Assessing the co-variability of DNA methylation across peripheral cells and tissues: Implications for the interpretation of findings in epigenetic epidemiology
Autoři:
Eilis Hannon aff001; Georgina Mansell aff001; Emma Walker aff001; Marta F. Nabais aff001; Joe Burrage aff001; Agnieszka Kepa aff003; Janis Best-Lane aff004; Anna Rose aff006; Suzanne Heck aff007; Terrie E. Moffitt aff003; Avshalom Caspi aff003; Louise Arseneault aff003; Jonathan Mill aff001
Působiště autorů:
University of Exeter Medical School, University of Exeter Medical School, University of Exeter, Exeter, United Kingdom
aff001; Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
aff002; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
aff003; Section of Anaesthetics, Pain Medicine and Intensive Care Medicine, Department of Surgery and Cancer, Imperial College London and Imperial College Healthcare NHS Trust, London, United Kingdom
aff004; Imperial Clinical Trials Unit, Imperial College London, London, United Kingdom
aff005; BRC Flow Cytometry Platform, NIHR GSTT/KCL Comprehensive Biomedical Research Centre, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
aff006; Biomedical Research Centre at Guy’s and St Thomas’ Hospitals and King’s College London, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
aff007; Department of Psychology and Neuroscience, Duke University, Durham, United States of America
aff008; Center for Genomic and Computational Biology, Duke University, Durham, United States of America
aff009; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States of America
aff010
Vyšlo v časopise:
Assessing the co-variability of DNA methylation across peripheral cells and tissues: Implications for the interpretation of findings in epigenetic epidemiology. PLoS Genet 17(3): e1009443. doi:10.1371/journal.pgen.1009443
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1009443
Souhrn
Most epigenome-wide association studies (EWAS) quantify DNA methylation (DNAm) in peripheral tissues such as whole blood to identify positions in the genome where variation is statistically associated with a trait or exposure. As whole blood comprises a mix of cell types, it is unclear whether trait-associated DNAm variation is specific to an individual cellular population. We collected three peripheral tissues (whole blood, buccal epithelial and nasal epithelial cells) from thirty individuals. Whole blood samples were subsequently processed using fluorescence-activated cell sorting (FACS) to purify five constituent cell-types (monocytes, granulocytes, CD4+ T cells, CD8+ T cells, and B cells). DNAm was profiled in all eight sample-types from each individual using the Illumina EPIC array. We identified significant differences in both the level and variability of DNAm between different sample types, and DNAm data-derived estimates of age and smoking were found to differ dramatically across sample types from the same individual. We found that for the majority of loci variation in DNAm in individual blood cell types was only weakly predictive of variance in DNAm measured in whole blood, although the proportion of variance explained was greater than that explained by either buccal or nasal epithelial samples. Covariation across sample types was much higher for DNAm sites influenced by genetic factors. Overall, we observe that DNAm variation in whole blood is additively influenced by a combination of the major blood cell types. For a subset of sites, however, variable DNAm detected in whole blood can be attributed to variation in a single blood cell type providing potential mechanistic insight about EWAS findings. Our results suggest that associations between whole blood DNAm and traits or exposures reflect differences in multiple cell types and our data will facilitate the interpretation of findings in epigenetic epidemiology.
Klíčová slova:
Blood – Blood cells – Cytotoxic T cells – DNA methylation – Epigenetics – Epithelial cells – Granulocytes – T cells
Zdroje
1. Murphy TM, Mill J. Epigenetics in health and disease: heralding the EWAS era. Lancet. 2014;383(9933):1952–4. doi: 10.1016/S0140-6736(14)60269-5 24630775.
2. Pidsley R, Zotenko E, Peters TJ, Lawrence MG, Risbridger GP, Molloy P, et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 2016;17(1):208. Epub 2016/10/07. doi: 10.1186/s13059-016-1066-1 27717381; PubMed Central PMCID: PMC5055731.
3. Jaffe AE, Irizarry RA. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol. 2014;15(2):R31. Epub 2014/02/04. doi: 10.1186/gb-2014-15-2-r31 24495553; PubMed Central PMCID: PMC4053810.
4. Elliott HR, Tillin T, McArdle WL, Ho K, Duggirala A, Frayling TM, et al. Differences in smoking associated DNA methylation patterns in South Asians and Europeans. Clin Epigenetics. 2014;6(1):4. doi: 10.1186/1868-7083-6-4 24485148; PubMed Central PMCID: PMC3915234.
5. Sugden K, Hannon EJ, Arseneault L, Belsky DW, Broadbent JM, Corcoran DL, et al. Establishing a generalized polyepigenetic biomarker for tobacco smoking. Transl Psychiatry. 2019;9(1):92. Epub 2019/02/15. doi: 10.1038/s41398-019-0430-9 30770782; PubMed Central PMCID: PMC6377665.
6. Tsaprouni LG, Yang TP, Bell J, Dick KJ, Kanoni S, Nisbet J, et al. Cigarette smoking reduces DNA methylation levels at multiple genomic loci but the effect is partially reversible upon cessation. Epigenetics. 2014;9(10):1382–96. doi: 10.4161/15592294.2014.969637 25424692.
7. Zeilinger S, Kühnel B, Klopp N, Baurecht H, Kleinschmidt A, Gieger C, et al. Tobacco smoking leads to extensive genome-wide changes in DNA methylation. PLoS One. 2013;8(5):e63812. doi: 10.1371/journal.pone.0063812 23691101; PubMed Central PMCID: PMC3656907.
8. Dick KJ, Nelson CP, Tsaprouni L, Sandling JK, Aïssi D, Wahl S, et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet. 2014;383(9933):1990–8. doi: 10.1016/S0140-6736(13)62674-4 24630777.
9. Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S, et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature. 2017;541(7635):81–6. Epub 2016/12/21. doi: 10.1038/nature20784 28002404; PubMed Central PMCID: PMC5570525.
10. Ventham NT, Kennedy NA, Adams AT, Kalla R, Heath S, O’Leary KR, et al. Integrative epigenome-wide analysis demonstrates that DNA methylation may mediate genetic risk in inflammatory bowel disease. Nat Commun. 2016;7:13507. Epub 2016/11/25. doi: 10.1038/ncomms13507 27886173; PubMed Central PMCID: PMC5133631.
11. McDermott E, Ryan EJ, Tosetto M, Gibson D, Burrage J, Keegan D, et al. DNA Methylation Profiling in Inflammatory Bowel Disease Provides New Insights into Disease Pathogenesis. J Crohns Colitis. 2016;10(1):77–86. Epub 2015/09/28. doi: 10.1093/ecco-jcc/jjv176 26419460; PubMed Central PMCID: PMC5013897.
12. Hannon E, Dempster E, Viana J, Burrage J, Smith AR, Macdonald R, et al. An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation. Genome Biol. 2016;17(1):176. Epub 2016/08/31. doi: 10.1186/s13059-016-1041-x 27572077; PubMed Central PMCID: PMC5004279.
13. Crawford B, Craig Z, Mansell G, White I, Smith A, Spaull S, et al. DNA methylation and inflammation marker profiles associated with a history of depression. Hum Mol Genet. 2018;27(16):2840–50. doi: 10.1093/hmg/ddy199 29790996.
14. Mansell G, Gorrie-Stone TJ, Bao Y, Kumari M, Schalkwyk LS, Mill J, et al. Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array. BMC Genomics. 2019;20(1):366. Epub 2019/05/14. doi: 10.1186/s12864-019-5761-7 31088362; PubMed Central PMCID: PMC6518823.
15. Hannon E, Lunnon K, Schalkwyk L, Mill J. Interindividual methylomic variation across blood, cortex, and cerebellum: implications for epigenetic studies of neurological and neuropsychiatric phenotypes. Epigenetics. 2015;10(11):1024–32. doi: 10.1080/15592294.2015.1100786 26457534.
16. Byun HM, Siegmund KD, Pan F, Weisenberger DJ, Kanel G, Laird PW, et al. Epigenetic profiling of somatic tissues from human autopsy specimens identifies tissue- and individual-specific DNA methylation patterns. Hum Mol Genet. 2009;18(24):4808–17. doi: 10.1093/hmg/ddp445 19776032.
17. Slieker RC, Bos SD, Goeman JJ, Bovée JV, Talens RP, van der Breggen R, et al. Identification and systematic annotation of tissue-specific differentially methylated regions using the Illumina 450k array. Epigenetics Chromatin. 2013;6(1):26. doi: 10.1186/1756-8935-6-26 23919675; PubMed Central PMCID: PMC3750594.
18. Jiang R, Jones MJ, Chen E, Neumann SM, Fraser HB, Miller GE, et al. Discordance of DNA methylation variance between two accessible human tissues. Sci Rep. 2015;5:8257. doi: 10.1038/srep08257 25660083; PubMed Central PMCID: PMC4321176.
19. Thompson TM, Sharfi D, Lee M, Yrigollen CM, Naumova OY, Grigorenko EL. Comparison of whole-genome DNA methylation patterns in whole blood, saliva, and lymphoblastoid cell lines. Behav Genet. 2013;43(2):168–76. doi: 10.1007/s10519-012-9579-1 23269419; PubMed Central PMCID: PMC3577999.
20. Walton E, Hass J, Liu J, Roffman JL, Bernardoni F, Roessner V, et al. Correspondence of DNA Methylation Between Blood and Brain Tissue and Its Application to Schizophrenia Research. Schizophr Bull. 2016;42(2):406–14. Epub 2015/06/08. doi: 10.1093/schbul/sbv074 26056378; PubMed Central PMCID: PMC4753587.
21. Braun PR, Han S, Hing B, Nagahama Y, Gaul LN, Heinzman JT, et al. Genome-wide DNA methylation comparison between live human brain and peripheral tissues within individuals. Transl Psychiatry. 2019;9(1):47. Epub 2019/01/31. doi: 10.1038/s41398-019-0376-y 30705257; PubMed Central PMCID: PMC6355837.
22. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115. doi: 10.1186/gb-2013-14-10-r115 24138928; PubMed Central PMCID: PMC4015143.
23. Reynolds LM, Magid HS, Chi GC, Lohman K, Barr RG, Kaufman JD, et al. Secondhand Tobacco Smoke Exposure Associations With DNA Methylation of the Aryl Hydrocarbon Receptor Repressor. Nicotine Tob Res. 2017;19(4):442–51. doi: 10.1093/ntr/ntw219 27613907; PubMed Central PMCID: PMC6075517.
24. Tantoh DM, Lee KJ, Nfor ON, Liaw YC, Lin C, Chu HW, et al. Methylation at cg05575921 of a smoking-related gene (AHRR) in non-smoking Taiwanese adults residing in areas with different PM. doi: 10.1186/s13148-019-0662-9 Clin Epigenetics. 2019;11(1):69. Epub 2019/05/06. 31060609; PubMed Central PMCID: PMC6503351.
25. Hannon E, Knox O, Sugden K, Burrage J, Wong CCY, Belsky DW, et al. Characterizing genetic and environmental influences on variable DNA methylation using monozygotic and dizygotic twins. PLoS genetics. 2018;14(8):e1007544. Epub 2018/08/10. doi: 10.1371/journal.pgen.1007544 30091980; PubMed Central PMCID: PMC6084815.
26. Hannon E, Gorrie-Stone TJ, Smart MC, Burrage J, Hughes A, Bao Y, et al. Leveraging DNA-Methylation Quantitative-Trait Loci to Characterize the Relationship between Methylomic Variation, Gene Expression, and Complex Traits. Am J Hum Genet. 2018;103(5):654–65. Epub 2018/11/08. doi: 10.1016/j.ajhg.2018.09.007 30401456; PubMed Central PMCID: PMC6217758.
27. Joehanes R, Just AC, Marioni RE, Pilling LC, Reynolds LM, Mandaviya PR, et al. Epigenetic Signatures of Cigarette Smoking. Circ Cardiovasc Genet. 2016;9(5):436–47. Epub 2016/09/20. doi: 10.1161/CIRCGENETICS.116.001506 27651444; PubMed Central PMCID: PMC5267325.
28. Ebrahimi P, Luthman H, McGuigan FE, Akesson KE. Epigenome-wide cross-tissue correlation of human bone and blood DNA methylation—can blood be used as a surrogate for bone? Epigenetics. 2020:1–14. Epub 2020/07/21. doi: 10.1080/15592294.2020.1788325 32692944.
29. Herzog EM, Eggink AJ, Willemsen SP, Slieker RC, Felix JF, Stubbs AP, et al. The tissue-specific aspect of genome-wide DNA methylation in newborn and placental tissues: implications for epigenetic epidemiologic studies. J Dev Orig Health Dis. 2020:1–11. Epub 2020/04/24. doi: 10.1017/S2040174420000136 32327008.
30. Davies MN, Volta M, Pidsley R, Lunnon K, Dixit A, Lovestone S, et al. Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biol. 2012;13(6):R43. Epub 2012/06/19. doi: 10.1186/gb-2012-13-6-r43 22703893; PubMed Central PMCID: PMC3446315.
31. Glossop JR, Nixon NB, Emes RD, Haworth KE, Packham JC, Dawes PT, et al. Epigenome-wide profiling identifies significant differences in DNA methylation between matched-pairs of T- and B-lymphocytes from healthy individuals. Epigenetics. 2013;8(11):1188–97. Epub 2013/09/04. doi: 10.4161/epi.26265 24005183.
32. Lowe R, Slodkowicz G, Goldman N, Rakyan VK. The human blood DNA methylome displays a highly distinctive profile compared with other somatic tissues. Epigenetics. 2015;10(4):274–81. doi: 10.1080/15592294.2014.1003744 25634226; PubMed Central PMCID: PMC4622544.
33. Varley KE, Gertz J, Bowling KM, Parker SL, Reddy TE, Pauli-Behn F, et al. Dynamic DNA methylation across diverse human cell lines and tissues. Genome Res doi: 10.1101/gr.147942.112 23325432 2013;23(3):555–67. Epub 2013/01/16. PubMed Central PMCID: PMC3589544.
34. Bell CG, Lowe R, Adams PD, Baccarelli AA, Beck S, Bell JT, et al. DNA methylation aging clocks: challenges and recommendations. Genome Biol. 2019;20(1):249. Epub 2019/11/25. doi: 10.1186/s13059-019-1824-y 31767039; PubMed Central PMCID: PMC6876109.
35. Field AE, Robertson NA, Wang T, Havas A, Ideker T, Adams PD. DNA Methylation Clocks in Aging: Categories, Causes, and Consequences. Mol Cell. 2018;71(6):882–95. doi: 10.1016/j.molcel.2018.08.008 30241605; PubMed Central PMCID: PMC6520108.
36. Shireby GL, Davies JP, Francis PT, Burrage J, Walker EM, Neilson GWA, et al. Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex. bioRxiv. 2020:2020.04.27.063719. doi: 10.1093/brain/awaa334 33300551
37. Zhang Q, Vallerga CL, Walker RM, Lin T, Henders AK, Montgomery GW, et al. Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Med. 2019;11(1):54. Epub 2019/08/23. doi: 10.1186/s13073-019-0667-1 31443728; PubMed Central PMCID: PMC6708158.
38. Voisin S, Harvey NR, Haupt LM, Griffiths LR, Ashton KJ, Coffey VG, et al. An epigenetic clock for human skeletal muscle. J Cachexia Sarcopenia Muscle. 2020;11(4):887–98. Epub 2020/02/17. doi: 10.1002/jcsm.12556 32067420; PubMed Central PMCID: PMC7432573.
39. Horvath S, Oshima J, Martin GM, Lu AT, Quach A, Cohen H, et al. Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and. Aging (Albany NY). 2018;10(7):1758–75. doi: 10.18632/aging.101508 30048243; PubMed Central PMCID: PMC6075434.
40. Knight AK, Craig JM, Theda C, Bækvad-Hansen M, Bybjerg-Grauholm J, Hansen CS, et al. An epigenetic clock for gestational age at birth based on blood methylation data. Genome Biol. 2016;17(1):206. Epub 2016/10/07. doi: 10.1186/s13059-016-1068-z 27717399; PubMed Central PMCID: PMC5054584.
41. Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics. 2010;11:587. Epub 2010/11/30. doi: 10.1186/1471-2105-11-587 21118553; PubMed Central PMCID: PMC3012676.
42. Ek WE, Rask-Andersen M, Karlsson T, Enroth S, Gyllensten U, Johansson A. Genetic variants influencing phenotypic variance heterogeneity. Hum Mol Genet. 2018;27(5):799–810. Epub 2018/01/13. doi: 10.1093/hmg/ddx441 29325024.
43. Wang H, Zhang F, Zeng J, Wu Y, Kemper KE, Xue A, et al. Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank. Sci Adv. 2019;5(8):eaaw3538. Epub 2019/08/28. doi: 10.1126/sciadv.aaw3538 31453325; PubMed Central PMCID: PMC6693916.
44. Oliver BR, Plomin R. Twins’ Early Development Study (TEDS): a multivariate, longitudinal genetic investigation of language, cognition and behavior problems from childhood through adolescence. Twin Res Hum Genet. 2007;10(1):96–105. Epub 2007/06/02. doi: 10.1375/twin.10.1.96 17539369.
45. Moffitt TE, Team ERS. Teen-aged mothers in contemporary Britain. J Child Psychol Psychiatry. 2002;43(6):727–42. Epub 2002/09/19. doi: 10.1111/1469-7610.00082 12236608.
46. R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2008.
47. Davis S, Du P, Bilke S, Triche J, Bootwalla M. methylumi: Handle Illumina methylation data. R package version 2.14.0. 2015.
48. Pidsley R, Wong CCY, Volta M, Lunnon K, Mill J, Schalkwyk LC. A data-driven approach to preprocessing Illumina 450K methylation array data. Bmc Genomics. 2013;14. Unsp 293. doi: 10.1186/1471-2164-14-293 ISI:000319819700001. 23631413
49. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363–9. doi: 10.1093/bioinformatics/btu049 24478339; PubMed Central PMCID: PMC4016708.
50. McCartney DL, Walker RM, Morris SW, M. MA, J. PD, L. EK. Identification of polymorphic and off-target probe binding sites on the Illumina Infinium MethylationEPIC BeadChip. Genomics Data. 2016;9(September):22–4. Epub 26 May 2016. doi: 10.1016/j.gdata.2016.05.012 27330998
Článek vyšel v časopise
PLOS Genetics
2021 Číslo 3
- Distribuce a lokalizace speciálně upravených exosomů může zefektivnit léčbu svalových dystrofií
- Prof. Jan Škrha: Metformin je bezpečný, ale je třeba jej bezpečně užívat a léčbu kontrolovat
- FDA varuje před selfmonitoringem cukru pomocí chytrých hodinek. Jak je to v Česku?
- Masturbační chování žen v ČR − dotazníková studie
- Vánoční dárky s přidanou hodnotou pro zdraví – nechte se inspirovat a poraďte svým pacientům
Nejčtenější v tomto čísle
- DNA polymerase theta suppresses mitotic crossing over
- IKAROS is required for the measured response of NOTCH target genes upon external NOTCH signaling
- activin-2 is required for regeneration of polarity on the planarian anterior-posterior axis
- The etiology of Down syndrome: Maternal MCM9 polymorphisms increase risk of reduced recombination and nondisjunction of chromosome 21 during meiosis I within oocyte