Please wait a minute...
Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front. Med.    2017, Vol. 11 Issue (1) : 74-86    https://doi.org/10.1007/s11684-017-0503-1
RESEARCH ARTICLE
Changes in lncRNAs and related genes in β-thalassemia minor and β-thalassemia major
Jing Ma1,2,Fei Liu2,Xin Du3,Duan Ma2(),Likuan Xiong1()
1. Central Laboratory, Bao’an Maternal and Children Health Hospital, Key Laboratory of Birth Defects Research, Birth Defects Prevention Research and Transformation Team, Shenzhen 518000, China
2. Key Laboratory of Metabolism and Molecular Medicine, Ministry of Education, Department of Biochemistry and Molecular Biology, Institute of Medical Sciences, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China
3. Department of Hematology, The Second People’s Hospital, Shenzhen 518035, China
 Download: PDF(514 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

β-thalassemia is caused by β-globin gene mutations. However, heterogeneous phenotypes were found in individuals with same genotype, and still undescribed mechanism underlies such variation. We collected blood samples from 30 β-thalassemia major, 30 β-thalassemia minor patients, and 30 matched normal controls. Human lncRNA Array v2.0 (8 × 60 K, Arraystar) was used to detect changes in long non-coding RNAs (lncRNAs) and mRNAs in three samples each from β-thalassemia major, β-thalassemia minor, and control groups. Compared with normal controls, 1424 and 2045 lncRNAs were up- and downregulated, respectively, in β-thalassemia major patients, whereas 623 and 349 lncRNAs were up- and downregulated, respectively, in β-thalassemia minor patients. Compared with β-thalassemia minor group, 1367 and 2356 lncRNAs were up- and downregulated, respectively, in β-thalassemia major group. We selected five lncRNAs that displayed altered expressions (DQ583499, X-inactive specific transcript (Xist), lincRNA-TPM1, MRFS16P, and lincRNA-RUNX2-2) and confirmed their expression levels in all samples using real-time polymerase chain reaction. Based on coding-non-coding gene co-expression network and gene ontology biological process analyses, several signaling pathways were associated with three common organ systems exhibiting β-thalassemia phenotypes: hematologic, skeletal, and hepatic systems. This study implicates that abnormal expression levels of lncRNAs and mRNA in β-thalassemia cases may be correlated with its various clinical phenotypes.

Keywords β-thalassemia      long non-coding RNA      mRNA      phenotypic heterogeneity      pathway     
Corresponding Author(s): Duan Ma,Likuan Xiong   
Just Accepted Date: 20 January 2017   Online First Date: 24 February 2017    Issue Date: 20 March 2017
 Cite this article:   
Jing Ma,Fei Liu,Xin Du, et al. Changes in lncRNAs and related genes in β-thalassemia minor and β-thalassemia major[J]. Front. Med., 2017, 11(1): 74-86.
 URL:  
https://academic.hep.com.cn/fmd/EN/10.1007/s11684-017-0503-1
https://academic.hep.com.cn/fmd/EN/Y2017/V11/I1/74
Fig.1  Differential expression profiles of lncRNAs in Tr, Tm, and NC samples.

Hierarchical clustering analysis of top 50 downregulated and top 50 upregulated lncRNAs, which were differentially expressed among Tm (A), Tr (B), and NC samples (C) (greater than 2.0-fold; P<0.05). Expression values are represented by red and green shades, indicating expressions above and below median expression level across all samples, respectively.

