Evolutionary characteristics and immune mutation of hepatitis C virus genotype 1b among intravenous drug users in mainland, China

China is one of the countries with the heaviest burden of hepatitis C virus (HCV) worldwide, especially subtype 1b. To better control hepatitis C, insights into the characteristics of dynamic spread and genomic mutations are urgently needed. We retrieved sequences of HCV-1b NS5B among intravenous drug users (IDUs) and general people (Non-IDUs) in China from 2000 to 2011 in NCBI. Bayesian phylogenetic and phylogeographic analyses were used to evaluate the transmission dynamics of HCV-1b. Non-synonymous substitutions were detected to illustrate immune adaptation. Evolutionary history demonstrated that HCV-1b effective population size experienced a sharp increase in 1990. HCV-1b sequences among IDUs had a higher estimated evolutionary rate (5.7185 × 10^-3  substitutions/site/year) than overall (7.7332 × 10^-4 ). 105/136 (77.2%) of HCV-1b sequences clustered into 38 networks. The average non-synonymous HCV-1b immune epitopes among IDUs were 0.211, higher than non-IDUs, especially in the HLA-A*02 molecular recognition region. All of these posed significant challenges for the prevention and treatment of HCV. Heterogeneity and genetic linkages of HCV-1b suggest that evolutionary surveillance of HCV in cities in east-central China and among IDUs could not be neglected.

Immunoinformatics: Predicting Peptide-MHC Binding

Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.

T Cell Epitope Predictions

Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.

A synthetic peptide analog of in silico-predicted immunogenic epitope unique to dengue virus serotype 2 NS1 antigen specifically binds immunoglobulin G antibodies raised in rabbits

Development of a serotyping-capable dengue detection test is hampered by the absence of an identified unique marker that can detect specific dengue virus (DENV) serotype. In the current commercially available antibody-capture diagnostic methods, immobilized nonstructural 1 (NS1) antigen indiscriminately binds and detects immunoglobulin M or immunoglobulin G against any serotype, thus limiting its capability to distinguish existing serotypes of dengue. Identification of dengue serotype is important because certain serotypes are associated with severe forms of dengue as well as dengue hemorrhagic fever. In this study, we aimed to identify an immunogenic epitope unique to DENV2 NS1 antigen and determine the binding specificity of its synthetic peptide mimotope to antibodies raised in animal models. Selection of a putative B-cell epitope from the reported DENV2 NS1 antigen was done using Kolaskar and Tongaonkar Antigenicity prediction, Emini surface accessibility prediction, and Parker hydrophilicity prediction available at the immune epitope database and analysis resource. Uniqueness of the B-cell epitope to DENV2 was analyzed by BLASTp. Immunogenicity of the synthetic peptide analog of the predicted immunogenic epitope was tested in rabbits. The binding specificity of the antibodies raised in animals and the synthetic peptide mimotope was tested by indirect ELISA. A synthetic peptide analog comprising the unique epitope of DENV2 located at the 170th-183rd position of DENV2 NS1 was found to be immunogenic in animal models. The antipeptide antibody produced in rabbits showed specific binding to the synthetic peptide mimotope of the predicted unique DENV2 NS1 immunogenic epitope.