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Integrated exosomal miRNA and transcriptome analysis of brain microvascular endothelial cells in spontaneously hypertensive rats

Hypertension Research volume 43, pages 90–98 (2020)Cite this article

Abstract

Endothelial cells, which regulate arterial stiffness via endothelial-derived substances, are independently and strongly associated with hypertension. However, the exact roles of exosome miRNAs from brain endothelial cells in the development of hypertension are still not fully explored. Here, we investigated the miRNA functions systematically by examining both exosomal small RNA and mRNA of endothelial cells in Wistar Kyoto (WKY) rats versus spontaneously hypertensive rats (SHRs). Our findings revealed that miRNAs, representing ~60–70%, account for the majority of small RNAs. Moreover, we found 159 novel miRNAs in total from the unannotated reads across the diverse samples. Afterwards, 76 differentially expressed miRNAs (37 upregulated, 39 downregulated) and 1709 differentially expressed mRNAs (775 upregulated, 934 downregulated) were identified between SHRs and WKY rats, respectively. Finally, 647 genes targeted by 36 miRNAs came to our attention via identification of the target genes of those abnormal miRNAs. The differentially expressed target genes induced by miRNA changes were mapped to a number of genes involved in various gene functions and pathways. These changes lead to dysregulation of angiogenesis, axonogenesis, neuron-to-neuron synapses, focal adhesion, axon guidance, cell adhesion molecules (CAMs), adherens junction, and ECM-receptor interaction pathways. Together, our study revealed that the miRNAs are changed and contribute to the dysregulated functions and pathways of their target genes and provided more insights into their regulation mechanisms during mammalian hypertension development.

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References

  1. Cohuet G, Struijker-Boudier H. Mechanisms of target organ damage caused by hypertension: therapeutic potential. Pharmcol Ther. 2006;111:81–98.

    Article  CAS  Google Scholar 

  2. Serne EH, de Jongh RT, Eringa EC, RG IJ, Stehouwer CD. Microvascular dysfunction: a potential pathophysiological role in the metabolic syndrome. Hypertension. 2007;50:204–11.

    Article  CAS  PubMed  Google Scholar 

  3. Wilkinson IB, Franklin SS, Cockcroft JR. Nitric oxide and the regulation of large artery stiffness: from physiology to pharmacology. Hypertension. 2004;44:112–6.

    Article  CAS  PubMed  Google Scholar 

  4. Targonski PV, Bonetti PO, Pumper GM, Higano ST, Holmes DR Jr., Lerman A. Coronary endothelial dysfunction is associated with an increased risk of cerebrovascular events. Circulation. 2003;107:2805–9.

    Article  PubMed  Google Scholar 

  5. Yuan X, Wu Q, Liu X, Zhang H, Xiu R. Transcriptomic profile analysis of brain microvascular pericytes in spontaneously hypertensive rats by RNA-Seq. Am J Transl Res. 2018;10:2372–86.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Pietri P, Vyssoulis G, Vlachopoulos C, Zervoudaki A, Gialernios T, Aznaouridis K, et al. Relationship between low-grade inflammation and arterial stiffness in patients with essential hypertension. J Hypertens. 2006;24:2231–8.

    Article  CAS  PubMed  Google Scholar 

  7. Recio-Mayoral A, Rimoldi OE, Camici PG, Kaski JC. Inflammation and microvascular dysfunction in cardiac syndrome X patients without conventional risk factors for coronary artery disease. JACC Cardiovasc Imaging. 2013;6:660–7.

    Article  PubMed  Google Scholar 

  8. Booth AD, Wallace S, McEniery CM, Yasmin, Brown J, Jayne DR, et al. Inflammation and arterial stiffness in systemic vasculitis: a model of vascular inflammation. Arthritis Rheum. 2004;50:581–8.

    Article  CAS  PubMed  Google Scholar 

  9. Fichtlscherer S, Rosenberger G, Walter DH, Breuer S, Dimmeler S, Zeiher AM. Elevated C-reactive protein levels and impaired endothelial vasoreactivity in patients with coronary artery disease. Circulation. 2000;102:1000–6.

    Article  CAS  PubMed  Google Scholar 

  10. Greenberg JI, Shields DJ, Barillas SG, Acevedo LM, Murphy E, Huang J, et al. A role for VEGF as a negative regulator of pericyte function and vessel maturation. Nature. 2008;456:809–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Stapor PC, Sweat RS, Dashti DC, Betancourt AM, Murfee WL. Pericyte dynamics during angiogenesis: new insights from new identities. J Vasc Res. 2014;51:163–74.

    Article  PubMed  Google Scholar 

  12. Garcia-Contreras M, Shah SH, Tamayo A, Robbins PD, Golberg RB, Mendez AJ, et al. Plasma-derived exosome characterization reveals a distinct microRNA signature in long duration Type 1 diabetes. Sci Rep. 2017;7:5998.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Ebrahimkhani S, Vafaee F, Young PE, Hur SSJ, Hawke S, Devenney E, et al. Exosomal microRNA signatures in multiple sclerosis reflect disease status. Sci Rep. 2017;7:14293.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Alipoor SD, Tabarsi P, Varahram M, Movassaghi M, Dizaji MK, Folkerts G, et al. Serum exosomal miRNAs are associated with active pulmonary tuberculosis. Dis Markers. 2019;2019:1907426.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Sugimachi K, Matsumura T, Hirata H, Uchi R, Ueda M, Ueo H, et al. Identification of a bona fide microRNA biomarker in serum exosomes that predicts hepatocellular carcinoma recurrence after liver transplantation. Br J Cancer. 2015;112:532–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hornick NI, Huan J, Doron B, Goloviznina NA, Lapidus J, Chang BH, et al. Serum exosome microRNA as a minimally-invasive early biomarker of AML. Sci Rep. 2015;5:11295.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Bhome R, Del Vecchio F, Lee GH, Bullock MD, Primrose JN, Sayan AE, et al. Exosomal microRNAs (exomiRs): small molecules with a big role in cancer. Cancer Lett. 2018;420:228–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. van Balkom BW, de Jong OG, Smits M, Brummelman J, den Ouden K, de Bree PM, et al. Endothelial cells require miR-214 to secrete exosomes that suppress senescence and induce angiogenesis in human and mouse endothelial cells. Blood. 2013;121:3997–4006. S3991-3915.

    Article  PubMed  CAS  Google Scholar 

  19. Bovy N, Blomme B, Freres P, Dederen S, Nivelles O, Lion M, et al. Endothelial exosomes contribute to the antitumor response during breast cancer neoadjuvant chemotherapy via microRNA transfer. Oncotarget. 2015;6:10253–66.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zhang J, Li S, Li L, Li M, Guo C, Yao J, et al. Exosome and exosomal microRNA: trafficking, sorting, and function. Genomics Proteomics Bioinformatics. 2015;13:17–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Nakagawa S, Deli MA, Nakao S, Honda M, Hayashi K, Nakaoke R, et al. Pericytes from brain microvessels strengthen the barrier integrity in primary cultures of rat brain endothelial cells. Cell Mol Neurobiol. 2007;27:687–94.

    Article  CAS  PubMed  Google Scholar 

  22. Wu Q, Jing Y, Yuan X, Li B, Wang B, Liu M, et al. The distinct abilities of tube-formation and migration between brain and spinal cord microvascular pericytes in rats. Clin Hemorheol Microcirc. 2015;60:231–40.

    Article  CAS  PubMed  Google Scholar 

  23. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.

    Article  CAS  PubMed  Google Scholar 

  24. Griffiths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR. Rfam: an RNA family database. Nucleic Acids Res. 2003;31:439–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008;36:D154–8.

    Article  CAS  PubMed  Google Scholar 

  26. Friedlander MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012;40:37–52.

    Article  PubMed  CAS  Google Scholar 

  27. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12:357–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS. MicroRNA targets in Drosophila. Genome Biol. 2003;5:R1.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L. Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol. 2011;12:R22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28:511–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Anders S, Pyl PT, Huber W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.

    Article  CAS  PubMed  Google Scholar 

  34. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. The Gene Ontology C. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res. 2017;45:D331–8.

  36. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45:D353–61.

    Article  CAS  PubMed  Google Scholar 

  37. Spijkers LJ, van den Akker RF, Janssen BJ, Debets JJ, De Mey JG, Stroes ES, et al. Hypertension is associated with marked alterations in sphingolipid biology: a potential role for ceramide. PLoS ONE. 2011;6:e21817.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Hannafon BN, Trigoso YD, Calloway CL, Zhao YD, Lum DH, Welm AL, et al. Plasma exosome microRNAs are indicative of breast cancer. Breast Cancer Res. 2016;18:90.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Liu X, Fortin K, Mourelatos Z. MicroRNAs: biogenesis and molecular functions. Brain Pathol. 2008;18:113–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Liu X, Yuan W, Yang L, Li J, Cai J. miRNA profiling of exosomes from spontaneous hypertensive rats using next-generation sequencing. J Cardiovasc Transl Res. 2019;12:75–83.

    Article  PubMed  Google Scholar 

  41. Santovito D, Mandolini C, Marcantonio P, De Nardis V, Bucci M, Paganelli C, et al. Overexpression of microRNA-145 in atherosclerotic plaques from hypertensive patients. Expert Opin Ther Targets. 2013;17:217–23.

    Article  CAS  PubMed  Google Scholar 

  42. Zagrean AM, Hermann DM, Opris I, Zagrean L, Popa-Wagner A. Multicellular crosstalk between exosomes and the neurovascular unit after cerebral ischemia. Therapeutic implications. Front Neurosci. 2018;12:811.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This study was supported by the innovation fund of the Chinese Academy of Medical Sciences and Peking Union Medical College (Nos. 3332014006 and 3332015123), the CAMS Initiative for Innovative Medicine (CAMS-I2M) (No. 2016-I2M-3-006), and the National Natural Science Foundation of China (81801433).

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  1. These authors contributed equally: Qingbin Wu, Xiaochen Yuan, Bingwei Li

Authors and Affiliations

  1. Key Laboratory for Microcirculation, Ministry of Health; Institute of Microcirculation, Chinese Academy of Medical Sciences & Pecking Union Medical College, Beijing, China

    Qingbin Wu, Xiaochen Yuan, Bingwei Li, Honggang Zhang & Ruijuan Xiu

  2. Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China

    Ruiqin Han

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  1. Qingbin Wu
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Correspondence to Xiaochen Yuan or Honggang Zhang.

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Wu, Q., Yuan, X., Li, B. et al. Integrated exosomal miRNA and transcriptome analysis of brain microvascular endothelial cells in spontaneously hypertensive rats. Hypertens Res 43, 90–98 (2020). https://doi.org/10.1038/s41440-019-0345-0

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