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For: Gismondi MI, Díaz Carrasco JM, Valva P, Becker PD, Guzmán CA, Campos RH, Preciado MV. Dynamic changes in viral population structure and compartmentalization during chronic hepatitis C virus infection in children. Virology 2013;447:187-96. [PMID: 24210114 DOI: 10.1016/j.virol.2013.09.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2013] [Accepted: 09/03/2013] [Indexed: 12/17/2022]

For:	Gismondi MI, Díaz Carrasco JM, Valva P, Becker PD, Guzmán CA, Campos RH, Preciado MV. Dynamic changes in viral population structure and compartmentalization during chronic hepatitis C virus infection in children. Virology 2013;447:187-96. [PMID: 24210114 DOI: 10.1016/j.virol.2013.09.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2013] [Accepted: 09/03/2013] [Indexed: 12/17/2022]

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Cited by Other Article(s)

Zhao L, Hall M, Giridhar P, Ghafari M, Kemp S, Chai H, Klenerman P, Barnes E, Ansari MA, Lythgoe K. Genetically distinct within-host subpopulations of hepatitis C virus persist after Direct-Acting Antiviral treatment failure. PLoS Pathog 2025;21:e1012959. [PMID: 40168433 DOI: 10.1371/journal.ppat.1012959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2024] [Accepted: 02/05/2025] [Indexed: 04/03/2025] Open

Abstract

Analysis of viral genetic data has previously revealed distinct within-host population structures in both untreated and interferon-treated chronic hepatitis C virus (HCV) infections. While multiple subpopulations persisted during the infection, each subpopulation was observed only intermittently. However, it was unknown whether similar patterns were also present after Direct Acting Antiviral (DAA) treatment, where viral populations were often assumed to go through narrow bottlenecks. Here we tested for the maintenance of population structure after DAA treatment failure, and whether there were different evolutionary rates along distinct lineages where they were observed. We analysed whole-genome next-generation sequencing data generated from a randomised study using DAAs (the BOSON study). We focused on samples collected from patients (N=84) who did not achieve sustained virological response (i.e., treatment failure) and had sequenced virus from multiple timepoints. Given the short-read nature of the data, we used a number of methods to identify distinct within-host lineages including tracking concordance in intra-host nucleotide variant (iSNV) frequencies, applying sequenced-based and tree-based clustering algorithms to sliding windows along the genome, and haplotype reconstruction. Distinct viral subpopulations were maintained among a high proportion of individuals post DAA treatment failure. Using maximum likelihood modelling and model comparison, we found an overdispersion of viral evolutionary rates among individuals, and significant differences in evolutionary rates between lineages within individuals. These results suggest the virus is compartmentalised within individuals, with the varying evolutionary rates due to different viral replication rates and/or different selection pressures. We endorse lineage awareness in future analyses of HCV evolution and infections to avoid conflating patterns from distinct lineages, and to recognise the likely existence of unsampled subpopulations.

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Bunimovich L, Ram A, Skums P. Antigenic cooperation in viral populations: Transformation of functions of intra-host viral variants. J Theor Biol 2024;580:111719. [PMID: 38158118 DOI: 10.1016/j.jtbi.2023.111719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 09/10/2023] [Accepted: 12/20/2023] [Indexed: 01/03/2024]

Bunimovich L, Ram A. Local Immunodeficiency: Combining Cross-Immunoreactivity Networks. J Comput Biol 2023;30:492-501. [PMID: 36625905 PMCID: PMC10125403 DOI: 10.1089/cmb.2022.0390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open

Hufsky F, Abecasis A, Agudelo-Romero P, Bletsa M, Brown K, Claus C, Deinhardt-Emmer S, Deng L, Friedel CC, Gismondi MI, Kostaki EG, Kühnert D, Kulkarni-Kale U, Metzner KJ, Meyer IM, Miozzi L, Nishimura L, Paraskevopoulou S, Pérez-Cataluña A, Rahlff J, Thomson E, Tumescheit C, van der Hoek L, Van Espen L, Vandamme AM, Zaheri M, Zuckerman N, Marz M. Women in the European Virus Bioinformatics Center. Viruses 2022;14:1522. [PMID: 35891501 PMCID: PMC9319252 DOI: 10.3390/v14071522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/05/2022] [Accepted: 07/07/2022] [Indexed: 02/01/2023] Open

