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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

Analysis of the Epigenetic Signature of Cell Reprogramming by Computational DNA Methylation Profiles

Author(s): Yongchun Zuo*, Mingmin Song, Hanshuang Li, Xing Chen, Pengbo Cao, Lei Zheng and Guifang Cao*

Volume 15, Issue 6, 2020

Page: [589 - 599] Pages: 11

DOI: 10.2174/1574893614666190919103752

Price: $65

Abstract

Background: DNA methylation plays an important role in the reprogramming process. Understanding the underlying molecular mechanism of reprogramming is crucial for answering fundamental questions regarding the transition of cell identity.

Methods: In this study, based on the genome-wide DNA methylation data from different cell lines, comparative methylation profiles were proposed to identify the epigenetic signature of cell reprogramming.

Results: The density profile of CpG methylation showed that pluripotent cells are more polarized than Human Dermal Fibroblasts (HDF) cells. The heterogeneity of iPS has a greater deviation in the DNA hypermethylation pattern. The result of regional distribution showed that the differential CpG sites between pluripotent cells and HDFs tend to accumulate in the gene body and CpG shelf regions, whereas the internal differential methylation CpG sites (DMCs) of three types of pluripotent cells tend to accumulate in the TSS1500 region. Furthermore, a series of endogenous markers of cell reprogramming were identified based on the integrative analysis, including focal adhesion, pluripotency maintenance and transcription regulation. The calcium signaling pathway was detected as one of the signatures between NT cells and iPS cells. Finally, the regional bias of DNA methylation for key pluripotency factors was discussed. Our studies provide new insight into the barrier identification of cell reprogramming.

Conclusion: Our studies analyzed some epigenetic markers and barriers of nuclear reprogramming, hoping to provide new insight into understanding the underlying molecular mechanism of reprogramming.

Keywords: DNA methylation profile, bioinformatics, epigenetic signature, regional distribution, cell reprogramming, molecular.

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[1]
Qin H, Diaz A, Blouin L, et al. Systematic identification of barriers to human iPSC generation. Cell 2014; 158(2): 449-61.
[http://dx.doi.org/10.1016/j.cell.2014.05.040 ] [PMID: 25036638]
[2]
Halley-Stott RP, Pasque V, Gurdon JB. Nuclear reprogramming. Development 2013; 140(12): 2468-71.
[http://dx.doi.org/10.1242/dev.092049 ] [PMID: 23715540]
[3]
Smith ZD, Sindhu C, Meissner A. Molecular features of cellular reprogramming and development. Nat Rev Mol Cell Biol 2016; 17(3): 139-54.
[http://dx.doi.org/10.1038/nrm.2016.6 ] [PMID: 26883001]
[4]
Vaskova EA, Stekleneva AE, Medvedev SP, Zakian SM. “Epigenetic memory” phenomenon in induced pluripotent stem cells. Acta Naturae 2013; 5(4): 15-21.
[http://dx.doi.org/10.32607/20758251-2013-5-4-15-21 ] [PMID: 24455179]
[5]
Long C, et al. Transcriptome comparisons of multi-species identify differential genome activation of mammals embryogenesis IEEE Access 2018; 7: 7794-802.
[6]
Tachibana M, Amato P, Sparman M, et al. Human embryonic stem cells derived by somatic cell nuclear transfer. Cell 2013; 153(6): 1228-38.
[http://dx.doi.org/10.1016/j.cell.2013.05.006 ] [PMID: 23683578]
[7]
Cibelli JB. Human somatic cell nuclear transfer is alive and well. Cell Stem Cell 2014; 14(6): 699-701.
[http://dx.doi.org/10.1016/j.stem.2014.05.013 ] [PMID: 24905159]
[8]
Polo JM, Anderssen E, Walsh RM, et al. A molecular roadmap of reprogramming somatic cells into iPS cells. Cell 2012; 151(7): 1617-32.
[http://dx.doi.org/10.1016/j.cell.2012.11.039 ] [PMID: 23260147]
[9]
Clancy JL, Patel HR, Hussein SM, et al. Small RNA changes en route to distinct cellular states of induced pluripotency. Nat Commun 2014; 5: 5522.
[http://dx.doi.org/10.1038/ncomms6522 ] [PMID: 25494340]
[10]
Takahashi K, Yamanaka S. A decade of transcription factor-mediated reprogramming to pluripotency. Nat Rev Mol Cell Biol 2016; 17(3): 183-93.
[http://dx.doi.org/10.1038/nrm.2016.8 ] [PMID: 26883003]
[11]
O’Malley J, Skylaki S, Iwabuchi KA, et al. High-resolution analysis with novel cell-surface markers identifies routes to iPS cells. Nature 2013; 499(7456): 88-91.
[http://dx.doi.org/10.1038/nature12243 ] [PMID: 23728301]
[12]
Pan G, Wang T, Yao H, Pei D. Somatic cell reprogramming for regenerative medicine: SCNT vs. iPS cells. BioEssays 2012; 34(6): 472-6.
[http://dx.doi.org/10.1002/bies.201100174 ] [PMID: 22419173]
[13]
Gurdon JB, Melton DA. Nuclear reprogramming in cells. Science 2008; 322(5909): 1811-5.
[http://dx.doi.org/10.1126/science.1160810 ] [PMID: 19095934]
[14]
Han F, Li X, Song D, Jiang S, Xu Q, Zhang Y. SCNT versus iPSCs: proteins and small molecules in reprogramming. Int J Dev Biol 2015; 59(4-6): 179-86.
[http://dx.doi.org/10.1387/ijdb.150042fh ] [PMID: 26505250]
[15]
Krupalnik V, Hanna JH. Stem cells: The quest for the perfect reprogrammed cell. Nature 2014; 511(7508): 160-2.
[http://dx.doi.org/10.1038/nature13515 ] [PMID: 25008516]
[16]
Sancho-Martinez I, Izpisua Belmonte JC. Will SCNT-ESCs be better than iPSCs for personalized regenerative medicine? Cell Stem Cell 2013; 13(2): 141-2.
[http://dx.doi.org/10.1016/j.stem.2013.07.013 ] [PMID: 23910080]
[17]
Ma H, Morey R, O’Neil RC, et al. Abnormalities in human pluripotent cells due to reprogramming mechanisms. Nature 2014; 511(7508): 177-83.
[http://dx.doi.org/10.1038/nature13551 ] [PMID: 25008523]
[18]
Liu W, Liu X, Wang C, et al. Identification of key factors conquering developmental arrest of somatic cell cloned embryos by combining embryo biopsy and single-cell sequencing. Cell Discov 2016; 2: 16010.
[http://dx.doi.org/10.1038/celldisc.2016.10 ] [PMID: 27462457]
[19]
Yongchun Z, Guanghua S, Lei C, et al. Coexpression analysis identifies nuclear reprogramming barriers of somatic cell nuclear transfer embryos. Oncotarget 2017; 8(39): 65847-59.
[http://dx.doi.org/10.18632/oncotarget.19504]
[20]
Chen J, Liu H, Liu J, et al. H3K9 methylation is a barrier during somatic cell reprogramming into iPSCs. Nat Genet 2013; 45(1): 34-42.
[http://dx.doi.org/10.1038/ng.2491 ] [PMID: 23202127]
[21]
Jullien J, Vodnala M, Pasque V, et al. Gene resistance to transcriptional reprogramming following nuclear transfer is directly mediated by multiple chromatin-repressive pathways. Mol Cell 2017; 65(5): 873-884.e8.
[http://dx.doi.org/10.1016/j.molcel.2017.01.030 ] [PMID: 28257702]
[22]
Vaseghi H, Liu J, Qian L. Molecular barriers to direct cardiac reprogramming. Protein Cell 2017; 8(10): 724-34.
[http://dx.doi.org/10.1007/s13238-017-0402-x ] [PMID: 28389873]
[23]
Ebrahimi B. Reprogramming barriers and enhancers: strategies to enhance the efficiency and kinetics of induced pluripotency. Cell Regen (Lond) 2015; 4: 10.
[http://dx.doi.org/10.1186/s13619-015-0024-9 ] [PMID: 26566431]
[24]
Takahashi K, Yamanaka S. A developmental framework for induced pluripotency. Development 2015; 142(19): 3274-85.
[http://dx.doi.org/10.1242/dev.114249 ] [PMID: 26443632]
[25]
Liu X, Wang C, Liu W, et al. Distinct features of H3K4me3 and H3K27me3 chromatin domains in pre-implantation embryos. Nature 2016; 537(7621): 558-62.
[http://dx.doi.org/10.1038/nature19362 ] [PMID: 27626379]
[26]
Wang Y, Bi Y, Gao S. Epigenetic regulation of somatic cell reprogramming. Curr Opin Genet Dev 2017; 46: 156-63.
[http://dx.doi.org/10.1016/j.gde.2017.07.002 ] [PMID: 28823984]
[27]
De Carvalho DD, You JS, Jones PA. DNA methylation and cellular reprogramming. Trends Cell Biol 2010; 20(10): 609-17.
[http://dx.doi.org/10.1016/j.tcb.2010.08.003 ] [PMID: 20810283]
[28]
Liu D, Li G, Zuo Y. Function determinants of TET proteins: the arrangements of sequence motifs with specific codes. Brief Bioinform 2019; 20(5): 1826-35.
[http://dx.doi.org/10.1093/bib/bby053 ] [PMID: 29947743]
[29]
Gao Y, Chen J, Li K, et al. Replacement of Oct4 by Tet1 during iPSC induction reveals an important role of DNA methylation and hydroxymethylation in reprogramming. Cell Stem Cell 2013; 12(4): 453-69.
[http://dx.doi.org/10.1016/j.stem.2013.02.005 ] [PMID: 23499384]
[30]
Meissner A, Mikkelsen TS, Gu H, et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 2008; 454(7205): 766-70.
[http://dx.doi.org/10.1038/nature07107 ] [PMID: 18600261]
[31]
Plath K, Lowry WE. Progress in understanding reprogramming to the induced pluripotent state. Nat Rev Genet 2011; 12(4): 253-65.
[http://dx.doi.org/10.1038/nrg2955 ] [PMID: 21415849]
[32]
Lee DS, Shin JY, Tonge PD, et al. An epigenomic roadmap to induced pluripotency reveals DNA methylation as a reprogramming modulator. Nat Commun 2014; 5: 5619.
[http://dx.doi.org/10.1038/ncomms6619 ] [PMID: 25493341]
[33]
Xu Y, Zhang M, Li W, et al. Transcriptional control of somatic cell reprogramming. Trends Cell Biol 2016; 26(4): 272-88.
[http://dx.doi.org/10.1016/j.tcb.2015.12.003 ] [PMID: 26776886]
[34]
Sandoval J, Heyn H, Moran S, et al. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 2011; 6(6): 692-702.
[http://dx.doi.org/10.4161/epi.6.6.16196 ] [PMID: 21593595]
[35]
Zheng X, Zhang N, Wu HJ, Wu H. Estimating and accounting for tumor purity in the analysis of DNA methylation data from cancer studies. Genome Biol 2017; 18(1): 17.
[http://dx.doi.org/10.1186/s13059-016-1143-5 ] [PMID: 28122605]
[36]
Zhang N, Wu HJ, Zhang W, Wang J, Wu H, Zheng X. Predicting tumor purity from methylation microarray data. Bioinformatics 2015; 31(21): 3401-5.
[http://dx.doi.org/10.1093/bioinformatics/btv370 ] [PMID: 26112293]
[37]
Wen Y, Wei Y, Zhang S, et al. Cell subpopulation deconvolution reveals breast cancer heterogeneity based on DNA methylation signature. Brief Bioinform 2017; 18(3): 426-40.
[PMID: 27016391]
[38]
Doi A, Park IH, Wen B, 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 2009; 41(12): 1350-3.
[http://dx.doi.org/10.1038/ng.471 ] [PMID: 19881528]
[39]
Miao YL, Stein P, Jefferson WN, Padilla-Banks E, Williams CJ. Calcium influx-mediated signaling is required for complete mouse egg activation. Proc Natl Acad Sci USA 2012; 109(11): 4169-74.
[http://dx.doi.org/10.1073/pnas.1112333109 ] [PMID: 22371584]
[40]
Boyer LA, Lee TI, Cole MF, et al. Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 2005; 122(6): 947-56.
[http://dx.doi.org/10.1016/j.cell.2005.08.020 ] [PMID: 16153702]
[41]
Jerabek S, Merino F, Schöler HR, Cojocaru V. OCT4: dynamic DNA binding pioneers stem cell pluripotency. Biochim Biophys Acta 2014; 1839(3): 138-54.
[http://dx.doi.org/10.1016/j.bbagrm.2013.10.001 ] [PMID: 24145198]
[42]
Chu SK, Yang HC. Interethnic DNA methylation difference and its implications in pharmacoepigenetics. Epigenomics 2017; 9(11): 1437-54.
[http://dx.doi.org/10.2217/epi-2017-0046 ] [PMID: 28882057]
[43]
Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res 2013; 41(Database issue): D991-5.
[PMID: 23193258]
[44]
Piras IS, Mills G, Llaci L, et al. Exploring genome-wide DNA methylation patterns in Aicardi syndrome. Epigenomics 2017; 9(11): 1373-86.
[http://dx.doi.org/10.2217/epi-2017-0060 ] [PMID: 28967789]
[45]
Du P, Zhang X, Huang CC, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 2010; 11: 587.
[http://dx.doi.org/10.1186/1471-2105-11-587 ] [PMID: 21118553]
[46]
Yang X, Gao L, Zhang S. Comparative pan-cancer DNA methylation analysis reveals cancer common and specific patterns. Brief Bioinform 2017; 18(5): 761-73.
[http://dx.doi.org/10.1093/bib/bbw063 ] [PMID: 27436122]
[47]
Smyth G. Limma: linear models for microarray dataBioinformatics and computational biology solutions using R and Bioconductor. New York: Springer 2005; pp. 397-420.
[http://dx.doi.org/10.1007/0-387-29362-0_23]
[48]
Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I. Controlling the false discovery rate in behavior genetics research. Behav Brain Res 2001; 125(1-2): 279-84.
[http://dx.doi.org/10.1016/S0166-4328(01)00297-2 ] [PMID: 11682119]
[49]
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30(15): 2114-20.
[http://dx.doi.org/10.1093/bioinformatics/btu170 ] [PMID: 24695404]
[50]
Pertea M, Kim D, Pertea GM, Leek JT, Salzberg SL. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc 2016; 11(9): 1650-67.
[http://dx.doi.org/10.1038/nprot.2016.095 ] [PMID: 27560171]
[51]
Huang W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009; 4(1): 44-57.
[http://dx.doi.org/10.1038/nprot.2008.211 ] [PMID: 19131956]
[52]
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 1995; 57: 289-300.
[http://dx.doi.org/10.1111/j.2517-6161.1995.tb02031.x]
[53]
Benjamini Y. Individual comparisons by ranking methods. Biom Bull 1945; 1(6): 80-3.
[http://dx.doi.org/10.2307/3001968]

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