• cyber-molecular assay
• infection stage
• machine learning
• quasispecies
• viral infection

Direct-acting antivirals (DAAs) revolutionized treatment of hepatitis C virus (HCV) infection. Resistance-associated substitutions (RASs) present at the baseline impair response to DAA due to rapid selection of resistant HCV strains. NS5A is indispensable target of the current DAA treatment regimens. We evaluated prevalence of RASs in NS5A in DAA-naïve patients infected with HCV 1a (n = 19), 1b (n = 93), and 3a (n = 90) before systematic DAA application in the territory of the Russian Federation. Total proportion of strains carrying at least one RAS constituted 35.1% (71/202). In HCV 1a we detected only M28V (57.9%) attributed to a founder effect. Common RASs in HCV 1b were R30Q (7.5%), L31M (5.4%), P58S (4.4%), and Y93H (5.4%); in HCV 3a, A30S (31.0%), A30K (5.7%), S62L (8.9%), and Y93H (2.2%). Prevalence of RASs in NS5A of HCV 1b and 3a was similar to that worldwide, including countries practicing massive DAA application, i.e., it was not related to treatment. NS5A with and without RASs exhibited different co-variance networks, which could be attributed to the necessity to preserve viral fitness. Majority of RASs were localized in polymorphic regions subjected to immune pressure, with selected substitutions allowing immune escape. Altogether, this explains high prevalence of RAS in NS5A and low barrier for their appearance in DAA-inexperienced population.

• NS5A
• amino acid covariance
• direct-acting antivirals
• hepatitis C virus (HCV)
• immune escape
• resistance-associated substitutions

At the sequence level it is hard to describe the complexity of viruses which allows them to challenge host immune system, some for a few weeks and others up to a complete compromise. Paradoxically, viral genomes are both complex and simple. Complex because amino acid mutation rates are very high, and yet viruses remain functional. Simple because they have barely around 10 types of proteins, so viral protein-protein interaction networks are not insightful. In this work we use fine-grained amino acid level information and their evolutionary characteristics obtained from large-scale genomic data to develop a statistical panel, towards the goal of developing quantitative descriptors for the biological complexity of viruses. Networks were constructed from pairwise covariation of amino acids and were statistically analyzed. Three differentiating factors arise: predominantly intra- vs inter-protein covariance relations, the nature of the node degree distribution and network density. Interestingly, the covariance relations were primarily intra-protein in avian influenza and inter-protein in HIV. The degree distributions showed two universality classes: a power-law with exponent −1 in HIV and avian-influenza, random behavior in human flu and dengue. The calculated covariance network density correlates well with the mortality strengths of viruses on the viral-Richter scale. These observations suggest the potential utility of the statistical metrics for describing the covariance patterns in viruses. Our host-virus interaction analysis point to the possibility that host proteins which can interact with multiple viral proteins may be responsible for shaping the inter-protein covariance relations. With the available data, it appears that network density might be a surrogate for the virus Richter scale, however the hypothesis needs a re-examination when large scale complete genome data for more viruses becomes available.

• Adenocarcinoma/virology
• Animals
• Breast Neoplasms/virology
• Carcinogenesis
• Cell Growth Processes
• Cell Line, Tumor
• Epithelial-Mesenchymal Transition
• Female
• HIV Infections/metabolism
• HIV Infections/pathology
• HIV Reverse Transcriptase/genetics
• HIV Reverse Transcriptase/metabolism
• HIV-1/genetics
• HIV-1/metabolism
• Humans
• Mammary Neoplasms, Experimental/virology
• Mice
• Mice, Inbred BALB C
• Mutation/genetics
• Neoplasm Metastasis
• Reactive Oxygen Species/metabolism
• Twist-Related Protein 1/genetics
• Twist-Related Protein 1/metabolism
• Up-Regulation

