Copyright
by
Karrie E. Wheatley
2010
The Dissertation Committee for Karrie Elizabeth Wheatley Certifies that this is the approved version of the following dissertation:
Energy Balance and Cancer Risk: Metabolic and Molecular Studies
Committee:
Stephen D. Hursting, Supervisor
Kimberly Kline
Michelle A. Lane
Jon M. Huibregtse
Robin Fuchs-Young
Energy Balance and Cancer Risk: Metabolic and Molecular Studies
by
Karrie Elizabeth Wheatley, B.A.
Dissertation Presented to the Faculty of the Graduate School of
The University of Texas at Austin
in Partial Fulfillment
of the Requirements
for the Degree of
Doctor of Philosophy
The University of Texas at Austin May 2010
Acknowledgements
I would like to first thank my mentor, Stephen D. Hursting for his years of
patience, for his willingness to pursue unfamiliar avenues of research, and for his
enthusiasm. I know that I will continue to rely on him for advice on my professional
development and am grateful to know that he will gladly continue to offer whatever
support he can.
I would also like to thank my committee members for their time and service.
They each contributed a unique attribute to the process. Dr. Kline was adamant in her support of realistic and achievable timelines. Dr. Lane encouraged me to publish as much as I could. Dr. Fuchs-Young rigorously questioned me about data and experimental design. Dr. Huibregtse, as he was less familiar with nutritional sciences, asked the most interesting questions that made me consider a broader audience as I wrote this dissertation.
I want to thank Dr. Susan Perkins for her many thoughtful and careful revisions over the years.
Finally, I would like to thank Dr. Lashinger and Dr. Smith for their daily contributions to my development.
iv Energy Balance and Cancer Risk: Metabolic and Molecular Studies
Publication No.______
Karrie Elizabeth Wheatley, Ph.D
The University of Texas at Austin, 2010
Supervisor: Stephen D. Hursting
Over 60% of U.S. adults are either overweight or obese. The effect of obesity on
cancer mortality is striking: approximately 90,000 deaths per year from cancer may be
avoided if Americans could maintain a BMI of <25.0 throughout adulthood. The aim of
this research was to employ and evaluate energy balance interventions designed to
reverse obesity-related risk factors. The overarching hypothesis was that energy balance
interventions would reduce cancer risk. To test this hypothesis, we used mostly animal
models of diet-induced obesity and tested the effect of a low carbohydrate diet, calorie
restriction, and exercise on adiposity, levels of circulating hormones, insulin resistance,
and oxidative stress. To extend on in vivo findings from the final animal study, we utilized a human breast cancer cell line to further characterize the gene expression of thioredoxin-interacting protein in the context of p53 deficiency. Calorie restriction was
v the most potent energy balance intervention. It caused weight loss, slowed tumor growth, reduced circulating IGF-1 and leptin levels, improved insulin resistance, and elicited a robust transcriptional response in visceral white adipose tissue following weight loss.
Although a low carbohydrate diet and exercise did decrease hormones associated with obesity, (IGF-1 and leptin respectively) calorie restriction proved to be the most effective at reducing multiple obesity-related factors. Finally, from our studies analyzing the effect of obesity and exercise on oxidative stress in the context of p53-deficiency, we discovered that thioredoxin-interacting protein is transcriptionally upregulated in response to increased glucose flux associated with metabolic dysregulation that occurs as a consequence of loss of p53.
vi Table of Contents
List of Tables ...... x
List of Figures...... xi
List of Illustrations...... xii
Chapter 1: Introduction...... 1 1.1 Obesity Defined ...... 1 1.2 The U.S. Obesity Epidemic ...... 1 1.3 Obesity and Cancer...... 2 1.4 Possible Mechanisms Underlying the Obesity-Cancer Connection 5 1.4.1 Chronic Hyperinsulinemia and IGF-1 ...... 6 1.4.2 Estrogen ...... 7 1.4.3 Oxidative Stress ...... 8 1.4.4 Inflammation...... 9 1.5. The effect of calorie restriction on cancer risk ...... 11 1.6. Studying Cancer Risk and Exercise...... 11 1.6.1 Breast cancer and exercise...... 13 1.6.2 Colon cancer and exercise ...... 14 1.7 Possible mechanisms underlying physical activity and cancer...... 14 1.7.1 Effects on circulating estrogen...... 14 1.7.2 Effects on hyperinsulinemia and IGF-1...... 15 1.7.3 Oxidative stress and inflammation...... 17 1.8 Obesity and tumor metabolism ...... 18 1.9 Dissertation objectives...... 19
Chapter 2: Low-Carbohydrate Diet versus Caloric Restriction: Effects on Weight Loss, Hormones and Colon Tumor Growth in Obese Mice ...... 23 2.1 Introduction...... 23 2.2 Materials and methods ...... 25 2.2.1 Animals and Diets...... 25
vii 2.2.2 Serum IGF-1 and Leptin Levels ...... 29 2.2.3 Percent Body Fat Analysis...... 29 2.2.4 Statistical Analysis...... 29 2.3 Results...... 30 2.3.1 Body Weight Analysis ...... 30 2.3.2 Serum IGF-1 ...... 34 2.3.3 Serum Leptin...... 34 2.3.5 Tumor Latency...... 36 2.3.6 Final Tumor Burden...... 36 2.4 Discussion...... 38
Chapter 3: Differential effects of calorie restriction and exercise on the adipose transcriptome in diet-induced obese mice...... 41 3.1 Introduction...... 41 3.2 Materials and Methods...... 42 3.2.1 Animal Study Design...... 42 3.2.2 Glucose Tolerance Test...... 44 3.2.3 Serum Hormones ...... 45 3.2.4 Microarray Analysis...... 45 3.2.5 Chromatin immunoprecipitation (ChIP) assay ...... 46 3.2.6 Statistics ...... 47 3.3 Results...... 47 3.4 Discussion...... 63
Chapter 4: p53, oxidative stress, and metabolism...... 69 4.1 Introduction...... 69 4.2 Materials and Methods...... 70 4.2.1 Animal Study Design...... 70 4.2.2 Glucose Tolerance Test...... 73 4.2.3 Quantitation of lipid and DNA oxidation ...... 73 4.2.4 Quantitative real-time PCR analysis...... 73 4.2.5 Cell culture...... 74 4.2.6 Small interfering RNA constructs and expression vectors 74 viii 4.2.7 Measurement of glucose consumption and assay production ...... 75 4.2.8 SDS-PAGE, Western blotting, and immunodetection...... 75 4.2.9 Statistics ...... 76 4.3 Results...... 76 4.4 Discussion...... 93
Chapter 5: Concluding Remarks...... 96
REFERENCES……………………………………………………………………99
VITA ……………………………………………………………………………..114
ix List of Tables
Table 2.1 Diet Composition...... 27
Table 3.1 Effect of weight loss induced by CR or EX on body composition...... 49
Table 3.2 Gene expression changes in CR and EX compared to control ...... 55
x List of Figures
Figure 2.1 Mean body weight per group of mice on 4 dietary regimens for 23 wks...... 32
Figure 2.2 Energy intake and body composition ...... 33
Figure 2.3 Effect of dietary regimen on hormone levels...... 35
Figure 2.4 Effect of dietary regimen on tumor latency and size...... 37
Figure 3.1 Effect of weight loss induced by calorie restriction or exercise on serum hormones and glucose tolerance ...... 50
Figure 3.2 Effect of weight loss induced by calorie restriction or exercise on mRNA expression in VWAT...... 53
Figure 3.3 Confirmation of microarray data analysis of mRNA expression in VWAT. Expression of mRNA transcripts in VWAT from Diet Control, CR and EX mice (n=7/group)...... 59
Figure 3.5 Relative quantification of Glut4 amplification...... 62
Figure 4.1 Body weight and energy intake ...... 77
Figure 4.2 Markers of oxidative stress are increased by obesity in p53+/- mice...... 80
Figure 4.3 No effect of p53 genotype on expression of genes related to oxidative stress in MFP of obese mice...... 81
Figure 4.4 No effect of energy balance intervention or genotype on oxidative DNA damage in MFP...... 84
Figure 4.5 Txnip mRNA levels are significantly increased in MFP of obese, p53 +/- mice...... 85 Figure 4.6 No effect of wild type p53 transfection on Txnip mRNA levels in MCF-7 cells ...... 87
Figure 4.7 Expression of p53 and Txnip mRNA in MCF-7 cells after transfection with siRNA constructs targeting p53 relative to cells transfected with scrambled siRNA...... 88
Figure 4.8 Effect of p53 knockdown on metabolism and Txnip mRNA levels ...... 91
Figure 4.9 Relative mRNA expression of glycolytic genes in MCF-7 cells in which the glycolytic switch occurred following knockdown of p53...... 92
xi List of Illustrations
Illustration 1.1 Possible factors linking obesity to increased cancer risk ...... 10
xii Chapter 1: Introduction
1.1 Obesity Defined
Several methods exist to measure adiposity, including waist to hip circumference,
percent body fat calculated using skin fold caliper measurements, and dual energy X-ray absorptiometry. The World Health Organization (WHO) utilizes the Body Mass Index
(BMI) as a simple index to classify a person’s weight as underweight (<18.50), normal
(18.50-24.99), overweight (≥25.00), or obese (≥30.00). BMI is calculated by dividing an individual’s mass in kilograms by the square of the height in meters (kg/m2). Although
BMI does not directly measure body fat, it is directly correlated to body fat. The degree to which BMI is associated with adiposity varies with age, sex, and ethnicity. For example, black individuals tend to have more lean mass and less fat mass than white individuals of the same BMI and sex. Despite these flaws, BMI is the most utilized classification method because of its ease of use and strong correlation to disease risk.
Indeed, the risk for developing chronic diseases increases when BMI exceeds 24.99. In fact, once BMI exceeds 25.0 kg/m2, every 5 kg/m2 increase is associated with about a
30% higher overall mortality (1).
1.2 The U.S. Obesity Epidemic
The Centers for Disease Control and Prevention (CDC) describe American
society as obesogenic in that there are readily available, cheap and unhealthy foods and a
general lack of physical activity (2). The results of the obesogenic environment are 1 reflected in the dramatically high prevalence of obesity which is calculated from
information gathered in The National Health and Nutrition Examination Survey
(NHANES). Obesity incidence rates began to climb from 1976-1980. There was another
period of increase from 1988-1994 and then again from 1999-2000. The latest report
indicates that the prevalence of obesity did not increase from 2007-2008. Currently,
32.2% of adult men and 35.5% of adult women are obese with another 17.1% of all
adults overweight (3).
Data collected by the CDC reveals differences in prevalence of obesity by
region. In the southern states (Alabama, Mississippi, Oklahoma, South Carolina,
Tennessee, and West Virginia) at least 30% of the adult population is obese, whereas in the western and northeastern states 20-24% of the adult population is obese. In only one state, Colorado, does the prevalence of obesity fall below 20%. Although there are differences by region, at least 25% of the adult population is obese in the majority of states (32 states) indicating that obesity is indeed a nationwide epidemic.
Obesity prevalence in the U.S. also varies by ethnicity. According to data collected from 2006-2008 by the CDC, blacks had 51% higher prevalence of obesity than whites. Hispanics had 21% higher prevalence of obesity than whites. Minority populations are prime targets for obesity intervention and prevention initiatives to minimize if not reverse the impact of obesity on public health.
1.3 Obesity and Cancer
2 In 2003, Calle et al published the first prospective study analyzing the relationship
between BMI in men and women and risk of death from all cancers and cancers at
individual sites (4). Prior to that time, it was known that obesity was a risk factor for
several chronic diseases including cardiovascular disease and type 2 diabetes. There had been reports of obesity increasing risk for certain cancers such colon and breast, but there were conflicting reports regarding most other cancers.
Calle et al (2003) studied a population of more than 900,000 adults from the years 1982-1998. Previous reports linking obesity to increased cancer risk had been conflicting for some sites (pancreas, prostate, liver, cervix, and ovary). For other sites where the results had been more consistent (endometrium, kidney, and colon) the magnitude of the risk was unknown. For these reasons the aim of the study was to determine the association of BMI and risk of death from cancer at individual sites.
Since cancer risk increases with age, participants were enrolled if everyone in the household was at least 30 years old and if at least one household member was 45 years old. By the end of the study, 24.0% of the participants had died. Of those, 32,303 men had died from cancer and 24,842 women had died from cancer.
For men and women, there was a positive, linear trend in mortality rates with increasing BMI for all cancers and for cancers of the colon, liver, gallbladder, pancreas, and non-Hodgkin’s lymphoma. Risk for cancer of the esophagus, stomach, prostate, and kidney also increased with BMI in men. Risk for cancer of the breast, uterus, cervix, and ovaries increased with BMI in women. There were no significant associations between
BMI and mortality from brain cancer, bladder cancer, and melanoma in men or women.
3 The researchers cited the number of cancer deaths in the cohort “sufficient to
permit only the death rates from all cancers to be examined separately for the two highest
body-mass index categories.” Therefore, the researchers were able to calculate the
relative risk of death from any cancer only in the BMI categories of 35.0 to 39.99 (Obese
Class I) and 40.0 or more (Obese Class II) in men and women. It should be noted that
these are the two highest classifications of obesity outlined by the WHO. For example, a
woman who stands 1.6 m (64 in) tall would have to weigh 105.2 kg (232 lb) to have a
BMI of 40.0. Although 68% of the adult population is overweight or obese, only 5.7% of
women have a BMI greater than 40.0 and 14.3% of men have a BMI greater than 35.0.
The relative risk of mortality from any cancer was 1.23 men with a BMI of 35.0 to 39.99,
and 1.52 for those with a BMI higher than 40.0. Women with a BMI 40.0 or greater had
a relative risk of 1.62.
Importantly, Calle et al. (2003) speculated that previous conflicting reports regarding the association between obesity and cancer risk may have been attributable to confounding factors unaccounted for such as smoking, which is in itself a significant risk factor for cancers at multiple sites. To avoid this pitfall, the researchers evaluated the possible association of BMI and cancer mortality in a subgroup of the population that had
never smoked. When the researchers analyzed the relation between BMI and cancer
mortality in the subgroup of the cohort that had never smoked, they discovered a positive
association of even a greater magnitude than in smokers. In individuals who had never
smoked the relative risk of cancer death was 1.38 for men with a BMI of at least 30, and
1.88 for women with a BMI of at least 40. The researchers concluded that public health
recommendations regarding BMI and cancer risk should be based on studies in 4 populations of nonsmokers. They estimated that the proportion of deaths from cancer
attributable to overweight and obesity in men was 4.25%-14.2%. The estimate for
women, 14.3%-19.8%, was strikingly higher than men. The lower end of the estimates is
more applicable to the overall population, whereas the higher end is more applicable to
nonsmokers. The marked difference in risk by sex is most likely attributable to increased
relative risk of breast (2.12) and uterine (6.25) cancer which account for roughly 18% of
all cancer deaths in women.
