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

doi:10.1007/s11684-016-0452-0

Front. Med.

2016, Vol. 10

Issue (3) : 297-310 https://doi.org/10.1007/s11684-016-0452-0

RESEARCH ARTICLE

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

Nan Ding^1,³,Jiafei Xi^2,⁴,Yanming Li¹,Xiaoyan Xie^2,⁴,Jian Shi^1,³,Zhaojun Zhang¹,Yanhua Li^2,⁴,Fang Fang^2,⁴,Sihan Wang^2,⁴,Wen Yue^2,⁴,Xuetao Pei^2,^4,^*(

),Xiangdong Fang^1,^*(

)

¹. CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
². Lab of Stem Cell and Regenerative Medicine, Beijing Institute of Transfusion Medicine, AMMS, Beijing 100850, China
³. University of Chinese Academy of Sciences, Beijing 100049, China
⁴. South China Research Center for Stem Cell &Regenerative Medicine, AMMS, Guangzhou 510300, China

Download: PDF(783 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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

Corresponding Author(s): Xuetao Pei,Xiangdong Fang

Just Accepted Date: 16 May 2016 Online First Date: 12 June 2016 Issue Date: 30 August 2016

Cite this article:

Nan Ding,Jiafei Xi,Yanming Li, et al. Global transcriptome analysis for identification of interactions between coding and noncoding RNAs during human erythroid differentiation[J]. Front. Med., 2016, 10(3): 297-310.

URL:

https://academic.hep.com.cn/fmd/EN/10.1007/s11684-016-0452-0
https://academic.hep.com.cn/fmd/EN/Y2016/V10/I3/297

Fig.1 Quantitative real-time PCR of several erythroid genes. (A) Expression patterns of several erythroid genes validated using qRT-PCR. (B) Bar plots show the expression patterns of erythroid genes during HSC development and in red blood cells.

Fig.2 mRNA, miRNA, and lncRNA expression profiles. (A) Bar plots showing numbers of expressed RNAs at various FPKM/RPKM levels. Most RNAs were expressed with FPKM lower than 30. A small number of mRNAs and miRNAs were expressed at a relatively high level at above 100 FPKM. (B) Scatter plot of expressed lncRNAs in different erythroid differentiation stages. The y-axis represents the FPKM value of each lncRNA (colored dots). Few lncRNAs were expressed with FPKM>30. (C–E) Heatmaps of mRNA, miRNA, and lncRNA expressions, respectively.

Fig.3 K-means clustering of differentially expressed genes. Genes that were highly expressed during differentiation but significantly downregulated at maturation stage were classified under subtype 1. Genes that were highly expressed in HSC, but decreased at the following stages were classified under subtype 2. In subtype 3, gene expressions gradually decreased during primary differentiation, but increased during maturation. The expressions of genes altered more than once during differentiation were classified under subtype 4. Transcription factors and hemoglobin, which were detected in distinct subtypes, are shown on the right side of the figure.

Fig.4 Expressions of genes in signaling pathways were activated/inhibited during erythroid differentiation. (A) Green nodes indicated genes that were decreased during maturation, whereas red nodes indicated genes that were increased. Most of the genes in P53 signaling pathway were inhibited. As TP53 was inhibited, the part of this pathway was supposed to be inactive. (B) Expression profiles of activated/inhibited genes in the P53 signaling pathway: 5 genes were upregulated during maturation, but 19 genes were observed downregulated during that period. (C) Expression profiles of chemokines in HSC. All known chemokines, except CCL13 and CCL18, were expressed in HSC and decreased during differentiation.

Fig.5 Correlation heatmap between miRNAs and their target genes in subtype 3. Fifty miRNAs and 256 genes are shown in the left side of the figure. Subtype 3 comprised 29 genes involved in “metabolism of heme” and “reactive to oxygen species,” among which 21 genes were possibly regulated by hsa-miR-532-3p, as highlighted by the red box. Green color boxes represent significant negative correlations between miRNAs and target genes with Pearson correlation coefficient of≤-0.9, P value≤0.01 (P values are not shown).

