variable binning (Davis et al., 2007; Meyer et al., 2008) has been
suggested. The alternative to bucketing is the direct integration of spectral peaks. This
can be more accurate and is used to quantify absolute concentrations of specific
metabolites.
Normalization applies for variations of the overall concentrations of samples. The
goal of normalization is to remove unwanted variation (e.g., dilution factor) between
samples and make the profiles comparable to each other. There are several technical
factors causing variation in data. Some of these factors are data recording with different
devices, different number of scans, or the absolute signal intensities data analysis.
Without normalizing data for these variations, the data analysis is less meaningful.
(Dieterle et al., 2006).
A number of normalization methods exist, including Probabilistic Quotient
Normalization (PQN) (Dieterle et al., 2006) and Constant Sum Normalization (CSN)
(Craig et al., 2007). Scaling has to be done before applying multivariate statistical
methods, as metabolite concentrations vary over many orders of magnitude. In the
absence of scaling, large and variable signals can be falsely picked as biomarker
candidates, and some low concentration metabolites that are true biomarkers can be
ignored. A number of scaling methods exist, such as variance scaling, scaling factor as
the standard deviation of peak intensities across the spectra, and Pareto scaling, where the
scaling factor is the square root of the standard deviation (Eriksson et al., 2004).
109
4.1.3.2 Statistical Methods
NMR and MS both generate huge and complex data sets. To interpret the data,
multivariate statistical analysis methods, also known as chemometrics, has been
extensively used (Cuperlovic-Culf et al., 2009; Khoo and Al-Rubeai, 2009, Van Vliet et
al., 2007).
Two approaches used to analyze metabolomics data sets are, 1) exploratory
(unsupervised) analysis and 2) confirmatory (unsupervised) analysis. Unsupervised data
analysis is used to find patterns in the data sets without prior knowledge of the groups.
Two examples of unsupervised data analysis are principal component analysis (PCA) and
hierarchical clustering analysis (HCA) (Mitchell et al., 2002). Supervised data analysis is
applied by assigning of spectra to classes. Examples of supervised analysis are the
univariate Student’s t-test, and multivariate methods that include, though are not limited
to, partial least square-discriminant analysis (PLS-DA), orthogonal signal correctionPLS-DA (O-PLS-DA), and logistic regression (Gottschalk et al., 2008).
PCA is one of the most popular data analysis techniques and has become a norm
for data visualization in the field of metabolomics (Khoo et al., 2007). PCA reduces the
large number of variables down to a smaller number of factors or principal components.
Each principal component (PC) is a linear combination of the original data parameters
and explains the maximum amount of variance possible that has not been accounted for
by the previous PCs. The PCA model is built on the basis of orthogonal vectors
(Eigenvectors), therefore principal components are independent from each other. The first
PC (PC1) describes the direction of the largest variations generated from the set of
110
spectra, the second PC (PC2) describes the direction for the largest portion of the
remaining variation, and likewise for the rest of the PCs.
Conversion of the original data to PCs produces, in two matrices, scores and
loadings. Scores and loadings are presented as scores and loading plots. Each point
represents a single sample spectrum in a score plot. Loadings explain how the old
variables are linearly combined and form the new variables. Loadings also indicate the
variables carrying the greatest weight and show the contribution of each variable in the
original data in the scores. Each point in the loading plots represents a different spectral
intensity. Therefore, loadings can be used to interpret relationships among variables that
cause any cluster separation in the score plot. From the output of PCA analysis,
compounds that are of great importance in classifying different samples are highlighted
and considered as the initial biomarker candidates.
PLS-DA has been used extensively as a powerful tool for the metabolomics field.
PLS-DA is the combination of partial least square (PLS) and discriminant analysis (DA).
PLS is one of the common supervised methods used in metabolomics data analysis. PLS
fits the data matrix of predictors X and class matrix of responses Y, and is used to find a
linear regression model of the new coordinate system. Each orthogonal axis in PLS is
named a latent variable (LV). DA is a statistical method for determination of a linear
combination of features to predict to which class a sample belongs (Wold et al., 2003;
Baker et al., 2003). PLS-DA create linear mathematical models from training sets of
spectral data within known classes. PLS-DA can be used to find the discriminant between
pre-assigned sample groups and recognize variables which best separates classes.
111
A drawback of PLS-DA is data over-fitting, therefore it is necessary and
important to perform the validation of the results (Westerhuis, 2008). Cross-validation is
the most common method for validation, in this method results are applied to a new set of
observations that was not used to build the model (Anderssen, et al. 2006). An example to
the cross-validation is leave-one-out cross-validation (LOOCV). This method is one of
the most commonly used methods for PLS-DA data validation in the field of
metabolomics (Stretch et al., 2012; Gu et al., 2011; Guan et al., 2009). To predict the
accuracy of the PLS-DA model, one single observation from the original sample set is
used. The rest of the samples are used as the training data in order to build the model.
This process is repeated until all the samples are used in the validation data once.
4.1.4 Metabolomic Application in the Present Study
Metabolomics is an emerging field with wide range of application in different
biological research areas that focuses on high-throughput identification of metabolites in
biological systems. Intrinsically, metabolomics is well positioned to be used in many
areas of food and nutrition science, where scientists are challenged to find better ways of
preventing or treating diseases such as obesity, diabetes, and cardiovascular disease
caused by unbalanced diets (German et al., 2005). A typical approached used to address
these problems is to first implement epidemiological studies to relate long-term food
consumption with health status, and then to design dietary interventions. This is based on
the hope to find associations between diet and health status (German et al., 2005; Zeisel,
2007), however correlative relationships do not necessarily indicate causation of diseases.
In order to relate diet effect to normalcy or to understand biochemical alterations in the
112
body due to diet, metabolomics techniques can be used to measure and compare small
molecule biomarkers (metabolites) in the presence and absence of diet or dietary factors
of interest (Gibney et al., 2005; Zeisel, 2007).
Based on the Dietary Guidelines for Americans 2010 (USDA), dietary
carbohydrates should provide 45-65% of total caloric intake. Carbohydrates are among
the macronutrients that provide energy and can, thus, contribute to excess energy intake.
However, the available carbohydrates differ in their rate of digestion and glucose
(mainly) liberation and absorption (e.g., for starch, rapidly or slowly digestible starch or
resistance starch), and healthiness. Pertinent to our discussion here, slow glucose release
has been shown to be beneficial for maintaining glucose homeostasis and may be
associated with health benefits through reduction of risk factors for some diet-associated
metabolic diseases (Jenkins et al., 2002).
Maltooligosaccharides are the primary product of starch digestion by salivary and
pancreatic α-amylases and are substrates for the small intestinal brush border-bound αglucosidases. We have recently shown that maltose acts as a signal molecule that changes
the molecular size of SI, which was postulated to stimulate the maturation and trafficking
of this enzyme complex to the enterocyte apical surface for digestion of the
maltooligosaccharides to glucose (Cheng et al., 2014). We view this action as another
potential point of control for starch-originated glucose release and absorption into the
body. While this finding is indicative of a maltooligosaccharides sensing ability by
enterocytes, it also suggests alteration in other events in small intestine enterocytes
leading to such things as secretion of gut hormones and production of tight junction
proteins. It is worth noting that such cellular events are stimulated by short chain fatty
113
acids (SCFAs) which are products of bacterial fermentation of undigested carbohydrates
in the intestine (Bond & Levitt, 1976; Sakata, 1987). They can enhance intestinal barrier
function as measured by increases in TEER (Schumann et al., 2005), and also affect
upper gut motility and satiety through triggering release of certain gut hormones
(Cherbut, 2006). The enteroendocrine L-cells secrete the gut hormones such as GLP-1,
PYY, and oxyntomodulin which are involved in appetite regulation (Delzenne et al.,
2006). In several studies it has been shown that fermentable carbohydrates such as inulin
(Delzenne et al., 2006), lactitol (Gee & Johnson, 2005), and FOS (Cani et al., 2005;
Delmee et al., 2006), increase satiety and decrease weight gain. From preliminary study
of the effect of maltooligosaccharides on these cell responses, a full study was conducted
to examine whether maltose and other maltooligosaccharides affect enterocyte tight
junctions and more generally cell differentiation. The purpose of this study was to
investigate the effect of maltooligosaccharides on cell differentiation biomarkers and to
identify other potential intracellular change by maltooligosaccharides using a
metabolomics approach.
