Design of Potential RNAi (miRNA and siRNA) Molecules for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Gene Silencing by Computational Method

  • Suza Mohammad Nur
  • Md. Anayet HasanEmail author
  • Mohammad Al Amin
  • Mehjabeen Hossain
  • Tahmina Sharmin
Original Research Article


The Middle East respiratory syndrome coronavirus (MERS-CoV) is a virus that manifests itself in viral infection with fever, cough, shortness of breath, renal failure and severe acute pneumonia, which often result in a fatal outcome. MERS-CoV has been shown to spread between people who are in close contact. Transmission from infected patients to healthcare personnel has also been observed and is irredeemable with present technology. Genetic studies on MERS-CoV have shown that ORF1ab encodes replicase polyproteins and play a foremost role in viral infection. Therefore, ORF1ab replicase polyprotein may be used as a suitable target for disease control. Viral activity can be controlled by RNA interference (RNAi) technology, a leading method for post transcriptional gene silencing in a sequence-specific manner. However, there is a genetic inconsistency in different viral isolates; it is a great challenge to design potential RNAi (miRNA and siRNA) molecules which can silence the respective target genes rather than any other viral gene simultaneously. In the current study, four effective miRNA and five siRNA molecules for silencing of nine different strains of MERS-CoV were rationally designed and corroborated using computational methods, which might lead to knockdown the activity of virus. siRNA and miRNA molecules were predicted against ORF1ab gene of different strains of MERS-CoV as effective candidate using computational methods. Thus, this method may provide an insight for the chemical synthesis of antiviral RNA molecule for the treatment of MERS-CoV, at genomic level.