Fig.6 Analysis of lncRNAs during erythroid differentiation. (A) Significant Pearson correlation coefficient between 60 lncRNAs and coding genes with functions associated with “hemopoiesis and leucocyte activation” and “DNA repair.” Positive correlations are colored red, whereas negative correlations are colored blue. (B) Expression patterns of RP11-326C3.2 and its downstream gene ATHL1 were validated using qPCR in the K562 cell line. (C) Integrative Genomics Viewer expressed reads for RP11-326C3.2 and its downstream coding gene ATHL1 on chromosome 5.

Fig.7 A comprehensive network illustrates the interactions of coding genes, miRNAs, and lncRNAs that participate in controlling red blood cell maturation. Coding genes are marked with square nodes, whereas miRNAs and lncRNAs are marked with “V” and round nodes, respectively. RNAs that were upregulated during erythroid cell development are colored red, and downregulated RNAs are colored green. RNAs downregulated during maturation, but not significant, were colored light green.

1	Palis J. Ontogeny of erythropoiesis. Curr Opin Hematol 2008; 15(3): 155–161 https://doi.org/10.1097/MOH.0b013e3282f97ae1 pmid: 18391778
2	McGrath K, Palis J. Ontogeny of erythropoiesis in the mammalian embryo. Curr Top Dev Biol 2008; 82: 1–22 https://doi.org/10.1016/S0070-2153(07)00001-4 pmid: 18282515
3	Loose M, Patient R. Global genetic regulatory networks controlling hematopoietic cell fates. Curr Opin Hematol 2006; 13(4): 229–236 https://doi.org/10.1097/01.moh.0000231419.15654.7f pmid: 16755218
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 https://doi.org/10.1016/j.bcmd.2009.01.020 pmid: 19329339
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 https://doi.org/10.1182/blood-2014-01-548305 pmid: 24637361
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 https://doi.org/10.1182/blood-2013-03-490425 pmid: 23974199
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 https://doi.org/10.1182/blood-2013-10-530683 pmid: 24200680
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 https://doi.org/10.1182/blood-2013-12-544494 pmid: 24497530
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 https://doi.org/10.2217/epi.11.104 pmid: 22332658
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 https://doi.org/10.1073/pnas.0610983104 pmid: 17293455
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 https://doi.org/10.1002/jcb.22156 pmid: 19350553
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 https://doi.org/10.1182/blood-2014-06-581926 pmid: 25515961
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 https://doi.org/10.1101/gad.1998711 pmid: 21196494
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 https://doi.org/10.1101/gad.1942810 pmid: 20679397
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 https://doi.org/10.1101/gad.1942110 pmid: 20679398
16	Wilusz JE, Sunwoo H, Spector DL. Long noncoding RNAs: functional surprises from the RNA world. Genes Dev 2009; 23(13): 1494–1504 https://doi.org/10.1101/gad.1800909 pmid: 19571179
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 https://doi.org/10.1007/s12185-014-1552-8 pmid: 24609766
18	Satpathy AT, Chang HY. Long noncoding RNA in hematopoiesis and immunity. Immunity 2015; 42(5): 792–804 https://doi.org/10.1016/j.immuni.2015.05.004 pmid: 25992856
19	Gallagher PG. Long noncoding RNAs in erythropoiesis. Blood 2014; 123(4): 465–466 https://doi.org/10.1182/blood-2013-12-538306 pmid: 24458276
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 https://doi.org/10.1016/j.bbrc.2007.10.077 pmid: 17964546
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 https://doi.org/10.1182/blood-2004-03-1002 pmid: 15358620
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 https://doi.org/10.1155/2013/807863 pmid: 23484161
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 https://doi.org/10.1083/jcb.200507073 pmid: 16418531