Affiliation(s)

Franziska Hufsky European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany
Ana Abecasis European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Global Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, New University of Lisbon, 1349-008 Lisbon, Portugal
Patricia Agudelo-Romero European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Wal-Yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Nedlands, WA 6009, Australia
Magda Bletsa European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece Department of Microbiology, Immunology and Transplantation, Rega Institute, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium
Katherine Brown European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Division of Virology, Department of Pathology, University of Cambridge, Cambridge CB2 1TN, UK
Claudia Claus European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Institute of Medical Microbiology and Virology, Medical Faculty, Leipzig University, 04103 Leipzig, Germany
Stefanie Deinhardt-Emmer European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Institute of Medical Microbiology, Jena University Hospital, 07747 Jena, Germany
Li Deng European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Institute of Virology, Helmholtz Centre Munich-German Research Center for Environmental Health, 85764 Neuherberg, Germany Microbial Disease Prevention, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
Caroline C. Friedel European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Institute of Informatics, Ludwig-Maximilians-Universität München, 80333 Munich, Germany
María Inés Gismondi European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Institute of Agrobiotechnology and Molecular Biology (IABIMO), National Institute for Agriculture Technology (INTA), National Research Council (CONICET), Hurlingham B1686IGC, Argentina Department of Basic Sciences, National University of Luján, Luján B6702MZP, Argentina
Evangelia Georgia Kostaki European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece
Denise Kühnert European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Transmission, Infection, Diversification and Evolution Group, Max Planck Institute for the Science of Human History, 07745 Jena, Germany
Urmila Kulkarni-Kale European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Bioinformatics Centre, Savitribai Phule Pune University, Pune 411007, India
Karin J. Metzner European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, 8091 Zurich, Switzerland Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
Irmtraud M. Meyer European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 10115 Berlin, Germany Institute of Chemistry and Biochemistry, Department of Biology, Chemistry and Pharmacy, Freie Universität Berlin, 14195 Berlin, Germany Faculty of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany
Laura Miozzi European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Institute for Sustainable Plant Protection, National Research Council of Italy, 10135 Torino, Italy
Luca Nishimura European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Department of Genetics, School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), Mishima 411-8540, Japan Human Genetics Laboratory, National Institute of Genetics, Mishima 411-8540, Japan
Sofia Paraskevopoulou European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Methods Development and Research Infrastructure, Bioinformatics and Systems Biology, Robert Koch Institute, 13353 Berlin, Germany
Alba Pérez-Cataluña European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) VISAFELab, Department of Preservation and Food Safety Technologies, Institute of Agrochemistry and Food Technology, IATA-CSIC, 46980 Valencia, Spain
Janina Rahlff European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Centre for Ecology and Evolution in Microbial Model Systems (EEMiS), Department of Biology and Environmental Science, Linneaus University, 391 82 Kalmar, Sweden
Emma Thomson European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow G51 4TF, UK MRC-University of Glasgow Centre for Virus Research, Glasgow G61 1QH, UK
Charlotte Tumescheit European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) School of Biological Sciences, Seoul National University, Seoul 08826, Korea
Lia van der Hoek European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Laboratory of Experimental Virology, Department of Medical Microbiology and Infection Prevention, Amsterdam UMC, University of Amsterdam, 1012 WX Amsterdam, The Netherlands Amsterdam Institute for Infection and Immunity, 1100 DD Amsterdam, The Netherlands
Lore Van Espen European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Department of Microbiology, Immunology and Transplantation, Rega Institute, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium
Anne-Mieke Vandamme European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Department of Microbiology, Immunology and Transplantation, Rega Institute, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, 1349-008 Lisbon, Portugal Institute for the Future, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium
Maryam Zaheri European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
Neta Zuckerman European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) Central Virology Laboratory, Public Health Services, Ministry of Health and Sheba Medical Center, Ramat Gan 52621, Israel
Manja Marz European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.) RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany

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Rios DA, Casciato PC, Caldirola MS, Gaillard MI, Giadans C, Ameigeiras B, De Matteo EN, Preciado MV, Valva P. Chronic Hepatitis C Pathogenesis: Immune Response in the Liver Microenvironment and Peripheral Compartment. Front Cell Infect Microbiol 2021;11:712105. [PMID: 34414132 PMCID: PMC8369367 DOI: 10.3389/fcimb.2021.712105] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 07/07/2021] [Indexed: 12/14/2022] Open

Abstract

Chronic hepatitis C (CHC) pathogenic mechanisms as well as the participation of the immune response in the generation of liver damage are still a topic of interest. Here, we evaluated immune cell populations and cytokines in the liver and peripheral blood (PB) to elucidate their role in CHC pathogenesis. B, CTL, Th, Treg, Th1, Th17, and NK cell localization and frequency were evaluated on liver biopsies by immunohistochemistry, while frequency, differentiation, and functional status on PB were evaluated by flow cytometry. TNF-α, IL-23, IFN-γ, IL-1β, IL-6, IL-8, IL-17A, IL-21, IL-10, and TGF-β expression levels were quantified in fresh liver biopsy by RT-qPCR and in plasma by CBA/ELISA. Liver CTL and Th1 at the lobular area inversely correlated with viral load (r = −0.469, p =0.003 and r = −0.384, p = 0.040). Treg correlated with CTL and Th1 at the lobular area (r = 0.784, p < 0.0001; r = 0.436, p = 0.013). Th17 correlated with hepatic IL-8 (r = 0.52, p < 0.05), and both were higher in advanced fibrosis cases (Th17 p = 0.0312, IL-8 p = 0.009). Hepatic cytokines were higher in severe hepatitis cases (IL-1β p = 0.026, IL-23 p = 0.031, IL-8 p = 0.002, TGF-β, p= 0.037). Peripheral NK (p = 0.008) and NK dim (p = 0.018) were diminished, while NK bright (p = 0.025) was elevated in patients vs. donors. Naïve Th (p = 0.011) and CTL (p = 0.0007) were decreased, while activated Th (p = 0.0007) and CTL (p = 0.0003) were increased. IFN-γ production and degranulation activity in NK and CTL were normal. Peripheral cytokines showed an altered profile vs. donors, particularly elevated IL-6 (p = 0.008) and TGF-β (p = 0.041). Total hepatic CTLs favored damage. Treg could not prevent fibrogenesis triggered by Th17 and IL-8. Peripheral T-lymphocyte differentiation stage shift, elevated cytokine levels and NK-cell count decrease would contribute to global disease.

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Knyazev S, Hughes L, Skums P, Zelikovsky A. Epidemiological data analysis of viral quasispecies in the next-generation sequencing era. Brief Bioinform 2021;22:96-108. [PMID: 32568371 PMCID: PMC8485218 DOI: 10.1093/bib/bbaa101] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 04/24/2020] [Accepted: 05/04/2020] [Indexed: 01/04/2023] Open

Icer Baykal PB, Lara J, Khudyakov Y, Zelikovsky A, Skums P. Quantitative differences between intra-host HCV populations from persons with recently established and persistent infections. Virus Evol 2020;7:veaa103. [PMID: 33505710 PMCID: PMC7816669 DOI: 10.1093/ve/veaa103] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open

Abstract

Detection of incident hepatitis C virus (HCV) infections is crucial for identification of outbreaks and development of public health interventions. However, there is no single diagnostic assay for distinguishing recent and persistent HCV infections. HCV exists in each infected host as a heterogeneous population of genomic variants, whose evolutionary dynamics remain incompletely understood. Genetic analysis of such viral populations can be applied to the detection of incident HCV infections and used to understand intra-host viral evolution. We studied intra-host HCV populations sampled using next-generation sequencing from 98 recently and 256 persistently infected individuals. Genetic structure of the populations was evaluated using 245,878 viral sequences from these individuals and a set of selected features measuring their diversity, topological structure, complexity, strength of selection, epistasis, evolutionary dynamics, and physico-chemical properties. Distributions of the viral population features differ significantly between recent and persistent infections. A general increase in viral genetic diversity from recent to persistent infections is frequently accompanied by decline in genomic complexity and increase in structuredness of the HCV population, likely reflecting a high level of intra-host adaptation at later stages of infection. Using these findings, we developed a machine learning classifier for the infection staging, which yielded a detection accuracy of 95.22 per cent, thus providing a higher accuracy than other genomic-based models. The detection of a strong association between several HCV genetic factors and stages of infection suggests that intra-host HCV population develops in a complex but regular and predictable manner in the course of infection. The proposed models may serve as a foundation of cyber-molecular assays for staging infection, which could potentially complement and/or substitute standard laboratory assays.

