Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 109
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate 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-
OCR for page 110
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate 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 biological 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 following subsections further illustrate this concept and its implications for assessing the impact of associations and interactions among social, behavioral, 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 Information1), and investigators around the world are engaged in performing genetic 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 expressed (i.e., transcribed in messenger RNA). Currently, most gene expression studies ignore individual-level variation in gene expression due to genetic variation. However, over the past few years several landmark stud- 1 See www.bioinfo.org.cn/relative/dbSNP%20Home%20Page.htm.
OCR for page 111
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate 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 humans 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 lymphocyte 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 levels. 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 measurement 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 expression (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
OCR for page 112
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate has been facilitated in large part by the creation of the Minimum Information 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 scientific 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 knowledge 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 initially 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. Cluster analysis has been one commonly used tool for multidimensional visualization 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 linkage databases, epigenetic information on imprinting, comparative genomic hybridization arrays, proteomic databases, and metabolic pathway databases provides an unparalleled opportunity for integration across the levels of the molecular universe that characterizes our human biology. For example, the Gene Ontology Project (www.geneontology.org) attempts to classify gene products, assigning proteins to groups specifying their molecular function, the biological process to which they contribute, and their cellular component (Ashburner et al., 2000). Similarly, using Enzyme Commission 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 technology is an incredibly powerful tool used to investigate complex gene expression relationships on a genome-wide scale, and it likely will be invaluable in assessing the relationships among social, behavioral, and genetic factors as they relate to health and disease.
OCR for page 113
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate Epigenetic Phenomenon Epigenesis originated as a term to describe the processes in embryonic development that transforms the undifferentiated cells in the newly fertilized 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 transform 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 processes 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 postnatal nutrition can have long-lasting epigenetic effects on an adult’s predisposition 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 offspring 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 phenotypic 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 development 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
OCR for page 114
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate environments (Carey, 2003; Gottesman and Hanson, 2005). From the standpoint of assessing the associations and interactions among social, behavioral, 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 associated 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 environment. CpG array-based technology is quickly advancing and now allows for the simultaneous detection of altered DNA methylation, histone acetylation, 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 encodes. 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-
OCR for page 115
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate tems, it can be seen that these variations in protein sequence now constitute a source of variation in the metabolic functionality of cellular and physiological 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 gelbased 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).
OCR for page 116
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate Metabonomic Technologies Metabonomics (also known as metabolomics) is the analysis of small molecular products of biochemical and physiological processes. Since metabolism is a highly complex, dynamic, and adaptive set of systems, measurement of the metabonome, as well as proteomes and transcriptomes, is expected to change in response to diet, stress, physical environment, circadian 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 molecules 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 pattern 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
OCR for page 117
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate 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 neuroendocrine 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 experiences 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 physiologic 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 pathways and feedback loops of the hypothalamic-pituitary-adrenal system (HPA). The HPA stimulates the production and release of steroid hormones, such as glucocorticoids, and neurotransmitters, such as catechol-amines. The release of cortisol, a glucocorticoid, and epinephrine, a catecholamine 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.
OCR for page 118
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate 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 anticipation 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 infantile 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 activity, 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), initiators 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 include diminished immune response, arthrosclerosis, resistance to glucocorticoids, and reproductive dysfunctions (Cavigelli and McClintock, 2003). Individuals 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.
OCR for page 119
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate 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 progression 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 atherosclerotic 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 durations have also revealed elevated levels of cholesterol, specifically low-density lipoproteins, triglycerides, and other molecules associated with negative 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 between 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-
OCR for page 121
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate 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 exposures to stressors is likely to have negative effects on normal development (IOM, 2000). Animal studies show that infants are particularly susceptible to stressful 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 grooming have also been implicated in the development of more stress-reactive animals (Liu et al., 1997). Introducing an infant that is genetically predisposed 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 during early life stages. Monkeys that are separated from their biological mothers 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 complete 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 hyperactive 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. However, 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 development. The report recommends pursuing integrative science that includes:
OCR for page 122
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate 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) understanding how biological processes, including neurochemical and neuroendocrine 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 resilience; 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 environment. 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 performing 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.
