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According to the 2000 National Health and Nutrition Examination Survey (NHANES), approximately 64% of American adults (>20 years of age) are either overweight (>25 body mass index (BMI)) or obese (>30 BMI) (CDC, 2003). Although a genetic component exists, behavioral patterns and food availability are the major factors in the recent increase in obese populations. Americans are eating more than previous generations due to the availability of a wide variety of good-tasting, inexpensive, energy-dense foods. Lower amounts of exercise at work, school, and home are also to blame. Obesity has been equated with aging 20 years, being more strongly linked to chronic diseases than living in poverty, smoking, or drinking (Sturm, 2002). Obesity is a major risk factor for several life-threatening diseases such as heart attack, stroke, non-insulin dependent diabetes, and certain cancers (e.g., colon, breast), leading to approximately 300,000 obesity-related deaths each year in the US (Marx, 2003).
Closely associated with obesity is diabetes, which continues to increase in the United States and other developed nations. Approximately 6% of Americans have diabetes and an equal number of people are thought to be in a ‘pre-diabetic’ state (Marx, 2002).
Diabetes mellitus is a group of metabolic diseases characterized by chronic hyperglycemia and disturbances in carbohydrate, fat, and protein metabolism associated with absolute or relative deficiencies in insulin production by pancreatic ßcells and/or insulin action at target tissues (Bennett, 1994). Several pathologies are responsible for the development of diabetes, which can take several forms. The majority of human diabetes cases fall into two broad etiopathogenetic categories. The first (type 1) is due to an absolute deficiency of insulin secretion, whereas the second (type 2) is due to a combination of resistance to insulin action and an inadequate compensatory insulin secretory response (ADA, 2002). Type 2 diabetes, also referred to as non-insulin dependent diabetes, is by far the most common form, accounting for approximately 90% of cases (Warram et al., 1994). Diabetes increases mortality and morbidity rates, as it is associated with several complications including heart disease and stroke, blindness, amputations, renal disease, and damage to the nervous system.
The parallels that exist between dogs and humans as regards recent lifestyle changes, increased lifespan, and increased incidence of obesity and associated diseases are remarkable. Obesity is likely the most common disease found in companion animals today. Up to 40% of dogs presented to veterinarians in the US are now overweight, which is significantly higher than just a few decades ago (Sunvold and Bouchard, 1998). Because the anomalies associated with canine obesity and diabetes are intertwined, it is not surprising that the incidence of diabetes also has increased by 3-fold in this same period (Guptill et al., 1999). Immune-mediated diabetes (type 1) is the most common form found in dogs (Hoenig and Dawe, 1992). Although the type 2 (non-insulin dependent) form accounts for only 1 in 5 canine diabetic cases (Feldman and Nelson, 1996), the etiology and gross clinical signs are similar to those of humans (Hoenig, 1995). This similarity will prove to be beneficial for both species, as dogs are important biomedical models for several human diseases. Conversely, clinical and nutritional information collected from human experiments may be applied to canines to improve nutritional and health status.
The dog has been crucial in understanding glucose metabolism, pancreatic function, and diabetes, as it was the first animal to become diabetic experimentally and was the animal used to determine pancreatic function (Mering and Minkowski, 1889). The cause of obesity in dogs is similar to that in humans; inadequate daily exercise and excessive intake of high quality foods (Mason, 1970). Many of the negative health outcomes of obesity observed in humans also are present in the dog. Weight gain is associated with increases in blood pressure, heart rate, plasma volume, cardiac output, and fasting insulin concentration (Rocchini et al., 1987). Many of the complications associated with diabetes in humans, including hypertension (Struble et al., 1998), hypercholesterolemia (Barrie et al., 1993), atherosclerosis (Sottiaux, 1999), and retinopathy (Wyman et al., 1988), also are present in canines. In fact, the dog is a popular model for ocular manifestations because diabetes causes cataracts and is the leading cause of blindness in dogs (Wyman et al., 1988). Finally, clinical signs of diabetes are similar to those of humans, with polydipsia and polyuria being the most common signs in newly diagnosed diabetic dogs (Plotnick and Greco, 1995).
