6
Embedded Relationships Among Social, Behavioral, and Genetic Factors

Over the past several decades, there has been an exponential increase in our understanding of the social, behavioral, and genetic components of health and disease. Accompanying that understanding is a need to more fully connect and integrate knowledge across all levels of these determinants of health. Such integration will provide a better understanding of how social factors are translated into physiological effects on cellular responses, including changes in gene expression. Likewise, the genomics revolution, catalyzed by the Human Genome Project, has stimulated widespread interest in how genetic variations may influence human behavior and response to social factors.

The previous chapters have implicitly used a linear, if not hierarchical, model to describe the strengths of and lacunae in our current understanding of reciprocal interactions among the various levels of organization: social factors, individual behavior and experience, physiological systems, and gene function. In this chapter we explore how future work must recognize that such a linear approach does not fully reflect the integrated nature of the social and physical environment and gene function that is the salient feature of biological systems. Instead, we must use a variety of models in order to address the fact that rarely is there a one-to-one relationship between genes and a trait.

Indeed, with only ~30,000 genes in the human genome, most genes are likely to serve different functions at different times and in different environments (McClintock et al., 2005). Moreover, the selection of our genome occurred when our ancestors migrated, through the interaction with differ-



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6 Embedded Relationships Among Social, Behavioral, and Genetic Factors Over the past several decades, there has been an exponential increase in our understanding of the social, behavioral, and genetic components of health and disease. Accompanying that understanding is a need to more fully connect and integrate knowledge across all levels of these determi- nants of health. Such integration will provide a better understanding of how social factors are translated into physiological effects on cellular re- sponses, including changes in gene expression. Likewise, the genomics revo- lution, catalyzed by the Human Genome Project, has stimulated wide- spread interest in how genetic variations may influence human behavior and response to social factors. The previous chapters have implicitly used a linear, if not hierarchical, model to describe the strengths of and lacunae in our current understanding of reciprocal interactions among the various levels of organization: social factors, individual behavior and experience, physiological systems, and gene function. In this chapter we explore how future work must recognize that such a linear approach does not fully reflect the integrated nature of the social and physical environment and gene function that is the salient feature of biological systems. Instead, we must use a variety of models in order to address the fact that rarely is there a one-to-one relationship between genes and a trait. Indeed, with only ~30,000 genes in the human genome, most genes are likely to serve different functions at different times and in different environ- ments (McClintock et al., 2005). Moreover, the selection of our genome occurred when our ancestors migrated, through the interaction with differ- 109

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110 GENES, BEHAVIOR, AND THE SOCIAL ENVIRONMENT ent social and physical environments. This affected not only their life-span trajectories, fertility, health, and disease and survival rates, but also those of their children and grandchildren (see Chapter 5 for additional discussion). Thus information in the genome is inextricably linked with the cellular, physiological, psychological, social, and physical environments in which it functions over a lifetime, and many of these nongenetic factors are passed on to subsequent generations. One of the limitations of a purely hierarchical perspective to integrating knowledge across levels is that, in reality, the effects of variation at any one level (e.g., gene, gene transcript, protein, metabolite, or tissue) are actually embedded in another level and are not simply “underneath” or “above” the other level. A well-established hierarchy is illustrated by the ways in which DNA is transcribed into messenger RNA, which is then translated into protein, which in turn is appropriately folded and chemically modified in order to perform a specific function in protein complexes. Conversely, an example of the complex, nonhierarchical, and embedded nature of biologi- cal information is the fact that some DNA variations affect transcription but are not found in the messenger RNA; other variants are transcribed and affect translation but are not found in the translated protein; and still others are transcribed, translated, and ultimately affect protein function. The fol- lowing subsections further illustrate this concept and its implications for assessing the impact of associations and interactions among social, behav- ioral, and genetic factors on health. THINKING FROM THE BOTTOM UP: GENOMIC INFORMATION INFLUENCING GENE EXPRESSION The Human Genome Project and many other international efforts have been focused on understanding the nature of the genome and its variations. Millions of single nucleotide polymorphisms (SNPs) have been identified (e.g., see dbSNP from the National Center for Biotechnology Informa- tion1), and investigators around the world are engaged in performing ge- netic association studies in order to better understand the influences of these variations on measures of health and disease. It is well known that genetic variations within a gene can alter its expression both quantitatively and qualitatively. For example, mutations within the promoter region of a gene can influence when, where, and how much a particular gene is ex- pressed (i.e., transcribed in messenger RNA). Currently, most gene expres- sion studies ignore individual-level variation in gene expression due to genetic variation. However, over the past few years several landmark stud- 1See www.bioinfo.org.cn/relative/dbSNP%20Home%20Page.htm.

