Non-CG DNA methylation is a biomarker for assessing endodermal differentiation capacity in pluripotent stem cells

Nature Communications

Volumn 7, Issue , 2016, Pages

  Butcher, Lee M ^a,b   Ito, Mitsuteru ^c   Brimpari, Minodora ^c   Morris, Tiffany J ^a,d   Soares, Filipa A C ^c,e   Ährlund Richter, Lars ^f   Carey, Nessa ^g   Vallier, Ludovic ^c,e   Ferguson Smith, Anne C ^c   Beck, Stephan ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b IMPERIAL COLLEGE LONDON   (United Kingdom)

^c UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^d CAMBRIDGE EPIGENETIX   (United Kingdom)

^e WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

^f KAROLINSKA INSTITUTE   (Sweden)

^g PraxisUnico   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DNA; DNA METHYLTRANSFERASE 1; DNA METHYLTRANSFERASE 3A; DNA METHYLTRANSFERASE 3B; DNMT3L PROTEIN; KRUPPEL LIKE FACTOR 4; MYC PROTEIN; OCTAMER TRANSCRIPTION FACTOR 4; PROTEIN; TRANSCRIPTION FACTOR SOX2; UNCLASSIFIED DRUG; BIOLOGICAL MARKER;

BIOMARKER; CELLS AND CELL COMPONENTS; GENE EXPRESSION; METHYLATION; PHENOTYPE;

ARTICLE; CELL DIFFERENTIATION; CELL DIVISION; CONTROLLED STUDY; DNA METHYLATION; EPIGENETICS; FIBROBLAST; GENE EXPRESSION PROFILING; GENETIC VARIABILITY; HUMAN; HUMAN CELL; HUMAN EMBRYONIC STEM CELL; PHENOTYPE; PLURIPOTENT STEM CELL; PROMOTER REGION; PYROSEQUENCING; QUANTITATIVE ANALYSIS; REVERSE TRANSCRIPTION POLYMERASE CHAIN REACTION; COHORT ANALYSIS; CYTOLOGY; ENDODERM; INDUCED PLURIPOTENT STEM CELL; METABOLISM;

BIOMARKERS; CELL DIFFERENTIATION; COHORT STUDIES; DNA METHYLATION; ENDODERM; HUMANS; INDUCED PLURIPOTENT STEM CELLS;

EID: 84961361545     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms10458     Document Type: Article

References (47)

