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Inflammation

, Volume 42, Issue 6, pp 1990–2002 | Cite as

Characterization of Circular RNA and microRNA Profiles in Septic Myocardial Depression: a Lipopolysaccharide-Induced Rat Septic Shock Model

  • Tie-Ning Zhang
  • Ni Yang
  • Julie E. Goodwin
  • Kali Mahrer
  • Da Li
  • Jing Xia
  • Ri Wen
  • Han Zhou
  • Tao Zhang
  • Wen-Liang Song
  • Chun-Feng LiuEmail author
Original Article

Abstract

Septic shock with heart dysfunction is common in intensive care units. However, the mechanism underlying myocardial depression is still unclear. Whether circular RNA (circRNA) or microRNA (miRNA) profiles differ between patients with and without myocardial depression is unknown. We generated a hypodynamic septic shock model induced by lipopolysaccharide (LPS) in adolescent rats. A total of 12 rats were utilized and heart tissue from each was collected. RNA sequencing was performed on left ventricular tissue. We focused on features of circRNAs and miRNAs, predicting their function by bioinformatic analysis and constructing circRNA-associated and miRNA-associated regulatory networks in heart tissue. We detected 851 circRNAs in heart samples, and 11 showed differential expression. A total of 639 annotated miRNAs and 91 novel miRNAs were explored including 78 showing differential expression between the two groups. We then constructed the most comprehensive circRNA-associated and miRNA-associated networks to explore their regulatory relationship in septic heart tissue, and demonstrated that different networks could potentially participate in and regulate the pathological process of sepsis. Furthermore, gene ontology term enrichment indicated miRNAs, and miRNA-mRNA networks could be associated with regulation and metabolic process, or influence cellular functions. The construction of regulator networks could improve the understanding of the basic molecular mechanisms underlying myocardial depression. It will be important for future investigations to ascertain the biological mechanisms present during the development of sepsis-induced myocardial depression to influence approaches to treatment.

KEY WORDS

circular RNA micro RNA sepsis regulatory network transcriptome 

Notes

Acknowledgments

T.N.Z., N.Y., and C.F.L. conceived and designed the study. T.N.Z., T.Z., and W.L.S. performed animal models and heart tissue collection. T.N.Z., N.Y., J.G., K.M., D.L., J.X., R.W., H.Z., and C.F.L performed RNA-seq data analysis and interpretation. T.N.Z., N.Y., J.G., K.M., and C.F.L. wrote the manuscript. All the authors have read and approved the final manuscript. T.N.Z. and N.Y. contributed equally to this work.

Funding

This study was funded by the National Natural Science Foundation of China (No. 81372039), the Natural Science Foundation of Liaoning Province (No. 2017225003, No. 2018108001), and the Science and Technology Foundation of Shenyang (No. F13-220-9-38).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary material

10753_2019_1060_MOESM1_ESM.pdf (225 kb)
ESM 1 (PDF 225 kb)
10753_2019_1060_MOESM2_ESM.pdf (6.4 mb)
Supplementary Figure 1 The density distribution of circRNAs in each chromosome of every heart sample. (PDF 6534 kb)
10753_2019_1060_MOESM3_ESM.pdf (2.9 mb)
Supplementary Figure 2 The length distribution of miRNAs in each heart sample. (PDF 2962 kb)
10753_2019_1060_MOESM4_ESM.pdf (10.9 mb)
Supplementary Figure 3 The density distribution of miRNAs in each chromosome of every heart sample. (PDF 11119 kb)
10753_2019_1060_MOESM5_ESM.pdf (6.8 mb)
Supplementary Figure 4 The first nucleotides bias of known miRNA in each heart sample. (PDF 6966 kb)
10753_2019_1060_MOESM6_ESM.pdf (5.4 mb)
Supplementary Figure 5 The first nucleotides bias of novel miRNA in each heart sample. (PDF 5539 kb)