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111 EMBEDDED RELATIONSHIPS AMONG FACTORS ies in humans have shown that genetic variation within a gene can have profound effects on gene expression. In a study by Lo et al. (2003), allele- specific expression of 602 transcribed SNPs was examined, and 54 percent of genes showed preferential expression of one allele over another. At least 25 percent of the 602 transcribed SNPs showed more than a four-fold difference in expression between the two alleles. Cheung et al. (2005) have demonstrated that the expression level of genes is highly heritable in hu- mans and map onto different regions of the genome. In a small study of 14 pedigrees, variation in more than 1,000 genes expressed in human lympho- cyte cell lines (out of 3,554 genes examined) was significantly heritable and linked to regions of the genome. Further, they found that only 374 of these 1,000 genes with heritable expression patterns showed evidence of possible mutations in their own gene region that directly affected transcription lev- els. Using a genome-wide association approach with >770,000 SNPs, Cheung et al. (2005) found 27 genes with the greatest evidence of inherited expression patterns could be divided into 2 approximately equal subsets— those with SNP associations in their genomic region (cis-effects) and those with SNP associations on different chromosomes (trans-effects). Functional analysis using allele-specific binding assays (HaploChip assay) were then used to confirm the results from the SNP association study. By utilizing transcriptomic and genomic data simultaneously, new insights into the causes of variability in gene expression are being discovered. This type of research (discussed in the following sections) could be very beneficial to understanding why some people in a population have adverse responses to environmental exposures while others do not. Transcriptomics Technologies Transcriptomics is a term used to describe the genome-wide measure- ment of mRNA transcripts in a particular tissue or cell line. The two main technologies used for genome-wide measurements of gene expression (mRNA expression) are DNA microarrays and serial analysis of gene ex- pression (SAGE). In DNA microarray technology, thousands of known DNA sequences are bound systematically to a solid platform, and mRNA that has been extracted from a particular sample (and fluorescently labeled) is then hybridized to the DNA sequences. In contrast, SAGE is a high- throughput technology based on the sequencing of short sequence tags within each mRNA transcript found within a particular tissue. It provides a method of directly sampling the population of mRNAs in a cell rather than being restricted to preselected gene transcripts that have been placed on a chip. A number of important issues have been identified involving the reproducibility and standardization associated with these technologies and the massive datasets they produce. Progress in data quality and data sharing

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112 GENES, BEHAVIOR, AND THE SOCIAL ENVIRONMENT has been facilitated in large part by the creation of the Minimum Informa- tion About a Microarray Experiment guidelines by the Microarray Gene Expression Data Society (Brazma et al., 2001; Ball et al., 2002a; Ball et al., 2002b; Ball et al., 2004a; Ball et al., 2004b). The tremendous emphasis on data sharing of transcriptomic studies has been a major asset to the scien- tific community, both as a source of independent data that can be used as a means of validating results in diverse sample populations and in cross- species comparisons. It also has stimulated the development of new knowl- edge about global patterns of gene expression that are associated with particular cellular systems (Malek et al., 2002; Stuart et al., 2003). The field of transcriptomics also has catalyzed the development of many novel statistical and pattern recognition methods, as researchers ini- tially struggled to analyze massive amounts of data to identify genes whose expression profiles were found to be altered, co-regulated, or representative of key pathways thought to be activated by environmental exposures. Clus- ter analysis has been one commonly used tool for multidimensional visual- ization and the discernment of underlying subgroups of individuals with similar expression profiles (reviewed by Brun et al., 2004). Network models and supervised machine learning algorithms also have been important in generating new insights about key pathways in disease development or even predicting disease outcomes using these high-dimensional data. Through advances in bioinformatics it is now possible to merge gene expression data with additional data sources in order to aid the investigative process. For example, the hundreds of genes found to be associated with a disease or environmental exposure in a transcriptomic study can easily be linked to PubMed abstracts or the associated Medical Subject Heading terms (Jenssen et al., 2001; Fink et al., 2003; Doniger et al., 2003; Djebbari et al., 2005). Likewise, merging gene expression results with SNP databases, genetic link- age databases, epigenetic information on imprinting, comparative genomic hybridization arrays, proteomic databases, and metabolic pathway data- bases provides an unparalleled opportunity for integration across the levels of the molecular universe that characterizes our human biology. For ex- ample, the Gene Ontology Project (www.geneontology.org) attempts to classify gene products, assigning proteins to groups specifying their molecu- lar function, the biological process to which they contribute, and their cellular component (Ashburner et al., 2000). Similarly, using Enzyme Com- mission numbers, genes can be mapped to metabolic and signaling pathway databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) (www.genome.ad.jp/kegg) (Kanehisa, 2002). In general, microarray tech- nology is an incredibly powerful tool used to investigate complex gene expression relationships on a genome-wide scale, and it likely will be in- valuable in assessing the relationships among social, behavioral, and ge- netic factors as they relate to health and disease.

