Scientia Agricultura Sinica ›› 2020, Vol. 53 ›› Issue (8): 1677-1687.doi: 10.3864/j.issn.0578-1752.2020.08.016

• ANIMAL SCIENCE·VETERINARY SCIENCE·RESOURCE INSECT • Previous Articles     Next Articles

Comparative Analysis of miRNA Expression Profiles in the Hearts of Tibetan Cattle and Xuanhan Cattle

CHEN LuLu,WANG Hui,WANG JiKun,WANG JiaBo,CHAI ZhiXin,CHEN ZhiHua(),ZHONG JinCheng()   

  1. Institute of Tibetan Plateau Research, Southwest Minzu University/Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Chengdu 610041
  • Received:2019-04-30 Accepted:2020-02-19 Online:2020-04-16 Published:2020-04-29
  • Contact: ZhiHua CHEN,JinCheng ZHONG E-mail:czh@swun.cn;zhongjincheng518@126.com

Abstract:

【Objective】As a kind of non-coding RNA, miRNA is widely involved in various life activities of the organism. This study was aimed to explore the differential expression profiles of miRNA in the heart tissues between Tibetan cattle and Xuanhan cattle, so as to provide the basic data for further study on molecular mechanism of hypoxia adaptation in Tibetan cattle. 【Method】Each three healthy Tibetan and Xuanhan cattle were randomly selected for heart tissue sampling. RNA was extracted from tissues using the Trizol method. An 18 to 30nt fragment was selected by agarose gel electrophoresis, and 3' connector and 5' liner was ligated and then the fragment was enlarged. After gel electrophoresis, three Tibetan cattle and Xuanhan cattle libraries were established, respectively. High-throughput sequencing was performed by using the Illumina HiSeq4000 sequencing platform. The sequence was then filtered and the differentially expressed miRNA of Tibetan cattle and Xuanhan cattle were screened by comparing GenBank and Rfam databases. Functional annotation and signal pathway enrichment analysis of differentially expressed miRNA in Tibetan cattle and Xuanhan cattle. Finally, in order to verify the accuracy of the sequencing data, 8 miRNAs were randomly selected and the expression level of miRNA was detected by RT-qPCR. 【Result】The results showed that Tibetan cattle and Xuanhan cattle had high-quality reads of 17 463 446 and 13 662 812, respectively, while the clean reads were 16 552 296 and 12 055 304, respectively. The highest enrichment of high-quality nucleic acid sequences in Tibetan cattle and Xuanhan cattle were 21 nt, which were 37.5% and 32.1%, respectively. A total of 219 differential expressed miRNAs (48 up-regulated and 171 down-regulated) were obtained. There were 22 terms in the GO function annotation that significantly enriched in the molecular function of differentially expressed miRNAs target genes, such as GO: 0005488 (binding), GO: 0005515 (protein binding) and GO: 0043167 (ion binding). GO: 0005623 (cell), GO: 0044464 (cell component) and GO: 0005622 (cell) were among the 20 terms, which were significantly enriched in the cellular components. While there were 13 terms, which were significantly enriched in biological processes, such as GO: 0035556 (intracellular signal transduction), GO: 0032774 (RNA biosynthesis process) and GO: 0006351 (transcription, DNA templated). Analysis of KEGG signaling pathways revealed that miRNA target genes were significantly enriched to 232 signaling pathways, including the insulin signaling pathway (139 target genes), the mTOR signaling pathway (38 target genes) and the HIF-1 signaling pathway (92 target genes). Among them, 12 miRNA target genes worked together on these three signaling pathways. These results suggested that the differentially expressed miRNAs might participate in the regulation of hypoxia adaptation in Tibetan cattle through these three signaling pathways. Eight miRNAs were randomly selected for RT-qPCR, and the expression profiles were consistent with the sequencing data, indicating that the high-throughput sequencing data was reliable. 【Conclusion】Taken together, the expression profiles of miRNAs in the heart tissues of Tibetan and Xuanhan cattle were obtained in the present study, which laid a foundation for further research on the hypoxia adaptation mechanism of Tibetan cattle.