Upregulated Downregulated
lncRNA Log2 fold-change (Tm/Tr) lncRNA Log2 fold-change (Tm/Tr)
NR_001564 475.39487 ENST00000436373 33.743073
BX648254 267.86087 BC039532 29.61081
ENST00000504249 175.74313 uc.239 16.860533
ENST00000419932 89.22369 ENST00000451201 16.580164
ENST00000454506 77.04313 ENST00000413075 16.414616
AF129266 76.97585 AL137603 15.069204
NR_027140 69.866486 CR593590 14.665389
AK054860 68.70579 ENST00000420273 13.7016115
ENST00000504402 68.41673 uc002zrw.1 13.520707
ENST00000451017 57.33751 uc003tgm.1 13.519277
ENST00000456917 52.96834 AJ238554 13.439914
ENST00000421322 52.823494 ENST00000503840 12.704102
uc001ays.2 50.784473 uc003wvy.3 12.201274
chr15:63377715-63386763− 49.922466 NR_026585 12.090083
BG191796 49.482784 exon396+ 11.7519455
ENST00000425301 36.75228 ENST00000454921 11.483874
AA219474 36.507854 DB303296 11.471096
chr1:112541627-112559477− 34.17949 ENST00000437377 11.097665
NR_028048 33.216347 ENST00000430733 10.95928
ENST00000506388 33.1418 ENST00000506852 10.442531
Tab.1  Differentially expressed lncRNAs as detected by microarray analysis between three Tm and three Tr patient samples
Upregulated Downregulated
lncRNA Log2 fold-change (Tm/ NC) lncRNA Log2 fold-change (Tm/ NC)
NR_001564 378.60168 BC039532 35.659904
BX648254 366.97815 ENST00000451201 27.744078
ENST00000504249 154.14091 uc002nvy.2 25.602827
NR_027140 108.20667 G36655 25.182463
ENST00000451017 103.37533 uc.450− 23.279758
ENST00000421322 102.09342 ENST00000456581 22.84255
chr15:63377715-63386763− 74.7675 ENST00000506852 22.808413
uc001ays.2 66.400475 uc.239− 22.165426
ENST00000456917 65.948685 ENST00000413075 21.496092
AA219474 59.865746 uc002zrw.1 20.751621
ENST00000449663 59.861076 ENST00000511060 19.96795
ENST00000454506 59.65685 ENST00000513379 19.080957
uc001oqe.1 59.002132 AK026273 18.400055
uc011mlh.1 57.100994 AJ238554 17.94416
ENST00000433329 56.26776 uc003jly.2 17.85088
ENST00000419932 55.829163 AK024532 17.562132
ENST00000444424 55.658886 NR_003951 17.489788
ENST00000504402 52.477528 NR_026585 17.187653
NR_001589 52.265377 NR_027474 16.352808
AF129266 52.17556 ENST00000509804 15.905267
Tab.2  Differentially expressed lncRNAs as detected by microarray analysis between three Tm patients and three NCs
Upregulated Downregulated
lncRNA Log2 fold-change (Tr/ NC) lncRNA Log2 fold-change (Tr/ NC)
NR_002312 57.89842774 NR_015392 44.86021511
NR_024409 23.06563811 AL832916 11.42493247
NR_027667 18.12046911 ENST00000393497 4.272346524
uc010rog.1 17.24279407 ENST00000487061 3.577817171
BC050410 14.72538416 ENST00000415533 3.505123231
NR_027167 14.12993477 chr1:62018062-62035387+ 3.426699147
NR_024451 14.0071027 G36655 3.343033472
ENST00000429866 12.88850539 ENST00000326632 3.320251594
uc001gyh.2 11.70743941 ENST00000450915 3.186528726
NR_027169 11.34831372 uc003cpw.1 3.145007956
ENST00000411669 11.33674446 ENST00000445179 3.103450568
NR_003009 10.25310664 CR617898 3.100735931
AK127031 9.965940541 ENST00000496098 3.077919002
AK026079 9.831391785 AK026273 3.003843881
uc003hkh.2 9.732431823 ENST00000485935 2.97249421
ENST00000393071 9.45461274 chr5:3737400-3749175+ 2.972319358
ENST00000399764 8.748704905 U23864 2.959563682
uc001zgr.1 8.023060261 ENST00000446281 2.954471263
ENST00000392459 7.836911472 CR592555 2.879068271
ENST00000513338 7.755808955 CR596648 2.853814114
Tab.3  Differentially expressed lncRNAs as detected by microarray analysis between three Tr patients and three NCs
Upregulated Downregulated
mRNA Log2 fold-change (Tm/ Tr) mRNA Log2 fold-change (Tm/ Tr)
GNB4 456.48813 RPS4Y1 29.137558
TATDN1 225.97804 GRIK4 19.252838