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113 EMBEDDED RELATIONSHIPS AMONG FACTORS Epigenetic Phenomenon Epigenesis originated as a term to describe the processes in embryonic development that transforms the undifferentiated cells in the newly fertil- ized egg into a complex, multitissue organism. Today, it is used in a much broader sense to represent everything from the general concept of the forces that shape how an individual’s genotype gives rise to a particular phenotype (Waddington, 1957; Petronis, 2003) to the specific molecular mechanisms by which cells differentiate, age, change metabolic functions, or even trans- form into cancerous cells (Jablonka and Lamb, 2002). The most well- known mechanism for the epigenetic regulation of cell phenotypes is DNA methylation, which turns off a gene or gene region (i.e., keeps it from being expressed) by changing the chemical structure of the DNA (Jaenisch and Bird, 2003). Many different factors can affect the methylation pattern of genes and thus affect their expression. For example, as a normal part of human development genes are turned on and off using methylation pro- cesses stimulated by other gene products in the embryo, fetus, newly born infant, child, adolescent, and aging adult. Environmental factors such as infection and diet are also known to affect gene methylation. For example, the work of Waterland and Jirtle (2004) suggests that prenatal and postna- tal nutrition can have long-lasting epigenetic effects on an adult’s predispo- sition to obesity, cardiovascular disease (CVD), type 2 diabetes, and cancer. Rett syndrome is an example of a clinical syndrome typified by mental retardation and autistic-like behaviors that arises through the failure of these methylation processes (Shahbazian and Zoghbi, 2002). In addition to the growing body of research on the environmental and developmental factors that affect epigenesis, there also is evidence that epigenetic patterns of gene expression may be inherited and can affect genetically inherited diseases. For example, through a process known as genetic imprinting, the methylation pattern in a parent is passed onto off- spring through the germline and in some cases this has been associated with differential disease patterns. In general, epigenetic phenomena are thought to govern a very wide array of biological processes that determine how genotypes interact with environmental factors in a complex, dynamic fashion to give rise to pheno- typic variability both across individuals with the same genotype or same set of environmental exposures as well as across a person’s lifetime. The ways in which these epigenetic processes impart a kind of cellular memory of activity and experience that is passed to daughter cells indicates that the timing of particular environmental exposures may be key to the develop- ment of particular diseases for individuals with particular genotypes. It also has been suggested that this cellular memory may lead individuals to select particular environments, thus creating a correlation between genotypes and

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114 GENES, BEHAVIOR, AND THE SOCIAL ENVIRONMENT environments (Carey, 2003; Gottesman and Hanson, 2005). From the standpoint of assessing the associations and interactions among social, be- havioral, and genetic factors, epigenetic processes are likely to play a major role in determining how these seemingly disparate factors operate together to give rise to the distribution of disease in a population. These processes also are likely to explain differences in research results across studies or populations when only simple single biomarkers or social indicators are examined. An increasing number of studies are starting to relate changes in DNA methylation patterns to altered patterns of gene expression that are associ- ated with disease risk (reviewed by Jones, 2005). These observations have led to the development of technologies that are capable of scanning the genome for altered patterns of DNA methylation (e.g., Kaminsky et al., 2005). Nickel, cadmium, and xenobiotics (such as diethylstilbesterol or DES) all have been shown to affect gene methylation (Sutherland and Costa, 2003; Bombail et al., 2004). Methylation, as a means of inhibiting gene expression semi-permanently, means that some toxicological agents could have permanent effects on the genomic capacity of the individual to adapt to changing environments, including other toxic agents in their environ- ment. CpG array-based technology is quickly advancing and now allows for the simultaneous detection of altered DNA methylation, histone acety- lation, and gene expression (Shi et al., 2003). As this field progresses, it will be important to integrate epigenetic and genetic approaches in order to better model the risk of disease caused by environmental toxicants. Models of how to merge epigenotype and genotype information are now starting to emerge (Bjornsson et al., 2004), and more theoretical, as well as applied, work is needed in this area of toxicogenomics. THINKING FROM THE BOTTOM UP: GENOMIC INFORMATION EMBEDDED IN BIOCHEMICAL SYSTEMS At the molecular level, SNPs are simple DNA substitutions of one A, T, G, or C base in a DNA sequence for another. By knowing which portions of the DNA sequence actually code for the protein sequence, it is possible to predict whether a DNA sequence change (i.e., an SNP) will change the sequence of the protein. If it does, then it is quite possible that the activity of the protein will be altered and thus affect its metabolic or biochemical functionality. Currently, there are 30,000 SNPs identified that alter the DNA sequence of a gene in a way that alters the protein sequence it en- codes. Approximately 60 percent of known genes have at least one SNP with a frequency of 1 percent or greater that changes its protein sequence. Moving our perspective to the level of biochemical and physiological sys-

