HIV drug resistance testing by high-multiplex "wide" sequencing on the MiSeq instrument.

Limited access to HIV drug resistance testing in low- and middle-income countries impedes clinical decision-making at the individual patient level. An efficient protocol to address this issue must be established to minimize negative therapeutic outcomes for HIV-1-infected individuals in such settings. This is an observational study to ascertain the potential of newer genomic sequencing platforms, such as the Illumina MiSeq instrument, to provide accurate HIV drug resistance genotypes for hundreds of samples simultaneously. Plasma samples were collected from Canadian patients during routine drug resistance testing (n = 759) and from a Ugandan study cohort (n = 349). Amplicons spanning HIV reverse transcriptase codons 90 to 234 were sequenced with both MiSeq sequencing and conventional Sanger sequencing methods. Sequences were evaluated for nucleotide concordance between methods, using coverage and mixture parameters for quality control. Consensus sequences were also analyzed for disparities in the identification of drug resistance mutations. Sanger and MiSeq sequencing was successful for 881 samples (80%) and 892 samples (81%), respectively, with 832 samples having results from both methods. Most failures were for samples with viral loads of <3.0 log10 HIV RNA copies/ml. Overall, 99.3% nucleotide concordance between methods was observed. MiSeq sequencing achieved 97.4% sensitivity and 99.3% specificity in detecting resistance mutations identified by Sanger sequencing. Findings suggest that the Illumina MiSeq platform can yield high-quality data with a high-multiplex "wide" sequencing approach. This strategy can be used for multiple HIV subtypes, demonstrating the potential for widespread individual testing and annual population surveillance in resource-limited settings.

Development and Validation of Two Screening Assays for the Hepatitis C Virus NS3 Q80K Polymorphism Associated with Reduced Response to Combination Treatment Regimens Containing Simeprevir.

Persons with hepatitis C virus (HCV) genotype 1a (GT1a) infections harboring a baseline Q80K polymorphism in nonstructural protein 3 (NS3) have a reduced virologic response to simeprevir in combination with pegylated interferon-alfa and ribavirin. We aimed to develop, validate, and freely disseminate an NS3 clinical sequencing assay to detect the Q80K polymorphism and potentially other HCV NS3 drug resistance mutations. HCV RNA was extracted from frozen plasma using a NucliSENS easyMAG automated nucleic acid extractor, amplified by nested reverse transcription-PCR, and sequenced using Sanger and/or next-generation (MiSeq) methods. Sanger chromatograms were analyzed using in-house software (RECall), and nucleotide mixtures were called automatically. MiSeq reads were iteratively mapped to the H77 reference genome, and consensus NS3 sequences were generated with nucleotides present at >20% called as mixtures. The accuracy, precision, and sensitivity for detecting the Q80K polymorphism were assessed in 70 samples previously sequenced by an external laboratory. A comparison of the sequences generated by the Sanger and MiSeq methods with those determined by an external lab revealed >98.5% nucleotide sequence concordance and zero discordant calls of the Q80K polymorphism. The results were both highly repeatable and reproducible (>99.7% nucleotide concordance and 100% Q80K concordance). The limits of detection (>2 and ∼5 log10 IU/ml for the Sanger and MiSeq assays, respectively) are sufficiently low to allow genotyping in nearly all chronically infected treatment-naive persons. No systematic bias in the under- or overamplification of minority variants was observed. Coinfection with other viruses (e.g., HIV and hepatitis B virus [HBV]) did not affect the assay results. The two independent HCV NS3 sequencing assays with the automated analysis procedures described here are useful tools to screen for the Q80K polymorphism and other HCV protease inhibitor drug resistance mutations.

Correlation of the virological response to short-term maraviroc monotherapy with standard and deep-sequencing-based genotypic tropism prediction methods.

Genotypic tropism testing methods are emerging as the first step before prescription of the CCR5 antagonist maraviroc (MVC) to HIV-infected patients in Europe. Studies validating genotypic tests have included other active drugs that could have potentially convoluted the effects of MVC. The maraviroc clinical test (MCT) is an in vivo drug sensitivity test based on the virological response to a short-term exposure to MVC monotherapy. Thus, our aim was to compare the results of genotypic tropism testing methods with the short-term virological response to MVC monotherapy. A virological response in the MCT was defined as a ≥ 1-log(10) decrease in HIV RNA or undetectability after 8 days of drug exposure. Seventy-three patients undergoing the MCT were included in this study. We used both standard genotypic methods (n = 73) and deep sequencing (n = 27) on MCT samples at baseline. For the standard methods, the most widely used genotypic algorithms for analyzing the V3 loop sequence, geno2pheno and PSSM, were used. For deep sequencing, the geno2pheno algorithm was used with a false-positive rate cutoff of 3.5. The discordance rates between the standard genotypic methods and the virological response were approximately 20% (including mostly patients without a virological response). Interestingly, these discordance rates were similar to that obtained from deep sequencing (18.5%). The discordance rates between the genotypic methods (tropism assays predictive of the use of the CCR5 coreceptor) and the MCT (in vivo MVC sensitivity assay) indicate that the algorithms used by genotypic methods are still not sufficiently optimized.

Detection of mammalian virulence determinants in highly pathogenic avian influenza H5N1 viruses: multivariate analysis of published data.

Highly pathogenic avian influenza (HPAI) virus H5N1 infects water and land fowl and can infect and cause mortality in mammals, including humans. However, HPAI H5N1 strains are not equally virulent in mammals, and some strains have been shown to cause only mild symptoms in experimental infections. Since most experimental studies of the basis of virulence in mammals have been small in scale, we undertook a meta-analysis of available experimental studies and used Bayesian graphical models (BGM) to increase the power of inference. We applied text-mining techniques to identify 27 individual studies that experimentally determined pathogenicity in HPAI H5N1 strains comprising 69 complete genome sequences. Amino acid sequence data in all 11 genes were coded as binary data for the presence or absence of mutations related to virulence in mammals or nonconsensus residues. Sites previously implicated as virulence determinants were examined for association with virulence in mammals in this data set, and the sites with the most significant association were selected for further BGM analysis. The analyses show that virulence in mammals is a complex genetic trait directly influenced by mutations in polymerase basic 1 (PB1) and PB2, nonstructural 1 (NS1), and hemagglutinin (HA) genes. Several intra- and intersegment correlations were also found, and we postulate that there may be two separate virulence mechanisms involving particular combinations of polymerase and NS1 mutations or of NS1 and HA mutations.

Estimating selection pressures on HIV-1 using phylogenetic likelihood models.

Human immunodeficiency virus (HIV-1) can rapidly evolve due to selection pressures exerted by HIV-specific immune responses, antiviral agents, and to allow the virus to establish infection in different compartments in the body. Statistical models applied to HIV-1 sequence data can help to elucidate the nature of these selection pressures through comparisons of non-synonymous (or amino acid changing) and synonymous (or amino acid preserving) substitution rates. These models also need to take into account the non-independence of sequences due to their shared evolutionary history. We review how we have developed these methods and have applied them to characterize the evolution of HIV-1 in vivo. To illustrate our methods, we present an analysis of compartment-specific evolution of HIV-1 env in blood and cerebrospinal fluid and of site-to-site variation in the gag gene of subtype C HIV-1.