Application of microarray technology in Drosophila ethanol behavioral research

Abstract

Gene expression profiling of Drosophila melanogaster, an invertebrate model organism, applied to DNA microarray promises to provide novel insights into the important pathways and molecules that may contribute to the risk of alcohol abuse and addiction. Instead of studying one gene at a time, the technology provides a snapshot of transcriptional changes at once, and offers unprecedented opportunities to understand the molecular complexity of alcohol-seeking behavior including addiction and dependence.

Keywords

Drosophila / behavior / microarray / gene expression / ethanol / addiction

References

[1] Affymetrix (2001a) Affymetrix Microarray Suite Users Guide, Affymetrix, Santa Clara, CA, version 5.0 edition

[2] Affymetrix (2001b) Statistical Algorithms Reference Guide. Technical report, Affymetrix, Santa Clara, CA

[3] Al-Shahrour F, Díaz-Uriarte R, Dopazo J (2004). FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics, 20(4): 578–580

[4] Allison D B, Cui X, Page G P, Sabripour M (2006). Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet, 7(1): 55–65

[5] Awofala A A (2011a). Acute Ethanol Regulation of Gene Expression Systems in Drosophila: A Computational and Behavioral Genetic Approach to Alcohol Addiction. Lambert Academic Publisher (LAP): Germany.

[6] Awofala A A (2011b). Genetic approaches to alcohol addiction: gene expression studies and recent candidates from Drosophila. Invert Neurosci, 11(1): 1–7

[7] Awofala A A, Jones S, Davies J A (2011). The heat shock protein 26 gene is required for ethanol tolerance in Drosophila. J Exp Neurosci, 5: 31–44

[8] Benjamini Y, Hochberg Y (1995). Controlling the false discovery rate: A practical and powerful approach to mMultiple testing. J Roy Stat Soc B Met, 57: 289–300

[9] Berger K H, Kong E C, Dubnau J, Tully T, Moore M S, Heberlein U (2008). Ethanol sensitivity and tolerance in long-term memory mutants of Drosophila melanogaster. Alcohol Clin Exp Res, 32(5): 895–908

[10] Bolstad B M, Irizarry R A, Astrand M, Speed T P (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 19(2): 185–193

[11] Bussey K J, Kane D, Sunshine M, Narasimhan S, Nishizuka S, Reinhold W C, Zeeberg B, Ajay W, Weinstein J N (2003). MatchMiner: a tool for batch navigation among gene and gene product identifiers. Genome Biol, 4(4): R27

[12] Cui X, Churchill G A (2003). Statistical tests for differential expression in cDNA microarray experiments. Genome Biol, 4(4): 210

[13] Dennis G Jr, Sherman B T, Hosack D A, Yang J, Gao W, Lane H C, Lempicki R A (2003). DAVID: Database for annotation, visualisation, and integrated discovery. Genome Biol, 4(5): 3

[14] Devineni A V, Heberlein U (2009). Preferential ethanol consumption in Drosophila models features of addiction. Curr Biol, 19(24): 2126–2132

[15] Doniger S W, Salomonis N, Dahlquist K D, Vranizan K, Lawlor S C, Conklin B R (2003). MAPPFinder: using gene ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol, 4(1): R7

[16] Dudoit S, Shaffer J P, Block J C (2003). Multiple hypothesis testing in microarray experiments. Stat Sci, 18(1): 71–103

[17] Eisen M B, Brown P O (1999). DNA arrays for analysis of gene expression. Methods Enzymol, 303: 179–205

[18] Eisen M B, Spellman P T, Brown P O, Botstein D (1998). Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA, 95(25): 14863–14868

[19] Ernst J, Bar-Joseph Z (2006). STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics, 7(1): 191

[20] Golub T R, Slonim D K, Tamayo P, Huard C, Gaasenbeek M, Mesirov J P, Coller H, Loh M L, Downing J R, Caligiuri M A, Bloomfield C D, Lander E S (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286(5439): 531–537

[21] Huber W, Irizarry R, Gentlemen R (2005). Preprocessing overview. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, pages 3-12 and 431-442. eds. Gentlemen, R., Carey, V., Huber, W., Irizarry, R. and Dudoit, S. Springer: New York