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115 EMBEDDED RELATIONSHIPS AMONG FACTORS tems, it can be seen that these variations in protein sequence now constitute a source of variation in the metabolic functionality of cellular and physi- ological systems. It may be helpful to consider this from the perspective of the human population. With more than 6 million SNPs identified and each SNP giving rise to 3 possible genotypes in the population, there are >36 million possible genome types. Analogously, if 30,000 of these are translated into protein sequence differences, there are >330,000 unique proteomes possible in the population. This variation in proteome types will impact how social and behavioral factors are translated into variation in health and disease. In other words, many genomic variations are embedded in protein variations that are embedded in variability in cellular and physiological systems. It also should be noted that not all SNPs have a functional effect. Determining whether a particular SNP is associated with a disease, that is, actually having a biological effect, rather than being a correlate of the functional polymorphism, currently is consuming much time and effort. Proteomics Technologies Proteomics is the study of the full collection of proteins that make up our cellular and metabolic machinery. Because proteins are dynamically created and turned over as a part of normal cellular processes, proteins change in both quantity and activity depending on diet, stress, physical activity, and other environmental exposures. Each protein may be present in multiple chemically modified forms, and these protein modifications may be more critical to its metabolic or biochemical function than the amount of protein that is found in the cell (Mann and Jensen, 2003). Two major approaches used to measure the large collection of proteins in cells are gel- based proteomics and “shotgun” proteomics. In the gel-based approach, proteins are first separated by electrophoresis and then further resolved by another separation method (e.g., pH). Shotgun proteomic analysis involves relatively random digestion of complex protein mixtures followed by mass spectrometry analysis (Yates, 1998; Washburn et al., 2002). Another type of proteome analysis that has attracted widespread attention is proteomic profiling—a spectral profile of the proteins in a tissue or biofluid (e.g., serum)—using matrix-assisted laser desorption ionization-time of flight mass (Chaurand et al., 1999; Petricoin et al., 2002; Villanueva et al., 2004). The signals in these spectra contain hundreds or thousands of signals and represent intact proteins, as well as protein fragments, that collectively reflect the cellular protein machinery. This approach has been used for discovering novel biomarkers of diseases, especially cancers, that could be used for early detection (Conrads et al., 2004; Baggerly et al., 2005).

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116 GENES, BEHAVIOR, AND THE SOCIAL ENVIRONMENT Metabonomic Technologies Metabonomics (also known as metabolomics) is the analysis of small molecular products of biochemical and physiological processes. Since me- tabolism is a highly complex, dynamic, and adaptive set of systems, mea- surement of the metabonome, as well as proteomes and transcriptomes, is expected to change in response to diet, stress, physical environment, circa- dian rhythms, physical activity, developmental changes, and aging, as well as during disease development. The range of metabolic molecules is quite large, spanning from electrolytes to short-chain proteins to large lipid mol- ecules or exogenous compounds (e.g., diet and drugs) that represent both anabolic and catabolic processes from multiple tissues and organ systems. The two main technologies for measuring the metabonome are nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry. Although NMR has been used more extensively, mass spectrometry-based methods have much greater sensitivity and can detect molecules at up to 10,000-fold lower levels than NMR (Wilson et al., 2005; Brown et al., 2005). In some cases, this level of sensitivity is necessary; however, in most cases it is probably not needed. NMR spectra of urine contain thousands of signals representing thousands of metabolites (Nicholson et al., 2002). Using pat- tern recognition approaches, NMR spectra can be compared across samples to identify distinguishing patterns that reflect differences in environmental exposures. Given the current sophisticated algorithms for data processing and analysis, it is possible to chemically identify most of the peaks in a complex metabonomic spectra (Beckwith-Hall et al., 1998; Holmes et al., 1998) and in some cases tissue-specific injury or disease (Azmi et al., 2002; Griffin et al., 2004). By quantifying metabolite levels and mapping them onto known metabolic pathways new inferences can be drawn about the biochemical and cellular consequences of certain diseases (Griffin et al., 2004). Interestingly, metabomonic studies also are raising awareness of the important role that gut flora (estimated 1.5 kg/person) play in augmenting normal metabolism and how they may be a significant source of metabolic variability across individuals (Nicholson et al., 2005). THINKING FROM THE TOP DOWN: SOCIAL FACTORS INFLUENCING CELLS, TISSUES, AND PHYSIOLOGY In contrast to the “bottom up” approach, in a “top down” approach, external and human behavioral factors are mapped onto an individual’s psychological response, which can then alter proteins, metabolites, and physiological processes. In some cases, these factors can influence signal transduction, which is a key pathway for modulating gene expression in response to environmental signals. As discussed earlier, variation in the

