>Gaceta Médica de México
>Year 2008, Issue 6
Bastarrache RA, López-Alvarenga JC, Kent Jr JW, Laviada-Molina HA, Cerda-Flores RM, Calderón-Garcidueñas AL, Torres-Salazar A, Gallegos-Cabrales EC, Tejero ME, Cole SA , Comuzzie AG
Transcriptoma en mexicanos. Metodología para analizar el perfil de expresión genética de gran escala en muestras simultáneas de tejido muscular, adiposo y linfocitos obtenidas en un mismo individuo
Gac Med Mex 2008; 144 (6)
PDF: 177.38 Kb.
Objective: We describe the methodology used to analyze multiple transcripts using microarray techniques in simultaneous biopsies of muscle, adipose tissue and lymphocytes obtained from the same individual as part of the standard protocol of the Genetics of Mexican Metabolic Disorders (GMMD) Family Study. Methods: We recruited 4 healthy male subjects with BMI 20-41, who signed an informed consent letter. Subjects participated in a clinical examination that included anthropometric and body composition measurements, muscle biopsies (vasus lateralis) subcutaneous fat, and a blood draw. All samples provided sufficient amplified RNA for microarray analysis. Total RNA was extracted from the biopsy samples and amplified for analysis. Results: Of the 48 687 transcript targets queried, 39.4% were detectable in a least one of the studied tissues. Leptin was not detectable in lymphocytes, weakly expressed in muscle, but overexpressed and highly correlated with BMI in subcutaneous fat. Another example was GLUT4, which was detectable only in muscle and not correlated with BMI. Expression level concordance was 0.7 (p‹0.001) for the three tissues studied. Conclusions: We demonstrated the feasibility of carrying out simultaneous analysis of gene expression in multiple tissues, concordance of genetic expression in different tissues, and confidence that this method corroborates the expected biological relationships among LEP and GLUT4. The GEMM study will provide a broad and valuable overview on metabolic diseases, including obesity and type 2 diabetes.
||Microarray analysis, messenger RNA, metabolic syndrome (syndrome X), genetic expression, untranslated RNA.
International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature 2004;431:931-945.
Forrest AR, Taylor DF, Crowe ML, Chalk AM, Waddell NJ, Kolle G, et al. Genome-wide review of transcriptional complexity in mouse protein kinases and phosphatases. Genome Biol 2006;7:R5.
Perkins DO, Jeffries C, Sullivan P. Expanding the ‘“central dogma”: the regulatory role of nonprotein coding genes and implications for the genetic liability to schizophrenia. Molecular Psychiatry 2005;10:69-78.
Kim VN, Nam JW. Genomics of microRNA. Trends Genet 2006;22:165-173.
Odom DT, Dowell RD, Jacobsen ES, Gordon W, Danford TW, MacIsaac KD, et al. Tissue-specific transcriptional regulation has diverged significantly between human and mouse. Nature Genetics 2007;39:730-732.
Bastarrachea RA, Kent JA, Rozada G, Cole SA, López-Alvarenga JC, Aradillas C, et al. Heritability and genetic correlations of metabolic disease– related phenotypes in Mexico: Preliminary report from the GEMM Family Study. Human Biology 2007;78:121-130.
Martínez-Marignac VL, Valladares A, Cameron E, Chan A, Perera A, Globus- Goldberg R, et al. Admixture in Mexico City: implications for admixture mapping of type 2 diabetes genetic risk factors. Hum Genet 2007;120:807-819.
Cerda-Flores RM, Budowle B, Jin L, Barton SA, Deka R, Chakraborty R. Maximum likelihood estimates of admixture in Northeastern Mexico using 13 short tandem repeat loci. Am J Hum Biol 2002;14:429-439.
Curran JE, Johnson MP, Dyer TD, Göring HH, Jack WK, Charlesworth JC, et al. Genetic determinants of mitochondrial content. Hum Mol Genet 2007;16:1504-1514.
Van Gelder RN, von Zastrow ME, Yool A, Dement WC, Barchas JD, Eberwine JH. Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc Natl Acad Sci USA 1990;87:1663-1667.
Illumina TotalPrep RNA Amplification Kit. Instruction manual. 2006.
Pruitt KD, Maglott T. NCBI reference sequence (RefSeq): acurated nonredundant sequence database of genomes, transcrips and proteins. Nucleid Acids Res 2005;33:D501-D504.
Goring HH, Curran JE, Jonson MP, Dyer T, Charlesworth J, Cole SA, et al. Discovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes. Nature Genetics 2007; advance online publication; doi: 10.1038/ng2119.
Katayama S, Tomaru Y, Kasukawa T, Waki K, Nakanishi M, Nakamura M, et al. RIKEN Genome Exploration Research Group; Genome Science Group (Genome Network Project Core Group); FANTOM Consortium. Antisense transcription in the mammalian transcriptome. Science 2005;309:1564-1566.
Ponnampalam AP, Weston GC, Trajstman AC, Susil B, Rogers PA. Molecular classification of human endometrial cycle stages by transcriptional profiling. Mol Hum Reprod 2004;10:879-893.
Holter JL, Humphries A, Crunelli V, Carter DA. Optimization of methods for selecting candidate genes from cDNA array screens: application to rat brain punches and pineal. J Neurosci Methods 2001;112:173-184.
Segal JP, Stallings NR, Lee CE, Zhao L, Socci N, Viale A, et al. Use of lasercapture microdissection for the identiûcation of marker genes for the ventromedial hypothalamic nucleus. J Neurosci 2005;25:4181-4188.
Jansen RC, Nap JP. Genetical genomics: the added value from segregation. Trends Genet 2001;17:388-391.
Hubner N, Wallace CA, Zimdahl H, Petretto E, Schulz H, Maciver F, et al. Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat Genet 2005;37:243-253.
Morley M, Molony CM, Weber TM, Devlin JL, Ewens KG, Spielman RS, et al. Genetic analysis of genome-wide variation in human gene expression. Nature 2004;430:743-747.
Almasy L, Blangero J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 1998;62:1198-1211.
Comuzzie GA, Williams JT, Marin LJ, Blangero J. Searching for genes underlying normal variation in human adiposity. J Mol Med 2001;79:57-70.
>Gaceta Médica de México
>Year 2008, Issue 6