Gene expression changes with age in skin, adipose tissue, blood and brain

Genome Biology

Tim D Spector¹  Veronique Bataille¹  Michael E Weale⁴  Emmanouil T Dermitzakis⁷  Panagiotis Deloukas⁹  Mark I McCarthy⁶  Richard Durbin⁹  the UK Brain Expression consortium¹  Mina Ryten⁴  Frank O Nestle⁸  Paola Di Meglio⁸  Alexandra C Nica⁷  Elin Grundberg⁹  Alfonso Buil⁷  Kerrin S Small⁹  Åsa K Hedman³  Andrew A Brown⁹  David Knowles⁵  Leopold Parts⁹  Adaikalavan Ramasamy⁴  Matthew N Davies¹  Ana Viñuela¹  Daniel Glass² 

[1] Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, Westminster Bridge Road, London SE1 7EH, UK;North West London Hospitals NHS Trust, Northwick Park Hospital, Watford Road, Harrow HA1 3UJ, UK;Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK;Department of Medical ƒ Molecular Genetics, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK;Stanford University, 450 Serra MallStanford, CA 94305, USA;Oxford Centre for Diabetes, Endocrinology ƒ Metabolism, University of Oxford, Churchill Hospital, Oxford, Headington OX3 7LJ,UK;Department of Genetic Medicine and Development, University of Geneva Medical School, 1 Rue Michel-Servet (CMU office 9088), Geneva 1211, Switzerland;St. John's Institute of Dermatology, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK;Wellcome Trust Sanger Institute, HinxtonCB10 1SA,UK

关键词: microarrays;    brain;    adipose;    skin;    gene expression;    Aging;   

Others  :  864141
DOI  :  10.1186/gb-2013-14-7-r75

 received in 2012-11-01, accepted in 2013-07-26,  发布年份 2013

【 摘 要 】

Background

Previous studies have demonstrated that gene expression levels change with age. These changes are hypothesized to influence the aging rate of an individual. We analyzed gene expression changes with age in abdominal skin, subcutaneous adipose tissue and lymphoblastoid cell lines in 856 female twins in the age range of 39-85 years. Additionally, we investigated genotypic variants involved in genotype-by-age interactions to understand how the genomic regulation of gene expression alters with age.

Results

Using a linear mixed model, differential expression with age was identified in 1,672 genes in skin and 188 genes in adipose tissue. Only two genes expressed in lymphoblastoid cell lines showed significant changes with age. Genes significantly regulated by age were compared with expression profiles in 10 brain regions from 100 postmortem brains aged 16 to 83 years. We identified only one age-related gene common to the three tissues. There were 12 genes that showed differential expression with age in both skin and brain tissue and three common to adipose and brain tissues.

Conclusions

Skin showed the most age-related gene expression changes of all the tissues investigated, with many of the genes being previously implicated in fatty acid metabolism, mitochondrial activity, cancer and splicing. A significant proportion of age-related changes in gene expression appear to be tissue-specific with only a few genes sharing an age effect in expression across tissues. More research is needed to improve our understanding of the genetic influences on aging and the relationship with age-related diseases.

