Rare genetic variation at transcription factor binding sites modulates local DNA methylation profiles
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 vyšel v časopise
PLOS Genetics
2020 Číslo 11
- Může hubnutí souviset s vyšším rizikem nádorových onemocnění?
- Raději si zajděte na oční! Jak souvisí citlivost zraku s rozvojem demence?
- Co způsobuje pooperační infekce? Na vině může být i naše vlastní mikrobiota
- Čeká nás průlom v diagnostice karcinomu pankreatu?
- Polibek, který mi „vzal nohy“ aneb vzácný výskyt EBV u 70leté ženy – kazuistika
Nejčtenější v tomto čísle
- Stability of SARS-CoV-2 phylogenies
- Formal commentary
- No association between SCN9A and monogenic human epilepsy disorders
- Oxidative stress antagonizes fluoroquinolone drug sensitivity via the SoxR-SUF Fe-S cluster homeostatic axis