A systematic, deep sequencing-based methodology for identification of mixed-genotype hepatitis C virus infections.

Hepatitis C virus (HCV) mixed genotype infections can affect treatment outcomes and may have implications for vaccine design and disease progression. Previous studies demonstrate 0-39% of high-risk, HCV-infected individuals harbor mixed genotypes however standardized, sensitive methods of detection are lacking. This study compared PCR amplicon, random primer (RP), and probe enrichment (PE)-based deep sequencing methods coupled with a custom sequence analysis pipeline to detect multiple HCV genotypes. Mixed infection cutoff values, based on HCV read depth and coverage, were identified using receiver operating characteristic curve analysis. The methodology was validated using artificially mixed genotype samples and then applied to two clinical trials of HCV treatment in high-risk individuals (ACTIVATE, 114 samples from 90 individuals; DARE-C II, 26 samples from 18 individuals) and a cohort of HIV/HCV co-infected individuals (Canadian Coinfection Cohort (CCC), 3 samples from 2 individuals with suspected mixed genotype infections). Amplification bias of genotype (G)1b, G2, G3 and G5 was observed in artificially mixed samples using the PCR method while no genotype bias was observed using RP and PE. RP and PE sequencing of 140 ACTIVATE and DARE-C II samples identified the following primary genotypes: 15% (n = 21) G1a, 76% (n = 106) G3, and 9% (n = 13) G2. Sequencing of ACTIVATE and DARE-C II demonstrated, on average, 2% and 1% of HCV reads mapping to a second genotype using RP and PE, respectively, however none passed the mixed infection cutoff criteria and phylogenetics confirmed no mixed infections. From CCC, one mixed infection was confirmed while the other was determined to be a recombinant genotype. This study underlines the risk for false identification of mixed HCV infections and stresses the need for standardized methods to improve prevalence estimates and to understand the impact of mixed infections for management and elimination of HCV.

Genotypic analysis of the V3 region of HIV from virologic nonresponders to maraviroc-containing regimens reveals distinct patterns of failure.

Changes in HIV tropism from R5 to non-R5 or development of drug resistance is often associated with virologic failure in patients treated with maraviroc, a CCR5 antagonist. We sought to examine changes in HIV envelope sequences and inferred tropism in patients who did not respond to maraviroc-based regimens. We selected 181 patients who experienced early virologic failure on maraviroc-containing therapy in the MOTIVATE trials. All patients had R5 HIV by the original Trofile assay before entry. We used population-based sequencing methods and the geno2pheno algorithm to examine changes in tropism and V3 sequences at the time of failure. Using deep sequencing, we assessed whether V3 sequences observed at failure emerged from preexisting subpopulations. From population genotyping data at failure, 90 patients had R5 results, and 91 had non-R5 results. Of the latter group, the geno2pheno false-positive rate (FPR) value fell from a median of 20 at screening to 1.1 at failure. By deep sequencing, the median percentage of non-R5 variants in these patients rose from 1.4% to 99.5% after a median of 4 weeks on maraviroc. In 70% of cases, deep sequencing could detect a pretreatment CXCR4-using subpopulation, which emerged at failure. Overall, there were two distinct patterns of failure of maraviroc. Patients failing with R5 generally had few V3 substitutions and low non-R5 prevalence by deep sequencing. Patients with non-R5 HIV who were failing developed very-high-prevalence non-R5 HIV (median, 99%) and had very low geno2pheno values.

Correlates of protective cellular immunity revealed by analysis of population-level immune escape pathways in HIV-1.