【 授权许可】

   
2013 Glass et al.; licensee BioMed Central Ltd.

【 图 表 】

【 参考文献 】

• [1]Reznick DN: The genetic basis of aging: an evolutionary biologist's perspective. Sci Aging Knowl Environ 2005, 2005:pe7.
• [2]Trabzuni D, Wray S, Vandrovcova J, Ramasamy A, Walker R, Smith C, Luk C, Gibbs JR, Dillman A, Hernandez DG, Arepalli S, Singleton AB, Cookson MR, Pittman AM, de Silva R, Weale ME, Hardy J, Ryten M: MAPT expression and splicing is differentially regulated by brain region: relation to genotype and implication for tauopathies. Hum Mol Genet 2012, 21:4094-4103.
• [3]Weindruch R, Kayo T, Lee C-K, Prolla TA: Gene expression profiling of aging using DNA microarrays. Mech Ageing Dev 2002, 123:177-193.
• [4]Ly DH, Lockhart DJ, Lerner RA, Schultz PG: Mitotic misregulation and human aging. Science 2000, 287:2486-2492.
• [5]Lu T, Pan Y, Kao S-Y, Li C, Kohane I, Chan J, Yankner BA: Gene regulation and DNA damage in the ageing human brain. Nature 2004, 429:883-891.
• [6]Rodwell GEJ, Sonu R, Zahn JM, Lund J, Wilhelmy J, Wang L, Xiao W, Mindrinos M, Crane E, Segal E, Myers BD, Brooks JD, Davis RW, Higgins J, Owen AB, Kim SK: A transcriptional profile of aging in the human kidney. PLoS Biol 2004, 2:e427.
• [7]de Magalhaes JP, Curado J, Church GM: Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics 2009, 25:875-881.
• [8]Grundberg E, Small KS, Hedman AK, Nica AC, Buil A, Keildson S, Bell JT, Yang TP, Meduri E, Barrett A, Nisbett J, Sekowska M, Wilk A, Shin SY, Glass D, Travers M, Min JL, Ring S, Ho K, Thorleifsson G, Kong A, Thorsteindottir U, Ainali C, Dimas AS, Hassanali N, Ingle C, Knowles D, Krestyaninova M, Lowe CE, Di Meglio P, et al.: Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat Genet 2012, 44:1084-1089.
• [9]Nica AC, Parts L, Glass D, Nisbet J, Barrett A, Sekowska M, Travers M, Potter S, Grundberg E, Small K, Hedman ÅK, Bataille V, Tzenova Bell J, Surdulescu G, Dimas AS, Ingle C, Nestle FO, di Meglio P, Min JL, Wilk A, Hammond CJ, Hassanali N, Yang T-P, Montgomery SB, O'Rahilly S, Lindgren CM, Zondervan KT, Soranzo N, Barroso I, Durbin R, et al.: The architecture of gene regulatory variation across multiple human tissues: The MuTHER Study. PLoS Genet 2011, 7:e1002003.
• [10]Moayyeri A, Hammond CJ, Valdes AM, Spector TD: Cohort Profile: TwinsUK and healthy ageing twin study. Int J Epidemiol 2013, 42:76-85.
• [11]Huang DW, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protocols 2008, 4:44-57.
• [12]Sullivan PF, Fan C, Perou CM: Evaluating the comparability of gene expression in blood and brain. Am J Med GenetB Neuropsychiatr Genet 2006, 141B:261-268.
• [13]Aguilera G: HPA axis responsiveness to stress: Implications for healthy aging. Exp Gerontol 2011, 46:90-95.
• [14]Tacutu R, Craig T, Budovsky A, Wuttke D, Lehmann G, Taranukha D, Costa J, Fraifeld VE, de Magalhães JP: Human Ageing Genomic Resources: Integrated databases and tools for the biology and genetics of ageing. Nucleic Acids Res 2013, 41:D1027-D1033.
• [15]Joehanes R, Johnson AD, Barb JJ, Raghavachari N, Liu P, Woodhouse KA, O'Donnell CJ, Munson PJ, Levy D: Gene expression analysis of whole blood, peripheral blood mononuclear cells, and lymphoblastoid cell lines from the Framingham Heart Study. Physiol Genomics 2012, 44:59-75.
