Global transcriptome analysis for identification of interactions between coding and noncoding RNAs during human erythroid differentiation

Nan Ding, Jiafei Xi, Yanming Li, Xiaoyan Xie, Jian Shi, Zhaojun Zhang, Yanhua Li, Fang Fang, Sihan Wang, Wen Yue, Xuetao Pei, Xiangdong Fang

PDF(783 KB)
PDF(783 KB)
Front. Med. ›› 2016, Vol. 10 ›› Issue (3) : 297-310. DOI: 10.1007/s11684-016-0452-0
RESEARCH ARTICLE

Global transcriptome analysis for identification of interactions between coding and noncoding RNAs during human erythroid differentiation

Author information +
History +

Abstract

Studies on coding genes, miRNAs, and lncRNAs during erythroid development have been performed in recent years. However, analysis focusing on the integration of the three RNA types has yet to be done. In the present study, we compared the dynamics of coding genes, miRNA, and lncRNA expression profiles. To explore dynamic changes in erythropoiesis and potential mechanisms that control these changes in the transcriptome level, we took advantage of high throughput sequencing technologies to obtain transcriptome data from cord blood hematopoietic stem cells and the following four erythroid differentiation stages, as well as from mature red blood cells. Results indicated that lncRNAs were promising cell marker candidates for erythroid differentiation. Clustering analysis classified the differentially expressed genes into four subtypes that corresponded to dynamic changes during stemness maintenance, mid-differentiation, and maturation. Integrated analysis revealed that noncoding RNAs potentially participated in controlling blood cell maturation, and especially associated with heme metabolism and responses to oxygen species and DNA damage. These regulatory interactions were displayed in a comprehensive network, thereby inferring correlations between RNAs and their associated functions. These data provided a substantial resource for the study of normal erythropoiesis, which will permit further investigation and understanding of erythroid development and acquired erythroid disorders.

Keywords

erythroid differentiation / hematopoietic stem cell / RNA-seq / miRNA / lncRNA

Cite this article

Download citation ▾
Nan Ding, Jiafei Xi, Yanming Li, Xiaoyan Xie, Jian Shi, Zhaojun Zhang, Yanhua Li, Fang Fang, Sihan Wang, Wen Yue, Xuetao Pei, Xiangdong Fang. Global transcriptome analysis for identification of interactions between coding and noncoding RNAs during human erythroid differentiation. Front. Med., 2016, 10(3): 297‒310 https://doi.org/10.1007/s11684-016-0452-0

