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      Addressing Human Variability in Next-Generation Human Health Risk Assessments of Environmental Chemicals

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          Abstract

          Background: Characterizing variability in the extent and nature of responses to environmental exposures is a critical aspect of human health risk assessment.

          Objective: Our goal was to explore how next-generation human health risk assessments may better characterize variability in the context of the conceptual framework for the source-to-outcome continuum.

          Methods: This review was informed by a National Research Council workshop titled “Biological Factors that Underlie Individual Susceptibility to Environmental Stressors and Their Implications for Decision-Making.” We considered current experimental and in silico approaches, and emerging data streams (such as genetically defined human cells lines, genetically diverse rodent models, human omic profiling, and genome-wide association studies) that are providing new types of information and models relevant for assessing interindividual variability for application to human health risk assessments of environmental chemicals.

          Discussion: One challenge for characterizing variability is the wide range of sources of inherent biological variability (e.g., genetic and epigenetic variants) among individuals. A second challenge is that each particular pair of health outcomes and chemical exposures involves combinations of these sources, which may be further compounded by extrinsic factors (e.g., diet, psychosocial stressors, other exogenous chemical exposures). A third challenge is that different decision contexts present distinct needs regarding the identification—and extent of characterization—of interindividual variability in the human population.

          Conclusions: Despite these inherent challenges, opportunities exist to incorporate evidence from emerging data streams for addressing interindividual variability in a range of decision-making contexts.

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          Most cited references88

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          Genetics of gene expression and its effect on disease.

          Common human diseases result from the interplay of many genes and environmental factors. Therefore, a more integrative biology approach is needed to unravel the complexity and causes of such diseases. To elucidate the complexity of common human diseases such as obesity, we have analysed the expression of 23,720 transcripts in large population-based blood and adipose tissue cohorts comprehensively assessed for various phenotypes, including traits related to clinical obesity. In contrast to the blood expression profiles, we observed a marked correlation between gene expression in adipose tissue and obesity-related traits. Genome-wide linkage and association mapping revealed a highly significant genetic component to gene expression traits, including a strong genetic effect of proximal (cis) signals, with 50% of the cis signals overlapping between the two tissues profiled. Here we demonstrate an extensive transcriptional network constructed from the human adipose data that exhibits significant overlap with similar network modules constructed from mouse adipose data. A core network module in humans and mice was identified that is enriched for genes involved in the inflammatory and immune response and has been found to be causally associated to obesity-related traits.
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            Variations in DNA elucidate molecular networks that cause disease.

            Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase beta (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors.
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              Molecular networks as sensors and drivers of common human diseases.

              The molecular biology revolution led to an intense focus on the study of interactions between DNA, RNA and protein biosynthesis in order to develop a more comprehensive understanding of the cell. One consequence of this focus was a reduced attention to whole-system physiology, making it difficult to link molecular biology to clinical medicine. Equipped with the tools emerging from the genomics revolution, we are now in a position to link molecular states to physiological ones through the reverse engineering of molecular networks that sense DNA and environmental perturbations and, as a result, drive variations in physiological states associated with disease.
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                Author and article information

                Journal
                Environ Health Perspect
                Environ. Health Perspect
                EHP
                Environmental Health Perspectives
                National Institute of Environmental Health Sciences
                0091-6765
                1552-9924
                19 October 2012
                January 2013
                : 121
                : 1
                : 23-31
                Affiliations
                [1 ]Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, California, USA
                [2 ]Institut National de l’Environnement Industriel et des Risques (INERIS), Verneuil en Halatte, France
                [3 ]National Center for Environmental Assessment, U.S. Environmental Protection Agency, Washington, DC, USA
                [4 ]George Perkins Marsh Institute, Clark University, Worcester, Massachusetts, USA
                [5 ]Department of Environmental Sciences and Engineering, University of North Carolina–Chapel Hill, Chapel Hill, North Carolina, USA
                Author notes
                Address correspondence to L. Zeise, California Environmental Protection Agency, 1515 Clay St., 16th Floor, Oakland, CA 94612 USA. Telephone: (510) 622-3195. Fax: (510) 622-3211. E-mail: Lauren.Zeise@ 123456oehha.ca.gov
                Article
                ehp.1205687
                10.1289/ehp.1205687
                3553440
                23086705
                f31398bb-8dd6-4e13-a670-75ba259451f9
                Copyright @ 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, properly cited.

                History
                : 28 June 2012
                : 19 October 2012
                Categories
                Review

                Public health
                environmental agents,genetics,human health risk assessment,modeling,omics technologies,susceptible populations,variability

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