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117 EMBEDDED RELATIONSHIPS AMONG FACTORS target gene may affect how the signal is translated into a change in gene expression. This is a molecular example of gene-environment interaction. One well-documented example of the top down approach of thinking is the study of the pathways involved in the physiological effects of stress. The direct connection between stress stimuli and the response of the neuroendo- crine system was demonstrated by the work of Walter Cannon in the 1920s (Cannon, 1932). The expression “fight or flight” was first used by Cannon to illustrate the body’s primitive physiological responses to perceived threats and other external stressors such as exposure to heat or cold. The Effects of Stress A vast body of research has been devoted to the study of the effects of stress on many biological processes throughout the life course, including CVD, immune function, and child development. Because psychologists, physiologists, and the general public use the word stress in many varying ways (Engle, 1985), there is no one agreed upon definition for the term. Individual perceptions of stress and the resulting response to the stressor depend on genetics, events that occur during early development, prior expe- riences with the stressor, and behavior, such as lifestyle choices (McEwen and Seeman, 1999). When using stress in relation to animals the term typically is used to describe the body and brain’s various responses to the presence of a threat that could compromise the physical or psychological well-being of the animal (Selye, 1973; Selye, 1975). The complex physi- ologic response to stress alters the natural priorities set by the body and can result in substantial effects on normal health maintenance and development (Johnson et al., 1992). Brain structures that mediate stress response (e.g., hypothalamus and brainstem) are also responsible for regulating vital body functions such as heart rate, respiration, digestion, reproduction, growth and development, sleep-wake cycles, and the establishment of energy stores in the absence of stress. When presented with a threat that surpasses the limits of the body’s available resources and capabilities, the brain initiates the intricate path- ways and feedback loops of the hypothalamic-pituitary-adrenal system (HPA). The HPA stimulates the production and release of steroid hor- mones, such as glucocorticoids, and neurotransmitters, such as catechol- amines. The release of cortisol, a glucocorticoid, and epinephrine, a cat- echolamine that is also referred to as adrenaline, results in a multitude of effects that allow for a quick protective response against the threat in the short term, but have the potential for adverse effects if continued for an extended period of time. The presence of cortisol and epinephrine activates and potentiates some biological processes of the body, while deactivating and dampening others.

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118 GENES, BEHAVIOR, AND THE SOCIAL ENVIRONMENT Important sympathetic nervous system responses elicited by increased levels of cortisol and epinephrine include increased heart rate and respiration, increased blood flow to muscles, mobilization of white blood cells in antici- pation of injury, and the degradation of energy stores, thus increasing levels of blood sugar. Increased levels of cortisol and epinephrine also suppress blood flow to the digestive system, dampen immune responses involved with fighting infection, and inhibit growth and reproductive hormones. Neurological effects of these important neurochemicals include sharpening vigilance and attention, while suppressing unnecessary short-term memory and learning functions (IOM, 2000). It has been postulated that exposure to stress at early life stages may have effects on the stress response system that persist throughout the entire life course. Meaney et al. (1996) used animal studies to demonstrate that infan- tile rats exposed to short-term stress, such as handling, had decreased HPA activity, thus depressing responses to stressors throughout the life course. Conversely, rats exposed to prolonged stressors, such as maternal separation, physical trauma, and administration of endotoxins, had increased HPA activ- ity, thus exacerbating response to stressors throughout the life course. In addition to these lasting HPA effects, increased levels of mRNA for corticotropin-releasing hormone (CRH) and arginine vasopressin (AVP), ini- tiators of the stress response, were observed in the hypothalamus. Evidence from this study further indicated that exposure to stress early in life also affects the gene expression of glucocorticoid receptors, explaining the high levels of CRH and AVP, which are typically regulated through a negative- feedback loop involving the glucocorticoid receptors. These findings indicate that exposure to stress early in life can have monumental effects on the development of the HPA system and future responsivity to stressors that are presented throughout life (Meaney et al., 1996). Chronic stress is implicated in many negative health outcomes that in- clude diminished immune response, arthrosclerosis, resistance to glucocorti- coids, and reproductive dysfunctions (Cavigelli and McClintock, 2003). Indi- viduals exposed to chronic stress can suffer from allostatic load, which is the accumulation of negative physiologic effects such as those listed above. It is associated with persistent high levels of catecholamines and glucocorticoids, as well as the continued struggle to achieve allostasis during times of chronic stress exposure. Genes, early development, and behaviors such as diet and exercise, and tobacco and alcohol use (see Chapter 4 for further discussions of these behaviors) all contribute to an individual’s allostatic load (McEwen and Seeman, 1999). In addition to allostatic load, Cavigelli and McClintock (2003) found that individuals with naturally high levels of glucocorticoid produced in response to stress also have decreased longevity.