4.2 Materials and Methods
4.2.1 Chemicals and Samples
4.2.1.1 Chemicals
Sodium phosphate dibasic anhydrous (Na2HPO4), sodium phosphate monobasic
anhydrous (NaH2PO4), sodium hydroxide (NaOH), and sodium chloride (NaCl) were
114
obtained from Fisher Scientific. Sodium 2,2-dimethyl-2-silapentane-5-sulfonate (DSS,
D6, 98%), and D2O (99.9% D) were purchased from Cambridge Isotope Laboratories,
Inc.
4.2.1.2 Cell Culture Procedure
Caco-2 cells (passage 30 to 45) were used for this study. Cells were seeded as 6.4
× 104 cells/well on a 6-well solid support (Corning, Lowell, MA). Cells were grown in
Dulbecco’s modified Eagle’s medium (25 mM, or equal to 4.5 g/l glucose DMEM,
Corning, Lowell, MA) supplied with 10% heat-inactivated (56°C, 30 min) fetal bovine
serum (FBS) (Lonza, Walkersville, MD), 50 µg/ml gentamycin sulfate (J R Scientific
Inc., Woodland, CA), 10 mM HEPES, 100 µg/ml streptomycin and 100 U/ml penicillin
(Lonza, Walkersville, MD), and 100 µM non-essential amino acid (Corning, Lowell,
MA). Cells were incubated at 37°C with 5% CO2, 95% air atmosphere, and at constant
humidity. Cells were grown to confluence for the complete differentiation of the cells and
maintained according to the procedures of Mahraoui et al. (1994) and Le Gall et al.
(2007).
4.2.1.3 Cell Treatment
Cells on transwells were washed twice with 1 ml PBS after they reached 100%
confluence and were fully differentiated. Cells were then fed with glucose-free DMEM
(Gibco, Carlsbad, CA). Test carbohydrates were supplied to the glucose-free DMEM
(Gibco, Carlsbad, CA). Different carbohydrate concentrations were used with mannitol to
115
adjust the osmolarity of the media and the treatment time points were according to each
experiment (Dudley et al., 1993).
4.2.1.4 Measurement of Epithelial Monolayer Electrical Resistance
In order to determine the integrity of the monolayer of Caco-2 cells,
transepithelial electrical resistance (TEER) of monolayers was measured by an epithelial
voltohmmeter (Millicell ERS-2 system, Millipore, Billerica, MA). Caco-2 monolayers
were grown to confluence on 12-well transwell inserts (0.4 µm pore size, polycarbonate
membrane; Corning, NY). After cells differentiated (15 days after seeding cells), high
glucose DMEM was switched to a modified DMEM supply with different added
carbohydrates (6 mM glucose, 25 mM glucose, 12.5 mM maltose, 8.33 mM maltotriose,
mixture of 6 mM glucose + 9.5 mM maltose, and 12.5 mM sucrose). TEER was
measured at 0, 12, 24, and 48 hours; and calculated and normalized to the TEER
measured at the day of differentiation (0 h).
4.2.1.5 Determination of Paracellular Permeability
Paracellular permeability was determined measuring the flux of fluorescein
isothiocyanate-dextran (FITC-dextran) concentration through cell monolayers. Cells were
seeded and fed as described in TEER experiment above. FITC-dextran was added to the
apical compartment and media was collected from the basolateral chambers at different
incubation times. Fluorescence intensity of FITC-dextran was measured at excitation
wavelength of 485 nm and emission wavelength of 538 nm using a fluorescence plate
116
reader (Molecular Devices, Chicago, IL). Paracellular permeability was calculated and
normalized to the FITC measured at day of differentiation (0 hour).
4.2.1.6 Sample Preparation for NMR Experiments
Caco-2 monolayers were grown to confluence on tissue-culture treated culture
dishes (100 mm x 20 mm; Corning, NY). After cells differentiated (20 days after seeding
cells), high glucose DMEM was switched to a modified DMEM supply with different
carbohydrates (6 mM glucose, 25mM glucose, 12.5 mM maltose, 8.33 mM maltotriose,
mixture of 5.5 mM glucose + 10 mM maltose, and 12.5 mM sucrose).
After treatment, cells were washed five times with 10 mL of ice-cold PBS with
the plates sitting on ice to remove media and other residues. Then cells were quickly
scrapped off and pelleted in a 15 mL tube using a refrigerated centrifuge. The cell pellet
was snap-frozen using dry ice and stored at -80 C until metabolite extraction. To isolate
metabolites, cell pellets were thawed on ice and resuspended in 1500 µL of 80%
methanol solution and vigorously mixed for every 5 min for 20 min. Then cells were
homogenized using FastPrep®-24 homogenizer (MP Biomedicals, Santa Ana, CA). After
that, homogenized cells were centerifuged at full speed (13,000 rpm) at 4°C for 20 min.
The supernatant was collected and methanol was evaporated using a SpeedVac
concentrator (Thermo Fisher Scientific, Waltham, MA). Phosphate buffer (150 µl, 0.1M,
pH=7.4) containing NaN3 (3 mM), and DSS-D6 (0.11 mM) was added to each sample,
vortexed, and the mixture transferred to a 2.5 mm NMR tube (Norell, Landisville, USA).
Twenty microliters of pellet was used for protein concentration measurements.
117
4.3 NMR Experiments and Data Processing
4.3.1 NMR Experiments
The spectra were collected on a 600 MHz proton frequency Avance II NMR
(Bruker Biospin, Germany) spectrometer equipped with a cryogenically cooled probe or
on a Bruker Avance-III-800 (Bruker Biospin, Germany) spectrometer equipped with a
TXI probe. All NMR data were acquired at 300oK and carried out in a random order. The
probe was locked to H2O+D2O (%90+%10), shimmed, tuned, and matched for each
sample. A 90° pulse length, the offset of the water signal, water suppression and receiver
gain for a data set were also determined.
One-dimensional (1D) nuclear Overhauser enhancement spectroscopy (NOESY)presat (noesypr1d) pulse sequence was used to acquire 1D proton spectra; 128 scans, 4
second relaxation delay, spectral width of 20 ppm and 65k data points were set to acquire
the data. Total acquisition time was 15 min.
Two-dimensional (2D) Heteronuclear Single Quantum Coherence (HSQC) and
Total Correlation Spectroscopy (TOCSY) were applied to one of the samples in each
group. All of the 2D spectra were collected on the 600 MHz proton frequency Avance II
NMR. Metabolites were assigned using Complex Mixture Analysis by NMR (COLMAR)
using 13C-1H HSQC. This information was combined and validated with 2D TOCSY
data.
The 2D 13C-1H HSQC spectra were acquired with 1024 and 2048 complex data
points in t1 and t2, respectively. The number of scans per t1 increment was 8 with a
corresponding spectral width of 24904.8 Hz along the indirect dimension and 7211.5 Hz
118
along the direct dimension. The transmitter frequency offsets were 70 ppm and 4.7 ppm
in the 13C and the 1H dimensions, respectively. Total acquisition time was 3 h and 49
min.
The 2D 1H-1H TOCSY spectra were acquired with 1024 and 2048 complex data
points in t1and t2, respectively. The number of scans per t1 increment was 8 with a
corresponding spectral width of 9014.4 Hz along the indirect dimension and 9003.4 Hz
along the direct dimension. The transmitter frequency offset was 4.7 ppm in both 1H
dimensions. Total acquisition time was 5 h and 50 min.
4.3.2 Data Processing
All 1D NMR spectra were phased and baseline corrected using Topspin 3.2. The
Bruker 1r files were imported into MATLAB (R2013B, the Mathworks, Inc., Natick,
MA). Spectra were all referenced to DSS peak at 0.0 ppm. Water regions (4.65- 4.80
ppm) and end points before -0.2 ppm and after 10 ppm were removed and the spectra
were aligned, normalized and scaled for further analysis. Ccow, PQN, and logoff
algorithms were used for alignment, normalization and scaling, respectively. PCA model
was performed to initially examine intrinsic variations in the data set.