C18orf19 148.5936 MYOM2 16.058887
GOLGA3 133.19485 CHRNA1 15.200347
ELF3 92.112854 HSD17B3 15.018887
APOL3 75.76791 HUWE1 14.704448
MPST 72.73486 MAPK8 13.582769
APEX1 56.74558 CPOX 13.553835
LRRC2 47.020897 NF2 13.0389805
ALDH4A1 43.859104 TUBB2A 12.91199
LRRK1 39.078003 EIF1AY 12.903257
FAM19A4 38.12581 PDE11A 12.609439
COLQ 36.34967 MFSD5 12.079321
IL4I1 36.200016 RBM6 11.949651
DIDO1 35.557583 OSBPL9 11.586617
PTF1A 34.60447 BCAR1 11.040795
CCDC30 33.951355 CSMD3 10.961061
YIPF5 31.10548 PRSS23 10.468734
BSG 29.767323 BHLHB9 10.462516
CSNK1E 29.138319 MAN2B1 10.450647
Tab.4  Relative mRNA expression levels between three Tm and three Tr patients
Upregulated Downregulated
mRNA Log2 fold-change (Tm/ NC) mRNA Log2 fold-change (Tm/ NC)
GNB4 372.06174 RPS4Y1 34.69026
TATDN1 221.8701 PDE11A 27.702385
C18orf19 211.72853 GRIK4 27.608694
TRPV4 129.0601 HUWE1 26.487883
GOLGA3 119.84536 HSD17B3 25.277945
ELF3 113.51989 SENP8 24.697273
G0S2 101.131325 FXYD6 21.712652
LRRC2 95.60653 CPOX 21.465477
FEM1B 74.425064 TMPRSS11D 21.21611
APOL3 73.60572 MAPK8 21.142706
DIDO1 67.15943 RYR3 20.668802
FGFR1 63.01436 SENP8 19.893684
APEX1 61.107117 CHRNA1 19.15899
TIMD4 61.07469 UBL7 19.042307
LRRK1 60.75276 BEST3 18.51797
PCBP4 60.26624 TSPAN4 18.372448
CDKN2D 59.539013 MFSD5 17.65863
COLQ 59.43424 TUBB2A 17.413591
CSNK1E 55.094025 STX16 16.526283
TCF7 53.867447 CDK15 16.43974
Tab.5  Relative mRNA expression levels between three Tm patients and three NCs
Upregulated Downregulated
mRNA Log2 fold-change (Tr/ NC) mRNA Log2 fold-change (Tr/ NC)
TRAK2 19.85068655 KRTAP10-10 11.32289398
SIGLEC14 19.17475581 NINJ2 5.184313637
FOLR3 19.03491646 SHF 4.740106696
STOM 15.77306582 RG9MTD3 4.659519321
CA1 15.02756373 RAD54L 4.383114905
GSTT1 13.99017238 GCAT 4.373991954
MAPKSP1 12.55405677 PRDX6 3.866922007
PAQR9 11.4443115 NXPH4 3.742348984
FEM1B 11.44187619 ERAP1 3.589259254
YPEL4 10.63987306 NCR3 3.520218956
SFRP2 10.40208745 C20orf141 3.40752994
CA1 9.970651436 KIAA1468 3.372775128
BAT3 9.951762805 TNFSF14 3.363273493
FAM46C 9.772573296 ZNF580 3.261258954
RUNDC3A 9.579029522 SCO2 3.259484994
PDCD10 8.686919088 TINAGL1 3.208801028
PIM1 8.624507653 TCEB2 3.228833393
HSN2 8.574794124 IGFALS 3.227206735
TCP11L2 8.458575768 SENP8 3.172190327
NPRL3 8.301852502 ZNF439 3.095239046
Tab.6  Relative mRNA expression levels between three Tr patients and three NCs
Fig.2  Comparison between microarray data and qPCR results.

In total, five lncRNAs were differentially expressed among 90 samples of Tm (A), Tr (B) patients, and NCs (C). Expression levels of DQ583499, MRFS16P, Xist, lincRNA-TPM1, and lincRNA-RUNX2-2 were validated using qPCR. Results showed that majority of differences between groups in all five lncRNAs were significant when compared with one another. Columns height in chart represent relative lncRNA expression levels, and bars represent standard errors. Validation experiments on five selected lncRNAs indicate that microarray data strongly correlate with qPCR data.

Fig.3  Distributions of lncRNA expression levels.

qPCR was used to validated expression levels of DQ583499, MRFS16P, Xist, lincRNA-TPM1, and lincRNA-RUNX2-2 in 30 pairs of Tm (A), Tr (B), and NC (C) samples. All experiments were conducted in triplicate, and Ct values were normalized to GAPDH. Median in each triplicate was used to calculate relative lncRNA concentrations (ΔCt= median Ct of given lncRNA − median Ct of GAPDH). Coordinate heights in chart represent relative lncRNA concentrations (ΔCt) across 30 Tm, 30 Tr, and 30 NC samples for each of five validated lncRNAs.

Fig.4  CNC network.

