Identification of bovine CpG SNPs as potential targets for epigenetic regulation via DNA methylation
Autoři:
Mariângela B. C. Maldonado aff001; Nelson B. de Rezende Neto aff002; Sheila T. Nagamatsu aff003; Marcelo F. Carazzolle aff003; Jesse L. Hoff aff006; Lynsey K. Whitacre aff006; Robert D. Schnabel aff006; Susanta K. Behura aff006; Stephanie D. McKay aff008; Jeremy F. Taylor aff006; Flavia L. Lopes aff001
Působiště autorů:
São Paulo State University (Unesp), School of Veterinary Medicine, Araçatuba, São Paulo, Brazil
aff001; Natural and Human Sciences Center, ABC Federal University, Santo André, São Paulo, Brazil
aff002; Genomics and Expression Laboratory, University of Campinas, Campinas, São Paulo, Brazil
aff003; Brazilian Bioethanol Science and Technology Laboratory (CTBE), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, São Paulo, Brazil
aff004; National Center for High Performance Computing (CENAPAD-SP), University of Campinas, Campinas, São Paulo, Brazil
aff005; Division of Animal Sciences, University of Missouri, Columbia, Missouri, United States of America
aff006; Informatics Institute, University of Missouri, Columbia, Missouri, United States of America
aff007; Department of Animal and Veterinary Sciences, University of Vermont, Burlington, Vermont, United States of America
aff008
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0222329
Souhrn
Methylation patterns established and maintained at CpG sites may be altered by single nucleotide polymorphisms (SNPs) within these sites and may affect the regulation of nearby genes. Our aims were to: 1) identify and generate a database of SNPs potentially subject to epigenetic control by DNA methylation via their involvement in creating, removing or displacing CpG sites (meSNPs), and; 2) investigate the association of these meSNPs with CpG islands (CGIs), and with methylation profiles of DNA extracted from tissues from cattle with divergent feed efficiencies detected using MIRA-Seq. Using the variant annotation for 56,969,697 SNPs identified in Run5 of the 1000 Bull Genomes Project and the UMD3.1.1 bovine reference genome sequence assembly, we identified and classified 12,836,763 meSNPs according to the nature of variation created at CpGs. The majority of the meSNPs were located in intergenic regions (68%) or introns (26.3%). We found an enrichment (p<0.01) of meSNPs located in CGIs relative to the genome as a whole, and also in differentially methylated sequences in tissues from animals divergent for feed efficiency. Seven meSNPs, located in differentially methylated regions, were fixed for methylation site creating (MSC) or destroying (MSD) alleles in the differentially methylated genomic sequences of animals differing in feed efficiency. These meSNPs may be mechanistically responsible for creating or deleting methylation targets responsible for the differential expression of genes underlying differences in feed efficiency. Our methyl SNP database (dbmeSNP) is useful for identifying potentially functional "epigenetic polymorphisms" underlying variation in bovine phenotypes.
Klíčová slova:
DNA – Biology and life sciences – Cell biology – Chromosome biology – Chromatin – Chromatin modification – DNA methylation – Genetics – Epigenetics – DNA modification – Gene expression – Genomics – Genome analysis – Biochemistry – Nucleic acids – Organisms – Eukaryota – Computational biology – Research and analysis methods – Molecular biology – Database and informatics methods – Animals – Genetic loci – Gene regulation – Molecular genetics – Alleles – Vertebrates – Amniotes – Mammals – Biological databases – Bovines – Cattle – Ruminants – Genomic databases
Zdroje
1. Levenson JM, Sweatt JD. Epigenetic mechanisms in memory formation. Nat Rev Neurosci. 2005;6: 108–118. doi: 10.1038/nrn1604 15654323
2. Chow JC, Yen Z, Ziesche SM, Brown CJ. Silencing of the mammalian X chromosome. Annu Rev Genomics Hum Genet. 2005;6: 69–92. doi: 10.1146/annurev.genom.6.080604.162350 16124854
3. Delaval K, Feil R. Epigenetic regulation of mammalian genomic imprinting. Curr Opin Genet Dev. 2004;14(2): 188–195. doi: 10.1016/j.gde.2004.01.005 15196466
4. Robertson KD. DNA methylation and human disease. Nat Rev Genet. 2005;6(8): 597–610. doi: 10.1038/nrg1655 16136652
5. Bird A. DNA methylation patterns and epigenetic memory. Genes Dev. 2002;16(1): 6–21. doi: 10.1101/gad.947102 11782440
6. Li E, Bestor TH, Jaenisch R. Targeted mutation of the DNA methyltransferase gene results in embryonic lethality. Cell. 1992;69(6): 915–926. doi: 10.1016/0092-8674(92)90611-F 1606615
7. Razin A, Cedar H. Distribution of 5-methylcytosine in chromatin. Proc Natl Acad Sci U S A. 1977;74(7): 2725–2728. doi: 10.1073/pnas.74.7.2725 268622
8. D'Alessio AC, Szyf M. Epigenetic tête-à-tête: the bilateral relationship between chromatin modifications and DNA methylation. Biochem Cell Biol. 2006;84(4): 463–476. doi: 10.1139/o06-090 16936820
9. Dayeh TA, Olsson AH, Volkov P, Almgren P, Rönn T, Ling C. Identification of CpG-SNPs associated with type 2 diabetes and differential DNA methylation in human pancreatic islets. Diabetologia. 2013;56(5): 1036–1046. doi: 10.1007/s00125-012-2815-7 23462794
10. Gutierrez-Arcelus M, Lappalainen T, Montgomery SB, Buil A, Ongen H, Yurovsky A, et al. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. Elife. 2013;2: e00523. doi: 10.7554/eLife.00523 23755361
11. Zhi D, Aslibekyan S, Irvin MR, Claas SA, Borecki IB, Ordovas JM, et al. SNPs located at CpG sites modulate genome-epigenome interaction. Epigenetics. 2013;8(8): 802–806. doi: 10.4161/epi.25501 23811543
12. 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(9): e1004663. doi: 10.1371/journal.pgen.1004663 25233095
13. Bell JT, Pai AA, Pickrell JK, Gaffney DJ, Pique-Regi R, Degner JF, et al. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. 2011;12(1): R10. doi: 10.1186/gb-2011-12-1-r10 21251332
14. Gertz J, Varley KE, Reddy TE, Bowling KM, Pauli F, Parker SL, et al. Analysis of DNA methylation in a three-generation family reveals widespread genetic influence on epigenetic regulation. PLoS Genet. 2011;7(8): e1002228. doi: 10.1371/journal.pgen.1002228 21852959
15. Hellman A, Chess A. Extensive sequence-influenced DNA methylation polymorphism in the human genome. Epigenetics Chromatin. 2010;3(1): 11. doi: 10.1186/1756-8935-3-11 20497546
16. Riley JH, Allan CJ, Lai E, Roses A. The use of single nucleotide polymorphisms in the isolation of common disease genes. Pharmacogenomics. 2000;1(1): 39–47. doi: 10.1517/14622416.1.1.39 11258595
17. Kim S, Misra A. SNP genotyping: technologies and biomedical applications. Annu Rev Biomed Eng. 2007;9: 289–320. doi: 10.1146/annurev.bioeng.9.060906.152037 17391067
18. Rafalski A. Applications of single nucleotide polymorphisms in crop genetics. Curr Opin Plant Biol. 2002;5(2): 94–100. doi: 10.1016/S1369-5266(02)00240-6 11856602
19. Du FX, Clutter AC, Lohuis MM. Characterizing linkage disequilibrium in pig populations. Int J Biol Sci. 2007;3(3): 166–178. doi: 10.7150/ijbs.3.166 17384735
20. Daetwyler HD, Capitan A, Pausch H, Stothard P, van Binsbergen R, Brøndum RF, et al. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nat Genet. 2014;46(8): 858–865. doi: 10.1038/ng.3034 25017103
21. NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2017;45(D1): D12–D7. doi: 10.1093/nar/gkw1071 27899561
22. McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, et al. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17(1): 122. doi: 10.1186/s13059-016-0974-4 27268795
23. Elsik CG, Unni DR, Diesh CM, Tayal A, Emery ML, Nguyen HN, et al. Bovine Genome Database: New tools for gleaning function from the Bos taurus genome. Nucleic Acids Res. 2016;44(D1): D834–D839. Epub 2015/10/19. doi: 10.1093/nar/gkv1077 26481361
24. Sambrook J, Fritsch EF, Maniatis T. Molecular Cloning: A laboratory manual. In., 2nd ed. Plainview, New York: Cold Spring Harbor Laboratory Press; 1989.
25. Almamun M, Levinson BT, van Swaay AC, Johnson NT, McKay SD, Arthur GL, et al. Integrated methylome and transcriptome analysis reveals novel regulatory elements in pediatric acute lymphoblastic leukemia. Epigenetics. 2015; 10(9): 882–890. doi: 10.1080/15592294.2015.1078050 26308964
26. Green BB, McKay SD, Kerr DE. Age dependent changes in the LPS induced transcriptome of bovine dermal fibroblasts occurs without major changes in the methylome. BMC Genomics. 2015; 16: 30. doi: 10.1186/s12864-015-1223-z 25623529
27. Chapple RH, Tizioto PC, Wells KD, Givan SA, Kim J, McKay SD, et al. Characterization of the rat developmental liver transcriptome. Physiol Genomics. 2013; 45(8): 301–311. doi: 10.1152/physiolgenomics.00128.2012 23429212
28. Zimin AV, Delcher AL, Florea L, Kelley DR, Schatz MC, Puiu D, et al. A whole-genome assembly of the domestic cow, Bos taurus. Genome Biol. 2009; 10(4): R42. doi: 10.1186/gb-2009-10-4-r42 19393038
29. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012; 9(4): 357–359. doi: 10.1038/nmeth.1923 22388286
30. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Series B Stat Methodol. 1995; 57(1): 289–300. doi: 10.2307/2346101
31. Mi H, Huang X, Muruganujan A, Tang H, Mills C, Kang D, et al. PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Res. 2017; 45(D1): D183–D189. doi: 10.1093/nar/gkw1138 27899595
32. Hu ZL, Park CA, Wu XL, Reecy JM. Animal QTLdb: an improved database tool for livestock animal QTL/association data dissemination in the post-genome era. Nucleic Acids Res. 2013; 41(D1): D871–D879. doi: 10.1093/nar/gks1150 23180796
33. Feinberg AP. Cancer epigenetics takes center stage. Proc Natl Acad Sci U S A. 2001;98(2): 392–394. doi: 10.1073/pnas.98.2.392 11209042
34. Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA, Lai SL, et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 2010;6(5): e1000952. doi: 10.1371/journal.pgen.1000952 20485568
35. Liu S, Yin H, Li C, Qin C, Cai W, Cao M, et al. Genetic effects of PDGFRB and MARCH1 identified in GWAS revealing strong associations with semen production traits in Chinese Holstein bulls. BMC Genet. 2017;18(1): 63. doi: 10.1186/s12863-017-0527-1 28673243
36. Liu X, Yang J, Zhang Q, Jiang L. Regulation of DNA methylation on EEF1D and RPL8 expression in cattle. Genetica. 2017;145(4–5): 387–395. doi: 10.1007/s10709-017-9974-x 28667419
37. Taylor KH, Kramer RS, Davis JW, Guo J, Duff DJ, Xu D, et al. Ultradeep bisulfite sequencing analysis of DNA methylation patterns in multiple gene promoters by 454 sequencing. Cancer Res. 2007;67(18): 8511–8518. doi: 10.1158/0008-5472.CAN-07-1016 17875690
38. Jiang Z, Wang Z, Kunej T, Williams GA, Michal JJ, Wu XL, et al. A novel type of sequence variation: multiple-nucleotide length polymorphisms discovered in the bovine genome. Genetics. 2007;176(1): 403–407. doi: 10.1534/genetics.106.069401 17409076
39. Goddard ME, Whitelaw E. The use of epigenetic phenomena for the improvement of sheep and cattle. Front Genet. 2014;5: 247. doi: 10.3389/fgene.2014.00247 25191337
40. Haile-Mariam M, Nieuwhof GJ, Beard KT, Konstatinov KV, Hayes BJ. Comparison of heritabilities of dairy traits in Australian Holstein-Friesian cattle from genomic and pedigree data and implications for genomic evaluations. J Anim Breed Genet. 2013;130(1): 20–31. doi: 10.1111/j.1439-0388.2013.01001.x 23317062
41. Abo-Ismail MK, Vander Voort G, Squires JJ, Swanson KC, Mandell IB, Liao X, et al. Single nucleotide polymorphisms for feed efficiency and performance in crossbred beef cattle. BMC Genet. 2014;15: 14. doi: 10.1186/1471-2156-15-14 24476087
42. Rolf MM, Taylor JF, Schnabel RD, McKay SD, McClure MC, Northcutt SL, et al. Genome-wide association analysis for feed efficiency in Angus cattle. Anim Genet. 2012;43(4): 367–374. doi: 10.1111/j.1365-2052.2011.02273.x 22497295
43. Seabury CM, Oldeschulte DL, Saatchi M, Beever JE, Decker JE, Halley YA, et al. Genome-wide association study for feed efficiency and growth traits in U.S. beef cattle. BMC Genomics. 2017;18(1): 386. doi: 10.1186/s12864-017-3754-y 28521758
44. Harris RA, Wang T, Coarfa C, Nagarajan RP, Hong C, Downey SL, et al. Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat Biotechnol. 2010;28(10): 1097–1105. doi: 10.1038/nbt.1682 20852635
45. Titus AJ, Gallimore RM, Salas LA, Christensen BC. Cell-type deconvolution from DNA methylation: a review of recent applications. Hum Mol Genet. 2017;26(R2): R216–R224. doi: 10.1093/hmg/ddx275 28977446
46. Robertson KD. DNA methylation and human disease. Nat Rev Genet. 2005;6(8): 597–610. doi: 10.1038/nrg1655 16136652
47. Zhang Y, Breitling LP, Balavarca Y, Holleczek B, Schöttker B, Brenner H. Comparison and combination of blood DNA methylation at smoking-associated genes and at lung cancer-related genes in prediction of lung cancer mortality. Int J Cancer. 2016;139(11): 2482–2492. doi: 10.1002/ijc.30374 27503000
48. Edvinsson Å, Bränn E, Hellgren C, Freyhult E, White R, Kamali-Moghaddam M, Olivier J, et al. Lower inflammatory markers in women with antenatal depression brings the M1/M2 balance into focus from a new direction. Psychoneuroendocrinology. 2017;80: 15–25. doi: 10.1016/j.psyneuen.2017.02.027 28292683
49. 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. doi: 10.1186/s13059-016-1041-x 27572077
50. Elliott HR, Shihab HA, Lockett GA, Holloway JW, McRae AF, Smith GD, et al. The role of DNA methylation in Type 2 diabetes aetiology: using genotype as a causal anchor. Diabetes. 2017;66(6): 1713–1722. doi: 10.2337/db16-0874 28246294
51. Soriano-Tárraga C, Jiménez-Conde J, Giralt-Steinhauer E, Mola-Caminal M, Vivanco-Hidalgo RM, Ois A, et al. Epigenome-wide association study identifies TXNIP gene associated with type 2 diabetes mellitus and sustained hyperglycemia. Hum Mol Genet. 2016;25(3): 609–619. doi: 10.1093/hmg/ddv493 26643952
52. Heiss JA, Brenner H. Epigenome-wide discovery and evaluation of leukocyte DNA methylation markers for the detection of colorectal cancer in a screening setting. Clin Epigenetics. 2017;9: 24. doi: 10.1186/s13148-017-0322-x 28270869
53. Jones PA. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet. 2012;13(7): 484–492. doi: 10.1038/nrg3230 22641018
54. Ota KT, Andres W, Lewis DA, Stockmeier CA, Duman RS. BICC1 expression is elevated in depressed subjects and contributes to depressive behavior in rodents. Neuropsychopharmacology. 2015;40(3): 711–718. doi: 10.1038/npp.2014.227 25178406
55. Lewis CM, Ng MY, Butler AW, Cohen-Woods S, Uher R, Pirlo K, et al. Genome-wide association study of major recurrent depression in the U.K. population. Am J Psychiatry. 2010;167(8): 949–957. doi: 10.1176/appi.ajp.2010.09091380 20516156
56. Komaki S, Ohmomo H, Hachiya T, Furukawa R, Shiwa Y, Satoh M, et al. An epigenome-wide association study based on cell type-specific whole-genome bisulfite sequencing: Screening for DNA methylation signatures associated with bone mass. Integr Mol Med. 2017;4(5): 1–7. doi: 10.15761/IMM.1000307
57. Mesner LD, Ray B, Hsu YH, Manichaikul A, Lum E, Bryda EC, et al. Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density. J Clin Invest. 2014;124(6): 2736–2749. doi: 10.1172/JCI73072 24789909
58. Seaborne RA, Strauss J, Cocks M, Shepherd S, O’Brien TD, van Someren KA, et al. Human skeletal muscle possesses an epigenetic memory of hypertrophy. Sci Rep. 2018;8(1): 1898. doi: 10.1038/s41598-018-20287-3 29382913
Článok vyšiel v časopise
PLOS One
2019 Číslo 9
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Těžké menstruační krvácení může značit poruchu krevní srážlivosti. Jaký management vyšetření a léčby je v takovém případě vhodný?
- Fixní kombinace paracetamol/kodein nabízí synergické analgetické účinky
Najčítanejšie v tomto čísle
- Graviola (Annona muricata) attenuates behavioural alterations and testicular oxidative stress induced by streptozotocin in diabetic rats
- CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer’s disease
- Comparison between Aptima Assays (Hologic) and the Allplex STI Essential Assay (Seegene) for the diagnosis of Sexually transmitted infections
- Assessment of glucose-6-phosphate dehydrogenase activity using CareStart G6PD rapid diagnostic test and associated genetic variants in Plasmodium vivax malaria endemic setting in Mauritania