OCR for page 123
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate 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 toward 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 methods 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 hierarchically. For example, a traditional hierarchical view of biology looks something like this: DNA → mRNA → protein → protein interactions → metabolic 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;
OCR for page 124
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate 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 system 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 instance, 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 searchable catalogs of known protein-protein interactions; the Transcription Factors Database (Wingender et al., 2000) and The Promoter Database of Saccharomyces cerevisiae (Zhu and Zhang, 1999) catalog interactions between proteins and DNA (i.e., transcription factor interactions), and databases 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 development 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, market gene-expression databases commercially. Notably lacking from this list, however, are repositories of information on the behavioral and social components of the system. Work toward developing publicly available information 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 technologies, 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.
OCR for page 125
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate To address these challenges and advance our understanding of the complex contributions to health of social, behavioral, and genetic factors, 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 Research. 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 research should involve the genuine collaboration of social, behavioral, 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 outcomes. 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 chromosomal 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. Furthermore, 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 endocrine system, and the immune system); intracellular pathways that mediate 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
OCR for page 126
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate 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 approaches, and the conduct of research in diverse groups and settings. Therefore, the committee proposes the following recommendations: Recommendation 3: Develop and Implement New Modeling Strategies to Build More Comprehensive, Predictive Models of Etiologically Heterogeneous Disease. The NIH should emphasize research aimed at developing and implementing such models (e.g., pattern recognition, multivariate statistics, and systems-oriented approaches) for incorporating social, behavioral, and genetic factors 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 constellations 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 emphasizes 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 patient-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 interactions 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, ultimately, to improved health for individuals and populations. This research provides an exciting opportunity to advance our understanding and our impact on improving health.
OCR for page 127