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 https://doi.org/10.1182/blood-2010-07-290825 pmid: 21270440
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 https://doi.org/10.1186/gb-2013-14-4-r36 pmid: 23618408
27	Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 2009; 25(9): 1105–1111 https://doi.org/10.1093/bioinformatics/btp120 pmid: 19289445
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 https://doi.org/10.1038/nprot.2012.016 pmid: 22383036
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 https://doi.org/10.1038/nbt.1621 pmid: 20436464
30	Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 2010; 26(12): 1572–1573 https://doi.org/10.1093/bioinformatics/btq170 pmid: 20427518
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 https://doi.org/10.1073/pnas.0506580102 pmid: 16199517
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 https://doi.org/10.1093/bioinformatics/btq562 pmid: 20926419
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 https://doi.org/10.1101/gr.1239303 pmid: 14597658
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 https://doi.org/10.1101/gr.135350.111 pmid: 22955987
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 https://doi.org/10.1093/nar/gkt1223 pmid: 24288371
36	Rice P, Longden I, Bleasby A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet 2000; 16(6): 276–277 https://doi.org/10.1016/S0168-9525(00)02024-2 pmid: 10827456
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 https://doi.org/10.1093/nar/25.17.3389 pmid: 9254694
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 https://doi.org/10.1093/nar/gkr688 pmid: 21911355
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 https://doi.org/10.1371/journal.pone.0042390 pmid: 22870325
40	Orkin SH. Transcription factors and hematopoietic development. J Biol Chem 1995; 270(10): 4955–4958 https://doi.org/10.1074/jbc.270.10.4955 pmid: 7890597
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 https://doi.org/10.1074/jbc.M109.038539 pmid: 19889636
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 https://doi.org/10.1016/j.critrevonc.2011.04.007 pmid: 21605981
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 https://doi.org/10.1093/carcin/bgq101 pmid: 20504879
44	Fatica A, Bozzoni I. Long non-coding RNAs: new players in cell differentiation and development. Nat Rev Genet 2014; 15(1): 7–21 https://doi.org/10.1038/nrg3606 pmid: 24296535
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 https://doi.org/10.1111/jcmm.12243 pmid: 24702795
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 https://doi.org/10.1016/j.cell.2011.09.028 pmid: 22000014
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 https://doi.org/10.1073/pnas.0600745103 pmid: 16574773
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 https://doi.org/10.1038/nsmb.2448 pmid: 23222641
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 https://doi.org/10.1128/MCB.02147-05 pmid: 17167174
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 https://doi.org/10.1016/j.exphem.2003.11.002 pmid: 15102476
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 https://doi.org/10.1074/jbc.M114.547380 pmid: 24982425
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 https://doi.org/10.1093/nar/gkt093 pmid: 23420868
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 https://doi.org/10.1093/nar/gkt848 pmid: 24049083
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 https://doi.org/10.1038/leu.2010.280 pmid: 21079614
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 https://doi.org/10.1016/j.crohns.2012.02.006 pmid: 22386737
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 https://doi.org/10.1038/nmeth.1682 pmid: 21892151
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 https://doi.org/10.1111/eci.12585 pmid: 26707063
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 https://doi.org/10.1038/cddis.2015.41 pmid: 25766316