Factors influencing incidence of obesity and diabetes
The pathogenesis of obesity and type 2 diabetes is a complex process that occurs over an extended period of time in both humans and dogs. Age, genetic profile, and dietary/lifestyle habits are highly associated with these metabolic disease states. In humans, diabetes affects only 0.2% of people under the age of 20, 8.6% of those between 20 and 65, and 20.1% of people over 65 years old (www.cdc.gov/ diabetes/pubs/estimates.htm). Dogs may develop diabetes at almost any age, but it is most common from 7 to 9 years of age (Nelson, 1995). Mattheeuws et al. (1984) reported that as dogs increased in age and became heavier in weight, regulation of plasma glucose and insulin worsened, which is similar in the human (Glass et al., 1981). Other reports also have suggested poor regulation of glucose metabolism in geriatric dogs (Sheffy et al., 1985; Mosier, 1989).
Nutrition also plays a major role in the development of diabetes. Over-consumption of food by normalweight subjects, resulting in weight gain and adiposity, has been shown to induce hyperinsulinemia in animal models and humans (Simms et al., 1973).
This relationship is corroborated by the fact that over 80% of human diabetics are also obese. Knowler et al. (1990) reported that individuals having a BMI >35 were 100-300% more likely to develop diabetes compared with those having a BMI <25. The presence of obesity is not always a positive predictor of diabetes, however, as only 10% of obese people are diabetic (Beck-Nielsen and Hother-Nielsen, 1996).
Diabetes resulting from over-nutrition is also common in dogs, as most dogs diagnosed with diabetes are overweight. In addition to total caloric intake, the type of food consumed also may affect the development of diabetes. Several animal experiments have demonstrated that glucose metabolism and homeostasis are influenced by high fat consumption. Several researchers have measured the glycemic and insulinemic responses of pet foods having different carbohydrate (e.g., starch, soluble and insoluble dietary fiber), fat, and protein contents or prepared using different processing methods (e.g., canned, soft moist, dry) (Holste et al., 1989; Nguyen et al., 1998).
These experiments usually were performed to identify diets useful for managing dogs suffering from obesity and diabetes. However, little has been done to identify ingredients that may contribute to the development of canine diabetes.
Finally, genetics plays an important role in the development of diabetes, as its incidence in racial groups differs substantially. The incidence of type 2 diabetes in African Americans, Hispanics, and whites living in the US is approximately 13, 10.2, and 6.5%, respectively (Marx, 2002). In comparison, approximately 50% of the Pima Indians of Arizona have the disease. Numerous genetic loci affecting diabetes susceptibility, unrelated to racial categorization, also likely exist. Genotype (genetic makeup) also plays an important role in canine metabolic disease states such as obesity and diabetes.
Breeds such as Labrador retrievers, Cairn terriers, cocker spaniels, long-haired dachshunds, Shetland sheepdogs, basset hounds, Cavalier King Charles spaniels, and beagles, have a greater prevalence of obesity than other breeds (Mason, 1970; Edney and Smith, 1986). In another experiment, Samoyeds, miniature schnauzers, miniature poodles, pugs, and toy poodles were found to be at high risk for developing diabetes, while German shepherds, golden retrievers, and American pit bull terriers were at low risk (Hess et al., 2000). Specific genetic mutations resulting in diabetes at a young age also have been identified in dogs. Inherited insulin-dependent diabetes has been identified in keeshond and Samoyed breeds (Kramer et al., 1980; Kimmel et al., 2002). A mutation in the glucose-6-phosphatase gene, a key regulatory enzyme in glucose homeostasis, also has been identified in dogs (Feng et al., 1997; Kishnani et al., 1997). As more genes and genetic polymorphisms (genetic variants) are identified in the canine genome, other populations prone to obesity and diabetes may be identified.
