HIV-1 Transmission Clustering and Phylodynamics Highlight the Important Role of Young Men Who Have Sex with Men

More persons living with HIV reside in the Southern United States than in any other region, yet little is known about HIV molecular epidemiology in the South. We used cluster and phylodynamic analyses to evaluate HIV transmission patterns in middle Tennessee. We performed cross-sectional analyses of HIV-1 pol sequences and clinical data collected from 2001 to 2015 among persons attending the Vanderbilt Comprehensive Care Clinic. Transmission clusters were identified using maximum likelihood phylogenetics and patristic distance differences. Demographic, risk behavior, and clinical factors were assessed evaluating “active” clusters (clusters including sequences sampled 2011–2015) and associations estimated with logistic regression. Transmission risk ratios for men who have sex with men (MSM) were estimated with phylodynamic models. Among 2915 persons (96% subtype-B sequences), 963 (33%) were members of 292 clusters (distance ≤1.5%, size range 2–39). Most clusters (62%, n = 690 persons) were active, either being newly identified (n = 80) or showing expansion on existing clusters (n = 101). Correlates of active clustering among persons with sequences collected during 2011–2015 included MSM risk and ≤30 years of age. Active clusters were significantly more concentrated in MSM and younger persons than historical clusters. Young MSM (YMSM) (≤26.4 years) had high estimated transmission risk [risk ratio = 4.04 (2.85–5.65) relative to older MSM] and were much more likely to transmit to YMSM. In this Tennessee cohort, transmission clusters over time were more concentrated by MSM and younger age, with high transmission risk among and between YMSM, highlighting the importance of interventions among this group. Detecting active clusters could help direct interventions to disrupt ongoing transmission chains.

Absence of HIV-1 Drug Resistance Mutations Supports the Use of Dolutegravir in Uganda

To screen for drug resistance and possible treatment with Dolutegravir (DTG) in treatment-naive patients and those experiencing virologic failure during first-, second-, and third-line combined antiretroviral therapy (cART) in Uganda. Samples from 417 patients in Uganda were analyzed for predicted drug resistance upon failing a first- (N = 158), second- (N = 121), or third-line [all 51 involving Raltegravir (RAL)] treatment regimen. HIV-1 pol gene was amplified and sequenced from plasma samples. Drug susceptibility was interpreted using the Stanford HIV database algorithm and SCUEAL was used for HIV-1 subtyping. Frequency of resistance to nucleoside reverse transcriptase inhibitors (NRTIs) (95%) and non-NRTI (NNRTI, 96%) was high in first-line treatment failures. Despite lack of NNRTI-based treatment for years, NNRTI resistance remained stable in 55% of patients failing second-line or third-line treatment, and was also at 10% in treatment-naive Ugandans. DTG resistance (n = 366) was not observed in treatment-naive individuals or individuals failing first- and second-line cART, and only found in two patients failing third-line cART, while 47% of the latter had RAL- and Elvitegravir-resistant HIV-1. Secondary mutations associated with DTG resistance were found in 2%-10% of patients failing third-line cART. Of 14 drugs currently available for cART in Uganda, resistance was readily observed to all antiretroviral drugs (except for DTG) in Ugandan patients failing first-, second-, or even third-line treatment regimens. The high NNRTI resistance in first-line treatment in Uganda even among treatment-naive patients calls for the use of DTG to reach the UNAIDS 90:90:90 goals.

The 2006 NESCent Phyloinformatics Hackathon: A Field Report

In December, 2006, a group of 26 software developers from some of the most widely used life science programming toolkits and phylogenetic software projects converged on Durham, North Carolina, for a Phyloinformatics Hackathon, an intense five-day collaborative software coding event sponsored by the National Evolutionary Synthesis Center (NESCent). The goal was to help researchers to integrate multiple phylogenetic software tools into automated workflows. Participants addressed deficiencies in interoperability between programs by implementing “glue code” and improving support for phylogenetic data exchange standards (particularly NEXUS) across the toolkits. The work was guided by use-cases compiled in advance by both developers and users, and the code was documented as it was developed. The resulting software is freely available for both users and developers through incorporation into the distributions of several widely-used open-source toolkits. We explain the motivation for the hackathon, how it was organized, and discuss some of the outcomes and lessons learned. We conclude that hackathons are an effective mode of solving problems in software interoperability and usability, and are underutilized in scientific software development.

