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I“§” UBRARY
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University

 

 

 

This is to certify that the
dissertation entitled

MODULATION OF INTESTINAL TUMORIGENESIS BY
DIETARY CARBOHYDRATE AND PROTEIN SOURCES IN
APCM'N MICE

presented by
Bing Wang

has been accepted towards fulﬁllment
of the requirements for the

Doctoral degree in Human Nutrition

 

 

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Major Professor’s Signature

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Date

Doctoral Dissertation

MS U is an Afﬁrmative Action/Equal Opportunity Institution

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MODULATION OF INTESTINAL TUMORIGENESIS BY DIETARY
CARBOHYDRATE AND PROTEIN SOURCES IN APCMIN MICE

By

Bing Wang

A DISSERTATION
Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Department of Food Science and Human Nutrition

2005

ABSTRACT

MODULATION OF INTESTINAL TUMORIGENESIS BY DIETARY
CARBOHYDRATE AND PROTEIN SOURCES IN APCMIN MICE

By
Bing Wang
Three experiments were conducted to test the inﬂuence of dietary carbohydrate
and protein sources on intestinal tumorigenesis in APCMin mice. In the ﬁrst experiment,
APCMin mice consuming sucrose (versus cornstarch) had signiﬁcantly more but smaller
adenomas in the proximal third of the small intestine after 10 weeks of dietary treatment.
Mice fed diets containing sucrose (versus comstarch) had a greater rate of colonic
epithelial cell proliferation, a greater serum glucose concentrations and hepatic IGF-I
mRNA expression, and they also tended (P = 0.07) to have higher serum insulin levels.
In the second experiment, global gene expression in small intestinal epithelium caused by
APC gene mutation and dietary carbohydrate source was assessed using cDNA
microarray analysis. Expression of 379 genes was signiﬁcantly different between
APCMin mice and wild-type mice. Among these differentially-expressed genes, 109 were
annotated and had expression altered by genotype by more than 50%. Some of these
genes were associated with cell growth control and carcinogenesis (APC gene mutation
increased expression of Clu, Ccnd2, Ccnbl , Btg4, Anxal and Gsptl, and decreased
expression of Camkld, sept2, LatSZ, Dab2 and Morf4ll). Expression of 306 genes was
signiﬁcantly inﬂuenced by dietary carbohydrate source. Among these genes, 87 were
annotated and had expression altered by carbohydrate source by more than 50%.
Several of these genes also were associated with cell growth control and carcinogenesis

(sucrose increased expression of Igf2, Pena, Csell, Idb2 and Cameg, and decreased

expression of Igfhp3, Tial, Fancg, Bmprla and Cul4b). Sucrose feeding increased
intestinal expression of IGF2 and decreased expression of IGFBP3, which may result in
elevated IGF signaling in the intestinal epithelium. In the third study, the inﬂuence of
dietary carbohydrate (sucrose versus cornstarch) and protein (soy versus casein) sources
on colonic adenoma development in APCMin mice was assessed. Sulindac (100 mg/ g)
was added to all diets to delay small intestinal tumorigenesis in this study. After a longer
duration of dietary treatment (i.e. 16 weeks), APCMin mice consuming sucrose (versus
cornstarch) had signiﬁcantly greater incidence of colon adenomas, as well as increased
epithelial cell proliferation and reduced apoptosis in colonic crypts. Female mice
consuming soy ﬂour had signiﬁcantly greater mammary gland tumor incidence (20%)

compared with those consuming casein-based diets (0%).

ACKNOWLEDGMENTS

I would like to sincerely thank my major professor, Dr. Leslie Bourquin, for his
guidance and constant support throughout the program. I really appreciate his
understanding and encouragement at my difﬁcult time to keep me moving forward. I
would also like to thank Dr. John LaPreS for allowing me to use his lab and his assistance
with the microarray experiments. I would also like to express my gratitude to Dr. Dale
Romsos for his precious advice and kindness. Thanks are also extended to Dr. Maurice
Bennink for his help and guidance.

I would like to thank Dr. Gerd Bobe and Crystal Ybarra for helping me with
animal care and experiments. I thank Elizabeth Rondini for her help and friendship for
all the years during my program. I would also like to thank Ajith Vengellur and KangAe
Lee for their generous help when I was doing microarray studies.

I would like to express my appreciation to my dear wife, Ying Qiu, for her love,
support and understanding through all the tough times. Finally, to my three kids, Allen,

Joshua and Angelina, they make me feel truly blessed.

iv

TABLE OF CONTENTS

LIST OF TABLES .............................................................................. vii

LIST OF FIGURES .............................................................................. x

INTRODUCTION ................................................................................ 1

CHAPTER 1.

REVIEW OF LITERATURE ................................................................... 3
1. Overview of colorectal cancer .................................................... 4
2. Epithelial cell turnover in colonic crypts .......................................... 5
3. Genetics alterations in colon carcinogenesis .................................... 7
4. APCMln mouse model for cancer research ........................................ 9
5. Dietary carbohydrates and risk for colon cancer ............................. 12
6. Soybean proteins and risk for colon cancer and

breast cancer ........................................................................ 25

7. Hypotheses and research objectives ............................................. 28

CHAPTER H.

INFLUENCE OF DIETARY CARBOHYDRATE SOURCE
(SUCROSE VERSUS CORNSTARCH) ON INTESTINAL

TUMORIGENESIS IN APCMIN MICE ...................................................... 31
1. Abstract ............................................................................ 32
2. Introduction ........................................................................ 34
3. Materials and methods ............................................................ 36
4. Results .............................................................................. 46
5. Discussion .......................................................................... 59
CHAPTER III.

GENE EXPRESSION PROFILING OF INTESTINAL
EPITHELIUM IN APCMIN MICE CONSUMING DIETS

CONTAINING DIFFERENT CARBOHYDRATE
SOURCES 66

m-wa—d

Abstract .............................................................................. 67
Introduction ........................................................................ 69
Materials and methods ........................................................... 71
Results .............................................................................. 8 1
Discussion ........................................................................ 104

CHAPTER IV.

INFLUENCE OF DIETARY CARBOHYDRATE
(SUCROSE VERSUS CORNSTARCH) AND PROTEIN
(CASEIN VERSUS SOY FLOUR) SOURCES ON
INTESTINAL TUMORIGENESIS IN SULINDAC-

TREATED APCMIN MICE .................................................................. 119
1 Abstract ........................................................................... 120
2 Introduction ...................................................................... 122
3 Materials and methods .......................................................... 124
4 Results ............................................................................. 127
5 Discussion ........................................................................ 145

CHAPTER V.

OVERALL SUMMARY AND CONCLUSIONS ......................................... 151

APPENDICES ................................................................... , ............ 1 59

REFERENCE ................................................................................... 189

vi

Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

Table 6.

Table 7

Table 8.

Table 9.

LIST OF TABLES

Composition of diets used in experiment 1 (g/kg

diet)

List of primers used for real time PCR for gene

.37

expression in experiment 1 .......................................................... 43

Adenoma incidence, average numbers of adenomas
per mouse, total adenoma burden per mouse and
avera .e size of adenomas in the small intestine of
APC '" mice consuming diets based on differing
carbohydrate sources in experiment 1 (means i

standard errors) ...........................................................

Adenoma incidence, average numbers of adenomas
per mouse, total adenoma burden per mouse and
average size of adenomas in the colon of APCMm mice
consuming diets based on differing carbohydrate

sources in experiment 1 (means i standard errors) ..................

Ki67 antigen expression in colon of APCMin mice
consuming diets containing different carbohydrate

sources (means i standard errors) .....................................

PCNA antigen expression in colon of APCMin mice

consuming diets containing different carbohydrate

sources (means 3: standard errors) .....................................

TUNEL assay results in colon of APCMin mice
consuming diets containing different carbohydrate

sources (means :1: standard errors) .....................................

Relative expression of IGF-H, IGFBPl and IGFBP3
mRNA in liver of mice consuming diets containing
different carbohydrate sources (means 3: standard

errors) .....................................................................

List of primers used for conﬁrmatory real time PCR

analyses of gene expression ............................................

vii

............. 51

............. 51

............. 52

............. 53

............. 53

............. 58

............. 76

Table 10.

Table 1 1.

Table 12.

Table 13.

Table 14.

Table 15.

Table 16.

Table 17.

Table 18.

Table 19.

Table 20.

Genes-up-regulated in small intestinal epithelium of

M . . .
APC m mice versus Wild-type mice ...................................

Genes down-regulated in small intestinal epithelium
of APCM‘" mice versus wild-type

mice

Genes up-regulated in small intestinal epithelial cells
of mice consuming diets based on sucrose versus

cornstarch .................................................................

Genes down-regulated in small intestinal epithelial
cells of mice consuming diets based on sucrose versus

cornstarch .................................................................

Genes whose expressions were altered differently by

............. 84

.87

............. 94

............. 97

APC genotype and dietary sucrose .................................................. 99

Inﬂuence of APC gene status on expression of
selected genes in small intestinal epithelial cells

measured by RT-PCR .............................................................. 102

Inﬂuence of APC gene status on expression of
selected genes in colon epithelial cells measured by

RT-PCR ............................................................................... 102

Inﬂuence of dietary sucrose versus cornstarch on
expression of selected genes in small intestinal

epithelial cells measured by RT-PCR ............................................. 103

Inﬂuence of dietary sucrose versus cornstarch on
expression of selected genes in colon epithelial cells

measured by RT-PCR ................................................................. 103

Composition of diets for experiment used in

experiment 3 .......................................................................... 125

Small intestinal adenoma numbers, average sizes of
small intestinal adenomas, and total burden of small
intestinal adenomas in APCMin mice consuming diets

based on differing protein and carbohydrate sources ........................... 131

viii

Table 21. Colonic adenoma numbers, average sizes of colonic
adenomas, and total burden of colonic adenomas in
APCMin mice consuming diets with protein and
carbohydrate sources ............................................................... 138

Table 22. Mammary tumor incidence in female APCMin mice
consuming diets based on differing protein and
carbohydrate sources ................................................................ 139

Table 23. Mean heights of colonic crypts and Ki-67 antigen
expression in colon of APCMin mice consuming diets
based on differing protein and carbohydrate sources ........................... 142

Table 24. PCNA expression in colon of APCMin mice
consuming diets based on differing protein and
carbohydrate sources ................................................................ 143

Table 25. TUNEL assay results in colon of APCMin mice
consuming diets based on differing protein and
carbohydrate sources ................................................................ 144

Table A1. Genes up-regulated in small intestinal epithelium of
mice consuming diets based on sucrose versus
cornstarch ............................................................................. 160

Table A2. Genes down-regulated in small intestinal epithelium
of mice consuming diets based on sucrose versus
cornstarch ............................................................................ 167

Table A3. Genesup-regulated in small intestinal epithelium of
APCM'" mice versus wild-type mice ............................................... 172

Table A4. Genes down-regulated in small intestinal epithelium
of APCM'" mice versus wild-type mice ........................................... 177

Table A5. Genes whose expressions were altered differently by
APC genotype and dietary sucrose ................................................ 186

ix

Figure 1.
Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

LIST OF FIGURES

Genetic alterations observed in colon carcinogenesis.

Function of APC protein in WNT signaling ...........

Proposed mechanism whereby dietary high sucrose

increased IGF signaling ....................................

IGF signaling pathway and increased risk for colon

cancer .........................................................

Weekly body weights of APCMin mice consuming
diets containing sucrose or cornstarch as the primary

carbohydrate source ........................................

Numbers of small intestinal adenomas in different
small intestinal regions of APCM‘" mice consuming
diets containing sucrose or cornstarch as the primary

carbohydrate source ........................................

Average sizes of small intestinal adenomas (mmz) in
different small intestinal regions of APCM'" mice
consuming diets containing sucrose or cornstarch as

the primary carbohydrate source .........................

Total burden of small intestinal adenomas.(mm2) in
different small intestinal regions of APCM'n mice
consuming diets containing sucrose or corn starch as

the primary carbohydrate source .........................

Glucose concentrations in blood serum of mice
consuming diets containing different dietary

carbohydrate sources .......................................

Insulin concentrations in blood serum of mice
consuming diets containing different dietary

carbohydrate sources .......................................

Relative expression of IGF-I mRNA in liver of mice
consuming diets containing different dietary
carbohydrate sources ......................................

........................... 8

......................... 11

......................... 18

......................... 22

......................... 47

......................... 48

......................... 49

......................... 50

......................... 55

......................... 56

........................ 57

Figure 12. Complete loop design for the microarray study ................................. 74

Figure 13 Body weights of APCMin mice consuming diets
containing different protein and carbohydrate
sources ............................................................................... 130

Figure 14. Main effect of carbohydrates on small intestinal
adenoma numbers in APCMin mice consuming diets
containing different carbohydrate and protein
sources .............................................................................. 132

Figure 15. Main effects of carbohydrates on small intestinal
adenoma sizes in APCMin mice consuming diets
containing different carbohydrate and protein
sources ............................................................................... 133

Figure 16. Main effects of carbohydrates on small intestinal
adenoma burden in APCMin mice consuming diets
containing different carbohydrate and protein
sources ............................................................................... 134

Figure 17 Main effect of proteins on adenoma numbers in
APCMin mice consuming diets containing different
carbohydrate and protein sources ................................................ 135

Figure 18 Main effect of proteins on adenoma sizes in
APCMin mice consuming diets containing different
carbohydrate and protein sources ................................................ 136

Figure 19. Main effect of proteins on adenoma burden in
APCMin mice consuming diets containing different
carbohydrate and protein sources ................................................ 137

xi

INTRODUCTION

Colon cancer is the second most common cause of cancer mortality and claims
more than 50,000 lives every year in the United States (ACS, 2004). Dietary factors
strongly modify colorectal cancer risk and approximately 66% to 75% of colon cancer
may be preventable by adequate diets and physical activity (AICR/WCRF, 1997). Diets
rich in reﬁned carbohydrates such as sucrose have been associated with high colon cancer
risk (Tuyns et al. 1988; La Vecchia et al. 1993; Bostick et al. 1994; Centonze et al. 1994;
De Stefani et al. 1998). Experiments using carcinogen-induced colon cancer also have
shown that feeding rats with high-sucrose diets promoted growth of aberrant crypt foci
(ACF), a proposed precancerous lesion of the colon, and also Were associated with
increased proliferation in colon crypts compared with feeding high-comstarch diets
(Kristiansen et al. 1995; Cademi et al. 1993, 1997; Poulsen et al. 2001). Colon
adenocarcinomas in carcinogen-treated rats cosuming high-sucrose diets were
Signiﬁcanatly larger and had more invasive potential compared with those in rats
consuming high-comstarch diets (Cademi et al. 1994). However, to date high-sucrose
diets have not been demonstrated to increase colon cancer incidence in animal studies.
Furthermore, the underlying mechanisms whereby high dietary sucrose may increase
colon cancer risk are unclear.

APCMin (multiple intestinal neoplasia) mice carry a gemline mutation in the
adenomatous polyposis coli (APC) gene (Su et al. 1992) which is similar to APC gene
mutations observed in humans with FAP (familial adenomatous polyposis). FAP patients
develop hundreds of colonic adenomas and are at increased risk of colon cancer

(N ishisho et al. 1991). APC gene mutations also are commonly present in sporadic colon

cancers (Powell et al. 1992). Kinzler and Vogelstein (1996) have proposed a
“gatekeeper” function for the APC gene in colorectal epithelia in which APC is
responsible for maintaining a constant census in renewing cell populations. Therefore,
APCMin mice represent a useful model to study diet and gene interactions during colon
carcinogenesis.

Animal studies suggest that high intakes of sucrose induce compensatory
hyperinsulinemia by causing a decline in insulin sensitivity in the liver and peripheral
tissues. Hyperinsulinemia may increase IGF-I (insulin-like growth factor 1) production
by the liver and thereby increase IGF-l concentrations in the circulation (Giovannucci
2001). Evidence also indicates possible associations between altered gene expressions of
IGFBP3 (insulin-like grth factor binding protein 3) and IGF-II (insulin-like growth
factor II) in the colon epithelium and colon carcinogenesis (Hassan et al. 2000; Kirman et
al. 2004).

The purpose of the current research is to determine if diets high in sucrose (vs.
cornstarch) promote colon carcinogenesis in APCMin mice, and if this action is associated
with elevated IGF signaling and increased cell proliferation in colonic epithelial cells. A
secondary objective of this research is to determine the inﬂuence of dietary protein

source (soy versus casein) on intestinal tumorigenesis in APCMm mice.

CHAPTER 1.

REVIEW OF LITERATURE

1. Overview of Colorectal Cancer

Worldwide, colorectal cancer (CC) is the third most common cancer in women
and fourth most common cancer in men. Colorectal cancer incidence varies more than
ten-fold across different regions on earth. The highest incidence rates are found in
developed regions such as North America, Western Europe, Japan and Australia. In
contrast, much lower colon cancer incidences are typically observed in less-developed
areas such as Asia and Africa (lARC, 2002).

The American Cancer Society estimates that 106,370 new cases of colon cancer
and 40,570 new cases of rectal cancer were diagnosed in the United States in 2004.
Colorectal cancer is the second most common cause of cancer death in the US, and was
expected to cause about 56,730 deaths in 2004, accounting for about 10% of cancer
deaths (ACS, 2004).

Cancer as a disease is multi-factorial in its causality. Many factors such as
hereditary defects, diet, smoking, and exposure to environmental carcinogenic chemicals
all may contribute to the generation of cancers. It is estimated that the lifetime risk of
developing CC is approximately 5% (Jemal et al. 2002). Most cases of CC arise
sporadically (no background of a family history of the disease), but inherited cancer
syndromes may account for up to 5% of all CC cases (Ilyas et al. 1999).

In 1969, Burkitt ﬁrst suggested that a low intake of dietary ﬁber might be
involved in the causation of colorectal cancer (Burkitt, 1969). Since that time, abundant
epidemiological evidence has revealed that dietary factors strongly inﬂuence colorectal
cancer incidence. High risk for developing colon cancer has been associated with higher

meat and fat consumptions (Stemmerman et al. 1984; Enstrom, 1975), whereas lower risk

is associated with consumption of diets rich in vegetables and fruits (Howe ct al. 1992).
Dietary composition may be one of the most important factors contributing to cross-
cultural differences in colorectal cancer incidence. The American Institute for Cancer
Research estimated that about 66% to 75% of colorectal cancer incidence may be

associated with inadequate diets and low physical activity (AlCR/WCRF, 1997).

2. Epithelial Cell Turnover in Colonic Crypts

The colon can be functionally divided through the transverse colon into two parts,
the right and left colon. The right colon (cecum and ascending colon) plays a major role
in water and electrolyte absorption and fermentation of undigested sugars, and the left
colon (descending colon, sigmoid colon and rectum) is predominantly involved in storage
and evacuation of stool. The colon is a muscular organ, and its wall consists of four basic
layers — the mucosa, submucosa, circular muscle and longitudinal muscle.

The colon mucosa is lined by a simple columnar epithelium that is folded into a
number of deep cavities or crypts. The colonic epithelium is in a constant state of cell
renewal. A stem cell population located in the base of the crypt proliferates, giving rise
to daughter cells. These daughter cells migrate towards the surface of the crypt, and
along the way undergo further cell divisions. This cell proliferation is typically localized
to the basal two-thirds of normal colonic crypts, which is referred as the proliferation
compartment (Lipkin et al. 1974). As the cells migrate toward the crypt surface, they
undergo terminal differentiation into a number of cell types, including absorptive cells,
mucin-producing goblet cells and endocrine cells. These differentiated cells will

continue to migrate toward the luminal surface of the crypt, and ultimately are removed

by programmed cell death (apoptosis) at the crypt surface. Apoptosis also may occur to
remove cells along the length of the crypt.

Colon carcinogenesis is associated with abnormal cell population renewal in the
colonic crypts. Deschner (1974) reported that mice injected with the colon carcinogen
1,2-dimethylhydrazine (DMH) had a signiﬁcantly higher proportion of crypt epithelial
cells in S-phase. Furthermore, they noted an expansion of the proliferative compartment
towards the luminal surface. Human patients with colon adenomas or cancer also
demonstrate abnormal patterns of cell proliferation, with increased proliferation and an
upward shiﬁ in the major zone of DNA synthesis (Deschner et al. 1981). Collectively,
human studies indicate that increased labeling index (LI) of proliferating cells and an
upward shift of the proliferative compartment zone in colonic crypts are biomarkers for
increased colorectal cancer risk (Lipkin et al. 1974, 1984; Bleiberg et al. 1985; Terpstra

et al. 1987; Ponz de Leon et al. 1988; Mills et al. 2001).

However, increased cell proliferation alone has not been directly associated with
increased cancer risk (Faber 1995). For example, tumors in the small intestine are
extremely rare despite the higher rates of epithelial cell proliferation observed in the
small intestine compared to the colon (Faber, 1995). Other processes, such as epithelial
cell differentiation and programmed cell death, also have been found to be associated
with risk of colonic carcinogenesis (Wyllie, 1985; Boland, 1992). Homeostasis of the
colorectal epithelium is dependent not only on the rate of epithelial cell production but
also on the rate of apoptosis (epithelial cell removal). Studies have demonstrated that the
transformation of colorectal epithelium to carcinomas is associated with a progressive

inhibition of apoptosis. The reduced rates of apoptosis observed in colorectal cancers

may contribute to tumor growth, promote neoplastic progression, and confer resistance to
anticancer agents (Bedi et al. 1995). Thus, colonic neoplasia can be considered as a
disease characterized by an abnormal increase of cell population in colonic crypts

resulting from disordered regulation of cell proliferation, differentiation and apoptosis.

3. Genetic Alterations in Colon Carcinogenesis

Cancer development is a multi-step process consisting of at least three stages —
initiation, promotion and progression (F oulds, 195 8). Colorectal cancer development can
be categorized by several histological steps, which are described as the "adenoma to
carcinoma sequence” (Muto et al. 1975). In this sequence, the smallest lesion is an
aberrant crypt focus (ACF), which can be either hyperplastic or dysplastic (N ucci et al.
1997). The most common type of ACF is the hyperplastic crypt, which has increased cell
numbers but relatively normal crypt structure and seldom develops into malignant
carcinomas. Dysplastic ACF, also termed unicryme adenomas, have abnormal crypt
structure and can further develop towards malignancy. Expansion of dysplastic ACFs
gives rise to larger adenomas of several centimeters. Adenomas can further progress into
carcinoma in situ. At this latter stage, carcinomas eventually may invade adjacent tissues
or metastasize to other organs.

Fearon and Vogelstein (1992) proposed a genetic model for colorectal
tumorigenesis related to their histological stages in the adenoma to carcinoma sequence
(Figure 1). Colonic tumorigenesis proceeds through a series of genetic alterations
involving mutation of proto-oncogenes such as the ras gene, and inactivation of tumor

suppressor genes such as the APC, p53, and DCC (Deleted in Colon Cancer) genes. The

APC gene has been proposed as the “gatekeeper” gene for colon carcinogenesis
(Volgestein et al. 1996). The majority of dysplastic ACFs bear APC mutations, whereas
nonmalignant hyperplastic ACFS often harbor mutations in K-ras (Jen et al. 1994; Nucci
et al. 1997). K-ras mutations also commonly occur in small adenomas, which may then
become larger and more dysplastic tumors through clonal expansion. Loss of the DCC
gene typically occurs at later stages of colorectal tumorigenesis. Mutation of the p53
gene often occurs before metastasis. In addition to these common genetic events, there
are likely additional unidentiﬁed gene mutations associated with colon carcinogenesis.
Each of these mutations confers the affected cell with some growth advantage, allowing it
to outgrow other neoplastic cells within the tumor and to become the predominant cell

type constituting the neoplasm (clonal expansion).

 

Figure 1. Genetic alterations observed in colon carcinogenesis. Modiﬁed from Kinzler
KW, Vogelstein B. (1996) Cell 87:159-70

12p qu 17p Other
Activation 1055 loss genetic
k-ras DCC p53 events

 

 

 

Amn‘H mm]: ArlefI‘SmH (mm Metastasis

 

 

 

4. APCMill Mouse Model for Cancer Research

(1) Colonrectal cancer: The APCMin (multiple intestinal neoplasia) mouse
model has been used extensively for colorectal cancer research in recent years. Min is a
gemline mutation in the murine APC (adenomatous polyposis coli) gene (Su et al. 1992),
which contains a mutation (Leu to stop) at codon 850 resulting in premature truncation of
the polypeptide. This mutation is similar to the APC gene mutations commonly observed
in humans with familial adenomatous polyposis (FAP). FAP patients develop hundreds
of colonic adenomas and have a very high risk of developing colon cancer (N ishisho et
al. 1991). APC gene mutations also are common in sporadic colon cancers (Powell et al.
1992). Kinzler and Vogelstein (1996) have proposed a “gatekeeper” function for the
APC gene in colorectal epithelia in which APC is believed to be responsible for
maintaining a constant census in renewing cell populations. Therefore, APCMin mice
offer an excellent model to study the genetic events leading to colon carcinogenesis
following APC gene mutation.

Recent studies have revealed several details on how APC mutations lead to
colonic tumorigenesis. The APC gene encodes a protein having 2843 amino acids and
containing several important functional domains. The middle third of the APC protein
contains two classes of B—catenin binding repeats. Through interactions with B-catenin,
the APC protein participates in two cellular processes related to colon tumorigenesis (Su
et al. 1993). The ﬁrst process is related to cellular adhesion. Interaction between B-
catenin and E-cadeherin is necessary for E-cadherin-mediated cell adhesion between
epithelial cells (Kemler, 1993). Disruption of epithelial cell adhesion junctions may

contribute to epithelial carcinogenesis. The APC protein also impacts colonic

tumorigenesis through its participation in the Wnt signaling pathway. Normally, APC
forms an intracellular complex with B-catenin, GSK3B (glycogen synthase kinase-3-l3),
and conductin/axin. In the absence of Wnt signaling, B-catenin is phosphorylated by
GSK3 [3. Subsequently, it is recognized and degraded through a ubiquitin-proteosome
mediated process. Following activation of the Wnt signaling pathway, GSK3B is
inactivated and nonphosphorylated B-catenin is released from the complex and
accumulates in the cytoplasm.

Mutations in the APC gene result in production of truncated APC proteins which
lack their domains for B-catenin binding. B-catenin cannot form a complex with
truncated APC, GSK3B and conductin/axin, and B—catenin will not be phosphorylated by
GSK3B and degraded. Thus, APC mutation causes accumulation of B—catenin in the
cytosol. B—catenin proteins in the cytosol form complexes with transcription factors such
as T-cell factor (ch) and lymphoid enhancer factor (Let) (Behrens et al. 1996), which
translocate into the nucleus and can increase the transcription of many genes having ch
binding sites (Figure 2). A comprehensive listing of these target genes is maintained at
http://www.stanford.edu/~musse/pathways/targets.html. Some of these target genes play
important roles in colon carcinogenesis. These include cyclin D1 (Zhang et al. 1997),
matrilysin (Crawford et al. 1999), two components of the AP-l transcription complex —
c-jun and fra-l (Mann et al. 1999), c-myc (He 1998) and peroxisome proliferator

activated receptor 5 (He et al. 1999).

10

 

Figure 2. Function of APC protein in WNT signaling.

/ Intracellular space \ / Intracellular space \

Ubiquitin-

mediated

roteol sis
. y

 

 

   
  

  
    

B-catenin

 

 

 

 

 

 

 

 

 

TCF
Targets I [owe |
B-catenin
Cyclin 01
\ Nucleus /
Normal APC Mutated APC

 

 

 

(2) Mammary cancer: Female APCMin mice having the 57BL/6J background
occasionally develop mammary gland tumors (Moser et al. 1992, 1993), which indicates
a tumor suppressor role for APC within the mammary epithelium. However, the
mammary gland tumor incidence observed in APCMin mice is generally low because
APCMi’n mice die around 15-16 weeks of age due to rapid small intestinal tumorigenesis.
The incidence of spontaneous mammary gland tumors has been reported to be
approximately 10% in female mice (Min/+) with a hybrid genetic background (Moser et
al. 1992). These mice have a reduced intestinal tumor load and an increased life span
when compared to C57BL/6J APCMin mice due to expression of some modiﬁer genes

(Moser et al. 1992).

ll

The APC protein is expressed at high levels within the mammary gland
epithelium. Loss of heterozygosity at 5q21 (the chromosomal location of human APC)
has been reported in sporadic tumors of the breast (Thompson et al. 1993; Kashiwaba et
al. 1994). Reduced APC protein expression also has been demonstrated in human breast
cancers (Ho et al. 1999). F uruuchi et al. (2000) reported that somatic APC mutations
were present in about 18% of primary breast cancers. J in et al. (2001) demonstrated that
the promoter region of the APC gene was frequently hypermethylated in breast cancers,
which leads to decreased APC gene expression. Ashkenazi Jews have a high presence of
APC 11307K polymorphisms, which renders APC proteins to be genetically unstable and
prone to somatic mutation. Ashkenazi Jews have increased risk for colon cancer, and
also have high risk of breast cancer when APC Il307K polymorphism is associated with

BRCA founder mutations (Woodage et al. 1998).

5. Dietary Carbohydrates and Risk for Colon Cancer

(1) Epidemiological and experimental studies: Dietary carbohydrate source and
concentration have been previously suggested to inﬂuence colon cancer risk. High
consumption of reﬁned carbohydrates such as sucrose has been associated with higher
colon cancer risk (AICR/WCRF, 1997). Using the Iowa Women’s Health Study cohort,
Bostick et al. (1994) reported that consumptions of diet containing non-dairy sucrose
were associated with increased colon cancer risk (RR = 2.0 for highest versus lowest
quintiles, CI = 121-33). The relative risk of colon cancer risk was 1.74 for highest
versus lowest quintiles of total sucrose-based food (CI = 1.06-2.87; Bostick et al. 1994).

Several case-control studies have reported signiﬁcant associations between diets high in

12

sucrose with increased risk of colorectal cancer (Bristol et al. 1985; Tuyns et al. 1988; La
Vecchia et al. 1993; Centonze et al. 1994; De Stefani et al. 1998). However, other
studies found no relationship or only weak associations between sucrose intake and
colorectal cancer with odds ratios between 1.0 and 1.3 (Manousos et al. 1983; Macquart-
Moulin et al. 1986; La Vecchia et al. 1988; Peters et al. 1992).

Results from experimental studies also suggest that higher sucrose intakes are
associated with increased colon cancer risk. Feeding sucrose is associated with greater
proliferation in colonic crypts, as well as a greater number of large aberrant crypt foci
(Cademi et al. 1993, 1997; Kristiansen et al. 1995; Poulsen et al. 2001). Feeding rats for
4 weeks with dietary sucrose versus cornstarch (46%) signiﬁcantly increased colonic
epithelial cell proliferation (Cademi et al. 1993). Rats treated with the colon carcinogen
dimethylhydrazine (DMH) had signiﬁcantly greater numbers of dysplastic aberrant crypt
foci (ACF) in colon when consuming diets containing sucrose (46%) compared to those
consuming cornstarch (Cademi et al. 1991, 1997). DMH-injected rats fed high-sucrose
diets (61%) had signiﬁcantly greater numbers of ACF during the post-initiation period
compared to rats fed starch diets (Pulsen et al. 2001). Cademi et al. (1994) reported that
rats fed high sucrose diets had signiﬁcantly greater numbers of colonic adenomas per rat
compared with those fed comstarch-based diets in a DMH-induced rat colon cancer
model. The adenocarcinomas in the rats fed sucrose were larger and had greater invasive
potential than those in rats fed the comstarch-based diets, although the incidence of total
intestinal tumors was not affected by the different carbohydrate sources (Cademi et al

1994).

13

Cademi et al. (1996) studied the inﬂuence of different dietary carbohydrate
sources on colon lumenal environment. Female rats were fed for one month with diets
containing different carbohydrates (sucrose, glucose, fructose, cornstarch, and Hylon 7, a
starch with a high amylose content). Colon epithelial cell proliferation was signiﬁcantly
greater in rats fed sucrose than in rats fed glucose, fructose, or cornstarch. Cecal pH was
lower in rats fed cornstarch and Hylon 7 than in rats fed sucrose, glucose, or fructose.
Cecal concentrations of short-chain fatty acids (SCFAS) were greater in rats fed Hylon 7
than in those fed glucose and fructose (Cademi et al. 1996). In this study, no association
was found between the inﬂuence of carbohydrate source on colon epithelial cell
proliferation and cecal SCF A concentrations or cecal pH (Cademi et al. 1996). Stamp et
al. (1993) reported that oral gavages of either sucrose or fructose in mice (10 g/kg body
weight) increased colonic epithelial cell proliferation (measured 16 h after gavage) when
compared to mice gavaged with an equivalent amount of glucose. Sucrose and fructose,
when administered 14 h prior to azoxymethane (AOM) injection, increased average
numbers of ACF in colon compared with glucose.

The effects of dietary carbohydrates on blood glucose levels vary depending upon
the glucose content of the carbohydrate and its rate of digestion, which can be predicted
by glycemic index (Jenkins et al. 1981; Wolever et al. 1991). Corpet et al. (1998) studied
the inﬂuence of diets having different glycemic indices on development of insulin
resistance and ACF in rats. After receiving an AOM injection, rats were randomly
assigned to AlN-76-based diets containing different carbohydrate sources (65% starch,

glucose or fructose). The average numbers of ACF per rat were not different among rats

14

fed starch, glucose or fructose. Indirect markers of insulin resistance did not correlate
with ACF multiplicity in this study (Corpet et al. 1998).

(2) High consumption of sucrose and hyperinsulinemia: Although sucrose has
a lower glycemic index than some complex carbohydrates such as white bread (Jenkins et
al. 1981; Wolever et al. 1991), animal studies have found that high intakes of sucrose,
relative to cornstarch, cause a decline in insulin sensitivity in the liver and later in
peripheral tissues as assessed by euglycemic hyperinsulinemic clamps (Pagliassotti et al.
1995; Storlien et al. 1988; Martinez et al. 1994; PamieS-Andreu et al. 1995). Pagliassotti
et al. (1995) reported that male Wistar rats consuming diets containing relatively low
levels of sucrose (18% of energy) also developed insulin resistance after 8 weeks when
compared to rats consuming cornstarch.

The decreased insulin sensitivity caused by high sucrose diets is likely related to
the fructose component of sucrose (Thorbum et al. 1989; Thresher et al. 2000). High
fructose intakes promote the development of hypertriglyceridemia (Zavaroni et al. 1982;
Thorbum et al. 1989). Rats fed high-fructose diets (35%), compared to rats fed glucose,
had increased fasting triglyceride levels within 2 weeks after initiation of dietary
treatments, and increased triglyceride levels in circulation may be responsible for the
development of impaired insulin actin in liver and peripheral tissues (Thorbum et al.
1989). High-fructose diets stimulated hepatic VLDL-TG secretion due to the increased
formation of glycerol-3-phosphate, a precursor of lipid synthesis (Zavaroni et al. 1982).
Increased plasma concentrations of nonesteriﬁed fatty acids may reduce insulin
sensitivity by increasing the intramyocellular lipid content (Virkamaki et al. 2001).

Increased portal delivery of nonesteriﬁed fatty acids increases hepatic glucose production

15

(Rebrin et al. 1995; Steil et al. 1998). High intakes of fructose also cause decreased
production of the adipocyte protein, adiponectin. Decreased circulating concentrations of
this hormone are associated with insulin resistance independently of body adiposity
(Weyer et al. 2001; Havel et al. 2002).

However, human studies examining the ability of dietary sucrose to induce
insulin resistance have not been convincing, possibly due to limited variation in dietary
sucrose intakes, heterogeneity in study populations and complexities of study design
(Giovannucci, 2001). Some studies showed that high intakes of sucrose or fructose
increased fasting or postprandial insulin concentrations (Reiser et al. 1979, 1981). Other
studies indicated that high fructose intakes (20% of carbohydrate, 45-65 g/day for 4
weeks) increased insulin sensitivity (Koivisto et al. 1993), whereas still others showed no
effect (Dunnigan et al. 1970). Bessesen (2001) suggested that most of these human
studies were conducted using patients who already had insulin resistance, and the effects
of diets containing high sucrose or fructose concentrations would more likely be detected
in younger individuals who do not already have some degree of insulin resistance.

