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Hepatitis C virus genotype 3A in a population of injecting drug users in Montenegro: Bayesian and evolutionary analysis

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Abstract

Few reports are available on HCV molecular epidemiology among IDUs in Eastern Europe, and none in Montenegro. The aim of this study was to investigate the HCV genotype distribution in Montenegro among IDUs and to perform Bayesian and evolutionary analysis of the most prevalent HCV genotype circulating in this population. Sixty-four HCV-positive IDUs in Montenegro were enrolled between 2013 and 2014, and the NS5B gene was sequenced. The Bayesian analysis showed that the most prevalent subtype was HCV-3a. Phylogenetic data showed that HCV-3a reached Montenegro in the late 1990s, causing an epidemic that exponentially grew between the 1995 and 2005. In the dated tree, four different entries, from 1990 (clade D), 1994 (clade A) to 1999 (clade B) and 2001 (clade C), were identified. In the NS5B protein model, the amino acids variations were located mainly in the palm domain, which contains most of the conserved structural elements of the active site. This study provides an analysis of the virus transmission pathway and the evolution of HCV genotype 3a among IDUs in Montenegro. These data could represent the basis for further strategies aimed to improve disease management and surveillance program development in high-risk populations.

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References

  1. World Health Organization (2009) The growing threats of hepatitis B and C in the Eastern Mediterranean region: a call for action. Technical paper EM/RC56/3

  2. Bernini F, Ebranati E, De Maddalena C, Shkjezi R, Milazzo L, Lo Presti A, Ciccozzi M, Galli M, Zehender G (2011) Within-host dynamics of the hepatitis C virus quasispecies population in HIV-1/HCV coinfected patients. PLoS One 6:e16551

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Simmonds P, Bukh J, Combet C, Deléage G, Enomoto N, Feinstone S, Halfon P, Inchauspé G, Kuiken C, Maertens G, Mizokami M, Murphy DG, Okamoto H, Pawlotsky JM, Penin F, Sablon E, Shin-I T, Stuyver LJ, Thiel HJ, Viazov S, Weiner AJ, Widell A (2005) Consensus proposals for a unified system of nomenclature of hepatitis C virus genotypes. Hepatology 42:962–973

    Article  CAS  PubMed  Google Scholar 

  4. Pybus OG, Charleston MA, Gupta S, Rambaut A, Holmes EC, Harvey PH (2001) The epidemic behavior of the hepatitis C virus. Science 292:2323–2325

    Article  CAS  PubMed  Google Scholar 

  5. Hauri AM, Armstrong GL, Hutin YJ (2004) The global burden of disease attributable to contaminated injections given in health care settings. Int J STD AIDS 15:7–16

    Article  PubMed  Google Scholar 

  6. Pybus OG, Markov PV, Wu A, Tatem AJ (2007) Investigating the endemic transmission of the hepatitis C virus. Int J Parasitol 37:839–849

    Article  CAS  PubMed  Google Scholar 

  7. Ciccozzi M, Zehender G, Cento V, Lo Presti A, Teoharov P, Pavlov I, Bogdanova V, Perno CF, Ciotti M (2011) Molecular analysis of hepatitis C virus infection in Bulgarian injecting drug users. J Med Virol 83:1565–1570

    Article  CAS  PubMed  Google Scholar 

  8. Zehender G, Sorrentino C, Lai A, Ebranati E, Gabanelli E, Lo Presti A, Vujoševic D, Lauševic D, Terzić D, Shkjezi R, Bino S, Vratnica Z, Mugosa B, Galli M, Ciccozzi M (2013) Reconstruction of the evolutionary dynamics of hepatitis C virus subtypes in Montenegro and the Balkan region. Infect Genet Evol 17:223–230

    Article  PubMed  Google Scholar 

  9. Judd A, Rhodes T, Johnston LG, Platt L, Andjelkovic V, Simić D, Mugosa B, Simić M, Zerjav S, Parry RP, Parry JV (2009) Improving survey methods in sero-epidemiological studies of injecting drug users: a case example of two cross sectional surveys in Serbia and Montenegro. BMC Infect Dis 9:14

