Comparing of backfat microRNAomes of Landrace and Neijiang pig by high-throughput sequencing

被引:4
|
作者
Li, Yanyue [1 ,2 ]
机构
[1] Southwest Med Univ, Affiliated Hosp, Luzhou 646000, Sichuan, Peoples R China
[2] Sichuan Univ, Coll Life Sci, Chengdu 610064, Sichuan, Peoples R China
关键词
MiRNAome; MiRNAs; Neijiang pig; Landrace pig; Backfat;
D O I
10.1007/s13258-021-01078-z
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Background MicroRNAs (miRNAs) could regulate the expression of target genes and play important roles in modulation of various metabolic processes. Nevertheless, little is known about the backfat microRNAome (miRNAome) of the Neijiang pig. Objectives The primary objective of this study was to analyse miRNAomes of Landrace and Neijiang pig backfat (LPB and NPB resp.). Furthermore, investigating differentially expressed miRNAs participating in lipid metabolism and mining potential biomarker for Neijiang pig breeding. Methods Here we used the Landrace pig with different metabolic characteristics as a control to analyse the Neijiang pig-specific backfat miRNAome. A comprehensive analysis of miRNAomes was performed by deep sequencing. Results Small RNA sequencing identified 326 unique miRNAs, 280 were co-expressed in both libraries. Only 11 and 35 miRNAs were specifically expressed in LPB and NPB respectively. Sixty seven differentially expressed miRNAs were identified by IDEG6. MiR-1-3p were identified that may participate in lipid metabolism. Furthermore, qPCR results revealed that lower expression of miR-1-3p in NPB could increase the expression of LXR alpha, which is an enzyme important for the synthesis and accumulation of lipid. The double luciferase report experiment suggested that LXR alpha was the direct target gene of miR-1-3p. In short, miR-1-3p could modulate the synthesis and accumulation of lipid by target LXR alpha. It may be a potential marker for pig breeding. Conclusion Our investigation has delineated the different miRNAs expression patterns of LPB and NPB, which may help understand the regulatory mechanisms of miRNAs in the lipid metabolism, and provide potential biomarkers for Neijiang pig breeding.
引用
收藏
页码:543 / 551
页数:9
相关论文
共 50 条
  • [21] Identification of high utility SNPs for population assignment and traceability purposes in the pig using high-throughput sequencing
    Ramos, A. M.
    Megens, H. J.
    Crooijmans, R. P. M. A.
    Schook, L. B.
    Groenen, M. A. M.
    ANIMAL GENETICS, 2011, 42 (06) : 613 - 620
  • [22] Small molecule regulators of microRNAs identified by high-throughput screen coupled with high-throughput sequencing
    Lien D. Nguyen
    Zhiyun Wei
    M. Catarina Silva
    Sergio Barberán-Soler
    Jiarui Zhang
    Rosalia Rabinovsky
    Christina R. Muratore
    Jonathan M. S. Stricker
    Colin Hortman
    Tracy L. Young-Pearse
    Stephen J. Haggarty
    Anna M. Krichevsky
    Nature Communications, 14 (1)
  • [23] Small molecule regulators of microRNAs identified by high-throughput screen coupled with high-throughput sequencing
    Nguyen, Lien D.
    Wei, Zhiyun
    Silva, M. Catarina
    Barberan-Soler, Sergio
    Zhang, Jiarui
    Rabinovsky, Rosalia
    Muratore, Christina R.
    Stricker, Jonathan M. S.
    Hortman, Colin
    Young-Pearse, Tracy L.
    Haggarty, Stephen J.
    Krichevsky, Anna M.
    NATURE COMMUNICATIONS, 2023, 14 (01)
  • [24] Standards for Sequencing Viral Genomes in the Era of High-Throughput Sequencing
    Ladner, Jason T.
    Beitzel, Brett
    Chain, Patrick S. G.
    Davenport, Matthew G.
    Donaldson, Eric F.
    Frieman, Matthew
    Kugelman, Jeffrey R.
    Kuhn, Jens H.
    O'Rear, Jules
    Sabeti, Pardis C.
    Wentworth, David E.
    Wiley, Michael R.
    Yu, Guo-Yun
    Sozhamannan, Shanmuga
    Bradburne, Christopher
    Palacios, Gustavo
    MBIO, 2014, 5 (03): : 1 - 5
  • [25] Genomics - from Neanderthals to high-throughput sequencing
    Matthew John Wakefield
    Genome Biology, 7
  • [26] Efficient and quantitative high-throughput tRNA sequencing
    Zheng G.
    Qin Y.
    Clark W.C.
    Dai Q.
    Yi C.
    He C.
    Lambowitz A.M.
    Pan T.
    Nature Methods, 2015, 12 (9) : 835 - 837
  • [27] High-throughput sequencing: a failure mode analysis
    Yang, GS
    Stott, JM
    Smailus, D
    Barber, SA
    Balasundaram, M
    Marra, MA
    Holt, RA
    BMC GENOMICS, 2005, 6 (1)
  • [28] Genome reassembly with high-throughput sequencing data
    Parrish, Nathaniel
    Sudakov, Benjamin
    Eskin, Eleazar
    BMC GENOMICS, 2013, 14
  • [29] High-Throughput Sequencing of a South American Amerindian
    Ribeiro-dos-Santos, Andre M.
    Santana de Souza, Jorge Estefano
    Almeida, Renan
    Alencar, Dayse O.
    Barbosa, Maria Silvanira
    Gusmao, Leonor
    Silva, Wilson A., Jr.
    de Souza, Sandro J.
    Silva, Artur
    Ribeiro-dos-Santos, Andrea
    Darnet, Sylvain
    Santos, Sidney
    PLOS ONE, 2013, 8 (12):
  • [30] Tools for mapping high-throughput sequencing data
    Fonseca, Nuno A.
    Rung, Johan
    Brazma, Alvis
    Marioni, John C.
    BIOINFORMATICS, 2012, 28 (24) : 3169 - 3177