By comparing and measuring covariations of viral protein sequences from isolates of the 2009 pH1N1 influenza A virus (IAV), specific substitutions that co-occur in the NP-NA pair were identified. To investigate the effect of these co-occurring substitution pairs, the V100I substitution in NP and the D248N substitution in NA were introduced into laboratory-adapted WSN IAVs. The recombinant WSN with the covarying NP^V100I-NA^D248N pair exhibited enhanced pathogenicity, as characterized by increased viral production, increased death and inflammation of host cells, and high mortality in infected mice. Although direct interactions between the NP^V100I and NA^D248N proteins were not detected, the RNA-binding ability of NP^V100I was increased, which was further strengthened by NA^D248N, in expression-plasmid-transfected cells. Additionally, the NA^D248N protein was frequently recruited within lipid rafts, indirectly affecting the RNA-binding ability of NP as well as viral release. Altogether, our data indicate that the covarying NP^V100I-NA^D248N pair obtained from 2009 pH1N1 IAV sequence information function together to synergistically augment viral assembly and release, which may explain the observed enhanced viral pathogenicity.

Amino acid mutations in proteins are random and those mutations which are beneficial or neutral survive during the course of evolution. Conservation or co-evolution analyses are performed on the multiple sequence alignment of homologous proteins to understand how important different amino acids or groups of them are. However, these traditional analyses do not explore the directed influence of amino acid mutations, such as compensatory effects. In this work we develop a method to capture the directed evolutionary impact of one amino acid on all other amino acids, and provide a visual network representation for it. The method developed for these directed networks of inter- and intra-protein evolutionary interactions can also be used for noting the differences in amino acid evolution between the control and experimental groups. The analysis is illustrated with a few examples, where the method identifies several directed interactions of functionally critical amino acids. The impact of an amino acid is quantified as the number of amino acids that are influenced as a consequence of its mutation, and it is intended to summarize the compensatory mutations in large evolutionary sequence data sets as well as to rationally identify targets for mutagenesis when their functional significance can not be assessed using structure or conservation.

Amino acid substitutions R70Q/H and L91M in HCV subtype 1b core protein can affect the response to interferon and are associated with the development of hepatocellular carcinoma. We found that the rate of R70Q/H in HCV 1b from Russia was 31.2%, similar to that in HCV strains from Asia (34.0%), higher than that in the European (18.0%, p = 0.0010), but lower than that in the US HCV 1b strains (62.8%, p < 0.0001). Substitution L91M was found in 80.4% of the Russian HCV 1b isolates, higher than in Asian isolates (43.8%, p < 0.0001). Thus, a significant proportion of Russian HCV 1b isolates carry the unfavorable R70Q/H and/or L91M substitution. In silico analysis of the epitopic structure of the regions of substitutions revealed that both harbor clusters of T-cell epitopes. Peptides encompassing these regions were predicted to bind to a panel of HLA class I molecules, with substitutions impairing peptide recognition by HLA I molecules of the alleles prevalent in Russia. This indicates that HCV 1b with R70Q/H and L91M substitutions may have evolved as the immune escape variants. Impairment of T-cell recognition may play a part in the negative effect of these substitutions on the response to IFN treatment.

• direct-acting antiviral agents
• hepatitis C virus
• resistance-associated substitutions
• unstructured proteins

Significant progress has been made in characterizing broadly neutralizing antibodies against the HIV envelope glycoprotein Env, but an effective vaccine has proven elusive. Vaccine development would be facilitated if common features of early founder virus required for transmission could be identified. Here we employ a combination of bioinformatic and operations research methods to determine the most prevalent features that distinguish 78 subtype B and 55 subtype C founder Env sequences from an equal number of chronic sequences. There were a number of equivalent optimal networks (based on the fewest covarying amino acid (AA) pairs or a measure of maximal covariance) that separated founders from chronics: 13 pairs for subtype B and 75 for subtype C. Every subtype B optimal solution contained the founder pairs 178–346 Asn-Val, 232–236 Thr-Ser, 240–340 Lys-Lys, 279–315 Asp-Lys, 291–792 Ala-Ile, 322–347 Asp-Thr, 535–620 Leu-Asp, 742–837 Arg-Phe, and 750–836 Asp-Ile; the most common optimal pairs for subtype C were 644–781 Lys-Ala (74 of 75 networks), 133–287 Ala-Gln (73/75) and 307–337 Ile-Gln (73/75). No pair was present in all optimal subtype C solutions highlighting the difficulty in targeting transmission with a single vaccine strain. Relative to the size of its domain (0.35% of Env), the α₄β₇ binding site occurred most frequently among optimal pairs, especially for subtype C: 4.2% of optimal pairs (1.2% for subtype B). Early sequences from 5 subtype B pre-seroconverters each exhibited at least one clone containing an optimal feature 553–624 (Ser-Asn), 724–747 (Arg-Arg), or 46–293 (Arg-Glu).