[1]

FMD-16218-of-FXD_suppl_1

Download

[1]	Ying Huo, Peng Yuan, Qingyuan Qin, Zhiqiang Yan, Liying Yan, Ping Liu, Rong Li, Jie Yan, Jie Qiao. Effects of vitrification and cryostorage duration on single-cell RNA-Seq profiling of vitrified-thawed human metaphase II oocytes[J]. Front. Med., 2021, 15(1): 144-154.
[2]	Ling Wang, Lining Wang, Xing Fan, Wei Tang, Jiong Hu. Fludarabine and intravenous busulfan conditioning with post-transplantation cyclophosphamide for allogeneic peripheral stem cell transplantation for adult patients with lymphoid malignancies: a prospective single-arm phase II study[J]. Front. Med., 2021, 15(1): 108-115.
[3]	Jing Ma, Shiyu Chen, Lili Hao, Wei Sheng, Weicheng Chen, Xiaojing Ma, Bowen Zhang, Duan Ma, Guoying Huang. Long non-coding RNA SAP30-2:1 is downregulated in congenital heart disease and regulates cell proliferation by targeting HAND2[J]. Front. Med., 2021, 15(1): 91-100.
[4]	Xin Zou, Ke Chen, Jiawei Zou, Peiyi Han, Jie Hao, Zeguang Han. Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection[J]. Front. Med., 2020, 14(2): 185-192.
[5]	Lijuan Hu, Qi Wang, Xiaohui Zhang, Lanping Xu, Yu Wang, Chenhua Yan, Huan Chen, Yuhong Chen, Kaiyan Liu, Hui Wang, Xiaojun Huang, Xiaodong Mo. Positive stool culture could predict the clinical outcomes of haploidentical hematopoietic stem cell transplantation[J]. Front. Med., 2019, 13(4): 492-503.
[6]	Xiaodong Mo, Xiaohui Zhang, Lanping Xu, Yu Wang, Chenhua Yan, Huan Chen, Yuhong Chen, Wei Han, Fengrong Wang, Jingzhi Wang, Kaiyan Liu, Xiaojun Huang. Minimal residual disease-directed immunotherapy for high-risk myelodysplastic syndrome after allogeneic hematopoietic stem cell transplantation[J]. Front. Med., 2019, 13(3): 354-364.
[7]	Xiaodong Mo, Xiaohui Zhang, Lanping Xu, Yu Wang, Chenhua Yan, Huan Chen, Yuhong Chen, Wei Han, Fengrong Wang, Jingzhi Wang, Kaiyan Liu, Xiaojun Huang. Interferon-α salvage treatment is effective for patients with acute leukemia/myelodysplastic syndrome with unsatisfactory response to minimal residual disease-directed donor lymphocyte infusion after allogeneic hematopoietic stem cell transplantation[J]. Front. Med., 2019, 13(2): 238-249.
[8]	Meng Lv, Yingjun Chang, Xiaojun Huang. Everyone has a donor: contribution of the Chinese experience to global practice of haploidentical hematopoietic stem cell transplantation[J]. Front. Med., 2019, 13(1): 45-56.
[9]	Fei Gao, Jingyu Chen, Dong Wei, Bo Wu, Min Zhou. Lung transplantation for bronchiolitis obliterans syndrome after allogenic hematopoietic stem cell transplantation[J]. Front. Med., 2018, 12(2): 224-228.
[10]	Xuying Pei, Xiangyu Zhao, Yu Wang, Lanping Xu, Xiaohui Zhang, Kaiyan Liu, Yingjun Chang, Xiaojun Huang. Comparison of reference values for immune recovery between event-free patients receiving haploidentical allografts and those receiving human leukocyte antigen-matched sibling donor allografts[J]. Front. Med., 2018, 12(2): 153-163.
[11]	Zhen He,Cheng Hu,Weiping Jia. miRNAs in non-alcoholic fatty liver disease[J]. Front. Med., 2016, 10(4): 389-396.
[12]	Lanping Xu,Huanling Zhu,Jianda Hu,Depei Wu,Hao Jiang,Qian Jiang,Xiaojun Huang. Superiority of allogeneic hematopoietic stem cell transplantation to nilotinib and dasatinib for adult patients with chronic myelogenous leukemia in the accelerated phase[J]. Front. Med., 2015, 9(3): 304-311.
[13]	Du Yan, Han Xue, Pu Rui, Xie Jiaxin, Zhang Yuwei, Cao Guangwen. Association of miRNA-122-binding site polymorphism at the interleukin-1 α gene and its interaction with hepatitis B virus mutations with hepatocellular carcinoma risk[J]. Front. Med., 2014, 8(2): 217-226.
[14]	Xiaodong Mo, Xiaojun Huang. Advancement of human leukocyte antigen-partially matched related hematopoietic stem cell transplantation[J]. Front Med, 2013, 7(3): 306-315.
[15]	Beicheng Sun, Michael Karin. Inflammation and liver tumorigenesis[J]. Front Med, 2013, 7(2): 242-254.

Viewed

Full text

Abstract

Cited

Shared

Discussed