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119 EMBEDDED RELATIONSHIPS AMONG FACTORS Stress and CVD The deleterious effects of stress on CVD were clearly defined in the 1950s by Selye (1956). Since that time, substantial evidence has amassed that supports the role of psychological factors in the etiology and progres- sion of CVD. For example, Manuck et al. (1988) used animal studies to illustrate an increased rate of atherosclerotic plaque buildup in individuals with chronically high blood pressure and elevated levels of catecholamines as a result of persistent socially stressful situations. The buildup of athero- sclerotic plaque is a factor in the development or complication of CVD, such as heart attack or stroke. Another factor implicated in the risk of CVD is cholesterol. High blood concentration of cholesterol and other lipids due to prolonged exposure to stress can increase the risk of developing arthrosclerosis and the risks of additional heart disease complications. Stoney et al. (1999a; 1999b) found that levels of cholesterol in the blood varied according to the degree of perceived stress, and operated independently of modifications to health behaviors that are traditionally associated with cholesterol levels such as diet and exercise. Studies of more mild exposure to stress for shorter dura- tions have also revealed elevated levels of cholesterol, specifically low- density lipoproteins, triglycerides, and other molecules associated with nega- tive health outcomes and cardiovascular disease (Stoney et al., 1999a; Stoney et al., 1999b). Traditionally it has been assumed that levels of cholesterol in the blood are indirectly linked to chronic stress through the direct effects stress has on health behavior (i.e., diet choices and physical activity). However, Stoney has proposed a model of direct effect between stress and lipid concentration. This new model hypothesizes that exposure to short-term stressors that activate the sympathetic nervous system also reduces lipase activity, the enzymes responsible for lipid metabolism and storage, thus increasing the blood lipid level in times of stress (Stoney et al., 1999a; Stoney et al., 1999b). Stress and Immune Function A considerable amount of evidence has established a relationship be- tween stress and the suppression of certain aspects of the immune system. It has also been determined that immune function during times of stress can be mediated by different factors in humans. After performing a meta- analysis of available literature, Herbert and Cohen (1993) determined for example that duration of exposure to stress played an important role in the level of the immune response. As previously mentioned, acute stress has a protective immune response. This is exhibited by increased levels of suppressor/cytotoxic T-cells. However, the presence of prolonged expo-

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121 EMBEDDED RELATIONSHIPS AMONG FACTORS the presence of recognizable caregivers (Ainsworth and Bell, 1970; Sroufe, 1979). These likely responses to short-term stressors play an important part in the emotional development of children and are not expected to have long-term adverse effects. However, as previously mentioned, stress responses inhibit normal growth and developmental processes that are an essential part of a healthy childhood, thus long-term or repeated expo- sures to stressors is likely to have negative effects on normal development (IOM, 2000). Animal studies show that infants are particularly susceptible to stress- ful events, such as neglect, that have the potential to permanently alter the HPA system, resulting in hyperactive stress responses (Meaney et al., 1996; Denenberg, 1999). Decreased maternal attention such as licking and groom- ing have also been implicated in the development of more stress-reactive animals (Liu et al., 1997). Introducing an infant that is genetically predis- posed to be more stress-reactive into the care of an adoptive mother that is genetically predisposed to be less stress-reactive causes the infant to develop with a higher than expected stress tolerance, implying a role of nurture in addition to the genetic predisposition that determines the characteristics of stress response. Primate studies demonstrate the importance of maternal presence dur- ing early life stages. Monkeys that are separated from their biological moth- ers at a very young age and reared with a cloth surrogate, but provided with daily peer interactions, are less socially inept than monkeys reared in com- plete isolation. However, the monkeys reared with the cloth surrogate still produce a number of physiological indicators that point toward anxiety and fear (Suomi, 1991). When faced with stress, these animals produce higher levels of stress response neurochemicals such as glucocorticoids and catecholamines. Other studies indicate that monkeys reared without a cloth surrogate and only in the presence of infant peers exhibit parallel hyperac- tive stress responses to those reared with the surrogate (Champoux et al., 1989; Champoux et al., 1992). As discussed, early life inputs, such as maternal presence and attention, can be crucial to the normal development of the stress response system as children grow into adults. These key inputs can keep stress response activity in check and result in the maturation of a response system that is capable of rapidly shutting down responses when the stressor has been removed. How- ever, lack of this positive input can create a system that is hyperactive and unable to modulate responses to stimuli (NRC/IOM, 2000). The 2000 National Research Council/Institute of Medicine report, From Neurons to Neighborhoods: The Science of Early Childood Development, highlights the importance and difficulty of crossing between disciplines to understand the multiple factors that influence early childhood develop- ment. The report recommends pursuing integrative science that includes:

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122 GENES, BEHAVIOR, AND THE SOCIAL ENVIRONMENT a) understanding how experience is incorporated into the developing nervous system and how the boundaries are determined that differentiate deprivation from sufficiency and sufficiency from enrichment; b) under- standing how biological processes, including neurochemical and neu- roendocrine factors, interact with environmental influences to affect the development of complex behaviors, including self-regulatory capacities, prosocial or anti-social tendencies, planning and sustained attention, and adaptive responses to stress; c) describing the dynamics of gene- environmental interactions that underlie the development of behavior and contribute to differential susceptibility to risk and capacity for resil- ience; and d) elucidating the mechanism that underlie nonoptimal birth outcomes and developmental disabilities (NRC/IOM, 2000). MOLECULAR MECHANISMS OF GENE-ENVIRONMENT INTERACTION From a molecular perspective, gene-environment interaction can mean different things to different researchers. For example, gene-environment interaction could refer to the regulation of gene transcription (i.e., gene expression) by signals from the environment binding to appropriate cell surface receptors and stimulating a signal transduction pathway that carries the molecular signal into the nucleus and eventually binding to the DNA in the promoter region of the gene to stimulate or inhibit its expression. In this case, environmental variation will increase variation in gene expression. From another perspective, gene-environment interaction could refer to how a DNA mutation in the gene alters its expression in response to the environ- ment. In this case, genetic variation is contributing to variation in gene expression even in the absence of environmental variation. From a third perspective, gene-environment interactions occur when a DNA mutation changes the protein sequence encoded by a gene and the altered protein has a different activity than the nonmutant and acts differently when perform- ing its role in a system that is processing an environmental factor. In this case, the molecularly embedded genetic information in the protein isoforms carried by the individual is translated into metabolic features that represent a gene-environment interaction. Our understanding of the molecular mechanisms of gene-environment interactions are likely to continue to expand as the “omic” technologies deliver more insight into the high-dimensional microcosms that self- organize into the macro properties of human biology that have been fine tuned to adapt to social, behavioral, and physical environments.