• Binding Sites
• Founder Effect
• Polymorphism, Genetic
• Protein Binding
• env Gene Products, Human Immunodeficiency Virus/chemistry
• env Gene Products, Human Immunodeficiency Virus/classification
• env Gene Products, Human Immunodeficiency Virus/genetics
• env Gene Products, Human Immunodeficiency Virus/metabolism

• University of New South Wales

• Amino Acid Sequence
• Amino Acids/metabolism
• Darunavir/pharmacology
• HEK293 Cells
• HIV Protease/chemistry
• HIV Protease/metabolism
• HIV-1/drug effects
• HIV-1/enzymology
• Humans
• Models, Molecular
• Molecular Sequence Data
• Mutation/genetics
• Protein Processing, Post-Translational/drug effects
• Structural Homology, Protein
• Structure-Activity Relationship
• gag Gene Products, Human Immunodeficiency Virus/chemistry
• gag Gene Products, Human Immunodeficiency Virus/metabolism

• R01 CA126807 NCI NIH HHS
• R21A1080351 PHS HHS
• CA126807 NCI NIH HHS
• R03AI108392-01A1 NIAID NIH HHS
• R03 AI108392 NIAID NIH HHS

Rapidly mutating viruses, such as hepatitis C virus (HCV) and HIV, have adopted evolutionary strategies that allow escape from the host immune response via genomic mutations. Recent advances in high-throughput sequencing are reshaping the field of immuno-virology of viral infections, as these allow fast and cheap generation of genomic data. However, due to the large volumes of data generated, a thorough understanding of the biological and immunological significance of such information is often difficult. This paper proposes a pipeline that allows visualization and statistical analysis of viral mutations that are associated with immune escape. Taking next generation sequencing data from longitudinal analysis of HCV viral genomes during a single HCV infection, along with antigen specific T-cell responses detected from the same subject, we demonstrate the applicability of these tools in the context of primary HCV infection. We provide a statistical and visual explanation of the relationship between cooccurring mutations on the viral genome and the parallel adaptive immune response against HCV.

• Adaptation
• Bayesian network
• Coevolution
• HEV ORFs
• Hepatitis E virus
• Prediction

• Adaptation, Physiological/genetics
• Animals
• Base Sequence
• Bayes Theorem
• Genetic Variation
• Hepatitis E/transmission
• Hepatitis E virus/classification
• Hepatitis E virus/genetics
• Hepatitis E virus/pathogenicity
• Host Specificity/genetics
• Humans
• Open Reading Frames/genetics
• Phylogeny
• Sequence Alignment
• Swine
• Swine Diseases/virology
• Viral Nonstructural Proteins/genetics

• CC999999 Intramural CDC HHS

• Antiviral Agents/therapeutic use
• Genotype
• Hepacivirus/genetics
• Hepatitis C/drug therapy
• Hepatitis C/virology
• Humans
• Interferon-alpha/therapeutic use
• Models, Biological
• Models, Statistical
• Multivariate Analysis
• Phylogeny
• Ribavirin/therapeutic use
• Treatment Outcome
• Viral Envelope Proteins/genetics

