Skip to main content
Log in

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

  • Research Article
  • Published:
Genes & Genomics Aims and scope Submit manuscript

Abstract

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 LXRα, which is an enzyme important for the synthesis and accumulation of lipid. The double luciferase report experiment suggested that LXRα was the direct target gene of miR-1-3p. In short, miR-1-3p could modulate the synthesis and accumulation of lipid by target LXRα. 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig.5
Fig. 6

Similar content being viewed by others

References

  • Ahmad A, Zhang W, Wu M, Tan S, Zhu T (2017) Tumor-suppressive miRNA-135a inhibits breast cancer cell proliferation by targeting ELK1 and ELK3 oncogenes. Genes Genom 40:243–251

    Article  Google Scholar 

  • Bellingham SA, Coleman BM, Hill AF (2012) Small RNA deep sequencing reveals a distinct miRNA signature released in exosomes from prion-infected neuronal cells. Nucleic Acids Res 21:10937–10949

    Article  Google Scholar 

  • Bu H, Chen Y, Liu J, Li S, Li Y (2000) RYR1 genotype of the Chinese Neijiang pig. Transpl Proc 32:1058

    Article  CAS  Google Scholar 

  • Carthew RW, Sontheimer EJ (2009) Origins and mechanisms of miRNAs and siRNAs. Cell 136:642–655

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chan S-P, Slack FJ (2006) Point of View microRNA-mediated silencing inside P-bodies. RNA Biol 3:97–100

    Article  CAS  PubMed  Google Scholar 

  • Chartoumpekis DV, Zaravinos A, Ziros PG, Iskrenova RP, Psyrogiannis AI, Kyriazopoulou VE, Habeos IG (2012) Differential expression of microRNAs in adipose tissue after long-term high-fat diet-induced obesity in mice. PLoS ONE 7:e34872

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cui S, Cao Z, Guo W, Yu H, Zhou Y (2019) Plasma miRNA-23a and miRNA-451 as candidate biomarkers for early diagnosis of nonsmall cell lung cancer: a case-control study. J South Med Univ 39:705–711

    Google Scholar 

  • Da Wei Huang BTS, Lempicki RA (2008) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57

    Article  Google Scholar 

  • Dávalos A, Goedeke L, Smibert P, Ramírez CM, Warrier NP, Andreo U, Cirera-Salinas D, Rayner K, Suresh U, Pastor-Pareja JC (2011) miR-33a/b contribute to the regulation of fatty acid metabolism and insulin signaling. Proc Natl Acad Sci 108:9232–9237

    Article  PubMed  Google Scholar 

  • Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS (2004) MicroRNA targets in Drosophila. Genome Biol 5:R1–R1

    Article  Google Scholar 

  • Esau C, Davis S, Murray SF, Yu XX, Pandey SK, Pear M, Watts L, Booten SL, Graham M, McKay R (2006) miR-122 regulation of lipid metabolism revealed by in vivo antisense targeting. Cell Metab 3:87–98

    Article  CAS  PubMed  Google Scholar 

  • Fang Y, Zeng HY, Yun-Cheng LV (2015) MiR-152 inhibits hepatocyte lipid uptake by targeting low density lipoprotein receptor. Chin J Arterioscler 23(8):0774–0778

    CAS  Google Scholar 

  • Fernandez-Valverde SL, Calcino AD, Degnan BM (2015) Deep developmental transcriptome sequencing uncovers numerous new genes and enhances gene annotation in the sponge Amphimedon queenslandica. BMC Genom 16:387

    Article  Google Scholar 

  • Gang L, Jiansheng W, Sida Q, Xin S, Hong R, Jing Z, Baocheng L (2019) Application of detection of circulating tumor cells and exosome miR-21 in diagnosis of lung ground glass opacity. J Jilin Univ (Med Ed) 299:E198–E206

