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Molecular biology reports
Oct, 2019;46 (5): 5511-5516.

Identification of novel rhesus macaque microRNAs from naïve whole blood.

Mary S Lopez, Jeanette M Metzger, Marina E Emborg
Author Information
  1. Mary S Lopez: Cellular and Molecular Pathology Graduate Program, University of Wisconsin-Madison, 1685 Highland Ave., Madison, WI, 53705, USA. ORCID
  2. Jeanette M Metzger: Cellular and Molecular Pathology Graduate Program, University of Wisconsin-Madison, 1685 Highland Ave., Madison, WI, 53705, USA. ORCID
  3. Marina E Emborg: Cellular and Molecular Pathology Graduate Program, University of Wisconsin-Madison, 1685 Highland Ave., Madison, WI, 53705, USA. emborg@primate.wisc.edu. ORCID
PMID: 31154603 DOI: 10.1007/s11033-019-04891-8

Abstract

MicroRNAs (miRNAs) are emerging as novel molecular tools for diagnosing and treating diseases. Rhesus monkeys (Macaca mulatta) are the most widely used nonhuman primate species for biomedical studies, yet only 912 mature miRNAs have been identified in this species compared to 2654 in humans and 1978 in mice. The aim of this project was to help bridge that gap in knowledge by evaluating circulating miRNA in naïve rhesus monkeys and comparing results with currently available databases in different species in order to identify novel, mature miRNAs. Total RNA was isolated from whole blood of ten healthy, adult rhesus macaques. After performing next generation sequencing (NGS), 475 novel, mature miRNAs were identified in rhesus macaques for the first time; of those, 423 were identified for the first time in any species. The most abundantly expressed novel rhesus macaque miRNA, hsa-miR-744-5p, has previously been described in humans. Database assessment of hsa-miR-744-5p potential gene targets showed that while the gene targets showed > 90% sequence similarity between rhesus and humans, many did not share the same consensus sequences. The identification of 475 novel miRNAs in the blood of rhesus macaque reflects the complexity and variety of miRNAs across species. Further NGS studies are needed to reveal novel miRNA that will inform on species-, tissue-, and condition-specific miRNAs.