Chronic infection by either hepatitis B virus (HBV) or hepatitis C virus (HCV) share epidemiological characteristics with risks for development of severe complications such as liver cirrhosis and hepatocellular carcinoma. HBV and HCV also share a high genetic variability. Among highly variable regions, viral genes encoding surface proteins (hepatitis B surface antigen, E1/E2 HCV glycoproteins) play key roles in the stimulation of the host-related immune response and viral entry into hepatocytes. Specific segments of HBV envelope proteins (preS1, “a” determinant) are crucial in the entry process into permissive cells. HCV entry is a complex multistep process involving multiple cell cofactors (glycosaminoglycans, low density lipoprotein receptor, SR-B1, CD81, claudin-1, occludin, EGFR, EphA2) in the interaction with HCV E1/E2 envelope glycoproteins. In vitro both viruses can be controlled by antibody-mediated neutralization targeting viral envelope, also essential in preventing HBV infection in vivo as observed through successful vaccination using HBs antigen. But preventive vaccination and/or therapeutic pressure can influence HBV and HCV variability. For HBV, the patterns of antiviral drug resistance in chronic hepatitis are complex and the original pol/S gene overlap has to be taken into account. Treatment-induced HBV mutations in pol could indeed generate S mutants with subsequent modified antigenicity or increased cancer induction. Variability of HBV and HCV envelope proteins combining high exposure to selective pressures and crucial functional roles require investigation in the context of diagnostic, vaccination and treatment tools. In this editorial a synthesis is performed of HBV and HCV envelope properties at the entry step and as antigenic proteins, and the subsequent clinical impact.

• Hepatitis B
• Hepatitis C
• Viral envelope glycoproteins
• Clinical outcome

• Animals
• Antiviral Agents/therapeutic use
• Drug Resistance, Viral
• Genotype
• Hepacivirus/drug effects
• Hepacivirus/genetics
• Hepacivirus/immunology
• Hepacivirus/metabolism
• Hepacivirus/pathogenicity
• Hepatitis B Vaccines
• Hepatitis B virus/drug effects
• Hepatitis B virus/genetics
• Hepatitis B virus/immunology
• Hepatitis B virus/metabolism
• Hepatitis B virus/pathogenicity
• Hepatitis B, Chronic/diagnosis
• Hepatitis B, Chronic/drug therapy
• Hepatitis B, Chronic/immunology
• Hepatitis B, Chronic/prevention & control
• Hepatitis B, Chronic/virology
• Hepatitis C, Chronic/diagnosis
• Hepatitis C, Chronic/drug therapy
• Hepatitis C, Chronic/immunology
• Hepatitis C, Chronic/prevention & control
• Hepatitis C, Chronic/virology
• Host-Pathogen Interactions
• Humans
• Phenotype
• Prognosis
• Viral Envelope Proteins/genetics
• Viral Envelope Proteins/metabolism

• Antiviral Agents/pharmacology
• Antiviral Agents/therapeutic use
• Biological Evolution
• Drug Resistance, Viral/genetics
• Drug Therapy, Combination
• Epistasis, Genetic
• Genetic Markers
• Genetic Variation
• Genome, Viral/genetics
• Genotype
• Hepacivirus/drug effects
• Hepacivirus/genetics
• Hepatitis C/drug therapy
• Hepatitis C/virology
• Humans
• Interferons/pharmacology
• Interferons/therapeutic use
• Ribavirin/pharmacology
• Ribavirin/therapeutic use
• Treatment Outcome

• CC999999 Intramural CDC HHS

Enzymes are powerful biocatalysts, however, so far there is still a large gap between the number of enzyme-based practical applications and that of naturally occurring enzymes. Multiple experimental approaches have been applied to generate nearly all possible mutations of target enzymes, allowing the identification of desirable variants with improved properties to meet the practical needs. Meanwhile, an increasing number of computational methods have been developed to assist in the modification of enzymes during the past few decades. With the development of bioinformatic algorithms, computational approaches are now able to provide more precise guidance for enzyme engineering and make it more efficient and less laborious. In this review, we summarize the recent advances of method development with significant biological outcomes to provide important insights into successful computational protein designs. We also discuss the limitations and challenges of existing methods and the future directions that should improve them.

• Base Sequence
• Biological Evolution
• Cluster Analysis
• DNA Primers/genetics
• Demography
• Erythema Infectiosum/virology
• Genome, Viral/genetics
• Genotype
• Humans
• Likelihood Functions
• Models, Genetic
• Molecular Sequence Data
• Netherlands
• Parvovirus B19, Human/genetics
• Phylogeny
• Sequence Analysis, DNA
• Species Specificity

Molecular covariation of highly polymorphic viruses is thought to have crucial effects on viral replication and fitness. This study employs association rule data mining of hepatitis C virus (HCV) sequences to search for specific evolutionary covariation and then tests functional relevance on HCV replication. Data mining is performed between nucleotides in the untranslated regions 5′ and 3′UTR, and the amino acid residues in the non-structural proteins NS2, NS3 and NS5B. Results indicate covariance of the 243^rd nucleotide of the 5′UTR with the 14^th, 41^st, 76^th, 110^th, 211^th and 212^th residues of NS2 and with the 71^st, 175^th and 621^st residues of NS3. Real-time experiments using an HCV subgenomic system to quantify viral replication confirm replication regulation for each covariant pair between 5′UTR₂₄₃ and NS2-41, -76, -110, -211, and NS3-71, -175. The HCV subgenomic system with/without the NS2 region shows that regulatory effects vanish without NS2, so replicative modulation mediated by HCV 5′UTR₂₄₃ depends on NS2. Strong binding of the NS2 variants to HCV RNA correlates with reduced HCV replication whereas weak binding correlates with restoration of HCV replication efficiency, as determined by RNA-protein immunoprecipitation assay band intensity. The dominant haplotype 5′UTR₂₄₃-NS2-41-76-110-211-NS3-71-175 differs according to the HCV genotype: G-Ile-Ile-Ile-Gly-Ile-Met for genotype 1b and A-Leu-Val-Leu-Ser-Val-Leu for genotypes 1a, 2a and 2b. In conclusion, 5′UTR₂₄₃ co-varies with specific NS2/3 protein amino acid residues, which may have significant structural and functional consequences for HCV replication. This unreported mechanism involving HCV replication possibly can be exploited in the development of advanced anti-HCV medication.

• Base Sequence
• DNA, Viral/genetics
• Genome, Viral
• Genotype
• Hepatitis B/genetics
• Hepatitis B/transmission
• Hepatitis B/virology
• Hepatitis B virus/classification
• Hepatitis B virus/genetics
• Humans
• Molecular Sequence Data
• Phylogeny
• Real-Time Polymerase Chain Reaction
• Sequence Analysis, DNA

• Aged
• Antiviral Agents/administration & dosage
• Female
• Genotype
• Hepatitis C, Chronic/drug therapy
• Humans
• Interferons
• Interleukins/genetics
• Male
• Middle Aged
• Mutation
• Polymerase Chain Reaction/methods
• Polymorphism, Single Nucleotide
• Prognosis
• Transition Temperature
• Treatment Outcome

• hepatitis c virus
• rna binding
• rna polymerase activity
• viral genetics

• Antiviral Agents/administration & dosage
• Antiviral Agents/pharmacology
• Antiviral Agents/therapeutic use
• DNA-Directed RNA Polymerases/drug effects
• DNA-Directed RNA Polymerases/genetics
• DNA-Directed RNA Polymerases/metabolism
• Drug Therapy, Combination
• Genetic Variation/genetics
• Hepacivirus/drug effects
• Hepacivirus/enzymology
• Hepacivirus/genetics
• Hepatitis C/drug therapy
• Hepatitis C/virology
• Humans
• Interferon-alpha/administration & dosage
• Interferon-alpha/pharmacology
• Interferon-alpha/therapeutic use
• RNA, Viral/genetics
• RNA-Binding Proteins/genetics
• RNA-Binding Proteins/metabolism
• Recurrence
• Ribavirin/administration & dosage
• Ribavirin/pharmacology
• Ribavirin/therapeutic use
• Viral Nonstructural Proteins/genetics
• Viral Nonstructural Proteins/metabolism

• R01 DK074515 NIDDK NIH HHS
• R21 CA125321 NCI NIH HHS
• CA125321 NCI NIH HHS
• DK074515-S2 NIDDK NIH HHS

• Adaptation, Biological
• Antiviral Agents/therapeutic use
• Drug Resistance, Viral
• Evolution, Molecular
• Hepacivirus/drug effects
• Hepacivirus/genetics
• Hepatitis C, Chronic/drug therapy
• Hepatitis C, Chronic/virology
• Humans
• Interferons/therapeutic use
• Polyproteins/genetics
• Ribavirin/therapeutic use
• Treatment Outcome

Chronic hepatitis C virus (HCV) infection is a major cause of liver disease worldwide. HCV infection is currently treated with IFNα plus ribavirin for 24 to 48 weeks. This demanding therapy fails in up to 50% of patients, so the use of pharmacogenetic biomarkers to predict the outcome of treatment would reduce futile treatment of non-responders and help identify patients in whom therapy would be justified. Both IFNα and ribavirin primarily act by modulating the immune system of the patient, and HCV uses multiple mechanisms to counteract the antiviral effects stimulated by therapy. Therefore, response to therapy is influenced by variations in human genes governing the immune system and by differences in HCV genes that blunt antiviral immune responses. This article summarizes recent advances in understanding how host and viral genetic variation affect outcome of therapy. The most notable human associations are polymorphisms within the IL28B gene, but variations in human leukocyte antigen and cytokine genes have also been associated with treatment outcome. The most prominent viral genetic association with outcome of therapy is that HCV genotype 1 is much less sensitive to treatment than genotypes 2 and 3, but genetic differences below the genotype level also influence outcome of therapy, presumably by modulating the ability of viral genes to blunt antiviral immune responses. Pharmacogenetic prediction of the outcome of IFN-based therapy for HCV will require integrating the efficacies of the immunosuppressive mechanisms of a viral isolate, and then interpreting the viral resistance potential in context of the genetic profile of the patient at loci associated with outcome of therapy. Direct-acting inhibitors of HCV that will be used in combination with IFNα are nearing approval, so genetic prediction for anti-HCV therapy will soon need to incorporate viral genetic markers of viral resistance to the new drugs.

• HBV
• HCV
• HIV
• antiviral therapy
• drug resistance
• evolution
• quasispecies

• R01 AI068581 NIAID NIH HHS
• R56 AI068581 NIAID NIH HHS

Current treatment of chronic hepatitis C virus (HCV) infection has limited efficacy −especially among genotype 1 infected patients−, is costly, and involves severe side effects. Thus, predicting non-response is of major interest for both patient wellbeing and health care expense. At present, treatment cannot be individualized on the basis of any baseline predictor of response. We aimed to identify pre-treatment clinical and virological parameters associated with treatment failure, as well as to assess whether therapy outcome could be predicted at baseline.

Forty-three HCV subtype 1b (HCV-1b) chronically infected patients treated with pegylated-interferon alpha plus ribavirin were retrospectively studied (21 responders and 22 non-responders). Host (gender, age, weight, transaminase levels, fibrosis stage, and source of infection) and viral-related factors (viral load, and genetic variability in the E1–E2 and Core regions) were assessed. Logistic regression and discriminant analyses were used to develop predictive models. A “leave-one-out” cross-validation method was used to assess the reliability of the discriminant models.

Lower alanine transaminase levels (ALT, p = 0.009), a higher number of quasispecies variants in the E1–E2 region (number of haplotypes, nHap_E1–E2) (p = 0.003), and the absence of both amino acid arginine at position 70 and leucine at position 91 in the Core region (p = 0.039) were significantly associated with treatment failure. Therapy outcome was most accurately predicted by discriminant analysis (90.5% sensitivity and 95.5% specificity, 85.7% sensitivity and 81.8% specificity after cross-validation); the most significant variables included in the predictive model were the Core amino acid pattern, the nHap_E1–E2, and gamma-glutamyl transferase and ALT levels.

Discriminant analysis has been shown as a useful tool to predict treatment outcome using baseline HCV genetic variability and host characteristics. The discriminant models obtained in this study led to accurate predictions in our population of Spanish HCV-1b treatment naïve patients.

• interferon
• stat-c
• combination therapy
• personalized therapy
• emerging drug targets
• viral life cycle

• R01 AI070827 NIAID NIH HHS
• R21 CA133266 NCI NIH HHS
• T32 DK007788 NIDDK NIH HHS

• antiviral
• boceprevir
• immunity
• interferon-stimulated genes
• pegylated-interferon
• polymerase inhibitors
• protease inhibitors
• stat-c
• sustained virological response
• telaprevir

• Adolescent
• Adult
• Aged
• Amino Acid Sequence
• Animals
• Antigens, CD/genetics
• Antigens, CD/immunology
• Cell Line
• Female
• Genetic Variation
• Hepacivirus/drug effects
• Hepacivirus/genetics
• Hepacivirus/isolation & purification
• Hepacivirus/physiology
• Hepatitis C/immunology
• Hepatitis C/transmission
• Hepatitis C/virology
• Hepatitis C Antibodies/pharmacology
• Humans
• Liver Transplantation/adverse effects
• Male
• Mice
• Mice, SCID
• Middle Aged
• Molecular Sequence Data
• Phylogeny
• Sequence Alignment
• Tetraspanin 28
• Viral Envelope Proteins/chemistry
• Viral Envelope Proteins/genetics
• Viral Envelope Proteins/immunology
• Virus Internalization/drug effects
• Young Adult

• MC_U130184144 Medical Research Council

• Adult
• Black or African American/statistics & numerical data
• Antiviral Agents/therapeutic use
• Drug Therapy, Combination
• Female
• Gene Frequency
• Genetic Variation
• Genome, Viral
• Genotype
• Hepacivirus/classification
• Hepacivirus/genetics
• Hepatitis C/drug therapy
• Hepatitis C/ethnology
• Hepatitis C/genetics
• Humans
• Interferon alpha-2
• Interferon-alpha/therapeutic use
• Male
• MicroRNAs/genetics
• Middle Aged
• Outcome Assessment, Health Care
• Phylogeny
• Polyethylene Glycols/therapeutic use
• Recombinant Proteins
• Ribavirin/therapeutic use
• Viral Core Proteins/genetics
• Viral Nonstructural Proteins/genetics
• White People/statistics & numerical data

• U01 DK060345 NIDDK NIH HHS
• U01 DK60345 NIDDK NIH HHS

• Adult
• Antiviral Agents/therapeutic use
• Australia
• Drug Therapy, Combination
• Female
• Gene Expression Regulation
• Genome-Wide Association Study
• Genotype
• Hepatitis C, Chronic/drug therapy
• Hepatitis C, Chronic/genetics
• Hepatitis C, Chronic/virology
• Humans
• Interferon-alpha/therapeutic use
• Interferons
• Interleukins/genetics
• Male
• Middle Aged
• Polyethylene Glycols/therapeutic use
• Polymorphism, Single Nucleotide
• Ribavirin/therapeutic use
• Treatment Outcome

• G0502028 Medical Research Council

• Drug Resistance, Viral
• Genotype
• Hepacivirus/classification
• Hepacivirus/genetics
• Hepatitis C, Chronic/drug therapy
• Hepatitis C, Chronic/virology
• Humans
• Interferons/therapeutic use
• Phylogeny

• F32 AI082930 NIAID NIH HHS
• R01 DK064223 NIDDK NIH HHS
• T32 AI070502 NIAID NIH HHS
• 1 F32 AI082930 NIAID NIH HHS

• Animals
• Biomarkers/analysis
• Hepacivirus/genetics
• Hepacivirus/immunology
• Hepacivirus/isolation & purification
• Hepacivirus/physiology
• Hepatitis C/diagnosis
• Hepatitis C/epidemiology
• Hepatitis C/therapy
• Hepatitis C/virology
• Humans
• Substance-Related Disorders/complications
• Substance-Related Disorders/diagnosis
• Substance-Related Disorders/immunology
• Substance-Related Disorders/virology

• U24 MH100929 NIMH NIH HHS
• U01 MH083500 NIMH NIH HHS
• AI07126 NIAID NIH HHS
• R01 GM056529 NIGMS NIH HHS
• CA16042 NCI NIH HHS
• DA14533 NIDA NIH HHS
• P30 CA016042 NCI NIH HHS
• GM056529 NIGMS NIH HHS
• DA07683 NIDA NIH HHS
• NS038841 NINDS NIH HHS
• T32 AI007126 NIAID NIH HHS
• R24 NS038841 NINDS NIH HHS
• R21 DA014533 NIDA NIH HHS
• R01 DA012580 NIDA NIH HHS
• DA12580 NIDA NIH HHS
• DA10442 NIDA NIH HHS