MERS-CoV RNAi Antiviral Gene silencing Computational method 


  1. 1.
    Orbalenya AE, Enjuanes L, Ziebuhr J, Snijder EJ (2006) Nidovirales: evolving the largest RNA virus genome. Virus Res 117:17–37CrossRefGoogle Scholar
  2. 2.
    Wertheim JO, Chu DK, Peiris JS, Kosakovsky Pond SL, Poon LL (2013) A case for the ancient origin of coronaviruses. J Virol 87:7039–7045CrossRefGoogle Scholar
  3. 3.
    De Groot RJ, Bake SC, Baric RS, Brown CS, Drosten C, Enjuanes L (2013) Middle East respiratory syndrome coronavirus (MERS-CoV): announcement of the Coronavirus Study Group. J Virol 87:7790–7792CrossRefGoogle Scholar
  4. 4.
    Memish ZA, Zumla Al, Assiri A (2013) Middle East respiratory syndrome coronavirus infections in health care workers. N Engl J Med 369:884–886CrossRefGoogle Scholar
  5. 5.
    Zaki AM, Van Boheemen S, Bestebroer TM, Osterhaus AD, Fouchier RA (2012) Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia. N Engl J Med 367:1814–1820CrossRefGoogle Scholar
  6. 6.
    Hui DS, Alimuddin Z (2014) Advancing priority research on the Middle East respiratory syndrome coronavirus. J Infect Dis 209:173–176CrossRefGoogle Scholar
  7. 7.
    Guery B, Poissy J, Mansouf L, Sejourne C, Ettahar N, Lemaire X (2013) Clinical features and viral diagnosis of two cases of infection with Middle East respiratory syndrome coronavirus: a report of nosocomial transmission. Lancet 38:2265–2272CrossRefGoogle Scholar
  8. 8.
    Assiri A, McGeer A, Perl T, Price C, Al Rabeeah A, Cummings D, For the KSA MERS-CoV Investigation Team (2013) Hospital outbreak of Middle East respiratory syndrome coronavirus. N Engl J Med 369:407–416CrossRefGoogle Scholar
  9. 9.
    Annan A, Baldwin HJ, Corman VM, Klose SM, Owusu M, Nkrumah EE (2013) Human betacoronavirus 2c EMC/2012-related viruses in bats, Ghana and Europe. Emerg Infect Dis 19:456–459CrossRefGoogle Scholar
  10. 10.
    Eckerle I, Corman VM, Müller MA, Lenk M, Ulrich RG, Drosten C (2014) Replicative capacity of MERS coronavirus in livestock cell lines. Emerg Infect Dis 20:276–279CrossRefGoogle Scholar
  11. 11.
    Müller MA, Raj VS, Muth D, Meyer B, Kallies S, Smits SL (2012) Human coronavirus EMC does not require the SARS-coronavirus receptor and maintains broad replicative capability in mammalian cell lines. MBio 3:515-12CrossRefGoogle Scholar
  12. 12.
    Eric JZ, Alexander ES, Gorbalenya E (2000) Virus-encoded proteinases and proteolytic processing in the Nidovirales. J Gen Virol 81:853–879CrossRefGoogle Scholar
  13. 13.
    Van Bohemeen S, De Graaf M, Lauber C, Bestebroer TM, Raj VS, Zaki AM (2012) Genomic characterization of a newly discovered coronavirus associated with acute respiratory distress syndrome in humans. MBio 3:e00473-12CrossRefGoogle Scholar
  14. 14.
    Pasternak AO, Spaan WJ, Snijder EJ (2006) Nidovirus transcription: how to make sense…? J Gen Virol 87:1403–1421CrossRefGoogle Scholar
  15. 15.
    Sawicki SG, Sawicki DL, Siddell SG (2007) A contemporary view of coronavirus transcription. J Virol 81:20–29CrossRefGoogle Scholar
  16. 16.
    Sola I, Mateos-Gomez PA, Almazan F, Zu niga S, Enjuanes L (2011) RNA–RNA and RNA–protein interactions in coronavirus replication and transcription. RNA Biol 8:237–248CrossRefGoogle Scholar
  17. 17.
    Taxman DJ, Livingstone LR, Zhang J, Conti BJ, Iocca HA, Williams KL, Lich JD, Ting JP, Reed W (2006) Criteria for effective design, construction, and gene knockdown by shRNA vectors. BMC Biotechnol 24:6–7Google Scholar
  18. 18.
    Ui-Tei K, Naito Y, Nishi K, Juni A, Saigo K (2008) Thermodynamic stability and Watson–Crick base pairing in the seed duplex are major determinants of the efficiency of the siRNA-based off-target effect. Nucleic Acids Res 36:7100–7109CrossRefGoogle Scholar
  19. 19.
    Jackson AL, Linsley PS (2010) Recognizing and avoiding siRNA off-target effects for target identification and therapeutic application. Nat Rev Drug Discov 9:57–67CrossRefGoogle Scholar
  20. 20.
    Chan CY, Carmack CS, Long DD, Maliyekkel A, Shao Y, Roninson IB, Ding Y (2009) A structural interpretation of the effect of GC-content on efficiency of RNA interference. BMC Bioinform 10(Suppl):1–S33CrossRefGoogle Scholar
  21. 21.
