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Frontiers in Biology

ISSN 1674-7984

ISSN 1674-7992(Online)

CN 11-5892/Q

Front Biol    2012, Vol. 7 Issue (1) : 65-72    https://doi.org/10.1007/s11515-011-1177-8
REVIEW
Application of microarray technology in Drosophila ethanol behavioral research
Awoyemi A. AWOFALA1,2()
1. School of Life Sciences, University of Sussex, Falmer, Brighton BN1 9QG, United Kingdom; 2. Department of Biological Sciences, Tai Solarin University of Education, Ijebu-Ode, Ogun State, Nigeria
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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     
Corresponding Author(s): AWOFALA Awoyemi A.,Email:a.a.awofala@gmail.com   
Issue Date: 01 February 2012
 Cite this article:   
Awoyemi A. AWOFALA. Application of microarray technology in Drosophila ethanol behavioral research[J]. Front Biol, 2012, 7(1): 65-72.
 URL:  
https://academic.hep.com.cn/fib/EN/10.1007/s11515-011-1177-8
https://academic.hep.com.cn/fib/EN/Y2012/V7/I1/65
Fig.1  Correlation of fold change between RMA and GCRMA normalization methods. (a) RMA Upregulated genes versus GCRMA upregulated genes. (b) RMA downregulated genes versus GCRMA downregulated genes. The solid lines represent a linear regression fit. Linear fit: RMA upregulated genes versus GCRMA upregulated genes, = 1.4594 + 0.0237, = 0.897; RMA downregulated genes versus GCRMA downregulated genes, = 1.584 + 0.0436, = 0.8464. The common genes were selected based on Benjamini and Hochberg’s false discovery rate method (fdr≤0.05).
Fig.2  Volcano plot with moderated t-statistics made from microarray data. It shows 100 potentially interesting genes from a biologic standpoint.
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
doi: 10.1093/bioinformatics/btg455 pmid:14990455
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
doi: 10.1038/nrg1749 pmid:16369572
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
doi: 10.1007/s10158-010-0113-y pmid:21153676
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
doi: 10.4137/JEN.S6280
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
doi: 10.1111/j.1530-0277.2008.00659.x pmid:18435628
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
doi: 10.1093/bioinformatics/19.2.185 pmid:12538238
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
doi: 10.1186/gb-2003-4-4-r27 pmid:12702208
12 Cui X, Churchill G A (2003). Statistical tests for differential expression in cDNA microarray experiments. Genome Biol , 4(4): 210
doi: 10.1186/gb-2003-4-4-210 pmid:12702200
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
doi: 10.1186/gb-2003-4-5-p3
14 Devineni A V, Heberlein U (2009). Preferential ethanol consumption in Drosophila models features of addiction. Curr Biol , 19(24): 2126–2132
doi: 10.1016/j.cub.2009.10.070 pmid:20005106
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
doi: 10.1186/gb-2003-4-1-r7 pmid:12540299
16 Dudoit S, Shaffer J P, Block J C (2003). Multiple hypothesis testing in microarray experiments. Stat Sci , 18(1): 71–103
doi: 10.1214/ss/1056397487
17 Eisen M B, Brown P O (1999). DNA arrays for analysis of gene expression. Methods Enzymol , 303: 179–205
doi: 10.1016/S0076-6879(99)03014-1 pmid:10349646
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
doi: 10.1073/pnas.95.25.14863 pmid:9843981
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
doi: 10.1186/1471-2105-7-191 pmid:16597342
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
doi: 10.1126/science.286.5439.531 pmid:10521349
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
doi: 10.1093/nar/gng015 pmid:12582260
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
doi: 10.1093/biostatistics/4.2.249 pmid:12925520
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
doi: 10.1038/nn.2805 pmid:21499254
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
doi: 10.1038/89044 pmid:11385503
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
doi: 10.1111/j.1530-0277.2009.01093.x pmid:19951294
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
doi: 10.1073/pnas.011404098 pmid:11134512
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
doi: 10.1101/gr.079558.108 pmid:18550803
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
doi: 10.1186/1471-2105-7-137 pmid:16539732
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
doi: 10.1093/gerona/56.2.B52 pmid:11213267
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
doi: 10.1016/S0092-8674(00)81205-2 pmid:9635429
33 Morozova T V, Anholt R R, Mackay T F (2006). Transcriptional response to alcohol exposure in Drosophila melanogaster. Genome Biol , 7(10): R95
doi: 10.1186/gb-2006-7-10-r95 pmid:17054780
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
doi: 10.1186/gb-2007-8-10-r231 pmid:17973985
35 Nadon R, Shoemaker J (2002). Statistical issues with microarrays: processing and analysis. Trends Genet , 18(5): 265–271
doi: 10.1016/S0168-9525(02)02665-3 pmid:12047952
36 Olson N E (2006). The microarray data analysis process: from raw data to biological significance. NeuroRx , 3(3): 373–383
doi: 10.1016/j.nurx.2006.05.005 pmid:16815220
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
doi: 10.1186/1471-2105-7-23 pmid:16417622
38 Reiner A, Yekutieli D, Benjamini Y (2003). Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics , 19(3): 368–375
doi: 10.1093/bioinformatics/btf877 pmid:12584122
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
doi: 10.1038/nature03864 pmid:16094367
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
doi: 10.1038/nbt1239 pmid:16964229
41 Singh C M, Heberlein U (2000). Genetic control of acute ethanol-induced behaviors in Drosophila. Alcohol Clin Exp Res , 24(8): 1127–1136
doi: 10.1111/j.1530-0277.2000.tb02075.x pmid:10968649
42 Slonim D K, Yanai I (2009). Getting started in gene expression microarray analysis. PLOS Comput Biol , 5(10): e1000543
doi: 10.1371/journal.pcbi.1000543 pmid:19876380
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
doi: 10.1073/pnas.96.6.2907 pmid:10077610
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
doi: 10.1038/10343 pmid:10391217
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
doi: 10.1186/1471-2105-7-105 pmid:16512908
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
doi: 10.1038/nrg2484 pmid:19015660
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
doi: 10.1038/nbt0604-656b pmid:15175677
52 Yamamoto M, Pohli S, Durany N, Ozawa H, Saito T, Boissl K W, Z?chling R, Riederer P, B?ning J, G?tz, 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
doi: 10.1186/gb-2003-4-4-r28 pmid:12702209
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