HLA class I-associated polymorphisms identified at the population level mark viral sites under immune pressure by individual HLA alleles. As such, analysis of their distribution, frequency, location, statistical strength, sequence conservation, and other properties offers a unique perspective from which to identify correlates of protective cellular immunity. We analyzed HLA-associated HIV-1 subtype B polymorphisms in 1,888 treatment-naïve, chronically infected individuals using phylogenetically informed methods and identified characteristics of HLA-associated immune pressures that differentiate protective and nonprotective alleles. Over 2,100 HLA-associated HIV-1 polymorphisms were identified, approximately one-third of which occurred inside or within 3 residues of an optimally defined cytotoxic T-lymphocyte (CTL) epitope. Differential CTL escape patterns between closely related HLA alleles were common and increased with greater evolutionary distance between allele group members. Among 9-mer epitopes, mutations at HLA-specific anchor residues represented the most frequently detected escape type: these occurred nearly 2-fold more frequently than expected by chance and were computationally predicted to reduce peptide-HLA binding nearly 10-fold on average. Characteristics associated with protective HLA alleles (defined using hazard ratios for progression to AIDS from natural history cohorts) included the potential to mount broad immune selection pressures across all HIV-1 proteins except Nef, the tendency to drive multisite and/or anchor residue escape mutations within known CTL epitopes, and the ability to strongly select mutations in conserved regions within HIV's structural and functional proteins. Thus, the factors defining protective cellular immune responses may be more complex than simply targeting conserved viral regions. The results provide new information to guide vaccine design and immunogenicity studies.

Automating HIV drug resistance genotyping with RECall, a freely accessible sequence analysis tool.

Genotypic HIV drug resistance testing is routinely used to guide clinical decisions. While genotyping methods can be standardized, a slow, labor-intensive, and subjective manual sequence interpretation step is required. We therefore performed external validation of our custom software RECall, a fully automated sequence analysis pipeline. HIV-1 drug resistance genotyping was performed on 981 clinical samples at the Stanford Diagnostic Virology Laboratory. Sequencing trace files were first interpreted manually by a laboratory technician and subsequently reanalyzed by RECall, without intervention. The relative performances of the two methods were assessed by determination of the concordance of nucleotide base calls, identification of key resistance-associated substitutions, and HIV drug resistance susceptibility scoring by the Stanford Sierra algorithm. RECall is freely available at http://pssm.cfenet.ubc.ca. In total, 875 of 981 sequences were analyzed by both human and RECall interpretation. RECall analysis required minimal hands-on time and resulted in a 25-fold improvement in processing speed (∼150 technician-hours versus ∼6 computation-hours). Excellent concordance was obtained between human and automated RECall interpretation (99.7% agreement for >1,000,000 bases compared). Nearly all discordances (99.4%) were due to nucleotide mixtures being called by one method but not the other. Similarly, 98.6% of key antiretroviral resistance-associated mutations observed were identified by both methods, resulting in 98.5% concordance of resistance susceptibility interpretations. This automated sequence analysis tool provides both standardization of analysis and a significant improvement in data workflow. The time-consuming, error-prone, and dreadfully boring manual sequence analysis step is replaced with a fully automated system without compromising the accuracy of reported HIV drug resistance data.

The relationship between resistance and adherence in drug-naive individuals initiating HAART is specific to individual drug classes.

OBJECTIVE: To investigate the relationship between HIV-1 drug resistance and adherence and the accumulation rate of resistance mutations in 1191 HIV-infected, antiretroviral-naive adults initiating highly active antiretroviral therapy in British Columbia, Canada. METHODS: Plasma samples with plasma viral load >1,000 copies per milliliter collected within 30 months of follow-up were genotyped for drug resistance. Adherence was estimated using prescription refills and plasma drug levels. The primary outcome measure was time to detection of drug resistance. Cox proportional hazard regression was used to calculate hazard ratios (HRs) associated with baseline variables. RESULTS: The accumulation rates of multiple primary and secondary mutations were similar in patients initiating highly active antiretroviral therapy with protease inhibitor versus nonnucleoside reverse transcriptase inhibitor (NNRTI). Rates decreased approximately 50% per additional mutation. At 80%-90% adherence based on refills, there was greater risk of detecting lamivudine (3TC) [HR 3.0, 95% confidence interval (CI): 1.9 to 4.7; P < 0.0001] and NNRTI mutations (HR 6.0, 95% CI: 3.3 to 10.9; P < 0.0001) compared with the >or=95% refill reference group. In a multivariate model, individuals with <95% refills and consistently detectable plasma drug levels were at increased risk for 3TC (HR 4.5, 95% CI: 2.6 to 7.9; P = 0.0001) and NNRTI resistance (HR 7.0, 95% CI: 3.4 to 14.5; P = 0.0001) compared with the reference group of >or=95% refills with consistently detectable drug levels. Adherence-resistance relationships were much weaker for protease inhibitors and nucleoside reverse transcriptase inhibitors as there was little variance in HRs among the different adherence strata compared with 3TC and NNRTIs. CONCLUSION: The relationships between resistance, adherence, and mutation accumulation differ between HIV drug classes.