The authors concluded that approximately 90,000 deaths per year from cancer
may be avoided if Americans could maintain a BMI of < 25.0 throughout adulthood.
This estimation spurred an already burgeoning area of cancer research aimed at
identifying the underlying mechanisms linking overweight and obesity with increased
cancer mortality.
1.4 Possible Mechanisms Underlying the Obesity-Cancer Connection
Excess adipose tissue results in profound metabolic disturbances that have been
linked to tumor promotion (5). These metabolic disturbances result from the failure of
the overly enlarged adipocytes to execute insulin-stimulated uptake of excess energy.
Chronic hyperinsulinemia ensues which is associated with increased bioavailability of the
mitogenic hormone insulin-like growth factor 1 (IGF-1)(6). Serum estrogen levels are
also elevated by obesity in post-menopausal women (7). Finally, insulin-resistant adipocytes secrete pro-inflammatory factors (8) that have been linked to proliferation of transformed cells and angiogenesis (9). The following section will discuss these
5 metabolic disturbances in greater detail as they relate to colon and breast cancer. We chose to focus on colon and breast cancer because they are the cancers with the highest death rates that are associated with obesity in men and women, respectively (4).
1.4.1 Chronic Hyperinsulinemia and IGF-1
The risk of developing hyperinsulinemia, a precursor to type 2 diabetes, is positively associated with BMI (10, 11). Hyperinsulinemia occurs when tissues fail to increase uptake of glucose and fatty acids in response to insulin. This results in the increased secretion of insulin from the pancreas to compensate for chronically high levels of blood glucose (12).
A recent meta-analysis concluded that hyperinsulinemia increases the risk of colon cancer in men by 2.34 (highest levels of insulin vs. lowest levels of insulin) (13).
In the same study, no effect of hyperinsulinemia on the risk of colon cancer was observed in women. However, a recent prospective study found insulin levels were positively associated with an increased risk of breast cancer (hazard ratio [HR] for highest versus lowest quartile of insulin level = 1.46) in postmenopausal women (14).
The effect of hyperinsulinemia on carcinogenesis is mediated in part through increased IGF-1 bioavailability, a mitogenic hormone, by decreasing production of IGF-1 binding proteins 1 and 2 (6). Due to the 40-60% sequence homology between the IGF-1 receptor (IFG-1R) and insulin receptor (IR) there can be crosstalk between these pathways (15). The IR and IGF-1R signal transduction pathways induce growth and proliferation as well as exert anti-apoptotic effects (6). Overexpression of IGF-1R has
6 been detected in multiple cancers, including colon and breast (16). In a recent study, total
IR levels and activated levels of IGF-1R were indicative of poor survival in breast cancer patients (17).
1.4.2 Estrogen
Estrogen is a steroid hormone produced mainly by the ovaries. As a lipid, estrogen can diffuse freely into cells and bind to one of its two nuclear receptors, estrogen
receptor alpha (ERα) or estrogen receptor beta (ERβ). These receptors mediate the
genomic effects of estrogen, resulting in increased proliferation of normal and
transformed breast cancer cells.
A sizable body of work suggests that the amount of estrogen exposure over the lifetime can affect risk of developing breast cancer (18). For example, early age at menarche and later age at menopause constitute a longer exposure to estrogen over the lifetime which is associated with a higher risk of developing breast cancer. After menopause, the primary source of estrogen production is adipose tissue (19).
Consequently, levels of estradiol and estrone are positively associated with BMI in postmenopausal women (20). Thus, exposure to estrogen beyond menopause can be prolonged by obesity. Several studies have shown that postmenopausal women with elevated levels of circulating estrogen are at increased risk for breast cancer (21, 22). In fact, a recent meta-analysis concluded that women with the highest levels of serum estrogen levels had a doubling of breast cancer risk.
7 Estrogen production in the adipose tissue is mediated by aromatase which
converts androgens into estrogen (23). Aromatase activity originating in the adipose
tissue of the breast can produce estrogen levels that are 20 fold higher in the breast
tumors than in circulation (24). Aromatase inhibitors effectively lower estrogen levels
and have been shown in clinical trials to be as effective as Tamoxifen, a selective
estrogen receptor modulator, at preventing recurrence of breast cancer. Therapeutic
and/or behavior interventions aimed at decreasing circulating estrogen levels in
postmenopausal women are viable strategies for reducing breast cancer risk.
1.4.3 Oxidative Stress
Reactive oxygen species (ROS) are generated continuously at low levels as a natural byproduct of aerobic respiration. ROS react with DNA, cellular proteins, and
membrane lipids to cause cellular damage. Oxidative stress occurs when the rate of free
radical production overwhelms the antioxidant capacity of the cell. This antioxidant
capacity is determined by expression and activity of antioxidant enzymes, such as
catalase and superoxide dismutase, as well as the abundance of dietary antioxidants such
as vitamins E and C.
Oxidative stress markers are positively associated with increased risk of colon
and breast cancer (25-30). Oxidative stress has been shown to be increased in insulin
resistant adipose tissue, and circulating markers of oxidative stress such as protein carbonyls (oxidized proteins) and 8-isoprostanes (oxidized lipids) are increased in persons with a BMI >30.0 (31, 32). For example, results from the Shanghai Women’s
8 Health Study show that urinary isoprostanes were positively associated with breast cancer
risk among women who were overweight (33). In fact, the odds ratio for breast cancer
incidence among women with a BMI > 29.0 was 10.23 for those in the highest tertile of
urinary isoprostanes compared to those in the lowest tertile.
1.4.4 Inflammation
Chronic, local inflammation has been linked to several cancers. For example,
pancreatitis, Barrett’s esophagus, and inflammatory bowel disease greatly increase risk for cancer of the pancreas, esophagus, and colorectal cancer respectively (9). However,
less is known as to what extent the systemic inflammation induced by obesity increases cancer risk.
The systemic inflammation associated with obesity is thought to be mediated by pro-inflammatory cytokines such as tumor necrosis factor alpha (TNF-α), monocyte
chemoattractant protein 1(MCP-1), and interleukin (IL)-6 that are secreted by the adipose
tissue (8, 34, 35). Some of these cytokines are produced by the adipocytes themselves
and others by infiltrating macrophages (36). At the same time, there is a decrease in
secretion of the anti-inflammatory adipokine, adiponectin (37). This pathological
endocrine profile characterizes the state of chronic low-grade inflammation that has
become synonymous with obesity. In fact, C-reactive protein, a serum marker of
inflammation, is elevated in people with higher BMI, as is serum IL-6 and TNF-α.
Furthermore, increases in these inflammatory markers are linked with increased cancer
risk (38).
9
Illustration 1.1 Possible factors linking obesity to increased cancer risk
10
1.5. The effect of calorie restriction on cancer risk
Calorie restriction has been shown in animal models of carcinogenesis to be as protective as obesity is deleterious in humans. There is a paucity of human studies
examining biological responses to long term calorie restriction. However, there are
results from “natural” studies regarding cancer risk and calorie restriction. For example,
in a Swedish study, there was 53% lower incidence of breast cancer in women
hospitalized for anorexia nervosa before age 40 (39). A prospective study analyzing the
effect of CR on overall mortality and incidence of age-related diseases in rhesus monkeys is still underway. The most recent report indicates that incidence of cancer in CR monkeys is reduced by 50% compared to control animals (40).
CR has been shown to be increase tumor latency and reduce tumor burden in
spontaneous, transplanted, and chemically induced models of colon and breast cancer
(41-44). Studies have shown that the protective effects of CR may be mediated by
lowered levels of circulating IGF-1, insulin, pro-inflammatory molecules, and oxidative
stress (45).
1.6. Studying Cancer Risk and Exercise
There are many obstacles to identifying associations between cancer risk and
exercise. Several confounding factors exist that may obfuscate any existing relationship
between exercise and cancer risk. Such factors may include, but are not limited to,
11 smoking; a history of hormone replacement; dietary intake of fats, fruits, and vegetables; age; alcohol use; and BMI. For example, people who exercise are more likely to maintain a normal weight and choose healthier foods, making it difficult to test the hypothesis that it is exercise that decreases risk independent of other associated lifestyle factors. Other factors may act as effect modifiers, further complicating connections between exercise and cancer. For example, a family history of breast or colon cancer may minimize or overcome any protective effect of exercise (46).
Still another complication arises from the fact that a variety of different methods are used for measuring exercise intensity, making meta-analyses especially challenging. Some studies group people into quintiles of activity from least to most active according to minutes per week that a person is active. Other investigators convert reported physical activity into Metabolic Equivalent Tasks (METs). One MET is the energy expenditure and caloric requirement at rest. This allows researchers to affix a standard unit of measurement to assess the energy expended during two very different activities, such as bowling (2.3-3 METs/hr) and running at a pace of 10 K/hr (10
METs/hr). Finally, studies vary according to when in the course of a lifetime exercise may or not confer protection from cancer later in life. For example some case-control studies ask patients diagnosed with cancer how much they exercised during the year or two prior to diagnosis. Other prospective studies have followed a cohort for several years, asking participants periodically for an estimation of exercise intensity. Still others have analyzed the effect of exercise frequency during adolescence, college, and after the age of 50 on cancer risk later in life.
12 1.6.1 Breast cancer and exercise
Despite the wide range of study designs, methods, and populations used to
determine the relationship between exercise and cancer risk, a 20-40% risk reduction for
breast cancer is consistently observed (47). For breast cancer risk, there are effect
modifiers that can undermine the benefits of exercise on cancer risk. For example, some
studies have shown that exercise conferred protection only to normal weight women and
not to obese women. In fact, one study found no protection in women with a BMI greater
than 28.4 kg/m2 (48). Also, exercise does not reduce risk in those with a family history
of breast cancer. Finally, the amount of protection conferred resulting from exercise
depends on the duration. A moderate reduction of 20% has been observed in women who walked approximately 45-60 min/day, 5-6 days/week, whereas a greater reduction was
observed in women who exercised 3-4 h/week at a moderate or higher intensity (48).
It is interesting to note while exercise is not protective against risk for
developing breast cancer in overweight and obese women, studies have shown that
exercise is protective against breast cancer recurrence regardless of BMI (49, 50).
Furthermore, reductions in mortality were observed in women with all stages of disease.
For example, women with stage III breast cancer that exercised for ≥9 MET hrs/week had
a relative risk of 0.36 compared to women who exercised for <9 MET hrs/week.
However, those who benefited the most from physical activity were women with
hormone responsive tumors (49).
13 Taken together, these studies suggest that although exercise is widely
regarded as an effective way to reduce breast cancer risk and mortality, its ability to
confer protection is highly variable.
1.6.2 Colon cancer and exercise
As in breast cancer, a 30-40% reduction in colon cancer is consistently observed
in active individuals compared to sedentary persons (51). In contrast, to reduce colon
cancer risk, more intense activity is required to achieve a risk reduction comparable to the
magnitude seen for breast cancer. For example, as mentioned above a reduction in
breast cancer risk is observed with brisk walking, whereas colon cancer reduction
requires vigorous activity such as slow jogging 45 min/day, 5 days/week (52). Also, as in breast cancer, exercise is beneficial even after diagnosis of disease as indicated by two recent studies in which the hazard ratio of disease-free survival was less than 0.51 for those who participated in more than 18 MET hrs/week compared to sedentary persons
(<3 MET hrs/week) (53, 54).
1.7 Possible mechanisms underlying physical activity and cancer
Physical activity is thought to mediate its protective effects by lowering many of the hormones that are elevated by obesity. The following sections discuss how exercise impacts obesity-related hormones that have been linked to cancer risk.
1.7.1 Effects on circulating estrogen
14 The primary hypothesis explaining the effects of physical activity on estrogen
levels is that weight loss results in decreased amounts of adipose tissue, the main site for
estrogen aromatization from androgen precursors. In support of this hypothesis is the finding that BMI is positively associated with free estradiol and negatively associated with serum hormone binding globulin in postmenopausal women. However, evidence
from intervention studies is conflicting. In fact, two different 12-month randomized,
controlled trials in sedentary, postmenopausal women performed by the same
investigator, Anne McTiernen, produced different results. In the first study, physical
activity lowered circulating levels of estradiol by 16.7% only in exercising women who
experienced a decrease in percent body fat, supporting the hypothesis that decreases in
estrogen are dependent on decreases in adiposity (55). In the second study, exercise
significantly reduced levels of circulating free estradiol, and this effect was unchanged by
adjustments for weight loss (56). The authors concluded that exercise may have “effects
independent of a change in adiposity, including a reduction in insulin levels, which, in
turn, increases SHBG levels and decreases estradiol bioavailability.” Furthermore, the
authors concluded that the clinical impact of a mean 1.2 pg/ml decrease in estradiol levels
was most likely very small. It may be that estrogen levels would continue to decrease
over time with continued physical activity. However, the relatively small change in
estradiol levels, although statistically significant, suggests that physical activity exerts its
protective affects through changes in other hormones.
1.7.2 Effects on hyperinsulinemia and IGF-1
15 Exercise improves insulin sensitivity in the skeletal muscle thereby reducing
elevated levels of circulating insulin for up to four hours following a single bout of
exercise (57). Therefore, it is hypothesized that regular exercise reduces levels of circulating insulin, a powerful mitogen that suppresses apoptosis and stimulates proliferation. Indeed, exercise has been shown to reduce fasting insulin levels in overweight, postmenopausal women, in healthy middle aged men, and men diagnosed with metabolic syndrome (58-62). On the other hand, there are reports of exercise
interventions having no effect on circulating insulin levels in obese subjects and breast
cancer survivors (63, 64). Conflicting results may be attributed to differences in duration
of intervention (ranging from 12 weeks to 12 months), subject adherence to prescribed
exercise protocol, and intensity of exercise performed.
Results from animal studies are more consistent than results from human
studies. Not only has exercise been shown to decrease circulating levels of insulin in rats
and mice, but reductions in circulating insulin have been associated with increased tumor
latency and decreased tumor burden in chemical and genetic models of breast and colon
cancer. For example, rats participating in volunteer, nonmotorized wheel running had
significantly lower insulin levels than sedentary rats. This reduction corresponded to
lower incidence and smaller size of chemically induced mammary tumors (65).
Evidence regarding connections between exercise and IGF-1 levels is
conflicting in the human and animal literature. In a 12-month randomized, clinical trial
there was no effect of exercise on serum IGF-1 levels in postmenopausal women (66).