(3) Elevated IGF signaling and increased colon cancer risk: McKeown-Eyssen
(1994) and Giovannucci (1995) noted the similarity in geographic distribution of diabetes
and colon cancer patients. Patients having either diabetes or colon cancer often share
common risk factors such as high body mass index, increased central obesity, physical
inactivity, excessive intakes of energy and dietary patterns that increase risk of
hyperinsulinemia. Epidemiological studies also found that subjects diagnosed with type

II diabetes have increased risk of colon cancer (La Vecchia et al. 1997; Le Marchand et

16

al. 1997; Will et al. 1998; Hu et al. 1999). Based on these observations, it has been
proposed that hyperinsulinemia promotes colorectal carcinogenesis.

Increased insulin levels were originally thought to promote colon cancer
development. However, insulin has been found to be a relatively weak mitogen in vitro,
acting as such only at very high concentrations (Giovannucci 1995). Alternatively,
Giovannucci (1995, 2001) proposed that the role of insulin in colorectal cancer is
mediated through IGF-I. Insulin has been found to increase bioactive IGF-I through
various mechanisms (Figure 3). Growth hormone up-regulates the production of IGF-I in
liver, and insulin increases IGF-I expression by up-regulating hepatic growth hormone
receptor number (Jones et al. 1995; Underwood et al. 1994). Insulin also reduces hepatic
secretion of IGFBPl (insulin-like growth factor binding protein 1; Ooi et al. 1992), which
in turn will increase the availability of IGF-I to the receptors of other tissues.

The IGF signaling system includes two insulin-like growth factors (IGF-I and
IGF ~11), which exert their actions by interacting with speciﬁc receptors for IGF-I and
IGF-II on the cell membrane, and their actions are regulated by a group of speciﬁc
binding proteins (i.e., IGFBP] through IGFBP6). In addition, a large group of IGFBP
proteases hydrolyze IGF BPS, resulting in the release of bound IGFs that then resume their

ability to interact with receptors (Stewart et al. 1996; Rajaram et al. 1997).

17

 

Figure 3. Proposed mechanism whereby high dietary sucrose increases IGF
signaling. Modiﬁed from Giovannucci (2001) J. Nutr. 131:3 109S-3120S.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

High sucrose diet Growth hormone
Hepatic
growth
Insulin P hormone ——>

_ receptor

V V

IGFBPl
................................................................... , IGF-I

 

 

 

*Solid lines indicate stimulating effect and broken lines indicate inhibition.

 

 

 

IGF-I is a potent mitogen and promotes growth of a wide variety of cells. IGF-I
also inhibits cell differentiation and apoptosis (Jones et al. 1995; Yu et al. 2000). Like
IGF-I, IGF-II has similar effects on cell proliferation, differentiation and apoptosis, and
this effect is also achieved through activation of the IGF-IR (Yu et a1. 2000). In contrast,
the IGF-IIR has no tyrosine kinase activity, and its binding with IGF-II results in
degradation of IGF-II, thereby decreasing IGF-II activity (Stewart et al. 1996; Rajaram et
al. 1997).

IGF-I and IGF-II in the circulation are primarily produced in liver. Although

IGF-II plays a key regulatory role during embryonic and fetal growth, IGF-II plays a less

18

important role in postnatal growth compared with IGF-I (Jones et al. 1995). Levels of
circulating IGF-I are mainly regulated by growth hormone and change substantially with
developmental stages. IGF-I expression also is altered by nutritional state. For instance,
fasting reduces serum IGF-I concentrations as much as 75% and negates the effects of
growth hormone (GH) stimulation. Refeeding rapidly restores IGF-I levels to normal
(Clemmons et al. 1981). Circulating levels of IGF-II are relatively stable after puberty,
and GH has little inﬂuence on them (Jones et al. 1995). Actions of IGF-I and IGF-II on
growth both are mediated through binding with IGF-IR. However, IGF-IR binding
afﬁnity with IGF-I is about 2-15 fold higher than that with IGF-II (Jones et al. 1995).
Three of the six IGF BPS have higher afﬁnity to IGF-II than to IGF-I, and the rest have
similar binding afﬁnity to both IGFs (Rajaram et al. 1997). The combination of high
afﬁnity to the receptor and low affinity to the binding proteins results in more IGF-I than
IGF-II interacting with IGF-IR in tissues.

Although it is believed that circulatory IGF-I, mainly produced in liver, is most
important for postnatal growth, research also has identiﬁed the importance of
extrahepatic production of IGF-I in growth regulation. Yakar et al. (1999) used the
Cre/loxP recombination system to Speciﬁcally delete the liver IGF-I gene one to two
weeks after birth, and reduced circulating IGF-I concentrations by approximately 80% in
mice. The growth rates of these transgenic animals were not signiﬁcantly different when
compared with the wild-type animals. This provided direct evidence for the importance
of the autocrine/paracrine role of IGF-I in the extrahepatic tissues.

The IGF-II gene is known to have parental allele-speciﬁc expression. In normal

cells, IGF-II is maternally imprinted in that it is expressed only from the paternal copy of

19

the gene. Loss of IGF-II imprinting has been reported in a variety of tumors (Yu et al.
2000). When loss of imprinting occurs, biallelic expression of IGF-II results in over-
expression of this potent growth factor.

Activation of the IGF signaling pathway greatly impacts cell proliferation,
differentiation, and apoptosis (Jones et al. 1995). Both IGF-I and IGF-II can activate the
IGF-I receptor, leading to receptor autophosphorylation and phosphorylation of
downstream proteins such as insulin receptor substrate (IRS). IRS serves as docking sites
for other cellular proteins and subsequently activates downstream signal transduction
pathways (Yu et al. 2000). At least two distinct signal transduction pathways have been
identiﬁed for IGF-IR: one is the MAPK (mitogen-activated protein kinase) pathway, and
increased MAPK pathway signaling leads to increased cell proliferation. Another is the
PI3K pathway, wherein activation of Akt results in the phosphorylation of several other
proteins that affect cell growth and cell survival. Phosphorylation of the F orkhead
transcription factor (FOXO) blocks transcription of P27kip, and decreased expression of
P27kip contributes to increased cell proliferation. Phosphorylation of the apoptosis-
inducing protein Bad prevents cells from undergoing apoptosis. A third target of Akt is
glycogen synthase kinase 3 (GSK3). Phosphorylation of GSK3 (both alpha and beta
isoforms) by Akt turns off its catalytic activity. GSK3B is required for phosphorylation
and degradation of B-catenin in the Wnt signaling pathway. Inactivation of GSK3B by
Akt also leads to the accumulation of B-catenin, which leads to increased cell
proliferation (Foulstone et al. 2005; Figure 4).

Recent research has indicated that various components in the IGF system are

involved in colon cancer development. Using in vitro studies, IGF-I and IGF-II have

20

been demonstrated to stimulate growth of various colon cancer cell lines (Koenuma et al.
1989; Lahm et al. 1992). IGFBP3 is found not only to regulate the mitogenic action of
IGFS but also to inhibit their antiapoptotic effect (Stewart et al. 1996; Rajaram et al.
1997)

Studies examined circulating IGF-I and IGFBP3 levels in relation to colon cancer
risk in humans and found that high blood IGF-I and low IGFBP3 levels are associated
with increased colon cancer risk (Ma et al. 1999; Giovannucci et al. 2000). Studies also
have demonstrated a direct role of IGFS in colon cancer development. Wu et al. (2002)
demonstrated that circulating IGF-I levels regulate colon cancer growth and metastasis in

a mouse model of colon cancer.

21

 

Figure 4. IGF signaling pathway and increased risk for colon cancer.

 

 

 

 

.\/ IGFBPS

/ l

Extracellular Space Q% L
n

 

 

IGFBP proteaseﬂ

 

 

llllllllllllllllllIlllll llllll ll llll llll Illl lll311lllllllllllllllll
Pll-4,5-P2 Pll-3,4,5-P3

Intracellular /‘\
space @ Grb2 @éypnu ﬂ;
K

@

MAPK p27kiP

 

 

 

 

 

 

 

 

 

 

 

 

GSK3B

 

 

 

 

L
- WNT Pathway

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

l . . . M /
I proliferation j proliferation I Apoptosrs Proliferation

/

 

 

 

 

 

 

 

 

 

 

Some studies also have reported associations between local expressions of
components of the IGF signaling system in colonic epithelium with colon carcinogenesis.
Both IGF-I and IGF-II mRNA levels in colon tumors were found to be slightly elevated
compared with normal colonic mucousa (Tricoli et al. 1986). Other studies did not
observe signiﬁcant differences in IGF-IR and IGF-11R expression when comparing colon
tumors and normal epithelial cells (Adenis et al. 1995; Zenilman et al. 1997). However,
IGF-IR overexpression has been reported in colon cancers, and its overexpression was

associated with aggressive tumors (Hakam et al. 1999). In ApcMin mice, it was found that

22

Mm“ mice with IGF-II gene

reducing IGF-II expression in the intestine by crossing Ape
knockout mice resulted in reduced adenoma size and frequency. On the other hand,
inducing over-expression of IGF-II in the intestine signiﬁcantly increased tumorigenesis
in ApcMW+ mice (Hassan et al. 2000). IGFBP3 has been demonstrated to inhibit cancer
cell growth induced by IGF-I by sequestering IGF-I from binding IGFI-R, but IGFBP3
also has functions independently from binding IGF-I (Cubbage et al. 1990). A mutated
form of IGFBP3 that is unable to bind IGF-I or IGF-II still inhibited tumor growth (I-Iong
et al. 2002). In addition, IGFBP3 induces tumor cell apoptosis even in the absence of
IGF-I receptors (Valentinis et al. 1995). Recently, Kirman et al (2004) further
demonstrated that IGFBP3 inhibits the development of colonic tumors in experimental
models. In the study, two tumor models were used. In AOM-induced colon
carcinogenesis, wild-type and IGFBP3 transgenic mice (with over-expression of IGFBP3
in tissues) were injected with AOM and the numbers of aberrant crypt foci (ACF) in the
colon of transgenic mice were signiﬁcantly lower compared with that in the wild-type
mice. In the syngeneic model, mice were inoculated with CT26 colon cancer cells. The
control group received saline, while the test group was injected with IGFBP3 around the
tumors weekly. CI26 tumors were signiﬁcantly smaller in mice that received IGFBP3
(Kirman et al. 2004).

Studies have examined circulating IGF-I and IGFBP3 levels in relation to colon
cancer risk in humans and found that high blood IGF-I and low IGFBP3 levels are

associated with increased colon cancer risk (Ma et al. 1999; Giovannucci et al. 2000).

Studies also have demonstrated a direct role of IGFS in colon cancer development. Wu et

23

al. (2002) demonstrated that circulating IGF-I levels regulate colon cancer growth and
metastasis in a mouse model of colon cancer.

Some studies also have reported associations between local expression of
components of the IGF signaling system in colonic epithelium with colon carcinogenesis.
Both IGF-I and IGF-II mRNA levels in colon tumors were found to be slightly elevated
compared with normal colonic mucousa (Tricoli et al. 1986). Other studies did not
observe signiﬁcant differences in IGF-IR and IGF-IIR expression when comparing colon
tumors and normal epithelial cells (Adenis et al. 1995; Zenilman et al. 1997). However,
IGF-IR overexpression has been reported in colon cancers, and its overexpression was
Min

associated with aggressive tumors (Hakam et al. 1999). In Apc mice, it was found that

Min/I mice with IGF-II gene

reducing IGF-II expression in the intestine by crossing Apc
knockout mice resulted in reduced adenoma size and frequency. On the other hand,
inducing over-expression of IGF-II in the intestine signiﬁcantly increased tumorigenesis
in ApcMim mice (Hassan et al. 2000). IGFBP3 has been demonstrated to inhibit cancer
cell growth induced by IGF-I by sequestering IGF-I from binding IGFI-R, but IGFBP3
also has functions independently from binding IGF-I (Cubbage et al. 1990). A mutated
form of IGFBP3 that is unable to bind IGF-I or IGF-II still inhibited tumor growth (Hong
et al. 2002). In addition, IGFBP3 induces tumor cell apoptosis even in the absence of
IGF-I receptors (Valentinis et al. 1995). Recently, Kirman et al (2004) further
demonstrated that IGFBP3 inhibits the development of colonic tumors in experimental
models. In the study, two tumor models were used. In AOM-induced colon

carcinogenesis, wild-type and IGFBP3 transgenic mice (with over-expression of IGFBP3

in tissues) were injected with AOM and the numbers of aberrant crypt foci (ACF) in the

24

colon of transgenic mice were Signiﬁcantly lower compared with that in the wild-type
mice. In the syngeneic model, mice were inoculated with CT26 colon cancer cells. The
control group received saline, while the test group was injected with IGFBP3 around the
tumors weekly. CT26 tumors were signiﬁcantly smaller in mice that received IGFBP3

(Kirman et al. 2004).

6. Soybean Proteins and Risk for Colon Cancer and Breast Cancer

In the United States, the incidence and mortality of colorectal, breast and prostate
cancers are higher than that observed in certain Asian countries such as Japan and China
(ACS, 1994). Since persons in those countries consume more soybean products than
Americans, consuming large amounts of soybean products has been suggested to be
associated with overall low cancer incidence and mortality rates (Messina et al. 1994;
Barnes et al. 1995). However, results from several epidemiological studies were not
supportive of a protective role of dietary soy against colon cancer. In a case-control
study in China, Hu et al. (1991) only found a protective effect of soybean products
against rectal cancer risk in man. Another study in Japan also only found that
consumption of beans and bean curd was associated with lower rectal cancer risk
(Watanabe et al. 1984). However, in a cross-cultural study of 38 countries, no
association between soybean intake and colon cancer risk was found (McKeown-Eyssen
et al. 1984).

Numerous studies have been conducted to examine the effects of speciﬁc
components in soybeans on colon cancer risk. Several compounds in soy products have

been shown to suppress carcinogenesis in vitro and in vivo. These compounds include the

25

isoﬂavone genistein, the Bowman-Birk protease inhibitor, inositol hexaphosphate (phytic
acid), saponins, and phytosterols such as B-sitosterol (Messina et al. 1994; Kennedy et al.
1995)

Results of studies examining effects of soy consumption on colon cancer risk
using carcinogen-induced colon cancer models are inconsistent. Thiagarajan et al. (1998)
reported that feeding defatted soy ﬂour and full-fat soy ﬂakes signiﬁcantly reduced the
numbers of ACF in azoxymethane (AOM)-treated rats. Bennink et al. (1999, 2000)
found that feeding soy ﬂour reduced colon tumor incidence during the promotional phase
of carcinogenesis. In another study, Hakkak et al. (2001) reported that the second
generation of rats had a signiﬁcantly lower colon tumor incidence after feeding rats with
soy protein isolate for two generations.

However, feeding soy protein was not protective against colon carcinogenesis in
several other studies using animal models of colon cancer. Feeding soybean meal tended
to increase tumor incidence in carcinogen-induced colon cancer in rats compared with
feeding casein (P = 0.12; McIntosh et al. 1995). Feeding soy protein isolates containing
high levels of isoﬂavones was found to increase the number of small ACF (less than 4
crypts per foci) after 12 weeks of dietary treatment compared with feeding soy protein
having low isoﬂavone concentrations, although the ﬁnal colon tumor incidence was not
different (Davies et al. 1999).

Few human clinical trials with soy products have been conducted to evaluate their
potential to protect against colon cancer risk. One double blind prospective study with
forty-seven subjects found that dietary supplementation with 39 g per day of isolated soy

protein for one year signiﬁcantly reduced the proliferative capacity in colonic crypts

26

compared with supplementation with an equivalent amount of casein (Thiagarajan et al.
1999)

Soy protein consumption also has been associated with reduced risk of breast
cancer. It has been reported that modest soy consumption was associated with a 50%
reduction in premenopausal breast cancer risk (Lee et al. 1991). However, most
epidemiological studies in postmenopausal women found no signiﬁcant protection of soy
consumption (Messina et al. 2001).

The soybean isoﬂavone genistein is often considered to be the component in soy
that is primarily responsible for its putative protective effects against breast cancer
development. Administration of high concentrations of genistein to female rats during
neonatal development or during the prepubertal period reduces the later development of
DMBA-induced mammary tumors (Lamartiniere et al. 1995; Murrill et al. 1996). The
protective effect of early genistein exposure against mammary cancer is due to promotion
of earlier mammary gland maturation, thereby rendering these animals less susceptible to
mammary carcinogenesis when exposed to the carcinogen later in life (Messina et al.
2001)

Feeding soy products has been reported to reduce mammary tumor incidence or
tumor multiplicity compared with feeding casein-based diets when given during tumor
initiation (Barnes et al. 1990; Hawrylewicz e t al. 1991; Hakkak et al. 2000). Studies also
have investigated effects of soy consumption on mammary tumor progression.
Hawrylewicz et al. (1995) reported that, after inducing breast tumors by injecting the
carcinogen MN U, rats fed soy protein isolate developed signiﬁcantly fewer tumors than

rats fed casein. Furthermore, tumors in the soy-fed rats were less aggressive than tumors

27

observed in the casein-fed rats (Hawrylewicz et al. 1995). However, Hsieh et al. (1998)
found that dietary genistein (750 ug/g) stimulated the growth of subcutaneously
implanted MCF-7 cells in ovariectomized athymic mice. Feeding soy protein containing
various amounts ofgenistein (15, 150 and 300 ug/g) also has been demonstrated to
increase implanted mammary tumor growth in a dose-dependent manner (Allred et al.
2001)

Effects of feeding isoﬂavones on mammary cancer development also were studied
using WTV—neu mice, which spontaneously develop mammary tumors due to
overexpression of the ErbB-2/neu/HER2 oncogene (J in etal. 2002). Feeding MMTV-neu
mice with diets containing different levels of isoﬂavones from 7 weeks of age, J in et al
(2002) found that mammary tumor latency was signiﬁcantly delayed in mice fed
isoﬂavones compared with the control. However, once tumors formed, isoﬂavone
feeding did not reduce the number or size of tumors such that at 34 weeks of age there

were no differences in tumor burden among the treatment groups (J in et al. 2002).

7. Hypotheses and Research Objectives

Dietary factors strongly modify colorectal cancer risk and approximately 66% to
75% of colon cancer may be preventable by adequate diets and physical activity
(AICR/WCRF 1997). Improving our understanding of the inﬂuence of dietary
carbohydrates on risk of colon cancer is of great importance. Although epidemiological
research and results of animal studies suggest that dietary carbohydrates inﬂuence colon

cancer risk, the effects of high dietary sucrose have not been tested in APCMin mice.

28

APCMin mice carry a gemline mutation in the APC gene and develop multiple
intestinal adenomas that can progress to adenocarcinomas. APC gene mutation is
considered as the most critical step in the multi-step colonic carcinogenic process, in
which it acts as a “gatekeeper” to ensure cell population census in intestinal crypts
(Kinzler and Vogelstein, 1996). Therefore, APCMin mice present a useful model to study
diet and gene interactions during colon carcinogenesis following APC mutation.

Animal studies suggest that high intakes of sucrose induce compensatory
hyperinsulinemia by causing a decline in insulin sensitivity in the liver and peripheral
tissues (Pagliassotti et al. 1995; Storlien et al. 1988; Thorbum et al. 1989; Pagliassotti et
al. 1994; Martinez et al. 1994; Pamies-Andreu et al. 1995). Hyperinsulinemia can cause
changes in the IGF signaling system, such as increased IGF-I expression in liver.
Evidence also indicates possible associations between high sucrose intakes and altered
gene expressions of IGFBP3 and IGF-II in colon epithelial cells and colon
carcinogenesis.

The central hypothesis of this research is high dietary sucrose promotes colon
carcinogenesis in APCMin mice, and this effect is mediated in part through elevating IGF
signaling in the intestinal epithelium. To test this hypothesis, experiments were
conducted to 1) test if high dietary sucrose promotes intestinal tumorigenesis in APCMin
mice; 2) determine the patterns of cell kinetic alterations in colonic crypts which indicate
increased risk of colon cancer; 3) determine if high dietary sucrose changes levels of
glucose and insulin in the blood, and mRNA levels of IGF-I, IGF-II and IGFBP] and
IGFBP3 in liver; and 4) explore the patterns of global gene expression alterations

associated with increased intestinal tumorigenesis in APCMI" mice. Results of these

29

studies will determine if high dietary sucrose increases colon cancer risk in APCMin mice,
and also identify potential mechanisms whereby dietary sucrose increases risk for colon
cancer. A secondary objective of this research was to examine the effects of dietary
protein source on intestinal tumorigenesis in APCMin mice. We hypothesized that dietary
soy, compared to casein, reduces intestinal tumorigenesis in APCMin mice. To test this
hypothesis, we designed one feeding experiment as a 2 x 2 factorial to test the effects of,

and interactions between, dietary carbohydrate (sucrose vs. cornstarch) and protein (soy

vs. casein) sources on intestinal tumorigenesis APCMm mice.

30

CHAPTER II.
INFLUENCE OF DIETARY CARBOHYDRATE SOURCE (SUCROSE

VERSUS CORNSTARCH) ON INTESTINAL TUMORIGENESIS IN APCMIN
MICE

31

ABSTRACT

Epidemiological evidence suggests that consumption of large amounts of reﬁned
carbohydrates may be associated with increased risk of colon cancer. The objectives of
this study were to test the effects of diets containing sucrose or cornstarch as the sole
carbohydrate source on intestinal adenoma development in APCMin mice, and possible
associations between elevated serum IGF concentrations caused by high dietary sucrose
and increased colon cancer risk. APCMin mice (n = 48) were randomly assigned at 4
weeks of age to one of two modiﬁed AIN-93G diets containing either sucrose or
cornstarch (523 g/kg of diet) as the sole carbohydrate source. Diets were fed ad libitum
for 10 weeks. Weekly body weights, intestinal adenoma development and epithelial cell
kinetics (Ki67, PCNA and TUNEL labeling) in colonic crypts were measured. The levels
of glucose and insulin in the circulation, as well as mRNA expressions of IGF-I, IGF-II,
IGFBP] and IGFBP3 in liver also were determined.

In comparison to cornstarch, sucrose promoted weight gain in mice (P < 0.05).
APCMin mice fed sucrose had signiﬁcantly greater numbers of adenomas in the proximal
third of the small intestine when compared to mice fed cornstarch (21.8 vs. 13.1, P <
0.01), however, the average Size of adenomas in the proximal third of the small intestine
were larger in cornstarch-fed mice (2.1 vs. 1.1 mmz, P < 0.01). The total number of
adenomas in the small intestine tended to be greater in sucrose-fed mice (72 vs. 60, P <
0.07). Colon tumor incidence was 56% in APCMin mice fed sucrose compared with 43%
in those fed cornstarch, but this difference was not statistically signiﬁcant. Mice
consuming sucrose had signiﬁcantly greater Ki-67 antigen labeling index in colonic crypt

epithelial cells compared to mice consuming cornstarch (37% vs. 32%, P < 0.05), which

32

indicated that mice consuming sucrose had a greater overall rate of cell proliferation in
the colon.

Mice consuming the high-sucrose diet had greater serum glucose concentrations
than mice consuming cornstarch (10.09 vs. 8.08 mM, P < 0.05). Mice consuming sucrose
tended to have higher serum insulin levels compared to mice consuming cornstarch
(262.6 vs. 174.4 pmol/L, P = 0.07). Relative IGF-I mRNA expression in liver of mice
fed sucrose was greater than that observed in mice fed cornstarch (0.166 vs. 0.092, p =
0.05). Hepatic mRNA expressions of IGF-II, IGFBP] and IGFBP3 were not inﬂuenced
by diet.

In summary, dietary carbohydrate source signiﬁcantly inﬂuenced the numbers and
sizes of adenomas in the proximal small intestine. Although carbohydrate source did not
inﬂuence colon tumor incidence after 10 weeks of treatment, dietary sucrose signiﬁcantly
increased the proliferation rate of colonic epithelial cells. The effects of dietary sucrose
on intestinal tumorigenesis may be related to increased levels of IGF-I mRNA expression

in liver.

33

INTRODUCTION

Colon cancer is the second most common cause of cancer mortality and claims
more than 50,000 lives every year in the United States (ACS, 2004). Dietary factors
strongly modify colorectal cancer risk and approximately 66% to 75% colon cancer may
be prevented by adequate diets and physical activity (AICR/WCRF, 1997). High dietary
intakes of reﬁned carbohydrates such as sucrose are associated with higher colon cancer
risk (Bostick et al. 1994; De Stefani et al., 1998). Furthermore, recent studies indicate
that the there is an increased consumption of simple sugars such as high fructose corn
syrup in the United States (Bray et al., 2004). Using US Department of Agriculture food
consumption tables from 1967 to 2000, Bray et al. (2004) found that the consumption of
high fructose corn syrup increased more than ten-fold between 1970 and 1990, far

exceeding the changes in intake of any other food or food group.

Experiments using carcinogen-induced colon cancer models have shown that
feeding rats with high sucrose diets promoted growth of aberrant crypt foci (ACF),
precancerous lesions in colon, and also were associated with increased epithelial cell
proliferation in colon crypts compared with feeding cornstarch diets (Kristiansen et al.
1995; Cademi et al. 1993, 1997; Poulsen et al. 2001). Colon adenocarcinomas in
carcinogen-treated rats cosuming high-sucrose diets were signiﬁcanatly larger and had
more invasive potential compared with those in rats consuming cornstarch (Cademi et al.
1994). However, to date high sucrose diets have not been demonstrated to increase colon
cancer incidence in animal studies. Furthermore, the underlying mechanisms whereby
high dietary sucrose may increase colon cancer risk are not clear. Studies have focused

on the possible inﬂuence of intestinal lumenal environment through fermentation and

34

production of short chain fatty acids (Cardemi et al. 1994). Because sucrose and
cornstarch are digested and their constituent monosaccharides are absorbed in the small
intestine, any inﬂuence on colon carcinogenesis is more likely to be mediated by
circulatory factors.

APCMin mice carry a mutation in one allele of the APC gene and develop
numerous adenomas in the intestine. APC gene mutations are a frequent and early event
in colon carcinogenesis. Thus, APCMin mice represent an excellent model to study diet
and gene interactions during colon carcinogenesis. The inﬂuence of dietary
carbohydrates on intestinal tumorigenesis in APCMin mice has not been reported.

The ﬁrst objective of the current study was to determine the inﬂuence of dietary
carbohydrate source (sucrose vs. cornstarch) on intestinal tumorigenesis in APCMin mice.
A second objective was to evaluate serum concentrations of insulin and IGF, as well as
hepatic expression of mRNA for IGF-I, IGF-II, IGFBP] and IGFBP3 and their
association with intestinal tumorigenesis. Our ﬁnal objective was to measure the
inﬂuence of dietary carbohydrate source on cell proliferation and programmed cell death

in colonic epithelium.

35

MATERIALS AND METHODS

Animals: Due to the high cost and limited commercial availability of APCMin
mice, we maintain a breeding colony of APCMin mice at Michigan State University. Mice
for the study were produced by mating C57BL/6J male APCMin mice with normal
C57BL/6J female mice. The resulting offspring (50% normal and 50% APCMin based on
simple Mendelian inheritance) were randomly assigned at weaning (4 weeks of age) to
one of two diets, which they were fed for 10 weeks or until they became morbid (> 10%
weight loss). Fifty mice were assigned to each treatment. Of these, we expected 50% to
be APCMin mice and 50% to have normal APC gene status. Mice used in this study were
genotyped by an allele-speciﬁc PCR procedure to identify APCMin progeny (Jacoby et al.,
1996). Mice were housed in a room with constant temperature and humidity (23°C, 60%
humidity) and a 12:12 hour light-dark cycle. Animal care and feeding were conducted
with approval of the Michigan State University All-University Committee on Animal Use
and Care.

Diets: Experimental diets were formulated based on AIN-93G diets (Reeves et
al., 1993) and contained either sucrose or cornstarch as the sole carbohydrate source
(Table l). Mice were fed diets ad libitum for the duration of the study (ten weeks or until

individual animals exhibited body weight loss of more than 10%).

36

Table 1. Composition of diets used in experiment 1 (g/kg diet).

 

 

Ingredient Corn Starch Diet Sucrose Diet
Casein 221 221
Corn Starch 523 0
Sucrose 0 523
Soybean Oil 150 150
Cellulose 50 50
AIN-93G-MX 39 39
AIN-93-VX ll 11
L—Cystine 3 3
Choline Bitartrate 3 3

 

Sample Collection: Mouse body weights were measured weekly. After ten
weeks of dietary treatments, mice were euthanized. Blood samples from each mouse
(approximately 300 ILL) were collected immediately from the heart using a needle and
syringe and transferred to microcentrifuge tubes and held on ice. Once the clot formed,
the samples were centrifuged and the serum was carefully aspirated into a fresh tube and
stored at —80 °C. A small piece of liver was sampled (usually at the lower edge of the
left lobe) and stored at —80 °C. The entire intestine was removed, opened longitudinally,
rinsed with water, pinned on cardboard, and ﬁxed with 10% neutral buffered formalin
overnight. A one-centimeter section was cut from the middle of the colon and processed
and parafﬁn-embedded for ﬁrrther analyses of immunohistochemistry. The ﬁxed
intestinal tissues were stained with 0.3% methylene blue for three minutes to facilitate

quantiﬁcation of adenoma numbers and sizes.

37

Quantiﬁcation of Intestinal Tumors: Numbers of adenomas in each intestinal
section (proximal, middle, and distal small intestine, cecum, and colon) were determined
by using a stereo microscope. The dimensions of each adenomas was measured using a
transparent grid placed under the specimen. The diameter of each adenoma was estimated
to the nearest 0.5 mm. For ﬂat tumors in the small intestine, the diameters were measured
in two dimensions (d1, d2), and the Sizes of the adenomas were calculated by using the
formula: a = (II * d1 * d2) / 4. For solid tumors in the large intestine, the diameters were
measured in three dimensions (d1, d2 and d3), and the spherical volumes were calculated
by using the formula: spherical volume = (1r "' d1 * d2 * d3) / 6.

Immunohistochemistry: Parafﬁn-embbed tissue blocks were cut into tissue
sections (4 pm thick) and oriented on Poly-L-lysine slides. Tissue sections were adhered
to slides by drying at 58°C in a vacuum oven for two hours, and then they were
deparafﬁnized with xylene (2 x 5 min), rehydrated through 100% and 95% ethanol (2 x 2
min each), respectively, then rinsed in nanopure water 3 times. Endogenous peroxidase
activity was blocked by immersion in 0.3% hydrogen peroxide for 10 min, followed by
washing with buffered solution (TBS or PBS, pH 7.4) 3 times. Antigen retrieval was
achieved by using citrate buffer (10 mM sodium citrate) at 95°C for 20 minutes followed
by cooling of 20 minutes. After thoroughly washing with TBS, the slides were covered
with capillary-action slide clips and mounted on Shandon IHC staining racks (Shandon
Immunohistochemical System, Thermo Electron Corporation). Speciﬁc
immunohistochemistry staining procedures were conducted for PCNA and Ki67 antigens.
Reagents were obtained from DakoCytomation (Carpinteria, CA) unless identiﬁed

otherwise.

38

A. Measurement of PCNA: Tissue sections were incubated with an anti-PCNA
antibody (PC10, Novcastra, UK; 1:100 dilution with 2% BSA in TBS) overnight at 4°C.
Slides were rinsed twice with TBS and then incubated with Link Antibody (goat anti-
mouse IgG) (4 drops) for 30 minutes. Slides were rinsed twice with TBS and then
incubated with strepavidin-peroxidase conjugate (4 drops) for another 30 minutes. Slides
were then washed with TBS twice and incubated in 0.05% DAB (3,3'—Diaminobenzidine;
Sigma) for 7-10 minutes. Slides were then rinsed and counterstained with Mayer's
hematoxylin (Sigma) for 15 seconds, rinsed and blued in 0.3% ammonia for 5 seconds.

Finally, slides were coverslipped using Faramount aqueous mounting media.

Ten full-length crypts of each colon were examined to determine labeling indices
(all the crypts appeared to have normal cryptal structure, and crypts adjacent to adenomas
were excluded). The number and the position of PCNA-stained nuclei in each crypt
column were recorded. PCNA labeling index (LI) was determined as the percentage of
cells positively stained in each colon hemi-crypt. The colon hemi-crypt was also divided
into three sections with equal numbers of cells: the bottom third, the middle third and the
top third. The labeling index also was calculated for each section as the percentage of
cells positively stained in each section. The proliferative zone was calculated using the

formula: proliferative zone = position of highest labeled cell in crypt / crypt height.

B. Measurement of Ki67: The tissue sections were incubated with an anti-Ki67
antibody (NCL-Ki67, Novcastra, UK; 1:100 dilution with 2% BSA in TBS) overnight at

4°C. All the subsequent steps were the same as that used for PCNA measurement.

39

C. Measurement of Apoptosis: The apoptosis index in colonic epithelial cells
was measured by the terminal deoxynucleotidyl transferase (TdT)-mediated dUTP nick
end-labeling (TUNEL) assay using the in situ cell death detection kit from Roche
Molecular Biochemicals (Nonnenwald, Germany), which labels DNA strand breaks
produced during apoptosis. Brieﬂy, tissue sections (4 pm) were de-parafﬁnized with
xylene, rehydrated as described before and washed with phosphate-buffered saline (PBS).
Antigen retrieval was achieved by using citrate buffer (10 mM sodium citrate) at 95°C for
20 minutes followed by cooling of 20 minutes. Slides were washed twice in PBS and
treated with proteinase K (2 jig/ml in 10 mM Tris—HCI) for 15 min at room temperature.
Slides were then rinsed twice with PBS, incubated with 3% H202 for 10 min at room
temperature, and again rinsed twice with PBS. Slides were then covered with a slide
cover slip and mounted on Shandon IHC staining racks (Thenno Electron Corporation).
Slides were exposed to 2% bovine serum albumin for 20 min to mask non-speciﬁc
antigens. TUNEL reaction mixture (50 pl) consisting of terminal deoxynucleotidyl
transferase and the nucleotide were added (the original Enzyme Solution in vial l was
diluted with 2 times volume of TUNEL Dilution Buffer, then 5 ul of the diluted enzyme
were mixed with 45 ul of the Label Solution to obtain the 50 ul working TUNEL reaction
mixture). The slides were incubated for 60 min at room temperature in dark. Slides were
then rinsed three times with PBS, incubated for 30 min at 37°C with converter-POD
peroxidase-conjugated antibody and rinsed three times with PBS. Slides were then
incubated for 10 min at room temperature with 100 pl of ABC chromogen substrate
solution, counterstained with hematoxylin for 15 seconds, rinsed in distilled water, blued

with 0.3% ammonia water, and coverslipped using Faramount aqueous mounting media.

40

Cells having red stained nuclei were identiﬁed as TUNEL positive. Ten intact intestinal
crypts were scored for each animal, and the apoptotic index was calculated as the number
of stained cells in each crypt divided by the total number of epithelial cells in each crypt.

Measurement of Glucose Concentrations in Serum: Serum glucose
concentrations were measured using the QuantiChrom Glucose Assay Kit (DIGL-200,
BioAssay System, Hayward, CA) by following the manufacturer’s instructions. Brieﬂy, 5
pl glucose standards and samples were transferred to appropriately labeled tubes. 500 pl
Glucose Assay Reagent was added to each tube. The tubes were closed tightly and
thoroughly mixed. Samples were then heated in a boiling water bath for 8 min and cooled
in cold water bath for 4 min. Duplicate samples (200 pl) from each sample were
transferred into a clear-bottom 96-well plate. Optical density was measured at 570 nm
using a microplate reader, and the absorbance data were analyzed using SOFTmax PRO
(Molecular Devices Corporation, Sunnyvale CA).

Measurement of Insulin Concentrations in Serum: Concentrations of insulin in
blood serum were measured using the Mercodia Mouse Insulin ELISA kit (Mercodia AB,
Uppsala, Sweden) by following the manufacturer’s instructions. Brieﬂy, 25 pl each of the
standards and samples were added to a 96-well plate coated with anti-insulin antibodies.
Enzyme conjugate (50pl) was added to each well and plates were incubated on a shaker
for 2 hours at room temperature. Plates were then washed 6 times with an automatic
plate washer using the wash buffer solution. The chromogenic substrate 3,3',5,5'-
tetramethylbenzidine (TMB; 200 pl) was added to each well and plates were incubated

for 15 min, at which time 50 pl stop solution was added. Optical density of each well

41

was measured at 450 nm using a microplate reader, and the absorbance data were
analyzed using SOFTmax PRO (Molecular Devices Corporation, Sunnyvale CA).

