Rare genetic variation at transcription factor binding sites modulates local DNA methylation profiles

English version

Autoři: Alejandro Martin-Trujillo ^aff001; Nihir Patel ^aff001; Felix Richter ^aff001; Bharati Jadhav ^aff001; Paras Garg ^aff001; Sarah U. Morton ^aff002; David M. McKean ^aff003; Steven R. DePalma ^aff004; Elizabeth Goldmuntz ^aff006; Dorota Gruber ^aff008; Richard Kim ^aff009; Jane W. Newburger ^aff010; George A. Porter, Jr. ^aff012; Alessandro Giardini ^aff013; Daniel Bernstein ^aff014; Martin Tristani-Firouzi ^aff015; Jonathan G. Seidman ^aff004; Christine E. Seidman ^aff003; Wendy K. Chung ^aff016; Bruce D. Gelb ^aff001; Andrew J. Sharp ^aff001
Působiště autorů: The Mindich Child Health and Development Institute and Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America ^aff001; Department of Newborn Medicine, Boston Children’s Hospital, Boston, Massachusetts, United States of America ^aff002; Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America ^aff003; Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America ^aff004; Howard Hughes Medical Institute, Harvard University, Boston, Massachusetts, United States of America ^aff005; Division of Cardiology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States of America ^aff006; Department of Pediatrics, University of Pennsylvania Perlman School of Medicine, Philadelphia, PA, United States of America ^aff007; Department of Pediatrics, Cohen Children’s Medical Center, Northwell Health, New Hyde Park, NY, Unites States of America ^aff008; Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America ^aff009; Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, United States of America ^aff010; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America ^aff011; Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, United States of America ^aff012; Cardiothoracic Unit, Great Ormond Street Hospital, London, England ^aff013; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States of America ^aff014; Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, United States of America ^aff015; Departments of Pediatrics and Medicine, Columbia University, New York, NY, United States of America ^aff016; Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, United States of America ^aff017; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America ^aff018
Vyšlo v časopise: Rare genetic variation at transcription factor binding sites modulates local DNA methylation profiles. PLoS Genet 16(11): e1009189. doi:10.1371/journal.pgen.1009189
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1009189

Souhrn

Although DNA methylation is the best characterized epigenetic mark, the mechanism by which it is targeted to specific regions in the genome remains unclear. Recent studies have revealed that local DNA methylation profiles might be dictated by cis-regulatory DNA sequences that mainly operate via DNA-binding factors. Consistent with this finding, we have recently shown that disruption of CTCF-binding sites by rare single nucleotide variants (SNVs) can underlie cis-linked DNA methylation changes in patients with congenital anomalies. These data raise the hypothesis that rare genetic variation at transcription factor binding sites (TFBSs) might contribute to local DNA methylation patterning.

In this work, by combining blood genome-wide DNA methylation profiles, whole genome sequencing-derived SNVs from 247 unrelated individuals along with 133 predicted TFBS motifs derived from ENCODE ChIP-Seq data, we observed an association between the disruption of binding sites for multiple TFs by rare SNVs and extreme DNA methylation values at both local and, to a lesser extent, distant CpGs. While the majority of these changes affected only single CpGs, 24% were associated with multiple outlier CpGs within ±1kb of the disrupted TFBS. Interestingly, disruption of functionally constrained sites within TF motifs lead to larger DNA methylation changes at nearby CpG sites. Altogether, these findings suggest that rare SNVs at TFBS negatively influence TF-DNA binding, which can lead to an altered local DNA methylation profile. Furthermore, subsequent integration of DNA methylation and RNA-Seq profiles from cardiac tissues enabled us to observe an association between rare SNV-directed DNA methylation and outlier expression of nearby genes.

In conclusion, our findings not only provide insights into the effect of rare genetic variation at TFBS on shaping local DNA methylation and its consequences on genome regulation, but also provide a rationale to incorporate DNA methylation data to interpret the functional role of rare variants.