• [16]Trabzuni D, Ryten M, Walker R, Smith C, Imran S, Ramasamy A, Weale ME, Hardy J: Quality control parameters on a large dataset of regionally dissected human control brains for whole genome expression studies. J Neurochem 2011, 119:275-282.
• [17]Insinga A, Monestiroli S, Ronzoni S, Carbone R, Pearson M, Pruneri G, Viale G, Appella E, Pelicci P, Minucci S: Impairment of p53 acetylation, stability and function by an oncogenic transcription factor. Embo J 2004, 23:1144-1154.
• [18]Menschikowski M, Hagelgans A, Tiebel O, Klinsmann L, Eisenhofer G, Siegert G: Expression and shedding of endothelial protein C receptor in prostate cancer cells. Cancer Cell Int 2011, 11:4. BioMed Central Full Text
• [19]Proescholdt MA, Mayer C, Kubitza M, Schubert T, Liao S-Y, Stanbridge EJ, Ivanov S, Oldfield EH, Brawanski A, Merrill MJ: Expression of hypoxia-inducible carbonic anhydrases in brain tumors. NeuroOncol 2005, 7:465-475.
• [20]Ohsawa S, Miura M: Caspase-mediated changes in Sir2α during apoptosis. FEBS Letters 2006, 580:5875-5879.
• [21]Luo J, Nikolaev AY, Imai S-i, Chen D, Su F, Shiloh A, Guarente L, Gu W: Negative control of p53 by Sir2α promotes cell survival under stress. Cell 2001, 107:137-148.
• [22]Brunet A, Sweeney LB, Sturgill JF, Chua KF, Greer PL, Lin Y, Tran H, Ross SE, Mostoslavsky R, Cohen HY, Hu LS, Cheng H-L, Jedrychowski MP, Gygi SP, Sinclair DA, Alt FW, Greenberg ME: Stress-dependent regulation of FOXO transcription factors by the SIRT1 deacetylase. Science 2004, 303:2011-2015.
• [23]Sakata T, Ferdous G, Tsuruta T, Satoh T, Baba S, Muto T, Ueno A, Kanai Y, Endou H, Okayasu I: L-type amino-acid transporter 1 as a novel biomarker for high-grade malignancy in prostate cancer. Pathol Int 2009, 59:7-18.
• [24]Hollingworth P, Harold D, Sims R, Gerrish A, Lambert J-C, Carrasquillo MM, Abraham R, Hamshere ML, Pahwa JS, Moskvina V, Dowzell K, Jones N, Stretton A, Thomas C, Richards A, Ivanov D, Widdowson C, Chapman J, Lovestone S, Powell J, Proitsi P, Lupton MK, Brayne C, Rubinsztein DC, Gill M, Lawlor B, Lynch A, Brown KS, Passmore PA, Craig D, et al.: Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease. Nat Genet 2011, 43:429-435.
• [25]Misra V, Lee H, Singh A, Huang K, Thimmulappa RK, Mitzner W, Biswal S, Tankersley CG: Global expression profiles from C57BL/6J and DBA/2J mouse lungs to determine aging-related genes. Physiol Genomics 2007, 31:429-440.
• [26]Grundberg E, Kwan T, Ge B, Lam KCL, Koka V, Kindmark A, Mallmin H, Dias J, Verlaan DJ, Ouimet M, Sinnett D, Rivadeneira F, Estrada K, Hofman A, van Meurs JM, Uitterlinden A, Beaulieu P, Graziani A, Harmsen E, Ljunggren Ö, Ohlsson C, Mellström D, Karlsson MK, Nilsson O, Pastinen T: Population genomics in a disease targeted primary cell model. Genome Res 2009, 19:1942-1952.
• [27]Grundberg E, Small KS, Hedman AK, Nica AC, Buil A, Keildson S, Bell JT, Yang T-P, Meduri E, Barrett A, Nisbett J, Sekowska M, Wilk A, Shin S-Y, Glass D, Travers M, Min JL, Ring S, Ho K, Thorleifsson G, Kong A, Thorsteindottir U, Ainali C, Dimas AS, Hassanali N, Ingle C, Knowles D, Krestyaninova M, Lowe CE, Di Meglio P, et al.: Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat Genet 2012, 44:1084-1089.
• [28]Njajou OT, Blackburn EH, Pawlikowska L, Mangino M, Damcott CM, Kwok P-Y, Spector TD, Newman AB, Harris TB, Cummings SR, Cawthon RM, Shuldiner AR, Valdes AM, Hsueh W-C: A common variant in the telomerase RNA component is associated with short telomere length. PLoS ONE 2010, 5:e13048.
• [29]McKay JD, Hung RJ, Gaborieau V, Boffetta P, Chabrier A, Byrnes G, Zaridze D, Mukeria A, Szeszenia-Dabrowska N, Lissowska J, Rudnai P, Fabianova E, Mates D, Bencko V, Foretova L, Janout V, McLaughlin J, Shepherd F, Montpetit A, Narod S, Krokan HE, Skorpen F, Elvestad MB, Vatten L, Njolstad I, Axelsson T, Chen C, Goodman G, Barnett M, Loomis MM, et al.: Lung cancer susceptibility locus at 5p15.33. Nat Genet 2008, 40:1404-1406.
• [30]Martin GM, Austad SN, Johnson TE: Genetic analysis of ageing: role of oxidative damage and environmental stresses. Nat Genet 1996, 13:25-34.
• [31]Wheeler HE, Kim SK: Genetics and genomics of human ageing. Philos Trans R SocLond B Biol Sci 2011, 366:43-50.
• [32]Zahn JM, Sonu R, Vogel H, Crane E, Mazan-Mamczarz K, Rabkin R, Davis RW, Becker KG, Owen AB, Kim SK: Transcriptional profiling of aging in human muscle reveals a common aging signature. PLoS Genet 2006, 2:e115.
• [33]Forte E, Luftig MA: MDM2-dependent inhibition of p53 is required for Epstein-Barr virus B-cell growth transformation and infected-cell survival. J Virol 2009, 83:2491-2499.
• [34]Viñuela A, Snoek LB, Riksen JAG, Kammenga JE: Genome-wide gene expression regulation as a function of genotype and age in C. elegans. Genome Res 2010, 20:929-937.
• [35]Viñuela A, Snoek LB, Riksen JAG, Kammenga JE: Aging uncouples heritability and expression-QTL in Caenorhabditis elegans. G3 (Bethesda) 2012, 2:597-605.
• [36]Wheeler HE, Metter EJ, Tanaka T, Absher D, Higgins J, Zahn JM, Wilhelmy J, Davis RW, Singleton A, Myers RM, Ferrucci L, Kim SK: Sequential use of transcriptional profiling, expression quantitative trait mapping, and gene association implicates MMP20 in human kidney aging. PLoS Genet 2009, 5:e1000685.
• [37]Kent JW Jr, Göring HHH, Charlesworth JC, Drigalenko E, Diego VP, Curran JE, Johnson MP, Dyer TD, Cole SA, Jowett JBM, Mahaney MC, Comuzzie AG, Almasy L, Moses EK, Blangero J, Williams-Blangero S: Genotype × age interaction in human transcriptional ageing. Mech Ageing Dev 2012, 133:581-590.
• [38]Herndon LA, Schmeissner PJ, Dudaronek JM, Brown PA, Listner KM, Sakano Y, Paupard MC, Hall DH, Driscoll M: Stochastic and genetic factors influence tissue-specific decline in ageing C. elegans. Nature 2002, 419:808-814.
• [39]Law MH, Montgomery GW, Brown KM, Martin NG, Mann GJ, Hayward NK, MacGregor S: Meta-analysis combining new and existing data sets confirms that the TERT-CLPTM1L locus influences melanoma risk. J Invest Dermatol 2012, 132:485-487.
• [40]Janzen V, Forkert R, Fleming HE, Saito Y, Waring MT, Dombkowski DM, Cheng T, DePinho RA, Sharpless NE, Scadden DT: Stem-cell ageing modified by the cyclin-dependent kinase inhibitor p16INK4a. Nature 2006, 443:421-426.
• [41]Acosta JC, O'Loghlen A, Banito A, Guijarro MV, Augert A, Raguz S, Fumagalli M, Da Costa M, Brown C, Popov N, Takatsu Y, Melamed J, d'Adda di Fagagna F, Bernard D, Hernando E, Gil J: Chemokine signaling via the CXCR2 receptor reinforces senescence. Cell 2008, 133:1006-1018.
• [42]Michaloglou C, Vredeveld LCW, Mooi WJ, Peeper DS: BRAFE600 in benign and malignant human tumours. Oncogene 2007, 27:877-895.
• [43]Serrano M, Blasco MA: Cancer and ageing: convergent and divergent mechanisms. Nat Rev Mol Cell Biol 2007, 8:715-722.
• [44]Acosta JC, Gil J: Senescence: a new weapon for cancer therapy. Trends Cell Biol 2012, 22:211-219.
• [45]Maser RS, DePinho RA: Connecting chromosomes, crisis, and cancer. Science 2002, 297:565-569.
• [46]Maser RS, DePinho RA: Keeping telomerase in its place. Nat Med 2002, 8:934-936.
• [47]O'Hagan RC, Chang S, Maser RS, Mohan R, Artandi SE, Chin L, DePinho RA: Telomere dysfunction provokes regional amplification and deletion in cancer genomes. Cancer Cell 2002, 2:149-155.
• [48]Meeker AK, Hicks JL, Gabrielson E, Strauss WM, De Marzo AM, Argani P: Telomere shortening occurs in subsets of normal breast epithelium as well as in situ and invasive carcinoma. AmJPathol 2004, 164:925-935.
• [49]Choudhury AR, Ju Z, Djojosubroto MW, Schienke A, Lechel A, Schaetzlein S, Jiang H, Stepczynska A, Wang C, Buer J, Lee H-W, von Zglinicki T, Ganser A, Schirmacher P, Nakauchi H, Rudolph KL: Cdkn1a deletion improves stem cell function and lifespan of mice with dysfunctional telomeres without accelerating cancer formation. Nat Genet 2007, 39:99-105.
• [50]Feng Z, Levine AJ: The regulation of energy metabolism and the IGF-1/mTOR pathways by the p53 protein. Trends Cell Biol 2010, 20:427-434.
• [51]Feng Z, Hu W, de Stanchina E, Teresky AK, Jin S, Lowe S, Levine AJ: The regulation of AMPK β1, TSC2, and PTEN expression by p53: stress, cell and tissue specificity, and the role of these gene products in modulating the IGF-1-AKT-mTOR pathways. Cancer Res 2007, 67:3043-3053.
• [52]The MuTHER project (Multiple Tissue Human Expression Resource). [http://www.muther.ac.uk] webcite
• [53]The R Project for Statistical Computing. [http://www.r-project.org/] webcite
• [54]Bates D, Maechler M, Bolker B: lme4: Linear mixed-effects models using S4 classes. Rpackage version 0.999375-41. 2011.
• [55]Dobbin K, Shih JH, Simon R: Statistical design of reverse dye microarrays. Bioinformatics 2003, 19:803-810.
• [56]Millar T, Walker R, Arango JC, Ironside JW, Harrison DJ, MacIntyre DJ, Blackwood D, Smith C, Bell JE: Tissue and organ donation for research in forensic pathology: the MRC Sudden Death Brain and Tissue Bank. J Pathol 2007, 213:369-375.
• [57]International Parkinson's Disease Genomics C, Wellcome Trust Case Control C: A two-stage meta-analysis identifies several new loci for Parkinson's disease. PLoS Genet 2011, 7:e1002142.
• [58]International Parkinson Disease Genomics Consortium, Nalls MA, Plagnol V, Hernandez DG, Sharma M, Sheerin UM, Saad M, Simon-Sanchez J, Schulte C, Lesage S, Sveinbjornsdottir S, Stefansson K, Martinez M, Hardy J, Heutink P, Brice A, Gasser T, Singleton AB, Wood NW: Imputation of sequence variants for identification of genetic risks for Parkinson's disease: a meta-analysis of genome-wide association studies. Lancet 2011, 377:641-649.
• [59]Li Y, Willer C, Sanna S, Abecasis G: Genotype imputation. Annu Rev Genomics Hum Genet 2009, 10:387-406.
• [60]Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR: MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 2010, 34:816-834.
• [61]Minimac. [http://genome.sph.umich.edu/wiki/Minimac] webcite
• [62]The UK Brain Expression Consortium and expression resource.