    Filipowicz W, Bhattacharyya SN, Sonenberg N (2008) Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat Rev Genet 9:102–114CrossRefGoogle Scholar
  22. 22.
    Naito Y, Yoshimura J, Morishita S, Ui-Tei K (2009) siDirect 2.0: updated software for designing functional siRNA with reduced seed-dependent off-target effect. BMC Bioinform 10:392CrossRefGoogle Scholar
  23. 23.
    Ahmed F, Ansari HR, Raghava GPS (2009) Prediction of guide strand of microRNAs from its sequence and secondary structure. BMC Bioinform 10:105CrossRefGoogle Scholar
  24. 24.
    Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31:3406–3415CrossRefGoogle Scholar
  25. 25.
    Bernhart SH, Tafer H, Mückstein U, Flamm C, Stadler PF, Hofacker IL (2006) Partition function and base pairing probabilities of RNA heterodimers. Algorithms Mol Biol 16:1–3Google Scholar
  26. 26.
    Rehmsmeier M, Steffen P, Höchsmann M, Giegerich R (2006) Fast and effective prediction of microRNA/target duplexes. RNA 10:1507–1517CrossRefGoogle Scholar
  27. 27.
    Markham NR, Zuker M (2005) DINAMelt web server for nucleic acid melting prediction. Nucleic Acids Res 33:577–581CrossRefGoogle Scholar
  28. 28.
    Bret SE, Harris HS, Bowers SC, Rossi JJ (2005) siRNA target site secondary structure predictions using local stable substructures. Nucleic Acid Res 33:e30CrossRefGoogle Scholar
  29. 29.
    Liu Y, Chang Y, Zhang C, Wei Q, Chen J, Chen H, Xu D (2013) Influence of mRNA features on siRNA interference efficacy. J Bioinform Comput Biol 11:1341004CrossRefGoogle Scholar
  30. 30.
    Hajiaghayi M, Condon A, Hoos HH (2012) Analysis of energy-based algorithms for RNA secondary structure prediction. BMC Bioinform 13:22CrossRefGoogle Scholar
  31. 31.
    Ding Y, han CY, Lawrence CE (2005) RNA secondary structure prediction by centroids in a Boltzmann weighted ensemble. RNA 11:1157–1166CrossRefGoogle Scholar
  32. 32.
    Mathews DH (2005) Predicting a set of minimal free energy RNA secondary structures common to two sequences. Bioinformatics 21:2246–2253CrossRefGoogle Scholar
  33. 33.
    Muckstein U, Tafer H, Hackermuller J, Bernhart SB, Stadler F, Hofacker IL (2006) Thermodynamics of RNA–RNA binding. Bioinformatics 22:1177–1182CrossRefGoogle Scholar
  34. 34.
    Bohula EA, Salisbury AJ, Sohail M, Playford MP, Riedemann J, Southern EM, Macaulay VM (2003) The efficacy of small interfering RNAs targeted to the type 1 insulin-like growth factor receptor (IGF1R) is influenced by secondary structure in the IGF1R transcript. J Biol Chem 278:15991–15997CrossRefGoogle Scholar
  35. 35.
    Nur SM, Amin MA, Alam R, Hasan MA, Hossain MA, Mannan A (2013) An In silico approach to design potential siRNA molecules for ICP22 (US1) gene silencing of different strains of human herpes simplex 1. J Young Pharm 5:46–49CrossRefGoogle Scholar
  36. 36.
    Vickers TA, Wyatt JR, Freier SM (2000) Effects of RNA secondary structure on cellular antisense activity. Nucleic Acids Res 28:1340–1347CrossRefGoogle Scholar
  37. 37.
    Bryan K, Terrile M, Bray IM, Domingo-Fernandéz R, Watters KM, Koster J, Versteeg R, Stallings RL (2014) Discovery and visualization of miRNA–mRNA functional modules within integrated data using bicluster analysis. Nucleic Acids Res 42:e17CrossRefGoogle Scholar
  38. 38.
    Stahlhut C, Slack FJ (2013) MicroRNAs and the cancer phenotype: profiling, signatures and clinical implications. Genome Med 5:111CrossRefGoogle Scholar
  39. 39.
    Harada M, Luo X, Murohara T, Yang B, Dobrev D, Nattel S (2014) MicroRNA regulation and cardiac calcium signaling: role in cardiac disease and therapeutic potential. Circ Res 114:689–705CrossRefGoogle Scholar

Copyright information

© International Association of Scientists in the Interdisciplinary Areas and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Suza Mohammad Nur
    • 1
  • Md. Anayet Hasan
    • 1
    Email author
  • Mohammad Al Amin
    • 1
  • Mehjabeen Hossain
    • 1
  • Tahmina Sharmin
    • 2
  1. 1.Department of Genetic Engineering and Biotechnology, Faculty of Biological SciencesUniversity of ChittagongChittagongBangladesh
  2. 2.Department of Biotechnology and Genetic EngineeringMawlana Bhashani Science and Technology UniversityTangailBangladesh

Personalised recommendations