However, Fairey et al. reported a decrease in serum IGF-1 levels, an increase in IGFBP-
3, and a decrease in molar ratio of IGF-1/IGFBP-3 in postmenopausal breast cancer 16 survivors after a 15 wk exercise intervention (64). Results from exercise studies in
rodents on the effects of IGF-1 are also conflicting. Although Zhu et al reported
significantly decreased IGF-1 levels in exercised female rats (65), Colbert et al. reported
that exercise did not have an effect on IGF-1 levels in exercised female mice (67). In two
different studies analyzing the effect of volunteer wheel running on polyp number using
ApcMin mice, exercise reduced the number of polyps. However, Colbert et al. found that
exercise actually increased circulating levels of IGF-1 (68). In contrast, Ju et al. found a
decrease in the molar ratio of serum IGF-1 to IGFBP-3, indicating that exercise resulted
in a decrease in bioavailability of IGF-1(69).
To summarize, although serum insulin levels have been consistently shown to
be reduced by exercise in humans and rodents, the effect of exercise on IGF-1 levels is
unclear.
1.7.3 Oxidative stress and inflammation
In general, markers of oxidative stress and inflammation are lowered by exercise in humans and mice (70-75). For example, in a 12-month randomized, controlled trial overweight or obese, postmenopausal women who significantly improved aerobic fitness, exhibited a 14.1% decrease in urinary 8-isoprostane compared to sedentary controls (76).
This decrease in oxidative stress was observed with minimal changes in body composition. In another study among overweight or obese individuals with metabolic syndrome, decreased levels of oxidative stress were observed in those who experienced at least a 10% weight reduction (58). Likewise, markers of inflammation are reduced in
17 response to exercise. Exercise interventions in obese, postmenopausal women as well as older, obese men consistently reduce circulating levels of C-reactive protein, MCP-1, and
IL-6 (58, 72, 76). Animal studies report that exercise decreases markers of macrophage
infiltration into adipose tissue of obese mice and rats as well as decreased production of
pro-inflammatory adipokines such as TNF-alpha and MCP-1 (73-75). Collectively, these
studies show that inflammation and oxidative stress, which are putative biological
stressors linking obesity to cancer risk, are highly responsive to exercise interventions.
1.8 Obesity and tumor metabolism
As previously discussed, obesity results in chronically elevated levels of
circulating mitogenic hormones. These hormones are known to stimulate signal
transduction pathways which initiate transcription of genes that support aerobic
glycolysis and proliferation [for review see (77)]. In 1924 Otto Warburg hypothesized
that the reliance of cancer cells on aerobic glycolysis for energy production rather than on
oxidative phosphorylation was the primary cause of cancer (78). Although Warburg was
incorrect—metabolic transformation in not an initiating event—his observation that
cancer cells preferentially utilize aerobic glycolysis has been dubbed the “7th hallmark of
cancer” because the rate of glucose metabolism is so closely correlated with
transformation.
The tumor suppressor p53 upregulates transcription of genes that support
oxidative phosphorylation (synthesis of cytochrome c oxidase and TP53-induced glycolysis and apoptosis regulator) (79, 80) and downregulates transcription of genes that
18 support aerobic glycolysis (glucose transporters)(81). In contrast, the phosphatidylinositol 3-kinase (PI3K)/Akt/mTOR pathways upregulates transcription of
genes that promote glucose uptake (77). This very pathway is highly responsive to the
obesity-associated hormones insulin, IGF-1, and leptin. There is considerable interest in understanding what combinations of oncogene activation and loss of tumor suppressors are favorable for the metabolic transformation utilized by cancer cells. Our lab is interested in how obesity affects this transformation.
1.9 Dissertation objectives
Given that overweight and obesity increases risk for breast and colon cancer and that 2/3 of American adults have a BMI >25.0, we can expect mortality rates from these cancers to increase in the upcoming decades unless significant strides are made towards breaking the obesity-cancer link. Therefore, the primary focus of my work was to evaluate the effectiveness of energy balance interventions in reducing cancer risk.
Since weight loss is so difficult to achieve and maintain, there is considerable interest in intervention strategies that ameliorate obesity-related risk factors independent of weight loss. To this end, we were intrigued by our finding from a previous study (82) that a low-carbohydrate diet tended to reduce serum IGF-1 levels in obese, male C57Bl/6 mice. We hypothesized that a significant reduction in serum IGF-1 levels, even in the context of obesity, would reduce tumor burden in a transplant model of colon cancer. To
test this hypothesis we utilized a mouse model of diet induced obesity to analyze the effect of a low-carbohydrate diet on serum IGF-1 levels and growth of transplanted colon
19 cancer cells. Surprisingly, although the low carbohydrate diet did significantly reduce serum IGF-1 levels, there was no effect on tumor growth. This finding supports the broader hypothesis that multiple biological factors mediate the cancer risk conferred by obesity and that interventions must be multitargeted to be successful.
Therefore, we turned our attention to more conventional interventions that have been shown to reduce cancer risk, namely calorie restriction and exercise. Although both have been shown to reduce hormones associated with cancer risk, it is unknown how these interventions impact gene expression in adipose tissue, the organ that is at the center of metabolic deregulation in the obese state. We hypothesized that calorie restriction and exercise would have differential effects on gene expression in adipose tissue because our laboratory had previously observed differential effects on gene expression in murine mammary glands (83).
To test our hypothesis, we used ovariectomized mice to model the postmenopausal state because in postmenopausal women obesity increases breast cancer risk. Following eight weeks of diet induced obesity, mice were either exercised (EX) or calorie restricted (CR) for another eight weeks. Microarray analysis of visceral white adipose tissue collected after the eight wk intervention revealed that CR exerted a more potent effect on gene transcription in visceral white adipose tissue than EX. More specifically, CR altered expression of more than 20 times the number of genes in the adipose tissue than were uniquely affected by EX. Of those genes, there was a striking upregulation of carbohydrate metabolism and transport genes which correlated with the increased insulin sensitivity. One of these genes, Slc2a4, codes for the insulin-responsive glucose transporter (Glut) in adipose tissue. We discovered a positive association 20 between acetylation of histone 4 at the promoter of Glut4 and increased transcription of this gene in CR mice. This raises the possibility that other genes critical to metabolism and glucose homeostasis may be transcriptionally regulated through changes to the histone code. Finally, this evidence suggests that adipose tissue may be more sensitive to decreases in energy intake rather than increases in energy expenditure. This was not the first piece of evidence that CR and EX can exert strikingly differential effects on biological outcomes related to energy balance.
Previously, our laboratory observed a differential impact of CR and EX on tumor latency in a spontaneous model of mammary cancer in which the Wnt-1 oncogene is constitutively expressed in the mammary gland (67). In this model, CR significantly delays tumor development independent of p53 gene status (a tumor suppressor gene found to be altered in ~50% of human breast cancers). Surprisingly, treadmill running decreased tumor latency compared to sedentary controls. As discussed in section 1.6.2, epidemiological studies show that the cancer protective effects of exercise are highly variable. We hypothesized that exercise, in the context of p53 deficiency, may actually promote tumor growth.
Several reports in the literature indicate a role for p53 in regulating cellular levels of oxidative stress. Furthermore, oxidative stress is increased in the obese state.
Therefore our aim was to characterize levels of oxidative stress in mammary glands of obese, overweight, and lean mice and then determine the effect of exercise on oxidative
DNA damage in obese mice after 20 weeks of treadmill running. We discovered that in obese mice, p53 deficiency was associated with increased oxidative DNA damage in the mammary gland and higher levels of circulating 8-isoprostane, a marker for oxidized 21 lipids. However, we did not observe an effect of exercise on oxidative DNA damage in
the mammary fat pad in obese mice. Even though we did not see differences in damage,
we hypothesized that there may be differences in the response to exercise by p53
genotype. To test this hypothesis, we conducted PCR array to analyze differences in
mRNA expression of genes related to oxidative stress. From these analyses, we
identified a gene, Thioredoxin-interacting protein (Txnip) that was increased more than
two fold in the mammary fat pad of p53 +/- mice compared to p53 +/+ mice. Using
siRNA to knockdown p53 in MCF-7 breast cancer cells, we were able to show that Txnip
levels increase more than 2 fold. This induction is associated with indicators of aerobic glycolysis, a metabolic transformation often exhibited by cancer cells. From these data, we concluded that Txnip induction occurs in response to the metabolic deregulation associated with loss of p53. This finding contributes to a newer, burgeoning field of
cancer research dedicated to understanding how aerobic glycolysis supports tumor
progression.
22 Chapter 2: Low-Carbohydrate Diet versus Caloric Restriction: Effects on Weight Loss, Hormones and Colon Tumor Growth in Obese Mice
2.1 Introduction
The percentage of people who are overweight or obese is increasing in the US
(84). Obesity is associated with increased cancer incidence and mortality and accounts
for an estimated 90,000 cancer deaths each year (4). The cancers most strongly associated
with obesity include colorectal, postmenopausal breast, endometrial, hepatic, kidney and
pancreatic (4). Of these, colorectal cancer is the second most common cause of cancer-
related death in the western world (4, 85). The mechanisms linking obesity and colon
cancer are unclear, but increased serum insulin and insulin-like growth factor (IGF-1),
insulin resistance, and inflammation are thought to be involved (5, 6).
Numerous studies implicate IGF-1 as a risk factor for colorectal cancer [For a
review please see: (6)]. Colon cancer cells overexpress the IGF-1 receptor (86) which,
when activated, inhibits apoptosis signaling and promotes progression through the cell
cycle (87). This is believed to increased proliferation of colonic epithelia and colon
cancer mortality rates as seen in individuals diagnosed with acromegaly, a condition
marked by elevated levels of serum IGF-1 (88). High circulating IGF-1 levels have been
linked to colon cancer in healthy individuals as well. For example, participants in the
Physician’s Health Study with a serum IGF-1 level in the first quintile had a relative risk
of 2.51 for developing colon cancer when compared to those in the fifth quintile (89).1
1 Chapter 2 is an electronic version of an article published in Wheatley KE, Williams EA, Smith N, Dillard A, Park, EU, Nunez NP, Hursting SD, Lane MA: Low carbohydrate diet versus calorie restriction: Effects 23 Therefore, serum IGF-1 is an attractive target for lowering colon cancer risk especially
since IGF-1 levels may be modified through dietary changes.
Previously, we have shown that caloric restriction (CR), accomplished by
providing a high-carbohydrate, low-fat, reduced-energy diet, decreases body weight in
obese mice (82, 90). CR results in lower serum levels of fasting insulin, IGF-1, and
leptin as well as decreased levels of multiple markers of inflammation when compared to
obese mice {Mai, 2003 #105; Park, 2005 #106; Yakar, 2006 #135; (82)}. These effects
are associated with the suppression of spontaneous, chemically induced and transplanted
tumors (91), including reduced MC38 colon tumor growth in mice (90). We have also
shown that a low carbohydrate/high fat ketogenic (LC) diet, which mimics the induction
phase of a popular low-carbohydrate diet, does not reduce weight in diet-induced obese
mice, but does tend to reduce serum IGF-1 levels (82). These results suggest that
although the LC diet does not induce weight loss, it may reduce colon cancer risk by
lowering serum IGF-1 levels.
To our knowledge, existing studies relating cancer to a LC diet are limited to
brain tumors (92-94). For example, the LC diet effectively reduced the growth of
transplanted astrocytomas in mice (92). In addition, a LC diet successfully treated two
pediatric patients with malignant astrocytomas (94). However, because brain tissue relies
on glucose for energy, and brain tumor growth may be especially responsive to ketone
bodies, it is not clear if the protective effects of the LC diet apply to epithelial-derived
cancers (95). To test if the LC diet is as effective as CR at suppressing colon cancer
on weight loss, hormones, and colon tumor growth in obese mice. Nutrition and Cancer 60, 61-67, 2008. Nutrition and Cancer is available online at http://www.ncbi.nlm.nih.gov/pubmed/18444137. 24 progression, we compared the effects of a LC diet, high-carbohydrate (HC) diet, high fat
(HF) diet, and a HC-CR diet on the growth of transplanted MC38 colon cancer cells in
diet-induced obese mice.
2.2 Materials and methods
2.2.1 Animals and Diets
Five-week old, male, C57BL/6 mice (n=80) were obtained from Charles River
Laboratories, Inc. (Animal Production Area, NCI-Frederick, Frederick, MD) and placed
on a chow diet (Harlan Diets #2018, Madison, WI) for one week upon arrival. All mice were housed individually, had ad libitum access to water, and were on a 12 h light/dark
cycle. The experimental diets were generously formulated and donated by Drs. Matt
Ricci and Ed Ulman of Research Diets, Inc. (New Brunswick, NJ). All animal protocols
were approved by the University of Texas at Austin IACUC, protocol #05083001.
For the first seven weeks of the study, all 80 mice had ad libitum access to the
diet-induced obesity (DIO) regimen (20% carbohydrate, 20% protein, 60% fat; Table
2.1). At the beginning of week 8, mice were randomized to receive either the HC, LC, or
HC-CR regimens or continue on the DIO diet. The LC group consumed a diet similar to
the induction phase of a popular low-carbohydrate diet (5% carbohydrate, 35% protein,
60% fat; Table 2.1) ad libitum. The formulation of the LC diet reflected that consumed
by humans on low-carbohydrate diets as reported in a recent meta-analysis study (96).
This formulation was also used in two recent rodent studies comparing weight loss
efficacy due to CR to low carbohydrate/high fat versus high-carbohydrate/low-fat diets
25 (82, 97). The HC group in the present study consumed a high-carbohydrate diet (70% carbohydrate, 20% protein, 10% fat; Table 2.1) ad libitum for the remainder of the study.
The HF group continued on the DIO regimen for the remainder of the study.
