Skip to main content
SpringerLink
Log in

Inhibition of miR-486 and miR-92a decreases liver and plasma cholesterol levels by modulating lipid-related genes in hyperlipidemic hamsters

  • Original Article
  • Published:
Molecular Biology Reports Aims and scope Submit manuscript

Abstract

In the present study we aimed to evaluate the potential of in vivo inhibition of miR-486 and miR-92a to reverse hyperlipidemia, then to identify and validate their lipid metabolism-related target genes. Male Golden-Syrian hamsters fed a hyperlipidemic (HL) diet (standard chow plus 3% cholesterol and 15% butter, 10 weeks) were injected subcutaneously with lock-nucleic acid inhibitors for either miR-486 or miR-92a. Lipids and miRNAs levels in liver and plasma, and hepatic expression of miRNAs target genes were assessed in all HL hamsters. MiR-486 and miR-92a target genes were identified by miRWalk analysis and validated by 3′UTR cloning in pmirGLO vectors. HL hamsters had increased liver (2.8-fold) and plasma (twofold) miR-486 levels, and increased miR-92a (2.8-fold and 1.8-fold, respectively) compared to normolipidemic hamsters. After 2 weeks treatment, liver and plasma cholesterol levels decreased (23 and 17.5% for anti-miR-486, 16 and 22% for miR-92a inhibition). Hepatic triglycerides and non-esterified fatty acids content decreased also significantly. Bioinformatics analysis and 3′UTR cloning in pmirGLO vector showed that sterol O-acyltransferase-2 (SOAT2) and sterol-regulatory element binding transcription factor-1 (SREBF1) are targeted by miR-486, while ATP-binding cassette G4 (ABCG4) and Niemann-Pick C1 (NPC1) by miR-92a. In HL livers and in cultured HepG2 cells, miR-486 inhibition restored the levels of SOAT2 and SREBF1 expression, while anti-miR-92a restored ABCG4, NPC1 and SOAT2 expression compared to scrambled-treated HL hamsters or cultured cells. In vivo inhibition of miR-486 and miR-92a could be a useful and valuable new approach to correct lipid metabolism dysregulation.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Libby P, Okamoto Y, Rocha VZ, Folco E (2010) Inflammation in atherosclerosis: transition from theory to practice. Circ J 74(2):213–220

    Article  PubMed  CAS  Google Scholar 

  2. Reiner Z, Catapano AL, De Backer G, Graham I, Taskinen MR, Wiklund O, Agewall S, Alegria E, Chapman MJ, Durrington P, Erdine S, Halcox J, Hobbs R, Kjekshus J, Filardi PP, Riccardi G, Storey RF, Wood D (2011) ESC/EAS Guidelines for the management of dyslipidaemias: the Task Force for the management of dyslipidaemias of the European Society of Cardiology (ESC) and the European Atherosclerosis Society (EAS). Eur Heart J 32(14):1769–1818

    Article  PubMed  Google Scholar 

  3. Moore KJ, Rayner KJ, Suarez Y, Fernandez-Hernando C (2010) microRNAs and cholesterol metabolism. Trends Endocrinol Metab 21(12):699–706. https://doi.org/10.1016/j.tem.2010.08.008

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Vickers KC, Palmisano BT, Shoucri BM, Shamburek RD, Remaley AT (2011) MicroRNAs are transported in plasma and delivered to recipient cells by high-density lipoproteins. Nat Cell Biol 13(4):423–433. https://doi.org/10.1038/ncb2210

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Wagner J, Riwanto M, Besler C, Knau A, Fichtlscherer S, Roxe T, Zeiher AM, Landmesser U, Dimmeler S (2013) Characterization of levels and cellular transfer of circulating lipoprotein-bound microRNAs. Arterioscler Thromb Vasc Biol 33(6):1392–1400. https://doi.org/10.1161/ATVBAHA.112.300741

    Article  PubMed  CAS  Google Scholar 

  6. Niculescu LS, Simionescu N, Sanda GM, Carnuta MG, Stancu CS, Popescu AC, Popescu MR, Vlad A, Dimulescu DR, Simionescu M, Sima AV (2015) MiR-486 and miR-92a identified in circulating HDL discriminate between stable and vulnerable coronary artery disease patients. PLoS ONE 10(10):e0140958. https://doi.org/10.1371/journal.pone.0140958

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Simionescu N, Niculescu LS, Carnuta MG, Sanda GM, Stancu CS, Popescu AC, Popescu MR, Vlad A, Dimulescu DR, Simionescu M, Sima AV (2016) Hyperglycemia determines increased specific MicroRNAs levels in sera and HDL of acute coronary syndrome patients and stimulates MicroRNAs production in human macrophages. PLoS ONE 11(8):e0161201. https://doi.org/10.1371/journal.pone.0161201