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate REFERENCES Aach J, Rindone W, Church GM. 2000. Systematic management and analysis of yeast gene expression data. Genome Research 10(4):431-445. Ainsworth MD, Bell SM. 1970. Attachment, exploration, and separation: Illustrated by the behavior of one-year-olds in a strange situation. Child Development 41(1):49-67. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. 2000. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics 25(1): 25-29. Azmi J, Griffin JL, Antti H, Shore RF, Johansson E, Nicholson JK, Holmes E. 2002. Metabolic trajectory characterisation of xenobiotic-induced hepatotoxic lesions using statistical batch processing of NMR data. Analyst 127(2):271-276. Bader GD, Donaldson I, Wolting C, Ouellette BF, Pawson T, Hogue CW. 2001. BIND—The Biomolecular Interaction Network Database. Nucleic Acids Research 29(1):242-245. Baggerly KA, Morris JS, Edmonson SR, Coombes KR. 2005. Signal in noise: Evaluating reported reproducibility of serum proteomic tests for ovarian cancer. Journal of the National Cancer Institute 97(4):307-309. Ball CA, Sherlock G, Parkinson H, Rocca-Sera P, Brooksbank C, Causton HC, Cavalieri D, Gaasterland T, Hingamp P, Holstege F, Ringwald M, Spellman P, Stoeckert CJ Jr, Stewart JE, Taylor R, Brazma A, Quackenbush J. 2002a. Standards for microarray data. Science 298(5593):539. Ball CA, Sherlock G, Parkinson H, Rocca-Sera P, Brooksbank C, Causton HC, Cavalieri D, Gaasterland T, Hingamp P, Holstege F, Ringwald M, Spellman P, Stoeckert CJ Jr, Stewart JE, Taylor R, Brazma A, Quackenbush J. 2002b. The underlying principles of scientific publication. Bioinformatics 18(11):1409. Ball C, Brazma A, Causton H, Chervitz S, Edgar R, Hingamp P, Matese JC, Parkinson H, Quackenbush J, Ringwald M, Sansone SA, Sherlock G, Spellman P, Stoeckert C, Tateno Y, Taylor R, White J, Winegarden N. 2004a. Standards for microarray data: An open letter. Environmental Health Perspectives 112(12):A666-A667. Ball CA, Brazma A, Causton H, Chervitz S, Edgar R, Hingamp P, Matese JC, Parkinson H, Quackenbush J, Ringwald M, Sansone SA, Sherlock G, Spellman P, Stoeckert C, Tateno Y, Taylor R, White J, Winegarden N. 2004b. Submission of microarray data to public repositories. PLoS Biology 2(9):E317. Beckwith-Hall BM, Nicholson JK, Nicholls AW, Foxall PJ, Lindon JC, Connor SC, Abdi M, Connelly J, Holmes E. 1998. Nuclear magnetic resonance spectroscopic and principal components analysis investigations into biochemical effects of three model hepatotoxins. Chemical Research in Toxicology 11(4):260-272. Bertalanffy L. 1968. General System Theory: Foundations, Development, Applications. New York: George Brazillier. Bjornsson HT, Fallin MD, Feinberg AP. 2004. An integrated epigenetic and genetic approach to common human disease. Trends in Genetics 20(8):350-358. Bombail V, Moggs JG, Orphanides G. 2004. Perturbation of epigenetic status by toxicants. Toxicology Letters 149(1-3):51-58. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M. 2001. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nature Genetics 29(4):365-371.
OCR for page 128
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate Bronson G. 1971. Fear of the unfamiliar in human infants. In: Schaffer H, editor. The Origin of Human Social Relations. London: Academic Press. Brown SC, Kruppa G, Dasseux JL. 2005. Metabolomics applications of FT-ICR mass spectrometry. Mass Spectrometry Reviews 24(2):223-231. Brun C, Herrmann C, Guenoche A. 2004. Clustering proteins from interaction networks for the prediction of cellular functions. BMC Bioinformatics 5:95. Cannon WB. 1932. The Wisdom of the Body. New York: Norton. Carey G. 2003. Human Genetics for the Social Sciences. Thousand Oaks, CA: Sage. Cavigelli SA, McClintock MK. 2003. Fear of novelty in infant rats predicts adult corticosterone dynamics and an early death. Proceedings of the National Academy of Sciences of the United States of America 100(26):16131-16136. Champoux M, Coe S, Schanberg S, Kuhn C, Suomi S. 1989. Hormonal effects of early rearing conditions in the infant Rhesus monkey. American Journal of Primatology 19:111-117. Champoux M, Byrne E, Delizio R, Suomi S. 1992. Motherless mothers revisited: Rhesus maternal behavior and rearing history. Primates 33(2):251-255. Chaurand P, Stoeckli M, Caprioli RM. 1999. Direct profiling of proteins