Metabolic diseases are highly complex and difficult to study
Despite substantial investments of time and money by many laboratories, the identification of genes contributing to type 2 diabetes has been difficult (Elbein, 1997; Neel, 1999). Type 2 diabetes frequently goes undiagnosed for several years because hyperglycemia develops slowly and is not severe enough in early stages for the patient to notice the classical signs associated with the disease (e.g., polyuria, polydipsia, polyphagia, weight loss) (ADA, 2002). Type 2 diabetes often is not diagnosed until overt diabetes, an irreversible part of the disease cycle, is present. This may be especially true for companion animals because owners may not notice clinical signs of disease until it has progressed to an irreversible stage. Therefore, screening tools are needed to identify the progression of diabetes that is undetected by serological indices (e.g., glucose and insulin concentrations). Although fasting plasma glucose and other serological indices may be helpful in that they are able to detect glucose intolerance and early stages of diabetes, they are not able to predict its development. Screening tools able to predict the disease are needed so nutritional and exercise strategies may be implemented to prevent or prolong its development. Gene expression profiling may be a method to detect early stages of diseases such as diabetes before gross clinical signs are noticeable.
Because previous efforts to identify genetic loci responsible for the complex pathogenesis of obesity and type 2 diabetes have had little success, a better understanding of metabolic pathways in healthy individuals may be needed before confronting such complex metabolic diseases. Nutritionists are beginning to use the powerful molecular biological techniques available, but the nutritional genomics field is still in its infancy. Metabolic pathways such as the glycolytic pathway, which was completely elucidated in 1940, have been studied to a great extent (Stryer, 1995). Substrate and enzyme structures, regulatory sites, and regulatory elements such as insulin and glucagon have been described. However, completely understanding the regulation and interaction of metabolic pathways is a complex problem that will not be easily solved. Energy balance and whole body metabolism is a complex process involving numerous genes and proteins that interact to determine metabolic status. The fact that most mammalian genomes are estimated to contain only ~30,000 to 40,000 genes suggests that in addition to the overall number of genes present in a genome, other factors such as temporal and spatial gene expression patterns, alternative splicing, posttransitional modification, and protein-protein interactions, greatly influence phenotype (physical characteristics). Recent advances in molecular biology, including high-throughput tools used for gene and protein functional analyses, will be instrumental in describing and characterizing normal and diseased metabolic states.
Importance of canine genome sequencing
Because genomic data are often needed to implement the use of the emerging molecular biological techniques, canine genome sequencing is crucial.
Because of their similarities to human physiology and genome structure, the dog is a powerful biomedical model. Canine models of human diseases are important in identifying disease-causing genes, analyzing gene and protein function, and developing treatment strategies. As approximately 50% of canine genetic diseases have a human counterpart, researchers have demonstrated the dog’s utility as a biomedical model of numerous human diseases including obesity and diabetes (O’Brien et al., 2002).
The importance of the dog as a biomedical model is indicated by its rank in the National Human Genome Research Initiative (NHGRI) genome sequencing scheme (http://genome.gov). The dog is the first nonrodent mammalian animal model to be sequenced and is expected to be completed (6.5 X coverage) by June 2004 (www.genome.wi.mit.edu/media/2003/ pr_03_tasha.html). In addition to the canine sequencing funded and coordinated by NIH, a privately-funded project recently sequenced ~70-80% of the canine genome (Kirkness et al., 2003). In addition to the continual supply of sequence data added to the public databases, researchers have access to canine genetic maps. Breen et al. (2001) published the first fully integrated, comprehensive map of the canine genome. This 1800-marker map (containing 320 genes and 1078 microsatellites) covered >90% of the canine genome. Shortly thereafter, Guyon et al. (2003) published another map of the canine genome that contained 3270 markers. These established genetic maps are crucial in ordering genes in the canine genome as the sequence information continues to be added to public databases.
Comparative mapping with human and mouse genome sequences, which have already been completed, will increase the speed at which this process occurs in dogs.