26 Table 2.1 Diet Composition
Diet Group HC DIO LC HC-CR Product Number D12450B D12492 D03062303R D04082701 Macronutrient Composition gm% kcal% gm% kcal% gm% kcal% gm% kcal% Protein 19 20 26 20 46 35 19 20
Carbohydrate 67 70 26 20 7 5 67 70
Fat 4 10 35 60 35 60 4 10
Total kcal/g 3.8 100 5.2 100 5.2 100 3.8 100
Ingredient gm kcal gm Kcal gm kcal gm kcal Casein, 80 Mesh 200 800 200 800 350 1400 200 800
L-cysteine 3 12 3 12 5 20 3 12
Corn Starch 315 1260 0 0 0 0 315 1260
Maltodextrin 10 35 140 125 500 42 168 35 140
Sucrose 350 1400 68.8 275 0 0 357.2 1429
Cellulose, BW200 50 0 50 0 50 0 50 0
Soybean Oil 25 225 25 225 25 225 25 225
Lard 20 180 245 2205 245 2205 20 180
Mineral Mix, S10026 10 0 10 0 10 0 14.67 0
DiCalcium Phosphate 13 0 13 0 13 0 18.98 0
Calcium Carbonate 5.5 0 5.5 0 5.5 0 8.03 0
Potassium Citrate, 1 H20 16.5 0 16.5 0 16.5 0 24.09 0
Vitamin Mix, V10001 10 40 10 40 10 40 10 40
Vitamin Mix, V10001C 0 0 0 0 0 0 0.45 2
Choline Bitartrate 2 0 2 0 2 0 3 0
FD&C Yellow Dye #5 0.05 0 0 0 0 0 0 0
FD&C Red Dye #40 0 0 0 0 0.05 0 0.025 0
FD&C Blue Dye #1 0 0 0.05 0 0 0 0.025 0
Total 1055.05 4057 773.85 4057 774.05 4058 1084.50 4088 27 The feed intake of the HC-CR group was restricted to provide 70% of the average kJ/d
consumed per mouse by the HF group. The HC-CR group consumed a high- carbohydrate diet (70% carbohydrate, 20% protein, 10% fat; Table 2.1) that was supplemented with 30% more vitamins and minerals to provide 70% of the energy but
100% of the micronutrients consumed by the HF group.
Body weight was measured weekly throughout the entire study. Feed intake of
the HC, HF and LC groups was measured every other day. During weeks 8–14, the HC-
CR group was fed daily aliquots and their feed intake was recorded at that time. The LC and HF diets were stored at -20°C and thawed in aliquots sufficient for one feeding to
prevent rancidity.
At the beginning of week 15, mice were fasted overnight with ad libitum access to
water. Following the 12 h fast, mice were anesthetized with isofluorane and blood was
collected via retro-orbital venipuncture. Whole blood was allowed to clot at room
temperature prior to centrifugation at 1000 x g for 20 min. The serum was removed and
stored at -20°C. Following venipuncture, 1.0 x 105 MC38 cells were injected
subcutaneously in the right flank. MC38 cells are a murine colon adenocarcinoma cell
line isolated from C57BL/6 mice (98). All mice remained on their respective diets until
termination of the study.
Mice were palpitated for tumors twice a week. Using calipers, the length and
width of the tumors were measured. When a tumor reached a surface area of 200 mm2,
the entire group was scheduled for sacrifice the following week. Mice were killed by
cervical dislocation. Carcasses were stored frozen at -20˚C.
28 2.2.2 Serum IGF-1 and Leptin Levels
Serum IGF-1 levels were determined by radioimmunoassay as per manufacturer’s
instructions (DSL-2900 kit, DSL Laboratories, Webster, Tx). Serum leptin levels were
determined using a Lincoplex, bead-based assay as per manufacturer’s instructions
(Linco, St. Charles, MO).
2.2.3 Percent Body Fat Analysis
Percent body fat was assessed on each mouse carcass by dual-energy X-ray
absorptiometry (DEXA) (GE Lunar PIXImus II, Madison, WI) as described in (99). The
head of each mouse was eliminated from the scan by using the region of interest
exclusion option provided in the software.
2.2.4 Statistical Analysis
All values shown are mean + SEM of n=20/group unless otherwise indicated. All
statistical analyses were conducted using SPSS (Apache Software Foundation,
Wilmington, DE, version 12.0 for Windows). Outliers were identified and excluded
using box-plot analysis. Univariate ANOVA was used to determine differences between
groups for body composition, tumor size, and hormone levels. Repeated measures
ANOVA was used to determine differences between groups for body weight over time.
Results were considered significant if P < 0.05. ANOVA was followed by Tukey’s HSD post-hoc test to determine significant between group differences.
29 2.3 Results
2.3.1 Body Weight Analysis
At the beginning of the study, the cohort of 80 mice had a mean body weight of
21.9 ± 0.31 g (Fig. 2.1). Following 7 weeks of the DIO regimen, the mean body weight for all mice increased by 10.4 ± 1.2 g to 32.3 ± 0.88 g and there was no significant difference in body weight between the four dietary groups (Fig. 2.1). Tumor cells were injected at the beginning of week 15, therefore weight gain and feed intake are discussed from weeks 8-14 below to ensure that tumor load did not affect body weight and intake.
DEXA analysis of body percent fat requires immobility and was thus performed on carcasses after sacrifice at 23 weeks. During weeks 8-14, the HC-CR group lost 10.6 ±
1.4 g of body weight (Fig. 2.1) and had the lowest percent body fat (29.4 ± 0.9%; Fig.
2.2) at the end of the study (week 23). The HC group did not experience a significant weight gain or loss from weeks 8-14 (Fig. 2.1). The HC group consumed fewer total calories during weeks 8-14 (553 kcal; Fig. 2.2) and had a lower percent body fat at the end of the study (40.2 ± 1.8%) than the HF and LC groups (Fig. 2.2). The HF and LC groups both increased in body weight from weeks 8-14, gaining 8.5 ± 2.2 and 6.9 ± 2.3 g, respectively (Fig. 2.1). Although the LC group consumed slightly more total calories than the HF group from weeks 8-14 (618 versus 583 kcal, respectively; Fig. 2.2), the HF and LC groups did not differ in body weight (40.4 ± 1.3 versus 39.0 ± 1.4g, respectively;
Fig. 2.1) at time of tumor injection or in percent body fat (50.9 ± 2.4 versus 47.7 ± 1.6%, respectively; Fig. 2.2) at week 23. Finally, both the HC and HC-CR groups weighed significantly less than the HF and LC groups from weeks 11-23 (Fig. 2.1). These data 30 show that only the HC-CR regimen successfully reversed the weight gain induced during the first seven weeks of DIO.
31 45
40
35 + + + 30 + + + + + + + + + 25 * 20 * * * * * * * * * * * 15 Body Weight(g) 10
5
0 0 2 4 6 8 10 12 14 16 18 20 22 24
Week
Figure 2.1 Mean body weight per group of mice on 4 dietary regimens for 23 wks.
Mean body weight ± SEM of mice in group HF(▲), LC(▼), HC(♦), and HC-CR(●) (n=19 for HF and HC groups). *, +Significant differences between groups (P < 0.05).
32 A
B
C
Figure 2.2 Energy intake and body composition
(A) Total energy intake for weeks 8-14. (B) Average body weight at time of tumor injection. (C) Average % body fat at the end of week 23. Data shown are mean ± SEM. Significant differences (P < 0.05) are indicated by different superscripts (n=19 for HF and HC groups).
33 2.3.2 Serum IGF-1
Mice consuming the LC, HC, and HC-CR diets displayed significantly lower serum IGF-1 levels (Fig. 2.3) when compared to mice consuming the HF diet (135 ± 56,
259 ± 25.0, and 320 ± 39.9 versus 604 ± 44.2 ng/ml, respectively). In addition, there were no significant differences in serum IGF-1 levels between mice on the HC and LC dietary regimens. Mice on the HC-CR diet had significantly lower serum IGF-1 levels than mice on the LC diet. There was no difference in serum IGF-1 levels between levels
HC-CR and HC dietary groups (Fig. 2.3). These data indicate that serum IGF-1 levels are affected not only by weight loss, as exhibited by the HC-CR group, but also by differences in macronutrient intake, as demonstrated by the decrease in serum IGF-1 concentration displayed by the mice consuming the LC and HC diets, which did not induce weight loss, relative to the mice consuming the HF diet.
2.3.3 Serum Leptin Serum leptin levels were significantly lower in mice fed the HC and HC-CR diets (7.0 ± 0.7 and 1.0 ± 0.3 ng/ml, respectively) when compared to mice consuming the
HF and LC diets (15.6 ± 2.2 and 12.3 ± 2.0 ng/ml, respectively; Fig. 2.3). Serum leptin levels did not differ between the HF and LC groups (Fig. 2.3).
34
Figure 2.3 Effect of dietary regimen on hormone levels.
(A) Average serum IGF-1 levels for each dietary group at time of tumor injection (n=19 for HC group; n=18 for HF group) (B) Average serum leptin levels for each dietary group at time of tumor injection (n=11 for HF group; n=11 for HC group; n= 8 for LC group; n=6 for HC-CR group) Data are shown mean ± SEM. Significant differences (P< 0.05) are indicated by different superscripts. 35 2.3.5 Tumor Latency
Tumor latency is defined as the time from tumor cell injection to the occurrence
of a palpable tumor. The HC-CR group exhibited the longest latency period, (20.1 ± 0.9
d; Fig. 2.4). The latency period of the HC-CR group was more than 60 % longer than
any other group. Despite a significant reduction in serum IGF-1 when compared to the
HF group, the LC mice did not have a longer period of tumor latency than the HF group
(11.2 ± 0.6 versus 11.4 ± 0.5 d; Fig. 2.4).
2.3.6 Final Tumor Burden
The HC-CR group exhibited the smallest final tumor size (162.4 ± 41.2 mm2; Fig.
2.4). In contrast, all other groups displayed significantly larger tumors averaging 397.2 ±
32.7 mm2 for the HF group, 351.6 ± 29.0 mm2 for the LC group, and 474.6 mm2 ± 61.7 for the HC group.
36
Figure 2.4 Effect of dietary regimen on tumor latency and size.
(A) Percentage of mice from each dietary group with palpable tumor from during weeks 20-23: HF(▲), LC(▼), HC(♦), and HC-CR(●). Data shown are mean ± SEM. (n=19 for HC and HF groups). (B) Average final tumor size for each dietary group at time of sacrifice. *Indicates significantly different (P < 0.05).
37 2.4 Discussion In a previous study, we discovered that the LC diet tended to reduce serum
IGF-1 and insulin levels in diet-induced obese, male, C57BL/6 mice relative to mice
consuming a high fat diet (82). This led us to determine if a LC diet and the resulting
decrease in IGF-1 could temper the increased colon cancer risk associated with obesity.
In the current study, following a period of DIO, mice were placed on LC, HC, or HC-CR
diets. A fourth group continued on the same HF diet used to induce obesity. After 7 weeks on their respective diets, mice were injected with MC38 colon cancer cells. At the end of the study, there was no significant difference in body weight or percent body fat between the LC and HF groups. Also, the LC diet did not reduce time to palpable tumor
(Fig. 2.4) or final tumor size (Fig. 2.4) despite its ability to significantly reduce serum
IGF-1 levels (Fig. 2.3) when compared to the HF diet. Thus, our hypothesis that the LC diet may be protective against colon tumor progression because of its ability to lower serum IGF-1 levels was not supported. These data indicate that a decrease in circulating
IGF-1 levels is insufficient to suppress the increased colon tumor growth associated with obesity.
The high amounts of circulating leptin observed in the LC group, but not in the HC-CR group (Fig. 2.3), could be one of the obesity-dependent mechanisms that may have promoted tumor growth. Leptin is secreted by adipocytes and its serum levels directly correlate to adipose mass Garofalo (100). When leptin binds to its receptor, it can regulate a number of pathways including Janus-family tyrosine kinase (JAK/STAT), signal transducers and activators of transcription, and phosphatidylinositol 3-kinase
(PI3K), AKT (protein kinase B) resulting in the transcription of genes related to cell
38 growth and survival, migration, and invasion (101-103). Leptin levels have also been
shown to be positively associated with colon cancer risk in men (104). In the current
study, the mice consuming the LC diet displayed significantly higher levels of circulating
leptin when compared to all other dietary groups except for the HF group (Fig. 2.3).
Given the established association between leptin and cancer, it is plausible that the
elevated serum leptin levels in the LC mice contributed to increased cancer growth
relative to the HC-CR group.
Other factors may have also contributed to the tumor growth in the LC mice.
Previous work by our group indicated that although mice consuming the identical LC diet
used in the current study exhibited reduced serum insulin when compared to mice
consuming the HF diet, the serum insulin level of the LC group was still significantly
elevated when compared to the HC-CR group (82). Both the insulin receptor (IR) and
IGF-1 receptor can be activated by hyperinsulinemia (105). The activated IR/IGFR can then promote cell proliferation and reduce apoptosis through the MAPK and PI3K pathways (106). Hyperinsulinemia can also exert oncogenic effects in tissues which are not typically the primary targets of insulin signaling. For example, both Corpet et al
(107) and Koohestani et al (108) have shown that insulin injections promote the growth of aberrant crypt foci and colon tumors in rats. Also, epidemiological studies have linked hyperinsulinemia to breast, endometrial, prostate, and colon cancer (5, 109). Therefore, the hyperinsulinemia exhibited by mice consuming a LC diet in our previous study (82) could have played a role in the promotion of cancer that occurred in this study despite the decrease in serum IGF-1 level exhibited by the LC group. Insulin levels were not measured in the current study due to lack of sufficient serum volume. 39 To our knowledge, this is the first study to evaluate the effect of a LC diet on colon tumor growth. Previous investigations concerning LC diets determined their ability to induce short and long term weight loss (110-112), compared subject adherence and attrition to other diets (113-116), measured how the LC diet altered risk factors for heart disease (114, 116-120), and affected non-insulin-dependent diabetes (121). In our colon tumor cell transplant model, the LC diet did not reduce tumor growth despite its ability to lower IGF-1 levels, potentially due to elevated leptin and insulin levels. However, a LC diet may be helpful in the treatment or prevention of other cancers, such as astrocytomas and other brain tumors, that are more responsive to changes in circulating IGF-1, glucose, or other metabolic alterations associated with the LC diet. However, based on the findings from the current study, we conclude that a low-fat/high-carbohydrate, reduced calorie diet, such as the HC-CR regimen used here, may be a more protective dietary strategy with respect to colorectal cancer prevention than the LC regimen.
40 Chapter 3: Differential effects of calorie restriction and exercise on the adipose transcriptome in diet-induced obese mice
3.1 Introduction Roughly two-thirds of all adults in the U.S. are either overweight or obese (3).
Obesity is associated with an increased risk of developing chronic diseases, including atherosclerosis, type 2 diabetes, and many types of cancer (4, 122, 123). At the crux of obesity-related diseases is insulin resistance, which is characterized by elevated levels of circulating insulin and glucose and is directly linked to excess adiposity. In the context of low adiposity, insulin activates signaling through the insulin receptor, resulting in translocation of the glucose transporter 4 (Glut4) to the cell membrane to increase glucose uptake into the adipocyte [reviewed in (124)]. In contrast, high levels of adiposity are marked by enlarged adipocytes which are unresponsive to insulin levels even under conditions of hyperinsulinemia (125). In the insulin-resistant state, adipose tissue secretes adipokines and pro-inflammatory factors that reduce insulin sensitivity in peripheral tissues, thereby affecting whole-body glucose homeostasis (126, 127).