Key words: Tibetan cattle, Xuanhan cattle, miRNA, heart, high-throughput sequencing, hypoxia adaptability

Table 1

Fluorescent quantitative primers"

miRNAs miRNAs 引物序列(5'-3') Primer sequence(5'-3')
bta-miR-99a-5p RT primer: GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACACAAGATC
F: CTGGAGAACCCGTAGATCCGAT
bta-miR-100 RT primer: GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACCACAAGTT
F: CTGGAGAACCCGTAGATCCGAA
miR-99-x RT primer: GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTCACAAGA
F: CTGGAG AACCCGTAGATCCGAT
bta-miR-1468 RT primer: GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTCAGCAAA
F: CTGGAGGTCGTATCCAGTGCAG
bta-miR-1 RT primer: GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACATACATCT
F: CTGGAGTCGGATCCGTCTGAGC
bta-miR-148a RT primer: GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACACAAAGTT
F: CTGGAGTCAGTGCACTACAGAA
miR-101-y RT primer: GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACAGTCAGTT
F: CTGGAGTACAGTACTGTGATAA
bta-miR-499 RT primer: GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACAAACATCA
F: CTGGAGTTAAGACTTGCAGTGA
miRNA统一反向引物
Unified reverse primer
GTGCAGGGTCCGAGGT

Table 2

Data filtering and de-joining statistics"

读段类型
Type of reads
藏黄牛1
数量Count
藏黄牛2
数量Count
藏黄牛3
数量Count
平均值
Average value
宣汉黄牛1
数量Count
宣汉黄牛2
数量Count
宣汉黄牛3
数量Count
平均值
Average value
总读值
Total reads
17274872 (100%) 14655372 (100%) 21634340 (100%) 17854861 14186785 (100%) 13820881 (100%) 13651263 (100%) 13886310
高质量读值
Reads with high quality
16864234 (97.6229%) 14331976 (97.7933%) 21194128 (97.9652%) 17463446 13970084 (98.4725%) 13591498 (98.3403%) 13426853
(98.3561%)
13662812
3' 端缺失
Reads with null 3'adapter
41744 (0.2475%) 31922 (0.2227%) 31922 (0.4363%) 35196 44975 (0.3219%) 29374 (0.2161%) 33735 (0.2513%) 36028
插入缺失读值
Reads with insert
101492 (0.6018%) 74047 (0.5167%) 51722 (0.2440%) 75754 108440 (0.7762%) 72639 (0.5344%) 93029 (0.6929%) 91369
5'端污染的读值
5'adapter contaminants
7946 (0.0471%) 15266 (0.1065%) 7948 (0.0375%) 10387 18678 (0.1337%) 20305 (0.1494%) 39930 (0.2974%) 26304
小于18nt的读值
Reads shorter than 18nt
437113 (2.5920%) 598024 (4.1727%) 480882 (2.2689%) 505340 811209 (5.8068%) 998246 (7.3446%) 1414065 (10.5316%) 1074507
含有poly(A)的序列
Reads with polyA
139 (0.0008%) 133 (0.0009%) 316 (0.0015%) 196 37 (0.0003%) 35 (0.0003%) 59
(0.0004%)
44
低频数读值(< 2)
Reads with Low frequency
228502 (1.3550%) 270838 (1.8897%) 292937 (1.3822%) 264092 369657 (2.6461%) 300701 (2.2124%) 467408 (3.4811%) 379255
干净读值
Clean reads
16047298 (95.1558%) 13341746 (93.0908%) 20267845 (95.6295%) 16552296 12617088 (90.3150%) 12170198 (89.5427%) 11378627 (84.7453%) 12055304

Fig. 1

Length distribution and frequency percentage of miRNAs in Tibetan cattle and Xuanhan cattle"

Fig. 2

Differential expression of miRNAs in Tibetan cattle and Xuanhan cattle"

Table 3

10 miRNAs with high expression levels in differential expression"

基因名 Gene name log2(fc) PP value 序列 Sequence
miR-2285-y -2.185471095 0.010019532 TCCAAGTGAACTTTTTGGCT
miR-2284-y -2.234579424 0.009299134 TCCAAGTGAACTTTTTGGCT
bta-miR-10a -3.418663256 1.30E-06 TACCCTGTAGATCCGAATTTGTG
bta-miR-208b -2.116094716 0.00109356 ATAAGACGAACAAAAGGTTTGT
bta-miR-146b -3.180709957 9.28E-07 TGAGAACTGAATTCCATAGGCTGT
bta-miR-141 -8.183090025 6.55E-27 TAACACTGTCTGGTAAAGATGG
bta-miR-200a -6.442558686 1.66E-10 TAACACTGTCTGGTAACGATGTT
bta-miR-2478 -2.342776751 0.000175165 GTATCCCACTTCTGACACCA
bta-miR-19b -2.540817607 5.05E-05 TGTGCAAATCCATGCAAAACTGA
bta-miR-200c -7.794012294 7.53E-22 TAATACTGCCGGGTAATGATGGA

Table 4

Predictive statistics of miRNA target genes loci in Tibetan cattle and Xuanhan cattle"

样品名
Sample name
miRNA数量
miRNA number
靶基因数量
Target gene number
靶基因位点数量
Target site number
总数 Total 2033 59271 5486903
藏黄牛Tibetan cattle 962 58111 2584253
宣汉黄牛Xuanhan cattle 1103 58541 3017725

Fig. 3

Quantitative histogram of GO enrichment of miRNA target genes in Tibetan cattle and Xuanhan cattle(A :Biological process B: Molecular function C: Cellular component)"

Fig. 4

Pathway analysis of miRNA target genes in Tibetan cattle and Xuanhan cattle"

Table 5

Target genes involved in the three signaling pathways"

富集通路
Enrichment pathway
靶基因
Target gene
胰岛素信号通路
Insulin signaling pathway
FASN, PYG, GYS, HK, PHKG, PIK3C, G6PC, PCK, ACACB, BAD, CALM, PIK3R, HRAS, RP-S6e, GSK3B, SOS, EIF4E, FBP, PKA, GRB2, BRAF, RAF1, MAP2K1, MAP2K2, MAPK13, MKNK, ELK1, CRK, JNK, AKT,INSR, RPS6KB, SOCS1, SOCS2, SOCS3, SOCS4, CBL, PRKAR, PTPRF, PTPN1, PRKCI, SORBS1, PPP1C, PDPK1, RAPGEF1, SHC1, IRS2, LIPE, PPP1R3, PHKA-B, SLC2A4, FLOT, APS, RHOQ, EXOC7, TRIP10, SREBP1, PRKAA, PRKAB, PRKAG, FOXO1, PPARGC1A, MTOR, RAPTOR, EIF4EBP1, TSC1, TSC2, RHEB, IKBKB, KRAS, NRAS, ARAF, ACACA, PKLR, GCK, PDE3B, SHIP2, IRS1, IRS3, IRS4, SHC2, SHC3, SHC4, PPP1R3F, PRKCZ, HRAS, GSK3B, CXCR4, PPP3C, RASA1, MAPK1_3, RAC1, PAK1, PAK2, RHOA, ROCK1, GNAI, MET, EPHA1, EPHA2, EPHA4, EPHA5, EPHA6, EPHA7, EPHA8, EPHB1, EPHB2, EPHB3, EPHB4, EPHB6, EFNA, EFNB, FYN, ITGB1, PTK2, PAK3, PAK4, PAK6, PAK7, LIMK1, LIMK2, CFL, PPP3R, SEMA4, SEMA7, L1CAM, PLXNC, ABL1, NRP1, ROBO1, ROBO2, DCC, PLXNA, PLXNB
mTOR信号通路
mTOR signaling pathway
PIK3C, PTEN, PIK3R, PRKCA, RP-S6e, TNF, EIF4B, EIF4E, BRAF, MAPK1_3, RPS6KA, AKT, RPS6KB, VEGFA, IGF1, PDPK1, PRKAA, MTOR, RAPTOR, EIF4EBP1, TSC1, TSC2, RHEB, IKBKB, STK11, MLST8, RICTOR, HIF1A, ULK1_2_3, DDIT4, STRADA, CAB39, IRS1, AKT1S1, RRAGA_B, RRAGC_D, PRKCB, PRKCG
HIF-1信号通路
HIF-1 signaling pathway
GAPDH, PDHA, PDHB, HK, PIK3C, PFKFB3, PLCG1, ENO, BCL2, NFKB1, PIK3R, PRKCA, RP-S6e, TNFRSF3, EIF4E, RBX1, CUL2, VHL, SERPINE1, EGF, EGFR, MAP2K1, MAPK1_3, MKNK, AKT, EP300, CAMK2, INSR, IFNG, RPS6KB, STAT3, RELA, IL6R, ERBB2, IGF1R, FLT1, TEK, IFNGR1, IFNGR2, IL6, EPO, VEGFA, IGF1, ANGPT1, ANGPT2, ANGPT4, PLCG2, TFRC, CDKN1B, CDKN1A, MTOR, EIF4EBP1, SLC2A1, NOX, HIF1A, EGLN, TLR4, PDK1, NOS2, NOS3, TF, EDN1, TIMP1, PRKCB, PRKCG

Fig. 5

Expression levels of 8 randomly selected genes in Tibetan cattle and Xuanhan cattle hearts (A: sequencing results; B: verification results) Different letters indicate significant difference (P<0.05)"

[1] TEKIN D, DURSUN A D, XI L . Hypoxia inducible factor 1 (HIF-1) and cardioprotection. Acta Pharmacologica Sinica, 2010,31(9):1085-1094.
[2] SEMENZA G L . Hypoxia-inducible factors in physiology and medicine. Cell, 2012,148(3):399-408.
[3] BARTEL D P . MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 2004,116(2):281-297.
[4] HE M, LU Y, XU S, MAO L, ZHANG L, DUAN W, LIU C, PI H, ZHANG Y, ZHONG M, YU Z, ZHOU Z . MiRNA-210 modulates a nickel-induced cellular energy metabolism shift by repressing the iron-sulfur cluster assembly proteins ISCU1/2 in Neuro-2a cells. Cell Death Disease, 2014, 5(2):e1090.
[5] CONTI A, ROMEO S G, CAMA A, LATORRE D, BARRESI V, PEZZINO G, TOMASELLO C, CARDALI S, ANGILERI F F, POLITO F, FERLAZZO G, DIGIORGIO R, GERMANO A, AGUENNOUZ M . MiRNA expression profiling in human gliomas: upregulated miR-363 increases cell survival and proliferation. Tumour Biology 2016, 37(10):14035-14048.
[6] PANDEY R K, SUNDAR S, PRAJAPAATI V K . Differential expression of miRNA regulates T Cell differentiation and plasticity during visceral leishmaniasis infection. Frontiers in Microbiology, 2016, 7(25):206-215.
[7] FULLAONDO A, LEE S Y . Identification of putative miRNA involved in Drosophila melanogaster immune response. Developmental and Comparative Immunology, 2012,36(2):267-273.
[8] FU X, MENG Z, LIANG W, TIAN Y, WANG X, HAN W, LOU G, WANG X, LOU F, YEN Y, YU H, JOVE R, HUANG W . MiR-26a enhances miRNA biogenesis by targeting Lin28B and Zcchc11 to suppress tumor growth and metastasis. Oncogene, 2014,33(34):4296-4306.
[9] CHENG Y, XIANG G, MENG Y, DONG R . MiRNA-183-5p promotes cell proliferation and inhibits apoptosis in human breast cancer by targeting the PDCD4. Reproductive Biology, 2016, 16(3): 225-233.
[10] HESSAM S, SAND M, SKRYGAN M, GAMBICHLER T, BECHARA F G . Expression of miRNA-155, miRNA-223, miRNA-31, miRNA-21, miRNA-125b, and miRNA-146a in the Inflammatory Pathway of hidradenitis suppurativa. Inflammation, 2017,40(2):464-472.
[11] MAL C, AFTABUDDIN M, KUNDU S . IIKmTA: Inter and intra kingdom miRNA-target analyzer. Interdisciplinary Sciences, Computational Life Sciences, 2018,10(3):538-543.
[12] JONAS S, LZAURRALDE E . Towards a molecular understanding of microRNA-mediated gene silencing. Nature Reviews Genetics, 2015,16(7):421-433.
[13] FENG B, CHAKRABARTI S . MiR-320 regulates glucose-induced gene expression in diabetes. ISRN Endocrinology, 2012, 6(7):1-7.
[14] CIMMINO A, CALIN GA, FABBRI M, LORIO M V, FERRACIN M, SHIMIZU M, WOJCIK S E, AQEILAN R I, ZUPO S, DONO M, RASSENTI L, ALDER H, VOLINIA S, LIU C G, KIPPS T J, NEGRINI M, CROCE C M . MiR-15 and miR-16 induce apoptosis by targeting BCL2. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(39):13944-13949.
[15] NALLAMSHETTY S, CHAN S Y, LOSCALZO J . Hypoxia: a master regulator of microRNA biogenesis and activity. Free Radical Biology Medicine, 2013,64:20-30.
[16] YAO M, WANG X, TANG Y, ZHANG W, Cui B, Liu Q, Xing L . Dicer mediating the expression of miR-143 and miR-155 regulates hexokinase II associated cellular response to hypoxia. American Journal of Physiology-Lung Cellular Physiology, 2014,307(11):829-837.
[17] XU X, LIU C, BAO J . Hypoxia-induced hsa-miR-101 promotes glycolysis by targeting TIGAR mRNA in clear cell renal cell carcinoma. Molecular Medicine Reports, 2017,15(3):1373-1378.
[18] ZHANG G, YIN S, MAO J, LIANG F, ZHAO C, LI P, ZHOU G, CHEN S, TANG Z . Integrated analysis of mRNA-seq and miRNA-seq in the liver of Pelteobagrus vachelli in response to hypoxia. Scientific Reports, 2016,10(6):22907.
[19] 张阳阳 . miR-378在牛前体脂肪细胞分化的作用与机制[D]. 长春: 吉林大学, 2014.
ZHANG Y Y . Effect and mechanism of bovine miR-378 in preadipocyte differention[D]. Changchun: Jilin University, 2014. (in Chinese)
[20] LI H J, LIU M, YE S, YANG F . De novo assembly, gene annotation, and molecular marker development using Illumina paired-end transcriptome sequencing in the clam Saxidomus purpuratus. Genes & Genomics, 2017,39(6):675-685.
[21] 郭胜祥, 刘永年 . 高原适应动物牦牛与普通黄牛肺血管反应性的比较研究. 中国病理生理杂志, 1995, 11(3):230-233.
GUO S X, LIU Y N . Comparative study on pulmonary vascular reactivity of plateau adapted animal Yak and common cattle. Chinese Journal of Pathophysiology, 1995,11(3):230-233. (in Chinese)
[22] 金澄艳, 吕晓阳, 高雯, 王悦, 陈炜昊, 盛水兴, 陈玲, 林杰, 孙伟 . 湖羊羔皮毛囊候选miRNA在不同花纹间的表达与毛囊发育特性关联的研究. 中国农业科学, 2018,51(14):2814-2824.
JIN C Y, LÜ X Y, GAO W, WANG Y, CHEN W H, SHEN S X, CHEN L, LIN J, SUN W . Study on the relationship between the expression of candidate miRNAs and the developmental characteristics in different patterns in Hu Sheep Lambskin. Scientia Agricultura Sinica, 2018,51(14):2814-2824. (in Chinese)
[23] NALLAMSHETTY S, CHAN S Y, LOSCALZO J . Hypoxia: a master regulator of microRNA biogenesis and activity. Free Radical Biology & Medicine, 2013,64:20-30.
[24] BURNSIDE J, OUYANG M, ANDERSON A, BERNBERG E, LU C, MEYERS B C, GREEN P J, MARKIS M, ISAACS G, HUANG E, MORGAN R W . Deep sequencing of chicken microRNAs. BMC Genomics, 2008,9(1):185.
[25] LORENZO F R, HUFF C, MYLLYMAKI M, OLENCHOCK B, SWIERCZEK S, TASHI T, GORDEUK V, WUREN T, RI-LI G, MCCLAIN D A, KHAN T M, KOUL P A, GUCHHAIT P, SALAMA M E, XING J, SEMENZA G L, LIBERZON E, WILSON A, SIMONSON T S, JORDE L B, KAELIN W G J, KOIVUNEN P, PRCHAL J T . A genetic mechanism for Tibetan high-altitude adaptation. Nature Genetics, 2014,46(9):951-956.
[26] QU Y, ZHAO H, HAN N, ZHOU G, SONG G, GAO B, TIAN S, ZHANG J, ZHANG R, MENG X, ZHANG Y, ZHANG Y, ZHU X, WANG W, LAMBERT D, ERICSON P G, SUBRAMANIAN S, YEUNG C, ZHU H, JIANG Z, LI R, LEI F . Ground tit genome reveals avian adaptation to living at high altitudes in the Tibetan plateau. Nature Communications, 2013,4:2071.
[27] VASUDEVAN S, TONG Y, STEITZ J A . Switching from repression to activation: microRNAs can up-regulate translation. Science, 2007, 318(5858):1931-1934.
[28] 贺大芳 . 牦牛和黄牛心脏、肺脏组织microRNA转录组的鉴定与差异表达分析[D]. 成都:四川农业大学, 2017.
HE D F . Identification and differential expression analysis of microRNAs in heart and lung tissues between yak and cattle[D]. Chengdu: Sichuan Agricultural University, 2017. (in Chinese)
[29] ZHANG Q, GOU W, WANG X, ZHANG Y, MA J, ZHANG H, ZHANG Y, ZHANG H . Genome resequencing identifies unique adaptations of Tibetan Chickens to hypoxia and high-dose ultraviolet radiation in high-altitude environments. Genome Biology and Evolution, 2016,8(3):765-776.
[30] 刘杰, 李景东 . 哺乳动物雷帕霉素靶蛋白信号通路在心脏发育和重构中作用的研究进展. 心血管病学进展, 2018(06):911-915.
LIU J, LI J D . Research progress of mTOR signaling pathway effect in cardiac development and reconstruction. Advances in Cardiovascular Diseases, 2018(06):911-915. (in Chinese)
[31] SCIARRETTA S, ZHAI P, MAEJIMA Y, DEL R D P, NAGARAJAN N, YEE D, LIU T, MAGNUSON M A, VOLPE M, FRATI G, LI H, SADOSHIMA J . mTORC2 regulates cardiac response to stress by inhibiting MST1. Cell Reports, 2015,11(1):125-136.
[32] VERMA P, SHARMA A, SODHI M, THAKUR K, KATERIA R S, NIRANJAN S K, BHARTI V K, KUMAR P, GIRI A, KALIA S, MUKESH M . Transcriptome analysis of circulating pbmcs to understand mechanism of high altitude adaptation in native cattle of ladakh region. Scientific Reports, 2018,8(1):7681.
[33] VERMA P, SHARMA A, SODHI M, THAKUR K, BHARTI V K, KUMAR P, GIRI A, KALIA S, SWAMI S K, MUKESH M . Overexpression of genes associated with hypoxia in cattle adapted to trans himalayan region of Ladakh. Cell Biology International, 2018,42(9):1141-1148.
[34] TAGUCHI A, YANAGISAWA K, TANAKA M, CAO K, MATSUYAMA Y, GOTO H, TAKAHASHI T . Identification of hypoxia-inducible factor-1 alpha as a novel target for miR-17-92 microRNA cluster. Cancer Research, 2008, 68(14):5540-5545.
[35] GIATROMANOLAKI A, BAI M, MARGARITIS D, BOURANTAS K L, KOUKOURAKIS M I, SIVRIDIS E, GATTER K C . Hypoxia and activated VEGF/receptor pathway in multiple myeloma. Anticancer Research, 2010,30(7):2831-2836.
[36] BEFANI C D, VLACHOSTERRGIOS P J, HATZIDAKI E, PATRIKIDOU A, BONANOU S, SIMOS G, PAPANDREOU C N, LIAKOS P . Bortezomib represses HIF-1α protein expression and nuclear accumulation by inhibiting both PI3K/Akt/TOR and MAPK pathways in prostate cancer cells. Journal of Molecular Medicine, 2012,90(1):45-54.
[37] MAJMUNDARR A J, WONG W J, SIMON M C . Hypoxia-inducible factors and the response to hypoxic stress. Molecular Cell, 2010,40(2):294-309.
[38] TAGUCHI A, YANAGISAWA K, TANAK A M, CAO K, MATSUYAMA Y, GOTO H, TAKAHASHI T . Identification of hypoxia-inducible factor-1 alpha as a novel target for miR-17-92 microRNA cluster. Cancer Research, 2008,68(14):5540-5545.
[39] CASCIO S, D'ANDREA A, FERLA R, SURMACZ E, GULOTTA E, AMODEO V, BAZAN V, GEBBIA N, RUSSO A . MiR-20b modulates VEGF expression by targeting HIF-1 alpha and STAT3 in MCF-7 breast cancer cells. Journal of Cellular Physiology, 2010,224(1):242-249.
[40] 岳莹, 吕风华, 陈玉磊, 王卓, 司澳洋 . miR-499对缺氧/复氧诱导的心肌细胞凋亡的影响. 郑州大学学报(医学版), 2018,53(04):503-507.
YUE Y, LÜ F H, CHEN Y L, WANG Z, SI A Y . Effect of miR-499 on apoptosis of primary cardiomyocytes induced by anoxia-reoxygenation. Journal of Zhengzhou University(Medical Sciences), 2018,53(04):503-507. (in Chinese)
[41] 赵欣 . MicroRNA-101a通过靶向调控心脏成纤维细胞TGFβRI的表达抑制缺氧诱导的心肌纤维化[D]. 武汉:华中科技大学, 2015.
ZHAO X . MicroRNA-101a inhibits hypoxia-induced myocardial fibrosis by targeting regulation of cardiac fibroblast TGFβRI expression[D]. Wuhan:Huazhong University of Science and Technology, 2015. (in Chinese)
[42] XI T Y, JIN F, ZHU Y, WANG J, TANG L, WANG Y, LIEBESKKIND D S, HE Z . MicroRNA-126-3p attenuates blood-brain barrier disruption, cerebral edema and neuronal injury following intracerebral hemorrhage by regulating PIK3R2 and Akt. Biochemical and Biophysical Research Communications, 2017,494(1-2):144-151.
[1] WU Yan,ZHANG Hao,LIANG ZhenHua,PAN AiLuan,SHEN Jie,PU YueJin,HUANG Tao,PI JinSong,DU JinPing. circ-13267 Regulates Egg Duck Granulosa Cells Apoptosis Through Let-7-19/ERBB4 Pathway [J]. Scientia Agricultura Sinica, 2022, 55(8): 1657-1666.
[2] MA XueMeng,YU ChengMin,SAI XiaoLing,LIU Zhen,SANG HaiYang,CUI BaiMing. PSORA: A Strategy Based on High-Throughput Sequence for Analysis of T-DNA Insertion Sites [J]. Scientia Agricultura Sinica, 2022, 55(15): 2875-2882.
[3] DU Yu,ZHU ZhiWei,WANG Jie,WANG XiuNa,JIANG HaiBin,FAN YuanChan,FAN XiaoXue,CHEN HuaZhi,LONG Qi,CAI ZongBing,XIONG CuiLing,ZHENG YanZhen,FU ZhongMin,CHEN DaFu,GUO Rui. Construction and Annotation of Ascosphaera apis Full-Length Transcriptome Utilizing Nanopore Third-Generation Long-Read Sequencing Technology [J]. Scientia Agricultura Sinica, 2021, 54(4): 864-876.
[4] WANG Yong,LI SiYan,HE SiRui,ZHANG Di,LIAN Shuai,WANG JianFa,WU Rui. Prediction and Bioinformatics Analysis of BLV-miRNA Transboundary Regulation of Human Target Genes [J]. Scientia Agricultura Sinica, 2021, 54(3): 662-674.
[5] SHAO MeiQi,ZHAO WeiSong,SU ZhenHe,DONG LiHong,GUO QingGang,MA Ping. Effect of Bacillus subtilis NCD-2 on the Growth of Tomato and the Microbial Community Structure of Rhizosphere Soil Under Salt Stress [J]. Scientia Agricultura Sinica, 2021, 54(21): 4573-4584.
[6] CHEN HuiFang,HUANG QiLiang,HU ZhiChao,PAN XiaoTing,WU ZhiSheng,BAI YinShan. Expression Differences and Functional Analysis of Exosomes microRNA in Porcine Mature and Atretic Follicles [J]. Scientia Agricultura Sinica, 2021, 54(21): 4664-4676.
[7] YU BaoJun,DENG ZhanZhao,XIN GuoSheng,CAI ZhengYun,GU YaLing,ZHANG Juan. Correlation Analysis of Inosine Monophosphate Specific Deposition Related LNC_003828-gga-miR-107-3P-MINPP1 in Jingyuan Chicken Muscle Tissue [J]. Scientia Agricultura Sinica, 2021, 54(19): 4229-4242.
[8] HUANG ZiYue,LIU WenJun,QIN RenLiu,PANG ShiChan,XIAO Jian,YANG ShangDong. Endophytic Bacterial Community Composition and PICRUSt Gene Functions in Different Pumpkin Varieties [J]. Scientia Agricultura Sinica, 2021, 54(18): 4018-4032.
[9] TAN ZhaoGuo,LI YanMei,BAI JianFang,GUO HaoYu,LI TingTing,DUAN WenJing,LIU ZiHan,YUAN ShaoHua,ZHANG TianBao,ZHANG FengTing,CHEN ZhaoBo,ZHAO FuYong,ZHAO ChangPing,ZHANG LiPing. Cloning of TaBG and Analysis of Its Function in Anther Dehiscence in Wheat [J]. Scientia Agricultura Sinica, 2021, 54(13): 2710-2723.
[10] ShuJun MENG,XueHai ZHANG,QiYue WANG,Wen ZHANG,Li HUANG,Dong DING,JiHua TANG. Identification of miRNAs and tRFs in Response to Salt Stress in Rice Roots [J]. Scientia Agricultura Sinica, 2020, 53(4): 669-682.
[11] ZHAO YuanYuan,LI PengFei,XU QinZhi,AN QingMing,MENG JinZhu. Screening and Analysis of Follicular Development Related Genes in Goat [J]. Scientia Agricultura Sinica, 2020, 53(17): 3597-3605.
[12] CHEN HuaZhi,ZHU ZhiWei,JIANG HaiBin,WANG Jie,FAN YuanChan,FAN XiaoXue,WAN JieQi,LU JiaXuan,XIONG CuiLing,ZHENG YanZhen,FU ZhongMin,CHEN DaFu,GUO Rui. Comparative Analysis of MicroRNAs and Corresponding Target mRNAs in Ascosphaera apis Mycelium and Spore [J]. Scientia Agricultura Sinica, 2020, 53(17): 3606-3619.
[13] ZHU JingJing,ZHOU XiaoLong,WANG Han,LI XiangChen,ZHAO AYong,YANG SongBai. Prediction and Verification of MicroRNAs Targeting Porcine Endoplasmic Reticulum Stress Pathway [J]. Scientia Agricultura Sinica, 2020, 53(15): 3169-3179.
[14] GENG SiHai,SHI CaiYun,FAN XiaoXue,WANG Jie,ZHU ZhiWei,JIANG HaiBin,FAN YuanChan,CHEN HuaZhi,DU Yu,WANG XinRui,XIONG CuiLing,ZHENG YanZhen,FU ZhongMin,CHEN DaFu,GUO Rui. The Mechanism Underlying MicroRNAs-Mediated Nosema ceranae Infection to Apis mellifera ligustica Worker [J]. Scientia Agricultura Sinica, 2020, 53(15): 3187-3204.
[15] DU Yu,FAN XiaoXue,JIANG HaiBin,WANG Jie,FAN YuanChan,ZHU ZhiWei,ZHOU DingDing,WAN JieQi,LU JiaXuan,XIONG CuiLing,ZHENG YanZhen,CHEN DaFu,GUO Rui. The Potential Role of MicroRNAs and MicroRNA-Mediated Competing Endogenous Networks During the Developmental Process of Apis mellifera ligustica Worker’s Midgut [J]. Scientia Agricultura Sinica, 2020, 53(12): 2512-2526.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!