Abstract
Whole-genome screening methods are unique approach to search for novel genes and molecular pathways involved in drug action. High-throughput profiling allows the gene expression levels of tens of thousands of transcripts to be measured simultaneously. Here, we describe transcriptional profiling in a specific area of the brain using DNA microarrays and next-generation sequencing.
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References
Butler D (2010) Human genome at ten: science after the sequence. Nature 465:1000–1001
Lamb J, Crawford ED, Peck D et al (2006) The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313:1929–1935
Yu H, Luscombe NM, Qian J et al (2003) Genomic analysis of gene expression relationships in transcriptional regulatory networks. Trends Genet 19:422–427
Liang M, Cowley AW, Greene AS (2004) High throughput gene expression profiling: a molecular approach to integrative physiology. J Physiol 554(Pt 1):22–30
Draghici S, Khatri P, Eklund AC et al (2006) Reliability and reproducibility issues in DNA microarray measurements. Trends Genet 22:101–109
Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63
Korostynski M, Piechota M, Dzbek J et al (2013) Novel drug-regulated transcriptional networks in brain reveal pharmacological properties of psychotropic drugs. BMC Genomics 14:606
Korostynski M, Piechota M, Kaminska D et al (2007) Morphine effects on striatal transcriptome in mice. Genome Biol 8:R128
Piechota M, Korostynski M, Solecki W et al (2010) The dissection of transcriptional modules regulated by various drugs of abuse in the mouse striatum. Genome Biol 11:R48
Ziolkowska B, Gieryk A, Bilecki W et al (2005) Regulation of alpha-synuclein expression in limbic and motor brain regions of morphine-treated mice. J Neurosci 25:4996–5003
Slonim DK (2002) From patterns to pathways: gene expression data analysis comes of age. Nat Genet 32(Suppl):502–508
Dunning MJ, Smith ML, Ritchie ME et al (2007) R classes and methods for Illumina bead-based data. Bioinformatics 23:2183–2184
Gentleman RC, Carey VJ, Bates DM et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80
da Huang W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57
Rothberg JM, Hinz W, Rearick TM et al (2011) An integrated semiconductor device enabling non-optical genome sequencing. Nature 475:348–352
Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25:1105–1111
Trapnell C, Williams BA, Pertea G et al (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28:511–515
Acknowledgments
This work was supported by NCN 2011/03/D/NZ3/01686 SONATA, POIG De-Me-Ter 3.1, and 2013/08/A/NZ3/00848 Maestro.
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Korostynski, M., Piechota, M., Golda, S., Przewlocki, R. (2015). High-Throughput Gene Expression Profiling of Opioid-Induced Alterations in Discrete Brain Areas. In: Spampinato, S. (eds) Opioid Receptors. Methods in Molecular Biology, vol 1230. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1708-2_5
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DOI: https://doi.org/10.1007/978-1-4939-1708-2_5
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