All the 2D spectra were zero-filled, Fourier transformed, and phase and baseline
corrected by NMRPipe (Delaglio et al., 1995). Peak pickings were done using
NMRViewJ (9.0.0-b127, One Moon Scientific, Inc., Westfield, NJ).
119
NMR metabolomics data was collected at the Southeast Center for Integrated
Metabolomics at the University of Florida using the National High Magnetic Field
Laboratory’s AMRIS Facility.
4.3.2.1 Statistical Analysis
Data are shown as means ± S.E.M. Statistical analysis was performed using SAS
9.4 software. Two-way ANOVA was used to determine significance of treatment effect
and interactions. Significant differences between group means were assessed by Tukey
method which was accepted at p < 0.05.
4.4 Results
4.4.1 Different Source of Carbohydrates Alters Intestinal Epithelial Tight Junction
Barrier
The effect of different carbohydrates on tight junction barrier function was
measured using an epithelial voltohmmeter. Cells were fed with different carbohydrates
in two different ways. First, cells were seeded and fed high glucose Dulbecco's Modified
Eagle's medium (DMEM). After cells were differentiated (15 days after seeding), they
were fed with carbohydrate-free media containing 25 mM glucose (high glucose), 5.5
mM glucose (low glucose), 12.5 mM maltose, 8.33 mM maltotriose, 10 mM maltose and
5.5 mM glucose (low glucose+maltose), or 12.5 mM sucrose for 12, 24, 48, and 72 hours.
120
In addition to tight junction barrier function, the effect of different carbohydrates
on epithelial paracellular permeability was measured using FITC-labeled dextran. In
parallel with the TEER experiment, cells were fed the same two ways as in the tight
junction barrier experiment.
Results from ANOVA analysis of the TEER experiment in cells fed with different
carbohydrates showed that only glucose and maltose maintained the highest level of tight
junction barrier over the 72 hours period (Figure 4.1). In addition, maltose promoted
better tight junction at 12 hours which was significantly higher than glucose at 12 hours
(Figure 4.2). The other carbohydrates showed lower tight junction formation compared to
glucose and maltose (Figure 4.1). ANOVA analysis also was performed on epithelial
paracellular permeability in Caco-2 cells fed with different carbohydrates (Figure 4.3).
Result showed that glucose, maltose, maltose+low glucose, and maltotriose promoted
similar permeability. However, sucrose and low glucose were significantly higher and
significantly lower in permeability levels compared to other carbohydrates. In addition,
permeability of cells fed with maltose and sucrose was significantly higher at 12 hours
compared to cells fed with other carbohydrates (Figure 4.4). The low glucose fed cells
had significantly lower permeability compared to the other treatments.
4.4.2 Different Source of Carbohydrate Alters Metabolite Profiles in Caco-2 Monolayers
In this study, high-resolution 1H NMR was used to monitor the metabolites
involved in cell differentiation in Caco-2 cells fed with different carbohydrates.
Metabolites were extracted using 80% methanol-water which was found to provide the
best extraction condition in preliminarily experiments run on the same cells. The first
121
aspect noticed from the NMR spectra is the richness of the spectra showing that a wide
variety of metabolites were detected. Thus, the extraction method yielded a variety of
metabolites. Figure 4.5 shows the full spectrum of a representative glucose sample.
Identified peaks were assigned and listed in Table 4.1, the annotated metabolites are
shown in Figures 4.6 and 4.7. In the 1H NMR spectra, the aliphatic regions from 1 to 4.5
ppm are dominated by peaks from isoleucine, leucine, valine, 3-hydroxybutyrate,
threonine, lactate, acetate, alanine, arginine, glutamate, glutamine, succinate, creatine,
phosphorylcholine, taurine, myo-inositol, glycine, proline, and glycerophosphocholine
(Figure 4.6). These metabolites were identified by COLMAR 13C-1H HSQC query
database and by comparison of the NMR spectra with literature reports (Beckonert,
2007). The NMR spectrum from 5 to 10 ppm represents chemical shifts of the aromatic
nucleoside and ribose signals as well as shows the aromatic amino acids (nicotinamide
dinucleotide (NAD), tyrosine, histidine, tryptophan, phenylalanine, UDP-derivate, AMPderivate, fumarate, and formate (Figure 4.7). The results from the spectra of metabolites
in cells fed with different carbohydrates were used to monitor differences in the
metabolic profiles in different time points.
Alteration in cell contents corresponds to a change in metabolite levels and can be
considered as the metabolic fingerprint of the cells. Lee at al. (2009) identified the
metabolites involved in differentiation of Caco-2 cells. They showed that taurine, creatine
and glycerophocholine increased and myo-inositol decreased during cell differentiation.
In this study, we monitored the alteration in these metabolites in presence of different
carbohydrates. Taurine levels were significantly different in cells fed with maltose and
glucose compared to cells fed with other carbohydrates over 72 hours (Figure 4.8). Cells
122
fed with maltose showed significantly higher taurine level at 12 hours compare to cells
fed with other carbohydrates at 12 hours. (Figure 4.9). Also, level of myo-inositol
metabolites was significantly higher in cells fed with maltose compared to other
carbohydrates (Figure 4.10). Phosphorycholine (Figure 4.12 and Figure 4.13) and
glycerophosphocholine (Figure 4.14 and Figure 4.15) levels were significantly increased
in cells fed with maltooligosaccharides (maltose, mixture of maltose and glucose,
maltotriose). Results showed no change in creatine level over the different treatments.
4.5 Discussion
In the present study, the effect of maltooligosaccharides (maltose and
maltotriose), glucose (high and low concentration), and sucrose on two important
characters of the Caco-2 cell line (intestinal permeability and cell differentiation) were
evaluated.
While the main purpose of the small intestine is digestion and absorption of
nutrients, it also functions as a barrier. This prevents harmful substances (such as
antigens) from entering the body. Increase in intestinal permeability is a factor in several
autoimmune conditions such as Crohn's disease, type 1 diabetes, and inflammatory bowel
disease (Fasano, 2011; Johnson, 2013; Watts et al., 2005). Tight junctions are important
in limiting intestinal permeability and maintaining the barrier integrity by sealing the
paracellular space between epithelial cells. Tight junctions also regulate the entry of
nutrients, ions, and water while restricting pathogen entry, and determine the barrier
function of the epithelium (Farquhar et al., 1963). The structures of tight junctions are
123
dynamic and change due to interactions with external stimuli, such as food (Hashimoto et
al., 1994; Jensen-Jarolim et al., 1998).
Cell proliferation, polarization, and differentiation has a significant role in
creating and maintaining epithelial barrier integrity through tight junctions (Matter et al.,
2005). Cell differentiation is the process in which a less specialized cell becomes a more
specialized cell type. The Caco-2 cells differentiation process occurs from
homogeneously undifferentiated to heterogeneously polarized and differentiated to
homogeneously polarized and differentiated (Vachon & Beaulieu, 1992). Cells in cell
culture can also go the other way and lose properties they originally had, such as protein
expression, physiological, morphological, and/or biochemical properties. This process is
termed dedifferentiation (Schnabel et al., 2002). Culture-related conditions (e.g. glucose
concentration) can trigger dedifferentiation in cells (Lu et al., 1996).
Recently, we reported that maltose may act as a signaling molecule that changes
the molecular size of SI (Cheng et al., 2014). As this finding is indicative of a maltose
sensing ability by enterocytes, it also suggests alteration in other events in small intestine
enterocytes leading to such things as secretion of gut hormones and production of tight
junction proteins. In this study, we investigated the effect of maltooligosaccharides on
cell differentiation biomarkers and identifying other potential intracellular changes using
the metabolomics method, as well as their effect on permeability of intestinal epithelia
using the Caco-2 cells model.