Although sequencing the dog genome was initiated because of its importance as a biomedical model for humans, dogs and their owners also will greatly benefit from this information. The most immediate impact of genome sequencing may be the detection and elimination of canine genetic diseases by identifying genes responsible for them. At present, approximately 450 canine genetic diseases have been identified (http://www.angis.org.au/Databases/BIRX/ omia). Test results may be used to eliminate carriers from the breeding population in order to decrease or eliminate incidence of disease. Due to high sequence homology among dogs, humans, and mice, comparative mapping will be helpful in correctly placing the genes in the canine genome. Once sequence information is known, genetic polymorphisms may be identified. Further testing will be required to identify which of these polymorphisms are important in health and disease. Some common genomic terms are presented in Table 1.
Table 1. Common genomic terms.
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| Functional genomics: the study of the function of every gene encoded in a genome. Genome: the totality of all DNA in an organism. Genomics: the study of genomes, including genome mapping and sequencing. Genotype: the genetic makeup of an organism. Nutritional genomics: the study of nutritional effects on gene expression. Phenotype: the physical characteristics of an organism. Polymorphism: a variation in DNA sequence. Proteomics: the study of the full complement of proteins found in an organism. Transcription: the creation of a messenger RNA strand from a DNA strand. Translation: the creation of a protein from a messenger RNA strand. |
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Utility of functional and nutritional genomics
Although the progress made in the past decade as regards genome sequencing has been astounding, the vast amount of information it provides comes without interpretation. Genome structure is important, but the function, regulation, and interaction of genes and gene products (proteins) have the major influence on phenotype. Therefore, assessing gene function by analyzing RNA and protein expression, localizing proteins, and determining the significance of proteinprotein interactions are of major importance (International Human Genome Sequencing Consortium, 2001). As the field of functional genomics and proteomics (study of protein profiles) matures, our understanding of gene and protein function, cellular function, and physiology will be greatly enhanced.
This newfound knowledge will allow scientists to fully utilize genome sequence data and lead to an improved understanding of the biological systems of the body and subsequently develop effective prevention and (or) treatment therapies for disease.
This area of research also will be very important for the field of nutritional sciences. If applied correctly, nutritional genomics will enhance our understanding of metabolic pathways and aid in maximizing dog nutritional and health status.
Dietary constituents can up-regulate or downregulate gene expression directly, as is the case for certain vitamins and minerals, or by indirect means (e.g., dietary fiber) through hormonal signaling, mechanical stimuli, or metabolites produced from gut microflora (Cousins, 1999). Changes in gene expression have been used to study a broad range of topics, including caloric restriction (Lee et al., 1999), vitamin (Nur et al., 2002) and mineral deficiencies (Blanchard et al., 2001), glucose metabolism (Uyeda et al., 2002), and diseases affecting nutritional status (Gannon and Nuttall, 1997). However, the effects of nutrition on gene expression in companion animals have not yet been tested. Several areas of companion animal nutrition are not well studied, but experimental work may be enhanced by using nutritional genomic techniques, including the determination of minimal, optimal, and toxic concentrations of all nutrients, efficacy and toxicity testing of novel ingredients, effects of nutrition on development, prevention, and treatment of complex diseases, and genetic polymorphisms important for nutritional metabolism and requirements and susceptibility to diseases.
Genomic technology may be used to identify genetic and dietary factors responsible for the development of a complex disease and aid in the development of prevention and treatment strategies and therapeutics. Continued advances in biotechnology have provided scientists with powerful research tools that will generate more informative and less invasive research. Many of these new tools have been in the area of functional genomics, the study of assessing gene function. One of these techniques, microarray technology, has become very popular because it enables researchers to measure the expression of hundreds to thousands of genes simultaneously (Pease et al., 1994; Schena et al., 1995). This snapshot of gene expression provides a global perspective of the cellular or tissue metabolic state, rather than measuring only a few genes at a time, which limited classical techniques. By using this technology, gene expression profiles may be generated and correlated with metabolic indices from blood or tissue samples to identify relationships important to understanding whole body metabolism.