    Google Scholar 

  • Gerin I, Bommer GT, McCoin CS, Sousa KM, Krishnan V, MacDougald OA (2010) Roles for miRNA-378/378* in adipocyte gene expression and lipogenesis. Am J Physiol Endocrinol Metab 299:E198–E206

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Glazov EA, Cottee PA, Barris WC, Moore RJ, Dalrymple BP, Tizard ML (2008a) A microRNA catalog of the developing chicken embryo identified by a deep sequencing approach. Genome Res 18:957–964

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Glazov EA, Kongsuwan K, Assavalapsakul W, Horwood PF, Mitter N, Mahony TJ (2009) Repertoire of bovine miRNA and miRNA-like small regulatory RNAs expressed upon viral infection. PLoS ONE 4:0006349

    Article  Google Scholar 

  • Gomez-Uchida D, Seeb LW, Warheit KI, Mckinney GJ, Seeb JE (2014) Deep sequencing of the transcriptome and mining of single nucleotide polymorphisms (SNPs) provide genomic resources for applied studies in Chinook salmon (Oncorhynchus tshawytscha). Conserv Genet Resour 6:807–811

    Article  Google Scholar 

  • Hai B, Min C, Hui C, Du L, Wang Y (2019) Transcriptome-wide identification of miRNA targets and a TAS3-homologous gene in Populus by degradome sequencing. Genes Genom 41:849–861

    Article  Google Scholar 

  • Huang YP, Guo WZ, Xiao-Qi LI (2007) Molecular cloning, sequencing and constructing eukaryotic expression vector of Neijiang pig IFN-γ. China Anim Husb Vet Med 34:60–63

    CAS  Google Scholar 

  • Iliopoulos D, Drosatos K, Hiyama Y, Goldberg IJ, Zannis VI (2010) MicroRNA-370 controls the expression of MicroRNA-122 and Cpt1α and affects lipid metabolism. J Lipid Res 51:1513–1523

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jingge L, Caibo N, Bojiang L, Rongyang W (2019) Wu H (2018) epatic microRNAome reveals potential microRNA-mRNA pairs association with lipid metabolism in pigs. Asian-Australas J Anim Sci 32(9):1458

    Article  Google Scholar 

  • Kuchenbauer F, Morin RD, Argiropoulos B, Petriv I, Griffith M, Heuser M, Yung E, Piper J, Delaney A, Prabhu A-L (2008a) In-depth characterization of the microRNA transcriptome in a leukemia progression model. Genome Res 18:1787–1797

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T (2001) Identification of novel genes coding for small expressed RNAs. Science 294:853–858

    Article  CAS  PubMed  Google Scholar 

  • Lai EC (2005) miRNAs: whys and wherefores of miRNA-mediated regulation. Curr Biol 15:R458–R460

    Article  CAS  PubMed  Google Scholar 

  • Larsen L, Rosenstierne MW, Gaarn LW, Bagge A, Pedersen L, Dahmcke CM, Nielsen JH, Dalgaard LT (2011) Expression and localization of microRNAs in perinatal rat pancreas: role of miR-21 in regulation of cholesterol metabolism. PLoS ONE 6:e25997

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lau NC, Lim LP, Weinstein EG, Bartel DP (2001) An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 294:858–862

    Article  CAS  PubMed  Google Scholar 

  • Lehrke M, Lebherz C, Millington SC, Guan HP, Millar J, Rader DJ, Wilson JM, Lazar MA (2005) Diet-dependent cardiovascular lipid metabolism controlled by hepatic LXRα. Cell Metab 1:297–308

    Article  CAS  PubMed  Google Scholar 

  • Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP, Burge CB (2003) Prediction of mammalian microRNA targets. Cell 115:787–798

    Article  CAS  PubMed  Google Scholar 

  • Lian C, Sun B, Niu S, Yang R, Liu B, Lu C, Meng J, Qiu Z, Zhang L, Zhao Z (2012) A comparative profile of the microRNA transcriptome in immature and mature porcine testes using Solexa deep sequencing. FEBS J 279:964–975

