Inter-individual methylation variability in differentially methylated regions between maternal whole blood and first trimester CVS

Molecular Cytogenetics

Philippos C Patsalis²  George Koumbaris³  Carolina Sismani²  Michael Hadjidaniel²  Christiana Spyrou³  Evdokia Tsaliki³  Anna Keravnou²  Elisavet A Papageorgiou³  Marios Ioannides¹ 

[1] Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus;The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus;NIPD Genetics Ltd, Nicosia, Cyprus

关键词: MeDIP;    Differentially methylated regions;    Inter-individual variability;    Non-invasive prenatal diagnosis;   

Others  :  1149562
DOI  :  10.1186/s13039-014-0073-8

 received in 2014-09-22, accepted in 2014-10-12,  发布年份 2014

【 摘 要 】

Background

DNA methylation is the most studied form of epigenetic regulation, a process by which chromatin composition and transcription factor binding is altered to influence tissue specific gene expression and differentiation. Such tissue specific methylation patterns are investigated as biomarkers for cancer and cell-free fetal DNA using various methodologies.

Results

We have utilized methylation DNA immunoprecipitation (MeDIP) and real-time quantitative PCR to investigate the inter-individual methylation variability of differentially methylated regions (DMRs) on chromosomes 18 and 21. We have characterized 15 newly selected and seven previously validated DMRs in 50, 1^st trimester Chorionic villus samplings (CVS) and 50 female non-pregnant peripheral blood (WBF) samples. qPCR results from MeDIP and genomic DNA (Input) assays were used to calculate fold enrichment values for each DMR. For all regions tested, enrichment was higher in CVS than in WBF samples with mean enrichments ranging from 0.22 to 6.4 and 0.017 to 1 respectively. Despite inter-individual variability, mean enrichment values for CVS were significantly different than those for WBF in all DMRs tested (p?

Conclusions

Our work provides an expansion in the biomarker panel available for non-invasive prenatal diagnosis (NIPD) using the MeDIP-qPCR methology for Down syndrome and can eventually provide the starting point towards the development for assays towards the detection of Edwards syndrome. Furthermore, our data indicate that inter-experimental and inter-individual variation in methylation is apparent, yet the difference in methylation status across tissues is large enough to allow for robust tissue specific methylation identification.