    Article  PubMed  PubMed Central  Google Scholar 

  10. Stamenkovic G, Zerjav S, Velickovic ZM, Krtolica K, Samardzija VL, Jemuovic L, Nozic D, Dimitrijevic B (2000) Distribution of HCV genotypes among risk groups in Serbia. Eur J Epidemiol 16:949–954

    Article  CAS  PubMed  Google Scholar 

  11. Svirtlih N, Delic D, Simonovic J, Jevtovic D, Dokic L, Gvozdenovic E, Boricic I, Terzic D, Pavic S, Neskovic G, Zerjav S, Urban V (2007) Hepatitis C virus genotypes in Serbia and Montenegro: the prevalence and clinical significance. World J Gastroenterol 13:355–360

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kalinina O, Norder H, Vetrov T, Zhdanov K, Barzunova M, Plotnikova V, Mukomolov S, Magnius LO (2001) Shift in predominating subtype of HCV from 1b to 3a in St. Petersburg mediated by increase in injecting drug use. J Med Virol 65:517–524

    Article  CAS  PubMed  Google Scholar 

  13. Krekulova L, Rehak V, Madrigal N, Johnson M, Killoran P, Riley LW (2001) Genotypic and epidemiologic characteristics of hepatitis C virus infections among recent injection drug user and nonuser populations. Clin Infect Dis 33:1435–1438

    Article  CAS  PubMed  Google Scholar 

  14. Lu L, Nakano T, He Y, Fu Y, Hagedorn CH, Robertson BH (2005) Hepatitis C virus genotype distribution in China: predominance of closely related subtype 1b isolates and existence of new genotype 6 variants. J Med Virol 75:538–549

    Article  CAS  PubMed  Google Scholar 

  15. Thompson JD, Gibson TJ, Plewniak F, Jeanmougin F, Higgins DG (1997) The CLUSTAL-X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Res 2:4876–4882

    Article  Google Scholar 

  16. Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Posada D, Buckley TR (2004) Model selection and model averaging in phylogenetics: advantages of Akaike information criterion and Bayesian approaches over likelihood ratio tests. Syst Biol 53:793–808

    Article  PubMed  Google Scholar 

  18. Lanave C, Preparata G, Saccone C, Serio G (1984) A new method for calculating evolutionary substitution rates. J Mol Evol 20:86–93

    Article  CAS  PubMed  Google Scholar 

  19. Huelsenbeck JP, Ronquist F (2001) MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17:754–755

    Article  CAS  PubMed  Google Scholar 

  20. Schmidt HA, Strimmer K, Vingron M, von Haeseler A (2002) TREE-PUZZLE: maximum likelihood phylogenetic analysis using quartets and parallel computing. Bioinformatics 18:502–504

    Article  CAS  PubMed  Google Scholar 

  21. Drummond AJ, Rambaut A (2007) BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol 7:214

    Article  PubMed  PubMed Central  Google Scholar 

  22. Drummond AJ, Rambaut A, Shapiro B, Pybus OG (2005) Bayesian coalescent inference of past population dynamics from molecular sequences. Mol Biol Evol 22:1185–1192

    Article  CAS  PubMed  Google Scholar 

  23. Kass RE, Raftery AE (1995) Bayes factors. J Am Stat Assoc 90:773–795

    Article  Google Scholar 

  24. Yang Z (1997) PAML: a program package for phylogenetic analysis by maximum likelihood. Comput Appl Biosci 13:555–556

    CAS  PubMed  Google Scholar 

  25. Yang Z, Nielsen R (2000) Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models. Mol Biol Evol 17:32–43

    Article  CAS  PubMed  Google Scholar 

  26. Nielsen R, Yang Z (1998) Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene. Genetics 148:929–936

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Pond SL, Frost SD, Muse SV (2005) HyPhy: hypothesis testing using phylogenies. Bioinformatics 21:676–679

    Article  CAS  PubMed  Google Scholar 

  28. Kosakovsky Pond SL, Frost SD (2005) Not so different after all: a comparison of methods for detecting amino acid sites under selection. Mol Biol Evol 22:1208–1222

