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

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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.

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References

  1. Palis J. Ontogeny of erythropoiesis. Curr Opin Hematol 2008; 15(3): 155–161

    Article  PubMed  Google Scholar 

  2. McGrath K, Palis J. Ontogeny of erythropoiesis in the mammalian embryo. Curr Top Dev Biol 2008; 82: 1–22

    Article  CAS  PubMed  Google Scholar 

  3. Loose M, Patient R. Global genetic regulatory networks controlling hematopoietic cell fates. Curr Opin Hematol 2006; 13(4): 229–236

    Article  CAS  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

    Article  CAS  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

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  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

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  PubMed  Google Scholar 

  10. Georgantas RW, 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

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  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

    Article  CAS  PubMed  PubMed Central  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

    Article  PubMed  PubMed Central  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 14-3-3. Genes Dev 2010; 24(15): 1614–1619

    Article  CAS  PubMed  PubMed Central  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 14-3-3. Genes Dev 2010; 24(15): 1620–1633

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Satpathy AT, Chang HY. Long noncoding RNA in hematopoiesis and immunity. Immunity 2015; 42(5): 792–804

    Article  CAS  PubMed  Google Scholar 

  19. Gallagher PG. Long noncoding RNAs in erythropoiesis. Blood 2014; 123(4): 465–466

    Article  CAS  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

    Article  CAS  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

    Article  CAS  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

    Article  PubMed  PubMed Central  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

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  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

    Article  PubMed  PubMed Central  Google Scholar 

  27. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 2009; 25(9): 1105–1111

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 2010; 26(12): 1572–1573

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  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

    Article  CAS  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

    Article  CAS  PubMed  PubMed Central  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

    Article  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Orkin SH. Transcription factors and hematopoietic development. J Biol Chem 1995; 270(10): 4955–4958

    Article  CAS  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

    Article  CAS  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

    Article  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

    Article  CAS  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

    Article  CAS  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 proteincoding gene and the role of lncRNA as ceRNA in pulmonary fibrosis. J Cell Mol Med 2014; 18(6): 991–1003

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. dos Santos CO, Duarte AS, Saad ST, Costa FF. Expression of a- hemoglobin stabilizing protein gene during human erythropoiesis. Exp Hematol 2004; 32(2): 157–162

    Article  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

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  PubMed  PubMed Central  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

    Article  CAS  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

    Article  CAS  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

    Article  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

    Article  CAS  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

    Article  CAS  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 Dis 2015; 6:e1677

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Correspondence to Xuetao Pei or Xiangdong Fang.

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Ding, N., Xi, J., Li, Y. et al. Global transcriptome analysis for identification of interactions between coding and noncoding RNAs during human erythroid differentiation. Front. Med. 10, 297–310 (2016). https://doi.org/10.1007/s11684-016-0452-0

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