Klíčová slova:

DNA methylation – Gene disruption – Gene expression – Genetic polymorphism – Genetics – Genomics – Methylation – Sequence motif analysis

Zdroje

1. McVicker G, van de Geijn B, Degner JF, Cain CE, Banovich NE, Raj A, et al. Identification of Genetic Variants That Affect Histone Modifications in Human Cells. Science (80-). 2013;342 : 747–749. doi: 10.1126/science.1242429 24136359

2. Degner JF, Pai AA, Pique-Regi R, Veyrieras JB, Gaffney DJ, Pickrell JK, et al. DNase-I sensitivity QTLs are a major determinant of human expression variation. Nature. 2012;482 : 390–394. doi: 10.1038/nature10808 22307276

3. Banovich NE, Lan X, McVicker G, van de Geijn B, Degner JF, Blischak JD, et al. Methylation QTLs Are Associated with Coordinated Changes in Transcription Factor Binding, Histone Modifications, and Gene Expression Levels. PLoS Genet. 2014;10 : 1–12. doi: 10.1371/journal.pgen.1004663 25233095

4. Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA, Lai S-L, et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 2010;6: e1000952. doi: 10.1371/journal.pgen.1000952 20485568

5. Drong AW, Nicholson G, Hedman AK, Meduri E, Grundberg E, Small KS, et al. The presence of methylation quantitative trait loci indicates a direct genetic influence on the level of DNA methylation in adipose tissue. PLoS One. 2013;8: e55923. doi: 10.1371/journal.pone.0055923 23431366

6. McVean GA, Altshuler (Co-Chair) DM, Durbin (Co-Chair) RM, Abecasis GR, Bentley DR, Chakravarti A, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491 : 56–65. doi: 10.1038/nature11632 23128226

7. Lek M, Karczewski KJ, Minikel E V., Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536 : 285–291. doi: 10.1038/nature19057 27535533

8. Ardlie KG, Deluca DS, Segre A V., Sullivan TJ, Young TR, Gelfand ET, et al. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science (80-). 2015;348 : 648–660. doi: 10.1126/science.1262110 25954001

9. Li X, Kim Y, Tsang EK, Davis JR, Damani FN, Chiang C, et al. The impact of rare variation on gene expression across tissues. Nature. 2017;550 : 239–243. doi: 10.1038/nature24267 29022581

10. Montgomery SB, Lappalainen T, Gutierrez-Arcelus M, Dermitzakis ET. Rare and common regulatory variation in population-scale sequenced human genomes. PLoS Genet. 2011;7: e1002144. doi: 10.1371/journal.pgen.1002144 21811411

11. Zhao J, Akinsanmi I, Arafat D, Cradick TJ, Lee CM, Banskota S, et al. A Burden of Rare Variants Associated with Extremes of Gene Expression in Human Peripheral Blood. Am J Hum Genet. 2016;98 : 299–309. doi: 10.1016/j.ajhg.2015.12.023 26849112

12. Robertson KD. DNA methylation and human disease. Nat Rev Genet. 2005;6 : 597–610. doi: 10.1038/nrg1655 16136652

13. Beard C, Li E, Jaenisch R. Loss of methylation activates Xist in somatic but not in embryonic cells. Genes Dev. 1995;9 : 2325–34. Available from: http://www.ncbi.nlm.nih.gov/pubmed/7557385. doi: 10.1101/gad.9.19.2325 7557385

14. Li E, Beard C, Jaenisch R. Role for DNA methylation in genomic imprinting. Nature. 1993;366 : 362–365. doi: 10.1038/366362a0 8247133

15. Lienert F, Wirbelauer C, Som I, Dean A, Mohn F, Schübeler D. Identification of genetic elements that autonomously determine DNA methylation states. Nature Genetics. 2011. pp. 1091–1097. doi: 10.1038/ng.946 21964573

16. Barbosa M, Joshi RS, Garg P, Martin-Trujillo A, Patel N, Jadhav B, et al. Identification of rare de novo epigenetic variations in congenital disorders. Nat Commun. 2018;9 : 2064. doi: 10.1038/s41467-018-04540-x 29802345

17. Stadler MB, Murr R, Burger L, Ivanek R, Lienert F, Schöler A, et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature. 2011;480 : 490–495. doi: 10.1038/nature10716 22170606

18. Wang J, Zhuang J, Iyer S, Lin XY, Greven MC, Kim BH, et al. Factorbook.org: A Wiki-based database for transcription factor-binding data generated by the ENCODE consortium. Nucleic Acids Res. 2013;41 : 171–176. doi: 10.1093/nar/gks1221 23203885

19. Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis C a., Doyle F, et al. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489 : 57–74. doi: 10.1038/nature11247 22955616

20. Blattler A, Farnham PJ. Cross-talk between site-specific transcription factors and DNA methylation states. J Biol Chem. 2013;288 : 34287–94. doi: 10.1074/jbc.R113.512517 24151070