    Article  PubMed  Google Scholar 

  29. Okonechnikov K, Golosova O, Fursov M, UGENE team, (2012) Unipro UGENE: a unified bioinformatics toolkit. Bioinformatics 28:1166–1167

    Article  CAS  PubMed  Google Scholar 

  30. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, Soding J, Thompson JD, Higgins DG (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7:539

    Article  PubMed  PubMed Central  Google Scholar 

  31. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410

    Article  CAS  PubMed  Google Scholar 

  32. Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234:779–815

    Article  CAS  PubMed  Google Scholar 

  33. Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 35:W407–W410

    Article  PubMed  PubMed Central  Google Scholar 

  34. Laskowski RA, Rullmannn JA, MacArthur MW, Kaptein R, Thornton JM (1996) AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J Biomol NMR 8:477–486

    Article  CAS  PubMed  Google Scholar 

  35. Ashkenazy H, Erez E, Martz E, Pupko T, Ben-Tal N (2010) ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Res 38:W529–W533

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Schroedinger L (2015) In: The PyMOL molecular graphics system, Version 1.7.4, LLC. Schrödinger

  37. Capriotti E, Fariselli P, Casadio R (2005) I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 33:W306–W310

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Parthiban V, Gromiha MM, Schomburg D (2006) CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Res 34:W239–W242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. World Health Organization (2010) Sixty-third World health assembly. Viral hepatitis, report by the Secretariat A63/15

  40. Alter MJ (2002) Prevention of spread of hepatitis C. Hepatology 36:S93–S98

    Article  PubMed  Google Scholar 

  41. Lauer GM, Walker BD (2001) Hepatitis C virus infection. N Engl J Med 345:41–52

    Article  CAS  PubMed  Google Scholar 

  42. Hamers FF, Downs AM (2003) HIV in central and eastern Europe. Lancet 361:1035–1044

    Article  PubMed  Google Scholar 

  43. Kelly JA, Amirkhanian YA (2003) The newest epidemic: a review of HIV/AIDS in central and eastern Europe. Int J STD AIDS 14:361–371

    Article  PubMed  Google Scholar 

  44. Cohen J (2010) Late for the epidemic: HIV/AIDS in eastern Europe. Science 329:160–164

    Article  CAS  PubMed  Google Scholar 

  45. Kruglov YV, Kobyshcha YV, Salyuk T, Varetska O, Shakarishvili A, Saldanha VP (2008) The most severe HIV epidemic in Europe: Ukraine’s national HIV prevalence estimates for 2007. Sex Transm Infect 84:37–41

    Article  Google Scholar 

  46. Shapatava E, Nelson KE, Tsertsvadze T, del Rio C (2006) Risk behaviors and HIV, hepatitis B, and hepatitis C seroprevalence among injection drug users in Georgia. Drug Alcohol Depend 82:35–38

    Article  Google Scholar 

  47. Djurić D (2003) The economic development of Montenegro. In: Bieber F (ed) Montenegro in transition: problems of identity and statehood. Nomos Verlagsgesellschaft, Baden-Baden, pp 139–158

    Google Scholar 

  48. Baćak V, Laušević D, Mugoša B, Vratnica Z, Terzić N (2013) Hepatitis C virus infection and related riskfactors among injection drug users in Montenegro. Eur Addict Res 19:68–73

    Article  PubMed  Google Scholar 

  49. Bernays S (2008) Trust, disruption and responsibility in accounts of injecting equipment sharing and hepatitis C risk. Health Risk Soc 10:221–240

    Article  Google Scholar 

  50. Paintsil E, Verevochkin SV, Dukhovlinova E, Niccolai L, Barbour R, White E, Toussova OV, Alexander L, Kozlov AP, Heimer R (2009) Hepatitis C virus infection among drug injectors in St Petersburg, Russia: social and molecular epidemiology of an endemic infection. Addiction 104:1881–1890