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123 EMBEDDED RELATIONSHIPS AMONG FACTORS THE NEED FOR SYSTEMS APPROACHES The preceding sections of this chapter described many different levels and agents of influence on health ranging from the social to the genomic to the chemical. One of the most important contributions of the research of the past few decades, climaxing for geneticists with the Human Genome Project, is that it is pushing scientists toward a more holistic view of human biology. As scientists try to put the pieces of the puzzle together, the natural step beyond examining single agents of health and disease is to move to- ward a systems view. Recently, there has been a resurgence in the amount of attention that has been given to systems biology because of the vast amount of data that can now be collected at the genomic, transcriptomic, proteomic, and metabolomic levels. However, systems theories and meth- ods have a long tradition in science. The development of path analysis by Sewall Wright in the 1940s—a correlational approach—was one of the first attempts at studying states and relationships among many variables in order to understand the whole. This work more recently has evolved to use the sophisticated statistical method called Structural Equation Modeling (Hoyle, 1995; Maruyama, 1997), which has been used successfully in the behavioral and social sciences. The development of a general systems theory approach by Bertalanffy (1968) to describe dynamical systems catalyzed the development of new methods of analysis such as Biochemical System Theory (Savageau, 1976) and Metabolic Control Theory (Kacser and Burns, 1973). Arthur Guyton’s work using control theory to model the regulation of physiological systems (Guyton, 1976) is another important example of the use of systems concepts to model wholes from parts. In attempting to build bridges between social, behavioral, and genetic information about health and disease, investing in new systems approaches is likely to yield many new insights in areas of investigations such as how small nonlinear effects result in significant health outcomes. One of the most difficult aspects of integrating this knowledge into a systems approach is that the information is organized somewhat but not exactly hierarchi- cally. For example, a traditional hierarchical view of biology looks some- thing like this: DNA → mRNA → protein → protein interactions → meta- bolic pathway → metabolic networks → cells → tissues → organs → organisms → populations → ecologies. However, there also is feedback from the ecology to the organism to metabolic pathways to the DNA, which does not strictly follow the same pathways. Biological information has several important features: it operates on multiple hierarchical levels of organization at the same time and thus is indeed embedded. It is processed in complex networks. These information networks are typically robust, such that many single perturbations will not greatly affect them. There are key nodes in the network where perturbations may have profound effects;

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124 GENES, BEHAVIOR, AND THE SOCIAL ENVIRONMENT these offer powerful targets for the understanding and manipulation of the system (Ideker et al., 2001). The central task of a systems approach is to (a) comprehensively gather information from each of the distinct levels, (b) examine relationships among the agents of the system, (c) hypothesize sys- tem topologies, (d) integrate data into predictive mathematical models of the system, (e) test predictions, and (f) identify key regulatory signals and relationships where intervention could stimulate new outcomes. There are a growing number of publicly available molecular databases and systems analysis software programs that could be used for initiating systems modeling of social, behavioral, and genetic interactions. For in- stance, the Database of Interacting Proteins (Xenarios et al., 2001), the Biomolecular Interaction Network Database (Bader et al., 2001), and the Munich Information Center for Protein Sequences of the German National Center for Environment and Health (Mewes et al., 1999) contain search- able catalogs of known protein-protein interactions; the Transcription Fac- tors Database (Wingender et al., 2000) and The Promoter Database of Saccharomyces cerevisiae (Zhu and Zhang, 1999) catalog interactions be- tween proteins and DNA (i.e., transcription factor interactions), and data- bases of metabolic pathways also recently have been established (e.g., EcoCyc [Karp et al., 2000], KEGG [Ogata et al., 1999], and What Is There [Selkov et al., 1998]). A growing number of databases are also under devel- opment for storing the now sizeable number of mRNA-expression datasets (Ermolaeva et al., 1998; Stoeckert et al., 1999; Hawkins et al., 1999; Ringwald et al., 2000; Aach et al., 2000); companies, such as Affymetrix, Rosetta, Spotfire, Informax, Incyte, Gene Logic, and Silicon Genetics, mar- ket gene-expression databases commercially. Notably lacking from this list, however, are repositories of information on the behavioral and social com- ponents of the system. Work toward developing publicly available informa- tion on these levels could open up significant possibilities for the computer modeling of health outcomes. The development and practice of systems approaches to model social, behavioral, and genetic interactions involves a number of requirements that will pose particular challenges for researchers. These include: (a) bridging disciplinary and language barriers encountered by teams of social scientists, behavioral scientists, molecular biologists, geneticists, and computational scientists; (b) the need for high-throughput facilities for molecular tech- nologies, such as DNA sequencing, DNA arrays, genotyping, proteomics, metabonomics, and tissue arrays; (c) a lack of integrated public health, medical, and biological informatics systems; (d) the need to develop novel analytical tools and efficient, powerful computational infrastructures; (e) a lack of integration of discovery-driven and hypothesis-driven science; and (f) the need to develop diverse partnerships among academia, community, industry, and government.

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125 EMBEDDED RELATIONSHIPS AMONG FACTORS To address these challenges and advance our understanding of the complex contributions to health of social, behavioral, and genetic fac- tors, it becomes imperative to move toward conducting research that assesses the interactions of these variables (see Chapter 8 for a detailed discussion of interactions). Therefore, the committee makes the following recommendations: Recommendation 1: Conduct Transdisciplinary, Collaborative Re- search. The National Institutes of Health (NIH) should develop Requests for Applications (RFAs) to study the impact on health of interactions among social, behavioral, and genetic factors and their interactive pathways (i.e., physiological). Such transdisciplinary re- search should involve the genuine collaboration of social, behav- ioral, and genetic scientists. Genuine collaboration is essential for the identification, incorporation, analysis, and interpretation of the multiple variables used. Recommendation 2: Measure Key Variables Over the Life Course and Within the Context of Culture. The NIH should develop RFAs for studies of interactions that incorporate measurement, over the life course and within the context of culture, of key variables in the important domains of social, behavioral, and genetic factors. Essential social variables include educational attainment, income and wealth, occupational status, social networks/social support, and the work conditions that have been linked consistently and robustly to health out- comes. Behavioral and psychological variables include tobacco/alcohol/drug use, eating behavior, physical activity, temperament, perceived stress and coping, perceived social support, emotional state, and motivation. Essential genetic factors include the DNA sequence variation, structural chromo- somal changes, gene expression, epigenetic modifications, and downstream targets of gene expression. Physiological measures should consider relevant hormones, neurotransmitters, signaling molecules, and cell types that serve as transducing mechanisms between the social world and genetics. Further- more, candidate physiological measures should be selected that recognize biological and clinical relevance; practical application in the context of large-scale field studies; interactions among multiple physiological systems that are traditionally compartmentalized (e.g., the nervous system, the en- docrine system, and the immune system); intracellular pathways that medi- ate the interaction between gene function and physiological systems; and the role of a given physiological measure in multiple biological systems. Finally, because of the complexity encountered in variables related to sex/ gender and race/ethnicity, such variables must be considered and analyzed

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126 GENES, BEHAVIOR, AND THE SOCIAL ENVIRONMENT from a variety of perspectives, including social, cultural, psychological, historical, political, genetic, and geographic/ancestral. Additionally, as discussed previously and in Chapter 8, the study of interactions will require new modeling strategies, the use of profiling ap- proaches, and the conduct of research in diverse groups and settings. There- fore, the committee proposes the following recommendations: Recommendation 3: Develop and Implement New Modeling Strat- egies to Build More Comprehensive, Predictive Models of Etio- logically Heterogeneous Disease. The NIH should emphasize re- search aimed at developing and implementing such models (e.g., pattern recognition, multivariate statistics, and systems-oriented approaches) for incorporating social, behavioral, and genetic fac- tors and their interactive pathways (i.e., physiological) in testable models within populations, clinical settings, or animal studies. Recommendation 4: Investigate Biological Signatures. Researchers should use genomic, transcriptomic, proteomic, metabonomic, and other high-dimensional molecular approaches to discover new con- stellations of genetic factors, biomarkers, and mediating systems through which interactions with social environment and behavior influence health. Recommendation 5: Conduct Research in Diverse Groups and Settings. The NIH should encourage research on the impact of interactions among social, behavioral, and genetic factors and their interactive pathways (i.e., physiological) on health that empha- sizes diversity in groups and settings. Furthermore, NIH should support efforts to ensure that the findings of such research are validated by replication in independent studies, translated to pa- tient-oriented research, conducted and applied in the context of public health, and used to design preventive and therapeutic approaches. Transdisciplinary research assessing the impact on health of interac- tions among social, behavioral, and genetic factors has the potential to bring to the fore new understanding of disease risk. Such an understanding could lead to the development of more effective interventions and, ulti- mately, to improved health for individuals and populations. This research provides an exciting opportunity to advance our understanding and our impact on improving health.

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