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Stancu CS, Sanda GM, Deleanu M, Sima AV (2014) Probiotics determine hypolipidemic and antioxidant effects in hyperlipidemic hamsters. Mol Nutr Food Res 58(3):559–568. https://doi.org/10.1002/mnfr.201300224

    Article  PubMed  CAS  Google Scholar 

  9. Simionescu N, Niculescu LS, Sanda GM, Margina D, Sima AV (2014) Analysis of circulating microRNAs that are specifically increased in hyperlipidemic and/or hyperglycemic sera. Mol Biol Rep 41(9):5765–5773. https://doi.org/10.1007/s11033-014-3449-2

    Article  PubMed  CAS  Google Scholar 

  10. Schmittgen TD, Livak KJ (2008) Analyzing real-time PCR data by the comparative C(T) method. Nat Protoc 3(6):1101–1108. https://doi.org/10.3410/f.5500956.5467055

    Article  PubMed  CAS  Google Scholar 

  11. Dweep H, Sticht C, Pandey P, Gretz N (2011) miRWalk-database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform 44(5):839–847. https://doi.org/10.1016/j.jbi.2011.05.002

    Article  PubMed  CAS  Google Scholar 

  12. Dweep H, Gretz N (2015) miRWalk2.0: a comprehensive atlas of microRNA-target interactions. Nat Methods 12(8):697. https://doi.org/10.1038/nmeth.3485

    Article  PubMed  CAS  Google Scholar 

  13. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, von Mering C (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43(Database issue):D447–D452. https://doi.org/10.1093/nar/gku1003

    Article  PubMed  CAS  Google Scholar 

  14. Mi H, Muruganujan A, Casagrande JT, Thomas PD (2013) Large-scale gene function analysis with the PANTHER classification system. Nat Protoc 8(8):1551–1566. https://doi.org/10.1038/nprot.2013.092

    Article  PubMed  CAS  Google Scholar 

  15. Dirkx E, Gladka MM, Philippen LE, Armand AS, Kinet V, Leptidis S, El Azzouzi H, Salic K, Bourajjaj M, da Silva GJ, Olieslagers S, van der Nagel R, de Weger R, Bitsch N, Kisters N, Seyen S, Morikawa Y, Chanoine C, Heymans S, Volders PG, Thum T, Dimmeler S, Cserjesi P, Eschenhagen T, da Costa Martins PA, De Windt LJ (2013) Nfat and miR-25 cooperate to reactivate the transcription factor Hand2 in heart failure. Nat Cell Biol 15(11):1282–1293. https://doi.org/10.1038/ncb2866

    Article  PubMed  CAS  Google Scholar 

  16. Stancu CS, Carnuta MG, Sanda GM, Toma L, Deleanu M, Niculescu LS, Sasson S, Simionescu M, Sima AV (2015) Hyperlipidemia-induced hepatic and small intestine ER stress and decreased paraoxonase 1 expression and activity is associated with HDL dysfunction in Syrian hamsters. Mol Nutr Food Res 59(11):2293–2302. https://doi.org/10.1002/mnfr.201500422

    Article  PubMed  CAS  Google Scholar 

  17. Parini P, Johansson L, Broijersen A, Angelin B, Rudling M (2006) Lipoprotein profiles in plasma and interstitial fluid analyzed with an automated gel-filtration system. Eur J Clin Investig 36(2):98–104. https://doi.org/10.1111/j.1365-2362.2006.01597.x

    Article  CAS  Google Scholar 

  18. Liu D, Zhang M, Xie W, Lan G, Cheng HP, Gong D, Huang C, Lv YC, Yao F, Tan YL, Li L, Zheng XL, Tang CK (2016) MiR-486 regulates cholesterol efflux by targeting HAT1. Biochem Biophys Res Commun 472(3):418–424. https://doi.org/10.1016/j.bbrc.2015.11.128

    Article  PubMed  CAS  Google Scholar 

  19. Rogers MA, Liu J, Song BL, Li BL, Chang CC, Chang TY (2015) Acyl-CoA:cholesterol acyltransferases (ACATs/SOATs): enzymes with multiple sterols as substrates and as activators. J Steroid Biochem Mol Biol 151:102–107. https://doi.org/10.1016/j.jsbmb.2014.09.008

    Article  PubMed  CAS  Google Scholar 

  20. Warrier M, Zhang J, Bura K, Kelley K, Wilson MD, Rudel LL, Brown JM (2016) Sterol O-acyltransferase 2-driven cholesterol esterification opposes liver X receptor-stimulated fecal neutral sterol loss. Lipids 51(2):151–157. https://doi.org/10.1007/s11745-015-4116-7

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Bennett DJ, Cooke AJ, Edwards AS (2006) Non-steroidal LXR agonists; an emerging therapeutic strategy for the treatment of atherosclerosis. Recent Pat Cardiovasc Drug Disc 1(1):21–46