[22] Irizarry R A, Bolstad B M, Collin F, Cope L M, Hobbs B, Speed T P (2003a). Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res, 31(4): 15e

[23] Irizarry R A, Hobbs B G, Collin F, Beazer-Barclay Y D, Antonellis K J, Scherf U, Speed T P (2003b). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4(2): 249–264

[24] Kaun K R, Azanchi R, Maung Z, Hirsh J, Heberlein U (2011). A Drosophila model for alcohol reward. Nat Neurosci, 14(5): 612–619

[25] Khan J, Wei J S, Ringnér M, Saal L H, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu C R, Peterson C, Meltzer P S (2001). Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med, 7(6): 673–679

[26] Kong E C, Allouche L, Chapot P A, Vranizan K, Moore M S, Heberlein U, Kong E C, Allouche L, Chapot P A, Vranizan K, Moore M S, Heberlein U, Wolf F W (2010). Ethanol-regulated genes that contribute to ethanol sensitivity and rapid tolerance in Drosophila. Alcohol Clin Exp Res, 34(2): 302–316

[27] Lee M L, Kuo F C, Whitmore G A, Sklar J (2000). Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations. Proc Natl Acad SciβUSA,β97:β9834–9839

[28] Li C, Wong W H (2001). Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci USA, 98(1): 31–36

[29] Marioni J C, Mason C E, Mane S M, Stephens M, Gilad Y (2008). RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res, 18(9): 1509–1517

[30] Millenaar F F, Okyere J, May S T, van Zanten M, Voesenek L A, Peeters A J (2006). How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results. BMC Bioinformatics, 7(1): 137

[31] Miller R A, Galecki A, Shmookler-Reis R J (2001). Interpretation, design, and analysis of gene array expression experiments. J Gerontol A Biol Sci Med Sci, 56(2): B52–B57

[32] Moore M S, DeZazzo J, Luk A Y, Tully T, Singh C M, Heberlein U (1998). Ethanol intoxication in Drosophila: Genetic and pharmacological evidence for regulation by the cAMP signaling pathway. Cell, 93(6): 997–1007

[33] Morozova T V, Anholt R R, Mackay T F (2006). Transcriptional response to alcohol exposure in Drosophila melanogaster. Genome Biol, 7(10): R95

[34] Morozova T V, Anholt R R, Mackay T F (2007). Phenotypic and transcriptional response to selection for alcohol sensitivity in Drosophila melanogaster. Genome Biol, 8(10): R231

[35] Nadon R, Shoemaker J (2002). Statistical issues with microarrays: processing and analysis. Trends Genet, 18(5): 265–271

[36] Olson N E (2006). The microarray data analysis process: from raw data to biological significance. NeuroRx, 3(3): 373–383

[37] Qin L X, Beyer R P, Hudson F N, Linford N J, Morris D E, Kerr K F (2006). Evaluation of methods for oligonucleotide array data via quantitative real-time PCR. BMC Bioinformatics, 7(1): 23

[38] Reiner A, Yekutieli D, Benjamini Y (2003). Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics, 19(3): 368–375

[39] Scholz H, Franz M, Heberlein U (2005). The hangover gene defines a stress pathway required for ethanol tolerance development. Nature, 436(7052): 845–847