Each <InlineMediaObject OutputMedium="Online"><ImageObject FileRef="fmd-16269-xlk.doc_images\fmd-16269-xlk-tu1.jpg" ScaleToFitWidth="10cm" ScaleToFit="1"/></InlineMediaObject><InlineMediaObject OutputMedium="All"><ImageObject FileRef="FMD-16269-XLK.doc_images\FMD-16269-XLK-tu1.tif" ScaleToFit="1" ScaleToFitWidth="10cm"/></InlineMediaObject> represents an lncRNA. lncRNAs are labeled using colors per number of mRNAs connected to the lncRNA. Lines represent gene co-expression relationship between specific lncRNA and mRNA. Dark gray lines represent positive correlation, and light gray lines indicate negative correlation.

Fig.5  GO pathway analysis.

GO biological process annotation shows that differentially expressed mRNAs among Tr, Tm, and NC groups compared in pairs may be associated with hypoxic response, muscle organ development, signal transduction, negative caspase activity regulation, apoptosis induction by extracellular signals, MAPK activation, chemotaxis, apoptosis induction, cytokine-mediated signaling pathway, inflammatory response, synaptic transmission, and anti-apoptosis pathway gene expression.

Pathway Upregulated Downregulated
Response tohypoxia TNF, ABAT, IL1B, CRYAA PRMT2, VHL, LETM1, MLH1
Muscle organ development BASP1, FOXO4, HMG20B, MEF2B, TEAD4, ?CHRNA1, TMEM8C, epidermal growth factor ?receptor (EGFR) TEAD4, MEF2B, fibroblast growth factor 8(FGF8), ?TMEM8C, EGFR
Signal transduction CUL7, VRK2, CORO2A, EFNA1, LRRK1, ?CCL3, HRK, NFKBIA, FOXO4, PDE9A, ?CCL3L1, TNF, ARPC2, SIGIRR, IER3, ?CSNK1E, OR7D4, KCNIP2, GIPC1, ?SEMA7A, DIDO1, FGF14, CLEC4M, IL1B, ?ELF3, ATP6V0E2, TEAD4, BSG, HPSE, ?PLCB2, PTAFR, PTF1A, KCNIP2, APOL3, ?GNB4, DUSP1, MRGPRX4, SPN, PLEKHG2, ?CHRNA1, ADIPOR2IL2RB, CACNA1C, ?ARAP1, NUMBL, GRIK4, ARRB2, EGFR, ?PTGDR, SH2D2A, IL20RB, PSD, HMGA2, ?NTRK1, EPHA6, PLIN5, RHOC, SH2D2A, ?LGALS12, GPR78, BAG1 CPZ, CLEC7A, CORO2A, CDKN2D, EFNA1, ?SIGIRR, PRMT2, superoxide dismutase 1(SOD1), ?DNAJB9, PDE9A, CAMK2G, FBXW11, SIPA1, ?CSNK1E, GDF15, TEAD4, DIDO1, CABYR, ?SUMO1, ARL5B, FGF8, PLCB2, MX1, PRR5, ?FPR1, VHL, TRPV4, PTAFRPLEKHG2, MYO1C, ?NUP133, COL15A1, FGD1, EXT2, COL16A1, ?NUMBL, CSF2RA, VEGFC, OR2A5, EGFR, ?MLH1, ARTN, SH2D2A, MAPK8, ALOXE3, PSD, ?HSP90AA1, MICAL1, RBMS3, HMGA2, EPHA6, ?PLIN5, RHOC, HSF4, SH2D2A, GPR78
Induction of apoptosis by extracellular signals HRK, TNF, heat shock protein 1B (HSPA1B), ?IER3, DIDO1, IL1B, SPN, PLEKHG2, EGFR, ?LGALS12 CDKN2D, SOD1, DIDO1, VHL, PLEKHG2, FGD1, ?ARRB2, MLH1, EGFR, MAPK8
Activation of MAPK activity EFNA1, NFKBIA, TNF, L1B, EGFR, NTRK1 EFNA1, SOD1, FPR1, EGFR
Induction of apoptosis HRK, TNF, HSPA1B, IER3, DIDO1, IL1B, ?PLEKHG2, SPN, EGFR, LGALS12, CUL7, ?MPO, GPI, ALKBH1, TNFAIP8, DPF1, ?FOXO4, NFKBIA, CCL3, CCL3L1, TATDN1, ?DUSP1, ZNF346, IL2RB, CRYAA, BHLHB9, ?PEG3, NTRK1, HMGA2, BAG1 CDKN2D, SOD1, DIDO1, PLEKHG2, VHL, FGD1, ?ARRB2, MLH1, EGFR, MAPK8, LHX3, TNFAIP8, ?PRMT2, PPP2R4, FGF8, MX1, KDM5C, LCMT1, ?MICAL1, HSP90AA1, BHLHB9, HMGA2
Cytokine mediated signaling pathway VRK2, TNF, SIGIRR, IL1B, PTAFR, ?ADIPOR2, IL2RB, NUMBL SIGIRR, PRMT2, CAMK2G, SUMO1, MX1, PTAFR, ?VHL, NUP133, CSF2RA, NUMBL
Inflammatory response SIGIRR, CCL3, CCL3L1, TNF, SIGIRR, IER3, ?SEMA7A, IL1B, ELF3, PTAFR, APOL3, SPN, ?PTGDR, EGFR, IL20RB, ITIH4 CLEC7A, SIGIRR, CXCL3, SIGIRR, PTAFR, EGFR, ?MAPK8