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Basodi S, Baykal PI, Zelikovsky A, Skums P, Pan Y. Analysis of heterogeneous genomic samples using image normalization and machine learning. BMC Genomics 2020;21:405. [PMID: 33349236 PMCID: PMC7751093 DOI: 10.1186/s12864-020-6661-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 03/09/2020] [Indexed: 11/10/2022] Open

Abstract

BACKGROUND

Analysis of heterogeneous populations such as viral quasispecies is one of the most challenging bioinformatics problems. Although machine learning models are becoming to be widely employed for analysis of sequence data from such populations, their straightforward application is impeded by multiple challenges associated with technological limitations and biases, difficulty of selection of relevant features and need to compare genomic datasets of different sizes and structures.

RESULTS

We propose a novel preprocessing approach to transform irregular genomic data into normalized image data. Such representation allows to restate the problems of classification and comparison of heterogeneous populations as image classification problems which can be solved using variety of available machine learning tools. We then apply the proposed approach to two important problems in molecular epidemiology: inference of viral infection stage and detection of viral transmission clusters using next-generation sequencing data. The infection staging method has been applied to HCV HVR1 samples collected from 108 recently and 257 chronically infected individuals. The SVM-based image classification approach achieved more than 95% accuracy for both recently and chronically HCV-infected individuals. Clustering has been performed on the data collected from 33 epidemiologically curated outbreaks, yielding more than 97% accuracy.

CONCLUSIONS

Sequence image normalization method allows for a robust conversion of genomic data into numerical data and overcomes several issues associated with employing machine learning methods to viral populations. Image data also help in the visualization of genomic data. Experimental results demonstrate that the proposed method can be successfully applied to different problems in molecular epidemiology and surveillance of viral diseases. Simple binary classifiers and clustering techniques applied to the image data are equally or more accurate than other models.

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Skums P, Bunimovich L. Graph fractal dimension and the structure of fractal networks. JOURNAL OF COMPLEX NETWORKS 2020;8:cnaa037. [PMID: 33251012 PMCID: PMC7673317 DOI: 10.1093/comnet/cnaa037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 09/28/2020] [Indexed: 06/12/2023]

Raghwani J, Wu CH, Ho CKY, De Jong M, Molenkamp R, Schinkel J, Pybus OG, Lythgoe KA. High-Resolution Evolutionary Analysis of Within-Host Hepatitis C Virus Infection. J Infect Dis 2020;219:1722-1729. [PMID: 30602023 PMCID: PMC6500553 DOI: 10.1093/infdis/jiy747] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 12/28/2018] [Indexed: 12/21/2022] Open

Characterization of Hepatitis C Virus (HCV) Envelope Diversification from Acute to Chronic Infection within a Sexually Transmitted HCV Cluster by Using Single-Molecule, Real-Time Sequencing. J Virol 2017;91:JVI.02262-16. [PMID: 28077634 DOI: 10.1128/jvi.02262-16] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Accepted: 12/29/2016] [Indexed: 12/18/2022] Open