Keywords

MicroRNA Monkeys Next generation sequencing Non-coding RNA Rhesus

References

  1. 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(3):228–234. https://doi.org/10.1038/ncb0309-228 [DOI: 10.1038/ncb0309-228]
  2. Macfarlane LA, Murphy PR (2010) MicroRNA: biogenesis, function and role in cancer. Curr Genomics 11(7):537–561. https://doi.org/10.2174/138920210793175895 [DOI: 10.2174/138920210793175895]
  3. Quiat D, Olson EN (2013) MicroRNAs in cardiovascular disease: from pathogenesis to prevention and treatment. J Clin Investig 123(1):11–18. https://doi.org/10.1172/jci62876 [DOI: 10.1172/jci62876]
  4. Li G, Morris-Blanco KC, Lopez MS, Yang T, Zhao H, Vemuganti R, Luo Y (2018) Impact of microRNAs on ischemic stroke: from pre- to post-disease. Prog Neurobiol 163–164:59–78. https://doi.org/10.1016/j.pneurobio.2017.08.002 [DOI: 10.1016/j.pneurobio.2017.08.002]
  5. Luna JM, Scheel TK, Danino T, Shaw KS, Mele A, Fak JJ, Nishiuchi E, Takacs CN, Catanese MT, de Jong YP, Jacobson IM, Rice CM, Darnell RB (2015) Hepatitis C virus RNA functionally sequesters miR-122. Cell 160(6):1099–1110. https://doi.org/10.1016/j.cell.2015.02.025 [DOI: 10.1016/j.cell.2015.02.025]
  6. Chakraborty C, Sharma AR, Sharma G, Doss CGP, Lee SS (2017) Therapeutic miRNA and siRNA: moving from bench to clinic as next generation medicine. Mol Ther Nucleic Acids 8:132–143. https://doi.org/10.1016/j.omtn.2017.06.005 [DOI: 10.1016/j.omtn.2017.06.005]
  7. Qu K, Zhang X, Lin T, Liu T, Wang Z, Liu S, Zhou L, Wei J, Chang H, Li K, Wang Z, Liu C, Wu Z (2017) Circulating miRNA-21-5p as a diagnostic biomarker for pancreatic cancer: evidence from comprehensive miRNA expression profiling analysis and clinical validation. Sci Rep 7(1):1692. https://doi.org/10.1038/s41598-017-01904-z [DOI: 10.1038/s41598-017-01904-z]
  8. Guo X, Lv X, Lv X, Ma Y, Chen L, Chen Y (2017) Circulating miR-21 serves as a serum biomarker for hepatocellular carcinoma and correlated with distant metastasis. Oncotarget 8(27):44050–44058. https://doi.org/10.18632/oncotarget.17211 [DOI: 10.18632/oncotarget.17211]
  9. Peng Q, Zhang X, Min M, Zou L, Shen P, Zhu Y (2017) The clinical role of microRNA-21 as a promising biomarker in the diagnosis and prognosis of colorectal cancer: a systematic review and meta-analysis. Oncotarget 8(27):44893–44909. https://doi.org/10.18632/oncotarget.16488 [DOI: 10.18632/oncotarget.16488]
  10. Phillips KA, Bales KL, Capitanio JP, Conley A, Czoty PW, t Hart BA, Hopkins WD, Hu SL, Miller LA, Nader MA, Nathanielsz PW, Rogers J, Shively CA, Voytko ML (2014) Why primate models matter. Am J Primatol 76(9):801–827. https://doi.org/10.1002/ajp.22281 [DOI: 10.1002/ajp.22281]
  11. Favre G, Banta Lavenex P, Lavenex P (2012) miRNA regulation of gene expression: a predictive bioinformatics analysis in the postnatally developing monkey hippocampus. PLoS ONE 7(8):e43435. https://doi.org/10.1371/journal.pone.0043435 [DOI: 10.1371/journal.pone.0043435]
  12. Duy J, Koehler JW, Honko AN, Schoepp RJ, Wauquier N, Gonzalez JP, Pitt ML, Mucker EM, Johnson JC, O’Hearn A, Bangura J, Coomber M, Minogue TD (2016) Circulating microRNA profiles of Ebola virus infection. Sci Rep 6:24496. https://doi.org/10.1038/srep24496 [DOI: 10.1038/srep24496]
  13. Chandra LC, Kumar V, Torben W, Vande Stouwe C, Winsauer P, Amedee A, Molina PE, Mohan M (2015) Chronic administration of Delta9-tetrahydrocannabinol induces intestinal anti-inflammatory microRNA expression during acute simian immunodeficiency virus infection of rhesus macaques. J Virol 89(2):1168–1181. https://doi.org/10.1128/JVI.01754-14 [DOI: 10.1128/JVI.01754-14]
  14. Wu Y, Wei B, Liu H, Li T, Rayner S (2011) MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences. BMC Bioinformatics 12(1):107. https://doi.org/10.1186/1471-2105-12-107 [DOI: 10.1186/1471-2105-12-107]
  15. Kozomara A, Griffiths-Jones S (2014) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42:D68–D73. https://doi.org/10.1093/nar/gkt1181 [DOI: 10.1093/nar/gkt1181]
  16. Agarwal V, Bell GW, Nam JW, Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. eLife. https://doi.org/10.7554/eLife.05005 [DOI: 10.7554/eLife.05005]
  17. Wong N, Wang X (2015) miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res 43:D146–D152. https://doi.org/10.1093/nar/gku1104 [DOI: 10.1093/nar/gku1104]
  18. Boratyn GM, Camacho C, Cooper PS, Coulouris G, Fong A, Ma N, Madden TL, Matten WT, McGinnis SD, Merezhuk Y, Raytselis Y, Sayers EW, Tao T, Ye J, Zaretskaya I (2013) BLAST: a more efficient report with usability improvements. Nucleic Acids Res 41:W29–W33. https://doi.org/10.1093/nar/gkt282 [DOI: 10.1093/nar/gkt282]
  19. Higashi S, Fournier C, Gautier C, Gaspin C, Sagot MF (2015) Mirinho: an efficient and general plant and animal pre-miRNA predictor for genomic and deep sequencing data. BMC Bioinformatics 16:179. https://doi.org/10.1186/s12859-015-0594-0 [DOI: 10.1186/s12859-015-0594-0]
  20. Wang Y, Ru J, Jiang Y, Zhang J (2019) Adaboost-SVM-based probability algorithm for the prediction of all mature miRNA sites based on structured-sequence features. Sci Rep 9(1):1521. https://doi.org/10.1038/s41598-018-38048-7 [DOI: 10.1038/s41598-018-38048-7]
  21. Mercken EM, Majounie E, Ding J, Guo R, Kim J, Bernier M, Mattison J, Cookson MR, Gorospe M, de Cabo R, Abdelmohsen K (2013) Age-associated miRNA alterations in skeletal muscle from rhesus monkeys reversed by caloric restriction. Aging 5(9):692–703. https://doi.org/10.18632/aging.100598 [DOI: 10.18632/aging.100598]
  22. Rivera A, Barr T, Rais M, Engelmann F, Messaoudi I (2016) microRNAs regulate host immune response and pathogenesis during influenza infection in rhesus macaques. Viral Immunol 29(4):212–227. https://doi.org/10.1089/vim.2015.0074 [DOI: 10.1089/vim.2015.0074]
  23. Hu Y, Song J, Liu L, Li J, Tang B, Wang J, Zhang X, Zhang Y, Wang L, Liao Y, He Z, Li Q (2016) Different microRNA alterations contribute to diverse outcomes following EV71 and CA16 infections: insights from high-throughput sequencing in rhesus monkey peripheral blood mononuclear cells. Int J Biochem Cell Biol 81:20–31. https://doi.org/10.1016/j.biocel.2016.10.011 [DOI: 10.1016/j.biocel.2016.10.011]
  24. Barr T, Girke T, Sureshchandra S, Nguyen C, Grant K, Messaoudi I (2016) Alcohol consumption modulates host defense in rhesus macaques by altering gene expression in circulating leukocytes. J Immunol 196(1):182–195. https://doi.org/10.4049/jimmunol.1501527 [DOI: 10.4049/jimmunol.1501527]

Grants

  1. P51OD011106/NIH HHS
  2. R21 NS084158/NINDS NIH HHS
  3. F31HL136047/NHLBI NIH HHS
  4. F31 HL136047/NHLBI NIH HHS
  5. P51 OD011106/NIH HHS

MeSH Term

Animals
Cell-Free Nucleic Acids
Gene Expression Profiling
High-Throughput Nucleotide Sequencing
Macaca mulatta
MicroRNAs

Chemicals

Cell-Free Nucleic Acids
MicroRNAs
Journal Article
Article has an altmetric score of 1

Word Cloud

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