Phylogenetic clustering of hepatitis C virus among people who inject drugs in Vancouver, Canada

Little is known about factors associated with hepatitis C virus (HCV) transmission among people who inject drugs (PWID). Phylogenetic clustering and associated factors were evaluated among PWID in Vancouver, Canada. Data were derived from the Vancouver Injection Drug Users Study. Participants who were HCV antibody-positive at enrolment and those with HCV antibody seroconversion during follow-up (1996 to 2012) were tested for HCV RNA and sequenced (Core-E2 region). Phylogenetic trees were inferred using maximum likelihood analysis and clusters were identified using ClusterPicker (90% bootstrap threshold, 0.05 genetic distance threshold). Factors associated with clustering were assessed using logistic regression. Among 655 eligible participants, HCV genotype prevalence was: G1a: 48% (n=313), G1b: 6% (n=41), G2a: 3% (n=20), G2b: 7% (n=46), G3a: 33% (n=213), G4a: <1% (n=4), G6a: 1% (n=8), G6e: <1% (n=1), and unclassifiable: 1% (n=9). The mean age was 36 years, 162 (25%) were female, and 164 (25%) were HIV+. Among 501 participants with HCV G1a and G3a, 31% (n=156) were in a pair/cluster. Factors independently associated with phylogenetic clustering included: age <40 (versus age≥40, adjusted odds ratio [AOR]=1.64; 95% confidence interval [CI] 1.03, 2.63), human immunodeficiency virus (HIV) infection (AOR=1.82; 95% CI 1.18, 2.81), HCV seroconversion (AOR=3.05; 95% CI 1.40, 6.66), and recent syringe borrowing (AOR 1.59; 95% CI 1.07, 2.36).

CONCLUSION

In this sample of PWID, one-third demonstrated phylogenetic clustering. Factors independently associated with phylogenetic clustering included younger age, recent HCV seroconversion, prevalent HIV infection, and recent syringe borrowing. Strategies to enhance the delivery of prevention and/or treatment strategies to those with HIV and recent HCV seroconversion should be explored, given an increased likelihood of HCV transmission in these subpopulations.

Comparison of population and 454 "deep" sequence analysis for HIV type 1 tropism versus the original trofile assay in non-B subtypes

HIV-1 tropism can be predicted using V3 genotypic algorithms. The performance of these prediction algorithms for non-B subtypes is poorly characterized. Here, we use these genotypic algorithms to predict viral tropism of HIV-1 subtype A, B, C, and D to find apparent sensitivity, specificity, and concordance against a recombinant phenotypic assay, the original Trofile assay. This is a substudy of an epidemiological study (Pfizer A4001064). Plasma samples were selected to represent a large number of DM/X4 and R5 viruses. The HIV-1 env gene V3 loop was genotyped by Sanger sequencing (N=260) or 454 "deep" sequencing (N=280). Sequences were scored with g2p[coreceptor], PSSM X4/R5, PSSM SI/NSI, and PSSM subtype C matrices. Overall, non-B subtypes tropism prediction had similar concordance and apparent sensitivity and specificity as subtype B in predicting Trofile's results in both population sequencing (81.3%, 65.6%, and 90.5% versus 84.2%, 78.5%, and 88.2%) and 454 "deep" sequencing (82.3%, 80.0%, and 83.6% versus 86.8%, 92.0%, and 82.6%) using g2p[coreceptor]. By population sequencing, subtype A had lower sensitivity, whereas subtype D had lower specificity for non-R5 predictions, both in comparison to subtype B. 454 "deep" sequencing improved subtype A sensitivity but not subtype D. Subtype C had greater concordance than subtype B regardless of sequencing methods. In conclusion, genotypic tropism prediction algorithms may be applied to non-B HIV-1 subtypes with caution. Collective analysis of non-B subtypes revealed a performance similar to subtype B, whereas a subtype-specific analysis revealed overestimation (subtype D) or underestimation (subtype A).

Dates of HIV infection can be estimated for seroprevalent patients by coalescent analysis of serial next-generation sequencing data

OBJECTIVE

To reconstruct dates of HIV infection by the coalescent analysis of longitudinal next-generation sequencing (NGS) data.

DESIGN

The coalescent predicts the time that has elapsed since the most recent common ancestor (MRCA) of a population. Because HIV tends to undergo severe bottlenecks upon transmission, the MRCA may be a good predictor of the time of infection. NGS provides an efficient means for performing large-scale clonal sequencing of HIV populations within patients, and the ideal raw material for coalescent analysis.

METHODS

Baseline and follow-up plasma samples were obtained from 19 individuals enrolled into the Montréal Primary HIV Infection cohort. Dates of infection were initially estimated at baseline from nongenetic data (clinical and serological markers and patient questionnaires). HIV RNA was extracted and seven regions of the genome were amplified, subjected to parallel-tagged 454 pyrosequencing, and analyzed using the software package BEAST.

RESULTS

Mean estimates of the time to the MRCA per patient were significantly correlated with nongenetic estimates (Spearman's ρ = 0.65, P = 4.4 × 10(-3)). The median absolute difference between coalescent and nongenetic date estimates was smallest (median 29.4 days) for highly variable regions of the HIV genome such as env V3, and greater (median 114.9 days) for more conserved regions such as pol.

CONCLUSION

This application of NGS represents an important advancement, not only because accurate estimates of dates of infection can be derived retrospectively from archived specimens, but also because each analysis is patient-specific and, therefore, robust to variation in rates of HIV evolution.