in biological tissue sections by MALDI mass spectrometry. Analytical Chemistry 71(23):5263-5270. Cheung VG, Spielman RS, Ewens KG, Weber TM, Morley M, Burdick JT. 2005. Mapping determinants of human gene expression by regional and genome-wide association. Nature 437(7063):1365-1369. Cohen S, Doyle WJ, Turner RB, Alper CM, Skoner DP. 2003. Emotional style and susceptibility to the common cold. Psychosomatic Medicine 65(4):652-657. Conrads TP, Hood BL, Issaq HJ, Veenstra TD. 2004. Proteomic patterns as a diagnostic tool for early-stage cancer: A review of its progress to a clinically relevant tool. Molecular Diagnosis 8(2):77-85. Denenberg VH. 1999. Commentary: Is maternal stimulation the mediator of the handling effect in infancy? Developmental Psychobiology 34(1):1-3. Djebbari A, Karamycheva S, Howe E, Quackenbush J. 2005. MeSHer: Identifying biological concepts in microarray assays based on PubMed references and MeSH terms. Bioinformatics 21(15):3324-3326. Doniger SW, Salomonis N, Dahlquist KD, Vranizan K, Lawlor SC, Conklin BR. 2003. MAPPFinder: Using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biology 4(1):R7. Engle B. 1985. Stress is a noun! No, a verb! No, an adjective. In: Field T, McCabe P, Sneiderman N, editors. Stress and Coping. Vol. 1. Hillsdale, NJ: Erlbaum. Pp. 3-12. Ermolaeva O, Rastogi M, Pruitt KD, Schuler GD, Bittner ML, Chen Y, Simon R, Meltzer P, Trent JM, Boguski MS. 1998. Data management and analysis for gene expression arrays. Nature Genetics 20(1):19-23. Fink JL, Drewes S, Patel H, Welsh JB, Masys DR, Corbeil J, Gribskov M. 2003. 2HAPI: A microarray data analysis system. Bioinformatics 19(11):1443-1445. Gottesman II, Hanson DR. 2005. Human development: Biological and genetic processes. Annual Review of Psychology 56:263-286. Griffin JL, Bonney SA, Mann C, Hebbachi AM, Gibbons GF, Nicholson JK, Shoulders CC, Scott J. 2004. An integrated reverse functional genomic and metabolic approach to understanding orotic acid-induced fatty liver. Physiological Genomics 17(2):140-149. Guyton AC. 1976. Interstitial fluid pressure and dynamics of lymph formation. Introduction. Federation Proceedings 35(8):1861-1862. Hawkins V, Doll D, Bumgarner R, Smith T, Abajian C, Hood L, Nelson PS. 1999. PEDB: The Prostate Expression Database. Nucleic Acids Research 27(1):204-208. Herbert TB, Cohen S. 1993. Stress and immunity in humans: A meta-analytic review. Psychosomatic Medicine 55(4):364-379.
OCR for page 129
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate Holmes E, Nicholls AW, Lindon JC, Ramos S, Spraul M, Neidig P, Connor SC, Connelly J, Damment SJ, Haselden J, Nicholson JK. 1998. Development of a model for classification of toxin-induced lesions using 1H NMR spectroscopy of urine combined with pattern recognition. NMR in Biomedicine 11(4-5):235-244. Hoyle RH. 1995. Structural Equation Modeling: Concepts, Issues, and Applications. Thousand Oaks, CA: Sage Publications. Ideker T, Galitski T, Hood L. 2001. A new approach to decoding life: Systems biology. Annual Review of Genomics and Human Genetics 2(1):343-372. Jablonka E, Lamb MJ. 2002. The changing concept of epigenetics. Annals of the New York Academy of Sciences 981:82-96. Jaenisch R, Bird A. 2003. Epigenetic regulation of gene expression: How the genome integrates intrinsic and environmental signals. Nature Genetics 33(Suppl):245-254. Jenssen TK, Laegreid A, Komorowski J, Hovig E. 2001. A literature network of human genes for high-throughput analysis of gene expression. Nature Genetics 28(1):21-28. Johnson EO, Kamilaris TC, Chrousos GP, Gold PW. 1992. Mechanisms of stress: A dynamic overview of hormonal and behavioral homeostasis. Neuroscience and Biobehavioral Reviews 16(2):115-130. Jones PA. 2005. Overview of cancer epigenetics. Seminars in Hematology 42(3 Suppl 2): S3-S8. Kacser H, Burns JA. 1973. The control of flux. Symposia of the Society for Experimental