References

[1]
Palis J. Ontogeny of erythropoiesis. Curr Opin Hematol 2008; 15(3): 155–161
CrossRef Pubmed Google scholar
[2]
McGrath K, Palis J. Ontogeny of erythropoiesis in the mammalian embryo. Curr Top Dev Biol 2008; 82: 1–22
CrossRef Pubmed Google scholar
[3]
Loose M, Patient R. Global genetic regulatory networks controlling hematopoietic cell fates. Curr Opin Hematol 2006; 13(4): 229–236
CrossRef Pubmed Google scholar
[4]
Peller S, Tabach Y, Rotschild M, Garach-Joshua O, Cohen Y, Goldfinger N, Rotter V. Identification of gene networks associated with erythroid differentiation. Blood Cells Mol Dis 2009; 43(1): 74–80
CrossRef Pubmed Google scholar
[5]
An X, Schulz VP, Li J, Wu K, Liu J, Xue F, Hu J, Mohandas N, Gallagher PG. Global transcriptome analyses of human and murine terminal erythroid differentiation. Blood 2014; 123(22): 3466–3477
CrossRef Pubmed Google scholar
[6]
Beck D, Thoms JA, Perera D, Schütte J, Unnikrishnan A, Knezevic K, Kinston SJ, Wilson NK, O’Brien TA, Göttgens B, Wong JW, Pimanda JE. Genome-wide analysis of transcriptional regulators in human HSPCs reveals a densely interconnected network of coding and noncoding genes. Blood 2013; 122(14): e12–e22
CrossRef Pubmed Google scholar
[7]
Alvarez-Dominguez JR, Hu W, Yuan B, Shi J, Park SS, Gromatzky AA, van Oudenaarden A, Lodish HF. Global discovery of erythroid long noncoding RNAs reveals novel regulators of red cell maturation. Blood 2014; 123(4): 570–581
CrossRef Pubmed Google scholar
[8]
Paralkar VR, Mishra T, Luan J, Yao Y, Kossenkov AV, Anderson SM, Dunagin M, Pimkin M, Gore M, Sun D, Konuthula N, Raj A, An X, Mohandas N, Bodine DM, Hardison RC, Weiss MJ. Lineage and species-specific long noncoding RNAs during erythro-megakaryocytic development. Blood 2014; 123(12): 1927–1937
CrossRef Pubmed Google scholar
[9]
Bianchi N, Zuccato C, Finotti A, Lampronti I, Borgatti M, Gambari R. Involvement of miRNA in erythroid differentiation. Epigenomics 2012; 4(1): 51–65
CrossRef Pubmed Google scholar
[10]
Georgantas RW 3rd, Hildreth R, Morisot S, Alder J, Liu CG, Heimfeld S, Calin GA, Croce CM, Civin CI. CD34+ hematopoietic stem-progenitor cell microRNA expression and function: a circuit diagram of differentiation control. Proc Natl Acad Sci USA 2007; 104(8): 2750–2755
CrossRef Pubmed Google scholar
[11]
Yang GH, Wang F, Yu J, Wang XS, Yuan JY, Zhang JW. MicroRNAs are involved in erythroid differentiation control. J Cell Biochem 2009; 107(3): 548–556
CrossRef Pubmed Google scholar
[12]
Wang LS, Li L, Li L, Chu S, Shiang KD, Li M, Sun HY, Xu J, Xiao FJ, Sun G, Rossi JJ, Ho Y, Bhatia R. MicroRNA-486 regulates normal erythropoiesis and enhances growth and modulates drug response in CML progenitors. Blood 2015; 125(8): 1302–1313
CrossRef Pubmed Google scholar
[13]
Zhang L, Flygare J, Wong P, Lim B, Lodish HF. miR-191 regulates mouse erythroblast enucleation by down-regulating Riok3 and Mxi1. Genes Dev 2011; 25(2): 119–124
CrossRef Pubmed Google scholar
[14]
Patrick DM, Zhang CC, Tao Y, Yao H, Qi X, Schwartz RJ, Jun-Shen Huang L, Olson EN. Defective erythroid differentiation in miR-451 mutant mice mediated by 14-3-3ζ. Genes Dev 2010; 24(15): 1614–1619
CrossRef Pubmed Google scholar
[15]
Yu D, dos Santos CO, Zhao G, Jiang J, Amigo JD, Khandros E, Dore LC, Yao Y, D’Souza J, Zhang Z, Ghaffari S, Choi J, Friend S, Tong W, Orange JS, Paw BH, Weiss MJ. miR-451 protects against erythroid oxidant stress by repressing 14-3-3ζ. Genes Dev 2010; 24(15): 1620–1633
CrossRef Pubmed Google scholar
[16]
Wilusz JE, Sunwoo H, Spector DL. Long noncoding RNAs: functional surprises from the RNA world. Genes Dev 2009; 23(13): 1494–1504
CrossRef Pubmed Google scholar
[17]
Alvarez-Dominguez JR, Hu W, Gromatzky AA, Lodish HF. Long noncoding RNAs during normal and malignant hematopoiesis. Int J Hematol 2014; 99(5): 531–541
CrossRef Pubmed Google scholar
[18]
Satpathy AT, Chang HY. Long noncoding RNA in hematopoiesis and immunity. Immunity 2015; 42(5): 792–804
CrossRef Pubmed Google scholar
[19]
Gallagher PG. Long noncoding RNAs in erythropoiesis. Blood 2014; 123(4): 465–466
CrossRef Pubmed Google scholar
[20]
Masaki S, Ohtsuka R, Abe Y, Muta K, Umemura T. Expression patterns of microRNAs 155 and 451 during normal human erythropoiesis. Biochem Biophys Res Commun 2007; 364(3): 509–514