21. Do C, Lang CF, Lin J, Darbary H, Krupska I, Gaba A, et al. Mechanisms and Disease Associations of Haplotype-Dependent Allele-Specific DNA Methylation. Am J Hum Genet. 2016;98 : 934–955. doi: 10.1016/j.ajhg.2016.03.027 27153397

22. Bell CG, Gao F, Yuan W, Roos L, Acton RJ, Xia Y, et al. Obligatory and facilitative allelic variation in the DNA methylome within common disease-associated loci. Nat Commun. 2018;9 : 8. doi: 10.1038/s41467-017-01586-1 29295990

23. Lambert SA, Jolma A, Campitelli LF, Das PK, Yin Y, Albu M, et al. The Human Transcription Factors. Cell. 2018;172 : 650–665. doi: 10.1016/j.cell.2018.01.029 29425488

24. Onuchic V, Lurie E, Carrero I, Pawliczek P, Patel RY, Rozowsky J, et al. Allele-specific epigenome maps reveal sequence-dependent stochastic switching at regulatory loci. Science (80-). 2018;361. doi: 10.1126/science.aar3146 30139913

25. Krivega I, Dale RK, Dean A. Role of LDB1 in the transition from chromatin looping to transcription activation. Genes Dev. 2014;28 : 1278–90. doi: 10.1101/gad.239749.114 24874989

26. Lee J, Krivega I, Dale RK, Dean A. The LDB1 Complex Co-opts CTCF for Erythroid Lineage-Specific Long-Range Enhancer Interactions. Cell Rep. 2017;19 : 2490–2502. doi: 10.1016/j.celrep.2017.05.072 28636938

27. Dixon JR, Selvaraj S, Yue F, Kim A, Li Y, Shen Y, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature. 2012;485 : 376–80. doi: 10.1038/nature11082 22495300

28. Phanstiel DH, Van Bortle K, Spacek D, Hess GT, Shamim MS, Machol I, et al. Static and Dynamic DNA Loops form AP-1-Bound Activation Hubs during Macrophage Development. Mol Cell. 2017;67 : 1037–1048.e6. doi: 10.1016/j.molcel.2017.08.006 28890333

29. Jolma A, Yan J, Whitington T, Toivonen J, Nitta KR, Rastas P, et al. DNA-binding specificities of human transcription factors. Cell. 2013;152 : 327–39. doi: 10.1016/j.cell.2012.12.009 23332764

30. Shi W, Fornes O, Mathelier A, Wasserman WW. Evaluating the impact of single nucleotide variants on transcription factor binding. Nucleic Acids Res. 2016;44 : 10106–10116. doi: 10.1093/nar/gkw691 27492288

31. Behera V, Evans P, Face CJ, Hamagami N, Sankaranarayanan L, Keller CA, et al. Exploiting genetic variation to uncover rules of transcription factor binding and chromatin accessibility. Nat Commun. 2018;9 : 782. doi: 10.1038/s41467-018-03082-6 29472540

32. Timothy E, Reddy TE, Gertz J, Pauli F, Kucera KS, Varley KE, et al. The effects of genome sequence on differential allelic transcription factor occupancy and gene expression. Genome Res. 2012;22 : 860–869. doi: 10.1101/gr.131201.111 22300769

33. Deplancke B, Alpern D, Gardeux V. The Genetics of Transcription Factor DNA Binding Variation. Cell. 2016;166 : 538–554. doi: 10.1016/j.cell.2016.07.012 27471964

34. Fujiki K, Shinoda A, Kano F, Sato R, Shirahige K, Murata M. PPARγ-induced PARylation promotes local DNA demethylation by production of 5-hydroxymethylcytosine. Nat Commun. 2013;4 : 2262. doi: 10.1038/ncomms3262 23912449

35. Suzuki T, Shimizu Y, Furuhata E, Maeda S, Kishima M, Nishimura H, et al. RUNX1 regulates site specificity of DNA demethylation by recruitment of DNA demethylation machineries in hematopoietic cells. Blood Adv. 2017;1 : 1699–1711. doi: 10.1182/bloodadvances.2017005710 29296817

36. Hervouet E, Peixoto P, Delage-Mourroux R, Boyer-Guittaut M, Cartron P-F. Specific or not specific recruitment of DNMTs for DNA methylation, an epigenetic dilemma. Clin Epigenetics. 2018;10 : 17. doi: 10.1186/s13148-018-0450-y 29449903

37. Sato N, Kondo M, Arai K. The orphan nuclear receptor GCNF recruits DNA methyltransferase for Oct-3/4 silencing. Biochem Biophys Res Commun. 2006;344 : 845–51. doi: 10.1016/j.bbrc.2006.04.007 16631596

38. Schoenherr CJ, Levorse JM, Tilghman SM. CTCF maintains differential methylation at the Igf2/H19 locus. Nat Genet. 2003;33 : 66–9. doi: 10.1038/ng1057 12461525

39. Sugden K, Hannon EJ, Arseneault L, Belsky DW, Corcoran DL, Fisher HL, et al. Patterns of Reliability: Assessing the Reproducibility and Integrity of DNA Methylation Measurement. Patterns. 2020;1 : 100014. doi: 10.1016/j.patter.2020.100014 32885222

40. 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 : 208. doi: 10.1186/s13059-016-1066-1 27717381

41. Gelb B, Brueckner M, Chung W, Goldmuntz E, Kaltman J, Pablo Kaski J, et al. The Congenital Heart Disease Genetic Network Study: Rationale, Design, and Early Results. Circ Res. 2013;112 : 698–706. doi: 10.1161/CIRCRESAHA.111.300297 23410879

42. Hoang TT, Goldmuntz E, Roberts AE, Chung WK, Kline JK, Deanfield JE, et al. The Congenital Heart Disease Genetic Network Study: Cohort description. PLoS One. 2018;13: e0191319. doi: 10.1371/journal.pone.0191319 29351346

43. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25 : 1754–60. doi: 10.1093/bioinformatics/btp324 19451168

44. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20 : 1297–303. doi: 10.1101/gr.107524.110 20644199

45. DePristo MA, Banks E, Poplin R, Garimella K V, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43 : 491–8. doi: 10.1038/ng.806 21478889

46. Auwera GA Van Der, Carneiro MO, Hartl C, Poplin R, Levy-moonshine A, Jordan T, et al. From FastQ data to high confidence varant calls: the Genonme Analysis Toolkit best practices pipeline. Curr Protoc Bioinformatics. 2014. doi: 10.1002/0471250953.bi1110s43.From

47. Du P, Kibbe W a., Lin SM. lumi: A pipeline for processing Illumina microarray. Bioinformatics. 2008;24 : 1547–1548. doi: 10.1093/bioinformatics/btn224 18467348

48. Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2013;29 : 189–96. doi: 10.1093/bioinformatics/bts680 23175756

49. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13 : 86. doi: 10.1186/1471-2105-13-86 22568884

50. Wang J, Zhuang J, Iyer S, Jie Wang A, Lin X, Whitfield TW, et al. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors Repository Citation Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res. 2012;9 : 1798–1812. doi: 10.1101/gr.139105.112 22955990

51. Richter F, Hoffman GE, Manheimer KB, Patel N, Sharp AJ, McKean D, et al. ORE Identifies Extreme Expression Effects Enriched for Rare Variants. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz202 30903145

52. Liao Y, Smyth GK, Shi W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 2013;41: e108. doi: 10.1093/nar/gkt214 23558742

53. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30 : 923–30. doi: 10.1093/bioinformatics/btt656 24227677

Článek A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population

Článek Mms19 promotes spindle microtubule assembly in Drosophila neural stem cells

Článek Genotype imputation using the Positional Burrows Wheeler Transform

Článek Formal commentary

Článek The DNA damage response is required for oocyte cyst breakdown and follicle formation in mice

Článek Loss of hepatocyte cell division leads to liver inflammation and fibrosis

Článek A genetic variant controls interferon-β gene expression in human myeloid cells by preventing C/EBP-β binding on a conserved enhancer

Článek Identity-by-descent with uncertainty characterises connectivity of Plasmodium falciparum populations on the Colombian-Pacific coast

Článek A proteomic survey of microtubule-associated proteins in a R402H TUBA1A mutant mouse

Článek Inferring causal direction between two traits in the presence of horizontal pleiotropy with GWAS summary data

Článek A complementary study approach unravels novel players in the pathoetiology of Hirschsprung disease

Článek Unique genetic signatures of local adaptation over space and time for diapause, an ecologically relevant complex trait, in Drosophila melanogaster