Synaptic transmission COLQ, ABAT, TNF, FGF14, KCNIP2, GIPC1, ?PLCB2, CHRNA1, CACNA1C, GRIK4, ?BHLHB9, SLITRK5, EGFR, NTRK1 CAMK2G, PLCB, BHLHB9, LETM1, COMT, ?MYO6, ARRB2, EGFR
Tab.7  Selected genes involved in GO biological process pathways
Organ Genes
Skeletal development CHRNA1, FGF8
Hematologic system TNF, IL1B, VHL, CCL3, NFKBIA, ?CCL3L1, SOD1, MRGPRX4, SPN, ?NUMBL, NTRK1, HSP90AA1, ?HSPA1B
Liver development EGFR
Tab.8  Selected genes related to β-thalassemia phenotype
1 Tuzmen S, Schechter AN. Genetic diseases of hemoglobin: diagnostic methods for elucidating β-thalassemia mutations. Blood Rev 2001; 15(1): 19–29
https://doi.org/10.1054/blre.2001.0147 pmid: 11333136
2 Cao A, Galanello R. β-thalassemia. Genet Med 2010; 12(2): 61–76
https://doi.org/10.1097/GIM.0b013e3181cd68ed pmid: 20098328
3 Galanello R, Ruggeri R, Paglietti E, Addis M, Melis MA, Cao A. A family with segregating triplicated α globin loci and β thalassemia. Blood 1983; 62(5): 1035–1040
pmid: 6313095
4 Harteveld CL, Refaldi C, Cassinerio E, Cappellini MD, Giordano PC. Segmental duplications involving the α-globin gene cluster are causing β-thalassemia intermedia phenotypes in β-thalassemia heterozygous patients. Blood Cells Mol Dis 2008; 40(3): 312–316
https://doi.org/10.1016/j.bcmd.2007.11.006 pmid: 18249014
5 Sollaino MC, Paglietti ME, Perseu L, Giagu N, Loi D, Galanello R. Association of α globin gene quadruplication and heterozygous β thalassemia in patients with thalassemia intermedia. Haematologica 2009; 94(10): 1445–1448
https://doi.org/10.3324/haematol.2009.005728 pmid: 19794088
6 Viprakasit V, Tanphaichitr VS, Chinchang W, Sangkla P, Weiss MJ, Higgs DR. Evaluation of α hemoglobin stabilizing protein (AHSP) as a genetic modifier in patients with β thalassemia. Blood 2004; 103(9): 3296–3299
https://doi.org/10.1182/blood-2003-11-3957 pmid: 14715623
7 Sankaran VGMT, Menne TF, Šćepanović D, Vergilio JA, Ji P, Kim J, Thiru P, Orkin SH, Lander ES, Lodish HF. MicroRNA-15a and-16-1 act via MYB to elevate fetal hemoglobin expression in human trisomy 13. Proc Natl Acad Sci USA 2011; 108(4): 1519–1524
https://doi.org/10.1073/pnas.1018384108 pmid: 21205891
8 Lee YT, de Vasconcellos JF, Yuan J, Byrnes C, Noh SJ, Meier ER, Kim KS, Rabel A, Kaushal M, Miller JL. LIN28B-mediated expression of fetal hemoglobin and production of fetal-like erythrocytes from adult human erythroblasts ex vivo. Blood 2013;122(6):1034–1041
https://doi.org/10.1182/blood-2012-12-472308 PMID:23798711
9  .Prensner JR, Chinnaiyan AM. The emergence of lncRNAs in cancer biology. Cancer Discov 2011; 1(5): 391–407
https://doi.org/10.1158/2159-8290.CD-11-0209 pmid: 22096659
10 Guttman M, Donaghey J, Carey BW, Garber M, Grenier JK, Munson G, Young G, Lucas AB, Ach R, Bruhn L, Yang X, Amit I, Meissner A, Regev A, Rinn JL, Root DE, Lander ES. lincRNAs act in the circuitry controlling pluripotency and differentiation. Nature 2011; 477(7364): 295–300
https://doi.org/10.1038/nature10398 pmid: 21874018
11 Ponting CP, Oliver PL, Reik W. Evolution and functions of long noncoding RNAs. Cell 2009; 136(4): 629–641
https://doi.org/10.1016/j.cell.2009.02.006 pmid: 19239885
12 Guttman M, Rinn JL. Modular regulatory principles of large non-coding RNAs. Nature 2012; 482(7385): 339–346
https://doi.org/10.1038/nature10887 pmid: 22337053
13 Wapinski O, Chang HY. Long noncoding RNAs and human disease. Trends Cell Biol 2011; 21(6): 354–361
https://doi.org/10.1016/j.tcb.2011.04.001 pmid: 21550244