To be less invasive and provide more accurate analysis, several microgenomic techniques also have been developed in the past decade. Laser capture microdissection (LCM) enables researchers to isolate and study pure cellular populations from a heterogeneous tissue. In combination with RNA isolation and amplification procedures, LCM has become a very important tool for researchers and clinicians with small sample sizes (Simone et al., 1998). Because LCM allows researchers to perform cell-specific genomic analysis on small samples such as biopsies, this technique has become very popular in cancer and gastrointestinal research. Because individual cells may be selected for analysis using LCM, results are not skewed or diluted by unwanted cell populations in the sample. Our laboratory is currently using LCM and microarray technology to compare gene expression profiles of intestinal villus and crypt cell populations.
Rather than attempting to tackle complex metabolic diseases such as obesity and diabetes immediately, comparison of gene expression profiles from simple differences in dietary status or composition (e.g., fasted vs fed, animal vs plant-based ingredients) may be the place to begin. Once relationships of normal metabolic status are established, gene expression profiles or ‘signatures’ of disease may be identified in future experiments. These signatures will reveal abnormalities in metabolism that may be important for understanding disease development, developing biomarkers of disease, and choosing effective therapies. Due to the lack of knowledge regarding the canine genome and the expense involved with microarray technology in the past, canine microarrays containing a limited number of known genes have been available only on a proprietary basis at an extremely high cost. However, increased knowledge in both key areas (canine genome, microarray technology) in the past decade is enabling the production of canine microarrays using gene-specific probes. Because options are currently limited, canine microarrays are still very expensive ($400 to $500/ slide). However, as competition in the marketplace increases, these costs will likely decrease. By generating gene expression profiles of canine blood and liver samples already known to have metabolic differences (but in healthy animals), relationships between these datasets may be established. Along with understanding gene function and the effects of genegene interactions, these relationships will lay the foundation for future experiments studying complex diseases such as obesity and type 2 diabetes.
Illinois canine nutritional genomics experiment
Our laboratory recently conducted a 12-month experiment evaluating the effects of diet on gene expression in healthy elderly (11 years old at baseline) and weanling (8 weeks old at baseline) dogs. Due to the lack of knowledge in this area, this initial experiment was designed to evaluate major differences in age and diet on canine gene expression profiles.
In this experiment, an animal product-based diet was compared with a plant product-based diet. Blood and liver biopsy samples were collected over the course of the experiment and tissue samples were harvested at its end for isolation of RNA to be used for microarray analysis. In addition to RNA isolation, blood samples were used for determining serum metabolite concentrations and complete blood count.
Fecal and colonic digesta samples were used to measure nutrient digestibility and fermentative endproduct concentrations. Tissue samples of various regions of the gut also were collected for the measurement of gut morphology and for RNA isolation using LCM, which will generate intestinal villus cell- and crypt cell-specific gene expression profiles. Our primary goal was achieved, as several metabolic differences due to age and diet were identified. Nutrient digestibility, hematology, and serum chemistry data were recently submitted for publication (Swanson et al., 2004).
We are currently in the process of generating gene expression profiles that may be correlated with the metabolic data already collected. Significant correlations may identify mechanisms responsible for changes observed in biological systems (e.g., metabolic pathways, immune function) due to age and diet. These results also will be used to design future experiments in our laboratory evaluating the effects of nutrition on obesity and diabetes. We plan to identify biomarkers of disease and test dietary regimens that may prevent disease or improve nutritional and health status.
Conclusions
Now that pets are living longer lives due to improved nutrition and veterinary care, the incidence of complex metabolic diseases such as obesity and diabetes has increased. These complex diseases are difficult to study experimentally because of the time they take to develop and the various environmental and genetic factors involved. However, emerging genomic technologies are enabling scientists to perform more definitive and less invasive research that may enhance our understanding of normal and diseased metabolic states. Nutritional genomics may enable researchers to identify biomarkers of these diseases so they may be detected in early stages and treated appropriately. The current canine genome sequencing initiative will be crucial to improving canine health, as it will allow researchers to locate and determine the function of genes and identify genetic polymorphisms that influence metabolism, immune status, and other biological systems. These technologies will not only identify populations prone to disease, but will play an important role in developing strategies for prevention and (or) treatment.
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