CrossRef Pubmed Google scholar
[21]
Leberbauer C, Boulmé F, Unfried G, Huber J, Beug H, Müllner EW. Different steroids co-regulate long-term expansion versus terminal differentiation in primary human erythroid progenitors. Blood 2005; 105(1): 85–94
CrossRef Pubmed Google scholar
[22]
Xi J, Li Y, Wang R, Wang Y, Nan X, He L, Zhang P, Chen L, Yue W, Pei X. In vitro large scale production of human mature red blood cells from hematopoietic stem cells by coculturing with human fetal liver stromal cells. Biomed Res Int 2013; 2013: 807863
CrossRef Pubmed Google scholar
[23]
Brown JM, Leach J, Reittie JE, Atzberger A, Lee-Prudhoe J, Wood WG, Higgs DR, Iborra FJ, Buckle VJ. Coregulated human globin genes are frequently in spatial proximity when active. J Cell Biol 2006; 172(2): 177–187
CrossRef Pubmed Google scholar
[24]
Merryweather-Clarke AT, Atzberger A, Soneji S, Gray N, Clark K, Waugh C, McGowan SJ, Taylor S, Nandi AK, Wood WG, Roberts DJ, Higgs DR, Buckle VJ, Robson KJ. Global gene expression analysis of human erythroid progenitors. Blood 2011; 117(13): e96–e108
CrossRef Pubmed Google scholar
[25]
FASTQC: a quality control tool for high throughput sequence data
[26]
Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 2013; 14(4): R36
CrossRef Pubmed Google scholar
[27]
Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 2009; 25(9): 1105–1111
CrossRef Pubmed Google scholar
[28]
Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 2012; 7(3): 562–578
CrossRef Pubmed Google scholar
[29]
Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 2010; 28(5): 511–515
CrossRef Pubmed Google scholar
[30]
Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 2010; 26(12): 1572–1573
CrossRef Pubmed Google scholar
[31]
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005; 102(43): 15545–15550
CrossRef Pubmed Google scholar
[32]
Montojo J, Zuberi K, Rodriguez H, Kazi F, Wright G, Donaldson SL, Morris Q, Bader GD. GeneMANIA Cytoscape plugin: fast gene function predictions on the desktop. Bioinformatics 2010; 26(22): 2927–2928
CrossRef Pubmed Google scholar
[33]
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13(11): 2498–2504
CrossRef Pubmed Google scholar
[34]
Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, Aken BL, Barrell D, Zadissa A, Searle S, Barnes I, Bignell A, Boychenko V, Hunt T, Kay M, Mukherjee G, Rajan J, Despacio-Reyes G, Saunders G, Steward C, Harte R, Lin M, Howald C, Tanzer A, Derrien T, Chrast J, Walters N, Balasubramanian S, Pei B, Tress M, Rodriguez JM, Ezkurdia I, van Baren J, Brent M, Haussler D, Kellis M, Valencia A, Reymond A, Gerstein M, Guigó R, Hubbard TJ. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res 2012; 22(9): 1760–1774
CrossRef Pubmed Google scholar
[35]
Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, Heger A, Hetherington K, Holm L, Mistry J, Sonnhammer EL, Tate J, Punta M. Pfam: the protein families database. Nucleic Acids Res 2014; 42(Database issue): D222–D230
CrossRef Pubmed Google scholar
[36]
Rice P, Longden I, Bleasby A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet 2000; 16(6): 276–277
CrossRef Pubmed Google scholar
[37]
Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997; 25(17): 3389–3402
CrossRef Pubmed Google scholar
[38]
Friedländer MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 2012; 40(1): 37–52
CrossRef Pubmed Google scholar
[39]
Lu TP, Lee CY, Tsai MH, Chiu YC, Hsiao CK, Lai LC, Chuang EY. miRSystem: an integrated system for characterizing enriched functions and pathways of microRNA targets. PLoS ONE 2012; 7(8): e42390
CrossRef Pubmed Google scholar
[40]
Orkin SH. Transcription factors and hematopoietic development. J Biol Chem 1995; 270(10): 4955–4958
CrossRef Pubmed Google scholar
[41]
Singh MK, Li Y, Li S, Cobb RM, Zhou D, Lu MM, Epstein JA, Morrisey EE, Gruber PJ. Gata4 and Gata5 cooperatively regulate cardiac myocyte proliferation in mice. J Biol Chem 2010; 285(3): 1765–1772
CrossRef Pubmed Google scholar
[42]
Vicente C, Conchillo A, García-Sánchez MA, Odero MD. The role of the GATA2 transcription factor in normal and malignant hematopoiesis. Crit Rev Oncol Hematol 2012; 82(1): 1–17
CrossRef Pubmed Google scholar
[43]
Molchadsky A, Rivlin N, Brosh R, Rotter V, Sarig R. p53 is balancing development, differentiation and de-differentiation to assure cancer prevention. Carcinogenesis 2010; 31(9): 1501–1508
CrossRef Pubmed Google scholar
[44]
Fatica A, Bozzoni I. Long non-coding RNAs: new players in cell differentiation and development. Nat Rev Genet 2014; 15(1): 7–21
CrossRef Pubmed Google scholar
[45]
Song X, Cao G, Jing L, Lin S, Wang X, Zhang J, Wang M, Liu W, Lv C. Analysing the relationship between lncRNA and protein-coding gene and the role of lncRNA as ceRNA in pulmonary fibrosis. J Cell Mol Med 2014; 18(6): 991–1003
CrossRef Pubmed Google scholar
[46]
Cesana M, Cacchiarelli D, Legnini I, Santini T, Sthandier O, Chinappi M, Tramontano A, Bozzoni I. A long noncoding RNA controls muscle differentiation by functioning as a competing endogenous RNA. Cell 2011; 147(2): 358–369
CrossRef Pubmed Google scholar
[47]
Ginger MR, Shore AN, Contreras A, Rijnkels M, Miller J, Gonzalez-Rimbau MF, Rosen JM. A noncoding RNA is a potential marker of cell fate during mammary gland development. Proc Natl Acad Sci USA 2006; 103(15): 5781–5786
CrossRef Pubmed Google scholar
[48]
Gokhman D, Livyatan I, Sailaja BS, Melcer S, Meshorer E. Multilayered chromatin analysis reveals E2f, Smad and Zfx as transcriptional regulators of histones. Nat Struct Mol Biol 2013; 20(1): 119–126
CrossRef Pubmed Google scholar
[49]
Timmers C, Sharma N, Opavsky R, Maiti B, Wu L, Wu J, Orringer D, Trikha P, Saavedra HI, Leone G. E2f1, E2f2, and E2f3 control E2F target expression and cellular proliferation via a p53-dependent negative feedback loop. Mol Cell Biol 2007; 27(1): 65–78
CrossRef Pubmed Google scholar
[50]
dos Santos CO, Duarte AS, Saad ST, Costa FF. Expression of α-hemoglobin stabilizing protein gene during human erythropoiesis. Exp Hematol 2004; 32(2): 157–162
CrossRef Pubmed Google scholar
[51]
Zhai PF, Wang F, Su R, Lin HS, Jiang CL, Yang GH, Yu J, Zhang JW. The regulatory roles of microRNA-146b-5p and its target platelet-derived growth factor receptor a (PDGFRA) in erythropoiesis and megakaryocytopoiesis. J Biol Chem 2014; 289(33): 22600–22613
CrossRef Pubmed Google scholar
[52]
Zhu Y, Wang D, Wang F, Li T, Dong L, Liu H, Ma Y, Jiang F, Yin H, Yan W, Luo M, Tang Z, Zhang G, Wang Q, Zhang J, Zhou J, Yu J. A comprehensive analysis of GATA-1-regulated miRNAs reveals miR-23a to be a positive modulator of erythropoiesis. Nucleic Acids Res 2013; 41(7): 4129–4143
CrossRef Pubmed Google scholar
[53]
Wang F, Zhu Y, Guo L, Dong L, Liu H, Yin H, Zhang Z, Li Y, Liu C, Ma Y, Song W, He A, Wang Q, Wang L, Zhang J, Li J, Yu J. A regulatory circuit comprising GATA1/2 switch and microRNA-27a/24 promotes erythropoiesis. Nucleic Acids Res 2014; 42(1): 442–457
CrossRef Pubmed Google scholar
[54]
Grabher C, Payne EM, Johnston AB, Bolli N, Lechman E, Dick JE, Kanki JP, Look AT. Zebrafish microRNA-126 determines hematopoietic cell fate through c-Myb. Leukemia 2011; 25(3): 506–514
CrossRef Pubmed Google scholar
[55]
Paraskevi A, Theodoropoulos G, Papaconstantinou I, Mantzaris G, Nikiteas N, Gazouli M. Circulating microRNAs in inflammatory bowel diseases. J Crohns Colitis 2012; 6(9):900–904
CrossRef Pubmed Google scholar
[56]
Keller A, Leidinger P, Bauer A, Elsharawy A, Haas J, Backes C, Wendschlag A, Giese N, Tjaden C, Ott K, Werner J, Hackert T, Ruprecht K, Huwer H, Huebers J, Jacobs G, Rosenstiel P, Dommisch H, Schaefer A, Müller-Quernheim J, Wullich B, Keck B, Graf N, Reichrath J, Vogel B, Nebel A, Jager SU, Staehler P, Amarantos I, Boisguerin V, Staehler C, Beier M, Scheffler M, Büchler MW, Wischhusen J, Haeusler SF, Dietl J, Hofmann S, Lenhof HP, Schreiber S, Katus HA, Rottbauer W, Meder B, Hoheisel JD, Franke A, Meese E. Toward the blood-borne miRNome of human diseases. Nat Methods 2011; 8(10): 841–843
CrossRef Pubmed Google scholar
[57]
Rudnicki M, Perco P, D Haene B, Leierer J, Heinzel A, Mühlberger I, Schweibert N, Sunzenauer J, Regele H, Kronbichler A, Mestdagh P, Vandesompele J, Mayer B, Mayer G. Renal microRNA- and RNA-profiles in progressive chronic kidney disease. Eur J Clin Invest 2016; 46(3): 213–226
CrossRef Pubmed Google scholar
[58]
Wang JX, Zhang XJ, Feng C, Sun T, Wang K, Wang Y, Zhou LY, Li PF. MicroRNA-532-3p regulates mitochondrial fission through targeting apoptosis repressor with caspase recruitment domain in doxorubicin cardiotoxicity. Cell Death Dis2015; 6:e1677
CrossRef Pubmed Google scholar