    Article  PubMed  PubMed Central  Google Scholar 

  51. van Asten L, Verhaest I, Lamzira S, Hernandez-Aguado I, Zangerle R, Boufassa F, Rezza G, Broers B, Robertson JR, Brettle RP, McMenamin J, Prins M, Cochrane A, Simmonds P, Coutinho RA, Bruisten S (2004) European and Italian Seroconverter Studies. Spread of hepatitis C virus among European injection drug users infected with HIV: a phylogenetic analysis. J Infect Dis 189:292–302

    Article  PubMed  Google Scholar 

  52. Tallo T, Norder H, Tefanova V, Krispin T, Schmidt J, Ilmoja M, Orgulas K, Pruunsild K, Priimägi L, Magnius LO (2007) Genetic characterization of hepatitis C virus strains in Estonia: fluctuations in the predominating subtype with time. J Med Virol 79:374–382

    Article  PubMed  Google Scholar 

  53. Markov PV, van de Laar TJ, Thomas XV, Aronson SJ, Weegink CJ, van den Berk GE, Prins M, Pybus OG, Schinkel J (2012) Colonial history and contemporary transmission shape the genetic diversity of hepatitis C virus genotype 2 in Amsterdam. J Virol 86:7677–7687

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Harris KA, Gilham C, Mortimer PP, Teo CG (1999) The most prevalent hepatitis C virus genotypes in England and Wales are 3a and 1a. J Med Virol 58:127–131

    Article  CAS  PubMed  Google Scholar 

  55. Ranjith-Kumar CT, Kao CC (2006) Biochemical activities of the HCV NS5B RNA-dependent RNA polymerase. In: Tan SL (ed) Hepatitis C viruses: genomes and molecular biology, Chapter 10. Horizon Bioscience, Norfolk

    Google Scholar 

  56. Lesburg CA, Cable MB, Ferrari E, Hong Z, Mannarino AF, Weber PC (1999) Crystal structure of the RNA-dependent RNA polymerase from hepatitis C virus reveals a fully encircled active site. Nat Struct Biol 6:937–943

    Article  CAS  PubMed  Google Scholar 

  57. Poch O, Sauvaget I, Delarue M, Tordo N (1989) Identification of four conserved motifs among the RNA-dependent polymerase encoding elements. EMBO J 8:3867–3874

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Lohmann V, Korner F, Herian U, Bartenschlager R (1997) Biochemical properties of hepatitis C virus NS5B RNA-dependent RNA polymerase and identification of amino acid sequence motifs essential for enzymatic activity. J Virol 71:8416–8428

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Cheney IW, Naim S, Lai VC, Dempsey S, Bellows D, Walker MP, Shim JH, Horscroft N, Hong Z, Zhong W (2002) Mutations in NS5B polymerase of hepatitis C virus: impacts on in vitro enzymatic activity and viral RNA replication in the subgenomic replicon cell culture. Virology 297:298–306

    Article  CAS  PubMed  Google Scholar 

  60. Behrens SE, Tomei L, De Francesco R (1996) Identification and properties of the RNA-dependent RNA polymerase of hepatitis C virus. EMBO J 15:12–22

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Di Maio VC, Cento V, Mirabelli C, Artese A, Costa G, Alcaro S, Perno CF, Ceccherini-Silberstein F (2014) Hepatitis C virus genetic variability and the presence of NS5B resistance-associated mutations as natural polymorphisms in selected genotypes could affect the response to NS5B inhibitors. Antimicrob Agents Chemother 58:2781–2797

    Article  PubMed  PubMed Central  Google Scholar 

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    Authors and Affiliations

    1. Institute of Public Health, Podgorica, Montenegro

      Boban Mugosa, Zoran Vratnica, Danijela Vujoševic & Dragan Lauševic

    2. Department of Infectious, Parasitic and Immunomediated Disease, National Institute of Health, Rome, Italy

      Eleonora Cella, Alessandra Lo Presti & Massimo Ciccozzi

    3. Public Health and Infectious Diseases, Sapienza University, Rome, Italy

      Eleonora Cella

    4. Department of Biomedical and Clinical Sciences “L. Sacco”, Infectious Diseases and Tropical Medicine Chair, University of Milan, Milan, Italy