Evidence of differential HLA class I-mediated viral evolution in functional and accessory/regulatory genes of HIV-1.

Despite the formidable mutational capacity and sequence diversity of HIV-1, evidence suggests that viral evolution in response to specific selective pressures follows generally predictable mutational pathways. Population-based analyses of clinically derived HIV sequences may be used to identify immune escape mutations in viral genes; however, prior attempts to identify such mutations have been complicated by the inability to discriminate active immune selection from virus founder effects. Furthermore, the association between mutations arising under in vivo immune selection and disease progression for highly variable pathogens such as HIV-1 remains incompletely understood. We applied a viral lineage-corrected analytical method to investigate HLA class I-associated sequence imprinting in HIV protease, reverse transcriptase (RT), Vpr, and Nef in a large cohort of chronically infected, antiretrovirally naïve individuals. A total of 478 unique HLA-associated polymorphisms were observed and organized into a series of "escape maps," which identify known and putative cytotoxic T lymphocyte (CTL) epitopes under selection pressure in vivo. Our data indicate that pathways to immune escape are predictable based on host HLA class I profile, and that epitope anchor residues are not the preferred sites of CTL escape. Results reveal differential contributions of immune imprinting to viral gene diversity, with Nef exhibiting far greater evidence for HLA class I-mediated selection compared to other genes. Moreover, these data reveal a significant, dose-dependent inverse correlation between HLA-associated polymorphisms and HIV disease stage as estimated by CD4(+) T cell count. Identification of specific sites and patterns of HLA-associated polymorphisms across HIV protease, RT, Vpr, and Nef illuminates regions of the genes encoding these products under active immune selection pressure in vivo. The high density of HLA-associated polymorphisms in Nef compared to other genes investigated indicates differential HLA class I-driven evolution in different viral genes. The relationship between HLA class I-associated polymorphisms and lower CD4(+) cell count suggests that immune escape correlates with disease status, supporting an essential role of maintenance of effective CTL responses in immune control of HIV-1. The design of preventative and therapeutic CTL-based vaccine approaches could incorporate information on predictable escape pathways.

Effects of human leukocyte antigen class I genetic parameters on clinical outcomes and survival after initiation of highly active antiretroviral therapy.

BACKGROUND: Human leukocyte antigen (HLA) class I variation influences the progression of untreated human immunodeficiency virus (HIV) disease; however, it is not known whether HLA class I variation may influence clinical outcomes after initiation of highly active antiretroviral therapy (HAART). METHODS: Associations between HLA class I genotypes and pretherapy clinical parameters were investigated in a cohort of 765 antiretroviral-naive adults initiating HAART. Cox proportional hazards regression was used to investigate the effects of HLA class I genotypes on time to suppression of the viral load to <500 HIV RNA copies/mL, time to an increase in the CD4 cell count to >100 cells/mm(3) above the baseline count, and time to nonaccidental death over a >5-year period after initiation of HAART. RESULTS: Homozygosity at any HLA class I locus and possession of common HLA alleles were associated with a higher pretherapy viral load (P<.05). In multivariate analyses controlling for sociodemographic and clinical parameters at baseline, HLA class I homozygosity was significantly associated with a poorer CD4 cell response (P=.008), whereas possession of uncommon HLA alleles was associated with slower virologic suppression after initiation of HAART (P=.02). We observed no significant association between HLA parameters and time to nonaccidental death after initiation of HAART (P>.05, univariate analysis). CONCLUSION: HLA class I zygosity-dependent and frequency-dependent effects may influence short-term HAART outcomes, and, thus, they deserve further investigation. No effects of these HLA parameters on survival after initiation of HAART were observed.

