Prediction of prognostic biomarkers for Interferon-based therapy to Hepatitis C Virus patients: a metaanalysis of the NS5A protein in subtypes 1a, 1b, and 3a

Abstract

BackgroundHepatitis C virus (HCV) is a worldwide health problem with no vaccine and the only approved therapy is Interferon-based plus Ribavarin. Response prediction to treatment has health and economic impacts, and is a multi-factorial problem including both host and viral factors (e.g: age, sex, ethnicity, pre-treatment viral load, and dynamics of the HCV non-structural protein NS5A quasispecies). We implement a novel approach for extracting features including informative markers from mutations in the non-structural 5A protein (NS5A), specifically its Interferon sensitivity determining region (ISDR) and V3 regions, and use a novel bioinformatics approach for pattern recognition on the NS5A protein and its motifs to find biomarkers for response prediction using class association rules and comparing the predictability of the different features.ResultsA total of 58 sequences from sustained responders and 94 from non-responders were downloaded from the HCV LANL database. Site-specific signatures for response prediction from the NS5A protein were extracted from the alignments. Class association rules were generated (e.g.: sustained response is associated with position A2368T in subtype 1a (support 100% and confidence 52.19%); in subtype 1b, response is associated with E2356G/D/K (support 76.3% and confidence 67.3%).ConclusionThe V3 region was a more accurate biomarker than the ISDR region. Subtype-specific class association rules gave better support and confidence than profile hidden Markov models HMMs scores, genetic distances or number of variable sites, and would thus aid in the prediction of prognostic biomarkers and improve the accuracy of prognosis. Sites-specific class association rules in the V3 region of the NS5A protein have given the best support and confidence.

Viral Quasispecies Evolution

“…However, amino acid substitutions in the core-coding regions of different genotypes appear to be related to insulin, oxidative stress, or steatosis resistance, variable responses in IFN-␣/ribavirin combination therapy, and hepatocellular carcinoma (HCC) (427). Core amino acid 70 and 91 polymorphisms have recently been used to predict IFN-␣ treatment response; for example, Q/H70 and M91 in patients with genotype 1b predict a slow or no response to IFN-␣, whereas patients infected with HCV harboring R70 and L91 are more likely to obtain a rapid sustained response (246,453,722).…”

Viral Factors Associated with Response to Antiviral Therapy for Chronic Hepatitis C Virus Infection

“…Another group suggested that IRRDR was unlikely to be an independent factor affecting the outcome of PEG-IFN plus RBV therapy [43]. Substitution of aa 2356 in HCV G1b and 2368 in HCV G1a were noted as markers of prognosis in IFN-based therapy on meta-analysis [44]. In contrast, aa substitution at particular positions within IRRDR was reported not to affect the outcome of PEG-IFN plus RBV therapy [41].…”

Protein Bioinformatics Applied to Virology

“…During the last decade bioinformatics approaches have been used to predict different aspects of viruses [7][8][9][10]. Recently these methods even have been used to predict highrisk types of human papillomaviruses (HPVs) which is the *Address correspondence to this author at the Department of Biotechnology, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan, Iran; Tel: +98311-7934391; Fax: +98311-7932342; E-mail: h.mohabatkar@ast.ui.ac.ir etiological agents in nearly all cases of cervical cancer.…”