References

  1. 1.
    Singer, M., C.S. Deutschman, C.W. Seymour, M. Shankar-Hari, D. Annane, M. Bauer, R. Bellomo, G.R. Bernard, J.D. Chiche, C.M. Coopersmith, R.S. Hotchkiss, M.M. Levy, J.C. Marshall, G.S. Martin, S.M. Opal, G.D. Rubenfeld, T. van der Poll, J.L. Vincent, and D.C. Angus. 2016. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 315: 801–810.CrossRefGoogle Scholar
  2. 2.
    Angus, D.C., and T. van der Poll. 2013. Severe sepsis and septic shock. The New England Journal of Medicine 369: 2063.CrossRefGoogle Scholar
  3. 3.
    Rhodes, A., L.E. Evans, W. Alhazzani, M.M. Levy, M. Antonelli, R. Ferrer, A. Kumar, J.E. Sevransky, C.L. Sprung, M.E. Nunnally, B. Rochwerg, G.D. Rubenfeld, D.C. Angus, D. Annane, R.J. Beale, G.J. Bellinghan, G.R. Bernard, J.D. Chiche, C. Coopersmith, D.P. De Backer, C.J. French, S. Fujishima, H. Gerlach, J.L. Hidalgo, S.M. Hollenberg, A.E. Jones, D.R. Karnad, R.M. Kleinpell, Y. Koh, T.C. Lisboa, F.R. Machado, J.J. Marini, J.C. Marshall, J.E. Mazuski, L.A. McIntyre, A.S. McLean, S. Mehta, R.P. Moreno, J. Myburgh, P. Navalesi, O. Nishida, T.M. Osborn, A. Perner, C.M. Plunkett, M. Ranieri, C.A. Schorr, M.A. Seckel, C.W. Seymour, L. Shieh, K.A. Shukri, S.Q. Simpson, M. Singer, B.T. Thompson, S.R. Townsend, T. Van der Poll, J.L. Vincent, W.J. Wiersinga, J.L. Zimmerman, and R.P. Dellinger. 2017. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive Care Medicine 43: 304–377.CrossRefGoogle Scholar
  4. 4.
    Angus, D.C., W.T. Linde-Zwirble, J. Lidicker, G. Clermont, J. Carcillo, and M.R. Pinsky. 2001. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Critical Care Medicine 29: 1303–1310.CrossRefGoogle Scholar
  5. 5.
    Raj, S., J.S. Killinger, J.A. Gonzalez, and L. Lopez. 2014. Myocardial dysfunction in pediatric septic shock. The Journal of Pediatrics 164: 72–77.CrossRefGoogle Scholar
  6. 6.
    Brierley, J., and M.J. Peters. 2008. Distinct hemodynamic patterns of septic shock at presentation to pediatric intensive care. Pediatrics 122: 752–759.CrossRefGoogle Scholar
  7. 7.
    Weiss, S.L., J.C. Fitzgerald, J. Pappachan, D. Wheeler, J.C. Jaramillo-Bustamante, A. Salloo, S.C. Singhi, S. Erickson, J.A. Roy, J.L. Bush, V.M. Nadkarni, and N.J. Thomas. 2015. Global epidemiology of pediatric severe sepsis: the sepsis prevalence, outcomes, and therapies study. American Journal of Respiratory and Critical Care Medicine 191: 1147–1157.CrossRefGoogle Scholar
  8. 8.
    Jeck, W.R., and N.E. Sharpless. 2014. Detecting and characterizing circular RNAs. Nature Biotechnology 32: 453–461.CrossRefGoogle Scholar
  9. 9.
    Chen, L.L., and L. Yang. 2015. Regulation of circRNA biogenesis. RNA Biology 12: 381–388.CrossRefGoogle Scholar
  10. 10.
    Memczak, S., M. Jens, A. Elefsinioti, F. Torti, J. Krueger, A. Rybak, L. Maier, S.D. Mackowiak, L.H. Gregersen, M. Munschauer, A. Loewer, U. Ziebold, M. Landthaler, C. Kocks, F. le Noble, and N. Rajewsky. 2013. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 495: 333–338.CrossRefGoogle Scholar
  11. 11.