【 授权许可】

   
2014 Ioannides et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

【 参考文献 】

• [1]Bird A: DNA methylation patterns and epigenetic memory. Genes Dev 2002, 16:6-21.
• [2]Gopalakrishnan S, Van Emburgh BO, Robertson KD: DNA methylation in development and human disease. Mutat Res 2008, 647:30-38.
• [3]Baylin SB, Jones PA: A decade of exploring the cancer epigenome¿biological and translational implications. Nat Rev Cancer 2011, 11:726-734.
• [4]Laird PW: Principles and challenges of genomewide DNA methylation analysis. Nat Rev Cancer 2010, 11:191-203.
• [5]You JS, Jones PA: Cancer genetics and epigenetics: two sides of the same coin? Cancer Cell 2012, 22:9-20.
• [6]Jakovcevski M, Akbarian S: Epigenetic mechanisms in neurological disease. Nat Med 2012, 18(8):1194-1204.
• [7]Rawson JB, Bapat B: Epigenetic biomarkers in colorectal cancer diagnostics. Expert Rev Mol Diagn 2012, 12:499-509.
• [8]Lo YM, Corbetta N, Chamberlain PF, Rai V, Sargent IL, Redman CW, Wainscoat JS: Presence of fetal DNA in maternal plasma and serum. Lancet 1997, 350:485-487.
• [9]Della Ragione F, Mastrovito P, Campanile C, Conti A, Papageorgiou EA, Hulten MA, Patsalis PC, Carter NP, D¿Esposito M: Differential DNA methylation as a tool for noninvasive prenatal diagnosis (NIPD) of X chromosome aneuploidies. J Mol Diagn 2010, 12:797-807.
• [10]Old RW, Crea F, Puszyk W, Hulten MA: Candidate epigenetic biomarkers for non-invasive prenatal diagnosis of Down syndrome. Reprod Biomed Online 2007, 15:227-235.
• [11]Tong YK, Chiu RW, Akolekar R, Leung TY, Lau TK, Nicolaides KH, Lo YM: Epigenetic-genetic chromosome dosage approach for fetal trisomy 21 detection using an autosomal genetic reference marker. PLoS One 2010, 5:e15244.
• [12]Papageorgiou EA, Fiegler H, Rakyan V, Beck S, Hulten M, Lamnissou K, Carter NP, Patsalis PC: Sites of differential DNA methylation between placenta and peripheral blood: molecular markers for noninvasive prenatal diagnosis of aneuploidies. Am J Pathol 2009, 174:1609-1618.
• [13]Papageorgiou EA, Karagrigoriou A, Tsaliki E, Velissariou V, Carter NP, Patsalis PC: Fetal-specific DNA methylation ratio permits noninvasive prenatal diagnosis of trisomy 21. Nat Med 2011, 17:510-513.
• [14]Tsaliki E, Papageorgiou EA, Spyrou C, Koumbaris G, Kypri E, Kyriakou S, Sotiriou C, Touvana E, Keravnou A, Karagrigoriou A, Lamnissou K, Velissariou V, Patsalis PC: MeDIP real-time qPCR of maternal peripheral blood reliably identifies trisomy 21. Prenat Diagn 2012, 32:996-1001.
• [15]Rakyan VK, Down TA, Thorne NP, Flicek P, Kulesha E, Graf S, Tomazou EM, Backdahl L, Johnson N, Herberth M, Howe KL, Jackson DK, Miretti MM, Fiegler H, Marioni JC, Birney E, Hubbard TJ, Carter NP, Tavare S, Beck S: An integrated resource for genome-wide identification and analysis of human tissue-specific differentially methylated regions (tDMRs). Genome Res 2008, 18(9):1518-1529.
• [16]Butcher LM, Beck S: AutoMeDIP-seq: a high-throughput, whole genome, DNA methylation assay. Methods 2010, 52:223-231.
• [17]Feinberg AP, Irizarry RA: Evolution in health and medicine Sackler colloquium: Stochastic epigenetic variation as a driving force of development, evolutionary adaptation, and disease. Proc Natl Acad Sci U S A 2010, 1(Suppl 107):1757-1764.
• [18]El-Maarri O, Walier M, Behne F, van Uum J, Singer H, Diaz-Lacava A, Nusgen N, Niemann B, Watzka M, Reinsberg J, van der Ven H, Wienker T, Stoffel-Wagner B, Schwaab R, Oldenburg J: Methylation at global LINE-1 repeats in human blood are affected by gender but not by age or natural hormone cycles. PLoS One 2011, 6:e16252.
• [19]Choi SH, Worswick S, Byun HM, Shear T, Soussa JC, Wolff EM, Douer D, Garcia-Manero G, Liang G, Yang AS: Changes in DNA methylation of tandem DNA repeats are different from interspersed repeats in cancer. Int J Cancer 2009, 125:723-729.
• [20]Fryer AA, Emes RD, Ismail KM, Haworth KE, Mein C, Carroll WD, Farrell WE: Quantitative, high-resolution epigenetic profiling of CpG loci identifies associations with cord blood plasma homocysteine and birth weight in humans. Epigenetics 2011, 6:86-94.
• [21]Wong CC, Caspi A, Williams B, Craig IW, Houts R, Ambler A, Moffitt TE, Mill J: A longitudinal study of epigenetic variation in twins. Epigenetics 2010, 5:516-526.
• [22]Lam LL, Emberly E, Fraser HB, Neumann SM, Chen E, Miller GE, Kobor MS: Factors underlying variable DNA methylation in a human community cohort. Proc Natl Acad Sci U S A 2012, 2(Suppl 109):17253-17260.
• [23]Schneider E, Pliushch G, El Hajj N, Galetzka D, Puhl A, Schorsch M, Frauenknecht K, Riepert T, Tresch A, Muller AM, Coerdt W, Zechner U, Haaf T: Spatial, temporal and interindividual epigenetic variation of functionally important DNA methylation patterns. Nucleic Acids Res 2010, 38:3880-3890.
• [24]Bock C, Walter J, Paulsen M, Lengauer T: Inter-individual variation of DNA methylation and its implications for large-scale epigenome mapping. Nucleic Acids Res 2008, 36:e55.
• [25]MacDonald JR, Ziman R, Yuen RK, Feuk L, Scherer SW: The Database of Genomic Variants: a curated collection of structural variation in the human genome. Nucleic Acids Re 2014, 42:D986-D992.
• [26]Rozen S, Skaletsky H: Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol 2000, 132:365-386.
• [27]Mann HB, Whitney DR: On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics 1947, 18(1):50-60.
• [28]Lance GN, Williams WT: A general theory of classificatory sorting strategies II. Clustering systems. The computer journal 1967, 10:271-277.