Quantitative Real Time PCR Analysis of Hepatic mRNA Expression of IGF-
I, IGF-II, IGFBPl and IGFBP3:

A. Total RNA Extraction: Total RNA from liver samples was extracted using
Trizol reagent according to the manufacture’s instructions. Brieﬂy, liver samples (200-
300 mg) were suspended in 1 ml Trizol, homogenized and incubated 10 minutes at room
temperature. chloroform (200 pl) was added to the suspension and phases were separated
by centrifugation for 10 minutes (12,000 x g) at 4°C. The aqueous phase was aspirated
into a new tube and RNA was precipitated with 0.5 ml isopropyl alcohol. The total RNA
pellets were washed with 75% ethanol and resuspended into RNAase free H2O. The
RNA concentrations were determined by spectrophotometry, and RNA integrity was
monitored by agarose gel electrophoresis.

B. Primer Design: Consensus sequences for genes of interest were obtained from
the NCBI website. The Primer3 program (http://www-genome.wi.mit.edu/cgi-
bin/primer/primer3_www.cgi) was used to design the primers for each gene of interest.
General conditions used in the Primer3 program included: primer size 15-27 hp, primer
melting temperature 57°-63°C, and product size 150-250 bp. The resulting primers
(Table 2) were synthesized in the Genomic Technology Support Facility in the

Department of Biochemistry at Michigan State University.

42

Table 2. List of primers used for real time PCR for gene expression in experiment 1.

 

 

Gene Forward primer (5’-3’) Reverse primer (5’-3’) Amplicon
Name length (bp)
B-ACTIN GCTACAGC'ITCACCACC TCTCCAGGGAGGAAGAG 123
ACA GAT
IGF 1 CTACCAAAATGACCGCA CACGAACTGAAGAGCAT 126
CCT CCA
IGF-II CCCTCAGCAAGTGCCTA TTAGGGTGCCTCGAGAT 121
AAG GIT
IGFBPI AGCCCAGAGATGACAGA GITGGGCTGCAGCTAAT 199
GGA CTC
IGFBP3 TGTI'ITCTGGTCCAGCC CAAGCCACTCCTCTTTC 122
TCT CTG

 

*Abbreviations: insulin-like growth factor I (IGF-I), insulin-like growth factor II (IGF-
II), insulin-like growth factor binding protein 1 (IGFBPI), insulin-like grth factor
binding protein 3 (IGFBP3).

C. Synthesis of cDNA by Reverse Transcription: cDNA was synthesized from
total RNA using the Superscript II system (Invitrogen). Each reaction tube contained 1.0
pg total RNA and 1.0 pl of 0.02 mM anchored oligo dTl8VN (primers having 18
thymidine residues with one G, C or A residue as the anchor at the 3' end to ensure
binding at the beginning of the mRNA message) and water up to a ﬁnal volume of 12 pl.
The reaction solution was mixed and incubated at 70°C for 10 minutes to denature the
templates, then held on ice for 1 minute to anneal primers to templates. Then, the
reaction tube was pulse-spinned, and 8 pl of master mix (containing 4 pl of 5x ﬁrst strand
buffer, 2 pl of 0.1 M DTT, 1 pl of 10 mM dNTPs, and 1 pl of Superscript II — Invitrogen)
was added to each reaction. The reaction solution was mixed and incubated for 2 hours
at 42 °C, then inactivated by heating for 15 minutes at 75 °C. The reverse-transcription

products were stored at —20 °C until use.

43

D. Quantitative Real time PCR:

Preparation of Standards: Prior to conducting quantitative RT-PCR, 1 pl of
cDNA mixture containing cDNAs from several samples in different treatment groups was
ampliﬁed through real-time PCR using primers of the gene of interest (Table 2). Real
time PCR assays were conducted using SYBR Green PCR Core Reagents (Perkin
Elmer/ABI), using the MicroAmp Optical 96-well reaction plate. The reaction mixture
(25 pl ﬁnal volume) contained 1 pl cDNA reverse-transcribed from 1 pg total RNA, 0.12
mM forward and reverse primers, 2.5 pl of 10x SYBR Green Reaction Buffer, 3 mM
MgCl2, 0.2 mM dNTPs, and 0.026 units of Taq polymerase (Perkin Elmer/ABI). PCR
reactions were performed in an A81 7700 system with the following cycling conditions:
10 minutes at 95°C, followed by 40 cycles of 15 seconds at 95°C and 1 minute at 60°C.

The PCR products were puriﬁed using Qiagen PCR puriﬁcation kits (Qiagen Inc.,
Valencia, CA) following the manufacturer’s instructions. DNA was eluted in 30 pl
elution buffer in the ﬁnal step of puriﬁcation, and concentrations of the ﬁnal PCR
products were measured by spectrophotometery. A sample of PCR products were used to
run electrophoresis on a 1% agarose gel containing ethidium bromide (4 pl of 10 mg/ml
stock per 100 ml of agarose) to verify that each amplicon was of expected size with no
non-speciﬁc products.

The molecular weight of the PCR product for each gene of interest was calculated
using the formula: molecular weight (g/mol) = 650 * length of amplicon. The number of
copies/ pl was calculated by using the following formula: copy Number (per pl) =
(concentration /molecular weight) * 6.023 * 10”. PCR products were each diluted to a

copy number of 10l0 and serial dilutions from lO'Oto 101 were created for each product.

44

Quantitative Real Time PCR of Samples: Quantitative real time PCR was
carried out by running PCR with 1 pl of each dilution and lpl cDNA of unknown
samples on the same plate at the same time. The PCR mixtures and setup conditions were
the same as used previously. The quantiﬁcation of the starting quantity of a speciﬁc
cDNA for a speciﬁc gene in an unknown sample was performed using a standard curve
generated by using known dilutions of the corresponding DNA product. For each
dilution, the ABI-Prism 7700 software generated a real-time ampliﬁcation curve
constructed by relating the ﬂuorescence signal intensity to the cycle number. The
standard curve was then generated on the basis of the linear relationship existing between
the Ct value (cycle threshold; corresponding to the cycle number at which a signiﬁcant
increase in the ﬂuorescence Signal was ﬁrst detected) and the logarithm of the starting
quantity. The levels of mRNA for genes of interest were expressed as a ratio between its
own expression and that of an internal control gene (beta-actin expression), which was
referred as normalized expression.

Statistical Analyses: Data were analyzed using the General Linear Models
procedure of SAS (Version 8.2). Least square means procedures were used for all the
measurements. When signiﬁcant dietary effects were detected (F-test Signiﬁcant at P <
0.05), the Least Signiﬁcant Difference method was used to compare appropriate means.
Differences between means were declared signiﬁcant at P S 0.05. The colon adenoma
incidence of mice consuming diets containing sucrose or cornstarch was analyzed using

the Frequency procedure of SAS (Version 8.2).

45

RESULTS

Intestinal Tumorigenesis: At the end of experiment, twenty-seven and twenty-
one APCMin mice were identiﬁed using a PCR-based genotyping method (Jacoby et al.,
1996) in the sucrose and cornstarch groups, respectively.

Mice consuming the sucrose-based diet gained signiﬁcantly more weight than
mice consuming the cornstarch-based diet during the experiment (Figure 5). Although
male mice gained more weight than female mice, there was no signiﬁcant interaction
between gender and diet. Based on visual observations during necropsy, much of the
observed difference in body weight for the two dietary treatments likely was due to
increased abdominal fat deposition in mice consuming the sucrose diet.

Dietary carbohydrate source inﬂuenced the numbers and average sizes of small
intestinal adenomas. These effects were observed exclusively in the proximal one-third
of the small intestine. APCMin mice fed sucrose had signiﬁcantly greater numbers of
adenomas in the proximal third of the small intestine than mice fed cornstarch (Figure 6;
21.9 vs. 13.1, P <0.001). However, the average size of adenomas was larger in
comstarch-fed mice (Figure 7; 2.1 vs. 1.1 mmz, P <0.01). Overall, there was no
difference in the total adenoma burden between the two groups (Figure 8). The total
number of adenomas in the small intestine tended to be greater in sucrose-fed mice
(Table 3, 72 vs. 60, P <0.07).

The colonic adenoma incidence was 56% in APCMin mice fed the sucrose-based
diet compared with 43% in APCMin mice fed the comstarch-based diet. However this
difference was not statistically signiﬁcant. Among the tumor-bearing mice, the numbers

and sizes of adenomas in colon were numerically greater in mice fed sucrose compared

46

with those fed cornstarch, however these difference did not reach statistical signiﬁcance

(Table 4).

28-
264
24-
22-
20-
18-
16-
14-
12-

   

 

   

+ Sucrose
+ Cornstarch

 

 

 

Bodyweight (g)

 

 

10 I I I 1 I I t j I I j

012345678910
Treatment weeks
Figure 5. Weekly body weights of APCMin mice consuming diets containing sucrose
or cornstarch as the primary carbohydrate source. Mice consuming sucrose-based

diets gained signiﬁcantly more body weight compared with mice consuming
comstarch-based diets (P <0.05).

47

 

 

 

 

 

 

 

 

 

 

 

40- lSucrose Diet
35_ I] Cornstarch Diet
is 30-
a ._..._.
g 25-
” 20-
E b
o 15'
5
3 10"
5-
Proximal Small Medial Small Distal Small
Intestine Intestine Intestine

Figure 6. Numbers of small intestinal adenomas in different small intestinal regions of
APCMm mice consuming diets containing sucrose or cornstarch as the primary
carbohydrate source.

* All APCMm mice had small intestinal adenomas.

1" Signiﬁcant diet effect (P < 0.01).

48

 

 

 

 

 

   

 

 

 

 

 

 

 

2.5~ b ISucrose
a“ __ DCornstarch
E 2~
E
o
.5 1.5-
(D a
a
E 1- .......
c
a:
'a
< 0.5-
a:
m
E .. ., _
g V ..... . .
< Proximal Small Medial Small Distal Small
Intestine Intestine Intestine

Figure 7. Average sizes of small intestinal adenomas (mmz) in different small
intestinal regions of APCMm mice consuming diets containing sucrose or cornstarch as
the primary carbohydrate source.

“‘b Signiﬁcant diet effect (P < 0.01).

49

 

El Sucrose

D Cornstarchl

 

 

 

 

 

 

 

r
E
E
c
on
1:
h
:I
In
«I
E
o
C
o
'o
<
Distal Small
Intestine Intestine Intestine

Figure 8. Total burden of small intestinal adenomas (mmz) in different small intestinal
regions of APCMm mice consuming diets containing sucrose or corn starch as the
primary carbohydrate source.

* No signiﬁcant diet effects were detected.

50

Table 3. Adenoma incidence, average numbers of adenomas per mouse, total
adenomaburden per mouse and average size of adenomas in the small intestine
of APCM‘" mice consuming diets based on differing carbohydrate sources in
experiment 1 (means i standard errors).

 

Parameter Sucrose (n=27) Cornstarch (n=21)

 

For entire small intestine:
Incidence of small intestinal

adenomas 100% 100%
Adenoma number 72.2 i 2.4a 59.7 :t 5.1b
Total adenoma burden (mmz) 77.3 :I: 7.3 69.3 i 8.5
Average size (mmz) 1.02 i 0.06 1.17 i 0.07

 

a,b Diet effect on small intestinal adenoma number (P = 0.07)

Table 4. Adenoma incidence, average numbers of adenomas per mouse, total _
adenoma burden per mouse and average Size of adenomas in the colon of APCMln
mice consuming diets based on differing carbohydrate sources in experiment 1
(means a: standard errors).

 

 

Parameter Sucrose (n=27) Cornstarch (n=21)

For all APCMin mice: 15/27 9/21
Incidence of colon adenomas 56% 43%
Adenoma number 0.66 i 0.14 0.52 3: 0.16
Total adenoma burden (mm3) 6.12 :h 2.00 3.51:1: 2.00
Average Size (mm3) 4.63 :t 1.17 3.27 d: 1.36

For adenoma-bearing mice only:
Adenoma number 1.20 i 0.14 1.25 :1: 0.19
Total adenoma burden (mm3) 11,09 :t 2.72 8.38 i 3.72
Average size (mm3) 8.36 :t 1.62 7.75 :l: 2.22

 

* No signiﬁcant dietary treatments were detected.

51

Colon Crypt Cell kinetics: Ki-67 antigen labeling was used as a surrogate
marker for cell proliferation in this study. Mice consuming sucrose-based diets had
signiﬁcantly greater total crypt Ki-67 labeling index compared to mice consuming
cornstarch (Table 5). Crypt height and Ki67 proliferative zone were not inﬂuenced by
dietary carbohydrate source. Total crypt PCNA labeling index and proliferative zone
(Table 6) were not inﬂuenced by diet in this experiment. Dietary carbohydrate sources
did not inﬂuence programmed cell death in colon after lO-weeks of dietary treatment
(Table 7). Essentially all programmed cell death occurred in the apical one-third of

epithelial cells in colonic crypts.

Table 5. Ki67 antigen expression in colon of APCMin mice consuming diets
containing different carbohydrate sources (means i standard errors).

 

 

 

Parameter Sucrose (n=18) Cornstarch (n=20)
Crypt Height (cells) 20.0 d: 0.30 19.9 d: 0.28
Total LI 0.37 d: 0.018a 0.32 a 0.016b
Proliferative Zone 0.48 :I: 0.019 0.45 :t 0.018
Bottom 1/3 L1 0.75 i 0.021“‘ 0.66 :1: 0.020b
Middle 1/3 Ll 0.28 i 0.037 0.23 :t 0.034
Top 1/3 LI 0.01 i 0.006 0.01 d: 0.006
a,b .

Diet effect (P <0.05).

52

Table 6. PCNA antigen expression in colon of APCMin mice consuming diets
containing different carbohydrate sources (means :I: standard errors).

 

 

Parameter Sucrose (n=25) Cornstarch (n=24)
Crypt Height (cells) 20.20 i 0.20 20.02 :I: 0.2
Total LI 0.358 t 0.014 0.357 :t 0.014
Proliferative Zone 0.43 i 0.019 0.45 d: 0.019
Bottom 1/3 LI 0.79 i 0.017 0.76 i 0.018
Middle 1/3 LI 0.22 :t 0.026 0.19 :L- 0.026
Top 1/3 LI 0.002 i 0.002 0.003 d: 0.002

 

‘ No signiﬁcant diet effect.

Table 7. TUNEL assay results in colon ofAPCMin mice consuming diets
containing different carbohydrate sources (means 5: standard errors).

 

 

Parameter Sucrose (n=18) Cornstarch (n=20)
Crypt Height (cells) 19.8 i: 0.14 19.6 i 0.12
Total LI 0.008 d: 0.001 0.008 :t 0.001
Bottom 1/3 LI 0.000i 0.000 0.000 :t 0.000
Middle 1/3 L1 0.000 :t 0.000 0.000 i 0.000
Top 1/3 LI 0.023 :t 0.005 0.026 d: 0.004

 

' No signiﬁcant diet effect.

53

Concentrations of Glucose and Insulin in Serum: Mice consuming high-
sucrose diets had signiﬁcantly greater serum glucose concentrations compared to mice
consuming cornstarch (10.09 :I: 0.57 vs. 8.08 at 0.57 mM, P < 0.05; Figure 9). Mice
consuming sucrose also had higher plasma insulin levels compared to mice consuming
cornstarch (262.6 :I: 34.7 vs. 174.4 1 34.3 pmol/L, P = 0.07; Figure 10). These results
suggest that mice fed high-sucrose diets developed insulin resistance and
hyperinsulinemia.

IGF-I, IGF-H, IGFBP] and IGFBP3 mRNA Expression in liver: As shown in
Figure 11, IGF-I relative expression in liver of mice consuming sucrose was greater than
that observed in mice fed cornstarch (0.166 vs. 0.092, p = 0.05). IGF-II, IGFBPI and

IGFBP3 mRNA expressions in liver were not inﬂuenced by diet (Table 8).

54

15_ CI Sucrose
CI Cornstarch

 

 

E. a
9

g 101 b
O

E.

M

E

a 5‘

'5

O

.2

a:

 

 

 

 

 

 

Figure 9. Glucose concentrations in blood serum of mice consuming diets
containing different dietary carbohydrate sources.

‘1’" Signiﬁcant diet effect (P < 0.05).

55

 

350- El Sucrose

 

 

 

 

 

E El Cornstarch
'5 300-
E a
5 250-
.E .
3 200- b
.E
S 150-
d)
w 100«
'U
8
E 50
0

 

 

 

 

 

Figure 10. Insulin concentrations in blood serum of mice consuming diets
containing different dietary carbohydrate sources.

a’b Diet effect (P = 0.07).

56

 

 

 

 

 

 

02- El Sucrose
g a D Cornstarch
.5 1 J
2 0. 5
S
I” b
.921 0.1-
a
2
E’
—. 0.05“
It.
9

 

 

 

 

 

Figure 11. Relative expression of IGF-I mRNA in liver of mice consuming diets
containing different dietary carbohydrate sources.

a,b Signiﬁcant diet effect (P < 0.05).

57

Table 8. Relative expression of IGF-II, IGFBPl and IGFBP3 mRNA in liver of
mice consuming diets containing different carbohydrate sources (means :I:
standard errors).

 

 

Parameter Sucrose (n=18) Cornstarch (n=20)
IGF-II 0.016 :t 0.004 0.011 :t 0.004
IGFBP] 0.0014 :1: 0.0003 0.0011 i 0.0003
IGFBP3 0.012 :t 0.007 0.010 t 0.007

 

* No signiﬁcant diet effets.

58

DISCUSSION

Epidemiological research and results from experiments using carcinogen-induced
colon cancer models suggest that high consumption of reﬁned carbohydrates such as
sucrose is associated with increased risk of colon cancer. The inﬂuence of high-sucrose
diets on intestinal adenoma development in APCMin mice has not been reported
previously.

In the current study, APCMin mice consuming high-sucrose diets had signiﬁcantly
greater numbers of adenomas in the proximal small intestine, and tended to have more
adenomas in the entire small intestine, when compared to mice consuming high-
comstarch diets. Although all cells in the intestine of APCMin mice carry a mutant copy
of APC, only a relatively small number of tumors form in the intestine. This indicates
that further somatic events are required before tumors can develop. It has been suggested
that the somatic mutation of the wild-type APC allele inherited ﬁom the unaffected
parent is the rate-limiting step in tumor initiation (Kinzler 1996). It is possible that high-
sucrose diets increased mutation rates in the small intestine, which resulted in increased
rate of somatic mutation of the second allele of APC. Dragsted et al. (2002) reported that
increasing sucrose in diets administered to Big Blue rats for 3 weeks [diet sucrose
concentrations = 3.4% (control), 6.9%, 13.8%, or 34.5%] resulted in a dose-dependent
increase in the mutation frequency at the CH site in the colonic mucosa, which indicates a
direct or indirect genotoxic effect of a sucrose-rich diet.

In the current study, the cell proliferation rate in the small intestinal epithelium of
APCMin mice was not determined due to difﬁculties in obtaining intact full-length small

intestinal crypts for assessment of cell proliferation. However, cell proliferation rate

59

measured by Ki67 labeling in the colon of APCMin mice consuming high-sucrose diets
was signiﬁcantly greater compared to that measured in the colon of mice fed the high-
comstarch diet. It is possible that sucrose-rich diets also increased cell proliferation rate
in the small intestinal epithlium in this experiment. It is possible that increased cell
proliferation in the small intestine of APCMin mice may result in increased mutation rates,
and thereby increase adenoma promotion in the small intestine.

APCMin mice fed the high-cornstarch diet had a greater average size of proximal
small intestinal adenomas when compared with mice fed the high-sucrose diet. It is
possible that small intestinal adenomas in APCMin mice compete for blood and nutrient
supply, which ultimately might lead to an upper limit on adenoma burden in the small
intestine of these mice. Overall, the adenoma burden in the proximal small intestine did
not differ signiﬁcantly for mice consuming sucrose- or comstarch-based diets.

In the current study, dietary carbohydrate source only inﬂuenced small intestinal
adenoma development in the proximal small intestine. The reason for this is not clear.
Dietary sucrose and cornstarch are primarily digested and absorbed in the proximal small
intestine in APCMin mice. It has been suggested that Short-term consumption of high-
sucrose diets may increase oxidative stress in rats (Busserolles et al. 2002), so it may be
possible that chronic exposure to high-sucrose diets may contribute to the observed
effects on tumorigenesis in proximal small intestine.

We are particularly interested in tumorigenesis in the colon of APCMin mice.
Although colon tumor incidence was numerically greater in APCMin mice consuming
sucrose versus those consuming cornstarch (56% vs. 43%), this difference was not

statistically signiﬁcant. If high dietary sucrose does increase the risk for colon

60

tumorigenesis, there may be two reasons why no signiﬁcant differences in colon
adenomas were observed in this experiment. Firstly, we may have insufﬁcient statistical
power to detect small difference in colonic adenoma incidence. Secondly, small
intestinal adenomas initiate and progress very rapidly in APCMin mice, which usually
cause severe bleeding, anemia and weight loss. APCMin mice often display morbidity
around 15 weeks of age. Because the adenomas in the colon of APCMin mice grow at a
slower pace, there may not be sufficient time for diets to exert their full impact on colonic
tumor development.

Dietary effects on early biomarkers associated with increased risk for colon
cancer also were determined using immunohistochemistry analyses. Ki67 labeling is
considered a valid biomarker of cell proliferation (Gerdes et al. 1990, 1991; Hall et al.
1990; Holt et al. 1997). In the present study, overall Ki67 labeling index of colonic
epithelial cells was signiﬁcantly greater in APCMin mice consuming sucrose,which
indicatsd that consumption of high-sucrose diets increased epithelial cell proliferation in
colon crypts. This result is consistent with many other studies in which high-sucrose
diets signiﬁcantly increased epithelial cell proliferation in colon crypts when compared to
diets based on cornstarch (Cademi et al. 1991, 1993, 1997). Increased cell proliferation in
colon crypts is associated with increased risk for colon cancer (Lipkin et al. 1974. 1984;

Bleiberg et al. 1985; Terpstra et al. 1987).

Overall, these results indicate that diets containing high sucrose concentrations
(versus cornstarch) increased adenoma numbers in proximal small intestine, and also may
increase risk for colon tumor development by elevating cell proliferation in colon crypts.

Several studies have been conducted to explore possible mechanisms whereby high-

61

sucrose diets increased risk for colon cancer. Cademi et a1. (1996) studied the inﬂuence
of different dietary carbohydrate sources on colon lumenal environment. Female rats
were fed for one month with diets containing different carbohydrate sources. They found
that colonic epithelial cell proliferation was signiﬁcantly increased in rats fed sucrose-
based diets when compared to rats fed diets based on glucose, fructose, or cornstarch.
However, the effects of carbohydrate source on colonic epithelial cell proliferation were
not associated with short-chain fatty acid production or cecal pH (Cademi et al. 1996).

Corpet et al. (1998) studied the inﬂuence of diets containing different
carbohydrate sources on development of insulin resistance and ACF in rats. After
receiving an AOM injection, rats were randomly assigned to AIN-76-based diets
containing different carbohydrate sources (65% starch, glucose or ﬁ'uctose), and ACF
development was measured. The average numbers of ACF per rat were not different for
rats fed glucose- or fructose-based diets. Insulin resistance was estimated using indirect
insulin resistance markers [FIRI index (blood glucose by insulin), blood triglycerides,
and visceral fat]. Indirect insulin resistance markers did not correlate with ACF
multiplicity (Corpet et al. 1998).

As sucrose and cornstarch both are well digested and the resulting
monosaccharides are well absorbed in the small intestine, it is possible that their
inﬂuence on colon cancer risk may be mediated by factors in the circulation. Animal
studies suggest that high intakes of sucrose induce compensatory hyperinsulinemia by
causing a decline in insulin sensitivity in the liver and peripheral tissues (Pagliassotti et
al., 1995; Storlien et al., 1988; Thorbum et al., 1989; Pagliassotti et al., 1994; Martinez et

al., 1994; Pamies-Andreu et al., 1995). Hyperinsulinemia can cause changes in the IGF

62

signaling system, such as increased IGF-I expression in liver, which leads to increased
availability of free IGF-I in circulation. Increased exposure of IGF-I receptors to IGF-I
in colon epithelium may caused elevated IGF signaling in colonic epithelial cells.
Increased IGF Signaling in colonic epithelium is associated with increased colon cancer
risk (Giovannucci 2001). In the current study, inﬂuence of high-sucrose diets (vs.
cornstarch) on weight gain, glucose metabolism, levels of insulin in the serum, and IGF
and IGFBP mRNA expressions in liver were further examined.

Mice in the sucrose group gained signiﬁcantly more weight compared with mice
consuming cornstarch after lO-weeks of treatment. Sucrose is comprised of two
monosaccharides - a glucose and a fructose. Unlike glucose, fructose does not stimulate
insulin secretion nor enhance leptin production. Insulin and leptin act as key afferent
Signals regulating food intake and body weight. Therefore, dietary fructose may
contribute to increased energy intake and weight gain (Elliott et al., 2002). Serum
concentrations of glucose and insulin were both greater in mice consuming diets
containing sucrose compared to that observed in mice consuming diets containing
cornstarch. Animal studies have found that high intakes of sucrose versus cornstarch
cause a decline in insulin sensitivity in the liver and later in peripheral tissues as assessed
by euglycemic hyperinsulinemic clamps (Pagliassotti et al., 1995; Storlien et al., 1988;
Martinez et al., 1994; Pamies-Andreu et al., 1995). Long-term feeding of sucrose-rich
diets will cause compensatory hyperinsulinemia. The decreased insulin sensitivity caused
by high-sucrose diets is likely related to the fructose component of sucrose (Thorbum et

al., 1989; Thresher et al. 2000).

63

It has been proposed that hyperinsulinemia promotes colorectal carcinogenesis and
the role of insulin in colorectal cancer is mediated through IGF-I (McKeown-Eyssen
1994; Giovannucci 1995, 2001). To further explore the underlying mechanisms whereby
dietary sucrose may modulate colon cancer risk, the inﬂuence of high-sucrose diets on
IGFS and IGF BPS also were determined. IGF-I and IGF-II in circulation are primarily
produced in liver. The relative expression of IGF-I mRNA as measured by quantitative
RT-PCR in the liver of mice fed sucrose was signiﬁcantly greater compared with that in
mice fed cornstarch. High insulin concentrations increase the number of growth hormone
receptors on hepatic cells, and increased binding of growth hormone to its receptors
stimulates the production of IGF-I in liver (Jones et al. 1995; Underwood et al. 1994).

The IGF system includes two insulin-like grth factors (IGF-I and IGF-II)
which exert their actions on cell growth, differentiation and apoptosis by interacting with
IGF-I receptors on the cell membrane. IGF-I and IGF-II are further regulated by a group
of speciﬁc binding proteins (i.e., IGFBPl through IGFBP6; Stewart et al. 1996; Rajaram
et al. 1997). In rodents, IGF-II concentrations are typically low compared with that of
IGF-I. In this experiment, the relative expression of IGF-II mRNA in liver was lower
compared with that of IGF-I. However, we observed no difference in IGF-II mRNA
expression in liver of mice fed sucrose- or comstarch-based diets. IGFBP3 is the major
IGF binding protein in the circulation and limits access of both IGF-I and IGF-II to the
peripheral tissues. Expression of IGFBPI has been reported to be down-regulated by
insulin (Ooi et al., 1992). In the current study, neither IGFBP3 nor IGFBP] mRNA
concentrations in liver were inﬂuenced by diet. In this experiment, we were not able to

measure the concentrations of free IGF-I in the circulation. However, we consider it

64

quite probably that high-sucrose diets would increase circulating IGF-I concentrations
due to increased liver IGF-I production.

High levels of circulating IGF-I are associated with increased colon cancer risk in
humans (Ma et al., 1999; Giovannucci et al., 2000). Wu et al. (2002) demonstrated that
the growth of colon adenocarcinomas which were transplanted to the surface of the
cecum in control (normal) mice was Signiﬁcantly greater than that observed in liver-
speciﬁc IGF-I-deﬁcient (LID) mice, in which serum IGF-I levels are only 25% of that
present in normal mice. Intra-peritoneal injections of IGF-I (2 mg/kg body weight) twice
daily for 6 weeks increased the serum levels of IGF-I and IGFBP3 in both control and
LID mice. Both control and LID mice treated with IGF-I in this manner displayed
signiﬁcantly increased rates of tumor development on the cecum and metastasis to the
liver, as compared with saline-injected mice. The number of metastatic nodules in the
liver was Signiﬁcantly greater in control mice when compared with LID mice. These
observations indicate that circulating IGF-I levels regulate colon cancer growth and
metastasis (Wu et al. 2002).

In summary, results of the this study indicate that high-sucrose diets increased
small intestinal tumorigenesis in APCMin mice, and this action was associated with
increased levels of insulin and IGF in the circulation. The results further indicate that
high-sucrose diets increase serum insulin levels and also may increase the levels of free
IGF-I in the circulation due to increased production of hepatic IGF-I. Ultimately, this
may increase IGF signaling in the peripheral tissues such as intestinal epithelium by
increasing the availability of IGF-I to interact with IGF-I receptors present on the

intestinal epithelial cells.

65

CHAPTER III.
GENE EXPRESSION PROFILING OF INTESTINAL EPITHELIUM IN APCMIN

MICE CON SUMIN G DIETS CONTAINING DIFFERENT CARBOHYDRATE
SOURCES

66

ABSTRACT

Dietary carbohydrate source inﬂuences intestinal adenoma development in
APCMin mice.. The objective of this study was to further investigate global gene
expression alterations in the intestine of APCMin mice caused by APC gene mutations and
by dietary carbohydrate source using cDNA microarray analysis. Mice were randomly
assigned at 4 weeks of age to one of two modiﬁed AIN-93G diets containing either
52.3% sucrose or cornstarch as the sole carbohydrate source. Mice were fed these diets
for 10 weeks, at which point they were sacriﬁced. Total RNA was extracted ﬁ'om
epithelial cells scraped from the proximal third of the small intestine. cDNA synthesized
using total RNA from small intestine was coupled with ﬂuorescent dyes (Cy3 or Cy5)
and hybridized on microarray slides printed with the NIA 15K mouse gene set using a
loop experimental design. Expression of 379 genes was signiﬁcantly different between
APCMin mice and wild-type mice. Among these differentially-expressed genes, 109 were
annotated and had expression altered by genotype by more than 50%. Many of the genes
inﬂuenced by APC status were associated with cell growth control and tumorigenesis
(APC mutation increased expressions of Clu, Ccnd2, Ccnbl, Btg4, Anxal, Gsptl and
decreased expressions of Camkld, septin 2, LatsZ, Dab2, Morf4ll). These observations
indicate that APC gene mutations have a broad inﬂuence on expression of many genes
associated with intestinal tumorigenesis. Expression of 306 genes was signiﬁcantly
inﬂuenced by dietary carbohydrate source. Among these genes, 87 were annotated and
had expression altered by carbohydrate source by more than 5 0%. The expression
patterns of genes altered by dietary carbohydrate source suggest that feeding the high-

sucrose diet altered the intestinal expression of genes in the insulin/IGF Signaling

67

pathway (sucrose feeding increased expression of IGF-II and decreased expression of
IGFBP3), which may result in elevated IGF signaling in the intestinal epithelium. High-
sucrose diets also inﬂuenced expressions of genes associated with cell growth control and
tumorigenesis (increased expressions of Pena, Csel l, Idb2, Camk2g; decreased
expressions of Tial, F ancg, Bmprla, Cul4B). These results yield insights on candidate
gene targets for future mechanistic studies to assess the impact of dietary carbohydrates

on colon carcinogenesis.

68

INTRODUCTION

Modern cDNA microarray technology enables the simultaneous monitoring of
mRNA expression of thousands of genes. The application of this technology to conduct
gene expression proﬁling provides a powerful tool to enable researchers to identify
candidate genes and pathways that may be involved in regulation of processes such as
carcinogenesis (Liefers et al. 2002).

APCMin mice have been extensively studied as a model for human colon cancer.
APC mutation causes accumulation of B-catenin in the cytosol. These B-catenin proteins
can translocate into the nucleus and bind to transcription factors such as T-cell factor
(T00 and lymphoid enhancer factor (Let), and increase transcription of a variety of genes
having ch/Lef binding sites (Behrens et al., 1996). APC gene mutation is an early event
in intestinal tumorigenesis, and APC gene mutations have broad inﬂuences on expression
of many genes involved in cellular pathways involved in promotion and progression of
colon carcinogenesis

Dietary carbohydrate concentrations and sources have been demonstrated to
impact the risk of colon cancer development. Our previous experiments indicate that
dietary carbohydrate source (sucrose versus cornstarch) inﬂuences small intestinal
adenoma development in APCMin mice. High-sucrose diets also induced
hyperinsulinemia and increased hepatic IGF-I mRNA expression in APCMin mice, which
suggests an increased availability of insulin and IGF-1 to interact with intestinal IGF-I
receptors. However, the potential inﬂuence of high-sucrose diets on local gene expression

in the intestinal epithelium is not known.

69

The ﬁrst objective of the current study was to explore broad gene expression
changes caused by APC mutations in the small intestinal epithelium of mice using cDNA
microarray analysis, and aim to identify gene targets or signaling pathways that are
important in colon cancer promotion and progression. The second objective was to
investigate early alterations of gene expression patterns (10 weeks of dietary treatment)
induced by diets containing different carbohydrate sources (sucrose versus cornstarch).
Such pattern alterations may help us to understand the underlying mechanisms whereby

dietary sucrose increases intestinal tumorigenesis. .

70

MATERIALS AND METHODS

Animals and Diets: APCMin mice used for the study were generated by mating
C57BL/6J male APCMin mice with normal C57BL/6J female mice in our breeding
colony at Michigan State University. The resulting offspring (about twenty mice with
roughly equal numbers of males and females) were assigned to one of two diets
containing either sucrose or cornstarch as the sole carbohydrate resource at 4 weeks of
age. Among those mice, we expected 50% to be APCMin mice and 50% to have normal
APC gene status based on simple Mendelian inheritance. Mice used in this study were
genotyped by an allele-speciﬁc PCR procedure to identify APCMin progeny (Jacoby et al.
1996). Mice were housed in an animal facility with controlled temperature (about 21-
24°C), humidity (40-70%), and light/dark cycle (12 hours light/ 12 hours dark). Animal
care and feeding were conducted with approval of the Michigan State University All-
University Committee on Animal Use and Care. Diets used in the experiment were the
same as those used in the ﬁrst experiment (Table 1). Mice were fed these diets ad libitum
for ten weeks.

Total RNA Extraction: At the end of experiment, mice were euthanized by C02
asphyxia. A sample of liver tissue was collected from each mouse and frozen for
conﬁrmatory APCMin genotyping. The entire intestine from each mouse was removed
and cut open longitudinally, rinsed clean with RNAase free H20 and sections for RNA
isolation were immersed in RNAlater TM (Ambion, Austin, TX) reagent immediately to
inactivate RNAase. Epithelial cells from the proximal small intestine and the entire colon
from each mouse were scraped separately with a glass slide into tubes containing Trizol

reagent (Invitrogen, Carlsbad, Ca), and frozen at -80°C for future RNA extraction. Total

71

RNA from epithelial cells was extracted using Trizol reagent according to the
manufacture’s instructions. The RNA concentrations were determined by
spectrophotometry, and RNA integrity was monitored by agarose gel electrophoresis.

Preparation of the Microarray Slides: Microarray Slides were printed with
genes from the National Institute on Aging’s mouse 15K cDNA clone as described by
Tanaka et al. (2000). Printing of the slides was carried out at the Plant Research
Laboratory at Michigan State University according to the manufacturer’s protocol
(Telechem, Sunnyvale, CA).

cDNA Synthesis and Fluorescence Labeling of Microarray Probes: Synthesis
of ﬂuorescence-labeled cDNA was conducted based on procedures developed by The
Institute of Genomic Research (Hegde et al., 2000). Brieﬂy, each RNA sample (20 pg
per reaction) extracted from proximal small intestine was denatured and primed with
Oligo dT, then reverse-transcribed into cDNA using Superscript II transcriptase
(Invitrogen, Carlsbad, CA) in the presence of aminoallyl modiﬁed dUTP (Sigma
Chemical Company, St. Louis, Mo) (aa-dUTP: dTTP = 2:1) according to manufacture’s
instructions.