    Salzman, J., R.E. Chen, M.N. Olsen, P.L. Wang, and P.O. Brown. 2013. Cell-type specific features of circular RNA expression. PLoS Genetics 9: e1003777.CrossRefGoogle Scholar
  12. 12.
    Hansen, T.B., T.I. Jensen, B.H. Clausen, J.B. Bramsen, B. Finsen, C.K. Damgaard, and J. Kjems. 2013. Natural RNA circles function as efficient microRNA sponges. Nature 495: 384–388.CrossRefGoogle Scholar
  13. 13.
    Ho, J., H. Chan, S.H. Wong, M.H. Wang, J. Yu, Z. Xiao, X. Liu, G. Choi, C.C. Leung, W.T. Wong, Z. Li, T. Gin, M.T. Chan, and W.K. Wu. 2016. The involvement of regulatory non-coding RNAs in sepsis: a systematic review. Critical Care 20: 383.CrossRefGoogle Scholar
  14. 14.
    Yang, N., X.L. Shi, B.L. Zhang, J. Rong, T.N. Zhang, W. Xu, and C.F. Liu. 2018. The trend of beta3-adrenergic receptor in the development of septic myocardial depression: a lipopolysaccharide-induced rat septic shock model. Cardiology 139: 234–244.CrossRefGoogle Scholar
  15. 15.
    Trapnell, C., B.A. Williams, G. Pertea, A. Mortazavi, G. Kwan, M.J. van Baren, S.L. Salzberg, B.J. Wold, and L. Pachter. 2010. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology 28: 511–515.CrossRefGoogle Scholar
  16. 16.
    Zhou, L., J. Chen, Z. Li, X. Li, X. Hu, Y. Huang, X. Zhao, C. Liang, Y. Wang, L. Sun, M. Shi, X. Xu, F. Shen, M. Chen, Z. Han, Z. Peng, Q. Zhai, J. Chen, Z. Zhang, R. Yang, J. Ye, Z. Guan, H. Yang, Y. Gui, J. Wang, Z. Cai, and X. Zhang. 2010. Integrated profiling of microRNAs and mRNAs: microRNAs located on Xq27.3 associate with clear cell renal cell carcinoma. Plos One 5: e15224.CrossRefGoogle Scholar
  17. 17.
    Young, M.D., M.J. Wakefield, G.K. Smyth, and A. Oshlack. 2010. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biology 11: R14.CrossRefGoogle Scholar
  18. 18.
    Kanehisa, M., M. Araki, S. Goto, M. Hattori, M. Hirakawa, M. Itoh, T. Katayama, S. Kawashima, S. Okuda, T. Tokimatsu, and Y. Yamanishi. 2008. KEGG for linking genomes to life and the environment. Nucleic Acids Re 36 (Database issue): D480–D484.Google Scholar
  19. 19.
    Langmead, B., C. Trapnell, M. Pop, and S.L. Salzberg. 2009. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology 10: R25.CrossRefGoogle Scholar
  20. 20.
    Wen, M., Y. Shen, S. Shi, and T. Tang. 2012. miREvo: an integrative microRNA evolutionary analysis platform for next-generation sequencing experiments. BMC Bioinformatics 13: 140.CrossRefGoogle Scholar
  21. 21.
    Friedlander, M.R., S.D. Mackowiak, N. Li, W. Chen, and N. Rajewsky. 2012. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Research 40: 37–52.CrossRefGoogle Scholar
  22. 22.
    Girardot, T., T. Rimmele, F. Venet, and G. Monneret. 2017. Apoptosis-induced lymphopenia in sepsis and other severe injuries. Apoptosis 22: 295–305.CrossRefGoogle Scholar
  23. 23.
    Qi, J., Y. Qiao, P. Wang, S. Li, W. Zhao, and C. Gao. 2012. microRNA-210 negatively regulates LPS-induced production of proinflammatory cytokines by targeting NF-kappaB1 in murine macrophages. FEBS Letters 586: 1201–1207.CrossRefGoogle Scholar
  24. 24.