    Article  CAS  Google Scholar 

  22. Brown MS, Goldstein JL (1997) The SREBP pathway: regulation of cholesterol metabolism by proteolysis of a membrane-bound transcription factor. Cell 89(3):331–340

    Article  PubMed  CAS  Google Scholar 

  23. Sato R, Miyamoto W, Inoue J, Terada T, Imanaka T, Maeda M (1999) Sterol regulatory element-binding protein negatively regulates microsomal triglyceride transfer protein gene transcription. J Biol Chem 274(35):24714–24720

    Article  PubMed  CAS  Google Scholar 

  24. Loh WP, Yang Y, Lam KP (2017) miR-92a enhances recombinant protein productivity in CHO cells by increasing intracellular cholesterol levels. Biotechnol J. https://doi.org/10.1002/biot.201600488

    Article  PubMed  Google Scholar 

  25. Wang N, Lan D, Chen W, Matsuura F, Tall AR (2004) ATP-binding cassette transporters G1 and G4 mediate cellular cholesterol efflux to high-density lipoproteins. Proc Natl Acad Sci USA 101(26):9774–9779. https://doi.org/10.1073/pnas.0403506101

    Article  PubMed  CAS  Google Scholar 

  26. Murphy AJ, Bijl N, Yvan-Charvet L, Welch CB, Bhagwat N, Reheman A, Wang Y, Shaw JA, Levine RL, Ni H, Tall AR, Wang N (2013) Cholesterol efflux in megakaryocyte progenitors suppresses platelet production and thrombocytosis. Nat Med 19(5):586–594. https://doi.org/10.1038/nm.3150

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Wang N, Ranalletta M, Matsuura F, Peng F, Tall AR (2006) LXR-induced redistribution of ABCG1 to plasma membrane in macrophages enhances cholesterol mass efflux to HDL. Arterioscler Thromb Vasc Biol 26(6):1310–1316. https://doi.org/10.1161/01.ATV.0000218998.75963.02

    Article  PubMed  CAS  Google Scholar 

  28. Helwak A, Kudla G, Dudnakova T, Tollervey D (2013) Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153(3):654–665. https://doi.org/10.1016/j.cell.2013.03.043

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Cluzeau CV, Watkins-Chow DE, Fu R, Borate B, Yanjanin N, Dail MK, Davidson CD, Walkley SU, Ory DS, Wassif CA, Pavan WJ, Porter FD (2012) Microarray expression analysis and identification of serum biomarkers for Niemann-Pick disease, type C1. Hum Mol Genet 21(16):3632–3646. https://doi.org/10.1093/hmg/dds193

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Zhang JR, Coleman T, Langmade SJ, Scherrer DE, Lane L, Lanier MH, Feng C, Sands MS, Schaffer JE, Semenkovich CF, Ory DS (2008) Niemann-Pick C1 protects against atherosclerosis in mice via regulation of macrophage intracellular cholesterol trafficking. J Clin Investig 118(6):2281–2290. https://doi.org/10.1172/JCI32561

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank prof. Leon de Windt and Serve Olieslagers from Molecular Cardiology Department, CARIM, Maastricht, The Netherlands for their kind gift of pmirGLO vector. The authors thank Ms. Cristina Dobre (Lipidomics Department) for her skilful technical assistance.

Funding

This work was supported by the Romanian Academy and the Romanian National Authority for Scientific Research and Innovation, CNCS-UEFISCDI (Grant Number PN-II-RU-TE-2014-4-0290).

Author information

Authors and Affiliations

  1. Lipidomics Department, Institute of Cellular Biology and Pathology “Nicolae Simionescu” of the Romanian Academy, 8 B.P. Hasdeu Street, 050568, Bucharest, Romania

    Loredan S. Niculescu, Natalia Simionescu, Elena V. Fuior, Camelia S. Stancu, Mihaela G. Carnuta, Madalina D. Dulceanu, Mina Raileanu, Emanuel Dragan & Anca V. Sima

  2. Centre of Advanced Research in Bionanoconjugates and Biopolymers, “Petru Poni” Institute of Macromolecular Chemistry, Iasi, Romania

    Natalia Simionescu

Authors
  1. Loredan S. Niculescu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Natalia Simionescu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elena V. Fuior
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Camelia S. Stancu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mihaela G. Carnuta
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Madalina D. Dulceanu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mina Raileanu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Emanuel Dragan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Anca V. Sima
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Loredan S. Niculescu.

Ethics declarations

Conflict of interest

The authors have declared that no conflict of interest exists.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 742 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Niculescu, L.S., Simionescu, N., Fuior, E.V. et al. Inhibition of miR-486 and miR-92a decreases liver and plasma cholesterol levels by modulating lipid-related genes in hyperlipidemic hamsters. Mol Biol Rep 45, 497–509 (2018). https://doi.org/10.1007/s11033-018-4186-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11033-018-4186-8

Keywords

Search

Navigation