1. Ramsahoye, B. H. et al. Non-CpG methylation is prevalent in embryonic stem cells and may be mediated by DNA methyltransferase 3a. Proc. Natl Acad. Sci. USA 97, 5237-5242 (2000).
2. Shirane, K. et al. Mouse oocyte methylomes at base resolution reveal genomewide accumulation of non-CpG methylation and role of DNA methyltransferases. PLoS Genet 9, e1003439 (2013).
3. Tiedemann, R. L. et al. Acute depletion redefines the division of labor among DNA methyltransferases in methylating the human genome. Cell Rep. 9, 1554-1566 (2014).
4. Ziller, M. J. et al. Genomic distribution and inter-sample variation of non-CpG methylation across human cell types. PLoS Genet. 7, e1002389 (2011).
5. Laurent, L. et al. Dynamic changes in the human methylome during differentiation. Genome Res. 20, 320-331 (2010).
6. Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315-322 (2009).
7. Li, Y. et al. The DNA methylome of human peripheral blood mononuclear cells. PLoS Biol. 8, e1000533 (2010).
8. Lister, R. et al. Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells. Nature 471, 68-73 (2011).
9. Bock, C. et al. Reference maps of human ES and iPS cell variation enable high-throughput characterization of pluripotent cell lines. Cell 144, 439-452 (2011).
10. Doi, A. et al. Differential methylation of tissue- and cancer-specific CpG island shores distinguishes human induced pluripotent stem cells, embryonic stem cells and fibroblasts. Nat. Genet. 41, 1350-1353 (2009).
11. Ji, H. et al. Comprehensive methylome map of lineage commitment from haematopoietic progenitors. Nature 467, 338-342 (2010).
12. Meissner, A. et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 454, 766-770 (2008).
13. Meissner, A. Epigenetic modifications in pluripotent and differentiated cells. Nat. Biotechnol. 28, 1079-1088 (2010).
14. Karnik, R. & Meissner, A. Browsing (Epi)genomes: a guide to data resources and epigenome browsers for stem cell researchers. Cell Stem Cell 13, 14-21 (2013).
15. Smith, Z. D. & Meissner, A. DNA methylation: roles in mammalian development. Nat. Rev. Genet. 14, 204-220 (2013).
16. Chen, P. Y., Feng, S., Joo, J. W., Jacobsen, S. E. & Pellegrini, M. A comparative analysis of DNA methylation across human embryonic stem cell lines. Genome Biol. 12, R62 (2011).
17. Muller, F. J. et al. A bioinformatic assay for pluripotency in human cells. Nat. Methods 8, 315-317 (2011).
18. Maherali, N. & Hochedlinger, K. Guidelines and techniques for the generation of induced pluripotent stem cells. Cell Stem Cell 3, 595-605 (2008).
19. Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).
20. Rakyan, V. K. et al. Identification of type 1 diabetes-associated DNA methylation variable positions that precede disease diagnosis. PLoS Genet. 7, e1002300 (2011).
21. The 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061-1073 (2010).
22. Nordlund, J. et al. Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia. Genome Biol. 14, r105 (2013).
23. Kim, K. et al. Donor cell type can influence the epigenome and differentiation potential of human induced pluripotent stem cells. Nat. Biotechnol. 29, 1117-1119 (2011).
24. Wu, S. M. & Hochedlinger, K. Harnessing the potential of induced pluripotent stem cells for regenerative medicine. Nat. Cell Biol. 13, 497-505 (2011).
25. Saha, K. & Jaenisch, R. Technical challenges in using human induced pluripotent stem cells to model disease. Cell Stem Cell 5, 584-595 (2009).
26. Daley, G. Q. et al. Broader implications of defining standards for the pluripotency of iPSCs. Cell Stem Cell 4, 200-201 (2009).
27. Kim, K. et al. Epigenetic memory in induced pluripotent stem cells. Nature 467, 285-290 (2010).
28. Gonzalez, F., Boue, S. & Izpisua Belmonte, J. C. Methods for making induced pluripotent stem cells: reprogramming a la carte. Nat. Rev. Genet. 12, 231-242 (2011).
29. Clark, S. J., Harrison, J. & Molloy, P. L. Sp1 binding is inhibited by mCpmCpG methylation. Gene 195, 67-71 (1997).
30. Brandeis, M. et al. Sp1 elements protect a CpG island from de novo methylation. Nature 371, 435-438 (1994).
31. Yang, Y. et al. Sp1 modification of human endothelial nitric oxide synthase promoter increases the hypoxia-stimulated activity. Microvasc. Res. 93, 80-86 (2014).
32. Douet, V., Heller, M. B. & Le, S. O. DNA methylation and Sp1 binding determine the tissue-specific transcriptional activity of the mouse Abcc6 promoter. Biochem. Biophys. Res. Commun. 354, 66-71 (2007).
33. Black, A. R., Black, J. D. & Azizkhan-Clifford, J. Sp1 and kruppel-like factor family of transcription factors in cell growth regulation and cancer. J. Cell Physiol. 188, 143-160 (2001).
34. Hannan, N. R., Segeritz, C. P., Touboul, T. & Vallier, L. Production of hepatocyte-like cells from human pluripotent stem cells. Nat. Protoc. 8, 430-437 (2013).
35. Cho, C. H. et al. Inhibition of activin/nodal signalling is necessary for pancreatic differentiation of human pluripotent stem cells. Diabetologia 55, 3284-3295 (2012).
36. Vallier, L. et al. Signaling pathways controlling pluripotency and early cell fate decisions of human induced pluripotent stem cells. Stem Cells 27, 2655-2666 (2009).
37. Morris, T. J. et al. ChAMP: 450k chip analysis methylation pipeline. Bioinformatics 30, 428-30 (2013).
38. Aryee, M. J. et al. Minfi: A flexible and comprehensive Bioconductor package for the analysis of Infinium DNA Methylation microarrays. Bioinformatics 30, 1363-1369 (2014).
39. Maksimovic, J., Gordon, L. & Oshlack, A. SWAN: Subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biol. 13, R44 (2012).
40. Teschendorff, A. E. et al. An epigenetic signature in peripheral blood predicts active ovarian cancer. PLoS ONE 4, e8274 (2009).
41. Du, P. et al. Comparison of beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11, 587 (2010).
42. Smyth, G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, 397-420 (2004).
43. Kurita, S. et al. DNMT1 and DNMT3b silencing sensitizes human hepatoma cells to TRAIL-mediated apoptosis via up-regulation of TRAIL-R2/DR5 and caspase-8. Cancer Sci. 101, 1431-1439 (2010).
44. Giardine, B. et al. Galaxy: a platform for interactive large-scale genome analysis. Genome Res. 15, 1451-1455 (2005).
45. Blankenberg, D. et al. Galaxy: a web-based genome analysis tool for experimentalists. Curr. Protoc. Mol. Biol. Chapter 19, Unit-19.10 1-21 (2010).
46. Goecks, J., Nekrutenko, A. & Taylor, J. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 11, R86 (2010).
47. Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).