    Ma, H., X. Wang, T. Ha, M. Gao, L. Liu, R. Wang, K. Yu, J.H. Kalbfleisch, R.L. Kao, D.L. Williams, and C. Li. 2016. MicroRNA-125b prevents cardiac dysfunction in polymicrobial sepsis by targeting TRAF6-mediated nuclear factor kappaB activation and p53-mediated apoptotic signaling. The Journal of Infectious Diseases 214: 1773–1783.CrossRefGoogle Scholar
  25. 25.
    Levy, M.M., A. Artigas, G.S. Phillips, A. Rhodes, R. Beale, T. Osborn, J.L. Vincent, S. Townsend, S. Lemeshow, and R.P. Dellinger. 2012. Outcomes of the surviving sepsis campaign in intensive care units in the USA and Europe: a prospective cohort study. The Lancet Infectious Diseases 12: 919–924.CrossRefGoogle Scholar
  26. 26.
    Romero-Bermejo, F.J., M. Ruiz-Bailen, J. Gil-Cebrian, and M.J. Huertos-Ranchal. 2011. Sepsis-induced cardiomyopathy. Current Cardiology Reviews 7: 163–183.CrossRefGoogle Scholar
  27. 27.
    Hochstadt, A., Y. Meroz, and G. Landesberg. 2011. Myocardial dysfunction in severe sepsis and septic shock: more questions than answers? Journal of Cardiothoracic and Vascular Anesthesia 25: 526–535.CrossRefGoogle Scholar
  28. 28.
    Sluijter, J.P., and P.A. Doevendans. 2016. Sepsis-associated cardiac dysfunction is controlled by small RNA molecules. Journal of Molecular and Cellular Cardiology 97: 67–69.CrossRefGoogle Scholar
  29. 29.
    Wang, X., W. Huang, Y. Yang, Y. Wang, T. Peng, J. Chang, C.C. Caldwell, B. Zingarelli, and G.C. Fan. 2014. Loss of duplexmiR-223 (5p and 3p) aggravates myocardial depression and mortality in polymicrobial sepsis. Biochimica et Biophysica Acta 1842: 701–711.CrossRefGoogle Scholar
  30. 30.
    Gao, M., X. Wang, X. Zhang, T. Ha, H. Ma, L. Liu, J.H. Kalbfleisch, X. Gao, R.L. Kao, D.L. Williams, and C. Li. 2015. Attenuation of cardiac dysfunction in polymicrobial sepsis by MicroRNA-146a is mediated via targeting of IRAK1 and TRAF6 expression. Journal of Immunology 195: 672–682.CrossRefGoogle Scholar
  31. 31.
    Wang, H., Y. Bei, S. Shen, P. Huang, J. Shi, J. Zhang, Q. Sun, Y. Chen, Y. Yang, T. Xu, X. Kong, and J. Xiao. 2016. miR-21-3p controls sepsis-associated cardiac dysfunction via regulating SORBS2. Journal of Molecular and Cellular Cardiology 94: 43–53.CrossRefGoogle Scholar
  32. 32.
    Zhang, S., D. Zhu, H. Li, H. Li, C. Feng, and W. Zhang. 2017. Characterization of circRNA-associated-ceRNA networks in a senescence-accelerated mouse prone 8 brain. Molecular Therapy 25: 2053–2061.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Tie-Ning Zhang
    • 1
  • Ni Yang
    • 1
  • Julie E. Goodwin
    • 2
  • Kali Mahrer
    • 2
  • Da Li
    • 3
  • Jing Xia
    • 1
  • Ri Wen
    • 1
  • Han Zhou
    • 2
  • Tao Zhang
    • 1
  • Wen-Liang Song
    • 1
  • Chun-Feng Liu
    • 1
    Email author
  1. 1.Department of Pediatrics, PICUShengjing Hospital of China Medical UniversityShenyang CityPeople’s Republic of China
  2. 2.Department of PediatricsYale University School of MedicineNew HavenUSA
  3. 3.Department of Obstetrics and GynecologyShengjing Hospital of China Medical UniversityShenyangChina

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