Unfortunately, the mechanisms underlying these changes in insulin responsiveness in adipocytes are poorly understood. Furthermore, mechanism-based lifestyle strategies for effectively offsetting obesity-induced insulin resistance are lacking.
Increased energy expenditure and decreased energy intake are the two most commonly recommended lifestyle changes to reduce adiposity and restore insulin sensitivity (128). Calorie restriction (CR) and exercise (EX) are both effective at improving insulin sensitivity and decreasing body weight and percent body fat (129,
41 130). However, there are conflicting reports concerning the effect of EX on body fat distribution and adipokine secretion, two key predictors of insulin resistance (129, 131-
133). Furthermore, studies on the beneficial effects of EX have focused mainly on molecular changes in the skeletal muscle and liver, while considerably less is known about changes in adipose tissue (130, 134, 135). Therefore, the aim of this study was to
compare the effect of CR and EX on visceral white adipose tissue (VWAT) gene
expression, along with changes in body composition and insulin resistance, in diet-
induced obese mice. We utilized an animal model of postmenopausal obesity because
postmenopausal women are especially at risk for developing diseases associated with
obesity such as type 2 diabetes (136) and breast cancer, the second leading cause of
cancer death in women (4).
In the present study we show that, despite comparable reductions in adiposity,
CR was more effective than EX at improving insulin sensitivity. Although both CR and
EX qualitatively affected a set of genes related to metabolism, CR had a stronger
quantitative effect on these genes. Furthermore, CR induced a dramatic change in
expression of an additional set of genes related to carbohydrate metabolism and transport
in VWAT that was not observed in the EX mice.
3.2 Materials and Methods
3.2.1 Animal Study Design
To model the postmenopausal state, 6-week-old ovariectomized C57BL/6 mice were
used (Charles River Labs, Inc. Frederick, MD). Ovariectomized mice exhibit
42 characteristics of the postmenopausal state in humans: decreased levels of circulating estrogen, loss of bone mineral density, and cessation of estrous cycles (137). Upon arrival, mice had ad libitum access to water and chow diet, and were on a 12:12 h light/dark cycle.
To compare the effects of CR and EX on reversal of obesity and insulin resistance, 48 mice were singly housed upon receipt and put on a diet-induced obesity
(DIO) regimen for eight wks consisting of ad libitum access to a 60 kcal% fat diet
(D12492; Research Diets, Inc, New Brunswick, NJ), beginning one week after arrival.
At week nine, the mice were randomized into the following treatment groups
(n=12/group): 1) calorie restriction (CR), 2) exercise (EX), 3) diet control, or 4) DIO regimen. The diet control and EX groups were switched from the DIO regimen to a 10 kcal% fat diet (D12450B; Research Diets, New Brunswick, NJ) consumed ad libitum, which is the base diet of our CR regimen. The diet control group was used as a feeding control for determining CR feed intake and to ensure that EX mice were not overeating to compensate for increased energy expenditure. We have previously shown that switching mice to the 10 kcal% fat diet maintains adiposity at the peak level achieved during the eight weeks of DIO (138). The CR group consumed a modified diet (D0302702, administered in daily aliquots) providing 30% fewer calories from carbohydrates compared to the control diet, with all other components being isonutrient when intake was limited to 70% of mean kcal consumption of the diet control group. The EX group were run on a variable speed treadmill 5 days/wk on a 5% grade, beginning with 10 min/day at 12 m/min. Time and intensity were increased gradually over the next two weeks until the EX group reached 40 min/day at a maximum rate of 20 m/min. The diet 43 control group was placed on the treadmill but not run. Body weights and feed intake
were measured weekly.
At the beginning of week 17, mice were euthanized. On the morning the mice
were killed, all mice received their respective dietary or exercise treatment, followed by a
6- hr fast. Mice were anesthetized with isofluorane for terminal blood collection via the
retroorbital venous plexus. Whole blood was allowed to clot at room temperature for 30
min prior to centrifugation at 1000 x g for 10 min. The serum was removed and stored at
-80◦ C for analyses. VWAT was collected and stored at -80◦ C until further analyses.
After euthanasia, carcasses were stored at -20◦ C. Percent body fat and lean mass were
determined using dual energy x-ray absorptiometry (DXA) (GE Lunar Piximus II,
Madison, WI) as described previously (139).
To further characterize the effect of CR on the histone code, 15 six-week-old
ovariectomized C57BL/6 mice (n=5/group) (Charles River Labs, Inc.) were randomized
to the DIO regimen, control diet, or the CR treatment. Mice were on diets for 8 wks.
Body composition was determined using quantitative magnetic resonance (Echo Medical
Systems, Houston, Texas). Animals were then sacrificed after an 8-hr fast and tissues
and serum collected as described above. All animal protocols were approved by the
University of Texas at Austin Institutional Animal Care and Use Committee.
3.2.2 Glucose Tolerance Test
To determine the effects of CR and EX on glucose regulation following weight
loss, we conducted a glucose tolerance test (GTT) at week 15. GTT was performed after
44 a 6- hour fast by administration of 20% glucose (2g/kg body weight IP). Blood samples were taken from the tail and analyzed for glucose concentration using an Ascencia Elite
XL 3901G glucose analyzer (Bayer Corporation, Mishawaka, IN). Glucose levels were
determined at baseline, 15, 30, 60, and 120 min after injection of glucose.
3.2.3 Serum Hormones
Leptin, insulin, and adiponectin were measured in serum collected at the terminal
bleed, using mouse adipokine LINCOplex®Multiplex Assays (Millipore, Inc., Billerica,
MA) analyzed on a BioRad Bioplex 200 analysis system (Biorad, Inc. Hercules, CA).
3.2.4 Microarray Analysis
Total RNA was isolated from VWAT tissues using an organic extraction and precipitation protocol with a DNAseI treatment step (Asuragen Inc., Austin, TX). Biotin-
labeled targets were prepared using modified MessageAmp™ based protocols (Ambion
Inc., Austin, TX) and hybridized to MOE 430A 2.0 arrays (Affymetrix, Santa Clara, CA).
The arrays were scanned on an Affymetrix GeneChip Scanner 3000 7G. A summary of
the image signal data, detection calls, and gene annotations for every gene interrogated
on the array was generated using Affymetrix Statistical Algorithm MAS 5.0 (GCOS
v1.3), with all arrays scaled to 500. Sample normalization was carried out using the
Robust Multichip Average (RMA) followed by multiple group analysis comparison using
ANOVA. Pair-wise comparisons were performed to identify expression fold differences
with false discovery rate (FDR) set at 0.05. Genes with expression differences equal or
45 greater than 2 fold compared to DIO, were selected to be analyzed by DAVID (140). The resulting Gene Ontology (GO) analysis was used to identify genes relevant to the different effects of CR and EX in reversing obesity, some of which were selected for further analysis. In the DAVID analysis, genes that were represented more than once in the microarray output were filtered. Some of the genes in the Gene Ontology analysis belonged to more than one functional category and are tabulated accordingly. Expression changes were verified in VWAT from a separate cohort of mice that underwent CR or
EX following DIO, as described above, using Taqman Gene Expression Assay (Applied
Bioystems Inc., Carlsbad CA). However, to control for changes in expression due to differences in fat consumption, the diet control was used as the reference group instead of mice on the DIO regimen. Gene expression data were normalized to the housekeeping gene β-actin.
3.2.5 Chromatin immunoprecipitation (ChIP) assay ChIP assays were performed per manufacturer’s instructions (Millipore). Briefly,
proteins from VWAT were formaldehyde crosslinked to DNA. After homogenization,
lysis, and sonication, proteins were incubated overnight with antibodies to acetyl-histone
H4 or tri-methyl histone H4 (Millipore). The DNA-protein complexes were washed,
DNA was eluted, and crosslinking was reversed by heating to 65◦ C overnight. DNA was
purified using QIAGEN PCR purification kit (QIAGEN, Valencia, CA). Quantitative,
real-time PCR was performed using SYBR Green (ABI) with the following Slc2a4
primers: forward primer 5'-CCCTTTAAGGCTCCATCTCC-3' and reverse primer 5'-
TGTGTGTATGCCCCGAAGTA-3' (ABI). GAPDH was used as the internal control for
46 analysis of acetylation with the following primers: forward primer 5'-
CATGGCCTTCCGTGTTCCTA-3' and reverse primer 5'-
CCTGCTTCACCACCTTCTTGAT-3'. For analysis of methylation, p16 was used as the
internal control with the following primers: forward primer 5'-
ACACTCCTTGCCTACCTGAA-3' and reverse primer 5'-
CGAACTCGAGGAGAGCCATC-3'.
3.2.6 Statistics Values are presented as mean ± standard error (SE). One-way analysis of variance (ANOVA) followed by Tukey’s Honestly Significant Differences test was used
to assess the effects of diet on mean weekly body weight at weeks 8 and 16, body
composition data at week 16, serum adipokine levels, and fasting glucose levels.
Repeated measures analysis was used to evaluate glucose tolerance tests. For serum
insulin, mRNA levels as measured by qRT-PCR, and relative quantification of Glut 4 in
ChIP experiments means were compared using Student’s t test. For all tests SPSS
software was used (SPSS Inc., Chicago, IL), and P < 0.05 was considered statistically
significant.
3.3 Results During the first eight weeks, the DIO regimen increased mean body weight from
20.3 ± 0.5 g to 30.7 ± 0.5 g. After eight weeks of undergoing either a CR or EX
intervention, the CR mice weighed significantly less than the DIO and EX mice (Table
3.1). As expected, the EX group displayed significantly more lean mass than DIO and
CR mice (Table 3.1). Although the CR mice weighed significantly less than the EX
47 mice, there was no difference in percent body fat, with both groups exhibiting 40% less
body fat compared to sedentary DIO mice. Achieving equal percent body fat between
CR and EX was a goal of the study design, given that percent body fat is associated with
insulin resistance (141). At the end of the study we measured two serum adipokines,
leptin and adiponectin, which are positively and negatively correlated with adiposity,
respectively (37). Consistent with decreased adiposity, leptin levels were roughly 80%
lower in CR and EX mice (Fig. 3.1). Although percent body fat in CR and EX mice was
comparable, only CR increased adiponectin levels compared to control mice (Fig. 3.1).
The higher levels of adiponectin observed in the CR mice were associated with decreased
fasting insulin levels (Fig. 3.1), decreased fasting glucose levels (Figure 3.1), and
increased insulin sensitivity as indicated by significantly lower blood glucose levels at
every time point following glucose challenge (Figure 3.1). In contrast, the EX mice did not display increased insulin sensitivity or decreased fasting insulin levels compared to sedentary DIO mice despite having a significantly lower percent body fat that was comparable to CR. Taken together, these data demonstrate that CR and EX differentially affected adipose tissue metabolism.
48
Table 3.1 Effect of weight loss induced by CR or EX on body composition
49
Figure 3.1 Effect of weight loss induced by calorie restriction or exercise on serum hormones and glucose tolerance
(A) Study design for microarray experiments (B) Serum leptin levels (C) Serum adiponectin levels (D) Serum insulin levels after 8 weeks of intervention (For all serum hormones, n=11 for DIO group; n=10 for CR group; n=10 for EX group) (E) Blood glucose concentrations during a glucose tolerance after 7 weeks of intervention. Data shown are mean ± SE. DIO (●), EX (Δ ), CR (□), n=12/group. Significance (P < 0.05) between groups is denoted by different letters.
50 Microarray analysis was performed on VWAT collected at the conclusion of the study. Pair-wise comparisons of DIO vs. CR and DIO vs. EX revealed that 725 transcripts were significantly altered (± 2.0 fold, p < 0.05, Fig. 3.2). Of those 725 transcripts, 209 were common to CR and EX (Fig. 3.2), possibly representing a suite of genes most sensitive to energy balance. GO analysis was used to categorize these genes according to function and revealed that the majority of genes altered both by CR and EX were related to metabolic process, immune response, and stress response (Fig. 3.2).
Within the metabolic process category, 24 of the genes were related to lipid metabolism, and overall the response of the genes to CR and EX was qualitatively similar. More specifically, a number of genes involved in fatty acid synthesis and transport were upregulated (Fig. 3.2). These included: stearoyl-CoA desaturase (Scd1), fatty acid synthase (Fasn), carnitine palmitoyltransferase 1 (Cpt1), and elongation of long chain fatty acids 3 and 6 (ELOVL3 and ELOVL6). In addition, 9 genes related to glucose metabolism were affected by CR and EX, including pyruvate dehydrogenase E1 alpha
1(Pdha1), leptin (Lep), and glycerol phosphate dehydrogenase 2 (Gpd2). As in lipid metabolism, genes related to carbohydrate metabolism were qualitatively responsive to both CR and EX, although CR had a stronger quantitative effect.
Reductions in adiposity are accompanied by lower levels of immune cell infiltrates into adipose tissue, which mediate the pro-inflammatory state associated with obesity (70). As expected, the reduced adiposity in CR and EX mice was associated with decreased expression of genes related to immune response (Fig. 3.2). These immune- related genes also comprised the majority of the genes in the stress response category, including downregulation of transcripts that code for chemokines that attract and are 51 produced by monocytes and macrophages, specifically chemokine (C-C motif) ligand
(Ccl) 2, 6, 7, and 9.
52
Figure 3.2 Effect of weight loss induced by calorie restriction or exercise on mRNA expression in VWAT.
(A) Venn Diagram of genes differentially expressed by CR and EX compared to DIO. (B) Classification of genes targeted by both CR and EX. (C) Heat map of genes related to metabolic processes affected by both CR and EX. (D) Classification of genes targeted uniquely by CR (n=6/group).
53
CR uniquely affected expression of 496 genes, whereas a mere 20 genes were responsive only to EX (Fig. 3.2). GO analysis of the genes uniquely responsive to EX revealed that only the grouping of genes related to mitochondrial transport was significant. Specifically, uncoupling proteins Ucp1 and Ucp2 were both upregulated by
EX. Given the robust transcriptional response to CR, we focused our analysis on those genes whose expression was affected by CR but not EX (Table 3.2). GO analysis showed that in every category of genes altered by both CR and EX, CR impacted an additional set of genes unaffected by EX. For example, in genes relating to cellular lipid metabolic processes, the largest subset of transcripts uniquely altered by CR, soluble carrier family 27 (Slc27a1) and Acetyl-Coenzyme A carboxylase alpha (Acaca) were upregulated. CR also uniquely increased expression of sterol regulatory element binding transcription factor 1 (Srebp1), a master regulator for lipid metabolism in adipoctyes.
With respect to immune response and stress response, CR resulted in a downregulation of gene expression, whereas expression of genes related to biosynthesis of steroids was upregulated.