Abstract

In contrast to other available next-generation sequencing platforms, PacBio single-molecule, real-time (SMRT) sequencing has the advantage of generating long reads albeit with a relatively higher error rate in unprocessed data. Using this platform, we longitudinally sampled and sequenced the hepatitis C virus (HCV) envelope genome region (1,680 nucleotides [nt]) from individuals belonging to a cluster of sexually transmitted cases. All five subjects were coinfected with HIV-1 and a closely related strain of HCV genotype 4d. In total, 50 samples were analyzed by using SMRT sequencing. By using 7 passes of circular consensus sequencing, the error rate was reduced to 0.37%, and the median number of sequences was 612 per sample. A further reduction of insertions was achieved by alignment against a sample-specific reference sequence. However, in vitro recombination during PCR amplification could not be excluded. Phylogenetic analysis supported close relationships among HCV sequences from the four male subjects and subsequent transmission from one subject to his female partner. Transmission was characterized by a strong genetic bottleneck. Viral genetic diversity was low during acute infection and increased upon progression to chronicity but subsequently fluctuated during chronic infection, caused by the alternate detection of distinct coexisting lineages. SMRT sequencing combines long reads with sufficient depth for many phylogenetic analyses and can therefore provide insights into within-host HCV evolutionary dynamics without the need for haplotype reconstruction using statistical algorithms.IMPORTANCE Next-generation sequencing has revolutionized the study of genetically variable RNA virus populations, but for phylogenetic and evolutionary analyses, longer sequences than those generated by most available platforms, while minimizing the intrinsic error rate, are desired. Here, we demonstrate for the first time that PacBio SMRT sequencing technology can be used to generate full-length HCV envelope sequences at the single-molecule level, providing a data set with large sequencing depth for the characterization of intrahost viral dynamics. The selection of consensus reads derived from at least 7 full circular consensus sequencing rounds significantly reduced the intrinsic high error rate of this method. We used this method to genetically characterize a unique transmission cluster of sexually transmitted HCV infections, providing insight into the distinct evolutionary pathways in each patient over time and identifying the transmission-associated genetic bottleneck as well as fluctuations in viral genetic diversity over time, accompanied by dynamic shifts in viral subpopulations.

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Echeverría N, Moreno P, Cristina J. Molecular Evolution of Hepatitis C Virus: From Epidemiology to Antiviral Therapy (Current Research in Latin America). HUMAN VIROLOGY IN LATIN AMERICA 2017:333-359. [DOI: 10.1007/978-3-319-54567-7_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2025]

Datta S, Chakravarty R. Role of RNA secondary structure in emergence of compartment specific hepatitis B virus immune escape variants. World J Virol 2016;5:161-169. [PMID: 27878103 PMCID: PMC5105049 DOI: 10.5501/wjv.v5.i4.161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Revised: 08/09/2016] [Accepted: 08/29/2016] [Indexed: 02/05/2023] Open

Abstract

AIM

To investigate the role of subgenotype specific RNA secondary structure in the compartment specific selection of hepatitis B virus (HBV) immune escape mutations.

METHODS

This study was based on the analysis of the specific observation of HBV subgenotype A1 in the serum/plasma, while subgenotype A2 with G145R mutation in the peripheral blood leukocytes (PBLs). Genetic variability found among the two subgenotypes was used for prediction and comparison of the full length pregenomic RNA (pgRNA) secondary structure and base pairings. RNA secondary structures were predicted for 37 °C using the Vienna RNA fold server, using default parameters. Visualization and detailed analysis was done using RNA shapes program.

RESULTS

In this analysis, using similar algorithm and conditions, entirely different pgRNA secondary structures for subgenotype A1 and subgenotype A2 were predicted, suggesting different base pairing patterns within the two subgenotypes of genotype A, specifically, in the HBV genetic region encoding the major hydrophilic loop. We observed that for subgenotype A1 specific pgRNA, nucleotide 358^U base paired with 1738^A and nucleotide 587^G base paired with 607^C. However in sharp contrast, in subgenotype A2 specific pgRNA, nucleotide 358^U was opposite to nucleotide 588^G, while 587^G was opposite to 359^U, hence precluding correct base pairing and thereby lesser stability of the stem structure. When the nucleotides at 358^U and 587^G were replaced with 358^C and 587^A respectively (as observed specifically in the PBL associated A2 sequences), these nucleotides base paired correctly with 588^G and 359^U, respectively.

CONCLUSION

The results of this study show that compartment specific mutations are associated with HBV subgenotype specific alterations in base pairing of the pgRNA, leading to compartment specific selection and preponderance of specific HBV subgenotype with unique mutational pattern.