      Alessia Lai, Erika Ebranati & Gianguglielmo Zehender

    5. Unit of Clinical Pathology and Microbiology, University Campus Bio-Medico, Via Alvaro del Portillo 200, 00128, Rome, Italy

      Aletheia Blasi, Silvia Angeletti & Massimo Ciccozzi

    6. Department of Gastrointestinal Diseases, Campus Bio-Medico University, Rome, Italy

      Michele Guarino

    7. Department of Biochemical Science “A. Rossi Fanelli”, Sapienza University, Rome, Italy

      Teresa Milano & Stefano Pascarella

    8. Internal Medicine Department, University Hospital Campus Bio-Medico, Rome, Italy

      Silvia Spoto

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    Correspondence to Silvia Angeletti.

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    All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standard.

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    B. Mugosa, E. Cella and A. Lai contributed equally.

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    705_2017_3224_MOESM1_ESM.tif

    Supplementary material 1 (TIFF 59 kb) Fig. S1 Likelihood mapping of the HCV-3a dataset of nucleotide sequences. The three corners represent fully resolved tree topologies, i.e., the presence of a tree-like phylogenetic signal, in the given dataset

    705_2017_3224_MOESM2_ESM.tif

    Supplementary material 2 (TIFF 367 kb) Fig. S2 Detail of the variant positions 250, 330 and 334. The protein is represented as a salmon- colored cartoon, while the side chain of the variable amino acids are shown by sticks. In A, reference amino acid Arg250 is shown, while the replacing Lys is represented in B. H-bonds between side chains of the residues in positions 250 and Gln241 are depicted by yellow dashed lines, and distances are shown. In C and D reference residues are shown with salmon-colored stick models, while the variant residues are represented by grey sticks

    705_2017_3224_MOESM3_ESM.tif

    Supplementary material 3 (TIFF 444 kb) Fig. S3 Detail of the variant positions 304, 305 and 307. The protein is represented as a cartoon, while the side chains of the variable amino acids are shown by sticks. In A, reference amino acids Lys304, Ala305, Asn307 are shown, while the variant amino acids are represented by grey sticks in B, C and D. In B, the interactions between residue side chains in position 304 and the residues Thr66 and Glu70 are indicated by yellow dashed lines, labeled with atomic distances

    705_2017_3224_MOESM4_ESM.tif

    Supplementary material 4 (TIFF 240 kb) Fig. S4 Residue conservation analysis. The residue conservation was analyzed using Consurf through the comparison of 250 NS5B protein sequences of different HCV genotypes obtained from the UniProt database. The reference sequence of the HCV-3a NS5B is displayed with the residue conservation score at each site color-coded into it. The conservation scale was defined from the most variable amino acid position (grade 1, colored turquoise) to the most conserves amino acid position (grade 9, colored maroon). Positions for which the inferred conservation level was assigned with low confidence are marked with light yellow. The first row below the sequence lists the predicted burial status of the site (i.e., ‘‘b’’– buried versus ‘‘e’’ – exposed). The second row indicates residues predicted to be structurally and functionally important: ‘‘s’’ and ‘‘f’’, respectively. Positions 219 and 221, marked by black arrows, are discussed in the text

    705_2017_3224_MOESM5_ESM.tif

    Supplementary material 5 (TIFF 678 kb) Fig. S5 Sequence alignment of HCV 3a NS5B protein sequences. Multiple sequence alignment of the NS5B sequences. Alignment was obtained with the program Clustal Omega and displayed with Jalview. Sequences are reported with the amino acid one-letter code; the last sequence is the HCV 3a NS5B reference sequence labeled with its RefSeq code. The black arrows indicate the 219, 221 and 307 positions (numbers are referred to the reference sequence), which have an high mutation frequency with respect to reference sequence. Residues are colored according to the ClustalX scheme, which for each alignment column, takes into account conservation and amino acid type

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    Mugosa, B., Cella, E., Lai, A. et al. Hepatitis C virus genotype 3A in a population of injecting drug users in Montenegro: Bayesian and evolutionary analysis. Arch Virol 162, 1549–1561 (2017). https://doi.org/10.1007/s00705-017-3224-5

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