In addition to affecting more genes in each functional category than EX, CR affected the transcription of genes in another category not modulated by EX, specifically
4 genes related to glucose transport. Complementing this increase in transcription of glucose transport genes, CR resulted in upregulation of another 14 genes related to carbohydrate metabolism processes. Taken together, these data are suggestive of increased glucose flux into the adipose tissue, which may underlie the enhanced insulin sensitivity observed in response to CR.
54
Table 3.2 Gene expression changes in CR and EX compared to control
55 56 57 Given that the DIO mice were on a high-fat diet, and the CR and EX consumed a low-fat diet, we were concerned that the observed differences in expression of metabolic genes might be due to differences in the macronutrient contents of the diets and not energy balance per se. To address this concern, confirmatory analysis of mRNA expression was done using the diet control mice as the reference group. A gene that was responsive to both CR and EX (Lep), two genes uniquely responsive to EX (Ucp1 and Ucp2), and three genes uniquely responsive to CR and relating to carbohydrate metabolism and transport
(Slc2a4, Acly, and Sh2b) were selected for validation. RT-PCR analysis verified that
Ucp1, but not Ucp2, was significantly increased by EX only (Fig. 3.3). Although according to the microarray analyses, Lep was reduced by both CR and EX, RT-PCR analyses revealed that only CR significantly reduced Lep expression (Fig. 3.3). All three genes relating to carbohydrate metabolism and transport, Acly, Slc2a4, and Sh2b2, were indeed significantly increased by CR only (Fig. 3.3). Importantly, Slc2a4 codes for the insulin-responsive glucose transporter, Glut4. Translocation of Glut4 from the cytosol to the plasma membrane in response to insulin signaling is the rate-limiting step of glucose transport into the adipocyte. Furthermore, downregulation of Glut4 at the messenger
RNA and protein levels has been implicated in obesity and insulin resistance. Finally, increases in the enzyme ATP-citrate lyase (Acly), which was also upregulated by CR but not EX, has recently been linked to increases in Glut4 mRNA levels (142). For these reasons, we decided to further characterize differences in transcriptional regulation of
Glut4 that may occur in response to CR.
58
Figure 3.3 Confirmation of microarray data analysis of mRNA expression in VWAT. Expression of mRNA transcripts in VWAT from Diet Control, CR and EX mice (n=7/group).
(A) Leptin (Lep) (B) Uncoupling protein 1 (Ucp1) (C) Uncoupling protein 2 (Ucp2) (D) Solute carrier 2, family 4 (Slc2a4) (E) ATP-citrate lyase (Acly) (F) SH2B adapter protein 2 (Sh2b2)
Data shown are mean ± SE, * (P < 0.05).
59
Because regulation of acetylation of histones has been shown to be nutrient
dependent (142), we hypothesized that increased Glut4 mRNA expression in CR mice
may be the result of histone modifications at the Glut4 promoter. To test this hypothesis, we generated obese and lean mice (relative to overweight control mice) through diet modulation. Body weight and percent body fat were positively associated with fasting blood glucose levels and inversely related to Glut4 mRNA levels in the VWAT (Fig.
3.4). Modifications to the histone code such as methylation, which can result in decreased transcription (143) or acetylation, which can result in increased transcription
(142) may account for the differences in Glut4 transcription in VWAT. There were no differences in tri-methylation of histone 4 at the Glut4 promoter (Fig. 3.5). However, CR significantly increased histone 4 acetylation at the Glut4 promoter compared to control mice (Fig. 3.5), which was associated with higher levels of Glut4 mRNA and increased insulin sensitivity.
60
Figure 3.4 Lean phenotype is associated with lower blood glucose levels and elevated levels of Glut4 mRNA relative to control mice. (A) Study design for ChIP experiments (B) Average body weight of mice on DIO, Control diet, or CR regimen to generate obese, control, or lean mice. (C) Fasting blood glucose levels of mice after 8 weeks of respective dietary regimen. (D) Relative mRNA expression of Glut4 in VWAT
All data shown are mean ± SE. (n=5/group). Significance (p<0.05) between groups is denoted by different letters
61
Figure 3.5 Relative quantification of Glut4 amplification
(A) Relative quantification of Glut4 DNA immunoprecipitated with anti-trimethyl H4 antibody. (B) Relative quantification of Glut4 DNA immunoprecipitated with anti-acetyl H4 antibody. Data shown are mean ± SE, * (P < 0.05), (n=3/group).
62 3.4 Discussion
With roughly two-thirds of American adults classified as overweight or obese (3), increased understanding of how best to reverse the harmful effects of obesity is urgently needed. Given the critical role of adipose tissue in regulating glucose homeostasis, analysis of the changes that occur in adipose tissue after weight loss could reveal key targets for treatment of insulin resistance. To our knowledge, this is the first study to compare the effects of CR and EX (the two most commonly recommended lifestyle modifications to prevent or reverse obesity) on gene expression in adipose tissue in a model of DIO. The direct comparison of these two obesity reversal interventions revealed the following novel findings: 1) the insulin-sensitizing effect of CR was associated with altered expression of more than 20 times the number of genes in the adipose tissue than were uniquely affected by EX, 2) alteration of expression of carbohydrate transport genes was uniquely affected by CR and correlated with the increased insulin sensitivity exhibited by CR, and 3) upregulation of Glut 4 by CR, but not by EX, may be explained in part by our finding that CR increased acetylation of histone 4 at the Glut4 promoter.
Both energy balance interventions resulted in significant weight loss compared to sedentary DIO controls. Although CR and EX groups displayed comparable levels of percent body fat at the end of the intervention, only CR significantly improved insulin sensitivity. Exercise has been shown to significantly improve insulin sensitivity in mice and humans (73, 144). The relatively short intervention in our study may explain why
EX was not as affective as CR at altering indices of insulin resistance. In other rodent
63 studies that showed a significant affect of EX, the intervention was either more than 10 weeks long (62, 73, 129) or the intervention period was longer than the period of diet- induced obesity (134). These differences in study design suggest that, in the short-term,
EX may not be as effective as CR in restoring insulin sensitivity.
CR has been shown to decrease expression of genes related to aging and tumorigenesis in multiple tissues (145). However, there is a paucity of studies examining the effect of CR on adipose tissue following weight loss. More importantly, there are no studies directly comparing the effect of CR and EX on gene expression in adipose tissue.
To our knowledge there are only two microarray studies comparing CR to EX, which were performed in the mouse mammary gland (146) and mouse skin (147). In these reports CR and EX exhibited distinct effects on gene expression, with CR impacting more than four times the number of genes than EX. In the present study, we found that this differential impact was more pronounced in adipose tissue, with CR affecting more than 20 times the number of genes altered by EX.
Our results confirm that DIO downregulates multiple genes that play a role in lipid metabolism and upregulates a profile of genes related to immune response (148,
149). Furthermore, many of the lipid metabolism genes shown to be decreased by DIO were increased by CR and EX in our study (148). Likewise, immune response genes that have been shown to be increased by DIO were decreased after weight loss induced by CR or EX (148). Together these data support previous findings that in the obese state, there is diminished fatty acid synthesis and transport, characteristic of insulin-resistant adipose tissue rich in immune cell infiltrates. Importantly, our data show that these processes are sensitive to both CR and EX interventions. 64 Many of the transcripts related to lipid and carbohydrate metabolism that were affected by both CR and EX in the present study were also shown by Shankar et al. to be induced by a high-carbohydrate diet (150). Increased transcription of these genes is consistent with increased uptake of glucose and fatty acids into the adipose tissue. In the study by Shankar et al. these transcriptional changes were measured in rats fed a high- carbohydrate diet for four weeks, during which time the rats gained weight and the adipocytes hypertrophied, whereas the mice in our study first underwent DIO but then lost weight for eight weeks before analysis.
The similarities between the two studies are indicative of increased signaling through the insulin receptor/phosphatidylinositol 3-kinase (IR/PI3K) pathway that mediates glucose uptake and the lipogenic effects of insulin in adipose tissue. This conclusion is consistent with the observation that the CR mice, which displayed increased insulin sensitivity compared to EX mice, exhibited a more robust transcriptional effect on metabolic genes than EX. Not only did CR induce a stronger quantitative effect than EX on genes that were qualitatively similar in their response to both CR and EX, but CR affected an additional 48 genes related to metabolism that were unaffected by EX. Of those genes, there was an overall upregulation of genes related to carbohydrate metabolism and glucose transport, including Glut4.
Glucose uptake into adipose tissue is mediated by two different Glut isoforms:
Glut1 and Glut4. Glut1 mediates basal uptake of glucose into adiopocytes. Although others have reported that Glut1 mRNA increases with obesity (151), we did not observe any changes in Glut1 mRNA expression in the microarray. Translocation of the GLUT4 transporter from the cytosol to the membrane is the rate-limiting step in insulin-mediated 65 glucose uptake in adipocytes and skeletal muscle (152). The importance of Glut4 function in adipose tissue is underscored by the finding that overexpression of Glut4 in adipocytes rescues insulin resistance in mice with muscle-specific knockout of Glut4
(153). However, expression of Glut4 in the muscle does not compensate for lack of
Glut4 activity in adipose tissue (154), further implicating adipose tissue as a key metabolic organ in the etiology of insulin resistance. There is considerable evidence that
Glut4 mRNA levels in adipose tissue decrease with obesity (155) and that increases in
Glut4 mRNA in adipose tissue can ameliorate insulin resistance (156, 157). Indeed, our finding that Glut4 mRNA levels were significantly increased by CR, but not by EX, and that this increase was associated with improved insulin sensitivity, supports this idea.
Therefore, increased transcription of Glut4 in VWAT during weight loss may be a critical event in reversing insulin resistance.
Studies into the transcriptional regulation of Glut4 in skeletal muscle implicate a histone deacetylase (HDAC5) as a crucial mediator of changes to Glut4 mRNA levels in response to exercise (158). Raychaudhuri et al. have also described a series of histone modifications mediated by histone deacetylases and histone methyltransferases that culminate in a metabolic knockdown of the Glut4 gene in the skeletal muscle of rats that had experienced intrauterine growth restriction (159). Collectively, these studies suggest that transcriptional regulation of the Glut4 gene is highly responsive to changes in energy balance. This led to our hypothesis that Glut4 mRNA levels in adipose tissue could be subjected to similar transcriptional regulation. In support of this hypothesis, Wellen et al. recently discovered that during adipocyte differentiation, levels of global histone acetylation are dependent on glucose availability (142). More specifically, acetylation of 66 histones 3 and 4 at the Glut4 promoter is linked to increased Glut4 mRNA expression in
response to higher concentrations of glucose during differentiation.
Our in vivo data show that increased acetylation of histone 4 at the Glut4
promoter, which was associated with higher levels of Glut4 mRNA, occurred in lean
mice that were highly insulin sensitive as indicated by significantly decreased fasting
glucose levels. Taken together, these data suggest that insulin-responsive adipose tissue
maintains H4 acetylation. This leads to increased transcription of Glut4 to facilitate
continued glucose uptake. However, as adiposity increases so does insulin resistance
(123). Deregulation of signal transduction downstream of the insulin receptor results in
decreased trafficking of Glut4 to the cell membrane (160, 161) and a decline in glucose
flux into the adipocyte (162). According to the findings of Wellen et al., limited glucose
availability results in diminished histone acetylation and decreased Glut4 mRNA
expression (142). Therefore, in the context of obesity and insulin resistance, the lower
levels of Glut4 mRNA expressed in adipose tissue may be a consequence of decreased
insulin-mediated uptake of glucose that results in diminished histone acetylation at the
Glut4 promoter. Alternatively, diminished histone acetylation may occur as direct result
of decreased transcription of the Glut4 gene. Conversely, the increased acetylation
observed in the CR mice may be a result of increased transcription of the Glut4 gene rather than an effector of increased transcription. To clarify, future experiments could utilize the adipoctye cell line, 3T3-L1. Measuring changes in acetylation of histone 3 and
4 at the Glut4 promoter following silencing of 2-MEF2A, a transcription factor necessary for Glut4 transcription in the 3T3-L1 cells, could determine if acetylation at the Glut 4 promoter plays a causative role in increased transcription of this gene. Also, future 67 research is necessary to determine if other modifications to the histone code at the Glut4 promoter may be mediating transcriptional repression of Glut4 mRNA in obesity.
In conclusion, these findings show that CR and EX differentially impact insulin resistance and the adipose transcriptome during obesity reversal. In particular, the expression of Glut4, the gene that codes for the sole insulin-responsive glucose transporter, was uniquely responsive to CR and found to be transcriptionally regulated through histone code modification.
68 Chapter 4: p53, oxidative stress, and metabolism
4.1 Introduction
Calorie restriction (as little as 14% relative to ad libitum-fed controls) significantly delays spontaneous tumor development, regardless of p53 status (163-165).
This includes MMTV-Wnt1 transgenic mice which develop spontaneous mammary tumors (Hursting, unpublished). In contrast, exercise (in the form of treadmill running) accelerates the development of spontaneous mammary tumors in p53-deficienty Wnt-1 transgenic mice (67). Since p53 is mutated in 20-40% of all breast cancers, identifying p53 gene dosage and exercise interactions that may impact disease outcome is critical
(166). This is especially important given that public health recommendations call for regular exercise at a moderate intensity to decrease overall disease risk and mortality.
The tumor suppressor protein p53 initiates repair mechanisms when DNA damage occurs and mediates apoptosis if the damage can not be reversed (167). This results in the prevention of the growth and proliferation of abnormal cells (167).
Recently Sablina et al demonstrated that downregulating p53 in vitro results in elevated levels of intracellular reactive oxygen species (ROS) and a notable decrease in mRNA transcripts for antioxidant proteins (168). This is consistent with the role of p53 as
“guardian of the genome” because ROS can damage DNA, causing mutations and genomic instability which have been linked to tumorigenesis (169). Furthermore, dietary supplementation with N-acetylcysteine (NAC), an antioxidant, completely prevents the
69 development of lymphomas in p53 -/- mice, while 90% of untreated p53 -/- mice develop malignant tumors(168).
Calorie restriction reduces oxidative stress (170) in several tissues including
muscle and liver (171, 172) as well as circulating markers of oxidative stress (173).
However, less is known regarding its efficacy in epithelial tissues. In contrast, exercise
increases oxidative stress in several tissues, including muscle tissue and immune cells
(174), although less is known about the effects of exercise in epithelial tissues.
Since obesity is associated with increases in circulating markers of oxidative
stress (175, 176), we hypothesized that obese, p53 +/- would exhibit increased oxidative
damage compared to obese, p53 +/+ mice and that this damage would be further
exacerbated by exercise. To test this hypothesis we measured the highly mutagenic
oxidative DNA adduct, 8-oxo-2'-deoxyguanosine (8-oxo-dg) in DNA isolated from the
mammary fat pad. In addition, we extracted mRNA to measure expression of p53-
inducible genes related to oxidative stress as well as antioxidant genes that may be
differentially activated independent of p53.
4.2 Materials and Methods
4.2.1 Animal Study Design
To model the postmenopausal state, 6-week-old ovariectomized C57BL/6 mice
were used (Charles River Labs, Inc. Frederick, MD). Ovariectomized mice exhibit
70 characteristics of the postmenopausal state in humans: decreased levels of circulating
estrogen, loss of bone mineral density, and cessation of estrous cycles. Upon arrival,
mice had ad libitum access to water and chow diet, and were on a 12:12 h light/dark
cycle.
To determine if p53 deficiency affects basal levels of oxidative DNA damage
and to characterize how this effect is modulated by varying degrees of adiposity, 48 p53
+/+ mice and 48 p53 +/- mice were singly housed upon receipt. One week after arrival, mice from each genotype were randomized into the following treatment groups (n=12 per group): 1) diet-induced obesity (DIO), 2) control, or calorie restriction (CR). Mice on the
DIO regimen had ad libitum access to a high fat diet (20% carbohydrate, 20% protein,
60% fat, D12492). Control mice had ad libitum access to a high carbohydrate diet (70% carbohydrate, 20% protein, 10% fat, D12450B). The CR group consumed a modified diet (D0302702, administered in daily aliquots) providing 30% fewer calories from carbohydrates compared to the control diet, with all other components being isonutrient when intake was limited to 70% of mean kcal consumption of the control group. Body weights and feed intake were measured weekly.
After 10 weeks of dietary treatment, mice were euthanized. Mice were anesthetized with isofluorane for terminal blood collection via cardiac puncture. Whole blood was allowed to clot at room temperature for 30 min prior to centrifugation at 1000 x g for 10 min. The serum was removed and stored at -80◦ C for analyses. Tissues were
collected and stored at -80◦ C until further analyses. After euthanasia, carcasses were
stored at -20◦ C.
71 To determine p53 deficiency results in increased oxidative stress in response
to exercise during obesity reversal, 48 p53 +/+ mice and 48 p53 +/- mice were singly
housed upon receipt and put on a DIO regimen as described above for 10 wks consisting
beginning one week after arrival. At week 11, the mice were randomized into the
following treatment groups (n=12/group): 1) control, 2) exercise (EX), or 3) CR. The
control and EX groups were switched from the DIO regimen to the high carbohydrate
diet regimen as described above. The CR group was treated as described above. The
control group was used as a feeding control for determining CR feed intake and to ensure
that EX mice were not overeating to compensate for increased energy expenditure. The
EX group were run on a variable speed treadmill 5 days/wk on a 5% grade, beginning
with 10 min/day at 12 m/min. Time and intensity were increased gradually over the next
two weeks until the EX group reached 40 min/day at a maximum rate of 20 m/min. The
diet control group was placed on the treadmill but not run. Feed intake was measured
weekly. Body composition was determined using quantitative magnetic resonance (Echo
Medical Systems, Houston, Texas) at the end of the DIO regimen (beginning of week 11)
and every 2 weeks during the obesity reversal phase (weeks 11-32).
After 22 weeks of obesity reversal through either CR or EX, mice were euthanized.
The day the mice were killed, all mice received their respective dietary or exercise
treatment, followed by a 6 hr fast. At the time of sacrifice, fasting blood glucose was measured in blood samples obtained from the tail vein using an Ascencia Elite XL 3901G glucose analyzer (Bayer Corporation, Mishawaka, IN) Mice were euthanized and tissues collected as described above.
72 4.2.2 Glucose Tolerance Test
To determine the effects of p53 genotype on glucose homeostasis, we conducted a glucose tolerance test (GTT) at the end of week 10 on mice placed on DIO, control, and
CR regimens. GTT was performed after an 8-hour fast by administration of 20% glucose
(2g/kg body weight IP). Blood samples were taken from the tail and analyzed for glucose
concentration using an Ascencia Elite XL 3901G glucose analyzer (Bayer Corporation,
Mishawaka, IN). Glucose levels were determined at baseline, 15, 30, 60, and 120 min
after injection of glucose.
4.2.3 Quantitation of lipid and DNA oxidation
Serum 8-isoprostane was measured using an EIA assay (Cayman Chemical,
Ann Arbor, MI) according to manufacturer’s instructions. To measure 8-oxo-dg, a
marker of oxidative DNA damage, 10-15 µg of DNA was extracted from mammary fat
pads using DNeasy Blood and Tissue Kit (Qiagen, Valencia CA) according to
manufacturer’s instructions. DNA was then digested in the presence of 0.1 mM desferal.
After digestion, nucleosides were analyzed using HPLC with electrochemical detection.
4.2.4 Quantitative real-time PCR analysis
To analyze mRNA expression of genes related to oxidative stress, RNA was isolated
from mammary fat pads using RNeasy Lipid Tissue Mini Kit (Qiagen, Valencia CA).
Reverse transcription was performed using the High Capacity cDNA Reverse
73 Transcription Kit (Applied Biosystems Inc., Carlsbad, CA) according to manufacturer’s instructions. Quantitative real-time PCR analysis was performed using Taqman Gene
Expression Assays (Applied Biosystems Inc., Carlsbad, CA). Gene expression data were normalized to the housekeeping gene β-actin and quantified using the ΔΔCT method.
4.2.5 Cell culture
MCF-7 cells were obtained from American Tissue Culture Collection (Manassas,
VA). The cells were cultured in high glucose DMEM media supplemented with 10% fetal bovine serum, glutamine (2mM), and antibiotics (1000 U/mL of penicillin).
4.2.6 Small interfering RNA constructs and expression vectors
For silencing p53 expression, siRNA constructs targeting either p53 or scrambled sequence (Ambion, Austin, TX) were mixed with serum free DMEM (final concentration of 30.0 nM) and then incubated with the transfection reagent siPORT-NeoFX (Ambion,
Austin, TX). The solution was added to cells in a 12-well plate. Media was changed 48 hrs after transfection. For transient transfection of WT p53, the transfection reagent
Fugene 6 (Roche) was mixed with serum free DMEM and then incubated with plasmid
DNA (from 1µg/µl stock). The solution was added to cells seeded the previous day in a
12-well plate. The WT (pC53-SN3) p53 expression vector was a kind gift from Dr. Bert
Vogelstein (Johns Hopkins University, Baltimore, MD).
74 4.2.7 Measurement of glucose consumption and assay production
Cells were incubated in fresh media for 4 hrs and then media was collected for
measurement of glucose and lactate concentration. Glucose concentration was measured
using a glucose (GO) assay kit (Sigma) and glucose consumption was measured as the
difference in concentration of glucose between the sample and the blank media. Lactate
concentration was measured using a lactate assay kit (MBL International Corp., Woburn,
MA).
4.2.8 SDS-PAGE, Western blotting, and immunodetection
Cells were lysed in RIPA buffer containing protease (Protease Inhibitor Cocktail
Tablets, Roche Applied Science) and phosphatase inhibitors (Phosphatase Inhibitor
Cocktail 1 and 2, Sigma-Aldrich, Saint Louis, MO). Protein concentration was
determined using the Bradford reagent (Bio-Rad, Hercules, CA). Samples were diluted at a 1:1 concentration with Laemmli buffer and 15-40 µg of protein was loaded on 10-
12% SDS-PAGE gels for separation by electrophoresis. The proteins were transferred to a polyvinylidene fluoride membrane (Millipore, Bedford MA) at 100 V in transfer buffer
(25 mM Tris base, 200 mM glycine, 20% methanol). Membranes were blocked for 1 hour with LI-COR Blocking Buffer (LI-COR Biotechnology, Lincoln NE). For immunodetection, the membrane was probed overnight with antibodies specific for p53 and β-actin (Cell Signaling). The membrane was washed with PBS-T (10 mM sodium phosphate, 150 mM sodium chloride, 0.1% Tween-20, pH 7.6) and incubated with IRDye secondary antibodies (Licor Biotechnology, Lincoln NE) for 35 minutes. Following a 75 series of washes with PBS-T, the fluorescent activity was detected and quantified using the LI-COR Odyssey Infrared Fluorescent Imaging System (Licor Biotechnology,
Lincoln NE).
4.2.9 Statistics
All values shown are mean ± SEM. All statistical analyses were conducted using
SPSS (SPSS Inc., Chicago, IL). Univariate ANOVA was used to determine differences between groups for body weight and feed intake. Results were considered significant if P
< 0.05. ANOVA was followed by Tukey’s HSD post-hoc test to determine significant between group differences. For all other results, means were compared using Student’s t test.
4.3 Results
The diet treatments succeeded in generating three different body weight phenotypes: lean (p53+/+: 21.2 ± 0.2 g, p53+/-: 21.3 ± 0.8 g), overweight (p53+/+: 33.0 ±
0.6 g, p53+/-: 33.7 ± 0.4 g), and obese (p53+/+: 40.9 ± 1.3 g, p53+/-: 43.5 ± 0.9 g).
Statistical analyses determined that differences in mean body weight between the phenotypes were significantly different (p < 0.001). As expected, p53 gene dosage did not affect mean feed intake or body weight (Figure 1).
76 A
B
Figure 4.1 Body weight and energy intake
(A) Average body weight weeks 1-14
(B) Average kcal consumed per mouse/wk
Data are presented mean ± SEM; n=12/group
77 Adiposity had no effect on oxidative DNA damage in either genotype. More
specifically, for both genotypes, the overweight and obese mice did not display higher
levels of damage compared to lean mice. We next analyzed the effect of genotype within
each treatment group. Differences in DNA damage by genotype between the p53+/-
overweight and p53+/+ overweight mice approached significance, (3.2 ± 0.8 versus 2.3 ±
0.4, p = 0.07), while there was no difference by genotype between the lean mice (p53+/-:
2.3 ± 0.2 versus p53+/+: 2.0 ± 0.4, p = 0.5) (Figure 4.2). Obese, p53+/- mice displayed
significantly higher levels of 8-oxo-dG in the mammary fat pad than obese, p53+/+ mice
(3.1 ± 0.6 versus 1.7 ± 0.1, p = 0.05) (Figure 4.2). However, we were skeptical about the
physiological relevance of these findings. The statistical difference in damage between
the obese p53 +/+ and p53 +/- is attributable to a slight lowering of damage in the p53+/+
mice. In fact, there is no difference in damage between the obese, p53 +/- mice and the lean mice of both genotypes. Measurements of serum 8-isoprostane, a marker for
oxidized lipids, revealed a similar pattern: there were no differences in serum 8-
isoprostane levels by genotype in the overweight or lean mice. However, levels of serum
8-isoprastane were significantly higher in obese, p53+/- mice compared to p53 +/+ mice
(196.0 ± 20.0 versus 121.2 ± 7.2, p = 0.5) (Figure 4.2). This may have increased our
confidence in the 8-oxo-dg data if analysis of the expression of p53-inducible antioxidant
genes had supported the differenced in oxidative DNA damage.
We had hypothesized that obesity constituted a stress that would have initiated
either a pro-or anti-oxidant transcriptional response from p53. In the context of p53-
deficiency, no such response, or possibly a compromised response, would occur resulting
in increased oxidative damage. However, there were no differences by genotype in the 78 obese mice in the expression of the p53-inducible antioxidant genes sestrin 1 (SESN1), glutathione peroxidase 1 (GPX1), superoxide dismutase 2 (SOD2), p73 α, or the p53- inducible pro-oxidant gene PUMA (BBC3) (Figure 4.3). These data do not support the hypothesis that the increased oxidative stress exhibited by obese, p53 +/- mice is attributable to an impaired p53 anti-oxidant response. Indeed, real-time PCR analysis showed that there were no differences in p53 mRNA levels between the obese, p53 +/+ and p53 +/- mice (Figure 4.3). This indicates that a single allele is sufficient to maintain levels of p53 mRNA that are comparable to those seen in wild type mice.
79
Figure 4.2 Markers of oxidative stress are increased by obesity in p53+/- mice
(A) Average levels of oxidative DNA damage in MFP (B) Average levels of serum 8-isoprostane Data shown are mean ± SEM, n=3-6/group, * (P < 0.05)
80
Figure 4.3 No effect of p53 genotype on expression of genes related to oxidative stress in MFP of obese mice.
Top panel depicts expression of antioxidant genes sestrin 1 (SESN1), glutathione peroxidase 1 (GPX1), and superoxide dismutase 2 (SOD2). Bottom panel depicts expression of proxidant genes p73α and BBC3 and of p53.
81 We hypothesized that the added physiological stress of exercise would exacerbate oxidative stress in obese, p53-deficient mice. We further hypothesized that the combined stressors of obesity and exercise would overwhelm the ability of the single allele in the p53 +/- mice to induce an appropriate transcriptional response to increased oxidative damage. However, 22 weeks of treadmill running had no effect on oxidative
DNA damage in the MFP of p53 +/- mice compared to p53 +/+ mice (Figure 4.4). Even more surprising was the lack of effect of genotype in the obese, sedentary mice on oxidative DNA damage. Our expectation was that even if there was no effect of exercise on oxidative DNA damage, the obese, sedentary p53 +/- mice would display higher levels of DNA damage than obese, sedentary p53 +/+ mice given the results from the previous animal study. In fact there was no effect of either genotype (p53 +/+ versus p53 +/-) or treatment (EX, CR, or obese controls) on oxidative DNA damage (Figure 4.4). This raises two possibilities: 1) The data from the HPLC-EC is correct and there was no effect of exercise or genotype or 2) There were differences in oxidative DNA damage that went undetected due to the occurrence of artifactual oxidation during analysis (177, 178).
We reasoned that even though there were no detectable differences in oxidative damage or p53 mRNA expression, there may have been cells within the MFP that had undergone loss of heterozygosity. Furthermore, these cells may have upregulated different antioxidant defenses to control oxidative stress than in the p53 +/+ mice.
Therefore, we broadened our search to include transcripts that were not p53-inducible.
Using a qPCR array, we measured the expression of 85 genes related to oxidative stress and screened for transcripts that were upregulated or downregulated at least 2 fold by either genotype or treatment. Expression of the gene encoding thioredoxin-interacting 82 protein (TXNIP) was upregulated by more than three fold in the MFP of obese p53 +/- compared to obese, p53 +/+ (n=3). We validated this finding using 6 mice from each group and found that Txnip mRNA was significantly increased by almost 3 fold in the
MFP of sedentary, obese p53 +/- mice compared to sedentary, obese p53 +/+ mice
(p<0.001) (Figure 4.5).
Txnip expression is increased by glucose flux and binds reduced thioredoxin, resulting in accumulation of ROS (179). Txnip expression is lost in many tumor types, including breast cancer (180). We hypothesized that higher levels of Txnip in the MFP of p53-deficient mice occur because either: 1) p53 negatively regulates transcription of
Txnip or 2) transcription of Txnip is being induced by some physiological consequence of p53 deficiency.
To test the first hypothesis, the human breast cancer cell line, MCF-7 (which endogenously express wild type p53), was transfected with wild type p53 expression vector and Txnip mRNA levels were measured. Marked increases in p53 protein levels were observed 48 hrs after transfection compared to cells transfected with empty vector
(Figure 4.6). However, there were no differences observed in Txnip mRNA levels at 72 hrs, indicating that Txnip is not transcriptionally regulated by p53 (Figure 4.6).
83
Figure 4.4 No effect of energy balance intervention or genotype on oxidative DNA damage in MFP
C =control, CR =calorie restriction, EX= exercise Data are shown mean ± SEM; n=4-5/group
84
Figure 4.5 Txnip mRNA levels are significantly increased in MFP of obese, p53 +/- mice.
Data shown are mean ± SEM, n = 6/group; * (P < 0.05)
85 To test the hypothesis that Txnip transcription is induced in response to
cellular alterations resulting from loss of p53, we silenced p53 expression in MCF-7 cells using siRNA. Expression of p53 mRNA was reduced by 50% 72 hrs after transfection (p
< 0.01) (Figure 4.7). Txnip mRNA levels were increased almost 2.5 fold 48 hrs after knockdown of p53 (n=2) (Figure 4.7). At 60 hrs after knockdown of p53, Txnip mRNA levels were upregulated significantly by more than 2 fold (p < 0.01) compared to control
samples treated with scrambled siRNA (n=3) (Figure 4.7).
86
Figure 4.6 No effect of wild type p53 transfection on Txnip mRNA levels in MCF-7 cells
(A) Western blot of p53 protein levels in MCF-7 cells transfected with empty vector or p53 expession vector. Image is representative of three independent experiments. (B) Relative Txnip mRNA levels from MCF-7 cells transfected with empty vector or p53 expression vector. Data are presented mean ± SEM; n=3.
87
Figure 4.7 Expression of p53 and Txnip mRNA in MCF-7 cells after transfection with siRNA constructs targeting p53 relative to cells transfected with scrambled siRNA.
Data are presented mean ± SEM; n=3 for all time points except 96 h and 120 h where n=2; * (P < 0.05)
88 Txnip mRNA expression has been shown to increase in response to stressors such as heat shock and H2O2 (181). More recently, there have been several reports that increased glucose flux stimulates transcription of Txnip in normal and transformed cells
(182-184). Higher levels of Txnip protein are also detected in the vasculature of mice with diabetes mellitus (179). We were struck by the observation that significantly higher levels of Txnip mRNA expression in the MFP of obese, p53 +/- mice compared to obese, p53 +/+ mice was associated with significantly lower fasting blood glucose levels. This suggests that there is more glucose uptake in the tissues of the obese, p53 +/- mice than the obese, p53 +/+ mice, supporting the idea that transcription of Txnip in our animals is being stimulated by increased glucose flux.
Importantly, p53 has also been linked to glucose metabolism (185). As a metabolic regulator, p53 suppresses aerobic glycolysis by negatively regulating transcription of genes relating to glucose transport (Glut1 and Glut4) (81) as well as the transcription factor c-myc (186) which has been shown to promote transcription of glycolytic genes (187). In fact, silencing of p53 in vitro has been shown to cause a metabolic switch in which glucose is preferentially metabolized using aerobic glycolysis instead of mitochondrial oxidative phosphorylation (79, 81). Therefore we hypothesized that the utilization of aerobic glycolysis resulting from p53 silencing would induce transcription of Txnip in MCF-7 cells.
To test this hypothesis, we measured glucose consumption and lactate production in MCF-7 cells transfected with p53-targeting siRNA constructs at the same time points that we had previously observed increased Txnip mRNA expression. In experiments in which knockdown of p53 approached 70% (n=2), glucose consumption 89 and lactate production were markedly increased compared to control cells. There were also increases in the expression of the glycolytic genes hexokinase 2, c-myc, and glut 1.
Txnip mRNA levels were also elevated. Since Txnip binds the antioxidant enzyme, thioredoxin, we reasoned that increased levels of Txnip would result in increased ROS production. As expected, increased Txnip mRNA levels were associated with higher levels of ROS production compared to control cells. In experiments in which knockdown of p53 was 60% or less (n=3), the glycolytic switch did not occur and Txnip mRNA levels were not increased compared to control cells transfected with scrambled siRNA.
90
Figure 4.8 Effect of p53 knockdown on metabolism and Txnip mRNA levels
(A) Glucose consumption, lactate production, and ROS production in MCF-7 cells in which knockdown of p53 was 60%. Data are presented mean ± SEM, n=3. (B) Relative Txnip mRNA expression in MCF-7 cells in which knockdown of p53 was 60%. Data are presented mean ± SEM, n=3. (C) Glucose consumption, lactate production, and ROS production in MCF-7 cells in which knockdown of p53 was ~70%. Data are presented mean, n=2. (D) Relative Txnip mRNA expression in MCF-7 cells in which knockdown of p53 was was ~70%. Data are presented mean, n=2.
91 3.0
2.5
2.0
1.5
1.0 expression Relative mRNA Relative 0.5
0.0 c-Myc Glut1 Hk2 Pdk1
Figure 4.9 Relative mRNA expression of glycolytic genes in MCF-7 cells in which the glycolytic switch occurred following knockdown of p53.
Expression is shown relative to controls. Data are shown mean ± SEM, n=2.
92 4.4 Discussion Recent findings connect p53 to cellular metabolism in that p53 suppresses aerobic glycolysis, the preferred mode of ATP production of most transformed cells
(188). Interestingly, some of the p53-inducible genes that modulate metabolism also alter production or scavenging of reactive oxygen species (ROS) (79, 80). Our original hypothesis asserted that obesity and exercise would stimulate the antioxidant function of p53. Although our data was conflicting in that regard, we did identify a novel context for the transcriptional upregulation of Txnip. Previously, it was known that increased glucose flux causes increased transcription of Txnip (179). We are the first to provide evidence that the reliance on aerobic glycolysis that results from loss of p53 is associated with increased Txnip mRNA levels.
This finding is consistent with what is little is known about the function of
Txnip. Txnip binds the reduced form of thioredoxin, an oxioreductase that collaborates with thioredoxin reductase and thioredoxin peroxidase to scavenge ROS and reduce oxidatively damaged proteins (189). Since Txnip is controlled primarily at the transcriptional level, increased transcription of Txnip leads to reduced antioxidant activity of the thioredoxin system and heightened ROS accumulation (179). Glucose flux into the cell is a potent transcriptional activator of Txnip (179). Hyperglycemia and oxidative stress have long been linked to Type 2 diabetes and evidence is mounting that
Txnip is the underlying mediator. In fact, Txnip is elevated in the cardiac and skeletal muscle, kidney, and vasculature of mice and humans with Type 2 diabetes (179, 190-
93 192). Furthermore, Txnip has recently been shown to mediate glucose toxicity in
pancreatic beta cells (193).
Our in vivo and in vitro data are the first to link Txnip to aerobic glycolysis
and p53. The metabolic switch from oxidative phosphorylation to aerobic glycolysis
resulting from silencing of p53 has been well characterized. We have shown that
transcriptional upregulation of Txnip responds to this metabolic switch. Furthermore, we
show that Txnip mRNA levels are increased in the mammary fat pad of obese, p53-
deficient mice compared to obese, wild type mice and that the p53-deficient mice have
significantly lower fasting blood glucose levels than wild type counterparts even though
there were no differences in body weight, percent body fat, or feed intake. Taken
together, this suggests a difference in glucose uptake and utilization attributable to p53
gene dosage. We were especially intrigued to see these differences given that these are
non-tumor bearing tissues since metabolic alterations caused by loss of p53 have all been
characterized in transformed cells and tissues.
Aerobic glycolysis, also known as the Warburg Effect, is not in itself an
initiating event. Rather it is thought to promote tumor cell growth and proliferation by
rapidly producing the metabolic fuel and reducing agents for biosynthesis of
macromolecules necessary for cell division (194). For example, proliferating cells
require massive amounts of ATP. Although aerobic respiration is more efficient at producing ATP, if glucose flux is high enough, glycolysis can outpace ATP production
(185). By transcriptionally upregulating genes relating to oxidative phosphorylation and downregulating genes relating to glycolysis, p53 suppresses the switch to aerobic glycolysis (185). We observed induction of Txnip in MCF-7 cells following silencing of 94 p53. This induction was associated with increased glucose consumption and lactate production. We also observed increased ROS production associated with higher levels of
Txnip. Although we have not yet analyzed the functional consequence of increased
Txnip expression in MCF-7 cells, induction of Txnip in pancreatic beta cells leads to apoptosis (195). A hallmark of cancer cells is the ability to evade apoptotic signals (196).
Consistent with this hallmark is the finding that Txnip expression is lost in many cancers, including breast cancer (180). Loss of Txnip in cancer tissues is not due to mutations but transcriptional silencing of the gene (180). Recently, glutamine was shown to be a powerful transcriptional repressor of Txnip (197), although it is unknown exactly what metabolite of glutaminolysis transcriptionally suppresses Txnip.
The work of Craig Thompson and Chi Dang has demonstrated that tumors
become glutamine addicted because glutaminolysis allows the cells to replenish TCA
cycle intermediates (187, 198). The oncogene c-myc has been shown to initiate the
transcriptional program necessary for increased glutaminolysis (187). Recently, p53 has
been shown to transcriptionally repress c-myc through upregulation of miR-145 (186). In
agreement with other reports, we observed more than a 2-fold increase in c-myc mRNA
following silencing of p53. Since c-myc drives glutaminolysis, we would expect to see
Txnip eventually become silenced (187). Given the transient effect of p53 silencing
using siRNA, we were not able to test this hypothesis. A more long term silencing of p53
using shRNA may be a useful model in outlining the timeline of events marking the
metabolic deregulation that occurs following loss of p53. Therefore, our finding that
Txnip levels are increased in the context of p53 deficiency may represent an ephemeral
response that is repressed as cancer cells become more metabolically dysregulated. 95 CHAPTER 5: CONCLUDING REMARKS
The prevalence of obesity in U.S. adults remains high (3). Even more
alarming, is the recent report that in the last ten years, obesity rates have increased in 6-
19 year old boys (199). Roughly 18% of U.S. adolescents and teenagers have high body
mass index (BMI at or above 95 percentile for their age) (199). If our children are our
future, then our future is obese. Unfortunately, we must prepare for the ramifications of
the obesity epidemic: continued increases in type 2 diabetes, cardiovascular disease, and
cancer.
Treatments and strategies aimed at breaking the obesity-cancer connection
must be multitargeted given the multitude of metabolic abnormalities resulting from
excess adiposity. For example, we found that significant reductions in IGF-1 did not
result in decreased colon tumor growth in obese mice. Other obesity-related factors
remained unchanged in these mice. Circulating levels of leptin, which promotes
angiogenesis, proliferation, and motility in human and mouse colon cancer cell lines,
were elevated (200-203). Certainly, leptin may have been sufficient to promote tumor
growth even in the context of lowered IGF-1 levels. To further investigate the role of
IGF-1 in obesity-related cancer, our laboratory is currently analyzing data from an experiment in which liver-specific-IGF-1-deficient (LID) mice were made obese, overweight, or lean through dietary manipulation. The mice were then injected with a mouse mammary tumor brie. We hypothesized that the obese LID mice would display larger tumors than overweight LID mice but smaller tumors than obese, wild-type mice.
More mechanistic studies are needed to evaluate the contribution of individual obesity-
96 related factors as well as the additive or synergistic effects of these factors on tumor growth.
Metformin as an anticancer drug has attracted considerable interest because it ameliorates many of the metabolic dysfunctions associated with obesity. It is a commonly prescribed drug used by those at risk for developing or already diagnosed with type 2 diabetes. Metformin reduces blood sugar levels and circulating insulin levels.
Additionally, metformin has direct effects on cancer cells (204). Through its activation of AMP-activated kinase (AMPK), metformin inhibits proliferation of breast and colon cancer (204, 205). A recent report shows that metformin significantly decreased risk of breast cancer in type 2 diabetics who had been using the drug for more than 5 years
(206). Currently, a phase III clinical trial is underway testing how well metformin works compared with placebo in treating patients with early-stage breast cancer.
Ideally, lifestyle changes would be coupled with therapeutic agents to offset, if not reverse, the harmful effects of obesity. Public health recommendations call for exercise and calorie restriction to induce weight loss. However, our studies indicate that these strategies exert differential effects on gene expression in visceral white adipose tissue, the endocrine organ at the crux of metabolic dysregulation associated with obesity.
Calorie restriction elicited a more potent transcriptional response than exercise. These transcriptional changes were associated with increased insulin sensitivity. Even more important was the finding that exercise did not have as a robust affect as calorie restriction even though both interventions caused a comparable reduction in percent body fat. The comparatively weak affect of exercise on gene expression in visceral white adipose tissue speaks to the intractable nature of obesity. 97 Much of the research investigating the effects of increased insulin and IGF-1 on transformed cells has focused on the growth, proliferation, and survival signal transduction pathways that result from binding of the these growth factors to their cognate receptors. More recently, a new line of research has been aimed at understanding how these growth factors affect cellular metabolism of cancer cells. Our finding that increases in Txnip mRNA are associated with the Warburg Effect in p53-deficient cancer cells contributes to the deepening appreciation for the critical role glycolysis serves in cancer.
Given that obesity loss is difficult to achieve and even more difficult to maintain, public health policies and interventions should be aimed at preventing obesity. Certainly, we must continue to develop novel treatments to minimize cancer risk in the obese, but in preventing obesity, we prevent cancer.
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113 VITA
Karrie Elizabeth Wheatley was born in Beaumont, TX and raised in Richmond,
TX. Karrie attended I.H. Kempner High School and graduated in 1995. She then attended The University of Texas at Austin from 1995-1999. After receiving a Bachelor of Arts degree, she taught middle school language arts for 4 years. In the fall of 2004, she entered the Graduate School at The University of Texas at Austin. Karrie was a graduate student in the Department of Nutritional Sciences under the mentorship of Dr.
Stephen D. Hursting.
Permanent Address: 1301 Ridgemont, Austin, Texas 78723.
This manuscript was typed by the author.
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