Biology 27:65-104. Kaminsky ZA, Assadzadeh A, Flanagan J, Petronis A. 2005. Single nucleotide extension technology for quantitative site-specific evaluation of metC/C in GC-rich regions. Nucleic Acids Research 33(10):E95. Kanehisa M. 2002. The KEGG database. Novartis Foundation Symposium 247:91-101; discussion 101-103, 119-128, 244-252. Karp PD, Riley M, Saier M, Paulsen IT, Paley SM, Pellegrini-Toole A. 2000. The EcoCyc and MetaCyc databases. Nucleic Acids Research 28(1):56-59. Liu D, Diorio J, Tannenbaum B, Caldji C, Francis D, Freedman A, Sharma S, Pearson D, Plotsky PM, Meany MJ. 1997. Maternal care, hippocampal glucocorticoid receptors, and hypothalamic-pituitary-adrenal responses to stress. Science 277:1659-1662. Lo HS, Wang Z, Hu Y, Yang HH, Gere S, Buetow KH, Lee MP. 2003. Allelic variation in gene expression is common in the human genome. Genome Research 13(8):1855-1862. Malek RL, Irby RB, Guo QM, Lee K, Wong S, He M, Tsai J, Frank B, Liu ET, Quackenbush J, Jove R, Yeatman TJ, Lee NH. 2002. Identification of Src transformation fingerprint in human colon cancer. Oncogene 21(47):7256-7265. Mann M, Jensen ON. 2003. Proteomic analysis of post-translational modifications. Nature Biotechnology 21(3):255-261. Manuck SB, Kaplan JR, Adams MR, Clarkson TB. 1988. Studies of psychosocial influences on coronary artery atherogenesis in cynomolgus monkeys. Health Psychology 7(2): 113-124. Maruyama GM. 1997. Basics of Structural Eqaution Modeling. Thousand Oaks, CA: Sage Publications. McClintock MK, Conzen SD, Gehlert S, Masi C, Olopade F. 2005. Mammary cancer and social interactions: Identifying multiple environments that regulate gene expression throughout the life span. Journals of Gerontology: Series B 60B(Special Issue 1):32-41. McEwen B, Seeman T. 1999. Allostatic Load and Allostasis. [Online]. Available: www.macses.ucsf.edu/Research/Allostatic/notebook/allostatic.html#Allostasis [accessed May 22, 2006].
OCR for page 130
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate Meaney MJ, Diorio J, Francis D, Widdowson J, LaPlante P, Caldji C, Sharma S, Seckl JR, Plotsky PM. 1996. Early environmental regulation of forebrain glucocorticoid receptor gene expression: Implications for adrenocortical responses to stress. Developmental Neuroscience 18(1-2):49-72. Mewes HW, Heumann K, Kaps A, Mayer K, Pfeiffer F, Stocker S, Frishman D. 1999. MIPS: A database for genomes and protein sequences. Nucleic Acids Research 27(1):44-48. Nicholson JK, Connelly J, Lindon JC, Holmes E. 2002. Metabonomics: A platform for studying drug toxicity and gene function. Nature Review Drug Discovery 1(2):153-161. Nicholson JK, Holmes E, Wilson ID. 2005. Gut microorganisms, mammalian metabolism and personalized health care. Nature Review Microbiology 3(5):431-438. NRC/IOM (National Research Council/Institute of Medicine). 2000. From Neurons to Neighborhoods: The Science of Early Childhood Development. Washington, DC: National Academy Press. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. 1999. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research 27(1):29-34. Petricoin EF, Ardekani AM, Hitt BA, Levine PJ, Fusaro VA, Steinberg SM, Mills GB, Simone C, Fishman DA, Kohn EC, Liotta LA. 2002. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359(9306):572-577. Petronis A. 2003. Epigenetics and bipolar disorder: New opportunities and challenges. American Journal of Medical Genetics. Part C, Seminars in Medical Genetics 123(1):65-75. Ringwald M, Eppig JT, Kadin JA, Richardson JE. 2000. GXD: A Gene Expression Database for the laboratory mouse: Current status and recent enhancements. The Gene Expresison Database group. Nucleic Acids Research 28(1):115-119. Savageau, MA. 1976. Biochemical Systems Analysis. Reading, MA: Addison-Wesley Publishing Co. Selkov E Jr, Grechkin Y, Mikhailova N, Selkov E. 1998. MPW: The Metabolic Pathways Database. Nucleic Acids