Acknowledgements

This research was supported by the National “Twelfth Five-Year” Plan for Science & Technology Support (No. 2013BAI01B09 to X.F.), National Natural Science Foundation of China (No. 31471115 to X.F., No. 31401160 to H.Q., No. 31201097 to J.X.), National Key Scientific Instrument and Equipment Development Projects of China (No. 2011YQ03013404 to X.F.), National High Technology Research and Development Program of China (Nos. 2015AA020101 and 2015AA020108), and State Key Laboratory of Experimental Hematology Pilot Project Grant (No. ZK13-05 to Z.Z.), National High Technology Research and Development Program of China (No. 2013AA020107 to X.P.), National Natural Science Foundation of China (No. 31301199 to X.Y., No. 31401260 to F.F.), Guangzhou Health Care and Cooperative Innovation Major Project (No. 201400000003 to X.P.), and Guangdong Major Scientific and Technological Project (No. 2013A022100005 to X.P.).

Compliance with ethics guidelines

Nan Ding, Jiafei Xi, Yanming Li, Xiaoyan Xie, Jian Shi, Zhaojun Zhang, Yanhua Li, Fang Fang, Sihan Wang, Wen Yue, Xuetao Pei, and Xiangdong Fang declare that they have no conflicts of interest related to this work. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients for being included in the study.
Electronic Supplementary Material Supplementary material is available in the online version of this article at http://dx.doi.org/10.1007/s11684-016-0452-0 and is accessible for authorized users.

RIGHTS & PERMISSIONS

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg
AI Summary AI Mindmap
PDF(783 KB)

Accesses

Citations

Detail

Sections
Recommended

/