14 Yang F, Zhang L, Huo XS, Yuan JH, Xu D, Yuan SX, Zhu N, Zhou WP, Yang GS, Wang YZ, Shang JL, Gao CF, Zhang FR, Wang F, Sun SH. Long noncoding RNA high expression in hepatocellular carcinoma facilitates tumor growth through enhancer of zeste homolog 2 in humans. Hepatology 2011; 54(5): 1679–1689
https://doi.org/10.1002/hep.24563 pmid: 21769904
15 Yu G, Yao W, Wang J, Ma X, Xiao W, Li H, Xia D, Yang Y, Deng K, Xiao H, Wang B, Guo X, Guan W, Hu Z, Bai Y, Xu H, Liu J, Zhang X, Ye Z. lncRNAs expression signatures of renal clear cell carcinoma revealed by microarray. PLoS ONE 2012; 7(8): e42377
https://doi.org/10.1371/journal.pone.0042377 pmid: 22879955
16 Akrami R, Jacobsen A, Hoell J, Schultz N, Sander C, Larsson E. Comprehensive analysis of long non-coding RNAs in ovarian cancer reveals global patterns and targeted DNA amplification. PLoS One 2013; 8(11): e80306
https://doi.org/10.1371/journal.pone.0080306 pmid: 24265805
17 Tsai MC, Manor O, Wan Y, Mosammaparast N, Wang JK, Lan F, Shi Y, Segal E, Chang HY. Long noncoding RNA as modular scaffold of histone modification complexes. Science 2010; 329(5992): 689–693
https://doi.org/10.1126/science.1192002 pmid: 20616235
18 Rinn JL, Kertesz M, Wang JK, Squazzo SL, Xu X, Brugmann SA, Goodnough LH, Helms JA, Farnham PJ, Segal E, Chang HY. Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs. Cell 2007; 129(7): 1311–1323
https://doi.org/10.1016/j.cell.2007.05.022 pmid: 17604720
19 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
20 Thein SL. The molecular basis of β-thalassemia. Cold Spring Harb Perspect Med 2013; 3(5): a011700
https://doi.org/10.1101/cshperspect.a011700 pmid: 23637309
21 Colah R, Nadkarni A, Gorakshakar A, Phanasgaonkar S, Surve R, Subramaniam PG, Bondge N, Pujari K, Ghosh K, Mohanty D. Impact of β globin gene mutations on the clinical phenotype of beta thalassemia in India. Blood Cells Mol Dis 2004; 33(2): 153–157
https://doi.org/10.1016/j.bcmd.2004.05.002 pmid: 15315795
22 Kim A, Kiefer CM, Dean A. Distinctive signatures of histone methylation in transcribed coding and noncoding human β-globin sequences. Mol Cell Biol 2007; 27(4): 1271–1279
https://doi.org/10.1128/MCB.01684-06 pmid: 17158930
23 Martin DI, Ward R, Suter CM. Germline epimutation: a basis for epigenetic disease in humans. Ann N Y Acad Sci 2005; 1054(1): 68–77
https://doi.org/10.1196/annals.1345.009 pmid: 16339653
24 Sankaran VG, Menne TF, Xu J, Akie TE, Lettre G, Van Handel B, Mikkola HK, Hirschhorn JN, Cantor AB, Orkin SH. Human fetal hemoglobin expression is regulated by the developmental stage-specific repressor BCL11A. Science 2008; 322(5909): 1839–1842
https://doi.org/10.1126/science.1165409 pmid: 19056937
25 Jawaid K, Wahlberg K, Thein SL, Best S. Binding patterns of BCL11A in the globin and GATA1 loci and characterization of the BCL11A fetal hemoglobin locus. Blood Cells Mol Dis 2010; 45(2): 140–146
https://doi.org/10.1016/j.bcmd.2010.05.006 pmid: 20542454
26 Zhou D, Liu K, Sun CW, Pawlik KM, Townes TM. KLF1 regulates BCL11A expression and γ- to β-globin gene switching. Nat Genet 2010; 42(9): 742–744
https://doi.org/10.1038/ng.637 pmid: 20676097
27 Thein SL. Genetic association studies in β-hemoglobinopathies. Hematology Am Soc Hematol Educ Program 2013;354–361
https://doi.org/10.1182/asheducation-2013.1.354 PMID:24319204