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Pérez PS, Di Lello FA, Mullen EG, Galdame OA, Livellara BI, Gadano AC, Campos RH, Flichman DM. Compartmentalization of hepatitis C virus variants in patients with hepatocellular carcinoma. Mol Carcinog 2016;56:371-380. [PMID: 27163636 DOI: 10.1002/mc.22500] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Revised: 04/03/2016] [Accepted: 05/02/2016] [Indexed: 12/24/2022]

Network Analysis of the Chronic Hepatitis C Virome Defines Hypervariable Region 1 Evolutionary Phenotypes in the Context of Humoral Immune Responses. J Virol 2015;90:3318-29. [PMID: 26719263 DOI: 10.1128/jvi.02995-15] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 12/22/2015] [Indexed: 02/06/2023] Open

Abstract

UNLABELLED

Hypervariable region 1 (HVR1) of hepatitis C virus (HCV) comprises the first 27 N-terminal amino acid residues of E2. It is classically seen as the most heterogeneous region of the HCV genome. In this study, we assessed HVR1 evolution by using ultradeep pyrosequencing for a cohort of treatment-naive, chronically infected patients over a short, 16-week period. Organization of the sequence set into connected components that represented single nucleotide substitution events revealed a network dominated by highly connected, centrally positioned master sequences. HVR1 phenotypes were observed to be under strong purifying (stationary) and strong positive (antigenic drift) selection pressures, which were coincident with advancing patient age and cirrhosis of the liver. It followed that stationary viromes were dominated by a single HVR1 variant surrounded by minor variants comprised from conservative single amino acid substitution events. We present evidence to suggest that neutralization antibody efficacy was diminished for stationary-virome HVR1 variants. Our results identify the HVR1 network structure during chronic infection as the preferential dominance of a single variant within a narrow sequence space.

IMPORTANCE

HCV infection is often asymptomatic, and chronic infection is generally well established in advance of initial diagnosis and subsequent treatment. HVR1 can undergo rapid sequence evolution during acute infection, and the variant pool is typically seen to diverge away from ancestral sequences as infection progresses from the acute to the chronic phase. In this report, we describe HVR1 viromes in chronically infected patients that are defined by a dominant epitope located centrally within a narrow variant pool. Our findings suggest that weakened humoral immune activity, as a consequence of persistent chronic infection, allows for the acquisition and maintenance of host-specific adaptive mutations at HVR1 that reflect virus fitness.

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Gonçalves Rossi LM, Escobar-Gutierrez A, Rahal P. Multiregion deep sequencing of hepatitis C virus: An improved approach for genetic relatedness studies. INFECTION GENETICS AND EVOLUTION 2015;38:138-145. [PMID: 26733442 DOI: 10.1016/j.meegid.2015.12.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2015] [Revised: 12/23/2015] [Accepted: 12/24/2015] [Indexed: 12/19/2022]

Antigenic cooperation among intrahost HCV variants organized into a complex network of cross-immunoreactivity. Proc Natl Acad Sci U S A 2015;112:6653-8. [PMID: 25941392 DOI: 10.1073/pnas.1422942112] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open

Rossi LMG, Escobar-Gutierrez A, Rahal P. Advanced molecular surveillance of hepatitis C virus. Viruses 2015;7:1153-88. [PMID: 25781918 PMCID: PMC4379565 DOI: 10.3390/v7031153] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Revised: 02/05/2015] [Accepted: 02/20/2015] [Indexed: 12/12/2022] Open

Preciado MV, Valva P, Escobar-Gutierrez A, Rahal P, Ruiz-Tovar K, Yamasaki L, Vazquez-Chacon C, Martinez-Guarneros A, Carpio-Pedroza JC, Fonseca-Coronado S, Cruz-Rivera M. Hepatitis C virus molecular evolution: Transmission, disease progression and antiviral therapy. World J Gastroenterol 2014;20:15992-16013. [PMID: 25473152 PMCID: PMC4239486 DOI: 10.3748/wjg.v20.i43.15992] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2014] [Revised: 06/22/2014] [Accepted: 08/28/2014] [Indexed: 02/06/2023] Open

Analysis of the evolution and structure of a complex intrahost viral population in chronic hepatitis C virus mapped by ultradeep pyrosequencing. J Virol 2014;88:13709-21. [PMID: 25231312 DOI: 10.1128/jvi.01732-14] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open