    Article  CAS  PubMed  Google Scholar 

  • Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25:402–408

    Article  CAS  Google Scholar 

  • Min F, Wang S, Zhang L (2015) Survey of programs used to detect alternative splicing isoforms from deep sequencing data in silico. Biomed Res Int 2015:1–9

    Google Scholar 

  • Orangi E, Motovali-Bashi M (2019) Evaluation of miRNA-9 and miRNA-34a as potential biomarkers for diagnosis of breast cancer in Iranian women. Gene 687:272–279

    Article  CAS  PubMed  Google Scholar 

  • Rehmsmeier M, Steffen P, Höchsmann M, Giegerich R (2004) Fast and effective prediction of microRNA/target duplexes. RNA 10:1507–1517

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Romualdi C, Bortoluzzi S, d’Alessi F, Danieli GA (2003) IDEG6: a web tool for detection of differentially expressed genes in multiple tag sampling experiments. Physiol Genomics 12:159–162

    Article  CAS  PubMed  Google Scholar 

  • Sharbati S, Friedländer MR, Sharbati J, Hoeke L, Chen W, Keller A, Stähler PF, Rajewsky N, Einspanier R (2010) Deciphering the porcine intestinal microRNA transcriptome. BMC Genom 11:275

    Article  Google Scholar 

  • Takanabe R, Ono K, Abe Y, Takaya T, Horie T, Wada H, Kita T, Satoh N, Shimatsu A, Hasegawa K (2008) Up-regulated expression of microRNA-143 in association with obesity in adipose tissue of mice fed high-fat diet. Biochem Biophys Res Commun 376:728–732

    Article  CAS  PubMed  Google Scholar 

  • Wang J, Long Y, Zhang J, Xue M, Huang G, Huang K, Yuan Q, Pei X (2018) Combined analysis and miRNA expression profiles of the flowering related genes in common wild rice (oryza rufipogon Griff.). Genes Genom 40(8):835–845

    Article  CAS  Google Scholar 

  • Winter J, Jung S, Keller S, Gregory RI, Diederichs S (2009) Many roads to maturity: microRNA biogenesis pathways and their regulation. Nat Cell Biol 11:228–234

    Article  CAS  PubMed  Google Scholar 

  • Zhang B, Stellwag EJ, Pan X (2009) Large-scale genome analysis reveals unique features of microRNAs. Gene 443:100–109

    Article  CAS  PubMed  Google Scholar 

  • Zhao J, Xu J, Zhang R (2018) SRPX2 regulates colon cancer cell metabolism by miR-192/215 via PI3K-Akt. Am J Transl Res 10:483–490

    PubMed  PubMed Central  Google Scholar 

  • Zhipeng S, Zongde Z, Yang L (2019) Clinical application of plasma miR-34b-3p and miR-302a-5p in the diagnosis of non-small cell lung cancer. Zhongguo fei ai za zhi Chin J Lung Cancer 22(4):216–222

    Google Scholar 

  • Zhong D, Huang G, Zhang Y, Zeng Y, Xu Z, Zhao Y, He X, He F (2013) MicroRNA-1 and microRNA-206 suppress LXRα-induced lipogenesis in hepatocytes. Cell Signal 25:1429–1437

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Funding was provided by Doctoral research start up fund of Affiliated Hospital of Southwest Medical University (Grant no. 17138).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yanyue Li.

Ethics declarations

Conflict of interest

Authors declare that they have no competing interests.

Ethical approval

Animal experiments were performed according to Chinese animal welfare laws and regulations, and approved by the Institutional Animal Care and Use Committee in Neijiang pig breeding farm under Permit No. B2016112.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y. Comparing of backfat microRNAomes of Landrace and Neijiang pig by high-throughput sequencing. Genes Genom 43, 543–551 (2021). https://doi.org/10.1007/s13258-021-01078-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13258-021-01078-z

Keywords

Navigation