First, we assessed intestinal epithelial tight junction barrier and epithelial
paracellular permeability function in the Caco-2 monolayer when exposed to different
carbohydrates. Results from TEER showed that that maltose and glucose significantly
124
increased tight junction formation compared to other carbohydrates (Figure 4.1). Maltose
induced a significant increase in cells at 12 hours when it compared to other
carbohydrates (Figure 4.2). Sucrose caused the lowest tight junction barrier formation in
Caco-2 cells. High glucose concentration is known to increase tight junctions (Madara &
Pappenheimer, 1987; Pappenheimer, 1987). Maltose had a similar effect to glucose
compare to other carbohydrates which decreased tight junction barrier over the 72 hour
treatment period. Although SI has the function of digesting maltose to glucose, in our
previous study (Cheng et al., 2014) using the same concentration of maltose in the
medium, we found a conversion at 12 hours of only about 2 mM glucose. This is far
below the 25 mM glucose in the high glucose treatment and well above the 5.5 mM
glucose of the low glucose treatment which had a low 12 hour TEER value. Hence,
maltose itself was implicated in the high tight junction barrier value, particularly at the 12
hour time point.
The results from the paracellular permeability experiment were contrary to
TEER for maltose and high glucose treatments at 12 hours with maltose showing higher
permeability than high glucose. The maltose+low glucose and maltotriose treatments had
similar effects to maltose on cell permeability (Figure 4.3). Sucrose promoted the highest
permeability and low glucose caused the lowest permeability in Caco-2 cells.
1
H NMR spectroscopy was used to examine the intracellular metabolic level of
Caco-2 cells in the presence of different small molecule carbohydrates over a 72 h period
and to identify cell biomarkers that are involved in cell differentiation. Lee at al. (2009)
showed changes in metabolite profiling during Caco-2 cell differentiation.
125
Results showed that taurine level was significant in cells fed with maltose and
glucose (Figure 4.8). Taurine level was significantly higher at 72 hours compared to cells
fed other carbohydrates (Figure 4.9). Myo-inositol level was significantly higher in cells
fed with maltose compared to cells fed with other carbohydrates (Figure 4.10). In cells
fed with maltose at 72 hour, myo-inositol was significantly higher compare to cells fed
with other carbohydrates (Figure 4.11). The DMEM used for culturing Caco-2 cells does
not contain taurine. Taurine is made from cysteine (Beetsch et al., 1998) and behaves as
an osmolyte to regulate extracellular osmolarity (Satsu et al., 1999). It has been shown
that in an acute hypotonic stimulus, cells release taurine and myo-inositol to respond the
volume increase and to avoid swelling-induced cell lysis (Ullrich et al., 2006). These
results suggest that maltooligosaccharides induced biosynthesizing of taurine and
glycerophosphorylcholine which are seen in more differentiated cells (it is important to
note that the osmolality of DMEM with different carbohydrates are same).
Phosphorylcholine level of cells fed with maltose, was significantly higher
compared cells fed with other carbohydrates (Figure 4.12 & 4.13). Results showed that
glycerophosphocholine level in cells fed with maltose, maltotriose, maltose+low glucose,
and sucrose were significantly higher than cells fed with glucose and low glucose (Figure
4.14 & Figure 4.16). Phosphorycholine was another metabolite that significantly
increased only in Caco-2 cells fed with maltooligosaccharides. This metabolite is
involved in phospholipids sphingomyelin structure that can be found abundantly in lipid
raft membrane (Yeagle, 2004). In Chapter 3, maltose was shown to induce a higher
molecular SI which are more lipid raft associated. This data may further supports that
higher lipid raft structure was caused by maltooligosaccharides.
126
As starch and its derived products (e.g., maltodextrins) are the principal dietary
supply of glucose, investigation of their physiological effects on intestinal enterocyte
cells is important for this aspect of human health. This is the first study to use
maltooligosaccharides as the energy source of cells for studying tight junction barrier,
cell permeability, and cell differentiation. This study revealed that maltooligosaccharides
have the ability to promote tight junction formation and cell differentiation compared to
glucose and sucrose. This event might be related to sensing maltose, perhaps by the sweet
taste receptor, to result in better formation of tight junctions and barrier function.
However, more investigation is required to study potential signaling pathways involved
in improving cell permeability in presence of maltose.
127
Table 4.1 Metabolite assignments and chemical shifts of identified peaks.
Assigned number Metabolite
Chemical shift (ppm)
1
Pantothenate
0.88, 0.93
2
Leucine
0.95
3
Isoleucine
0.95, 1.00
4
Valine
0.98, 1.03
5
3-hydroxy butyrate
1.21
6
Lactate
1.32, 4.10
7
Alanine
1.47
8
Arginine
1.69
9
Acetate acid
1.91
10
Proline
1.99, 2.06
11
Glutamate
2.04, 2.11, 2.34
12
Glutamine
2.12, 2.45
13
Succinate
2.39
14
Beta-Alanine
2.54, 3.16
15
Creatine
3.03, 3.92
16
Glycerophosphocholine 3.20, 4.32
17
Phosphorylcholine
3.21
18
Taurine
3.26, 3.42
19
Myo-Inositol
3.53
20
Glycine
3.56
21
NAD
6.02, 6.09,6.13, 8.82, 9.32, 9.12
22
Fumarate
6.51
23
Tyrosine
6.89, 7.18
24
Histidine
7.10
25
Tryptophane
7.53, 7.32
26
Phenylalanine
7.32, 7.34, 7.42
27
UDP derivate
7.93
28
Formate
8.41
29
AMP derivate
8.59, 8.16
128
120
A
A
TEER
(% of differentiated cell in 0 hour)
100
B
80
BC
High Glucose
C
Low Glucose
60
Maltose
Maltotriose
D
Low Glucose + Maltose
Sucrose
40
20
0
Type of carbohydrate
Figure 4.1 Intestinal epithelial tight junction barrier function in Caco-2 cells in presence
of different types of carbohydrates. Cells were fed carbohydrate-free media containing 25
mM glucose (high glucose), 5.5 mM glucose (low glucose), 12.5 mM maltose, 8.33 mM
maltotriose, 10 mM maltose and 5.5 mM glucose (low glucose+maltose), or 12.5 mM
sucrose over 72 hours. The data was normalized with time zero (after cells
differentiated). Results represent the mean ± SEM of at least three wells of treated Caco2 cells. Different letters denote significant differences at α=0.05.
129
140
*
TEER
(% of differentiated cell in 0 hour)
120
100
*
*
*
80
*
* *
*
*
*
60
*
*
*
*
40
*
*
*
20
0
12
24
48
72
Time (h)
High Glucose
Low Glucose
Maltose
Maltotriose
Low Glucose + Maltose
Sucrose
Figure 4.2 Intestinal epithelial tight junction barrier function in Caco-2 cells in presence
of different types of carbohydrates. Cells were fed carbohydrate-free media containing 25
mM glucose (high glucose), 5.5 mM glucose (low glucose), 12.5 mM maltose, 8.33 mM
maltotriose, 10 mM maltose and 5.5 mM glucose (low glucose+maltose), or 12.5 mM
sucrose for 12, 24, 48, and 72 hours. The data was normalized with time zero (after cells
differentiated). Results represent the mean ± SEM of at least three wells of treated Caco2 cells. Asterisks denote significant differences at α=0.05 between the carbohydrate
treatments at that time point using high glucose as the reference.
130
160
A
Paracellular Permeability
(% of differentiated cell in 0 hour)
140
120
B
B
B
B
100
High glucose
Low glucose
80
Maltose
Maltotriose
C
Maltose+low glucose
60
Sucrose
40
20
0
Type of carbohydrate
Figure 4.3 Epithelial paracellular permeability in Caco-2 cells in presence of different
types of carbohydrates. Cells were fed carbohydrate-free media containing 25 mM
glucose (high glucose), 5.5 mM glucose (low glucose), 12.5 mM maltose, 8.33 mM
maltotriose, 10 mM maltose and 5.5 mM glucose (low glucose+maltose), or 12.5 mM
sucrose over 72 hours. The data was normalized with time zero (after cells
differentiated). Results represent the mean ± SEM of at least three wells of treated Caco2 cells. Different letters denote significant differences at α=0.05.
131
180
Paracellular Permeability
(% of differentiated cell in 0 hour)
160
140
*
*
*
*
*
120
100
*
80
60
*
*
*
40
20
0
12
24
48
72
Time (h)
High glucose
Low glucose
Maltose
Maltotriose
Maltose+low glucose
Sucrose
Figure 4.4 Epithelial paracellular permeability in Caco-2 cells in presence of different
types of carbohydrates. Cells were fed carbohydrate-free media containing 25 mM
glucose (high glucose), 5.5 mM glucose (low glucose), 12.5 mM maltose, 8.33 mM
maltotriose, 10 mM maltose and 5.5 mM glucose (low glucose+maltose), or 12.5 mM
sucrose for 12, 24, 48, and 72 hours. The data was normalized with time zero (after cells
differentiated). Results represent the mean ± SEM of at least three wells of treated Caco2 cells. Asterisks denote significant differences at α=0.05 between the carbohydrate
treatments at that time point using high glucose as the reference.
10.0
9.5
9.0
8.5
8.0
7.5
7.0
6.5
6.0
5.5
5.0
4.5
4.0
Chemical Shift (ppm)
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
132
Figure 4.5 Representative 1D 1H NMR noesygppr1d spectrum of metabolites extracted from Caco-2 cells fed with glucose.
6
2,3
17
1
4
13
14
4
10,11
11,12
3
12
2.75
2.70
2.65
2.60
2.55
2.50
2.45
2.40
2.35
2.30
2.25
2.20
2.15
2.10
2.05
2.00
5
7
9
16
1
1.95
14
1.25
1.20
1.15
1.10
1.05
1.00
0.95
0.90
0.85
6
18
20
19
16
4.6
4.4
4.2
4.0
3.8
3.6
15
18
3.4
3.2
3.0
10
2.8
2.6
2.4
2.2
2.0
1.8
Chemical Shift (ppm)
DSS
8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Figure 4.6 Aliphatic region of a representative 1D 1H NMR noesygppr1d spectrum of metabolites extracted from Caco-2 cells fed
with glucose. Identified metabolites have been labeled with numbers, according to Table 4.1.
133
29
21
27
28
29
21
23
23
26
26
26
21
21
21
24
25
9.4
9.2
9.0
8.8
21
8.6
8.4
8.2
8.0
7.8
25
7.6
22
7.4
7.2
7.0
6.8
Chemical Shift (ppm)
6.6
6.4
6.2
6.0
5.8
5.6
5.4
5.2
Figure 4.7 Aromatic region of a representative 1D 1H NMR noesygppr1d spectrum of metabolites extracted from Caco-2 cells fed
with glucose. Identified metabolites have been labeled with numbers, according to Table 4.1
134
135
1.2
Relative populations of taurine
1
A
A
AB
0.8
High glucose
BC
BC
Low glucose
C
0.6
Maltose
Maltotriose
Maltose+low glucose
Sucrose
0.4
0.2
0
Type of carbohydrate
Figure 4.8 Relative population of taurine in Caco-2 cells in presence of different types of
carbohydrates. Cells were fed carbohydrate-free media containing 25 mM glucose (high
glucose), 5.5 mM glucose (low glucose), 12.5 mM maltose, 8.33 mM maltotriose, 10 mM
maltose and 5.5 mM glucose (low glucose+maltose), or 12.5 mM sucrose over 72 hours.
The data was normalized with time zero (after cells differentiated). Results represent the
mean ± SEM of at least three wells of treated Caco-2 cells. Different letters denote
significant differences at α=0.05.
136
1.6
*
Relative populations of taurine
1.4
1.2
1
*
0.8
*
0.6
* *
*
*
*
*
*
*
*
0.4
0.2
0
12
24
36
48
72
Time (h)
High glucose
Low glucose
Maltose
Maltotriose
Maltose+low glucose
Sucrose
Figure 4.9 Relative population of taurine in Caco-2 cells in presence of different types of
carbohydrates. Cells were fed carbohydrate-free media containing 25 mM glucose (high
glucose), 5.5 mM glucose (low glucose), 12.5 mM maltose, 8.33 mM maltotriose, 10 mM
maltose and 5.5 mM glucose (low glucose+maltose), or 12.5 mM sucrose for 12, 24, 48,
and 72 hours. Results represent the mean ± SEM of at least three wells of treated Caco-2
cells. Asterisks denote significant differences at α=0.05 between the carbohydrate
treatments at that time point using high glucose as the reference.
137
2.5
A
Relative populations of myo-inositol
2
AB
1.5
B
B
B
AB
High glucose
Low glucose
Maltose
Maltotriose
1
Maltose+low glucose
Sucrose
0.5
0
Type of carbohydrate
Figure 4.10 Relative population of myo-inositole in Caco-2 cells in presence of different
types of carbohydrates. Cells were fed carbohydrate-free media containing 25 mM
glucose (high glucose), 5.5 mM glucose (low glucose), 12.5 mM maltose, 8.33 mM
maltotriose, 10 mM maltose and 5.5 mM glucose (low glucose+maltose), or 12.5 mM
sucrose over 72 hours. The data was normalized with time zero (after cells
differentiated). Results represent the mean ± SEM of at least three wells of treated Caco2 cells. Different letters denote significant difference at α=0.05.
138
3.5
Relative populations of myo-inositol
3
2.5
*
2
*
1.5
*
* *
*
*
*
*
*
*
1
0.5
0
12
24
36
48
72
Time (h)
High glucose
Low glucose
Maltose
Maltotriose
Maltose+low glucose
Sucrose
Figure 4.11 Relative population of myo-inositol in Caco-2 cells in presence of different
types of carbohydrates. Cells were fed carbohydrate-free media containing 25 mM
glucose (high glucose), 5.5 mM glucose (low glucose), 12.5 mM maltose, 8.33 mM
maltotriose, 10 mM maltose and 5.5 mM glucose (low glucose+maltose), or 12.5 mM
sucrose for 12, 24, 48, and 72 hours. The data was normalized with time zero (after cells
differentiated). Results represent the mean ± SEM of at least three wells of treated Caco2 cells. Asterisks denote significant differences at α=0.05 between the carbohydrate
treatments at that time point using high glucose as the reference.
139
3
Relative populations of phosphorylcholine
A
2.5
B
2
B
C
B
B
High glucose
Low glucose
Maltose
1.5
Maltotriose
Maltose+low glucose
Sucrose
1
0.5
0
Type of carbohydrate
Figure 4.12 Relative population of phosphorylcholine in Caco-2 cells in presence of
different types of carbohydrates. Cells were fed carbohydrate-free media containing 25
mM glucose (high glucose), 5.5 mM glucose (low glucose), 12.5 mM maltose, 8.33 mM
maltotriose, 10 mM maltose and 5.5 mM glucose (low glucose+maltose), or 12.5 mM
sucrose over 72 hours. The data was normalized with time zero (after cells
differentiated). Results represent the mean ± SEM of at least three wells of treated Caco2 cells. Different letters denote significant difference at α=0.05.
140
3
Relative populations of phosphorylcholine
*
*
*
2.5
*
*
*
* *
* *
*
* *
*
* *
*
*
*
*
2
*
1.5
1
0.5
0
12
24
36
48
72
Time (h)
High glucose
Low glucose
Maltose
Maltotriose
Maltose+low glucose
Sucrose
Figure 4.13 Relative population of phosphorylcholine in Caco-2 cells in presence of
different types of carbohydrates. Cells were fed carbohydrate-free media containing 25
mM glucose (high glucose), 5.5 mM glucose (low glucose), 12.5 mM maltose, 8.33 mM
maltotriose, 10 mM maltose and 5.5 mM glucose (low glucose+maltose), or 12.5 mM
sucrose for 12, 24, 48, and 72 hours. The data was normalized with time zero (after cells
differentiated). Results represent the mean ± SEM of at least three wells of treated Caco2 cells. Asterisks denote significant differences at α=0.05 between the carbohydrate
treatments at that time point using high glucose as the reference.
141
Relative populations of glycerophosphocholine
3
AB
2.5
AB
A
BC
C
2
D
High glucose
Low glucose
Maltose
1.5
Maltotriose
Maltose+low glucose
Sucrose
1
0.5
0
Type of carbohydrate
Figure 4.14 Relative population of glycerophosphocholine in Caco-2 cells in presence of
different types of carbohydrates. Cells were fed carbohydrate-free media containing 25
mM glucose (high glucose), 5.5 mM glucose (low glucose), 12.5 mM maltose, 8.33 mM
maltotriose, 10 mM maltose and 5.5 mM glucose (low glucose+maltose), or 12.5 mM
sucrose over 72 hours. The data was normalized with time zero (after cells
differentiated). Results represent the mean ± SEM of at least three wells of treated Caco2 cells. Different letters denote significant difference at α=0.05.
142
Relative populations of glycerophosphocholine
3
*
*
*
2.5
2
*
* *
*
*
*
*
*
* *
*
*
*
*
*
*
*
*
*
*
*
1.5
1
0.5
0
12
24
36
48
72
Time (h)
High glucose
Low glucose
Maltose
Maltotriose
Maltose+low glucose
Sucrose
Figure 4.15 Relative population of glycerophosphocholine in Caco-2 cells in presence of
different types of carbohydrates. Cells were fed carbohydrate-free media containing 25
mM glucose (high glucose), 5.5 mM glucose (low glucose), 12.5 mM maltose, 8.33 mM
maltotriose, 10 mM maltose and 5.5 mM glucose (low glucose+maltose), or 12.5 mM
sucrose for 12, 24, 48, and 72 hours. The data was normalized with time zero (after cells
differentiated). Results represent the mean ± SEM of at least three wells of treated Caco2 cells. Asterisks denote significant differences at α=0.05 between the carbohydrate
treatments at that time point using high glucose as the reference.
143
4.6 References
Anderssen, E., Dyrstad, K., Westad, F., & Martens, H. (2006). Reducing over-optimism
in variable selection by cross-model validation. Chemometrics and Intelligent Laboratory
Systems, 84(1), 69-74.
Barker, M., & Rayens, W. (2003). Partial least squares for discrimination. Journal of
Chemometrics, 17(3), 166-173.
Beckonert, O., Keun, H. C., Ebbels, T. M., Bundy, J., Holmes, E., Lindon, J. C., &
Nicholson, J. K. (2007). Metabolic profiling, metabolomic and metabonomic procedures
for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nature Protocols,
2(11), 2692-2703.
Beetsch, J. W., & Olson, J. E. (1998). Taurine synthesis and cysteine metabolism in
cultured rat astrocytes: effects of hyperosmotic exposure. American Journal of
Physiology - Cell Physiology, 274, C866–874.
Bolten, C. J., & Wittmann, C. (2008). Appropriate sampling for intracellular amino acid
analysis in five phylogenetically different yeasts. Biotechnology Letters, 30(11), 19932000.
Bond Jr, J. H., & Levitt, M. D. (1976). Fate of soluble carbohydrate in the colon of rats
and man. Journal of Clinical Investigation, 57(5), 1158.
Cani, P. D., Neyrinck, A. M., Maton, N., & Delzenne, N. M. (2005). Oligofructose
Promotes Satiety in Rats Fed a High‐Fat Diet: Involvement of Glucagon‐Like Peptide‐1.
Obesity Research, 13(6), 1000-1007.
Castrillo, J. I., Hayes, A., Mohammed, S., Gaskell, S. J., & Oliver, S. G. (2003). An
optimized protocol for metabolome analysis in yeast using direct infusion electrospray
mass spectrometry. Phytochemistry, 62(6), 929-937.
Cherbut, C. (2003). Motor effects of short-chain fatty acids and lactate in the
gastrointestinal tract. Proceedings of the Nutrition Society, 62(01), 95-99.
Craig, A., Cloarec, O., Holmes, E., Nicholson, J. K., & Lindon, J. C. (2006). Scaling and
normalization effects in NMR spectroscopic metabonomic data sets. Analytical
Chemistry, 78(7), 2262-2267.
144
Davis, R. A., Charlton, A. J., Godward, J., Jones, S. A., Harrison, M., & Wilson, J. C.
(2007). Adaptive binning: An improved binning method for metabolomics data using the
undecimated wavelet transform. Chemometrics and Intelligent Laboratory Systems,
85(1), 144-154.
De Meyer, T., Sinnaeve, D., Van Gasse, B., Tsiporkova, E., Rietzschel, E. R., De
Buyzere, M. L., & Van Criekinge, W. (2008). NMR-based characterization of metabolic
alterations in hypertension using an adaptive, intelligent binning algorithm. Analytical
Chemistry, 80(10), 3783-3790.
Delaglio, F., Grzesiek, S., Vuister, G. W., Zhu, G., Pfeifer, J., & Bax, A. D. (1995).
NMRPipe: a multidimensional spectral processing system based on UNIX pipes. Journal
of Biomolecular NMR, 6(3), 277-293.
Delmée, E., Cani, P. D., Gual, G., Knauf, C., Burcelin, R., Maton, N., & Delzenne, N. M.
(2006). Relation between colonic proglucagon expression and metabolic response to
oligofructose in high fat diet-fed mice. Life Sciences, 79(10), 1007-1013.
Delzenne, N. M., Cani, P. D., Daubioul, C., & Neyrinck, A. M. (2005). Impact of inulin
and oligofructose on gastrointestinal peptides. British Journal of Nutrition, 93(S1), S157S161.
Dettmer, K., Nürnberger, N., Kaspar, H., Gruber, M. A., Almstetter, M. F., & Oefner, P.
J. (2011). Metabolite extraction from adherently growing mammalian cells for
metabolomics studies: optimization of harvesting and extraction protocols. Analytical and
Bioanalytical Chemistry, 399(3), 1127-1139.
Dieterle, F., Ross, A., Schlotterbeck, G., & Senn, H. (2006). Probabilistic quotient
normalization as robust method to account for dilution of complex biological mixtures.
Application in 1H NMR metabonomics. Analytical Chemistry, 78(13), 4281-4290.
Hashimoto, K., Matsunaga, N., & Shimizu, M. (1994). Effect of vegetable extracts on the
transepithelial permeability of the human intestinal Caco-2 cell monolayer. Bioscience,
Biotechnology, and Biochemistry, 58(7), 1345-1346.
Jenkins, D. J., & Kendall, C. W. (2002). Glycemic index: overview of implications in
health and disease. The American Journal of Clinical Nutrition. 76(1): 266S- 273S.
Jensen-Jarolim, E., Gajdzik, L., Haberl, I., Kraft, D., Scheiner, O., & Graf, J. (1998). Hot
spices influence permeability of human intestinal epithelial monolayers. The Journal of
Nutrition, 128(3), 577-581.
145
Faijes, M., Mars, A. E., & Smid, E. J. (2007). Comparison of quenching and extraction
methodologies for metabolome analysis of Lactobacillus plantarum. Microbial Cell
Factories, 6(1), 27.
Farquhar, M. G., & Palade, G. E. (1963). Junctional complexes in various epithelia. The
Journal of Cell Biology, 17(2), 375-412.
Fasano, A. (2011). Zonulin and its regulation of intestinal barrier function: the biological
door to inflammation, autoimmunity, and cancer. Physiological reviews, 91(1), 151-175.
Johnson, K. (2013). Leaky Gut Syndrome. Encyclopedia of Autism Spectrum Disorders,
1706-1712.
Fiehn, O. (2002). Metabolomics–the link between genotypes and phenotypes. Plant
Molecular Biology, 48(1-2), 155-171.
Florian, C. L., Preece, N. E., Bhakoo, K. K., Williams, S. R., & Noble, M. D. (1995).
Cell type-specific fingerprinting of meningioma and meningeal cells by proton nuclear
magnetic resonance spectroscopy. Cancer Research, 55(2), 420-427.
Gates, S. C., & Sweeley, C. C. (1978). Quantitative metabolic profiling based on gas
chromatography. Clinical Chemistry, 24(10), 1663-1673.
Gee, J. M., & Johnson, I. T. (2005). Dietary lactitol fermentation increases circulating
peptide YY and glucagon-like peptide-1 in rats and humans. Nutrition, 21(10), 10361043.
Gottschalk, M., Ivanova, G., Collins, D. M., Eustace, A., O'connor, R., & Brougham, D.
F. (2008). Metabolomic studies of human lung carcinoma cell lines using in vitro1H
NMR of whole cells and cellular extracts. NMR in Biomedicine, 21(8), 809-819.
Gu, H., Pan, Z., Xi, B., Asiago, V., Musselman, B., & Raftery, D. (2011). Principal
component directed partial least squares analysis for combining nuclear magnetic
resonance and mass spectrometry data in metabolomics: Application to the detection of
breast cancer. Analytica Chimica Acta, 686(1), 57-63.
Guan, W., Zhou, M., Hampton, C. Y., Benigno, B. B., Walker, L. D., Gray, A., &
Fernández, F. M. (2009). Ovarian cancer detection from metabolomic liquid
chromatography/mass spectrometry data by support vector machines. BMC
Bioinformatics, 10(1), 259.
146
Horning, E. C., & Horning, M-G. (1971). Metabolic profiles: gas-phase methods for
analysis of metabolites. Clinical Chemistry 17.8, 802-809.
Khoo, S. H. G., & Al-Rubeai, M. (2009). Metabolic characterization of a hyperproductive state in an antibody producing NS0 myeloma cell line. Metabolic
Engineering, 11(3), 199-211.
Khoo, S. H. G., & Al-Rubeai, M. (2007). Metabolomics as a complementary tool in cell
culture. Biotechnology and Applied Biochemistry, 47(2), 71-84.
Koning, W. D., & Dam, K. V. (1992). A method for the determination of changes of
glycolytic metabolites in yeast on a subsecond time scale using extraction at neutral pH.
Analytical Biochemistry, 204(1), 118-123.
Lee, I. J., Hom, K., Bai, G., & Shapiro, M. (2009). NMR metabolomic analysis of caco-2
cell differentiation. Journal of Proteome Research, 8(8), 4104-4108.
Le Gall, M. L., Tobin, V., Stolarczyk, E., Dalet, V., Leturque, A., & Brot-Laroche, E.
(2007). Sugar sensing by enterocytes combines polarity, membrane bound detectors and
sugar metabolism. Journal of Cellular Physiology. 213(3): 834-843.
Le Belle, J. E., Harris, N. G., Williams, S. R., & Bhakoo, K. K. (2002). A comparison of
cell and tissue extraction techniques using high resolution 1H NMR spectroscopy. NMR
in Biomedicine, 15(1), 37-44.
Lu, S., Gough, A. W., Bobrowski, W. F., & Stewart, B. H. (1996). Transport properties
are not altered across Caco-2 cells with heightened TEER despite underlying
physiological and ultrastructural changes. Journal of Pharmaceutical Sciences, 85(3),
270-273.
Madara, J. L., & Pappenheimer, J. R. (1987). Structural basis for physiological regulation
of paracellular pathways in intestinal epithelia. The Journal of Membrane Biology,
100(1), 149-164.
Matter, K., Aijaz, S., Tsapara, A., & Balda, M. S. (2005). Mammalian tight junctions in
the regulation of epithelial differentiation and proliferation. Current Opinion in Cell
Biology, 17(5), 453-458.
Mitchell, S., Holmes, E., & Carmichael, P. (2002). Metabonomics and medicine: the
Biochemical Oracle. Biologist, 49(5), 217-221.
147
Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999). 'Metabonomics': understanding the
metabolic responses of living systems to pathophysiological stimuli via multivariate
statistical analysis of biological NMR spectroscopic data. Xenobiotica, 29(11), 11811189.
Oliver, S. G., Winson, M. K., Kell, D. B., & Baganz, F. (1998). Systematic functional
analysis of the yeast genome. Trends in Biotechnology, 16(9), 373-378.
Pappenheimer, J. R. (1987). Physiological regulation of transepithelial impedance in the
intestinal mucosa of rats and hamsters. The Journal of Membrane Biology, 100(1), 137148.
Patterson, A. D., Li, H., Eichler, G. S., Krausz, K. W., Weinstein, J. N., Fornace, A. J., &
Idle, J. R. (2008). UPLC-ESI-TOFMS-based metabolomics and gene expression
dynamics inspector self-organizing metabolomic maps as tools for understanding the
cellular response to ionizing radiation. Analytical Chemistry, 80(3), 665-674.
Ritter, J. B., Genzel, Y., & Reichl, U. (2008). Simultaneous extraction of several
metabolites of energy metabolism and related substances in mammalian cells:
optimization using experimental design. Analytical Biochemistry, 373(2), 349-369.
Sakata, T. (1987). Stimulatory effect of short-chain fatty acids on epithelial cell
proliferation in the rat intestine: a possible explanation for trophic effects of fermentable
fibre, gut microbes and luminal trophic factors. British Journal of Nutrition, 58(01), 95103.
Satsu, H., Miyamoto, Y., & Shimizu, M. (1999). Hypertonicity stimulates taurine uptake
and transporter gene expression in Caco-2 cells. Biochimica et Biophysica Acta, 1419,
89–96.
Sellick, C. A., Hansen, R., Maqsood, A. R., Dunn, W. B., Stephens, G. M., Goodacre, R.,
& Dickson, A. J. (2008). Effective quenching processes for physiologically valid
metabolite profiling of suspension cultured mammalian cells. Analytical Chemistry,
81(1), 174-183.
Schnabel, M., Marlovits, S., Eckhoff, G., Fichtel, I., Gotzen, L., Vecsei, V., & Schlegel,
J. (2002). Dedifferentiation-associated changes in morphology and gene expression in
primary human articular chondrocytes in cell culture. Osteoarthritis and Cartilage, 10(1),
62-70.
148
Schumann, A., Nutten, S., Donnicola, D., Comelli, E. M., Mansourian, R., Cherbut, C. &
Garcia-Rodenas, C. (2005). Neonatal antibiotic treatment alters gastrointestinal tract
developmental gene expression and intestinal barrier transcriptome. Physiological
Genomics, 23(2), 235-245.
Sousa S.A.A., Magalhães A., & Ferreira M.M.C. (2013). Optimized bucketing for NMR
spectra: Three case studies. Chemometrics and Intelligent Laboratory Systems, 122:93–
102.
Stretch, C., Eastman, T., Mandal, R., Eisner, R., Wishart, D. S., Mourtzakis, M., &
Baracos, V. E. (2012). Prediction of skeletal muscle and fat mass in patients with
advanced cancer using a metabolomic approach. The Journal of Nutrition, 142(1), 14-21.
Tate, A. R., Crabb, S., Griffiths, J. R., Howells, S. L., Mazucco, R. A., Rodrigues, L. M.,
& Watson, D. (1995). Lipid metabolite peaks in pattern recognition analysis of tumour in
vivo MR spectra. Anticancer Research, 16(3B), 1575-1579.
Tate, A. R., Griffiths, J. R., Martínez-Pérez, I., Moreno, A., Barba, I., Cabañas, M. E., ...
& Arús, C. (1998). Towards a method for automated classification of 1H MRS spectra
from brain tumours. NMR in Biomedicine, 11(45), 177-191.
Teng, Q., Huang, W., Collette, T. W., Ekman, D. R., & Tan, C. (2009). A direct cell
quenching method for cell-culture based metabolomics. Metabolomics, 5(2), 199-208.
Tweeddale, H., Notley-McRobb, L., & Ferenci, T. (1998). Effect of slow growth on
metabolism of Escherichia coli, as revealed by global metabolite pool (“metabolome”)
analysis. Journal of Bacteriology, 180(19), 5109-5116.
Ullrich, N., Caplanusi, A., Brone, B., Hermans, D., Lariviere, E., Nilius, B., Van
Driessche, W., & Eggermont, J. (2006). Stimulation by caveolin-1 of the hypotonicityinduced release of taurine and ATP at basolateral, but not apical, membrane of Caco-2
cells. American Journal of Physiology - Cell Physiology, 290, C1287–1296.
Vachon, P. H., & Beaulieu, J. F. (1992). Transient mosaic patterns of morphological and
functional differentiation in the Caco-2 cell line. Gastroenterology, 103(2), 414-423.
Van Vliet, E., Morath, S., Eskes, C., Linge, J., Rappsilber, J., Honegger, P., Hartung, T.,
& Coecke, S. (2008). A novel in vitro metabolomics approach for neurotoxicity testing,
proof of principle for methyl mercury chloride and caffeine. Neurotoxicology, 29(1), 112.
149
Veselkov, K. A., Lindon, J. C., Ebbels, T. M., Crockford, D., Volynkin, V. V., Holmes,
E., & Nicholson, J. K. (2008). Recursive segment-wise peak alignment of biological 1H
NMR spectra for improved metabolic biomarker recovery. Analytical Chemistry, 81(1),
56-66.
Viant, M. R., Bundy, J. G., Pincetich, C. A., de Ropp, J. S., & Tjeerdema, R. S. (2005).
NMR-derived developmental metabolic trajectories: an approach for visualizing the toxic
actions of trichloroethylene during embryogenesis. Metabolomics, 1(2), 149-158.
Watts, T., Berti, I., Sapone, A., Gerarduzzi, T., Not, T., Zielke, R., & Fasano, A. (2005).
Role of the intestinal tight junction modulator zonulin in the pathogenesis of type I
diabetes in BB diabetic-prone rats. Proceedings of the National Academy of Sciences
USA, 102(8), 2916-2921.
Westerhuis, J. A., Hoefsloot, H. C., Smit, S., Vis, D. J., Smilde, A. K., van Velzen, E. J.,
& Van Dorsten, F. A. (2008). Assessment of PLSDA cross validation. Metabolomics,
4(1), 81-89.
Wold, S., & Sjöström, M. (1987). Comments on a recent evaluation of the SIMCA
method. Journal of Chemometrics, 1(4), 243-245.
Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of
chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109-130.
Yeagle, P. L. (Ed.). (2010). The structure of biological membranes. CRC press.
150
CHAPTER 5. OVERALL CONCLUSIONS AND FUTURE RECOMMENDATIONS
The findings of this thesis supports the overall hypothesis that
maltooligosaccharides (generated by α-amylase degradation of dietary starch) cause the
maturation, trafficking, and activation of HMW SI to the apical membrane of intestinal
enterocytes. It additionally provided further evidence of maltose sensing at the enterocyte
surface that signaled cell differentiation events and an increase in monolayer tight
junctions as measured by cell permeability tests. Maltooligosaccharide sensing appeared
to occur via the gut sweet taste receptor to trigger the processing and trafficking of SI.
The expression of sweet taste receptor subunits (T1R2/T1R3) was increased in presence
of maltose. The increase of T1R2 expression was 30-fold for maltose compared to 18fold for glucose. Also the expression of T1R3 increased 9-fold for maltose compared to
6-fold for glucose. It seems sweet taste receptor subunits sense the existence of
maltooligosaccharides via the gastrointestinal track and signal the cell to express a higher
molecular weight species of SI that is mobilized to the enterocyte apical surface and
therein activated.
This increase in molecular weight in SI is due to a post-translational events. The
deglycosylation endo F and endo H enzyme treatments showed that most of the new
glycosylations on SI are the N-glycosylation type, with some possible O-glycosylation.
SI glycosylation occurs at its co-translation and post-translation stages and is critical to
151
the maturation of the SI enzyme active form. As shown in Chapter 3, the new Nand O-linked glycans promoted different events related to maturation and trafficking of
SI, as shown in a series of experiments described as follows.
First, the change in glycosylation of SI induced by maltose altered the distribution
of SI in cell compartments (intercellular vs. apical membrane). The result from this
experiment showed significant increase in the amount of SI on apical side of membrane.
Second, this study showed that the extra O-linked glycans on SI induced by
maltose enhanced association of SI with lipid rafts. SI is more associated with the lipid
raft region on the enterocyte membrane (Le Bivic et al., 1990), which has significance to
starch digestion as SI has higher activity when it is found in lipid raft regions (Alfalah et
al., 1999). This sorting of SI to lipid raft happens via O-linked glycans (Jacob et al.,
2000).
Third, the pulse-chase study clearly revealed that the mature form of SI (fully
glycosylated and active form) was induced by maltose within one hour. This shows that
maltose not only induces the formation of higher N- and O-glycosylated SI, but also the
mature form of SI trafficks faster to the apical membrane for digestion of
maltooligosaccharides in small intestine. This could be of practical importance to the
body related to the ileal brake mechanism, as ileal received maltooligosaccharides would
signal the final maturation and activation of SI within a short time period where it could
digest well the starchy food retained in the stomach to be releases over longer
postprandial period.
152
Fourth, the enzyme activity of high molecular SI revealed that maltase and
sucrase activity was similar to SI induce by glucose. This is an important finding
considering the high amount of N- and O-glycosylation did not have an effect on enzyme
activity of HMW SI.
Finally, we study the effect of maltooligosaccharides on two important characters
of Caco-2 cell line; intestinal permeability and cell differentiation. While the main
function of small intestine is digestion and absorption of nutrients, it also functions as a
barrier preventing harmful substances from entering the body. While high glucose
concentration induces tight junctions, maltose promoted lower permeability compared to
other carbohydrates tested.
Cell differentiation is another important character of small intestinal enterocytes.
Cell differentiation is the process by which a less specialized cell becomes a more
specialized cell type. Monitoring alteration of metabolites in presence of
maltooligosaccharides showed that maltose induced a significant increase in taurine level.
In addition, glycerophosphocholine and phosphorycholine levels significantly increased
in presence of maltooligosaccharides. Considering these metabolites are known to be
biomarkers for cell differentiation, it appears maltooligosaccharides improve this
important cell function. Enhancement of cell differentiation rate with maltose may be
then related to the observed lower monolayer permeability.
Maltooligosaccharides, as important digestion products of starch by α-amylase,
symbolize the presence of glucose-composed starch, which is the main glycemic
carbohydrate on which humans evolved. Chronic rapid digestion of glycemic
carbohydrates and absorption of glucose can be detrimental to health, particularly for
153
diabetics and pre-diabetics, due to glycemic spikes that stress the glucose homeostasis
regulatory system. This study presented evidence that the sweet taste receptor senses
maltooligosaccharides and triggers intracellular pathways to express HMW SI. We
showed that HMW SI can traffic faster to the apical membrane and digest starch presence
in small intestine. An approach might be envisioned to be developed to manipulate the
block sweet taste receptor so as to modulate postprandial glycemic response for improved
health.
To apply this approach (control of glucogenesis) with the concept of controlled
glucose delivery to the body, further investigations are required related to pathway
analysis of the sweet taste receptor and formation of HMW SI. Further study is also
recommended to evaluate the expression of gut hormones in presence of
maltooligosaccharides, as this may be the mechanism of carbohydrates triggering the ileal
brake and gut-brain access for appetite and food intake control. Since Caco-2 cells only
express SI, in vivo study is needed to evaluate the effect of maltooligosaccharides also on
MGAM.
154
1.5 References
Alfalah, M., Jacob, R., Preuss, U., Zimmer, K. P., Naim, H., & Naim, H. Y. (1999). Olinked glycans mediate apical sorting of human intestinal sucrase-isomaltase through
association with lipid rafts. Current Biology, 9(11), 593-S2.
Jacob, R., Weiner, J. R., Stadge, R., & Naim, H. Y. (2000). Additional N-glycosylation
and its impact on the folding of intestinal lactase-phlorizin hydrolase, Journal of
Biological Chemistry, 275: 10630–10637.
Le Bivic, A., Quaroni, A., Nichols, B., & Rodriguez-Boulan, E. (1990). Biogenetic
pathways of plasma membrane proteins in Caco-2, a human intestinal epithelial cell line.
The Journal of Cell Biology, 111(4), 1351-1361.
VITA
155
VITA
Mohammad Chegeni was born in Iran. He received the degree of Bachelor of
Science in Biology from Ferdowsi University, Mashhad, Iran, in 2001. He entered the
Graduate School in the Department of Health Science at Ball State University, Muncie,
IN, in 2007, under the supervision of Dr. James McKenzie, and received a Master's of
Science degree in May 2009. Mohammad started his Ph.D. studies in the Department of
Food Science at Purdue University, West Lafayette, IN, in 2010. While at Purdue
University he joined Professor Bruce Hamaker’s research group where he studied
maltose sensing and sucrase-isomaltase maturation and trafficking.