28 Engreitz JM, Pandya-Jones A, McDonel P, Shishkin A, Sirokman K, Surka C, Kadri S, Xing J, Goren A, Lander ES, Plath K, Guttman M. The Xist lncRNA exploits three-dimensional genome architecture to spread across the X chromosome. Science 2013; 341(6147): 1237973
https://doi.org/10.1126/science.1237973 pmid: 23828888
29 Jin X, Kim JG, Oh MJ, Oh HY, Sohn YW, Pian X, Yin JL, Beck S, Lee N, Son J, Kim H, Yan C, Wang JH, Choi YJ, Whang KY. Opposite roles of MRF4 and MyoD in cell proliferation and myogenic differentiation. Biochem Biophys Res Commun 2007; 364(3): 476–482
https://doi.org/10.1016/j.bbrc.2007.10.042 pmid: 17959144
30 Sweetman D, Goljanek K, Rathjen T, Oustanina S, Braun T, Dalmay T, Münsterberg A. Specific requirements of MRFs for the expression of muscle specific microRNAs, miR-1, miR-206 and miR-133. Dev Biol 2008; 321(2): 491–499
https://doi.org/10.1016/j.ydbio.2008.06.019 pmid: 18619954
31 Perry SV. Vertebrate tropomyosin: distribution, properties and function. J Muscle Res Cell Motil 2001; 22(1): 5–49
https://doi.org/10.1023/A:1010303732441 pmid: 11563548
32 van de Meerakker JB, Christiaans I, Barnett P, Lekanne Deprez RH, Ilgun A, Mook OR, Mannens MM, Lam J, Wilde AA, Moorman AF, Postma AV. A novel α-tropomyosin mutation associates with dilated and non-compaction cardiomyopathy and diminishes actin binding. Biochim Biophys Acta 2013; 1833(4): 833–839
https://doi.org/10.1016/j.bbamcr.2012.11.003 pmid: 23147248
33 Jones PA, Laird PW. Cancer epigenetics comes of age. Nat Genet 1999; 21(2): 163–167
https://doi.org/10.1038/5947 pmid: 9988266
34 Boyd J, Risinger JI, Wiseman RW, Merrick BA, Selkirk JK, Barrett JC. Regulation of microfilament organization and anchorage-independent growth by tropomyosin 1. Proc Natl Acad Sci USA 1995; 92(25): 11534–11538
https://doi.org/10.1073/pnas.92.25.11534 pmid: 8524798
35 Otto F, Kanegane H, Mundlos S. Mutations in the RUNX2 gene in patients with cleidocranial dysplasia. Hum Mutat 2002; 19(3): 209–216
https://doi.org/10.1002/humu.10043 pmid: 11857736
36 Lanaya H, Natarajan A, Komposch K, Li L, Amberg N, Chen L, Wculek SK, Hammer M, Zenz R, Peck-Radosavljevic M, Sieghart W, Trauner M, Wang H, Sibilia M. EGFR has a tumour-promoting role in liver macrophages during hepatocellular carcinoma formation. Nat Cell Biol 2014; 16(10): 972–981, 1–7
https://doi.org/10.1038/ncb3031 pmid: 25173978
37 Yannakoudakis BZ, Liu KJ. Common skeletal features in rare diseases: new links between ciliopathies and FGF-related syndromes. Rare Dis 2013; 1(1): e27109
https://doi.org/10.4161/rdis.27109 pmid: 25003013
38 Vasikova A, Belickova M, Budinska E, Cermak J. A distinct expression of various gene subsets in CD34+ cells from patients with early and advanced myelodysplastic syndrome. Leuk Res 2010; 34(12): 1566–1572
https://doi.org/10.1016/j.leukres.2010.02.021 pmid: 20303173
39 Lee K, Briehl MM, Mazar AP, Batinic-Haberle I, Reboucas JS, Glinsmann-Gibson B, Rimsza LM, Tome ME. The copper chelator ATN-224 induces peroxynitrite-dependent cell death in hematological malignancies. Free Radic Biol Med 2013; 60: 157–167
https://doi.org/10.1016/j.freeradbiomed.2013.02.003 pmid: 23416365
[1] FMD-16269-OF-XLK_suppl_1 Download
[1] Heng Fang, Aihua Zhang, Xiaohang Zhou, Jingbo Yu, Qi Song, Xijun Wang. High-throughput metabolomics reveals the perturbed metabolic pathways and biomarkers of Yang Huang syndrome as potential targets for evaluating the therapeutic effects and mechanism of geniposide[J]. Front. Med., 2020, 14(5): 651-663.