Dye coupling was conducted by incubating cDNA with the monoreactive dyes
cyanin 3 (Cy3) or cyanin 5 (Cy5) (Amersham, Buckinghamshire, UK) for 1 hour at room
temperature in the dark. Cy3- and Cy5- labeled samples for a given slide were mixed
according to the experimental design. Unincorporated dyes were removed using the
Qiagen cleanup kit (Quiagen Inc.). Each probe (Cy3 and Cy5 labeled pair) was dried in a

speed vacuum and resuspended in pre-hybridization buffer.

72

Microarray Hybridization: before hybridization, microarray slides were pre-
treated to remove unbound DNA-molecules and buffer substances. The slides were rinsed
in the following steps: ﬁve minutes in 0.1% Trition-X 100, two rinses of two minutes
each in HCl solution (100 pl of 37% HCl in 1000 ml H20), and ten minutes in 100 mM
KCl solution. The microarray slides were then rinsed in boiling H2O for three minutes
and washed for one minute in H2O at room temperature. The slides were then treated
with 1X QMT Blocking Solution (Quantifoil, Jena, Germany) at 50°C for 15 minutes,
and rinsed in H2O for one minute before hybridization.

Each probe was hybridized to pre-treated microarray slides according to the
experimental design (Figure 12). All of the hybridization steps were conducted in a dark
room. Brieﬂy, the dried probes were re-suspended in 50 pl of formamide-based
hybridization buffer (28 pl of formamide, 2 pl of 20% SDS, 4 pl of 50X Denhardt’s and
16 pl of 20X SSPE), which was preheated to 42°C. Then, 2 pl of polyA+ (10 pg/pl;
Gibco), 2 pl of mouse COT-1 DNA (10 pg/pl; Gibco) and 1 pl of yeast tRNA (10 pg/pl;
Gibco) were added. The hybridization solution containing the probe was vortexed lightly
and incubated at 42°C for two minutes, and this step was repeated three times. The probe
was then denatured at 100°C for three minutes and then held on ice for one minute. The
probe was centrifuged at 12000 x g for one minute and was applied to the microarray
slide immediately. After adding the probe, each slide was cover-slipped and placed in the
hybridization chambers (Coming), in which the slides were incubated at 42°C for 18 to
24 hours.

After incubation, the microarray slides were washed clean in dark room. Brieﬂy,

the slides were removed from hybridization chambers and placed in slide racks in

73

preheated (42°C) 2X SSC solution containing 0.2% SDS until the cover slips slid off.
Slides were then washed in 2X SSC solution containing 0.2% SDS at room temperature
for ﬁve minutes. Slides were then transferred to a new slide rack and washed in 0.2X
SSC solution containing 0.2% SDS for ﬁve minutes. They were then transferred into

another new slide rack and washed in 0.2X SSC for two minutes, and this step was

repeated twice. Finally, the Slides were centrifuged at 1200 rpm to dry and were scanned

immediately.

 

Wt Starch 1 _'_—_‘: :7» Wt Sucrose

ti M
l: :1
l i
ii ii

Min Starch —ﬂ ——+ Min Sucrose

F igure12. Complete loop design for the microarray study. Wt Starch, Wt Sucrose,
Min Starch and Min Sucrose represent mice with different APC mutation status
and dietary carbohydrate source. Each arrow represents a single microarray slide.
In each slide the appropriate samples were labeled with Cy3 (tail of the arrow) and
also with Cy5 (head of the arrow). The entire scheme was repeated three times
with RNA samples from different mice, which resulted in three independent
replicates of the design.

 

 

 

74

Slide Scanning and Data Processing: The microarray slides were scanned using
an Affymetrix 425TM scanner (Santa Clara, CA) and the image ﬁles were processed
using the GenePix Pro 6.0 program (Molecular Devices Corporation, Union City, CA).
The resulting GenePix ﬁles were imported into the MIDAS (microarray data analysis
system) program (www.tigr.org), and the Cy3 and Cy5 signals within each chip were
normalized using the Lowess algorithm. The normalized output signals were used for
further analyses.

Quantitative Real Time PCR (RT-PCR) Analysis: The same RNA samples
(n=12) ﬁ'om the proximal small intestine used for cDNA microarrays were also used to
conﬁrm gene expression changes with RT-PCR. RT-PCR ampliﬁcation and data
analyses were performed using an ABI Prism 7700 Sequence Detector System (PE
Applied Biosystems, Foster City, CA).

Consensus sequences for genes of interest were obtained from the NCBI website.
The Primer3 program (http://www-genome.wi.mit.edu/cgi-bin/primer/primer3_www.cgi)
was used to design the primers for each gene of interest. General conditions used in the
Primer3 program included: primer size 15-27 hp, primer melting temperature 57-63°C,
and product size 150-250 bp. Primers (Table 9) were synthesized in the Genomic
Technology Support Facility in the Department of Biochemistry at Michigan State

University.

75

Table 9. List of primers used for conﬁrmatory real time PCR analyses of gene
expression.

 

 

Gene Forward primer (5’-3’) Reverse primer (5’-3’) Amplicon

Name length (bp)

B-ACTIN GCTACAGCTTCACCACC TCTCCAGGGAGGAAG 123
ACA AGGAT

IGFI CTACCAAAATGACCGCA CACGAACTGAAGAGCA 126
CCT TCCA

IGF-II CCCTCAGCAAGTGCCTA TTAGGGTGCCTCGAGAT 121
AAG GIT

IGFBP3 TGTT'ITCTGGTCCAGCC CAAGCCACTCCTC'ITTC 122
TCT CTG

PCNA CCACATTGGAGATGCTG CCGCCTCCTC'ITCTTTA 128
'ITG TCC

PIK3C2A CAGTCGAAGCTCTCCTC TTCCAAAATACCAGGA 137
AGC CCTCA

CLU CACATGTCTCCAGGCGA AAGGCGGCTT’I'I‘ATTGG 1 33
GTA ATT

LTF CAGGGGATCTGGTITCT AGTGCAAGGAGTGCACA 1 3 1
TCA CAG

 

*Abbreviations: insulin-like growth factor binding protein 3 (IGFBP3), insulin-like
growth factor II (IGF-II), insulin-like factor I (IGF l), proliferating cell nuclear antigen
(PCNA), phosphatidylinositol 3-kinase, C2 domain containing, alpha polypeptide
(PIK3C2A), clusterin (Clu), lactotransferrin (LTF).

76

cDNA was synthesized from total RNA using the Superscript II system
(Invitrogen). Each reaction tube contained 1.0 pg total RNA and 1.0 pl of 0.02 mM
Anchored oligo dT18VN, which were primers having 18 thymidine residues with one G,
C or A residue as the anchor at the 3' end to ensure binding at the beginning of the
mRNA message, and water up to a ﬁnal volume of 12 pl. The reaction solution was
mixed and incubated at 70°C for 10 minutes to denature the templates, then held on ice
for 1 minute to anneal primers to templates. Then, the reaction tube was pulse-spinned,
and 8 pl of the master mix was added to each reaction, which included 4 pl of 5X First
strand buffer, 2 pl of 0.1 M DTT, 1 pl of 10 mM dNTPs, and 1 pl of Superscript II
(Invitrogen). The reaction solution was mixed and incubated for 2 hours at 42 °C, then
inactivated for 15 minutes at 75 °C. Finally, the reverse-transcription product was stored

at —20 °C until use.

Quntitaive real time PCR assays were conducted using SYBR Green PCR Core
Reagents (Perkin Elmer/ABI), using the MicroAmp Optical 96-well reaction plate. The
reaction mixture (25 pl ﬁnal volume) contained 1 pl cDNA reverse-transcribed from 1 pg
total RNA, 0.12mM forward and reverse primers, 2.5 pl of 10x SYBR Green Reaction
Buffer, 3mM MgCl2, 0.2 mM dNTPs, and 0.026 units of Taq polymerase (Perkin
Elmer/ABI). PCR reactions were performed in an ABI 7700 system with the following
cycling conditions (10 minutes at 95°C, followed by 40 cycles of 15 seconds at 95°C and

1 minute at 60°C).

77

Statistical Analyses:

A. cDNA Microarray Analysis: As shown in the microarray hybridization
design (Figure 12), each loop was comprised by four mice having different dietary
carbohydrate treatment or APC gene status. Four intensities (two Cy3 and two Cy5
intensities) were obtained for individual gene on the microarray chip for each mouse in
the loop. For each gene in each animal, the mean and standard deviation were calculated
for the four intensity values. Values that were greater than one standard deviation from
the mean were eliminated, and the averages of the remaining values were used to
represent the gene expression intensities for each gene for that mouse. Three loops of
microarray hybridization were conducted separately, which provided three complete
biological replicates to allow ﬁirther statistical analyses. To account for variations
between the three loops due to differences in RNA quantity, hybridization and seaming,
the individual gene expression intensities for each mouse were further normalized against
the median intensity of all gene expression intensities for that mouse using the
GeneSpring 6.0 program (Silicon Genetics, Redwood City, CA). Two-way ANOVA
analyses were performed using GeneSpring for main effects of genotype (wild-type

versus APCM'") and diet (sucrose versus cornstarch) on expression of each gene.

Differentially-expressed gene transcripts (P < 0.05) were classiﬁed into
functional groups based on known gene ontologies and putative ﬁInctions
reported in the literature. Gene ontologies were retrieved from the Rat Genome
Database (RGD) maintained at the Medical College of Wisconsin (Milwaukee,

Wisconsin; URL: http://rgd.mcw.edu/).

78

To compare APC status effects (APCMin vs. wild-type) on relative gene
expression, a ratio for each gene was calculated by dividing the expression in
mice carrying APC gene mutations by that observed in wild-type mice. To
compare the effect of diets containing different carbohydrate sources (sucrose vs.
cornstarch) on relative gene expression, a ratio for each gene was calculated by
dividing the gene expression observed in mice consuming sucrose by that
observed in mice consuming cornstarch. Because relative expressions of a large
number of genes were signiﬁcantly (P < 0.05) inﬂuenced by diet or APC
mutation, a cutoff (if up-regulated, ratio 2 1.5; if down-regulated, ratio 5 0.6) was
used to identify genes whose expression changes are biologically meaningﬁIl and
also to reduce the numbers of genes in the list presented in the results. Genes
listed in the results include those that were signiﬁcantly inﬂuenced by genotype or
diet (P <0.05) and also meet these cutoff conditions. Many of the gene elements
corresponding to unknown genes based on the current gene ontology were not
reported here even though their expression levels were signiﬁcantly altered by
either APC gene mutation or diet (complete lists of genes whose expressions were
Signiﬁcantly altered by diets or APC gene mutation are attached as Table A1 —

Table A5 in the appendix).

B. Quantitative Real-Time PCR Analysis: The standard curve method was used
to quantitate the mRNA levels. Quantiﬁcation of the starting quantity of a speciﬁc
cDNA generated for an unknown sample was performed using a standard curve using
known dilutions of the corresponding DNA product. For each dilution, the AbI-Prlsm

7700 software generated a real-time ampliﬁcation curve constructed by relating the

79

ﬂuorescence signal intensity to the cycle number. The standard curve was then generated
on the basis of the linear relationship existing between the Ct value (cycle threshold;
corresponding to the cycle number at which a signiﬁcant increase in the ﬂuorescence
signal was ﬁrst detected) and the logarithm of the starting quantity. The normalized
expression of a gene of interest was calculated as a ratio of its own mRNA expression
against the mRNA expression of the internal control gene (beta-actin). Normalized
expression from RT-PCR analyses were analyzed as a 2 x 2 factorial design (diet,
genotype) using the General Linear Models procedure of SAS (SAS Institute, Inc.; Cary,

NC, Version 8.0).

80

RESULTS

A. Effect of APC gene mutation on gene expression: As shown in table 10 and

l 1, we observed that there were 55 genes up-regulated (with ratio 2 1.5) and 54 genes
down-regulated (with ratio 3 0.6) in mice which were heterozygous for the APC gene
mutation when compared with wild-type mice. When classiﬁed into functional
categories, these genes included those coding for cell cycle and growth control (up 8,
down 5), Signal transduction and receptors (up 10, down 9), transcriptional and
translation regulation (up 10, down 11), cell adhesion and cytoskeleton (up 4, down 2),
cellular component and structure proteins (up 5, down 3), transport and carrier proteins

(up 7, down 12), and metabolism and enzymes (up 11, down 12).

Several genes related with the Wnt signaling pathway were Signiﬁcantly
inﬂuenced by APC gene status (APCMin vs. wild-type). Wnt4 expression was increased
about 2.4 fold in APCMin mice compared with normal mice. Expression of Dab2, which
is considered as an antagonist of the Wnt signaling pathway (Prunier et al. 2004; Dote et
al. 2005), was down-regulated in APCMin mice. Expression of cyclin-D2, but not that of
cyclin-D1, was found to be signiﬁcantly increased in APCMin mice compared with normal

mice.

A large group of genes involved in cell growth control and tumorigenesis was
differently expressed in APCMin mice and normal mice. The genes overexpressed in
APCMin mice included clusterin (Clu), cyclin-D2 (Ccnd2), cyclin-Bl (Ccnbl), B-cell
translocation gene 4 (Btg4), annexin A1 (Anxal), G1 to S phase transition 1 (Gsptl) and

bone morphogenetic protein receptor, type 1A(Bmpr1a). Genes in this category that

81

were down-regulated in APCMin mice included calcium/calmodulin-dependent protein
kinase ID (Camkld), septin 2, large tumor suppressor 2 (Lats2), disabled homolog 2

(Dab2), and mortality factor 4 like 1 (Morf4ll).

Genes up-regulated in APCMin mice included those involved in various signal
transduction pathways. These genes included protein phosphatase 5 (Ppp5c), Wnt4,
mitogen-activated protein kinase 6 (Mapk6), protein tyrosine phosphatase 4a3 (Ptp4a3),
T-complex expressed gene 1 (Tcel), Rho guanine nucleotide exchange factor 10
(Arhgefl 0), immunoglobulin-like domain containing receptor 1 (lldrl), endoplasmic
reticulum (ER) to nucleus signalling 1 (Eml) and ADP-ribosylation factor-like 3 (Arl3).
Genes involved in various signal transduction pathways that were down-regulated in
APCMin mice included receptor (calcitonin) activity modifying protein 2 (Ramp2),
estrogen receptor 1 (alpha) (Esrl), bisphosphate 3'-nucleotidase 1 (Bpntl), HIV-1 Rev
binding protein-like (Hrbl), interleukin l7 receptor (Ill7r), PDZ domain containing 2
(szk2), tumor necrosis factor receptor superfamily, member 19 (Tnfrsf19) and

homeodomain interacting protein kinase 3 (Hipk3).

Another group of genes regulated differentially by APC gene status were those
involved in regulation of transcription and translation. Almost equal numbers of genes in
this class were up- or down-regulated in APCMin mice. The genes that were up-regulated
in APCMin mice included high mobility group box 3 (ngb3), zinc ﬁnger protein 592
(pr592), general transcription factor 11 H, polypeptide l (th2h1), MYB binding protein
(P160) 1a (Mybbpla), interferon induced transmembrane protein 3 (Iﬁtm3) and

eukaryotic translation initiation factor 2, subunit 2 (Eif252). The down-regulated genes in

82

APCMin mice include ubinuclein l (Ubnl), transcription factor 19 (ch19), transforming
growth factor beta 1 induced transcript 4 (Tgfbli4), thyroid hormone receptor interactor 4
(Trip4), nuclear receptor co-repressor 1 (Ncorl), stem-loop binding protein (Slbp) and

replication protein A1 (Rpal).

Expressions of several transport proteins were increased in APCMin mice. These
included synaptotagmin 11 (Sytl 1), ubiquitously expressed transcript (Uxt),
lactotransferrin (Ltf), metallothionein 2 (Mt2), abl-interactor 2 (Ab12), solute carrier
family 15, member 4 (Slc15a4) and solute carrier family 11, member 2 (Slc11a2). Genes
coding for transporter proteins that were signiﬁcantly down-regulated in APCMin mice
included nucleoporin 62 and 37 (Nup62 and Nup37), sideroﬂexin 1 (Sfxnl), ADP-
ribosylation factor-like 6 interacting protein 5 (Arl6ip5), oxysterol binding protein-like 8

(Osbp18) and F K506 binding protein 10 (Fkbp10).

83

Table 10 Genes up-regulated in small intestinal epithelium of APCMin mice
versus wild-type mice.

 

 

GeneSymbol GeneName APCMin/Wt CloneID
Ratio

Cell cycle and growth control

Clu clusterin 5.6 H3108A04

Btg4 B-cell translocation gene 4 14.9 H3051H08

Ccnbl cyclin B1 2.9 H3 105810

Anxal annexin A1 2.0 H3008H01

Ccnd2 cyclin D2 1.8 H3152D01

Gsptl G1 to S phase transition 1 1.6 H3114F01

Psmd8 proteasome (prosome, macropain) 1.5 H3081H11
26S subunit, non-ATPase, 8

Bmprla bone morphogenetic protein 1.6 H3031E08
receptor, type 1A

Signal transduction and receptors

Ppp5c protein phosphatase 5, catalytic 1.7 H3158H01
subunit

Wnt4 Wingless-related MMTV integration 2.4 H3159E07
Site 4

Tcel T-complex expressed gene 1 2.1 H3138A07

Mapk6 mitogen-activated protein kinase 6 2.0 H3064B03

Ptp4a3 protein tyrosine phosphatase 4a3 1.9 H3088F03

ArhgeflO Rho guanine nucleotide exchange 1.8 H3137A10
factor (GEF) 10

Arhgef3 Rho guanine nucleotide exchange 2.9 H3034F 08
factor (GEF) 3

Ildrl immunoglobulin-like domain 2.3 H3102H08
containing receptor 1

Eml endoplasmic reticulum (ER) to 2.5 H3 144812
nucleus signalling 1

Arl3 ADP-ribosylation factor-like 3 2.6 H3092810

Transcription and translation regulation

ngb3 high mobility group box 3 1.8 H3030H10

Jaridla jumonji, AT rich interactive domain 3.5 H3154D09
1A (Rbp2 like)

Nhlrc2 NHL repeat containing 2 3.5 H3096B06

pr592 zinc ﬁnger protein 592 2.9 H3148F06

84

Table 10 (cont’d)

 

th2hl

Mybbpl a
Iﬁtm3

Cpsfl

Rpsl 1
Eif252

general transcription factor 11 H, 2.7
polypeptide l

MYB binding protein (P160) la 2.3
interferon induced transmembrane 2.3

protein 3

cleavage and polyadenylation 2.4
speciﬁc factor 1

ribosomal protein 811 1.5
eukaryotic translation initiation 1.5

factor 2, subunit 2 (beta)

Extracellular matrix, cell adhesion and cytoskeleton

Was
Kif14
Btbd9
Gpcl

Was

Wiskott-Aldrich syndrome homolog 1.9
(human)
kinesin family member 14 1.8

BTB (POZ) domain containing 9 2.0
Glypican 1 2.2

Wiskott-Aldrich syndrome homolog 1.9
(human)

Cellular component and structure proteins

Psmdl 1
Ap251
Psme4

Rps 1 7
Rp524

proteasome (prosome, macropain) 1.6
26S subunit, non-ATPase, 11
adaptor-related protein complex 2, 1.6
sigma 1 subunit

proteasome (prosome, macropain) 1.6
activator subunit 4

ribosomal protein 817 1.5
ribosomal protein 824 1.5

Transport and carrier proteins

Sytll
Slc15a4
Slc11a2

Uxt
Abi2
Ltf
Mt2

synaptotagmin 11 3.2
solute carrier family 15, member 4 3.1
solute carrier family 11 (proton- 2.2

coupled divalent metal ion
transporters), member 2
ubiquitously expressed transcript 2.0

abl-interactor 2 1.5
Lactotransferrin 2.5
metallothionein 2 1.5

85

H3 142H06

H3008A10
H3107D05

H3150D10

H3010A10
H3009C11

H3020F08

H3049F12
H3142F10
H3144G12

H3020F08

H3112D08

H3102F01

H3 120D03

H3118GO4
H3145D11

H3106E08
H3109B08
H3029B01

H3127Bl l
H3024D04
H3087A12
H3013D11

Table 10 (cont’d)

 

Metabolism an enzymes

Fbxo6b
Sepxl
Mat2b

Pgm211
Expi
B3 gntl

Ube2r2
Hprtl

Uchll

Slk
Acbd3

F -box only protein 6b 1.6
selenoprotein X 1 3.7
methionine adenosyltransferase II, 2.2
beta

phosphoglucomutase 2-like 1 1.9
extracellular proteinase inhibitor 1.8
UDP-GlcNAczbetaGal beta-1,3-N- 1 .8
acetylglucosaminyltransferase 1
ubiquitin-conjugating enzyme E2R 2 1.7
hypoxanthine guanine 1.5
phosphoribosyl transferase l

ubiquitin carboxy-tenninal hydrolase 1.5
L1

STE20-like kinase (yeast) 3.0
acyl-Coenzyme A binding domain 1.6
containing 3

86

H3013A04
H3002E01
H3152D10

H3 0993 1 1
H3097H03
H3002H04

H3008B03
H3152G02

H3059F01

H3 040D06
H3 125 E03

Table 1] Genes down-regulated in small intestinal epithelium of APCMin mice
versus wild-type mice.

 

GeneSymbol

GeneName

APCM‘"/ CloneID
Wt
Ratio

 

Cell cycle and growth control

Camkl d

septin 2
LatsZ
Dab2
Setdbl
Morf4l 1
Stkl 1

calcium/calmodulin-dependent protein
kinase ID

septin 2

large tumor suppressor 2

Disabled homolog 2 (Drosophila)
SET domain, bifurcated 1

mortality factor 4 like 1
serine/threonine kinase 11

Signal transduction and receptors

Ramp2
Ppp4r1

Esrl
Bpntl
Hrbl

Il 17r
szk2
Tnfrsfl 9

Hipk3

receptor (calcitonin) activity modifying
protein 2

0.2 H3104G10

0.6 H3152Ell
0.6 H3112G08
0.6 H3112G10
0.7 H3062Cll
0.5 H3048C04
0.7 H3052C12

0.3 H3 109A04

protein phosphatase 4, regulatory subunit 0.6 H3022D11

1

estrogen receptor 1 (alpha)
bisphosphate 3'-nucleotidase 1
HIV-1 Rev binding protein-like
interleukin 17 receptor

PDZ domain containing 2
tumor necrosis factor receptor
superfamily, member 19

homeodomain interacting protein kinase
3

Transcription and translation regulation

Ubnl
chl9
Tgfbl i4

Trip4
Nabl
Ncorl
Slbp

F em 1 a
Twist2

ubinuclein 1

transcription factor 19

transforming growth factor beta 1
induced transcript 4

thyroid hormone receptor interactor 4
Ngﬁ-A binding protein 1

nuclear receptor co-repressor 1
stem-loop binding protein
feminization 1 homolog a (C. elegans)
twist homolog 2 (Drosophila)

87

0.6 H3095A05
0.5 H3012G10
0.5 H3116F04
0.6 H3008A03
0.5 H3076F01
0.6 H3076A10

0.2 H3155D07

0.2 H3080D07
0.3 H3074GI 1
0.3 H31 12G06

0.5 H3018D01
0.6 H3056A11
0.6 H3070A10
0.4 H3113A01
0.3 H3030A09
0.6 H31 16C08

Table 1 l (cont’d)

 

Ddx3x DEAD/H (Asp-Glu-Ala-Asp/His) box
polypeptide 3, X-linked
Rpal replication protein A1

Extraceullar matrix, cell adhesion and cytoskeleton

Spg20 spastic paraplegia 20, spartin (Troyer
syndrome) homolog (human)

Wdrl WD repeat domain 1

Cellular component and structure proteins

Fin14 ﬁbroblast growth factor inducible 14

Mpvl 7 Mpv17 transgene, kidney disease mutant

Anxa4 Annexin A4

Transport and carrier proteins

Nup62 nucleoporin 62

Sfxnl sideroﬂexin 1

Nup37 nucleoporin 37

Grikl glutamate receptor, ionotropic, kainate 1

SlclSa2 solute carrier family 15 (H+/peptide
transporter), member 2

Uncl3c unc-13 homolog C (C. elegans)

Slc23a2 solute carrier family 23 (nucleobase
transporters), member 2

Arl6ip5 ADP-ribosylation factor-like 6
interacting protein 5

Mttp microsomal triglyceride transfer protein

Osbpl8 oxysterol binding protein-like 8

Gcipip GCIP-interacting protein p29

F kbp10 FK506 binding protein 10

Metabolism and enzymes

Ndufs7 NADH dehydrogenase (ubiquinone) F e-
S protein 7

Spint2 serine protease inhibitor, Kunitz type 2

Reel Ras and a-factor-converting enzyme 1
homolog (S. cerevisiae)

Acaa2 acetyl—Coenzyme A acyltransferase 2
(mitochondrial 3-oxoacyl-Coenzyme A
thiolase)

Asah2 N-acylsphingosine amidohydrolase 2

Senp6 SUMO/sentrin speciﬁc protease 6

88

0.6

0.5

0.6

0.5

0.6
0.5
0.6

0.3
0.5
0.6
0.2
0.3

0.3
0.5

0.6
0.6
0.6

0.5
0.6

0.6
0.6

0.5

0.6
0.6

H3068C04

H3030A08

H3016G03

H3024D09

H3068C04
H3 140G06
H3087E01

H3105F08
H3026E07
H3067A12
H3147C06
H3073BO3

H3076H09
H3 1 12E08

H3020G12
H3023G06
H3077F04

H3056C1 1
H3 046A09

H3098A08

H3109G10
H3035F09

H31 12307

H3 1 12H01
H3 049A01

Table l l (cont’d)

 

Helicl
Ddhdl
Ac02
Hibadh
Tpil
Cblll

Helicase, ATP binding 1

DDHD domain containing 1
aconitase 2, mitochondrial
3-hydroxyisobutyrate dehydrogenase
triosephosphate isomerase 1

Casitas B-lineage lymphoma-like 1

0.6
0.6
0.6
0.6
0.6
0.2

H3142C01
H3101Cl l
H3146GO6
H3069A10
H3149C10
H3068E07

 

89

B. Effect of dietary carbohydrates on gene expression: As Shown in Tables 12
and 13, there were 54 genes up-regulated (with ratio 2 1.5) and 33 genes down-regulated
(with ratio 3 0.6) when mice consumed high-sucrose versus high-comstarch diets. Based
on gene ontology annotations, these genes can be classiﬁed into several functional
categories, including cell cycle and grth control (up 9, down 5), signal transduction
and receptors (up 8, down 4), transcription and translation regulation (up 9, down 2), cell
adhesion and cytoskeleton (up 2, down 4), cellular component and structure proteins (up
4, down 3), transport and carrier protein (up 5, down 3), host defense and immunity

(down 2), and metabolism and enzymes (up 17, down 10)

mRNA expression levels of several genes involved in the insulin-like growth
factor (IGF) pathway were signiﬁcantly changed by diets containing different
carbohydrate sources (sucrose vs. cornstarch). Gene expression of insulin-like grth
factor 2 (IGF-II) in mice fed sucrose was signiﬁcantly greater than IGF-II expression in
mice fed cornstarch, whereas expression of insulin-like growth factor binding protein 3
(IGFBP3) was signiﬁcantly greater in mice fed cornstarch. Expressions of several genes
down-stream in the IGF signaling pathway also were altered. The mRNA levels of Grb7
were induced in mice consuming sucrose, and Grb7 is a homolog of gene Grb2 in the
MAPK pathway. The expression of Pik3c2a, the alpha polypeptide of
phosphatidylinositol 3-kinase in the PI3K pathway also was elevated by consuming the
high-sucrose diet. These gene expression patterns suggest that consuming high sucrose

diets may elevate IGF signaling in intestinal epithelial cells.

90

Several genes whose mRNA expression was up-regulated by diets containing
sucrose are positive regulators of the cell cycle, and overexpression of these genes are
generally associated with increased cell proliferation and growth. These genes included
IGF-II, proliferating cell nuclear antigen (PCNA), calcium/calmodulin-dependent protein
kinase II gamma (Camk2g), chromosome segregation l-like (Csell), inhibitor of DNA
binding 2 (Idb2), multiple endocrine neoplasia 1 (menl), netrin l (Ntnl) and ring ﬁnger
protein 2 (ng2). Additionally, several genes whose expression were repressed by
sucrose containing diets are negative regulators of the cell cycle, and under-expression of
these genes are generally associated with increased cell growth. These genes include
IGFBP3, cytotoxic granule-associated RNA binding protein 1 (Tial) and bone
morphogenetic protein receptor type 1A (Bmprla).

Among the genes involved in signal transduction, expression of several genes
related to G-protein signaling were induced by high-sucrose diets, including a G protein-
coupled receptor (GprcSc), RAS p21 protein activator 3 (Rasa3), Rap guanine nucleotide
exchange factor (GEF) 2 (Rapgef2), regulator of G-protein signaling 12 (Rgle) and
guanine nucleotide binding protein (Gnaq). However, expression of Rho-guanine
nucleotide exchange factor (Rgnef) was down-regulated in mice consuming sucrose.
Expressions of the casein kinase 1(Csnkl g2), phosphatidylinositol 3-kinase(Pik3c2a),
and phosphodiesterase 7A were elevated, but sphingomyelin phosphodiesterase 3
(Smpd3), ciliary neurotrophic factor receptor (Cntfr) and a signal sequence receptor
(Ssr4) were down-regulated by consumption of sucrose-containing diets.

Expressions of several genes coding for transcription factors were induced by

feeding sucrose. These genes included lymphoid nuclear protein related to AF4 (Laf4),

91

Mad homolog 3 (Smad3) and nuclear factor I/A (Nﬁa). Conversely, expressions of T-
box 20 (Tbx20) and basic leucine zipper and W2 domains 2 (Bzw2) were repressed by
dietary sucrose.

A large group of genes whose expressions were altered by dietary carbohydrates,
code for metabolic enzymes. Some of the genes whose expression was induced by
dietary sucrose are involved in carbohydrate and fatty acid metabolism. These included
phosphofructokinase (Pﬂd), aldolase 1 A (Aldoa), stearoyl-Coenzyme A desaturase 2
(Scd2), dihydrolipoamide S-acetyltransferase (Dlat), NADH dehydrogenase (ubiquinone)
1 alpha subcomplex 10 (NdufalO),and aldehyde reductase (aldose reductase)-like 6
(Aldrl6). On the other hand, expression of camitine palmitoyltransferase 1a(Cpt1a), and
phospholipid scramblase 3 (Plscr3) were down-regulated.

Fibronectin 1 (F ml) and defensin related cryptins (chr), which are involved in
host defense and immunity, were found to be down-regulated in mice fed high-sucrose
diets.

Expression of genes involved in cell adhesion or cytoskeletal structure was also
altered by diet. Expression of catenin alpha 1 (Catnal) and thymosin, beta 10 (TmsblO)
were induced, whereas, adenylate cyclase-associated protein 1 (Capl), shroom (Shrm),
stathmin 1 (Stmnl) and reticulon 4 (Rtn4) were repressed by feeding high-sucrose diet.

Other groups of genes regulated by dietary carbohydrates are genes coding for

cellular components, and transporter or carrier proteins.

C. Interactions of APC gene status and dietary carbohydrate source on

eXpression of genes: Table 14 lists genes for which a signiﬁcant interaction between

92

APC gene status and dietary carbohydrate source was detected. These genes included
Rgle (regulator of G protein signaling), Eif356ip (ukaryotic translation initiation factor
3, subunit 6 interacting protein), Cacybp (Calcium binding protein) and several other

proteins belonging to cellular component and enzyme categorizes.

93

Table 12. Genes up-regulated in small intestinal epithelial cells of mice

consuming diets based on sucrose versus cornstarch.

 

 

Gene Gene Name Sucrose vs. Clone ID

Symbol Cornstarch

Cell cycle and growth control

Ntnl netrin l 1.5 H3019E06

Csell chromosome segregation l-like 1.7 H3029A05

Igf2 insulin-like growth factor 2 1.5 H3126A04

Menl multiple endocrine neoplasia l 2.2 H3134F11

Pcna proliferating cell nuclear antigen 2.7 H3021F12

Camk2g calcium/calmodulin -dependent 3.6 H3147D07
protein kinase II gamma

Rnf2 ring ﬁnger protein 2 2.2 H3001812

Idb2 inhibitor of DNA binding 2 3.3 H3118H06

GRB7 Growth factor receptor bound 1.6 H3 006F12
protein 7

Signal transduction and receptors

Rasa3 RAS p21 protein activator 3 3.1 H3054E01

Gprc5c G protein-coupled receptor, family 2.9 H3076D03
C, group 5, member C

Rapgef2 Rap guanine nucleotide exchange 2.6 H3054804
factor (GEF) 2

Rg312 regulator of G-protein signaling 12 2.2 H3076F 12

Pik3c2a phosphatidylinositol 3-kinase, C2 1.7 H3124D09
domain containing, alpha
polypeptide

Gnaq guanine nucleotide binding 1.6 H3147806
protein, alpha q polypeptide

Csnkl g2 casein kinase 1, gamma 2 2.5 H3119H06

Pde7a phosphodiesterase 7A 1.7 H3085D03

Transcription and translation regulation

Laf4 lymphoid nuclear protein related to 1.8 H3093D02
AF4

Smad3 MAD homolog 3 (Drosophila) 3.1 H3149G02

Nﬁa nuclear factor VA 1.4 H3062812

Nolcl nucleolar and coiled-body 1.9 H3001E10
phosphoprotein l

Gcipip GCIP-interacting protein p29 1.6 H3056C11

Pscd3 pleckstrin homology, Sec7 and 3.2 H3095H05

coiled-coil domains 3

94

Table 12 (cont’s)

 

Prdm2

Rbm22

PR domain containing 2, with ZNF 2.5
domain
RNA binding motif protein 22 2.2

AI317223 expressed sequence AI317223 1.5

Cell adhesion and cytoskeleton

Tmsb l 0
Catnal

thymosin, beta 10 1.9
Catenin alpha 1 1.6

Cellular component and structural proteins

Srp68
Dibdl

an
Tbc1d15

signal recognition particle 68 5.3
disrupted in bipolar disorder 1 3.4
homolog (human)

tenascin R 2.5
TBCl domain family, member 15 1.6

Transport and carrier proteins

Kcnhl

Eif4enif1
Timm23

thdl
Uqcrb

potassium voltage-gated channel, 2.0
subfamily H (eag-related), member

1

eukaryotic translation initiation 2.5
factor 48 nuclear import factor 1
translocase of inner mitochondrial 3.5
membrane 23 homolog (yeast)

EF hand domain containing 1 2.1
ubiquinol-cytochrome c reductase 2.1
binding protein

Metabolism and enzymes

Aldrl6

aldehyde reductase (aldose 4.1
reductase)-like 6

PCYTlA Phosphate cytidylyltransferase 1, 2.1

DLAT

Tatdn 1
th40

Ndufal 0

Rps6kal

choline, alpha isoform
Dihydrolipoamide S- 2.0
acetyltransferase

TatD DNase domain containing 1 3.5
DEAH (Asp-Glu-Ala-His) box 2.9
polypeptide 40

NADH dehydrogenase 2.8
(ubiquinone) 1 alpha subcomplex

10

ribosomal protein S6 kinase 1.9
polypeptide 1

95

H3050F05

H3082D03
H3098F12

H3001H10
H3047H12

H3132E08
H3023E04

H3053F08
H3076810

H3033GO9

H3 099A03

H3028F10

H3077C06

H3 107F05

H305 1 E06

H3145805

H3133G11

H3086D07
H3159F10

H3041 A02

H3 002D07

 

Aldoa aldolase 1, A isoform 1.9 H3031D03
Table 12 (cont’d)
Agps alkylglycerone phosphate synthase 1.8 H3031802
Tbkl TANK-binding kinase 1 1.5 H3084809
Pcytla phosphate cytidylyltransferase 1, 2.1 H3145805
choline, alpha isoform
Hacel HECT domain and ankyrin repeat 1.7 H3079F02
containing, E3 ubiquitin protein
ligase 1
Smpdl3b sphingomyelin phosphodiesterase, 1.7 H3014H09
acid-like 3B
Pfkl phosphoﬁ'uctokinase, liver, B-type 1.6 H3025D11
Dlat dihydrolipoamide S- 2.0 H3133G11
acetyltransferase (E2 component
of pyruvate dehydrogenase
complex)
Elovl6 ELOVL family member 6, 2.1 H3077806
elongation of long chain fatty
acids (yeast)
Scd2 stearoyl-Coenzyme A desaturase 2 3.7 H3025803

 

96

Table 13 Genes down-regulated in small intestinal epithelial cells of mice

consuming diets based on sucrose versus cornstarch.

 

 

Gene Symbol Gene Name Sucrose vs. Clone ID
Cornstarch

Cell cycle and growth control

Cul4b cullin 48 0.4 H3043809

Tial cytotoxic granule-associated RNA 0.5 H3120F02
binding protein 1

Igfbp3 insulin-like growth factor binding 0.3 H3154G07
protein 3

Fancg Fanconi anemia, complementation 0.4 H3106EO9
group G

Bmprla bone morphogenetic protein 0.6 H3031E08
receptor, type 1A

Signal transduction and receptors

Smpd3 sphingomyelin phosphodiesterase 3, 0.4 H3007A08
neutral

ref Rho-guanine nucleotide exchange 0.5 H3139A08
factor

Cntfr ciliary neurotrophic factor receptor 0.4 H3075F09

Ssr4 signal sequence receptor, delta 0.5 H3039A08

Transcription and translation regulation

Tbx20 T-box 20 0.3 H3040G02

Bzw2 basic leucine zipper and W2 0.3 H3018ElO
domains 2

Cell adhesion and cytoskeleton

Capl CAP, adenylate cyclase-associated 0.3 H3013E04
protein 1 (yeast)

Shrm Shroom 0.5 H3039A02

Stmnl stathmin 1 0.4 H3122C07

Rtn4 reticulon 4 0.6 H3093EOl

Cellular component and structural proteins

Surf4 surfeit gene 4 0.3 H3014C08

Nduf58 NADH dehydrogenase (ubiquinone) 0.5 H3021805
F e-S protein 8

Rpl6 ribosomal protein L6 0.5 H3144E09

97

Table 13 (cont’d)

 

Transport and carrier proteins

Cog8 component of oligomeric golgi 0.4 H3059C05
complex 8

Hba-al hemoglobin alpha, adult chain 1 0.4 H3125H07

Nqo3a2 NAD(P)H:quinone oxidoreductase 0.5 H3043H09
type 3, polypeptide A2

Metabolism and enzymes

Cptla camitine palmitoyltransferase 1a, 0.6 H3140A09
liver

Plscr3 phospholipid scramblase 3 0.4 H3031809

Aurkc . aurora kinase C 0.1 H3053G07

Pdir protein disulﬁde isomerase-related 0.3 H3093809

Rcel Ras and a-factor-converting enzyme 0.3 H3035F09
l homolog (S. cerevisiae)

Maoa monoamine oxidase A 0.5 H3030809

Xrn2 5'-3' exoribonuclease 2 0.5 H3002C12

Cryzll crystallin, zeta (quinone reductase)- 0.6 H3123G05
like 1

Pgm3 phosphoglucomutase 3 0.3 H3038E09

_Pgm211 phosphoglucomutase 2-like 1 0.4 H3099811

Host defense and immunity

Defer defensin related cryptdins 0.3 H3083C07

Fnl ﬁbronectin l 0.3 H3116A10

 

98

Table 14. Genes whose expressions were altered differently by APC genotype and

dietary sucrose

 

Gene Symbol Gene Name

M_C w_C M_S w_s Clone ID

 

Signal transduction and receptors
Rg512 Regulator of G protein signaling

Trancription and translation regulation

Eif356ip eukaryotic translation initiation
factor 3, subunit 6 interacting
protein

Transport and carrier proteins
Cacybp Calcium binding protein

CD28P2 CD2 antigen (cytoplasmic tail)
binding protein 2
Abcf ATP binding cassette, sub family

Cellular component and structure proteins

Psmb7 proteasome (prosome, macropain)
subunit, beta type 7

Rga recombination activating gene 1
gene activation

Sept2 septin 2

Metabolism and Enzyme
Gmppb GDP mannose pyrophosphorylase

Clk3 CDC-like kinase 3
Ppp5c Serine/threonine protein
phosphatase 5

Ppppl r12a Myosin phosphatase

0.0 0.3 0.3 0.5

1.5

10.0 18.2 18.2 17.9

1.0 0.9

1.1

0.2 0.2 0.2 0.1

0.8 0.6 0.5 0.8

0.7

0.4
2.3
1.5

6.1

2.5 2.0 2.9
0.8 0.9 1.3
1.7 1.1 1.3
0.8 0.8 0.6
5.3 5.9 4.5
0.5 0.6 0.7
11.9 12.1 5.6

H3076F 12

H3025A05

H3147H06
H3106C05

H3031801

H3008G03

H3121C06

H3152E11

H3003E10
H3154809
H3158H01

H3084802

 

1Data represent normalized mean intensity values of mRNA expression for the
corresponding gene in the treatment groups: M_C (APCMm mouse consuming
cornstarch), W_C (wild-type mouse consuming cornstarch), M_S (APCMln mouse
consuming sucrose), W_S (wild-type mouse consuming sucrose).

99

RT-PCR Confirmatory Analysis of Gene Changes: For APC status effects on
gene expression, the mRNA expression of two selected genes (clusterin and
lactotransferrin) was further conﬁrmed by RT—PCR using the same RNA samples as in
the microarray analysis. Expression of both genes was signiﬁcantly increased in the
proximal small intestinal epithelium of APCMin mice compared with wild-type mice
(Table 15). Furthermore, expression of both clusterin and lactotransferrin was also

signiﬁcantly induced in the colon of APCMm mice (Table 16).

For effects of dietary carbohydrate source on gene expression, the mRNA
expression of ﬁve selected genes (IGF-II, IGFBP3, PCNA, PIK3C2A, IGF l) was further
conﬁrmed using the same RNA samples from the proximal small intestine by RT-PCR.
Although IGF-I expression was not signiﬁcantly changed by carbohydrate source in the
cDNA microarray analysis, we considered it important to further test if IGF-I mRNA
expression was altered by using RT-PCR. RT-PCR analysis conﬁrmed that expression of
PCNA was signiﬁcantly (P = 0.05) up-regulated in the small intestinal epithelium of mice
fed sucrose compared with those fed cornstarch (Table 17). mRNA expression of IGF 1,
IGF-II and PIK3C2A tended to increase, and expression of IGF 8P3 tended to decrease in
the proximal small intestine of mice consuming sucrose versus those in mice consuming
cornstarch. Although these differences did not reach statistical signiﬁcance, they were

correlated with the results of cDNA microarry analysis.

RT-PCR analysis also was performed using total RNA extracted from the colon of
the same mice. mRNA expression of IGF-II was signiﬁcantly up-regulated in the colon

epithelium of mice fed dietary sucrose (P < 0.05; Table 18). Expressions of PCNA and

100

PIK3C2A also tended to be greater in mice consuming sucrose diets. However,
expression pattern changes of IGFBP3 and IGF-1 caused by dietary carbohydrate source
were different in colon compared with that in proximal small intestine. Expression of
IGFBP3 in the colon of mice consuming sucrose-based diets tended to be greater than
that in mice consuming cornstarch-based diets, whereas, expression of IGF-I in the colon

tended to be repressed by dietary sucrose (Table 18).

101

Table 15. Inﬂuence of APC gene status on expression of selected genes in small
intestinal epithelial cells measured by RT-PCR.

 

 

Gene name APCMin Wild-type Fold SEM P-value
Change

Clusterin 4.90E-03 1.20E-03 4.1 1.00E-03 0.03

LTF 1.18E-04 1.93E-05 6.1 2.20E-05 0.01

 

Table 16. Inﬂuence of APC gene status on expression of selected genes in colon
epithelial cells measured by RT-PCR.

 

 

Gene name APCMin Wild-type Fold SEM P-value
Change

Clusterin 1.25E-02 8.76E-04 14.3 3 .30E-03 0.04

LTF 8.93E-05 6.99E-06 12.8 2.34E-05 0.04

 

102

Table 17. Inﬂuence of dietary sucrose versus cornstarch on expression of selected
genes in small intestinal epithelial cells measured by RT-PCR.

 

 

Gene name Sucrose Cornstarch Fold SEM P-value
Change

IGF-II 2.78E-04 2.15E-04 1.3 5 .22E-05 0.42

IGFBP3 2.94E-05 4.80E-05 0.6 1.48E-05 0.39

PCNA 1348-02 3.1 1E-03 4.3 3 .20E-03 0.05

PIK3C2A 2.63E-03 9.5 88-04 2.7 1.02E-03 0.28

IGF] 2.53E-04 5898-05 4.3 1.34E-04 0.34

 

Table 18. Inﬂuence of dietary sucrose versus cornstarch on expression of selected
genes in colon epithelial cells measured by RT-PCR.

 

 

Gene name Sucrose Cornstarch Fold SEM P-value
Change

IGF-II 1.19E-03 3288-04 3.6 5.22E-05 0.03

IGFBP3 9.17E-05 5.98E-05 1.5 2888-05 0.46

PCNA 1.06E-02 6.55E-03 1.6 2.90E-03 0.36

PIK3C2A 8.8 lE-04 8.09E—04 1.1 2.29E-04 0.83

IGFl 2.42E-05 5.13E-05 0.5 1028-05 0.10

 

103

DISCUSSION

APCMin mice have been widely used as a model for colon cancer research.
Mutations in the APC gene result in production of truncated APC proteins that lack B-
catenin binding domains. Thus, B-catenin is not degraded and accumulates in the cytosol.
B-catenin proteins then can translocate into nucleus, form complexes with transcription
factors such as T-cell factor (ch) and lymphoid enhancer factor (Lef) (Behrens et al.,
1996), and subsequently increase transcription of many genes having a ch/Lef binding
motif. Currently, there are more than 120 genes have been reported to be ch/Lef
responsive genes (http://www.stanford.edu/~musse/pathways/targets.html). Some of
these genes play an important role in colon carcinogenesis. Examples of these genes
include MMP-7 (Crawford et al., 1999), Cyclin D1 (Zhang et al., 1997), C-MYC and
peroxisome proliferator activated receptor 5 (He et al., 1999).

We examined the NIA 15K gene library used in this experiment for genes which
have ch/Lef binding motifs and have been reported as target genes in the Wnt pathway.
Only some of these genes (or their homologs) were present in the NIA 15 K library,
These genes included Wingless, C-MYC, cyclin D1, Axin, MYC binding protein, IGF-II,
MMP-7, CD44, IRX3, and ID2. The small intestinal expression of several possible target
genes in the Wnt signaling pathway was signiﬁcantly different in this experiment for
mice having different APC gene status (APCMin vs. wild-type). Expression of Wnt4
(homolog of Wingless) and cyclin-D2 (homolog of cyclin-D1) was signiﬁcantly increased
in APCMin mice compared with normal mice. It was previously reported that expression
of RAMP3 (receptor activity modifying protein 3) was reduced when Wnt signaling was

induced in C57MG/Wnt-l cells (Ziegler et al. 2005). In the current study, expression of

104

RAMP2 also was reduced in the APCMin mice compared with normal mice. Expression of
several other genes related with the Wnt signaling pathway also were signiﬁcantly
altered. Expression of Dab2, an antagonist of the Wnt signaling pathway (Prunier et al.
2004; Dote et al. 2005), was down-regulated in APCMin mice. Bone morphogenetic
protein receptor, type 1A (Bmprla) expression was increased in APCMin mice. BMP
signaling suppresses Wnt signaling to control the duplication of intestinal stem cells,
thereby preventing crypt ﬁssion and the subsequent increase in crypt number (He et al.
2004). Thus, increased expression of Bmprl a may be a compensatory mechanism to
limit intestinal epithelial growth.

Most experiments studying Wnt target genes have used homogenous cell
populations such as colon cancer cells. In the current study, the RNA was scraped from
the small intestinal epithelium of APCMin mice, which inevitably contained not only the
epithelial cells but also some cells from underlying tissues (e.g. lamina propria).
Furthermore, the small intestinal mucosal cell scrapings obtained from APCMin mice in
this experiment would contain both tumor cells (loss both alleles of APC gene) and
normal-appearing epithelial cells (with one mutated APC gene). Hence, the cell
populations used for gene expression proﬁling in this experiment are somewhat
heterogeneous. This heterogeneity of cell types may dilute our ability to detect some
gene expression changes caused by APC gene mutation, and thus may reduce our ability
to identify gene expression changes of some Wnt target genes in the current study.

Recently, Samson et al. (2005) conditionally knocked out both alleles of the APC
gene in the intestine of mice at 8-10 weeks of age using a novel inducible Ahcre

transgenic line in conjunction with a loxP-ﬂanked Apc allele. Affymetrix microarray

105

analysis was performed to compare gene expression proﬁles in RNA samples from the
intestine of these APC knockout mice and wild-type mice. They conﬁrmed expression
changes of several previously-reported Wnt target genes, including c-Myc, CD44, Tiam
l, Sema3c, Soxl7, Axin2 and Eph83 (Samson et al. 2005). However, in the Samson et
al. study, both alleles ofAPC gene were deleted in all intestinal epithelial cells, which
would provide for a more homogenous cell population compared with those used in our
study.

APCMin mice develop colonic adenomas with high frequency, and disregulation of
the cell cycle is one of the hallmarks of cancer. In the present study, a large group of
genes involved in cell cycle control were differentially expressed in the intestine of
APCMin mice and normal mice. Expression of cyclin D2 and cyclin B 1, genes associated
with increased cell proliferation, were increased in APCMin mice. D-type and B-type
cyclins are essential for progression through the G1 and G2/M phases of the cell cycle.
mRNA expression of clusterin was signiﬁcantly increased in APCMin mice. Clusterin is
implicated in immune regulation, cell adhesion, morphological transformation and cell-
cell interactions. Overexpression of clusterin has been found in prostate, kidney and
ovarian carcinomas. In breast tumor cells, clusterin protein accumulation correlates with
the aggressiveness of breast tumor (Trougakos et al. 2004; Pucci et al. 2005).

Several genes that we found to be down-regulated in APCMin mice also are
involved in cell cycle control. Large tumor suppressor 2 (LatsZ) belongs to a novel tumor
suppressor gene family. Recent studies reported that LatsZ negatively regulates the cell

cycle by controlling Gl/S and G2/M transition (Ke et al. 2004). The decreased expression

of LATSl or LATS2 mRNA was signiﬁcantly associated with a large tumor size

106

(Takahashi et al. 2005). LatsZ also induces apoptosis by down-regulating anti-apoptotic
proteins such as BCL-2 in human lung cancer cells (Ke et al. 2004), so decreased
expression of LatsZ may lead to decreased cell apoptosis. Expression of
calcium/calmodulin-dependent protein kinase ID also has been related to inhibition of
apoptosis. Repression of its expression may contribute to decreased apoptosis (Y amada et
al. 2005). Expression of mortality factor 4 like 1(Morf 411) was decreased in APCMin
mice. Morf 411 is involved in cell growth regulation and cellular aging, and induction of
this gene reverses the immortal phenotype of immortal cell lines (Bertram et al., 1999).
DA82 was originally isolated as a potential tumor suppressor gene from human ovarian
carcinoma (Mok et a1. 1998). Decreased expression of DA82 has been observed in
several cancers, including colon cancer (Kleeff et al. 2002).

Several genes involved in signal transduction were differentially expressed in
APCM’" mice and normal mice in this experiment. Wnt4 was overexpressed in APCMin
mice, which would further amplify B-catenin accumulation caused by APC gene mutation
and contribute to increased cell proliferation and risk of intestinal tumorigenesis.
Increased expression of mitogen-activated protein kinase 6 (Mapk6) in APCMin mice may
also lead to increased cell proliferation. Immunoglobulin-like domain containing receptor
1 (Ildrl) was overexpressed in APCMin mice, it has been reported that expression of its
cytosolic form may be related to the development and progression of cancer (Hauge et al.
2004).

Expression of estrogen receptor 1 (alpha) (Esrl) was decreased in APCMin mice.
Promoter hyperrnethylation of Esrl in sporadic colon adenomas has been reported (Xiong

et al. 2001). This hypennethylation represses expression of Esrl and may be associated

107

with increased risk of colon cancer. TNFRSF19 is a novel member of the TNFR family
that is highly expressed during embryonic development. TNFR superfamily proteins play
important regulatory roles in cell proliferation, differentiation and apoptosis (Baker et a1,
1998). Decreased expression ofTNFRSF19 may contribute to decreased apoptosis in the
intestinal epithelium in APCMin mice.

Several genes involved in transcription regulation whose expression was
inﬂuenced by APC gene status in this experiment also are associated with carcinogenesis.
Overexpression of the HMGA2 gene in transgenic mice leads to the onset of pituitary
adenomas (Fedele et al 2002). Expression of interferon-induced transmembrane protein 3
(Iﬁtm3) in the intestinal epithelium may be related with severe inﬂammation and
increased risk of colon cancer (Hisamatsu, 1999).

Transforming growth factor beta-l-induced transcript 4 (Tgfbl i4) was down-
regulated in APCMin mice. Signiﬁcantly decreased levels of Tgfbli4 mRNA have been
reported in several types of cancers such as human brain and salivary gland tumors, and
Tgfbl i4 has been suggested to have antiproliferative and tumor suppression roles
(Shostak et al. 2003). Nabl is a repressor of Egrl (Russo et al 1995), and Egrl is an
early growth response gene which has been implicated in cell proliferation and apoptosis.
Overexpression of Egrl has a signiﬁcant role in carcinogenesis and in cancer
progression, especially metastasis (Kabayashi eta12002). Decreased expression ofNab
1 may result in increased expression of Egrl, thereby increasing risk of carcinogenesis.
Rpal is the largest subunit of replication protein A, which is a single-stranded DNA-
binding protein complex that is required for multiple processes in eukaryotic DNA

metabolism, including DNA replication, DNA repair, and recombination. Mutation in

108

Rpal results in defective DNA double-strand break repair, chromosomal instability and
cancer in mice, which indicates that Rpal function is essential for the maintenance of
chromosomal stability and tumor suppression (Wang et al. 2005).

Uxt, the ubiquitously expressed transcript, was up-regulated in APCMin mice.
Although the function of Uxt is not clear, it has been previously reported that Uxt is Often
overexpressed in tumor tissues (Schroer et al. 1999). Lactotransferrin (Ltf), an iron-
binding protein, possesses antibacterial, antineoplastic and anti-inﬂammatory activity
(Weinberg 2001). In experimental studies, bovine lactoferrin (bLF) signiﬁcantly inhibited
colon, esophagus, lung, and bladder carcinogenesis in rats when administered orally in
the post-initiation stage. Furthermore, concomitant administration of bLF with
carcinogens resulted in inhibition of colon carcinogenesis (Tsuda et al. 2003). However,
in our study, expression of lactotransferrin in the intestinal epithelium of APCMin mice
was up-regulated compared with that in normal mice. The reason for this increased
expression of Ltf was not clear and needs further study.

Dietary carbohydrate concentration and source have been suggested to affect risk
of colon cancer. High dietary consumption of reﬁned carbohydrates such as sucrose are
associated with higher colon cancer risk (Bostick et al. 1994; Kristiansen et al. 1995;
Cademi et al. 1993, 1994, 1997). Studies conducted in our laboratory indicate that
consuming high-sucrose diets increases adenoma development in the small intestine and
colon of APCMin mice. The second objective of the current study was to investigate the
inﬂuence of dietary carbohydrate source (sucrose vs. cornstarch) on global gene
expression in the intestinal epithelium of APCMin and wild-type mice using cDNA

microarray proﬁling. Microarray analysis has been used to study gene expression

109

changes caused by dietary factors such as soy protein and vegetables in colon cancer
research (van Breda et al. 2005; Xiao et al. 2005). Cui et al. (2004) used microarrays to
study fructose-responsive genes in the small intestine of neonatal rats. However, the
effect of dietary carbohydrate source on intestinal gene expression proﬁling has not been
previously reported.

Although small intestinal expressions of 87 genes were altered by diets containing
different carbohydrate sources, we were particularly interested in genes involved in
certain signaling pathways and genes directly involved in cell growth control and
tumorigenesis.

When the main effects of dietary carbohydrate source on gene expression in the
intestinal epithelium were examined, expression of several genes involved in the insulin-
like growth factor (IGF) pathway was signiﬁcantly altered by dietary carbohydrate source
(sucrose versus cornstarch). These genes included insulin-like growth factor 2 (IGF-II,
up-regulated by sucrose versus cornstarch) and insulin-like growth factor binding protein
3 (IGFBP3, down-regulated by sucrose versus cornstarch). Expression of several other
genes down-stream in the IGF signaling pathway also was inﬂuenced by dietary
carbohydrate source. These genes included homolog of Grb2 (Grb7, up-regulated by
sucrose) in the mitogen-activated protein kinase (MAPK) pathway and
phosphatidylinositol 3-kinase (Pik302a) in the phosphatidylinositol 3-kinase (PI3K)
pathway (up-regulated by sucrose). These gene expression patterns suggest that
consuming high-sucrose diets is associated with elevated IGF signaling in intestinal

epithelial cells.

110

Activation of the IGF Signaling pathway greatly impacts cell proliferation,
differentiation, and apoptosis (Jones et al., 1995). Like IGF-I, IGF-II can bind to the IGF-
I receptor, leading to receptor activation. At least two distinct signal transduction
pathways have been identiﬁed for IGF-IR: one is the MAPK pathway, and increased
MAPK pathway signaling leads to increased cell proliferation. Another is the PI3K
pathway, wherein activation of Akt results in the phosphorylation of several other
proteins that affect cell growth and cell survival. Phosphorylation of the F orkhead
transcription factor (FOXO) blocks transcription of P27kip, and decreased expression of
P27kip contributes to increased cell proliferation. Phosphorylation of the apoptosis-
inducing protein Bad prevents cells from undergoing apoptosis. A third target of Akt is
glycogen synthase kinase 3 (GSK3). Phosphorylation of GSK3 (both alpha and beta
isoforms) by Akt turns off the catalytic activity. GSK3B is required for phosphorylation
and degradation of B-catenin in the Wnt Signaling pathway. Thus, inactivation of GSK3B
by Akt could lead to the accumulation of B-catenin, which could ultimately result in
increased cell proliferation (Y u et al. 2000; F oulstone et al. 2005). Increased cell
proliferation and decreased apoptosis are associated with increased risk for colon cancer.

In the current study, feeding high-sucrose diets signiﬁcantly induced IGF-II
mRNA levels and, at the same time, reduced IGFBP3 mRNA levels in the intestinal
epithelium. The ultimate outcome of these combined effects would presumably be
elevated IGF signaling in the intestinal epithelium, which may contribute to increased
risk of intestinal tumorigenesis in the sucrose fed animals. This result is consistent with
results of other studies, in which it was reported that inducing over-expression of IGF-II

in the intestine signiﬁcantly increased tumorigenesis in ApcMm mice. Conversely,

lll

reducing IGF-II expression in the intestine resulted in reduced adenoma size and
frequency (Hassan et al. 2000). It also has been observed that IGF-II mRNA levels in
colon tumors are mildly elevated compared with that in normal colonic mucousa (Tricoli
et al. 1986). IGF 8P3 inhibits cancer cell growth by sequestering IGF-I or IGF-II from
binding IGFI-R (Cubbage et al. 1990), and can also directly inhibit tumor growth (Hong
et al. 2002) and induce tumor cell apoptosis (Valentinis et al. 1995).

Carcinogenesis is associated with disrupted cell cycle control that results in
increased cell proliferation and growth. Among the genes whose mRNA levels were
induced by the high-sucrose diet in this experiment, several function as positive cell cycle
regulators. Overexpression of these genes is associated with increased cell growth and
increased risk for intestinal tumorigenesis. Idb2 proteins (inhibitor of DNA binding 2) act
as inhibitors of differentiation and promote cell proliferation and oncogenesis (Ruzinova
et al., 2003; Lasorella et al., 2000, 2001). The Idb2 protein forms complexes with the
retinoblastoma (Rb) protein and, when overexpressed, can override the Rb tumor
suppressor function and lead to increased risk of oncogenesis (Lasorella et al., 2000).
Proliferating cell nuclear antigen (PCNA) is an auxiliary protein (cofactor) for DNA
polymerase delta and is known to be associated with S phase and DNA replication of the
cell cycle (Bravo, 1986). Increased expression of PCNA is associated with increased cell
proliferation, which is often related with increased risk for intestinal tumor development.
Csell (chromosome segregation l-like) functions in the mitotic Spindle checkpoint, and
overexpression of Csell has been reported in colonic and breast cancers (Behrens et al.,

2003)

112

Among the genes whose mRNA levels were reduced by feeding high-sucrose
diets in this experiment, several function as negative cell cycle regulators. Decreased
expressions of these genes are associated with increased cell proliferation and increased
risk of colon cancer. Tial (cytotoxic granule-associated RNA binding protein 1) is an
RNA binding protein and functions as a translational silencer of cyclooxygenase-2 gene
expression (Dixon et al., 2003). Overexpreesion of COX-2 is associated with increased
risk of colon cancer (Eberhart et al., 1994). A null mutation for COX-2 markedly
reduced the number and size of intestinal tumors in APCA716 knockout mice (Oshima et
al., 1996). Fanconi anemia (FA) is an autosomal recessive disease marked by bone
marrow failure, birth defects, and cancer. The FA proteins ﬁmction at the level of
chromatin during S phase to regulate and maintain genomic stability (Mi et al., 2005), so
decreased expression of Fancg caused by dietary sucrose may increase genomic
instability, and therefore increase risk for intestinal tumorigenesis. Sucrose suppressed
the expression of Bmprla (bone morphogenetic protein receptor, type 1A). Inactivation
of Bmprla in mice disturbs homeostasis of intestinal epithelial cell regeneration
characterized by an expansion of the stem and progenitor cell populations, eventually
leading to intestinal polyposis resembling human juvenile polyposis syndrome (He et al.
2004). It also has been suggested that 8MP signaling suppresses Wnt signaling to limit
the duplication of intestinal stem cells, thereby preventing crypt ﬁssion and the
subsequent increase in crypt number (He et al. 2004).

Expression of several genes related to G-protein signaling was induced by high-
sucrose diets. Gprc5c is a G protein-coupled receptor which can stimulate normal and

aberrant cell growth and has transforming potential (Young et a1. 1986). Rapgef2 is a

113

guanine nucleotide exchange factor speciﬁc for Rapl and Rap2 (de Rooij et al. 1999)
which has been implicated in a variety of cellular processes including the control of cell
morphology, proliferation, and differentiation. It has been widely reported that the small
GTP-binding protein Rapl has an anti-Ras and anti-mitogenic activity. However, it also
has been reported that Rapl could have mitogenic effects, and is considered as a
conditional oncoprotein (Daniel et al. 1998). Regulator of G-protein signaling 12
(Rgle) accelerates GTPase-activity intrinsic to the alpha subunits of heterotrimeric G-
proteins and acts as a negative regulator of G-protein signaling (Dohlman et al. 1997).
Although the overall effects of these alterations in expression of genes involved in G-
protein signaling are not clear, their potential effects on intestinal tumorigenesis merit
further investigation. Expression of phosphodiesterase 7A also was up-regulated by
high-sucrose diets. cAMP-mediated effects are largely inhibitory in nature and include
cell cycle arrest and apoptosis (Beavo 1995). Thus, overexpression of phosphodiesterase
7A may decrease the inhibitory effects of cAMP and increase risk for intestinal
tumorigenesis.

Expression of neutral sphingomyelin phosphodiesterase (Smpd3) was decreased
in mice consuming sucrose. Spmd3 hydrolyzes sphingomyelin to phosphocholine and
ceramide, which can inhibit cell proliferation and induce differentiation and apoptosis.
(Chatterjee 1999; Stoffel et al. 2005). Decreased Spmd3 results in lower levels of
ceramide, which is associated with tumorigenesis in various tissues (Duan 2005).

The Aurora family kinases are pivotal to the successful execution of cell division, which
together ensure the formation of a bipolar mitotic spindle, accurate segregation of

chromosomes and the completion of cytokinesis. The expression of these kinases are

114

frequently deregulated in cancer and the Aur-C gene maps to 19q13.43, a region often
translocated or deleted in certain cancer tissues (Crane et al. 2004).

A large group of genes coding for metabolic enzymes had their expression
inﬂuenced by dietary carbohydrate source in this experiment. Some genes induced by
high-sucrose diets are involved in carbohydrate and fatty acid metabolism. These
included phosphofructokinase (Pﬂd), aldolase l A (Aldoa), stearoyl-Coenzyme A
desaturase 2 (Scd2), dihydrolipoamide S-acetyltransferase (Dlat), NADH dehydrogenase
(ubiquinone) 1 alpha subcomplex 10 (NdufalO) and aldehyde reductase (aldose
reductase)-like 6 (Aldrl6). Recent studies found that stearoyl-coenzyme A desaturase 2
also plays a role in carcinogenesis. Stearoyl-COA desaturase converts saturated fatty acids
into monounsaturated fatty acids, and a positive correlation between high
monounsaturated fatty acid levels and neoplastic transformation has been reported
(Scaglia et a1, 2005). Endogenously synthesized monounsaturated fatty acids are
involved in the control of cell proliferation, anchorage-independent growth, and survival
in human transformed cells (Scaglia et a1, 2005).

Phospholipid scramblase 3 (Plscr3) was down-regulated in mice consuming
sucrose. Mice having a targeted deletion of PLSCR3 displayed aberrant accumulation of
abdominal fat when maintained on standard rodent chow, and also developed insulin
resistance, glucose intolerance, and dyslipidemia at 2 months of age (Wiedmer, 2004).
Thus, the role of decreased expression of Plscr3 in the development of insulin resistance
in mice fed high-sucrose diets merits further study.

Defensin related cryptins (chr) and Fibronectin 1, which are involved in host

defense and immunity, were found to be down-regulated in mice fed the high-sucrose

115

diet. Paneth cells in the intestinal epithelium produce several types of defensins which
exert bactericidal activity. Defensin also was found to possess anticancer effects in vitro
(Wong et al. 2005). Fibronectin 1 (FNl) is a homologue of ﬁbronectin, which is a major
component of the extracellular matrix and is involved in cell adhesion and migration
processes. Finbronectin forms polymers which have antimetastatic effects against
multiple tumor types (Pasqualini et al., 1996; Yi et al. 2001).

Expressions of several genes associated with the cytoskeleton were altered by
dietary carbohydrate source. Expressions of stathmin and adenylate cyclase-associated
protein 1 (CAPI) were decreased by high-sucrose diets. Stathmin, which sequesters free
tubulin and impedes microtubule formation, plays a pivotal role in cell motility (Currni et
a1. 1999). Stathmin downregulation may be related to defective cell migration in the
intestinal crypt. Cyclase-associated proteins are highly-conserved actin monomer
binding proteins and depletion of CAPl can result in cytoskeletal defects (Bertling et al.
2004)

The establishment and maintenance of adhesion at adherens junctions requires the
stabilization of cell adhesion molecules at speciﬁc sites of cell—cell contact. Cell
adhesion molecules, in particular E- and N-cadherin, engage in homophilic interactions
and thereby promote the recruitment of or-catenin by B-catenin, which in turn directly or
indirectly binds actin ﬁlaments. Actin ﬁlaments are believed to stabilize E-cadherin—B-
catenin—or-catenin complexes (Lecuit 2005). Thus, increased expression of or-catenin in
mice fed the high-sucrose diet may have some consequence on stability of adherens

junctions.

116

mRNA expression of several genes in the proximal small intestinal epithelium
which were found to be signiﬁcantly inﬂuenced by dietary carbohydrate source or APC
gene status using cDNA microarray analysis was further assessed using RT-PCR
analysis. Expression of PCNA was signiﬁcantly (P = 0.05) increased by high dietary
sucrose, and feeding diets containing high-sucrose also tended to alter expression of IGF-
II, IGFBP3 and PIK3C2A in the same direction as identiﬁed in cDNA microarray
analysis. Expression of clusterin and lactotransferrin were conﬁrmed to be signiﬁcantly
up-regulated in APCMin mice measured by RT-PCR. Overall, the results from RT-PCR
analysis are support the ﬁndings of cDNA microarray analysis.

Expression of several genes in colonic epithelial cells obtained from the same
mice was measured using RT-PCR. Expression of IGF-II in the colonic epithelium was
signiﬁcantly (P < 0.05) up-regulated in mice consuming high-sucrose diets.
Overexpression of IGF-II in colon has been associated with increased risk for colon
Min

cancer. In Apc mice, it was found that reducing IGF-II expression in the intestine by

Mm” mice with IGF-II gene knockout mice resulted in reduced adenoma size

crossing Apc
and frequency. Conversely, inducing over-expression of IGF-II in the intestine
signiﬁcantly increased tumorigenesis in ApcMW+ mice (Hassan et al. 2000). The IGF-II
gene is maternally imprinted in that it is expressed only from the paternal copy of the
gene. Loss of imprinting (LOI) of the insulin-like growth factor II gene is an epigenetic
alteration that results in a modest increase in IGF-II expression, and it is present in the

normal colonic mucosa of about 30% of patients with colorectal cancer (Sakatani et al.

(2005)

117

In summary, cDNA microarray analysis identiﬁed 109 genes whose expression in
small intestine was signiﬁcantly different in APCMin mice and wild-type mice. Many of
these genes are associated with cell growth control and tumorigenesis (increased
expressions of Clu, Ccnd2, Ccnbl, Btg4, Anxal, Gsptl; decreased expressions of
Camkld, septin 2, LatsZ, Dab2, Morf4ll). These observations indicate that APC gene
mutations have a broad inﬂuence on expression of many genes associated with intestinal
tumorigenesis. Consumption of high-sucrose diets signiﬁcantly altered expression of 87
genes in the small intestinal epithelium when compared to consumption of high-
comstarch diets. High-sucrose diets also increased expression of IGF-II and decreased
expression of IGFBP3. Collectively, these observations suggest that increased risk of
colon carcinogenesis caused by high levels of dietary sucrose is associated with elevated

IGF signaling in the intestinal epithelium.

118

CHAPTER IV.
INFLUENCE OF DIETARY CARBOHYDRATE (SUCROSE VERSUS

CORN STARCH) AND PROTEIN (CASEIN VERSUS SOY) SOURCES ON
INTESTINAL TUMORIGENESIS IN SULINDAC-TREATED APCMIN MICE

119

ABSTRACT

Our previous research indicated that dietary sucrose increased adenoma numbers
in the proximal small intestine and cell proliferation in colon crypts in APCMin mice after
10-weeks of dietary treatment. Soy protein has been demonstrated to inhibit colon cancer
development in carcinogen-induced colon cancer models. In the current study, the non-
steroidal anti-inﬂammatory drug sulindac was added to all diets at 100 mg/kg to retard
small intestinal tumorigenesis of APCMin mice, thereby allowing them to survive for a
longer period of time before experiencing signiﬁcant morbidity secondary to small
intestinal adenoma development. The objective of this study was to utilize this extended-
progression protocol to assess the impact of dietary carbohydrate and protein sources on
colonic adenoma development and progression in APCMin mice.

Mice were randomly assigned at four weeks of age to one of four modiﬁed AIN-
93G diets containing different protein (soy versus casein) and carbohydrate (cornstarch
versus sucrose) sources. Mice were fed ad libitum for 16 weeks or until they became
moribund. Weekly body weights, intestinal adenoma development and colon crypt
epithelial cell kinetics were determined. Mice consuming either sucrose or soy ﬂour
gained signiﬁcantly more weight than those consuming cornstarch (P < 0.01) or casein (P
< 0.05), respectively. Feeding sucrose promoted colon tumor development in APCMin
mice compared with feeding cornstarch, with greater tumor incidence (59 vs. 30%, P <
0.01), greater tumor numbers (0.94 vs. 0.46 per mouse P < 0.01) and heavier tumor burden
(6.8 vs. 2.5 mm3 per mouse, P < 0.01). Ki67 antigen immunohistochemistry indicated that
mice fed sucrose had higher rates of epithelial cell proliferation in colon crypts compared

to mice fed cornstarch (labeling index: 40 versus 32% for sucrose and cornstarch,

120

respectively, P < 0.01; proliferative zone: 61% versus 53%, P < 0.01), and reduced
apoptosis (by TUNEL assay, apoptotic index: 3.8 versus 5.6%, P < 0.05). Feeding diets
containing soy ﬂour promoted small intestinal adenoma development in APCMin mice
compared with feeding casein, with increased sizes of adenomas (1.37 versus 1.04 mmz, P
< 0.01) and greater tumor burden (64 versus 42 mm2 per mouse, P < 0.01). Dietary soy
protein source had no effects on colon tumor development. Seven (7/35; 20% incidence)
female mice consuming soy ﬂour developed mammary gland tumors, whereas none (0/26;
0% incidence) of the female mice consuming casein-based diets developed mammary
tumors (P < 0.05).

In summary, diets containing high sucrose concentrations signiﬁcantly increased
colonic tumor incidence compared with cornstarch in APCMin mice. Dietary soy protein
source had no effects on colon tumor development. Female APCMin mice consuming soy
ﬂour had signiﬁcantly higher incidence of mammary gland tumors compared with that

consuming casein-based diets.

121

INTRODUCTION

Our previous research indicated that high-sucrose diets increased proximal small
intestinal adenoma numbers and colonic epithelial cell proliferation in APCMmmice.
However, high-sucrose diets did not signiﬁcantly inﬂuence colonic adenoma
development. We speculate that the lack of effect of sucrose on colonic adenoma
development in the previous experiment (Chapter 11) may be due to the relatively short
time period (10 weeks) of dietary treatment for APCMin mice, which may not have been
sufﬁcient in duration for these diets to exert their full impact on colon tumor progression.

APCMin mice typically exhibit morbidity at 14-16 weeks of age due to rapid
adenoma development in the small intestine, which causes bleeding, severe anemia and
weight loss. Colon tumors in APCMin mice typically develop more slowly than small
intestinal adenomas. Sulindac is a non-steroidal anti-inﬂammatory drug which has been
shown to reduce the rate of small intestinal tumor development in APCMin mice.
However, sulindac generally does not reduce colonic tumor growth in studies using
APCMin mice (Jacoby et al., 1996; Chiu et al., 1997; Sorensen et al., 1998). By adding a
low dose of sulindac to the diet (100 mg/kg), we can expand the life-span of APCMin mice
by inhibiting small intestinal tumor development. This allows more time for
development of the colonic tumors, therefore increasing the possibility to detect dietary
effects on colonic tumor development.

The objective of this study was to utilize this extended-progression protocol to
assess the impact of dietary carbohydrate and protein sources on colonic adenoma

development and progression in APCMm mice. As was the case in the previous

122

experiment, the carbohydrate sources examined were sucrose and cornstarch. We also
were particularly interested in assessing the impact of dietary protein source on intestinal
adenoma development in APCMin mice, as soy protein has been demonstrated to be
protective against development of colon cancer in rodent models of human colon cancer.
To date, the effects of soy protein feeding have not been fully explored in APCMin mice.
Hence, we also compared the impact of dietary protein source (soy versus casein) on

intestinal adenoma development in APCMin mice.

123

MATERIALS AND METHODS

Animals and Diets: . Mice for the study were produced by mating C5 7BL/6J
male APCMin mice with normal C57BL/6J female mice. The resulting offspring (50%
normal and 50% APCMin based on simple Mendelian inheritance) were randomly assigned
at weaning (4 weeks of age) to one of four diets containing different protein and
carbohydrate sources. Each group had similar numbers of males and females. Sixty mice
were assigned to group. Of these, we expected 50% to be APCMin mice, thus we expected
appoximately 30 APCMin mice per treatment group. Mice used in this study were
genotyped by an allele-speciﬁc PCR procedure to identify APCMin progeny (Jacoby et al.,
1996). Mice were housed in a room with constant temperature and humidity (23°C, 60%
humidity) and a 12: 12 hour light-dark cycle. Animal care and feeding were conducted
with approval of the Michigan State University All-University Committee on Animal Use
and Care.

The treatment design was a 2 X 2 factorial having two carbohydrate sources
(cornstarch or sucrose) and two protein sources (casein or soy ﬂour). The diets (Table
19) were based on modiﬁed AIN-93G diets (Reeves et al., 1993). Additionally, all diets
contained 100 mg/kg sulindac. Mice were fed diets ad libitum for the duration of the
study (Sixteen weeks or until individual animals exhibited body weight loss of more than

10%).

124

Table 19. Composition of diets for experiment 3 (g/kg diet).

 

 

Ingredient Casein & Casein & Soy & Soy &
Starch Sucrose Starch Sucrose

Defatted Soy Flour 0.00 0.00 411.36 411.36
Casein 222.20 222.20 0.00 0.00
Corn Starch 504.19 0.00 382.41 0.00
Sucrose 0.00 504.19 0.00 382.41
Soybean Oil 150.00 150.00 150.00 150.00
Cellulose 67.38 67 .38 0.00 0.00
AIN-93G-MX 38.90 38.90 38.90 38.90
AIN-93-VX 11.10 11.10 11.10 11.10
L-cysteine 3.30 3.30 3.30 3.30
Tert-Butylhydroquinone 0.03 0.03 0.03 0.03
Choline Bitartrate 2.80 2.80 2.80 2.80
Sulindac 0.10 0.10 0.10 0.10

 

125

Measurements: After sixteen weeks of dietary treatment, mice began to exhibit
morbidity due to intestinal tumor development. At this time, all mice were euthanized by
C02 asphyxiation. Liver samples were collected from each mouse to determine the APC
gene status using PCR (Jacoby et al., 1996). The entire intestine was removed, opened
longitudinally, rinsed with water, pinned on cardboard, and ﬁxed with 10% neutral
buffered formalin overnight. The numbers and sizes of adenomas in each section were
determined with the aid of a stereo-microscope as described in Chapter II. A one-
centimeter section was removed from the distal colon and processed for
immunohistochemistry analyses of epithelial cell kinetics (Ki67, PCNA, TUNEL assay)
in colonic crypts as described in Chapter II.

We also noted that several female mice developed mammary tumors in this
experiment. When detected, these mammary tumors were excised, weighed, and ﬁxed in
neutral buffered formalin to allow for histological grading at a later date.

Statistical Analyses: Data were analyzed using the General Linear Models
procedure of SAS (Version 8.2). The experimental design was a completely randomized
design with a 2 X 2 factorial arrangement of treatments (protein source, carbohydrate
source). When signiﬁcant treatment effects or interactions were detected (F-test
signiﬁcant at P < 0.05), the Least Signiﬁcant Difference method was used to compare
appropriate main effect or treatment means. The incidence rates of colonic adenomas and

mammary tumors in mice were analyzed using the Frequency procedure of SAS.

126

RESULTS

Body weight: Weekly body weights of APCMin mice consuming the four diet
combinations are shown in Figure 13. APCMin mice fed diets containing soy ﬂour gained
more weight during the study than mice fed diets containing casein (14.1 i 0.28 vs. 13.2
i 0.32 grams; P < 0.05). Mice fed diets containing sucrose also gained more weight than
mice fed diets containing cornstarch (14.5 i 0.31 vs. 12.8 i 0.29; P <0.01). Dietary
intakes by mice were not measured in the study.

Effects of diets on intestinal tumorigenesis: There were no interactions
between effects of carbohydrate and protein sources on intestinal tumorigenesis, so the
main effects of carbohydrate and protein sources on intestinal adenomas are presented.

When adenoma development in the whole small intestine was compared, there
were no signiﬁcant differences in adenoma numbers, sizes or total burden for APCMin
mice fed diets containing cornstarch or sucrose (Table 20). Mice that consumed soy as
the primary protein source had small intestinal adenomas that were signiﬁcantly larger in
size when compared to those observed in mice consuming casein, which resulted in the
soy-fed mice having a greater total burden (number X size) of small intestinal adenomas
when compared to casein-fed mice (Table 20).

Dietary carbohydrate source signiﬁcantly inﬂuenced the numbers and average
sizes of adenomas in proximal small intestine (Figure 14, Figure 15). These effects were
similar to that observed in the ﬁrst experiment (Chapter 11) despite adding sulindac in the
diets of the current study. APCMin mice fed sucrose-based diets had Signiﬁcantly greater
numbers of adenomas in the proximal third of the small intestine than mice fed

comstarch-containing diets (18.1 vs. 13.3, P < 0.05; Figure 14). However, the average

127

size of adenomas in the proximal small intestine was greater in mice fed cornstarch-
containing diets (1.96 vs. 1.26 mmz, P < 0.05; Figure 15). Overall, the total adenoma
burden in the proximal small intestine for the two groups was not signiﬁcantly different
(Figure 16).

Mice that consumed soy as the primary protein source had a signiﬁcantly greater
number of adenomas in the distal small intestine compared to mice that consumed casein.
(17.9 vs. 13.6, P < 0.05; Figure 17). Adenomas in the medial and distal small intestine of
mice consuming soy also were signiﬁcantly larger in average size when compared to
those in mice consuming casein (Figure 18). The total tumor burdens in the medial and
distal regions of the small intestine of APCMin mice consuming soy protein also were
signiﬁcantly greater than those in mice consuming casein (Figure 19).

Colonic adenoma incidence, numbers, and burdens are presented in Table 21.
The incidence of colonic adenomas in APCMin mice in this experiment was signiﬁcantly
greater in mice consuming sucrose-based diets (58.7%) when compared to mice
consuming comstarch-based diets (30.1%). Averaged across all APCMin mice, those
consuming sucrose had signiﬁcantly greater colonic adenoma numbers per mouse, larger
average size of colonic adenomas, and greater total adenoma burden per mouse relative to
that in mice consuming cornstarch (Table 21). Among colonic tumor-bearing mice only,
there was no difference in colonic adenoma number per mouse. Thus, all of the
differences observed among treatments in adenoma numbers were explained by the
difference in adenoma incidence among APCMin mice. Dietary protein source did not

inﬂuence colonic adenoma incidence.

128

Although female APCMin mice are known to be susceptible to mammary
tumorigenesis, no research conducted to date has indicated that diet can inﬂuence
mammary tumor development in this model. Mammary tumor incidence of female
APCMin mice is presented in Table 22. In the current study, seven (7/35; 20% incidence)
female mice consuming soy ﬂour developed mammary gland tumors, whereas none
(0/26; 0% incidence) of the female mice consuming casein-based diets developed
mammary tumors. This difference in mammary tumor incidence was statistically
signiﬁcant (P < 0.05). Dietary carbohydrate source did not inﬂuence mammary tumor

incidence.

129

 

3° ‘ —A— Soy-Sucrose

—O—Casein-Sucrose

—*—Soy-Comstarch :

26 - +Casein-Cornstarch rim-0"“
f—F"

28-

 

 

 

BodyWeight (grams)

 

 

 

10 I I I I I I I I I I I T I ﬁr I r I

012 3 4 5 6 7 8 910111213141516
Weeks of Dietary Treatment

Figure 13. Body weights of APCMin mice consuming diets containing different
protein and carbohydrate sources.

130

Table 20. Small intestinal adenoma numbers, average Sizes of small intestinal
adenomas, and total burden of small intestinal adenomas in APCMm mice consuming
diets based on differing protein and carbohydrate sources (main effects; means i

standard errors).

 

 

Carbohydrate Source Protein Source
Parameter Cornstarch Sucrose Casein Soy
(n=63) @54) (n=50) (n=67)
Adenoma number 43.5 :t 3.3 46.4 i 3.5 43.1 is 3.7 46.7 i 3.1
Average size (mmz) 1.27 i 0.06 1.13 d: 0.07 1.04 :l: 0.07a 1.37 d: 0.06b
Total burden (mm2) 54.5 :I: 4.5 52.0 :1: 4.8 42.4 :t 5.021 64.2 i 4.3b

 

a’bProtein source effect (P < 005):

131

 

 

 

     

 

 

 

 

30- El Sucrose-based diet
E1 Cornstarch-based diet

25-
‘- a
g 20-
E
c 15‘
E
o 10-
5
'U 5-
<

Proximal Small Medial Small Distal Small
Intestine Intestine Intestine

Figure 14. Main effect of dietary carbohydrate source on small intestinal adenoma
numbers in APC“.

* All APCMin mice had small intestinal adenomas.

a’b Signiﬁcant diet effect (P<0.01).

132

 

 

 

 

 

 

 

 

 

 

25. Sucrose-based diet
to" b Cornstarch-based diet
E 2-
§
§ 1.5- a
(I)
re
E 1-
o
c
8
< 0.5"
Proximal Small Medial Small Distal Small
Intestine Intestine Intestine

Figure 15. Main effect of dietary carbohydrate source on small intestinal average

adenoma sizes in APC M'"
a,b Signiﬁcant diet effect (P<0.05).

133

 

 

 

 

 

   

 

 

35 - El Sucrose-based diet
:5" El Cornstarch-based diet
E 30-
E
E, 25‘
“g 20-
3
ED 15.
E
o 10-
5
13 5‘
< n :1 , . . ,
u . f A
Proximal Small Medial Small Distal Small
Intestine Intestine Intestine

Figure 16. Main effect of dietary carbohydrate source on total small intestinal adenoma
burden in APCMm mice

134

 

 

 

 

     

 

 

 

30_ El Soy-based diet
[I Casein-based diet
25-
I-
3 20- a
E
c 15-
E
o 10-
5
'c 5-
<
Proximal Small Medial Small Distal Small
Intestine Intestine Intestine

Figure 17. Main effect of dietary protein source on small intestinal adenoma numbers
in APCMm mice.

a,b Signiﬁcant diet effect (P<0.05).

135

 

2,5- El Soy-based diet

 

 

 

 

 

 

 

D Casein-based diet
2-
:5"
E 1.5- a
I
.5 1-
U)
E
o 0.5-
c
e
'u
< c . . *
Proximal Small Medial Small Distal Small
Intestine Intestine Intestine

Figure 18. Main effect of dietary protein source on average small intestinal adenoma
sizes in APCM‘" mice.

a,b Signiﬁcant dict effect (P<0.05).

136

 

4o . El Soy-based diet

 

 

 

 

 

 

 

35- CICasein-based diet
.1‘ 30-
E 25-
: 20- a
o
'2 , b
5 15
g 10
2 5‘
a, 0 .
2 Proximal Small Medial Small
Intestine Intestine Intestine

Figure 19. Main effect of dietary protein source on total small intestinal adenoma
burden in APCM‘".

a,b Signiﬁcant diet effect (P<0.05).

137

Table 21. Colonic adenoma numbers, average sizes of colonic adenomas, and total
burden of colonic adenomas in APCMm mice consuming diets with protein and
carbohydrate sources (main effects; means :t standard errors).

 

Carbohydrate Source

Protein Source

 

Sucrose
Parameter (n=54)
A11 APCMill mice:

n e a
Adenoma Incidence (%) 58.7
a

Adenoma number 0.94 i 0.12
Adenoma size (mm3) 4.95 i 0.8421
Adenoma burden (mm3) 6.83 :t 1.1721
Tumor-bearing mice:
Adenoma number 1.57 d: 0.15
Adenoma size (mm3) 8.45 :t 1.49
Adenoma burden (mm3) 11.23 :t 2.05

Starch
(n=63)

30.1b

0.46 3: 0.11b
1.32 3: 0.77b

b

2.51 :1: 1.08

1.57 :1: 0.19
4.40 i 1.96
8.26 i 2.69

Soy
(n=67)

44.1
0.70 i 0.11
4.02 :1: 0.74
5.84 i 1.04

1.61 i016
8.101 1.63

12.08 i 2.24

Casein
(n=50)

44.7
0.70 :1: 0.13
2.25 :t 0.87
3.50 :1: 1.21

1.53 :t 0.18
4.75 :t 1.85
7.40 :t 2.53

 

a’bCarbohydrate source effect (P <0'05)°

138

Table 22. Mammary tumor incidence in female APCMin mice consuming diets
based on differing protein and carbohydrate sources.

 

Carbohydrate Source Protein Source
Parameter Cornstarch Sucrose Casein Soy
@=36) (n=25) (n=26) (n=3 5)

 

Mammary tumor a b
incidence (%) 11 (4/36) 12 (3/25) 0 (0/26) 20 (7/35)

a’bProtein source effect (P < 0.05).

139

Effects of diets on intestinal crypt cell kinetics: Ki-67 antigen was used as a
surrogate marker for cell proliferation in this study. Mice consuming sucrose had
Signiﬁcantly greater total crypt Ki-67 labeling index (labeling index = labeled cells per
hemicrypt / crypt height) when compared to mice consuming cornstarch (Table 23).
Dietary sucrose also signiﬁcantly increased Ki-67 proliferative zone (proliferative zone =
position of highest labeled cell in crypt / crypt height) relative to cornstarch, indicating
that sucrose feeding expanded the overall zone of cell proliferation in the colon. These
results were further conﬁrmed by our observation that Ki-67 labeling index was increased
in the middle and top (luminal) thirds of the colonic epithelium when mice consumed
sucrose versus corn starch. Dietary protein source did not inﬂuence Ki-67 labeling
indices in this experiment.

Proliferating cell nuclear antigen (PCNA) is expressed in colonic epithelial cells
that have not undergone terminal differentiation. Total crypt PCNA labeling index and
proliferative zone were not inﬂuence by diet in this experiment (Table 24). However,
PCNA labeling in the upper one-third of colonic crypts was signiﬁcantly greater in mice
consuming sucrose when compared to mice consuming cornstarch. This observation
indicates that there were a slight upward shift in PCNA labeling in mice consuming
sucrose, and would suggest a delay in terminal differentiation as colonic epithelial cells
migrate up the crypt axis when mice consumed the high-sucrose diet. Dietary protein
source did not inﬂuence PCNA labeling indices in this experiment.

As colonic epithelial cells migrate to the crypt surface, they undergo programmed

cell death and are eliminated from the crypt apex. Programmed cell death of colonic

140

epithelial cells was assessed by the TUNEL assay, and these results are presented in
Table 25. Mice consuming the high-sucrose diets had signiﬁcantly fewer TUNEL-
positive cells in the total crypt and the upper third of the crypts when compared to mice
consuming the comstarch-based diets. This suggests that colonic epithelial cells were
less likely to undergo programmed cell death at the crypt surface when mice consumed
the high-sucrose diets. Mice consuming the soy diets had signiﬁcantly fewer TUNEL-
positive cells in the total crypt and the middle third of the crypts when compared to mice

consuming the casein-based diets.

141

Table 23. Mean heights of colonic crypts and Ki-67 antigen expression in colon of
APCMm mice consuming diets based on differing protein and carbohydrate sources
(main effects; means i standard errors).

 

Carbohydrate Source

Protein Source

 

 

Parameter Sucrose Corn Starch Soy Casein

(n=22L Jn=18) (n=22) (n=18)
Crypt Height (cells) 22.1 e 0.4 21.3 e 0.5 21.9 e 0.4 21.5 3: 0.5
Total L1 0401i00123 0320i0013b 0.366i0.012 036020.013
Bottom 1/3 LI 0.643 i 0.025 0.601 :1: 0.028 0.603 i 0.025 0.641 3: 0.028
Middle 1/3 LI 0 485 i 0 024a 0 350 i 0 026b 0.447 i 0.024 0.388 :1: 0.026
Top 1/3L1 007910014“ 0022,5001; 0.049e0.014 00522.0.015
a,b

Carbohydrate source effect (P < 0.05).

142

Table 24. PCNA expression in colon of APCMin mice consuming diets based on
differing protein and carbohydrate sources (main effects; means :1: standard errors).

 

Carbohydrate Source

Carbohydrate Source

 

Parameter Sucrose Corn Starch Soy Casein
(n=32) (n=3 8) (n=40L (n=30)
Total LI 0.591 i 0.019 0.557 d: 0.017 0.589 :t 0.016 0.559 :t 0.019
Proliferative 0.708 t 0.019 0.660 :1: 0.017 0.697 :1: 0.017 0.671 :1: 0.020
Zone
Bottom 1/3 LI 0.818 i 0.018 0.844 :1: 0.016 0.831 :1: 0.016 0.831 t 0.018
Middle 1/3 LI 0.756 t 0.032 0.71221: 0.028 0.775 :t 0.028 0.693 :t 0.033
Top 1/3 L1 0198 i 0.02321 0.114 3. 0.020 b 0.159 i 0.020 0.156 i 0.023

 

a’bCarbohydrate source effect (P < 0.05).

143

Table 25. TUNEL assay results in colon of APCMin mice consuming diets based on
differing protein and carbohydrate sources (main effects; means i standard errors).

 

Carbohydrate Source

Sucrose Corn Starch
(n=2 1) (n=23)

0.038 i 0.0063

Parameter

Total LI

Middle 1/3 LI 0.00] i 0.000 0.000 i 0.000

TOP “3 H 0.111 e 0.019a

0.056 3: 0.006b
Bottom “3 L1 0.001 :t 0.000 a 0.0000: 0.000b

0.167:i_-0.018b

Protein Source
Soy Casein
(n=22) (n=22)
0.039 i 0.006c 0.055 i 0.006 d
0.001 i 0.000 0.001 :t 0.000

d 0.002 :t 0.000cl

0.163:1:0.018f

0.000 3. 0.000
0.115 e 0.018e

 

a’bCarbohydrate source effect (P < 0.05).
c’dProtein source effect (P < 0.05).

e’fProtein source effect (P < 0.06).

144

DISCUSSION

APC gene mutations occur in the early stage of colonic carcinogenesis (Fearon et
al., 1990; Kinzler et al., 1996). Therefore, APCMin mice represent an excellent model to
study the inﬂuence of dietary factors on colon cancer progression. However, a major
disadvantage of using APCMin mice for colon cancer research is that most tumors
typically are located in the small intestine. Because the small intestinal adenomas in
APCMin mice grow rapidly and cause bleeding, severe anemia and weight loss, APCMin
mice typically display signs of morbidity by 14-16 weeks of age.

We previously examined the effects of high-sucrose diets on colon tumorigenesis
in APCMin mice (Chapter 11). Due to morbidity associated with small intestinal
adenomas, dietary treatments had to be ended after 10 weeks. We found that high-
sucrose diets signiﬁcantly increased adenoma numbers in the proximal small intestine. In
the previous experiment, adenoma development in the colon was not signiﬁcantly
inﬂuenced by dietary carbohydrate source. However, high-sucrose diets signiﬁcantly
increased the overall rate of cell proliferation in the colonic crypts of mice consuming
sucrose compared to that in mice consuming cornstarch in the previous study. Because
colon tumors develop more slowly than small intestinal tumors, we speculated that a
longer duration of dietary treatment would assist in identifying carbohydrate source
effects on colon tumor development in this model.

Sulindac has been widely studied as potential chemopreventive agent for colon
cancer. Previous studies in APCMin mice have shown that sulindac signiﬁcantly reduces
the number and size of small intestinal adenomas, but does not signiﬁcantly affect colon

tumor development in most studies (Chiu et al., 1997; Sorensen 1998). In the current

145

study, a low dosage of sulindac (100 mg/kg diet) was added to each of the diets to limit
the development of small intestinal adenomas. This approach signiﬁcantly extended the
duration of diet treatments from 10 weeks to 16 weeks in this Study.

Feeding sucrose is associated with greater proliferation in colonic crypts, as well
as a greater number of large aberrant crypt foci (Cademi et al., 1993, 1997; Kristiansen et
al., 1995; Poulsen et al., 2001). Cademi et al. (1994) reported that rats fed high-sucrose
diets had signiﬁcantly greater numbers of colonic adenomas per rat compared with those
fed cornstarch-based diets in a DMH-induced rat colon cancer model. The
adenocarcinomas in the rats fed sucrose were larger and had greater invasive potential
than those in rats fed the comstarch-based diets, although the incidence of total intestinal
tumors was not affected by the different carbohydrate sources (Cademi et al., 1994). In
this study, we demonstrated that feeding high sucrose diets signiﬁcantly increased colon
adenoma incidence compared with feeding cornstarch diets in APCMin mice. We believe
this is the ﬁrst study to directly demonstrate that high-sucrose diets stimulate colon tumor
development.

The colonic epithelium is in a constant state of cell renewal. Abnormal net
increase of cell population in the colonic crypts is associated with increased risk of colon
cancer (Lipkin et al., 1974. 1984; Bleiberg et al., 1985; Terpstra et al., 1987; Ponz de
Leon et al., 1988; Mills et al., 2001). Increased cell population in colonic crypts would
result from increased cell proliferation, delayed cell differentiation, or reduced
programmed cell death of colonic epithelial cells.

In the current study, Ki-67 antigen was used as a surrogate marker for cell

proliferation. PCNA is expressed in colonic epithelial cells that have not undergone

146

terminal differentiation and was used in this study to assess cell differentiation. The
TUNEL assay was used to identify cells undergoing programmed cell death.
Immunohistochemistry analyses indicate that feeding high-sucrose diets was associated
with increased cell proliferation, delayed cell differentiation, and reduced programmed
cell death of colonic epithelial cells. These observations are consistent with the observed
increase in colonic adenoma incidence when mice consumed the sucrose-based diets.

Although it has been demonstrated that consumption of soy ﬂour protects against
colon cancer in carcinogen-induced rat colon cancer models (Bennink et al., 1999, 2000),
consumption of soy ﬂour did not have any protective effects on colon tumor development
in the current study. Rather, soy consumption promoted tumorigenesis in the medial and
distal small intestine. The mechanisms whereby dietary soy might stimulate colonic
adenoma development are unclear. Only one study to date has tested the effects of soy
protein isolates containing different levels of isoﬂavones in APCMin mice (Sorensen et al.,
1998), and it found that soy isoﬂavones did not inﬂuence the development of intestinal
adenomas. In the current study, TUNEL analysis indicated that the apoptotic rates of
colonic epithelial cells of mice consuming soy-based diets were signiﬁcantly reduced
compared to that observed in mice consuming casein-based diets. However, the colonic
epithelial cell proliferation rate was not different for mice consuming soy or casein. We
were unable to assess cell kinetics in small intestine in the current study due to
difﬁculties in preparing intact, full-length small intestinal crypts. Future studies on this
may provide some insights into the mechanisms whereby soy diets promote small

intestinal adenoma development in APCMin mice.

147

Female APCMin mice in 57BL/6J background occasionally develop mammary
gland tumors (Moser et al., 1992, 1993), which indicate a tumor suppressor role for APC
within the mammary epithelium. However, the mammary gland tumor incidence
observed in APCMm mice is low because APCMin mice die around 15-16 weeks of age due
to rapid small intestinal tumorigenesis. In the current study, female APCMin mice
consuming soy ﬂour had signiﬁcantly greater incidence of mammary gland tumors
compared those consuming casein-based diets (20% vs. 0%, P < 0.05) after 16 weeks of
dietary treatment. No research conducted to date has reported that diet can inﬂuence
mammary tumor development in this model.

The inﬂuence of soy protein on mammary gland tumor development has been
extensively studied using carcinogen-induced animal models. The isoﬂavone genistein
has been considered to be responsible for the effects of soy protein on breast cancer
development (Messina et al., 2001). The effects of dietary soy products on mammary
tumor development are still controversial. Early exposures in life to high concentrations
of genistein were found to be protective against DMBA-induced mammary tumor
development (Lamartiniere et al., 1995; Murrill et al., 1996). This effect is believed to
results from the stimulation by genistein of early mammary gland maturation, making
these animals less susceptible to mammary gland tumor development when exposed to
carcinogens later in life (Messina et al., 2001). Hawrylewicz et al. (1995) reported that,
after induced breast tumors by injecting MNU, rats consuming soy proteins developed
signiﬁcantly fewer tumors, and tumors in the soy-fed rats were less aggressive than those
observed in casein-fed rats. However, in ovariectomized athymic mice, dietary soy

protein or genistein have been demonstrated to stimulate the growth of subcutaneously

148

implanted MCF-7 cells (estrogen-dependent human breast cancer cells) in a dose-
dependent manner (Hsieh et al., 1998; Allred et al., 2001).

Effects of isoﬂavones on mammary tumor development also were studied using
MMTV-neu mice, which spontaneously develop mammary tumors due to overexpression
of the Erb8-2/neu/HER2 oncogene (J in etal. 2002). Feeding MMTV-neu mice with
AIN-93G diets containing isoﬂavones from 7 weeks of age signiﬁcantly delayed
mammary tumorigenesis compared to the feeding of a low-isoﬂavone control diet.
However, feeding dietary isoﬂavones did not reduce the numbers or sizes of tumors in
these mice once tumors formed (J in et al., 2002).

In the current study, feeding APCMin mice soy ﬂour from 4 weeks of age
signiﬁcantly increased mammary gland tumor incidence compared to feeding mice with
casein-based diets. Unlike ovariectomized athymic mice, female APCMin mice have
normal ovary function and estrogen production. Despite having a normal estrogen
background in the mice used in our study, soy protein containing isoﬂavones still was
able to stimulate mammary tumor development. Further studies on the effects of soy-
containing diets on mammary tumorigenesis in APCMin mice are warranted.

In summary, this experiment demonstrated that feeding high-sucrose diets
signiﬁcantly increased colonic tumor incidence compared with feeding comstarch-based
diets in sulindac-treated APCMin mice. High-sucrose diets also caused changes in
epithelial cell kinetics in the colonic crypts (increased proliferation, delayed
differentiation and reduced programmed cell death) consistent with increased risk of
colon cancer. Previous studies in rodents indicate that high-sucrose intakes cause

compensatory hyperinsulinemia, which leads to increased expression of IGF-I in liver

149

(Chapter 11; Jones et al., 1995; Giovannucci 1995). A previous study in our laboratory
also suggested that high dietary sucrose induces local overexpression of IGF-II in colon
(Chapter III). Elevated signaling via the IGF pathway is related to accelerated cell cycle
and repressed apoptosis, which ultimately leads to increased risk for colon cancer
development (Yu et al., 2000). Further research is warranted to determine if the
stimulatory effects of high-sucrose diets on colonic tumorigenesis are associated with
increased IGF signaling on colonic epithelial cells, or if other mechanisms explain this

effect.

150

CHAPTER V.

OVERALL SUMMARY AND CONCLUSIONS

151

Colon cancer is the second most common cause of cancer mortality and claims
more than 50,000 lives every year in the United States (ACS, 2004). Dietary factors
strongly modify colorectal cancer risk and approximately 66% to 75% colon cancer may
be prevented by adequate diets and physical activity (AICR/WCRF, 1997). Diets rich in
reﬁned carbohydrates such as sucrose have been associated with high colon cancer risk
(Tuyns et al., 1988; La Vecchia et al., 1993; Bostick et al., 1994;Centonze et al., 1994;
De Stefani et al., 1998). Experiments using carcinogen-induced colon cancer also have
shown that feeding rats with high sucrose diets promoted growth of aberrant crypt foci, a
proposed precancerous lesion of the colon, and also were associated with increased
proliferation in colon crypts compared with feeding cornstarch diets (Kristiansen et al.,
1995; Cademi et al., 1993, 1997; Poulsen et al., 2001). Colon adenocarcinomas in
carcinogen-treated rats consuming high-sucrose diets were signiﬁcantly larger and had
more invasive potential compared with those in rats consuming cornstarch (Cademi et al.,
1994). However, to date high dietary sucrose has not been demonstrated to increase
colon cancer incidence in animal studies. Furthermore, the underlying mechanisms
whereby high dietary sucrose may increase colon cancer risk are unclear.

APCMin (multiple intestinal neoplasia) mice carry a germline mutation in the
adenomatous polyposis coli (APC) gene (Su et al., 1992), which is similar to APC gene
mutations observed in humans with F AP (familial adenomatous polyposis). FAP patients
develop hundreds of colonic adenomas and are at increased risk of colon cancer
(N ishisho et al., 1991). APC gene mutations also are commonly present in sporadic
colon cancers (Powell et al., 1992). Kinzler and Vogelstein (1996) have proposed a

“gatekeeper” function for APC gene in colorectal epithelia in which APC is responsible

152

for maintaining a constant census in renewing cell populations. Therefore, APCMin mice
represent a useful model to study diet and gene interactions during colon carcinogenesis.

Animal studies suggest that high intakes of sucrose induce compensatory
hyperinsulinemia by causing a decline in insulin sensitivity in the liver and peripheral
tissues. Hyperinsulinemia may increase IGF-I (insulin-like grth factor 1) production in
liver and free IGF-I in the circulation (Giovannucci, 2001). Evidence also indicates
possible associations between altered gene expressions of IGFBP3 (insulin-like growth
factor binding protein 3) and IGF-II (insulin-like growth factor II) in the colon epithelia
and colon carcinogenesis (Hassan et al., 2000; Kirman et al., 2004).

The purpose of the current research was to determine if diets high in sucrose
(versus cornstarch) promote colon carcinogenesis in APCMin mice, and if this action was
associated with elevated IGF signaling and increased cell proliferation in colonic
epithelia. To conﬁrm the hypothesis, three experiements were conducted.

The objectives of the ﬁrst study were to test the effects of diets containing sucrose
or cornstarch as the sole carbohydrate source on intestinal adenoma development in
APCMin mice, and possible associations between elevated blood IGF concentrations
caused by high dietary sucrose and increased colon cancer risk. APCMin mice (n = 48)
were randomly assigned at 4 weeks of age to one of two modiﬁed AIN-93G diets
containing either sucrose or cornstarch (523 g/kg of diet) as the sole carbohydrate source.
Diets were fed ad libitum for 10 weeks. Weekly body weights, intestinal adenoma
development and cell kinetics (Ki67, PCNA and TUNEL labeling) in colonic crypts were
measured. The levels of glucose and insulin in the circulation, as well as mRNA

expressions of IGF-I, IGF-II, IGF BF] and IGFBP3 in liver also were determined.

153

In comparison to cornstarch, sucrose feeding promoted weight gain in mice (P <
0.05). APCMin mice fed sucrose had signiﬁcantly greater numbers of adenomas in the
proximal third of the small intestine than mice fed cornstarch (21.8 vs. 13.1, P < 0.01).
However, the average size of proximal small intestinal adenomas was larger in
comstarch- versus sucrose-fed mice (2.1 vs. 1.1 mmz, P < 0.01). The total number of
adenomas present in the entire small intestine tended to be greater in sucrose-fed mice
(72 vs. 60, P < 0.07). The colon tumor incidence was 56% in APCMin mice fed sucrose
compared with 43% in those fed cornstarch, but this difference was not statistically
signiﬁcant. Mice consuming sucrose had signiﬁcantly greater Ki-67 labeling index in
colonic crypts compared to mice consuming cornstarch (37% vs. 32%, P <0.05), which
indicated that mice consuming sucrose had a greater overall rate of cell proliferation in
the colon.

Mice consuming the high-sucrose diet had Signiﬁcantly greater serum glucose
concentrations compared to mice consuming cornstarch (10.09 :L- 0.57 vs. 8.08 :1: 0.57
mM, P < 0.05). Mice consuming sucrose tended to have higher serum insulin levels
compared to mice consuming cornstarch (262.6 i 34.7 vs. 174.4 i 34.3 pmol/L, P =
0.07). Relative IGF-I mRNA expression in liver of mice fed sucrose was greater than
that observed in mice fed cornstarch (0.166 vs. 0.092, p = 0.05). Hepatic mRNA
expressions of IGF-II, IGF 8P1 and IGFBP3 were not inﬂuenced by diet.

The objective of the second study was to further investigate alterations in global
gene expression caused by APC gene mutation and by dietary carbohydrate source by
using cDNA microarray analysis. Mice were assigned randomly at four weeks of age to

one of two modiﬁed AIN-93G diets containing either 52.3% sucrose or cornstarch as the

154

sole carbohydrate source. Mice were fed these diets for 10 weeks, at which point they
were sacriﬁced. Total RNA was extracted from epithelial cells scraped from the
proximal third of the small intestine using Trizol reagent. cDNA synthesized using total
RNA from small intestine was coupled with the ﬂuorescent dyes Cy3 or Cy5 and
hybridized on microarray slides printed with the NIA 15K gene set using a loop design.
Intestinal expressions of 109 genes were signiﬁcantly different between APCMin mice and
wild-type mice, and many of them were associated with cell growth control and
tumorigenesis (increased expressions of Clu, Ccnd2, Ccnbl, Btg4, Anxal, Gsptl;
decreased expressions of Camkld, Septin 2, LatsZ, Dab2, Morf4ll). These observations
indicate that APC gene mutations have a broad inﬂuence on expression of many genes
associated with intestinal tumorigenesis. Expression of 306 genes was signiﬁcantly
inﬂuenced by dietary carbohydrate source. Among these genes, 87 were annotated and
had expression altered by carbohydrate source by more than 50%. The expression
patterns of genes altered by the high-sucrose diet suggest that sucrose altered the
intestinal expression of genes in the insulin/IGF signaling pathway (increased expression
of IGF-II and decreased expression of IGFBP3) in a manner consistent with elevated IGF
Signaling in the intestinal epithelium. High-sucrose diets also changed expressions of
genes associated with cell growth control and tumorigenesis (increased expressions of
IGF2, PCNA, Csell, Idb2, Camk2g; decreased expressions of IGFBP3, Tial, Fancg,
Bmprla, Cul4B), and the overall pattern of gene expression was consistent with the

hypothesis that high dietary sucrose increases risk of intestinal tumorigenesis.

155

Dietary sucrose increased adenoma numbers in the proximal small intestine and
cell proliferation in colon crypts in APCMin mice after lO-week of dietary treatment, but
did not inﬂuence colonic adenoma development. We speculated that the lack of effect of
sucrose on colonic adenoma development could be due to the relatively short time period
(10 weeks) of dietary treatment for APCMin mice, which may not have been sufﬁcient in
duration for diets to exert their full impact on colon tumor progression. In the ﬁnal study,
the NSAID sulindac was added to all diets at 100 mg/kg to retard small intestinal
tumorigenesis, thus allowing APCMin mice to live longer without signiﬁcant morbidity
from small intestinal adenomas. The objective of the third study was to utilize this
extended-progression protocol to assess the impact of dietary carbohydrate and protein
sources on colonic adenoma development and progression in APCMin mice. Mice were
assigned randomly at four weeks of age to one of four modiﬁed AIN-93G diets
containing different carbohydrate (cornstarch versus sucrose) and protein (soy versus
casein) sources. Mice were fed these diets for 16 weeks. Mice consuming either sucrose
or soy ﬂour gained signiﬁcantly more weight than those consuming cornstarch (P < 0.01)
or casein (P < 0.05), respectively. Feeding sucrose promoted colon tumor development
in APCMin mice compared with feeding cornstarch, with greater tumor incidence (59 vs.
30%, P < 0.01), greater tumor numbers (0.94 vs. 0.46 per mouse, P < 0.01) and greater
tumor burden (6.8 vs. 2.5 mm3 per mouse, P < 0.01). Ki67 antigen
immunohistochemistry indicated that mice fed sucrose had a higher epithelial cell
proliferation rate in colon crypts (labeling index: 40 vs. 32%, P < 0.01; proliferative zone:

61% vs. 53%, P < 0.01), and reduced apoptosis (for TUNEL assay, apoptotic index: 3.8

156

vs. 5.6%, P < 0.05). Feeding diets containing soy ﬂour promoted small intestinal
adenoma development in APCMin mice compared with feeding casein, with increased
sizes of adenomas (1.37 vs. 1.04 mmz, P < 0.01) and greater tumor burden (64 vs. 42
mm2 per mouse, P < 0.01). Dietary protein source had no effects on colon tumor
development. Seven (7/35; 20% incidence) female mice consuming soy ﬂour developed
mammary gland tumors, whereas none (0/26; 0% incidence) of the female mice
consuming casein-based diets developed mammary tumors (P < 0.05).

In summary, diets containing high concentrations of sucrose signiﬁcantly
increased colon tumorigenesis in APCMin mice compared with cornstarch, and caused an
increased rate of epithelial cell proliferation and decreased rate of programmed cell death
in colonic crypts of APCMin mice. High-sucrose diets also altered gene expression
patterns in intestinal epithelium that are associated with increased colon cancer risk.
Increased risk for colon cancer caused by dietary sucrose in APCMin mice is associated
with elevated insulin/IGF signaling in intestinal epithelium.

Based on results of this research, ﬁIture research in three areas is of interest. First,

the inﬂuence of dietary glucose and fructose on development of intestinal adenomas in

APCMin mice needs to be further elucidated. In recent years, use of high fructose corn
syrup (HFCS) as a sweetener in food and beverage has increased dramatically, so the
potential inﬂuence of high intakes of HFCS on intestinal tumorigenesis is of particular
interest. Second, further study on the role of hyperinsulinemia in promoting intestinal
tumorigenesis should be pursued using animal models. For example, crossing APCMin
mice with ob/ob mice, which carry a mutation in the leptin gene and become obese and

exhibit diabetic-like symptoms early in life, would generate mice carrying both APC

157

gene and Lep°b gene mutations. If hyperinsulinemia directly stimulates intestinal
tumorigenesis, mice carrying both defects would be expected to develop
hyperinsulinemia and have increased risk for colon cancer compared with mice which
carry APC gene mutations alone. This mouse model also could be used to test if dietary
intervention, caloric restriction, or supplementation with recombinant leptin can correct
hyperinsulinemia and therefore reduce risk for colon cancer. Finally, the results of gene
expression proﬁling conducted as part of this research indicate that expression of many
genes are modiﬁed by APC gene mutation or dietary carbohydrate source. Further
research should be conducted to conﬁrm these alterations in gene expression and explore

the potential roles of these changes in intestinal tumorgenesis.

158

APPENDICES

159

Table A1. Genes up-regulated in small intestinal epithelium of mice

consuming diets based on sucrose versus comstarchl.

 

Clone ID Gene Name
H3132808 hypothetical protein MGC38208
(MGC38208)
H3131808 cadherin 2 (th2)
H3111F06 Unknown
H3051806 aldehyde reductase (aldose reductase)-like 6
(Aldrl6)
H3041D10 expressed sequence AI327354 (AI327354)
H3051C02 Unknown
H3025803 stearoyl-Coenzyme A desaturase 2 (Scd2)
H3 044H07 LOC241402 (LOC241402)
H3088H02 Unknown
H3147D07 Unknown
H3 086D07 hypothetical Metallo-dependent hydrolases
structure containing protein
H3146802 IRON RESPONSIVE ELEMENT BINDING
PROTEIN 1 IRE BP 1 IRON
REGULATORY PROTEIN 1 IRPl
F ERRITIN REPRESSOR PROTEIN
ACONITATE HYDRATASE EC 4.2.1.3
CITRATE HYDRO LYASE
H3 028F10 nuclear receptor coactivator 4 (Ncoa4)
H3 023804 RIKEN cDNA 8230402H15 gene
(8230402H15Rik)
H3 068C01 Unknown
H3 O70D01 expressed sequence C81457 (C81457)
H3 1 l 8H06 inhibitor of DNA binding 2 (Idb2)
H30 1 9H05 Unknown
H3 1 5 5D05 similar to WD repeat domain 7 protein,
isoform 1; TGF-beta resistance associated
gene [Homo sapiens] (LOC208914)
H3 095 H05 PSCD3
H3054E01 RAS p21 protein activator 3 (Rasa3)
H3 1 49G02 MAD homolog 3 (Drosophila) (Madh3)
H303 1 H12 SIMILAR T0 KIAA0971 PROTEIN
(FRAGMENT).
H3 1 321301 Unknown
H3 076D03 RIKEN cDNA 3200002M13 gene
(3200002M13Rik)

160

S/C2 Genebank ID

5.3 BG074237

5.2 BG074167
4.5 BG072515
4.1 BG067183

4.0 BG066309
3.9 BG067157
3.78G064900
3.6 BG066685
3.6 BG070577
3.6 BG075454
3.5 BG070351

3.5 BG075340

3.5 BG065226
3.4 BG064755

3.3 BG068683
3.3 BG068883
3 .3 80073126
3.3 8G064456
3.3 BG076077

3.2 8G071202
3.1 BG067440
3.1 BG075644
3.0 BG065510

2.9H3132801-5
2.9 BG069347

Table A1 (cont’d)

 

H3159F10 Unknown
H3046A08 Unknown
H3032H12 Unknown
H3071H07 Unknown

H3041A02 RIKEN cDNA 2900053813 gene
(290005 381 3Rik)

H3021F12 proliferating cell nuclear antigen (Pena)

H3082801 RIKEN cDNA 5730403810 gene
(5730403810Rik)

H3109F05 Unknown

H3120008 Unknown

H3100F08 membrane-associated protein 17 (Map17-
pending)

H3025805 serine/threonine kinase 3 (Ste20, yeast
homolog) (Stk3)

H3054804 LOC213321 (LOC213321)

H3094D11 0DZ4

H3050F05 RETINOBLASTOMA PROTEIN
INTERACTING ZINC FINGER PROTEIN
RIz MTE BINDING PROTEIN MTB
H3050E08 diaphorase 1 (NADH) (Dial)

H3119H06 expressed sequence AI463719 (AI463719)

H3135F02 SEC8 (S. cerevisiae) (Sec8)

H3097H06 Unknown

H3137812 Unknown

H3045F02 SIMILAR T0 HY POTHETICAL PROTEIN
F LJ20509 (F RAGMENT).

H3099A03 Unknown

H3050F09 Unknown

H3053F08 Unknown

H3114F10 CDNA FIS, CLONE , WEAKLY SIMILAR
T0 RHO GTPASE ACTIVATING
PROTEIN

H3046H08 Unknown

H3134F11 multiple endocrine neoplasia 1 (Menl)

H3013D12 Unknown

H3113H06 feminization l homolog b (C. elegans)
(F emlb)

H3096D08 Unknown

113001312 POLYCOMB-M33 INTERACTING
PROTEIN RINGIB (FRAGMENT).

161

2.9 80076498
2.9 80066706
2.9 80065588
2.9 80069024
2.8 80066269

2.7 80064598
2.7CK334664

2.7 80072360
2.6
2.6 H3100F08-5

2.6 80064902

2.6 80067409
2.6 80071076
2.5 80067103

2.5 80067095
2.5 80073199
2.5 80074476
2.5 80071361
2.5 80074612
2.5 80066578

2.5 80071438
2.5 80067107
2.5 80067371
2.3 80072763

2.3 80066785
2.2 80074403
2.2 80063926
2.2 80072700

2.2 80071239
2.2 80063026

Table A1 (cont’d)

 

H3076F12 REGULATOR OF 0 PROTEIN
SIGNALING
H3082D03 RIKEN cDNA 8430430L24 gene

H3027A12 Unknown

H3077806 long chain fatty acyl elongase (Lee-pending)

H3077C06 RIKEN cDNA 4931430101 gene
(493 1430101 Rik)

H3095D03 Unknown

H3142A01 RWl protein (Rwl-pending)

H3145805 phosphate cytidylyltransferase 1, choline,
alpha isoform (Pcytla)

H3097D03 cyclin-dependent kinase inhibitor 1C (P57)
(Cdknlc)

H3132803 EST X83328 (X83328)

H3107F05 RIKEN cDNA 2210415M14 gene
(2210415M14Rik)

H3033009 Unknown

H3111H02 kallikrein 8, plasma 1 (Klkbl)

H3057811 RIKEN cDNA 3110001D03 gene
(31 10001 D03Rik)

H3076D01 RIKEN cDNA 3110023802 gene
(31 10023802Rik)

H3 128812 hypothetical protein MGC19174
(MOC19174)

H3142806 ZINC FINGER PROTEIN

H3075C12 cysteine sulﬁnic acid decarboxylase (Csad)

H3099F06 hypothetical Glutathione synthetase ATP-
binding domain-like structure containing

protein
H3089F05 Unknown

H3126H08 Unknown

H3 13301 1 dihydrolipoamide S-acetyltransferase
precursor (PDC-EZ)

H3064804 Unknown

H3044D12 Unknown

H3002H07 RIKEN cDNA 1700019D03 gene
(1700019D03Rik)

H3038C01 PR8 MRNA CLEAVAGE COMPLEX II
PROTEIN PCFll

H3002D07 ribosomal protein S6 kinase polypeptide l
(Rps6ka1)

H3041D02 Unknown

162

2.2 80069379

2.2 80070065
2.2 80065075
2.1 80069509
2.1 80069521

2.1 80071159
2.1 80074995
2.] 80075254

2.180071313

2.1 80074232
2.1 80072177

2.0 80065662

2.0H3111H02-5

2.0 80067731

2.0 80069345

2.0H3128812-5

2.0 80075037
2.0 80069417
2.0 80071495

2.0 80070649

2.0H3126H08-5

2.0 80074331

2.0 80068347
1.9 80066644
1.9 80062997

1.9 80066040

1.9 80062954

1.9 H3041D02-5

Table A1 (cont’d)

 

H3072A08 Similar to: similar to glyceraldehyde-3-
phosphate dehydrogenase [Mus musculus]
(LOC212429)

H3001H10 THYMOSIN, BETA 10; PROTHYMOSIN
BETA 10.

H3100C10 Unknown

H3001810 nucleolar and coiled-body phosphoprotein 1

(N olc 1)
H3100D07 Unknown

H3042F10 SIMILAR T0 RIKEN CDNA C08

H3031D03 aldolase 1, A isoform (Aldol)

H3151D04 expressed sequence AW557061 (AW557061)

H3033006 Unknown

H3096D03 Unknown

H303 1802 ALKYLDIHYDROXYACETONEPHOSPH
ATE SYNTHASE, PEROXISOMAL
PRECURSOR EC 2.5.1.26 ALKYL DI-IAP
SYNTHASE ALKYLGLYCERONE
PHOSPHATE

H3098005 inhibitor of DNA binding 2 (Idb2)

H3093D02 Similar to: similar to LAP-4 protein
(Lymphoid nuclear protein related to AF 4)
(LOC329124)

H3097F04 RIKEN cDNA 5730469M10 gene
(5730469M10Rik)

H3019F06 tumor necrosis factor receptor superfamily,
member 23 (Tnfrsf23)

H3022C01 phosphate cytidylyltransferase 1, choline,
alpha isoform (Pcytla)

H3044A06 Unknown

H3027D09 MARCKS-like protein (Mlp)

H3032C02 SIMILAR T0 C01 49

H3079F02 Unknown

H3046A01 RIKEN cDNA 2210008F15 gene
(2210008F15Rik)

H3085D03 phosphodiesterase 7A (Pde7a)

H3048D04 RPBS-mediating protein (Rmp-pending)

H3014H09 RIKEN cDNA 1110054A24 gene
(1 110054A24Rik)

H3085801 casein kinase 1, alpha 1 (Csnklal)

163

1.9 80069037

1.9 80063081

1.9H3100C10-5

1.9 80063054

1.9 80071554
1.9 80066409
1.9 80065457
1.9 80075762
1.9 80065659
1.8 80071234
1.8 80065433

1.8 80071421
1.8 80070978

1.8 80071335

1.8 80064436

1 .8 80064642

1.8 80066606
1.8 80065106
1.7 80065533
1.7CK334657
1.7 80066700

1.7 80070255
1.7 80066904
1.7 80064047

1.7 80070230

Table A1 (cont’d)

 

H3044A02 SUCRASE ISOMALTASE, INTESTINAL
[CONTAIN S: SUCRASE EC 3.2.1.48 ;
ISOMALTASE 8C 3.2.1.- 10

H3124D09 phosphatidylinositol 3-kinase, C2 domain
containing, alpha polypeptide (Pik3c2a)

H3092012 CELL DIVISION CYCLE 2 RELATED
PROTEIN KINASE 7 8C 2.7.l.- CDC2
RELATED PROTEIN KINASE 7

H3159804 Unknown

H3029A05 hypothetical gene supported by NM_023565;
AK011791 (LOC269412)

H3076F01 natrium-phosphate cotransporter IIa C-
terminal-associated protein 2 (AF 334612)

H3084001 Unknown

H3138F12 syndecan 4 (Sdc4)

H3047H12 catenin alpha 1 (Catnal)

H3062F11 Similar to: similar to hypothetical protein
MOC955 [Homo sapiens] (8C035522)

H3134D02 RIKEN cDNA 2610511017 gene
(2610511017Rik)

H3056C11 RIKEN cDNA 1110018L13 gene
(1110018L13Rik)

H3137C12 Similar to: 13 days embryo heart cDNA,
RIKEN full-length enriched library,
clonezD330024811 productzunknown EST,
full insert sequence '

H3029012 Unknown

H3101F11 tuftelin1(Tuftl)

H3147806 Unknown

H3006F12 growth factor receptor bound protein 7 (Grb7)

H3048801 DNA segment, Chr 3, ERATO Doi 330,
expressed (D38rtd330e)

H3042D09 RIKEN cDNA 5730481H23 gene

H3142C01 1500034806RIK

H3023H03 Unknown

H3060H05 Unknown

H3076810 RIKEN cDNA 4432405K22 gene
(4432405K22Rik)

H3025D11 phosphofructokinase, liver, B-type

H3036A12 Unknown

H3147D09 RIKEN CDNA 2310061K06 gene
(23 10061K06Rik)

164

1 .7 80066602

1.780073587

1.7 80070845

1.7H3159804-5
1.7 80065256

1.7 80069369

1.780070194
1.6 80074739
1.6 80066871
1.6 80068186

1.6 80074375

1.6 80067614

1.6 80074623

1.6 H3029012-5
1.6 80071659
1.680075431
1.6 80063390
1.6 80066882

1.6 80066389
1.6CK335109
1.6

1.6 80068020
1.6 80069332

1.6 80064930
1.6 80065861
1.6 H3147D09-5

Table A1 (cont’d)

 

H3104803 ACTIN-ASSOCIATED PROTEIN
PALLADIN (F RAGMENT).

H3027C05 RIKEN cDNA 2400003C14 gene
(2400003C14Rik)

H3098F12 Similar to: Mus musculus, Similar to
chromosome 17 open reading frame 31, clone
IMAGEz5346419

H3100F02 Similar to: SplOO-rsl mRNA, partial
sequence

H3084809 Unknown

H3126A04 insulin-like grth factor 2 (Igf2)

H3009009 HYPOTHETICAL PROTEIN 8ST00098.

H3019D05 Unknown

H3074A11 Similar to CG8726 gene product
(LOC218699)

H3101C10 similar to glyceraldehyde-3-phosphate
dehydrogenase [Mus musculus]
(LOC237275)

H3019806 hypothetical gene supported by 8C029161;
8C019633 (LOC268425)

H3016Hll Similar to: cold shock domain protein A
(Csda)

H3039012 XRN l

H3035D10 Unknown

H3072A04 hypothetical Leucine-rich repeat, typical
subtype containing protein

H3062812 Unknown

H3020D05 gene trap locus 1-13

H3083H04 Unknown

H3030810 cathepsin B (Ctsb)

H3085803 RNA binding motif protein 14 (Rbm14)

H3004001 guanine nucleotide binding protein, alpha q
polypeptide (Gnaq)

H3056811 Unknown

H3099009 calmodulin-like 4 (Calml4)

H3041C02 2310030N02RIK

H3029805 interleukin enhancer binding factor 2 (Ilf2)

H3097H11 Unknown

H3088A02 expressed sequence AA960436 (AA960436)

H3015001 ubiquitin-conjugating enzyme 82A, RAD6
homolog (S. cerevisiae) (Ube2a)

165

1.680071905
1.5 80065091

1.580071418

1.5 80071570

1.5 80070144
1.5 80073613
1.5 80063647
1.5 80064417
1.5 80069222

1.5 80071626

1.5 80064425

1.5 80064216

1.5 80066178
1.4 80065801
1.4 80069033

1.4 80068141
1.4 80064494
1.4 800701 18
1.4 80065392
1.4 80070232
1.4 80063237

1.4 80067602
1.4 80071510
1.4 80066291
1.4 80065299

1.4H3097H11-5

1.4 80070496

1.4H3015001-5

Table A1 (cont’d)

 

H3023804 hypothetical Thioredoxin-like structure 1.4 80064721
containing protein

H3085001 IG MU CHAIN C REGION. 1.4 80070287

H3156A03 pericentriolar material 1 (Pcml) 1.4 80076129

H3044F11 Unknown 1.4 80066667

H3033D10 Unknown 1.4 80065629

113072812 LOC244840 (LOC244840) 1.3 80069052

H3054807 RIKEN cDNA 2810484M10 gene 1.3 80067412
(2810484M10Rik)

H3132A02 melanoma antigen, family D, 2 (Maged2) 1.3 80074195

H3154811 RIKEN cDNA 9030425811 gene 1.3 80075988
(903042581 lRik)

H3147H06 CALCYCLIN BINDING PROTEIN 1.3 80075491
(F RAGMENT).

H3089804 ZINC FINGER PROTEIN 1.2 80070636

 

1mRNA expression of all the genes were signiﬁcantly up-regulated by sucrose versus
cornstarch (P < 0.05).
2S/C: intensity ratio, sucrose versus cornstarch.

166

Table A2. Genes down-regulated in small intestinal epithelium of mice
consuming diets based on sucrose versus comstarch'.

 

 

Clone ID Gene Name S/C2 Genebank ID
H3028D11 40S RIBOSOMAL PROTEIN 0.6 80065203
H3002C12 5'-3' exoribonuclease 2 (Xrn2) 0.5 80062948
H3148805 60$ RIBOSOMAL PROTEIN 0.7H3148805-5
H3115012 60S RIBOSOMAL PROTEIN 0.6 80072862
H3004011 60S RIBOSOMAL PROTEIN L29. 0.7 80063246
H3042C03 adaptor-related protein complex AP-3, mu 1 0.7 80066375
subunit (Ap3m1)
H3013804 adenylyl cyclase-associated CAP protein 0.3 80063930
homolog 1(Cap1)
H3030809 AMINE OXIDASE [F LAVIN CONTAINING] 0.5 80065358
EC 1.4.3.4 MONOAMINE OXIDASE MAO
H3029H01 ATP synthase mitochondrial F 1 complex 0.7 80065328
assembly factor 2 (Atpaf2)
H3018810 basic leucine zipper and W2 domains 2 (82w2) 0.3 80064351
H3031808 bone morphogenetic protein receptor, type 1A 0.6 80065473
(Bmprla)
H3145810 brain protein 16 (8rp16) 0.3 80075292
H3083806 cAMP responsive element binding protein 3 0.7 80070002
(Creb3)
H3140A09 CARNITINE 0- 0.6 80074843
PALMITOYLTRANSFERASE I,
MITOCHONDRIAL LIVER ISOFORM (EC
2.3.1.21) (CPT I) (CPTI-L) (FRAGMENT).
H3106C05 CD2 antigen (cytoplasmic tail) binding protein 0.8 80072060
2 (Cd2bp2)
H307SFO9 ciliary neurotrophic factor receptor (Cntfr) 0.4 80069461
H3043809 cullin 48 (Cul4b) 0.4 80066453
H3120F02 cytotoxic granule-associated RNA binding 0.5 80073264
protein 1 (Tial)
H3015F06 DJ 126A5.1.2 (NOVEL DNAJ DOMAIN 0.680064114
PROTEIN) (ISOFORM 2) (F RAGMENT)
homolog [Homo sapiens]
H3148C09 EST AA792894 (AA792894) 0.6 80075525
H3025A05 eukaryotic translation initiation factor 3, 0.8 80064891
subunit 6 interacting protein (Eif336ip)
H3118D09 expressed sequence AW548221 (AW548221) 0.7 80073078
H3106EO9 Fanconi anemia, complementation group G 0.4 80072083

(Fancg)

167

Table A2 (cont’d)

 

H3102812
H3135011
H3116A10

H3009804
H31 14F01
H309981 1

H3125H07

H3125H11

H3075010
H3017EO3
H3050F12

H3122C11

H3085801

H3 069D02

H3085A01

H303 8809

H3075A09
H3021805
H3154007

H3087A12
H3043H09
H3122D09

H3096C05
H3016D12
H3031809
H3031A08

f-box and leucine-rich repeat protein 10
Fe receptor, IgG, low afﬁnity III (F cgr3)

FIBRONECTIN PRECURSOR (FN)
(F RAGMENTS).
ﬂap structure speciﬁc endonuclease 1 (F enl)

01 to phase transition 1 (Gsptl)

glutathione peroxidase 2, pseudogene 1 (pr2—

psl) on chromosome 7

hemoglobin X, alpha-like embryonic chain in
Hba complex (Hba-x)

hemoglobin X, alpha-like embryonic chain in
Hba complex (Hba-x)

histone 4 protein (Hist4)

HTPAP homolog [Homo sapiens]

hydroxysteroid (l7-beta) dehydrogenase 4
(Hsd17b4)

HY POTHETICAL 39.3 KDA PROTEIN
(FRAGMENT).

hypothetical EF-hand/Myb DNA binding
domain/ELMZ domain containing protein
hypothetical Glycerophosphoryl diester
phosphodiesterase/Glycosyl hydrolase, starch-
binding domain containing protein
hypothetical Glycerophosphoryl diester
phosphodiesterase/Glycosyl hydrolase, starch-
binding domain containing protein
hypothetical P-loop containing nucleotide
triphosphate hydrolases structure containing
protein

hypothetical protein MGC 1 8894 (MGC 1 8894)
hypothetical protein MOC37950 (MOC37950)

insulin-like growth factor binding protein 3

(Igfbp3)
lactotransferrin (Ltf)

NADH CYTOCHROME 85 REDUCTASE
nuclear distribution gene C homolog
(Aspergillus) (Nude)

nuclear receptor eo-repressor l (Ncorl)
nucleolin (Nel)

phospholipid scramblase 3 (Plscr3)

polymerase (RNA) 11 (DNA directed)
polypeptide 8 (25kDa) (Polr2e)

168

0.7 80071702
0.6 80074494
0.3 80072878

0.6 80063590
0.7 80072757
0.4 80071456

0.4 80073608

0.680073610

0.4 80069452
0.7 80064258
0.780067110

0.6 80073413

0.5 80070265

0.5 80068791

0.5 80070218

0.3 80066070

0.4 80069315
0.5 80064555
0.3 80076032

0.2 80070413
0.5 80066520
0.7 80073422

0.6 80071228
0.780064177
0.4 80065439
0.8 80065429

Table A2 (cont’d)

 

H3114802 proteasome (prosome, macropain) 26S subunit, 0.7 80072746
non-ATPase, 7 (Psmd7)

H3093809 PROTEIN DISULFIDE ISOMERASE A5 0.3 80070874
PRECURSOR EC 5.3.4.1 PROTEIN
DISULFIDE ISOMERASE RELATED

H3030808 PROTEIN KINASE C, TYPE EC 2.7.1.- 0.7 80065357

H3095006 RA827A, member RAS oncogene family 0.780071194
(Rab27a)

H3035F09 Ras and a-factor-converting enzyme 1 0.3 80065824
homolog (Reel)

H3093801 reticulon 4 (Rtn4) 0.6 80070989

H3139A08 Rho-guanine nucleotide exchange factor 0.5 80074770
(Rgnef)

113144809 ribosomal protein L6 (Rpl6) 0.5 80075206

H3010A10 ribosomal protein S11 (Rpsl 1) 0.780063667

H3006C11 ribosomal protein 818 (Rpsl8) 0.6 80063361

H3016811 ribosomal protein S18 (Rp818) 0.6 80064187

H3098009 ribosomal protein SS (RpsS) 0.6 H3098009-5

H3137809 RIKEN cDNA 0610007L05 gene 0.780074642
(0610007L05Rik)

H3012C01 RIKEN cDNA 1110020104 gene 0.780063739
(1 1 10020104Rik)

H3138A04 RIKEN cDNA 1300006C06 gene 0.8 80074679
(1300006C06Rik)

H3015C05 RIKEN cDNA 2010320M17 gene 0.8 80064078
(2010320M17Rik)

H3117D09 RIKEN cDNA 2210402A09 gene 0.7 80072996
(22 10402A09Rik)

83123005 RIKEN cDNA 2210407J23 gene 0.6 80073533
(2210407J23Rik)

H3114805 RIKEN cDNA 2410127L17 gene 0.6 80072749
(2410127L17Rik)

H3059C05 RIKEN cDNA 2610019N19 gene 0.4 80067882
(2610019N19Rik)

H3121806 RIKEN cDNA 2900057D21 gene 0.5 80073343
(2900057D21Rik)

H3057D02 RIKEN cDNA 6720463802 gene 0.7 80067712
(6720463802Rik)

H3120D04 RNA BINDING 0.6 80073242

H3013C04 sequestosome 1 (qutml) 0.6 H3013C04-5

H3039A08 signal sequence receptor, delta (Ssr4) 0.5 80066111

H3001005 similar to CG9578 gene product (LOC226591) 0.7 80063069

169

Table A2 (eont’d)

 

H3007A08 similar to Protein arginine N-methyltransferase 0.4 80063417
(LOC214572)

H3055A09 SIMILAR T0 RIBOSOMAL PROTEIN S9 0.7 80067495
(UNKNOWN) (PROTEIN FOR MGC: 14341)
(PROTEIN FOR MOC:2458) (PROTEIN FOR
MOC:4138) homolog [Homo sapiens]

H3076009 SIMILAR T0 SPLICING FACTOR, 0.6 80069388
ARGININE/SERINE-RICH 7 (35KD).

H3083C07 Similar to: defensin related cryptdin 3 (Defcr3) 0.3 80069980

H3093001 Similar to: Mus musculus, clone 0.6 80071009
IMAGE:4456744, partial eds
H3053007 Similar to: serine/threonine kinase 13 0.180067380

(aurora/IPL-like) (Stk13)

H3008A02 solute carrier family 25 (mitochondrial carrier; 0.6 80063501
oxoglutarate carrier), member 11 (Slc25a11)

H3084A08 SPECKLE TYPE POZ 0.6 80070133

H3122C07 STATHMIN (PHOSPHOPROTEIN P19) 0.4 BG073409
(PP19) (ONCOPROTEIN 18) (OP18)
(LEUKEMIA-ASSOCIATED
PHOSPHOPROTEIN P18) (PP17)

(PROSOLIN) (METABLASTIN) (PR22
PROTEIN) (LEUKEMIA-ASSOCIATED

GENE PROTEIN).
H3014C08 surfeit gene 4 (Surf4) 0.3 80063996
H3142D08 TIP120 FAMILY PROTEIN 0.6 80075030
H3086804 transcriptional regulator, SIN3A (yeast) 0.7 80070358
(Sin3a)
H3082H12 ubiquitin conjugating enzyme 6 (ch6p- 0.7 80069952
pending)
H3140806 ubiquitin-conjugating enzyme 82L 3 (Ube213) 0.5 H3140806-5
H3119005 WD REPEAT PROTEIN 0.680073188
H3014D06 ZINC FINGER PROTEIN 219 homolog [Mus 0.780064004
musculus]
H3101C05 4930488P06RIK 0.7 80071621
H3095C04 AU067695 0.880071149
H3068A02 D10JHU828 0.4 80068660
H3049805 EIF285 0.7 80067005
H3114F05 HBA-Al 0.5 80072760
H3062801 L3M8TL3 0.4 80068130
H3089A01 MAPILC3 0.7 80070588
H3037F09 NCORl 0.5 80065991
H3037D12 Unknown 0.2 80065971

170

Table A2 (cont’d)

 

H3037C10
H3107802
H3044008
H3050003
H3040002
H3055C01
H3133805
H3091010
H3137A08
H3034808
H3089805
H3051012
H3095003
H3097A01
H3100H05
H3040A1 1
H3101809
H3133A01
H3062802
H3100A12
H3039A02
H3001A06
H303 8A03
H3065A12
H3092F02
H3094001
H3095A07
H31 15802
H3120A09
H3135C08
H3137804
H3139808
H3 145C 1 0

Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown

0.2 80065957
0.3 H3107802-5
0.3 80066675
0.3 800671 12
0.3 80066250
0.3 80067510
0.3 80074309
0.4 80070926
0.4 80074597
0.4 80065727
0.4 80070604
0.4 80067212
0.4 80071 191
0.4 H3097A01-5
0.4 80071590
0.4 H3040Al 1-5
0.4 80071648
0.5 H3133A01-5
0.5 80068131
0.5 H3100A12-5
0.5 80066106
0.6
0.6H3038A03-5
0.6 80068401
0.7 H3092F02-5
0.780071 102
0.7 80071 131
0.6 80072832
0.6 80073213
0.6 80074448
0.6 80074604
0.6 H3139808-5
0.6

 

1mRNA expression of all the genes were signiﬁcantly down-regulated by sucrose versus

cornstarch (P < 0.05).

2S/C: intensity ratio, sucrose versus cornstarch.

171

Table A3. Genes up-regulated in small intestinal epithelium of APCMin mice versus wild-type

mice'.

 

 

Clone ID Gene Name M/W2 Genebank ID
H3051H08 8-cell translocation gene 4 (Btg4) 14.9 80067220
H3034806 Unknown 10.1 H3034806-5
H3143005 Unknown 7.9 80075141
H3108A04 clusterin (Clu) 5.6 80072209
H3051C05 Similar to: Mus musculus, Similar to hypothetical protein 4.9 80067159
MOC5576, clone MOC154819 IMAGE:6308771, complete
eds
H3146F09 similar to chromosome 22 open reading frame 3 [Homo 4.4 80075392
sapiens] (LOC279765)
H3002801 selenoprotein R (Sepr) 3.7 80062960
H3071H01 Unknown 3.7 80069019
H3159003 TISSUE FACTOR PATHWAY INIHBITOR PRECURSOR 3.6 H3159003-5
(TFPI) (LIPOPROTEIN- ASSOCIATED COAGULATION
INHIBITOR) (LACI) (EXTRINSIC PATHWAY
INHIBITOR) (8P1).
H3064H11 Unknown 3.6 80068390
H3154D09 hypothetical protein MOC11659 (MGCl 1659) 3.5 80076005
H3096806 Unknown 3.5 80071222
H3108A05 hypothetical protein MOC28622 (MGC28622) 3.4 80072210
H3074003 Unknown 3.3 80069286
H3106808 Similar to: similar to Synaptotagmin XI (SthI) 3.2 80072082
(LOC229521)
H3146H06 weakly similar to CTL2 PROTEIN [Homo sapiens] 3.1 80075411
H3030D02 Unknown 3.1 80065374
H3054H08 RIKEN cDNA 1110033009 gene (1110033009Rik) 3.1 80067483
H3109808 expressed sequence AA987064 (AA987064) 3.1 80072315
H3040D06 Unknown 3.0 80066222
H3112D06 RIKEN cDNA 2610027Ll6 gene (2610027L16Rik) 3.0 H3112D06-5
H3034F08 SIMILAR T0 RHO GUANINE NUCLEOTIDE 2.9 80065738
EXCHANGE FACTOR (GEF) 3.
H3148F06 HY POTHETICAL ZINC FINGER PROTEIN 2.9 80075555
H3105810 cyclin 81, related sequence 1 (Ccnbl-rsl) 2.9 80071965
H3071A01 Unknown 2.9
H3142H06 general transcription factor 11 H, polypeptide 1 (62kD 2.7 80075067
subunit) (0tf2h1)
H3130F08 Similar to: Mus musculus, clone IMA08:3497440 2.7 80074083
H3092810 ADP-ribosylation-like 3 (Arl3) 2.6 80070958

172

Table A3 (eont’d)

 

H3096F12 Unknown 2.6 80071263
H3098810 similar to KIAA0475 gene product (LOC215015) 2.6 80071392
H3144812 Unknown 2.5 80075179
H3087A12 lactotransferrin (Ltf) 2.5 80070413
H3146806 Unknown 2.5 80075344
H3124808 Unknown 2.4 80073563
H3147C02 GRIP ASSOCIATED PROTEIN l 2.4 80075438
H3159807 Unknown 2.4 80076410
H3051012 Unknown 2.4 80067212
H3150D10 cleavage and polyadenylation speciﬁcity factor 1 (Cpsfl) 2.4 80075692
H3143H10 Unknown 2.4 80075157
H3154F06 upstream transcription factor 1 (Usfl) 2.4 H3154F06-5
H3036A12 Unknown 2.4 80065861
H3148F04 Unknown 2.4 80075553
H3008A10 MYB binding protein (P160) la (Mybbpla) 2.3 80063508
H3102H08 expressed sequence AU041483 (AU041483) 2.3 80071761
H3149H02 Unknown 2.3 80075654
H3128812 hypothetical protein MOC19174 (MOC19174) 2.3 H3128812-5
H3060810 Unknown 2.3 H3060810-5
H3107D05 RIKEN cDNA 1110004C05 gene (1110004C05Rik) 2.3 80072156
H3152D10 expressed sequence AIl82287 (AIl82287) 2.2 80075841

H3029801 solute carrier family 11 (proton-coupled divalent metal ion 2.2 80065264
transporters), member 2 (Slc11a2)

H3095F12 Unknown 2.2 80071188
H3144012 glypican 1 (Gpcl) 2.2 80075228
H3138A07 T-complex expressed gene 1 (Tcel) 2.1 80074682
#Num! ARA9 2.1

H3127811 ubiquitously expressed transcript (Uxt) 2.0 80073796
H3121F05 hypothetical Lipocalins structure containing protein 2.0 80073353
H3064803 mitogen-activated protein kinase 6 (Mapk6) 2.0 CK334608
H3142810 Unknown 2.0 80075050
H3158D03 protein tyrosine phosphatase, non-receptor type 18 (Ptpn18) 2.0 80076324
H3008H01 annexin A1 (Anxal) 2.0 80063567
H3088F03 protein tyrosine phosphatase 4a3 (Ptp4a3) 1.9 80070554
H3096F 01 Unknown 1.9 H3096F 01-5
H3041D06 FALSE P73 TARGET 1.9 80066305
H3020F08 Similar to: Wiskott-Aldrich syndrome homolog (human) 1.9 80064517

(Was)

173

Table A3 (cont’d)

 

H3099811 glutathione peroxidase 2, pseudogene 1 (pr2-psl) on 1.9 80071456
chromosome 7

H3098009 ribosomal protein SS (RpsS) 1.9 H3098009-5
H3144806 H19 FETAL LIVER MRNA. 1.9 80075175
H3152F10 Nedd4 WW binding protein 4 (N4wbp4-pending) 1.9 80075859
H3012H04 similar to R29144_1 [Homo sapiens] 1.8 80063794
H3106C05 CD2 antigen (cytoplasmic tail) binding protein 2 (Cd2bp2) 1.8 80072060
H3120D04 RNA BINDING 1.8 80073242
H3030H10 high mobility group box 3 (ngb3) 1.8 80065420
H3141808 Unknown 1.8 80074966
H3097H03 extracellular proteinase inhibitor (Expi) 1.8 80071358
H3137A10 hypothetical Dbl domain (dbl/cdc24 rhOGEF family) 1.8 80074599
containing protein
H3049F12 60S RIBOSOMAL PROTEIN 1.8 80067024
H3118H11 SMALL NUCLEAR RIBONUCLEOPROTEIN 1.8 80073131
POLYPEPTIDE G.
H3002H04 Similar to: UDP-GlcNAczbetaGal beta-1,3-N- 1.8 80062994
acetylglucosaminyltransferase l (83gnt1), mRNA
H3012H06 expressed sequence AA408877 (AA408877) 1.8 80063884

H3118803 PLASMA MEMBRANE CALCIUM TRANSPORTING 1.8 80073084
ATPASE EC 3.6.3.8 PLASMA MEMBRANE CALCIUM
PUMP ISOFORM PLASMA MEMBRANE CALCIUM

ATPASE ISOFORM
H3132801 ZINC FINGER PROTEIN 1.8 80074240
H3012D10 WHSCI . 1.8 BG063759
H3152D01 cyclin D2 (Ccnd2) 1.8 80075832
H3008BO3 RIKEN cDNA1200003M11 gene (1200003M11Rik) 1.7 BG063512

H3158H01 SERINE/THREONINE PROTEIN PHOSPHATASE 5 (EC 1.7 BG076363
3.1.3.16) (PPS) (PROTEIN PHOSPHATASE T) (PPT).

H3141D09 Unknown 1.7 80074957
H3097F12 Unknown 1.7 80071343
H3158H12 RIKEN cDNA 2310047M15 gene (2310047M15Rik) 1.7 80076372

H3050D06 SH3-binding domain glutamic acid-rich protein (Sh3bgr) 1.7 80067083

H3098F12 Similar to: Mus musculus, Similar to chromosome 17 open 1.7 80071418
reading frame 31, clone IMAGE:5346419

H3088D12 weakly similar to MOLYBDOPTERIN SYNTHASE 1.6 80070540
SULFURYLASE [Homo sapiens]
H3137F04 Unknown 1.6 80074647

174

Table A3 (cont’d)

 

H31 12D08

H3065A12
H3148801
H3031808
H3125803
H3025812
H3014F10

H3013A04
H31 14F01
H3120D03
H3 1 02F 01

H3101A11
H3010A10
H3102F10
H3024D04
H3081H11

H31 18004
H3026D04
H3125Hl 1

H3145C10
H3152002
H3156802

H3009C1 1

H3145D1 1
H3059F01

H3096802
H3122D09
H3013D1 l

26S PROTEASOME NON ATPASE REGULATORY
SUBUNIT 11 268 PROTEASOME REGULATORY
SUBUNIT S9 26S PROTEASOME REGULATORY
SUBUNIT P44

Unknown

inhibitor of grth family, member 1 (Ingl)

bone morphogenetic protein receptor, type 1A (Bmprla)
Unknown

hypothetical protein MGC19174 (MGC19174)
CEROID LIPOFUSCINOSIS NEURONAL PROTEIN 5
CLNS

f-box only protein 6b (Fbxo6b)

01 to phase transition 1 (Gsptl)

SIMILAR T0 TEMO

CLATHRIN COAT ASSEMBLY PROTEIN AP17
(CLATHRIN COAT ASSOCIATED PROTEIN AP17)
(PLASMA MEMBRANE ADAPTOR AP-2 17 KDA
PROTEIN) (HA2 17 KDA SUBUNIT) (CLATHRIN
ASSEMBLY PROTEIN 2 SMALL CHAIN).

spindlin (Spin)

ribosomal protein S1 1 (Rpsl 1)

ZINC FINGER PROTEIN

BINDING .
proteasome (prosome, macropain) 26S subunit, non-
ATPase, 8 (Psmd8)

ribosomal protein S17

cyclin D2 (Ccnd2)

hemoglobin X, alpha-like embryonic chain in Hba complex

(Hba-x)
Unknown
hypoxanthine guanine phosphoribosyl transferase (Hprt)

YIPPEE PROTEIN (FRAGMENT) homolog [Homo
sapiens]

1.6

1.6
1.6
1.6
1.6
1.6
1.6

1.6
1.6
1.6
1.6

1.6
1.5
1.5
1.5
1.5

1.5
1.5
1.5

1.5
1.5
1.5

EUKARYOTIC TRANSLATION INITIATION FACTOR 21.5

SUBUNIT 2 EUKARYOTIC TRANSLATION
INITIATION FACTOR 2 BETA SUBUNIT 81F 2
ribosomal protein S24 (Rps24)

ubiquitin carboxy-terrninal hydrolase L1 (Uchll)

Unknown

nuclear distribution gene C homolog (Aspergillus) (Nude)

METALLOTHIONEIN-II (MT-II).

175

1.5
1.5
1.5
1.5
1.5

80072569

80068401
80075539
80065473
80073745
80064909
80064027

80063892
80072757
80073241
80071735

CK334730
80063667
80071744
80064834
80070030

80073104
80065010
80073610

80075862
80076174

80063 608

80075284
80067910

H3096802-5

80073422
80063925

Table A3 (cont’d)

 

 

H3151008 PEPTIDYLPROLYL ISOMERASE MATRIN CYP (BC 1.5 80075790
5.2.1.8) (PEPTIDYLPROLYL CIS- TRANS ISOMERASE)
(MATRIN CYCLOPHILIN) (MATRIN CYP) (PPIASE)
homolog [Rattus norvegicus]
H3092F02 Unknown 1.4 H3092F02-5
H3108803 histocompatibility 13 (H13) 1.4 80072220
H3111A06 ribosomal protein L22 (Rp122) 1.4 80072480
H3013C04 sequestosome 1 (qutrnl) 1.4 H3013C04-5
H3041F11 Unknown 1.4 80066329
H3029H01 ATP synthase mitochondrial F1 complex assembly factor 2 1.4 80065328
(Atpaf2)
H309581] Unknown 1.3 H3095811-5
H3134D02 RIKEN cDNA 2610511017 gene (2610511017Rik) 1.3 80074375
H3155H04 hypothetical ARM repeat structure containing protein 1.3 80076118
H3135C08 Unknown 1.3 80074448
H3001D02 heparan sulfate (glucosamine) 3-0-sulfotransferase l 1.3 80063038
(Hs3stl)
H3084010 Similar to: 13 days embryo male testis cDNA, RIKEN full- 1.3 80070203
length enriched library, clonez6030477023
produetzunclassiﬁable, full insert sequence
H3138A04 RIKEN cDNA 1300006C06 gene (1300006C06Rik) 1.3 80074679
H3022F07 TROPOMYOSIN CHAIN 1.2 80064681
ImRNA expressions of all the genes were signiﬁcantly up-regulated in APC?Vlln mice vs.

wild-type mice (P < 0.05).
2M/W: intensity ratio, Min versus wild-type.

176

Table A4. Genes down-regulated in small intestinal epithelium of APCMin

mice versus wild-type mice‘.

 

Clone ID Gene Name

M/W2 Genebank ID

 

H3055804 Unknown
H3150805 GATA binding protein 2 (Gata2)

H3074A09 PROBABLE N 2 ,N 2
DIMETHYLGUANOSINE TRNA
METHYLTRANSFERASE EC 2.1.1.32
TRNA GUANINE 26,N 2 N 2
METHYLTRANSFERASE TRNA 2,2
DIMETHYLGUANOSINE 26
METHYLTRANSFERASE TRNA M 2,2 026

H3108804 8D4_MOUSE Ubiquitin-protein ligase Nedd-4

H3046C05 Unknown

H3088C08 Unknown

H3155D07 homeodomain interacting protein kinase 3

(Hipk3)

H3147C06 Unknown

83042801 Unknown

H3104010 8030025C11R1K

H3053H04 Unknown

H3063Ell Unknown

H3044804 DNA segment, Chr 13, ERATO Doi 275,
expressed (D13Ertd275e)

H3038801 JAK3_MOUSE Tyrosine-protein kinase JAK3
(Janus kinase 3) (JAK-3)

H3048A03 Unknown

H3099D07 hypothetical Serine/threonine speciﬁc protein
phosphatase containing protein

H3068807 expressed sequence AI467391 (AI467391)

H3034003 Unknown

H3073A08 Unknown

H3080D07 UBINUCLEIN homolog [Homo sapiens]

H3003C07 MUC2_RAT Mucin 2 precursor (Intestinal
mucin 2)

H3053C05 HYPOTHETICAL 46.8 KDA PROTEIN.

H3055C07 Unknown

H3063F07 CALRETICULIN PRECURSOR CRP55
CALREGULIN HACBP

H3047C06 Unknown

177

0.1 80067537

0.1H3150805-5

0.1 80069220

0.2 80072221
0.2 80066727
0.2 80070525
0.2 80076079

0.2 80075441
0.2 80066391
0.2 80071931
0.2 80067387
0.2 80068267
0.2 80066648

0.2 80066063

0.2 H3048A03-5

0.2 80071474

0.2 80068712
0.2 80065745
0.2 80069128
0.2 80069812
0.3 80063119

0.3 80067336
0.3 80067516
0.3 80068274

0.3 80066817

Table Amont’d)

 

H3073803 Similar to: proton-dependent high afﬁnity
oligopeptide transporter (Pept2) gene,
complete eds

H3074011 RIKEN cDNA 5730403J10 gene
(5730403J 10Rik)

H3105F08 nucleoporin 62 (Nup62)

H3076H09 hypothetical protein MGC40770 (MGC40770)

H3121007 weakly similar to hypothetical protein
DKFZp434D193.1 (fragment) [Homo sapiens]

H3026809 RIKEN cDNA 2810460C24 gene
(28 10460C24Rik)

H3053805 AV028368

H3111H02 kallikrein 8, plasma 1 (Klkbl)

H3109A04 receptor (calcitonin) activity modifying protein
2

H3055F05 D88RTD4578

H31 12006 transforming growth factor beta 1 induced
transcript 4 (Tgfbli4)

H3112C04 Unknown

H3030A09 feminization 1 homolog a (C. elegans)
(Femla)

H3026H05 D11MOH45

H3043F09 gene rich cluster, C8 gene (Grce8)

H3032804 kinesin family member 58 (Kibe)

H3056A09 inositol 1,4,5-triphosphate receptor 2 (Itpr2)

H3092C10 Similar to: Mus musculus, clone
IMAGE:4010812

H3076807 ZINC FINGER PROTEIN

H3088D05 dystonin (Dst)

H305 1H12 RETINOBLASTOMA PROTEIN
INTERACTING ZINC FINGER PROTEIN
RIZ MTE BINDING PROTEIN MT 8

H3032A09 peroxisomal integral membrane protein
(Pmp47)

H3158802 RIKEN cDNA 5730555F13 gene

H3084D03 UDP-GLCNAC:A- l ,3-D-MANNOSIDE 8-
1,4-N-
ACETYLGLUCOSAMINYLTRANSFERASE
IV (EC 2.4.1.145) homolog [Homo sapiens]

H3105001 ring ﬁnger protein

178

0.3 80069134

0.3 80069294

0.3 80072008
0.3 80069400
0.3 80073367

0.3 80065025

0.3 80067358

0.3 H3111H02-5

0.3 80072299

0.3 80067550
0.3 80072602

0.3 80072603
0.3 80065347

0.3 80072605
0.3 80072606
0.3 80072607
0.3 80072608
0.3 80072609

0.3 80072610
0.4 8007261 1
0.4 80072612

0.4 80072613

0.4 80072614
0.4 80072615

0.4 80072616

Table A4 (eont’d)

 

H305 7803 hypothetical Prokaryotic membrane
lipoprotein lipid attachment site containing
protein

H303 8809 hypothetical P-loop containing nucleotide
triphosphate hydrolases structure containing
protein

H3046D03 ITGAV

H3115811 protein kinase C, lambda (Prkcl)

H3098F06 SERINE/THREONINE PROTEIN
PHOSPHATASE 2A, 56 KDA
REGULATORY SUBUNIT, EPSILON
ISOFORM (PP2A, B SUBUNIT, 8' EPSILON
ISOFORM) (PP2A, 8 SUBUNIT, 856
EPSILON ISOFORM) (PP2A, 8 SUBUNIT,
PR61 EPSILON ISOFORM) (PP2A, 8
SUBUNIT, R5 EPS

H3035805 Unknown

H307001 1 Unknown
H3076F 12 REGULATOR 0F 0 PROTEIN SIGNALING
H3041F12 Unknown

H3 141006 hypothetical Acyl-COA N-acyltransferases
(Nat) structure containing protein
H3121C02 follistatin-like (Fstl)

H3046808 hypothetical Homeobox domain containing
protein

H3062809 PROTEIN TYROSINE PHOSPHATASE 4A1.

H3050D09 similar to claspin [Homo sapiens]
(LOC277677)
H3072C03 Unknown

H3041A02 RIKEN cDNA 2900053813 gene
(2900053813Rik)
H3157006 Unknown

H3097F03 Similar to: Mus musculus, clone
IMAGE:5011849
H3123A06 Unknown

H3035801 TR8-3 (TR8-3)

H3057808 Unknown

H3116C04 SEC8 (S. cerevisiae) (Sec8)
H31 1 1H06 Unknown

H3096811 Unknown

H3022C06 Similar to: hypothetical protein L00208146
(LOC208146)

179

0.4 80072617

0.4 80072618

0.4 80072619
0.4 80072620
0.4 80072621

0.4 80072622
0.4 80072623
0.4 80072624
0.4 80072625
0.4 80072626

0.4 80072627
0.4 80072628

0.4 80072629
0.4 80072630

0.4 80072631
0.4 80072632

0.4 80072633
0.4 80072634

0.4 80072635
0.4 80072636
0.4 80072637
0.4 80072638
0.4 80072639
0.4 80072640
0.4 80072641

Table A4 (eont’d)

 

H3032811 RAD26L HYPOTHETICAL PROTEIN,
ALTERNATIVELY SPLICED PRODUCT,
SIMILAR T0 (AF217319) PUTATIVE
REPAIR AND RECOMBINATION
HELICASE RAD26L (FRAGMENT)
homolog [Homo sapiens]

H3085C02 Traf and Tnf receptor associated protein

H3080003 ribnuclease inhibitor
H3132A02 melanoma antigen, family D, 2 (Maged2)
H3131F06 developmentally regulated GTP binding

protein 1 (Drgl)
H3065C07 Unknown

H3035F03 Unknown

H3035809 hypothetical protein MGC46970 (MGC46970)
H3025F02 8C030932

H3001D04 HISTONE H2A.G (H2A/G) (H2A.3).
H3098GO2 Unknown

H3085805 RIKEN cDNA 5730533P17 gene
(5730533P17Rik)
H3058805 RIKEN cDNA 1110008L16 gene

H3041F09 solute carrier family 2, (facilitated glucose
transporter), member 8 (Slc2a8)
H3127F08 RIKEN cDNA 5530600P05 gene

H3068F07 Unknown

H303 5004 peptidase 4 (Pep4)

H3044805 Unknown

H3040810 81NG4 protein (8ing4)
H3025006 2810410P22RIK

H3046C09 2310035N15RIK

H3095H11 Unknown

H3103810 MACGAP homolog [Homo sapiens]
H3008004 GATA binding protein 2 (Gata2)
H31 12012 Unknown

H3096F03 Unknown

H3026C09 ubiquitin speciﬁc protease 23
H3098A11 Unknown

H3024001 mini chromosome maintenance deﬁcient 5 (S.
cerevisiae) (MemdS)
H3122006 neuropilin (Nrp)

H3050C07 Unknown

180

0.4 80072642

0.4 80072643
0.4 80072644
0.4 80072645
0.4 80072646

0.4 80072647
0.4 80072648
0.4 80072649
0.4 80072650
0.4 8007265 1
0.5 80072652
0.5 80072653

0.5 80072654
0.5 80072655

0.5 80072656
0.5 80072657
0.5 80072658
0.5 80072659
0.5 80072660
0.5 80072661
0.5 80072662
0.5 80072663
0.5 80072664
0.5 80072665
0.5 80072666
0.5 80072667
0.5 80072668
0.5 80072669
0.5 80072670

0.5 80072671
0.5 80072672

Table A4 (cont’d)

 

H3097F04 RIKEN cDNA 5730469M10 gene
(5730469M10Rik)
H3138806 lethal giant larvae homolog (nglh)

H3132F06 pleiomorphie adenoma gene-like 2 (Plag12)
H3120H09 Unknown

H3018D01 thyroid hormone receptor interactor 4 (Trip4)
H3070810 SIMILAR T0 RIKEN CDNA N24
H3070H05 Unknown

H3026808 human ubiquitin

H3102Cll Unknown

H3121002 RIKEN cDNA 3100004P22 gene
(3100004P22Rik)

H3120008 Unknown

H3140006 Mpv17 transgene, kidney disease mutant
(Mpv17)

H3030A08 replication protein Al (70 kDa) (Rpal)

H3001A06 Unknown

H3036004 Unknown

H3116F04 SIMILAR T0 REV/REX ACTIVATION
DOMAIN BINDING PROTEIN

H3112807 3 KETOACYL COA THIOLASE,
MITOCHONDRIAL EC 2.3.1.16 BETA
KETOTHIOLASE ACETYL COA
ACYLTRANSFERASE MITOCHONDRIAL
3 0XOACYL COA

H3112808 Unknown

H3120H12 Unknown

H3076F01 natrium-phosphate cotransporter IIa C-
tenninal-associated protein 2 (AF 334612)
H3024D09 WD repeat domain 1 (Wdrl)

H306581 1 Unknown

H3098A08 RIKEN cDNA 1010001M04 gene
(1010001M04Rik)
H3026807 sideroﬂexin 1 (Sfxnl)

H3012010 bisphosphate 3'-nueleotidase 1 (Bpntl)

H3056C11 RIKEN cDNA 1110018L13 gene
(1110018L13Rik)
H3101C09 Unknown

H3048C04 testis expressed gene 189
H3068C01 Unknown
H3027A12 Unknown

181

0.5 80072673

0.5 80072674
0.5 80072675
0.5

0.5 80064330
0.5 80068903
0.5 80068934
0.5 H3026808-5

0.5H3102C11-5

0.5 80073362

0.5
0.5 80074905

0.5 80065346
0.5

0.5 80065911
0.5 80072928

0.5 80072552

0.5 80072580
0.5 80073295
0.5 80069369

0.5 80064839
0.5 80068443
0.5 80071372

0.5 80065024
0.5 80063789
0.5 80067614

0.5 80071625
0.5 80066894
0.5 80068683
0.5 80065075

Table A4 (eont’d)

 

H3047A01 hypothetical RabGAP/TBC domain containing
protein

H3097A12 Unknown

H3120801 CADHERIN

H3056A11 Ngﬁ-A binding protein 1 (Nabl)

H3035003 Unknown

H3026FO3 RIKEN cDNA 5730536A07 gene
(5730536A07Rik)

H3112010 disabled homolog 2 (Drosophila) (Dab2)

H3067A12 RIKEN cDNA 2410003L22 gene
(2410003L22Rik)

H3116C08 Unknown

H3023006 microsomal triglyceride transfer protein (Mttp)

H3050809 RIKEN cDNA 2900054P12 gene
(2900054P12Rik)

H3 044A01 Unknown

H3008A03 interleukin 17 receptor (Ill7r)

H3112008 exportin 4 (Xpo4-pending)

H3140801 Unknown

H3068807 RIKEN cDNA 4833425H18 gene
(4833425H18Rik)

H3068C04 DEAD (aspartate-glutamate-alanine-aspartate)
box polypeptide 3 (Ddx3)

H3082806 LIM-DOMAIN PROTEIN LMP-l homolog
[Rattus norvegicus]

H3070A10 nuclear receptor co-repressor 1 (N eorl)

H3026A12 Unknown

H3089810 Unknown

H3049A01 SUMO 1 SPECIFIC PROTEASE 1 EC
3.4.22.- SENTRIN SPECIFIC PROTEASE
SENP6 PROTEASE

H3142C01 1500034806RIK

H3075A10 RIKEN cDNA 2810489L22 gene
(2810489L22Rik)

H3088C03 expressed sequence AU015605 (AU015605)

83016003 hypothetical protein KIAA0610 (fragment)
homolog [Homo sapiens]
H3124D09 phosphatidylinositol 3-kinase, C2 domain
containing, alpha polypeptide (Pik3c2a)
H3099801 Similar to: 7 days neonate cerebellum cDNA,
RIKEN full-length enriched library,
clone:A730047D01 product:unknown EST

0.5 80066790

0.5 H3097A12-5

0.6 80073251
0.6 80067591
0.6 80065830
0.6 80065031

0.6 80072606
0.6 80068579

0.6 80072898
0.6 80064780
0.6 80067096

0.6 80066601
0.6 80063502
0.6 80072604
0.6 80074877
0.6 80068677

0.6 80068686

0.6 80070079

0.6 80068856

0.6H3026A12-5

0.6 80070642
0.6 80066959

0.6CK335109
0.680069316

0.6 80070520
0.6 80064200

0.6 80073587

0.6 80071447

 

182

Table A4 (eont’d)

 

H3088A02 expressed sequence AA960436 (AA960436)
H3065810 SPIR 2

H3027D02 MYO INOSITOL MONOPHOSPHATASE
H3007A09 SET translocation

H3007F08 poliovirus receptor-related 3 (Pvrl3)

H3046A01 RIKEN cDNA 2210008F15 gene
(2210008F15Rik)
H3020012 Unknown

H3076A10 Unknown

H3109010 KUNITZ-TYPE PROTEASE INHIBITOR 2
PRECURSOR (HEPATOCYTE GROWTH
FACTOR ACTIVATOR INHIBITOR TYPE
2) (HAl-2).

H3101C11 RIKEN cDNA 4921528807 gene
(4921528807Rik)

H3062F1 1 Similar to: similar to hypothetical protein
MGC955 [Homo sapiens] (8C035522)

H3097H1 1 Unknown

H3152811 septin 2 (Sept2)

H3053D11 Unknown

H3095A05 ESRI

H3039C09 pMS protein (PmS-pending)
H3077F04 OSBPL8

H3022D11 protein phosphatase 4, regulatory subunit 1

(PPP4r1)
H3046A09 F K506 binding protein 10 (65 kDa) (F kbplO)

H3133A08 Unknown
H3133A12 zinc ﬁnger protein 94 (pr94)

H3052808 similar to DKFZP56400823 protein
(L00231440)

H3035F09 Ras and a-factor-converting enzyme 1
homolog (S. cerevisiae) (Reel)

H3111C09 aminolevulinic acid synthase 1 (Alasl)

H3087801 annexin A4 (Anxa4)

H3023D04 hypothetical Integral membrane protein, DUF6
containing protein
H3146006 aconitase 2, mitochondrial (Ae02)

H3034804 RCL SIMILAR T0 PUTATIVE C MYC
H3 069A 1 0 Unknown

H3033809 similar to Herrnansky-Pudlak syndrome
protein variant (LOC276962)

183

0.6 80070496
0.6 80068442
0.6 80065100
0.6 80063418
0.6 80063474
0.6 80066700

0.6 80064531
0.6 80069486
0.6 80072373

0.6 80071627

0.6 80068186

0.6H3097H11-5

0.6 80075848
0.6 80067352
0.6 80071 129
0.6 80066132
0.6 80069555
0.6 80064662

0.6 80066707
0.6 80074270
0.6 80074273
0.6 80067276

0.680065824

0.6H3111C09-5

0.6 80070448
0.6 80064744

0.6 80075401
0.6 80065723
0.6 80068763
0.6 80065639

Table A4 (eont’d)

 

H3149C10 triosephosphate isomerase (Tpi)

H3056801 actin related protein 2/3 complex, subunit 5

(165 kDa) (ArpcS)
H3110C01 erythrocyte protein band 4.1-like 4b

(pr4.ll4b)
H3015C01 B-cell receptor-associated protein 31 (8eap3l)

H3021A12 tryptophanyl-tRNA synthetase (Wars)
H3091C03 Unknown

H3092002 Similar to: Mus musculus, clone
IMAGE:4010812
H3011805 Unknown

H3100005 Unknown
H3139801 heat shock 70kD protein 8
H3088805 galactosidase, alpha (Gla)

H3054807 RIKEN cDNA 2810484M10 gene
(2810484M10Rik)

H3077801 Similar to: X-linked lymphocyte-regulated 5
(Xlr5)

H3079C11 ECGFl

H3063002 espin (Espn)

H3144A09 AUTOSOMAL HIGHLY CONSERVED

H3129806 expressed sequence AW546258 (AW546258)

H3015001 ubiquitin-conjugating enzyme 82A, RAD6
homolog (S. cerevisiae) (Ube2a)

H3068A08 kinesin-like 5 (mitotic kinesin-like protein 1)
(KnslS)

H3098804 Unknown

H3014005 RIKEN cDNA 0610038P07 gene
(0610038P07Rik)
H3062812 Unknown

H3001H02 Unknown
H3098003 STROMAL INTERACTION MOLECULE
H3095C11 Unknown
H3060H05 Unknown

H3042C03 adaptor-related protein complex AP-3, mu 1
subunit (Ap3m1)

H3016811 ANKYRIN

H3032C02 SIMILAR T0 C01 49

H301 8001 DEAD (aspartate-glutamate-alanine-aspartate)
box polypeptide 3 (Ddx3)

H3099009 calmodulin-like 4 (Calml4)

184

0.6 80075608
0.7 80067628

0.7 8007241 1

0.7 80064074
0.7 80064551
0.7 80070794
0.7CK334685

0.7 80063851
0.780071582
0.7 80074803
0.7 80070545
0.780067412

0.7 80069540

0.7 80069714
0.7 80068280
0.7 80075168
0.7 80073952

0.7 H3015001-5

0.7 80068666

0.7 80071402
0.7 80064033

0.780068141
0.7

0.7H3098GO3-5
0.7H3095C11-5

0.7 80068020
0.7 80066375

0.7 80064159
0.780065533
0.7 80064364

0.780071510

Table A4 (eont’d)

 

H3042C09 weakly similar to GASC-l PROTEIN [Homo
sapiens]

H3116812 AL024097

H3052C12 serine/threonine kinase 11 (Stkl 1)

H3062C11 SET domain, bifurcated l (Setdbl)

H3079A09 cyclin A2 (Ccna2)

H3041C02 2310030N02RIK

83078003 IMPORTIN BETA 2 SUBUNIT
KARYOPHERIN BETA 2 SUBUNIT
TRANSPORTIN M9 REGION
INTERACTION PROTEIN

H3060C01 INTERLEUKIN l RECEPTOR
ASSOCIATED KINASE EC 271- IRAK

H3085005 SIMILAR T0

H3024011 hypothetical Ubiquitin earboxyl-terrninal
hydrolase family 2 containing protein

H3141D02 EFNAS

H3042A07 stromal cell derived factor 4 (Sdf4)

H3095809 Similar to: similar to mastigoneme-like protein
[Chlamydomonas reinhardtii] (LOC277139)

H3138811 sema domain, immunoglobulin domain (Ig),
transmembrane domain (TM) and short
cytoplasmic domain, (semaphorin) 4G
(Sema4g)

H3089005 collagen, type IV, alpha 3 (Goodpasture
antigen) binding protein (Col4a3bp)

H3027C05 RIKEN cDNA 2400003C14 gene
(2400003C14Rik)

H3069C09 Rho-associated coiled-coil forming kinase 1
(Rockl)

H3003803 Unknown

H3147H06 CALCYCLIN BINDING PROTEIN
(FRAGMENT).

ImRNA expressions of all the genes were signiﬁcantly down-regulated in APCMin mice

vs. wild-type mice (P < 0.05).
2M/W: intensity ratio, Min versus wild-type.

185

0.7 80066379

0.7 80072924
0.7 80067258
0.7 80068152
0.7 80069688
0.7 80066291

0.7 H3 078003-5

0.7 80067964

0.7 80070291
0.7 80064874

078007495]
0.7 80066355
0.7 80071 174

0.7 80074727

0.7 80070661

0.880065091

0.8 80068786

0.8 80063137
0.8 80075491

Table A5. Genes whose expressions were altered differently by APC genotype and
dietary sucrose”.

 

 

Clone ID Gene Name M_C W_C M_S W_S Genebank 1D

H3113003 AGRIN 4.1 2.2 2.3 2.980072686

H3031801 ATP BINDING CASSETTE, 0.8 0.6 0.5 0.880065432
SUB FAMILY F, MEMBER 2
IRON INHIBITED ABC
TRANSPORTER 2 HUSSY

H3145810 brain protein16(8rp16) 0.1 0.0 0.0 0.080075292

H3147H06 CALCYCLIN BINDING 10.0 18.2 18.2 17.980075491
PROTEIN (FRAGMENT).

H3106C05 CD2 antigen (cytoplasmic tail) 0.2 0.2 0.2 0.180072060
binding protein 2 (Cd2bp2)

H3025A05 eukaryotic translation initiation 1.5 1.0 0.9 1.180064891
factor 3, subunit 6 interacting
protein (Eif356ip)

H3088A02 expressed sequence AA960436 0.1 0.3 0.2 0.380070496
(AA960436)

H3003810 GDPMANNOSE 0.4 0.8 0.8 0.680063142
PYROPHOSPHORYLASE

H3072A04 hypothetical Leucine-rieh 0.3 0.7 0.7 0.680069033
repeat, typical subtype
containing protein

H3038809 hypothetical P-loop containing 0.2 0.5 0.0 0.280066070
nucleotide triphosphate
hydrolases structure containing
protein

H3104812 hypothetical protein 0.6 0.3 0.4 0.580071880
MGC31247 (MGC31247)

H3037A07 hypothetical TPR repeat 0.5 0.1 0.1 0.280065934
containing protein

H3024011 hypothetical Ubiquitin 1.0 1.8 1.2 1.180064874
carboxyl-terrninal hydrolase
family 2 containing protein

H3132A02 melanoma antigen, family D,2 0.2 0.7 0.4 0.780074195
(Maged2)

H3084802 MYOSIN PHOSPHATASE 6.1 11.9 12.1 5.680070139

H3089004 neuregulin4(Nrg4) 3.2 4.4 4.3 3.980070660

186

Table A5 (cont’d)

 

H3075810

H3 008003

H3154809

H3121C06

H3076F12

H3138A04

H3085806

H3031802

H3054807

H3050809

H3152811
H3158H01

H3062C1 1

H3074Al 1

H3015810

NON KINASE CDC42
8F F ECTOR PROTEIN

proteasome (prosome,

macropain) subunit, beta type 7

(Psmb7)

PROTEIN KINASE CLK3
(EC 2.7.1.-) (CDC-LIKE
KINASE 3).

recombination activating gene
1 gene activation (Rga)

REGULATOR OF G
PROTEIN SIGNALING

RIKEN cDNA 1300006C06
gene (1300006C06Rik)

RIKEN cDNA 1810003N24
gene (1810003N24Rik)

RIKEN cDNA 2810410M20
gene (2810410M20Rik)

RIKEN cDNA 2810484M10
gene (2810484M10Rik)

RIKEN cDNA 2900054P12
gene (2900054P12Rik)

septin 2 (Sept2)
SERINE/THREONINE
PROTEIN PHOSPHATASE 5
(EC 3.1.3.16) (PPS)

(PROTEIN PHOSPHATASE
T) (PPT).

SET domain, bifurcated 1
(Setdbl)

similar to CG8726 gene
product (LOC21 8699)

similar to HIPPOCALCIN-
LIKE I [Mus musculus]

187

3.4

2.9

2.3

1.3

0.0

4.0

0.5

2.1

4.1

1.2

0.7
1.5

1.0

2.5

4.4

5.9

2.5

5.3

0.8

0.3

2.6

0.6

1.1

10.0

2.8

1.7
0.5

2.6

3.7

2.8

5.0

2.0

5.9

0.9

0.3

2.6

0.7

1.2

9.1

1.6

1.1
0.6

2.2

4.7

2.9

4.2 80069327

2.9 80063558

4.5 80075986

1.3 80073320

0.5 80069379

2.5 80074679

0.5 80070281

1.6 80065467

9.6 80067412

2.0 80067096

1.3 80075848
0.7 80076363

1.9 80068152

4.4 80069222

3.480064107

Table ASLcont’d)

 

H3099801

H3101F 1 1

H3012807
H3025H08
H3041C02
H3042805
H3060H05
H3062812
H3064D1 l
H3089D05
H3093A08
H3096802
H3101C05
H310201 1
H31 12012
H3145C10
H3156H09

Similar to: 7 days neonate
cerebellum cDNA, RIKEN
full-length enriched library,

clonezA730047D01

product:unknown EST, ﬁIll

insert sequence

tuftelin 1 (Tuftl)

Unknown
Unknown
Unknown
Unknown
Unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown

1.2

0.2
5.2
4.5
0.4
0.0
0.2
0.4
8.8
0.2
1.4
3.0
1.6
0.5
0.6
5.1
0.3

3.3 2.4

0.6 0.7
7.8 9.1
7.3 8.0
0.9 0.9
0.1 0.2
0.6 0.6
1.2 1.2
12.7 12.8
0.2 0.1
3.0 3.0
1.5 1.7
1.3 0.9
1.7 1.1
2.4 1.3
2.6 2.6
0.1 0.1

2.7 80071447

0.6 80071659
6.3 H3012807-5
6.7 80064969
0.9 80066291
0.1 H3042805-5
0.6 80068020
1.1 80068141
10.0 80068342
0.4 80070626
2.4 80070862
1.7 H3096802-5
1.1 80071621
1.3 H3102C11-5
1.7H3112012-5
2.4
0.2 8007621 1

 

1Data represent normalized mean intensity values of mRNA expression for the
corresponding gene in the treatment groups: M_C (APCMm mouse consuming
cornstarch), W_C (wild-type mouse consuming cornstarch), M_S (APCMm mouse
consuming sucrose), W_S (wild-type mouse consuming sucrose).

2Signiﬁcant interaction (P < 0.05) between effects of diet and genotype.

188

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