[40] Shi L, Reid L H, Jones W D, Shippy R, Warrington J A, Baker S C, Collins P J, de Longueville F, Kawasaki E S, Lee K Y, Luo Y, Sun Y A, Willey J C, Setterquist R A, Fischer G M, Tong W, Dragan Y P, Dix D J, Frueh F W, Goodsaid F M, Herman D, Jensen R V, Johnson C D, Lobenhofer E K, Puri R K, Schrf U, Thierry-Mieg J, Wang C, Wilson M, Wolber P K, Zhang L, Amur S, Bao W, Barbacioru C C, Lucas A B, Bertholet V, Boysen C, Bromley B, Brown D, Brunner A, Canales R, Cao X M, Cebula T A, Chen J J, Cheng J, Chu T M, Chudin E, Corson J, Corton J C, Croner L J, Davies C, Davison T S, Delenstarr G, Deng X, Dorris D, Eklund A C, Fan X H, Fang H, Fulmer-Smentek S, Fuscoe J C, Gallagher K, Ge W, Guo L, Guo X, Hager J, Haje P K, Han J, Han T, Harbottle H C, Harris S C, Hatchwell E, Hauser C A, Hester S, Hong H, Hurban P, Jackson S A, Ji H, Knight C R, Kuo W P, LeClerc J E, Levy S, Li Q Z, Liu C, Liu Y, Lombardi M J, Ma Y, Magnuson S R, Maqsodi B, McDaniel T, Mei N, Myklebost O, Ning B, Novoradovskaya N, Orr M S, Osborn T W, Papallo A, Patterson T A, Perkins R G, Peters E H, Peterson R, Philips K L, Pine P S, Pusztai L, Qian F, Ren H, Rosen M, Rosenzweig B A, Samaha R R, Schena M, Schroth G P, Shchegrova S, Smith D D, Staedtler F, Su Z, Sun H, Szallasi Z, Tezak Z, Thierry-Mieg D, Thompson K L, Tikhonova I, Turpaz Y, Vallanat B, Van C, Walker S J, Wang S J, Wang Y, Wolfinger R, Wong A, Wu J, Xiao C, Xie Q, Xu J, Yang W, Zhang L, Zhong S, Zong Y, Slikker W Jr, MAQC Consortium (2006). The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol, 24(9): 1151–1161

[41] Singh C M, Heberlein U (2000). Genetic control of acute ethanol-induced behaviors in Drosophila. Alcohol Clin Exp Res, 24(8): 1127–1136

[42] Slonim D K, Yanai I (2009). Getting started in gene expression microarray analysis. PLOS Comput Biol, 5(10): e1000543

[43] Stekel D (2003). Microarray Bioinformatics. Cambridge University Press: Cambridge

[44] Su A l, Welsh J B, Sapinoso L M, Kern S G, Dimitrov P, Lapp H (2001). Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res, 61:7388–93

[45] Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander E S, Golub T R (1999). Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA, 96(6): 2907–2912

[46] Tavazoie S, Hughes J D, Campbell M J, Cho R J, Church G M (1999). Systematic determination of genetic network architecture. Nat Genet, 22(3): 281–285

[47] Verhaak R G, Staal F J, Valk P J, Lowenberg B, Reinders M J, de Ridder D (2006). The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies. BMC Bioinformatics, 7(1): 105

[48] Wand G, Levine M, Zweifel L, Schwindinger W, Abel T (2001). The cAMP-protein kinase a signal transduction pathway modulates ethanol consumption and sedative effects of ethanol. J Neurosci,21: 5297–5303

[49] Wang Z, Gerstein M, Snyder M (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet, 10(1): 57–63

[50] Wu Z, Irizarry A R, Gentleman R, Martinez-Murillo F, Spencer F (2004). A model-based background adjustment for oligonucleotide expression arrays. JASA, 99: 909–917

[51] Wu Z, Irizarry R A (2004). Preprocessing of oligonucleotide array data. Nat Biotechnol, 22(6): 656–658, author reply 658

[52] Yamamoto M, Pohli S, Durany N, Ozawa H, Saito T, Boissl K W, Zöchling R, Riederer P, Böning J, Götz, M E (2001). Increased levels of calcium-sensitive adenylyl cyclase subtypes in the limbic system of alcoholics: evidence for a specific role of cAMP signaling in the human addictive brain. Brain Res, 895: 233–237

[53] Zeeberg B R, Feng W, Wang G, Wang M D, Fojo A T, Sunshine M, Narasimhan S, Kane D W, Reinhold W C, Lababidi S, Bussey K J, Riss J, Barrett J C, Weinstein J N (2003). GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol, 4(4): R28

Acknowledgments

This work was supported by a fellowship from Tai Solarin University of Education. The content is solely the responsibility of the author and does not necessarily reflect the official view of the funding institution.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg