Genetic Analysis of the Cardiac Methylome at Single Nucleotide Resolution in a Model of Human Cardiovascular Disease

English version České info

Epigenetic marks provide information that is not encoded in the primary DNA sequence itself but in modifications of genomic DNA and of the associated proteins. Methylation of genomic DNA at cytosine residues is an important epigenetic modification that is associated with developmental processes, carcinogenesis and other diseases. Genome-wide extent of, and reasons for inter-individual differences in cytosine methylation, and their association with phenotypic variation are poorly characterised. To address these questions we have determined and compared the genome-wide methylation patterns in heart tissue of two inbred rat strains, the spontaneously hypertensive rat, an animal model of human disease and a control rat strain. Comparison of methylation differences between genetically identical animals from the same strain and differences between animals from different strains allowed us to quantify association of epigenetic and genetic differences. We show that differences in an individual's germline DNA sequence are important determinants of the variability in methylation between individuals. Comparison with previous reports implicates common mechanisms for regulation of cytosine methylation that are highly conserved across species. Finally, we find correlation between a proposed blood biomarker for heart failure and variation in DNA methylation, suggesting a link between germline DNA sequence variation, methylation and a disease-related phenotype.

Vyšlo v časopise: Genetic Analysis of the Cardiac Methylome at Single Nucleotide Resolution in a Model of Human Cardiovascular Disease. PLoS Genet 10(12): e32767. doi:10.1371/journal.pgen.1004813
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004813

Souhrn

Zdroje

1. LiE, BestorTH, JaenischR (1992) Targeted mutation of the DNA methyltransferase gene results in embryonic lethality. Cell 69: 915–926.

2. OkanoM, BellDW, HaberDA, LiE (1999) DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell 99: 247–257.

3. BirdA (2002) DNA methylation patterns and epigenetic memory. Genes Dev 16: 6–21.

4. KerkelK, SpadolaA, YuanE, KosekJ, JiangL, et al. (2008) Genomic surveys by methylation-sensitive SNP analysis identify sequence-dependent allele-specific DNA methylation. Nat Genet 40: 904–908.

5. SchillingE, El ChartouniC, RehliM (2009) Allele-specific DNA methylation in mouse strains is mainly determined by cis-acting sequences. Genome Res 19: 2028–2035.

6. GibbsJR, van der BrugMP, HernandezDG, TraynorBJ, NallsMA, et al. (2010) Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet 6: e1000952.

7. SchalkwykLC, MeaburnEL, SmithR, DempsterEL, JeffriesAR, et al. (2010) Allelic skewing of DNA methylation is widespread across the genome. Am J Hum Genet 86: 196–212.

8. ShoemakerR, DengJ, WangW, ZhangK (2010) Allele-specific methylation is prevalent and is contributed by CpG-SNPs in the human genome. Genome Res 20: 883–889.

9. ZhangD, ChengL, BadnerJA, ChenC, ChenQ, et al. (2010) Genetic control of individual differences in gene-specific methylation in human brain. Am J Hum Genet 86: 411–419.

10. GertzJ, VarleyKE, ReddyTE, BowlingKM, PauliF, et al. (2011) Analysis of DNA methylation in a three-generation family reveals widespread genetic influence on epigenetic regulation. PLoS Genet 7: e1002228.

11. LienertF, WirbelauerC, SomI, DeanA, MohnF, et al. (2011) Identification of genetic elements that autonomously determine DNA methylation states. Nat Genet 43: 1091–1097.

12. XieW, BarrCL, KimA, YueF, LeeAY, et al. (2012) Base-resolution analyses of sequence and parent-of-origin dependent DNA methylation in the mouse genome. Cell 148: 816–831.

13. SchmitzRJ, HeY, Valdes-LopezO, KhanSM, JoshiT, et al. (2013) Epigenome-wide inheritance of cytosine methylation variants in a recombinant inbred population. Genome Res 23: 1663–1674.

14. HubnerN, WallaceCA, ZimdahlH, PetrettoE, SchulzH, et al. (2005) Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat Genet 37: 243–253.

15. PetrettoE, MangionJ, DickensNJ, CookSA, KumaranMK, et al. (2006) Heritability and tissue specificity of expression quantitative trait loci. PLoS Genet 2: e172.

16. AtanurSS, BirolI, GuryevV, HirstM, HummelO, et al. (2010) The genome sequence of the spontaneously hypertensive rat: Analysis and functional significance. Genome Res 20: 791–803.

17. SimonisM, AtanurSS, LinsenS, GuryevV, RuziusFP, et al. (2012) Genetic basis of transcriptome differences between the founder strains of the rat HXB/BXH recombinant inbred panel. Genome Biol 13: r31.

18. MontiJ, FischerJ, PaskasS, HeinigM, SchulzH, et al. (2008) Soluble epoxide hydrolase is a susceptibility factor for heart failure in a rat model of human disease. Nat Genet 40: 529–537.

19. PetrettoE, SarwarR, GrieveI, LuH, KumaranMK, et al. (2008) Integrated genomic approaches implicate osteoglycin (Ogn) in the regulation of left ventricular mass. Nat Genet 40: 546–552.

20. PravenecM, ChurchillPC, ChurchillMC, ViklickyO, KazdovaL, et al. (2008) Identification of renal Cd36 as a determinant of blood pressure and risk for hypertension. Nat Genet 40: 952–954.

21. HeinigM, PetrettoE, WallaceC, BottoloL, RotivalM, et al. (2010) A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk. Nature 467: 460–464.

22. McDermott-RoeC, YeJ, AhmedR, SunXM, SerafinA, et al. (2011) Endonuclease G is a novel determinant of cardiac hypertrophy and mitochondrial function. Nature 478: 114–118.

23. PravenecM, KlirP, KrenV, ZichaJ, KunesJ (1989) An analysis of spontaneous hypertension in spontaneously hypertensive rats by means of new recombinant inbred strains. J Hypertens 7: 217–221.

24. AitmanTJ, CritserJK, CuppenE, DominiczakA, Fernandez-SuarezXM, et al. (2008) Progress and prospects in rat genetics: a community view. Nat Genet 40: 516–522.

25. BaileyTL, BodenM, BuskeFA, FrithM, GrantCE, et al. (2009) MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res 37: W202–208.

26. AlischRS, BarwickBG, ChopraP, MyrickLK, SattenGA, et al. (2012) Age-associated DNA methylation in pediatric populations. Genome Res 22: 623–632.

27. SatiS, TanwarVS, KumarKA, PatowaryA, JainV, et al. (2012) High resolution methylome map of rat indicates role of intragenic DNA methylation in identification of coding region. PLoS One 7: e31621.

28. HaasJ, FreseKS, ParkYJ, KellerA, VogelB, et al. (2013) Alterations in cardiac DNA methylation in human dilated cardiomyopathy. EMBO Mol Med 5: 413–429.

29. MovassaghM, ChoyMK, KnowlesDA, CordedduL, HaiderS, et al. (2011) Distinct epigenomic features in end-stage failing human hearts. Circulation 124: 2411–2422.

30. RosjoH, HusbergC, DahlMB, StridsbergM, SjaastadI, et al. (2010) Chromogranin B in heart failure: a putative cardiac biomarker expressed in the failing myocardium. Circ Heart Fail 3: 503–511.

31. BellJT, PaiAA, PickrellJK, GaffneyDJ, Pique-RegiR, et al. (2011) DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol 12: R10.

32. RaoRH (1993) Insulin resistance in spontaneously hypertensive rats. Difference in interpretation based on insulin infusion rate or on plasma insulin in glucose clamp studies. Diabetes 42: 1364–1371.

33. PaternostroG, ClarkeK, HeathJ, SeymourAM, RaddaGK (1995) Decreased GLUT-4 mRNA content and insulin-sensitive deoxyglucose uptake show insulin resistance in the hypertensive rat heart. Cardiovasc Res 30: 205–211.

34. AitmanTJ, GlazierAM, WallaceCA, CooperLD, NorsworthyPJ, et al. (1999) Identification of Cd36 (Fat) as an insulin-resistance gene causing defective fatty acid and glucose metabolism in hypertensive rats. Nat Genet 21: 76–83.

35. PriceAL, HelgasonA, ThorleifssonG, McCarrollSA, KongA, et al. (2011) Single-tissue and cross-tissue heritability of gene expression via identity-by-descent in related or unrelated individuals. PLoS Genet 7: e1001317.

36. Shen-OrrSS, TibshiraniR, KhatriP, BodianDL, StaedtlerF, et al. (2010) Cell type-specific gene expression differences in complex tissues. Nat Methods 7: 287–289.

37. SiedleckaU, AroraM, KolettisT, SoppaGK, LeeJ, et al. (2008) Effects of clenbuterol on contractility and Ca2+ homeostasis of isolated rat ventricular myocytes. Am J Physiol Heart Circ Physiol 295: H1917–1926.

38. JohnsonMD, MuellerM, GameL, AitmanTJ (2012) Single nucleotide analysis of Cytosine methylation by whole-genome shotgun bisulfite sequencing. Curr Protoc Mol Biol Chapter 21: Unit21 23.

39. KarimiM, JohanssonS, EkstromTJ (2006) Using LUMA: a Luminometric-based assay for global DNA-methylation. Epigenetics 1: 45–48.

40. LiH, DurbinR (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25: 1754–1760.

41. McKennaA, HannaM, BanksE, SivachenkoA, CibulskisK, et al. (2010) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20: 1297–1303.

42. LiH, HandsakerB, WysokerA, FennellT, RuanJ, et al. (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25: 2078–2079.

43. BenjaminiY, HochbergY (1995) Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B-Methodological 57: 289–300.

44. WardJH (1963) Hierarchical grouping to optimize an objective function. J Am Statist Assoc 48: 236–244.

45. R Development Core Team (2010) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.

46. SaarK, BeckA, BihoreauMT, BirneyE, BrocklebankD, et al. (2008) SNP and haplotype mapping for genetic analysis in the rat. Nat Genet 40: 560–566.

47. ChurchillGA, DoergeRW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138: 963–971.

48. AndersS, HuberW (2010) Differential expression analysis for sequence count data. Genome Biol 11: R106.

49. ErillI, O'NeillMC (2009) A reexamination of information theory-based methods for DNA-binding site identification. BMC bioinformatics 10: 57.

50. QuinlanAR, HallIM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26: 841–842.

51. Huang daW, ShermanBT, LempickiRA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4: 44–57.

52. Huang daW, ShermanBT, LempickiRA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37: 1–13.

53. SupekF, BosnjakM, SkuncaN, SmucT (2011) REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One 6: e21800.