Abstract

UNLABELLED

Hepatitis C virus (HCV) causes chronic infection in up to 50% to 80% of infected individuals. Hypervariable region 1 (HVR1) variability is frequently studied to gain an insight into the mechanisms of HCV adaptation during chronic infection, but the changes to and persistence of HCV subpopulations during intrahost evolution are poorly understood. In this study, we used ultradeep pyrosequencing (UDPS) to map the viral heterogeneity of a single patient over 9.6 years of chronic HCV genotype 4a infection. Informed error correction of the raw UDPS data was performed using a temporally matched clonal data set. The resultant data set reported the detection of low-frequency recombinants throughout the study period, implying that recombination is an active mechanism through which HCV can explore novel sequence space. The data indicate that polyvirus infection of hepatocytes has occurred but that the fitness quotients of recombinant daughter virions are too low for the daughter virions to compete against the parental genomes. The subpopulations of parental genomes contributing to the recombination events highlighted a dynamic virome where subpopulations of variants are in competition. In addition, we provide direct evidence that demonstrates the growth of subdominant populations to dominance in the absence of a detectable humoral response.

IMPORTANCE

Analysis of ultradeep pyrosequencing data sets derived from virus amplicons frequently relies on software tools that are not optimized for amplicon analysis, assume random incorporation of sequencing errors, and are focused on achieving higher specificity at the expense of sensitivity. Such analysis is further complicated by the presence of hypervariable regions. In this study, we made use of a temporally matched reference sequence data set to inform error correction algorithms. Using this methodology, we were able to (i) detect multiple instances of hepatitis C virus intrasubtype recombination at the E1/E2 junction (a phenomenon rarely reported in the literature) and (ii) interrogate the longitudinal quasispecies complexity of the virome. Parallel to the UDPS, isolation of IgG-bound virions was found to coincide with the collapse of specific viral subpopulations.

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Valva P, Gismondi MI, Casciato PC, Galoppo M, Lezama C, Galdame O, Gadano A, Galoppo MC, Mullen E, De Matteo EN, Preciado MV. Distinctive intrahepatic characteristics of paediatric and adult pathogenesis of chronic hepatitis C infection. Clin Microbiol Infect 2014;20:O998-1009. [PMID: 24942073 DOI: 10.1111/1469-0691.12728] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2014] [Revised: 05/23/2014] [Accepted: 06/15/2014] [Indexed: 12/17/2022]

Abstract

Mechanisms leading to liver damage in chronic hepatitis C (CHC) are being discussed, but both the immune system and the virus are involved. The aim of this study was to evaluate intrahepatic viral infection, apoptosis and portal and periportal/interface infiltrate in paediatric and adult patients to elucidate the pathogenesis of chronic hepatitis C. HCV-infected, activated caspase-3(+) and TUNEL(+) hepatocytes, as well as total, CD4(+), CD8(+), Foxp3(+) and CD20(+) lymphocytes infiltrating portal and periportal/interface tracts were evaluated in 27 paediatric and 32 adult liver samples by immunohistochemistry or immunofluorescence. The number of infected hepatocytes was higher in paediatric than in adult samples (p 0.0078). In children, they correlated with apoptotic hepatocytes (activated caspase-3(+) r = 0.74, p < 0.0001; TUNEL(+) r = 0.606, p 0.0017). Also, infected (p = 0.026) and apoptotic hepatocytes (p = 0.03) were associated with the severity of fibrosis. In adults, activated caspase-3(+) cell count was increased in severe hepatitis (p = 0.009). Total, CD4(+), CD8(+) and Foxp3(+) lymphocyte count was higher in adult samples (p < 0.05). Paediatric CD8(+) cells correlated with infected (r = 0.495, p 0.04) and TUNEL(+) hepatocytes (r = 0.474, p = 0.047), while adult ones correlated with activated caspase-3(+) hepatocytes (r = 0.387, p 0.04). In adults, CD8(+) was associated with hepatitis severity (p < 0.0001) and correlated with inflammatory activity (CD8(+) r = 0.639, p 0.0003). HCV, apoptosis and immune response proved to be involved in CHC pathogenesis of both paediatric and adult patients. However, liver injury in paediatric CHC would be largely associated with a viral cytopathic effect mediated by apoptosis, while in adults it would be mainly associated with an exacerbated immune response.

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