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      Convergence and divergence in gene expression among natural populations exposed to pollution

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      1 , 3 , , 2 , 3
      BMC Genomics
      BioMed Central

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          Abstract

          Background

          Natural populations of the teleost fish Fundulus heteroclitus tolerate a broad range of environmental conditions including temperature, salinity, hypoxia and chemical pollutants. Strikingly, populations of Fundulus inhabit and have adapted to highly polluted Superfund sites that are contaminated with persistent toxic chemicals. These natural populations provide a foundation to discover critical gene pathways that have evolved in a complex natural environment in response to environmental stressors.

          Results

          We used Fundulus cDNA arrays to compare metabolic gene expression patterns in the brains of individuals among nine populations: three independent, polluted Superfund populations and two genetically similar, reference populations for each Superfund population. We found that up to 17% of metabolic genes have evolved adaptive changes in gene expression in these Superfund populations. Among these genes, two (1.2%) show a conserved response among three polluted populations, suggesting common, independently evolved mechanisms for adaptation to environmental pollution in these natural populations.

          Conclusion

          Significant differences among individuals between polluted and reference populations, statistical analyses indicating shared adaptive changes among the Superfund populations, and lack of reduction in gene expression variation suggest that common mechanisms of adaptive resistance to anthropogenic pollutants have evolved independently in multiple Fundulus populations. Among three independent, Superfund populations, two genes have a common response indicating that high selective pressures may favor specific responses.

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          Most cited references48

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            Molecular Evolutionay Genetics

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              Assessing gene significance from cDNA microarray expression data via mixed models.

              The determination of a list of differentially expressed genes is a basic objective in many cDNA microarray experiments. We present a statistical approach that allows direct control over the percentage of false positives in such a list and, under certain reasonable assumptions, improves on existing methods with respect to the percentage of false negatives. The method accommodates a wide variety of experimental designs and can simultaneously assess significant differences between multiple types of biological samples. Two interconnected mixed linear models are central to the method and provide a flexible means to properly account for variability both across and within genes. The mixed model also provides a convenient framework for evaluating the statistical power of any particular experimental design and thus enables a researcher to a priori select an appropriate number of replicates. We also suggest some basic graphics for visualizing lists of significant genes. Analyses of published experiments studying human cancer and yeast cells illustrate the results.
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                Author and article information

                Journal
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                2007
                25 April 2007
                : 8
                : 108
                Affiliations
                [1 ]Department of Biology, University of Hawai'i, Hilo, 200 W. Kawili St., Hilo, HI 96720, USA
                [2 ]Rosenstiel School of Marine & Atmospheric Sciences, University of Miami, 4600 Rickenbacker Cswy, Miami, FL 33149 USA
                [3 ]Department of Environmental and Molecular Toxicology, North Carolina State University, Campus Box 7633, Raleigh, NC 27695-7633 USA
                Article
                1471-2164-8-108
                10.1186/1471-2164-8-108
                1868758
                17459166
                018116ec-7f92-4bf0-acb3-d4f2beb6a844
                Copyright © 2007 Fisher and Oleksiak; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 November 2006
                : 25 April 2007
                Categories
                Research Article

                Genetics
                Genetics

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