A simple screening approach to reduce B*5701-associated abacavir hypersensitivity on the basis of sequence variation in HIV reverse transcriptase.

BACKGROUND: Abacavir hypersensitivity is strongly associated with the human leukocyte antigen (HLA)-B*5701 allele; however, the cost of routine high-resolution HLA typing before initiation of therapy remains prohibitive. We propose a simple approach to reduce B*5701-associated abacavir hypersensitivity based on the screening of human immunodeficiency virus (HIV) reverse transcriptase (RT) for a signature B*5701-associated cytotoxic T lymphocyte escape mutation at RT codon 245. METHODS: The correlation between HLA-B*5701 and RT codon 245 variation was investigated in 392 HIV-infected, antiretroviral-naive adults who were initiating highly active antiretroviral therapy. The relationship between codon 245 variation and premature abacavir discontinuation was investigated in a larger cohort of treated individuals (n=982). Associations between HLA-B*5701 and codon 245 variants were determined using Fisher's exact test or the chi (2) test. RESULTS: A very strong association between HLA-B*5701 and RT codon 245 variation was observed. Only 1 (4.2%) of 24 subjects with B*5701 harbored virus with the clade B "wild-type" amino acid 245V, compared with 278 (75.5%) of 368 who did not have B*5701 (P<.001). The sensitivity and specificity of codon 245 substitutions for predicting HLA-B*5701 were 96% and 75%, respectively, and the positive and negative predictive values were 20% and 99.6%, respectively. This association remained robust even after antiretroviral treatment was administered (negative predictive value, 100%; n=269). In abacavir-treated individuals (n=982), codon 245 substitutions were predictive of premature abacavir discontinuation (P=.02). CONCLUSIONS: As HIV RT sequence is incidentally obtained as a part of routine drug-resistance testing, the examination of sequence variation at RT codon 245 could be adopted as a simple, low-cost screening method to identify individuals who could be safely treated with abacavir and/or who could benefit from HLA characterization.

HIV VprR77Q mutation does not influence clinical response of individuals initiating highly active antiretroviral therapy.

VprR77Q has been associated with long-term nonprogressive (LTNP) HIV infection. We wished to investigate the prevalence, clinical correlates, and effect on treatment response of VprR77Q in a cohort of antiretroviral- naïve individuals initiating highly active antiretroviral therapy (HAART). Baseline plasma samples from 728 subjects were genotyped using RT-PCR and direct DNA sequencing. Cox proportional hazards regression was used to model the effects of VprR77Q on virologic and immunologic responses, and survival following initiation of HAART, over a median 4.5 years follow-up. We found that 308 subjects (42.3%) harbored VprR77Q alone or in combination with another amino acid, while 420 (57.7%) harbored an amino acid other than Q. A cross-sectional analysis found no correlation between R77Q and baseline plasma viral load (pVL), CD4 count, diagnosis of AIDS, or sociodemographic characteristics including age, gender, and history of injection drug use (p > 0.1). In multivariate analyses, no significant associations between VprR77Q and initial pVL and CD4 responses to HAART or survival following initiation of treatment were observed (p > 0.1). The high prevalence and the lack of association with pretherapy clinical parameters in this cohort argue against an association of R77Q with LTNP status. These results do not support an association between R77Q and HAART response.