Large scale screening of human sera for HCV RNA and GBV-C RNA.

North Carolina locates acute HIV cases by pooled nucleic acid testing of HIV-antibody negative serum samples. Here, 224 pools of 80 HIV-negative samples (N = 17,920) were screened for viral RNA from HCV, GBV-C, and influenza A. No evidence of influenza A was found, but HCV and GBV-C were common (1.2% and 1.7% prevalence, respectively), demonstrating the utility of pooled testing in locating individuals that may remain undiagnosed otherwise. By sequencing positive pools, potential transmission clusters may be located as well.

Assortativity coefficient-based estimation of population patterns of sexual mixing when cluster size is informative.

OBJECTIVES: Population sexual mixing patterns can be quantified using Newman's assortativity coefficient (r). Suggested methods for estimating the SE for r may lead to inappropriate statistical conclusions in situations where intracluster correlation is ignored and/or when cluster size is predictive of the response. We describe a computer-intensive, but highly accessible, within-cluster resampling approach for providing a valid large-sample estimated SE for r and an associated 95% CI. METHODS: We introduce needed statistical notation and describe the within-cluster resampling approach. Sexual network data and a simulation study were employed to compare within-cluster resampling with standard methods when cluster size is informative. RESULTS: For the analysis of network data when cluster size is informative, the simulation study demonstrates that within-cluster resampling produces valid statistical inferences about Newman's assortativity coefficient, a popular statistic used to quantify the strength of mixing patterns. In contrast, commonly used methods are biased with attendant extremely poor CI coverage. Within-cluster resampling is recommended when cluster size is informative and/or when there is within-cluster response correlation. CONCLUSIONS: Within-cluster resampling is recommended for providing valid statistical inferences when applying Newman's assortativity coefficient r to network data.

Triple-class antiretroviral drug resistance: risk and predictors among HIV-1-infected patients.

BACKGROUND: HIV-1 triple-class antiretroviral drug resistance (TC-DR) may substantially limit therapeutic options and compromise clinical outcomes. OBJECTIVE: To estimate TC-DR prevalence and incidence, and identify risk factors for TC-DR acquisition. METHODS: We identified patients in the University of North Carolina HIV Cohort Study with TC-DR HIV-1 variants. Nucleos(t)ide reverse transcriptase inhibitor (NRTI), non-nucleoside reverse transcriptase inhibitor (NNRTI), and major protease inhibitor (PI) mutations, were based on the International AIDS Society - USA guidelines. Prevalence was estimated with the exact binomial distribution, incidence with the exact Poisson distribution, and multivariable analyses were performed using logistic regression. RESULTS: Of 1587 patients, half initiated therapy with HAART (N = 789), 20% (N = 320) with non-HAART combination therapy, and 30% (N = 478) with one NRTI. The median time on therapy was 5.7 years [interquartile range (IQR) 2.9, 8.6], the median number of previous antiretroviral agents was six (IQR 4, 8), and 47% (N = 752) were exposed to at least one NRTI, NNRTI and PI. Assuming patients without genotypes did not harbor TC-DR virus, the prevalence of TC-DR among all antiretroviral-experienced patients was 8% [95% confidence interval (CI) 6%, 9%]. The prevalence was 3% (95% CI 2%, 4%) and 12% (95% CI 10%, 15%) among patients treated initially with HAART and non-HAART, respectively. The number of antiretroviral agents received and initiating therapy with non-HAART or an unboosted PI, increased TC-DR risk in multivariable analyses. CONCLUSION: The majority of patients with TC-DR have extensive antiretroviral exposure, particularly to non-HAART regimens, whereas HAART initiators are at low risk of acquiring TC-DR during a median of 4 years of follow-up.

Primer ID Informs Next-Generation Sequencing Platforms and Reveals Preexisting Drug Resistance Mutations in the HIV-1 Reverse Transcriptase Coding Domain.

Sequencing of a bulk polymerase chain reaction (PCR) product to identify drug resistance mutations informs antiretroviral therapy selection but has limited sensitivity for minority variants. Alternatively, deep sequencing is capable of detecting minority variants but is subject to sequencing errors and PCR resampling due to low input templates. We screened for resistance mutations among 184 HIV-1-infected, therapy-naive subjects using the 454 sequencing platform to sequence two amplicons spanning HIV-1 reverse transcriptase codons 34-245. Samples from 19 subjects were also analyzed using the MiSeq sequencing platform for comparison. Errors and PCR resampling were addressed by tagging each HIV-1 RNA template copy (i.e., cDNA) with a unique sequence tag (Primer ID), allowing a consensus sequence to be constructed for each original template from resampled sequences. In control reactions, Primer ID reduced 454 and MiSeq errors from 71 to 2.6 and from 24 to 1.2 errors/10,000 nucleotides, respectively. MiSeq also allowed accurate sequencing of codon 65, an important drug resistance position embedded in a homopolymeric run that is poorly resolved by the 454 platform. Excluding homopolymeric positions, 14% of subjects had evidence of ≥1 resistance mutation among Primer ID consensus sequences, compared to 2.7% by bulk population sequencing. When calls were restricted to mutations that appeared twice among consensus sequence populations, 6% of subjects had detectable resistance mutations. The use of Primer ID revealed 5-15% template utilization on average, limiting the depth of deep sequencing sampling and revealing sampling variation due to low template utilization. Primer ID addresses important limitations of deep sequencing and produces less biased estimates of low-level resistance mutations in the viral population.