[2] Xiaoqing Li, Xinxin Li, Genbei Wang, Yan Xu, Yuanyuan Wang, Ruijia Hao, Xiaohui Ma. Xiao Ke Qing improves glycometabolism and ameliorates insulin resistance by regulating the PI3K/Akt pathway in KKAy mice[J]. Front. Med., 2018, 12(6): 688-696.
[3] Zhilin Hu, Qiang Zou, Bing Su. Regulation of T cell immunity by cellular metabolism[J]. Front. Med., 2018, 12(4): 463-472.
[4] Jichun Yang, Kaiyue Jin, Jiajun Xiao, Jing Ma, Duan Ma. Endogenous tissue factor pathway inhibitor in vascular smooth muscle cells inhibits arterial thrombosis[J]. Front. Med., 2017, 11(3): 403-409.
[5] Hongli Yin,Tianyi Liu,Ying Zhang,Baofeng Yang. Caveolin proteins: a molecular insight into disease[J]. Front. Med., 2016, 10(4): 397-404.
[6] Yujiao Liu,Chao Liu,Wen Dong,Wei Li. Physiological functions and clinical implications of the N-end rule pathway[J]. Front. Med., 2016, 10(3): 258-270.
[7] Muhammad Waqas,Shasha Zhang,Zuhong He,Mingliang Tang,Renjie Chai. Role of Wnt and Notch signaling in regulating hair cell regeneration in the cochlea[J]. Front. Med., 2016, 10(3): 237-249.
[8] Dahong Ju,Meijie Liu,Hongyan Zhao,Jun Wang. Mechanisms of “kidney governing bones” theory in traditional Chinese medicine[J]. Front. Med., 2014, 8(3): 389-393.
[9] Xiaoxuan Zhu,Xinyi Zeng,Chao Sun,Shilin Chen. Biosynthetic pathway of terpenoid indole alkaloids in Catharanthus roseus[J]. Front. Med., 2014, 8(3): 285-293.
[10] Jingyi Lu, Guoxiang Xie, Weiping Jia, Wei Jia. Metabolomics in human type 2 diabetes research[J]. Front Med, 2013, 7(1): 4-13.
[11] Megan A. Hatlen, Lan Wang, Stephen D. Nimer. AML1-ETO driven acute leukemia: insights into pathogenesis and potential therapeutic approaches[J]. Front Med, 2012, 6(3): 248-262.
[12] Sicheng Wang, He Yu, Jianping Liu, Baoyan Liu. Exploring the methodology and application of clinical pathway in evidence-based Chinese medicine[J]. Front Med, 2011, 5(2): 157-162.
[13] Miao Jiang, Cheng Xiao, Gao Chen, Cheng Lu, Qinglin Zha, Xiaoping Yan, Weiping Kong, Shijie Xu, Dahong Ju, Pu Xu, Youwen Zou, Aiping Lu. Correlation between cold and hot pattern in traditional Chinese medicine and gene expression profiles in rheumatoid arthritis[J]. Front Med, 2011, 5(2): 219-228.
[14] Li LI, Jianxin JIANG. Regulatory factors of mesenchymal stem cell migration into injured tissues and their signal transduction mechanisms[J]. Front Med, 2011, 5(1): 33-39.
[15] Wen YAN MD, Min FENG MD, Pei-Hua WANG MD, Dao-Wen WANG MD, . Effect of bradykinin on bradykinin-B2 receptor in rat aortic vascular smooth muscle cells and the involved signal transduction pathways[J]. Front. Med., 2010, 4(2): 225-228.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed