Gene expression differences in mice divergently selected for methamphetamine sensitivity |
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Authors: | Abraham A Palmer Miguel Verbitsky Rathi Suresh Helen M Kamens Cheryl L Reed Na Li Sue Burkhart–Kasch Carrie S McKinnon John K Belknap T Conrad Gilliam Tamara J Phillips |
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Institution: | (1) Columbia Genome Center, Columbia University, 1150 St. Nicholas Ave., New York, New York 10032, USA;(2) Department of Genetics and Development, Columbia University, New York, New York 10032, USA;(3) Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon 97239, USA;(4) Portland Alcohol Research Center, Portland, Oregon 97239, USA;(5) Research Service, Portland Veterans Affairs Medical Center, Portland, Oregon 97239, USA;(6) Department of Human Genetics, University of Chicago, Chicago, Ilinois 60637, USA |
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Abstract: | In an effort to identify genes that may be important for drug-abuse liability, we mapped behavioral quantitative trait loci (bQTL) for sensitivity to the locomotor stimulant effect of methamphetamine (MA) using two mouse lines that were selectively bred for high MA-induced activity (HMACT) or low MA-induced activity (LMACT). We then examined gene expression differences between these lines in the nucleus accumbens, using 20 U74Av2 Affymetrix microarrays and quantitative polymerase chain reaction (qPCR). Expression differences were detected for several genes, including Casein Kinase 1 Epsilon (Csnkle), glutamate receptor, ionotropic, AMPA1 (GluR1), GABA B1 receptor (Gabbr1), and dopamine- and cAMP-regulated phosphoprotein of 32 kDa (Darpp-32). We used the
database to identify QTL that regulate the expression of the genes identified by the microarrays (expression QTL; eQTL). This approach identified an eQTL for Csnkle on Chromosome 15 (LOD=3.8) that comapped with a bQTL for the MA stimulation phenotype (LOD=4.5), suggesting that a single allele may cause both traits. The chromosomal region containing this QTL has previously been associated with sensitivity to the stimulant effects of cocaine. These results suggest that selection was associated with (and likely caused) altered gene expression that is partially attributable to different frequencies of gene expression polymorphisms. Combining classical genetics with analysis of whole-genome gene expression and bioinformatic resources provides a powerful method for provisionally identifying genes that influence complex traits. The identified genes provide excellent candidates for future hypothesis-driven studies, translational genetic studies, and pharmacological interventions. |
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