Research 26(1):43-45. Selye H. 1956. The Stress of Life. New York: McGraw-Hill. Selye H. 1973. The evolution of the stress concept. American Scientist 61(6):692-699. Selye H. 1975. Confusion and controversy in the stress field. Journal of Human Stress 1(2): 37-44. Shahbazian MD, Zoghbi HY. 2002. Rett syndrome and MeCP2: Linking epigenetics and neuronal function. American Journal of Human Genetics 71(6):1259-1272. Shi H, Wei SH, Leu YW, Rahmatpanah F, Liu JC, Yan PS, Nephew KP, Huang TH. 2003. Triple analysis of the cancer epigenome: An integrated microarray system for assessing gene expression, DNA methylation, and histone acetylation. Cancer Research 63(9):2164-2171. Sroufe L. 1979. Socioemotional development. In: Osofsky J, editor. Handbook of Infant Development. New York: John Wiley & Sons. Pp. 462-515. Stefanski V, Raabe C, Schulte M. 2005. Pregnancy and social stress in female rats: Influences on blood leukocytes and corticosterone concentrations. Journal of Neuroimmunology 162(1-2):81-88. Stoeckert CJ Jr, Salas F, Brunk B, Overton GC. 1999. EpoDB: A prototype database for the analysis of genes expressed during vertebrate erythropoiesis. Nucleic Acids Research 27(1):200-203. Stoney CM, Bausserman L, Niaura R, Marcus B, Flynn M. 1999a. Lipid reactivity to stress: II. Biological and behavioral influences. Health Psychology 18(3):251-261. Stoney CM, Niaura R, Bausserman L, Matacin M. 1999b. Lipid reactivity to stress: I. Comparison of chronic and acute stress responses in middle-aged airline pilots. Health Psychology 18(3):241-250.
OCR for page 131
Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate Stuart JM, Segal E, Koller D, Kim SK. 2003. A gene-coexpression network for global discovery of conserved genetic modules. Science 302(5643):249-255. Suomi S. 1991. Adolescent depression and depressive symptoms: Insights from longitudinal studies with Rhesus monkeys. Journal of Youth and Adolescence 20(2):273-287. Sutherland JE, Costa M. 2003. Epigenetics and the environment. Annals of the New York Academy of Sciences 983:151-160. Villanueva J, Philip J, Entenberg D, Chaparro CA, Tanwar MK, Holland EC, Tempst P. 2004. Serum peptide profiling by magnetic particle-assisted, automated sample processing and MALDI-TOF mass spectrometry. Analytical Chemistry 76(6):1560-1570. Waddington CH. 1957. The Strategy of the Genes: A Discussion of Some Aspects of Theoretical Biology. New York: The MacMillan Company. Washburn MP, Ulaszek R, Deciu C, Schieltz DM, Yates JR 3rd. 2002. Analysis of quantitative proteomic data generated via multidimensional protein identification technology. Analytical Chemistry 74(7):1650-1657. Waterland RA, Jirtle RL. 2004. Early nutrition, epigenetic changes at transposons and imprinted genes, and enhanced susceptibility to adult chronic diseases. Nutrition 20(1): 63-68. Waters E, Matas L, Sroufe LA. 1975. Infants’ reactions to an approaching stranger: description, validation, and functional significance of wariness. Child Development 46(2): 348-356. Wilson ID, Plumb R, Granger J, Major H, Williams R, Lenz EM. 2005. HPLC-MS-based methods for the study of metabonomics. Journal of Chromatography B Analytical Technologies in the Biomedical and Life Sciences 817(1):67-76. Wingender E, Chen X, Hehl R, Karas H, Liebich I, Matys V, Meinhardt T, Pruss M, Reuter I, Schacherer F. 2000. TRANSFAC: An integrated system for gene expression regulation. Nucleic Acids Research 28(1):316-319. Xenarios I, Fernandez E, Salwinski L, Duan XJ, Thompson MJ, Marcotte EM, Eisenberg D. 2001. DIP: The Database of Interacting Proteins: 2001 update. Nucleic Acids Research 29(1):239-241. Yates J. 1998. Mass spectrometry and the age of the proteome. Journal of Mass Spectrometry 33(1):1-19. Zhu J, Zhang MQ. 1999. SCPD: A promoter database of the yeast Saccharomyces cerevisiae. Bioinformatics 15(7-8):607-611.
Representative terms from entire chapter: