Insights into HIV-1 Transmission Dynamics Using Routinely Collected Data in the Mid-Atlantic United States

Abstract

Background: Molecular epidemiological approaches provide opportunities to characterize HIV transmission dynamics. We analyzed HIV sequences and virus load (VL) results obtained during routine clinical care, and individual’s zip-code location to determine utility of this approach. Methods: HIV-1 pol sequences aligned using ClustalW were subtyped using REGA. A maximum likelihood (ML) tree was generated using IQTree. Transmission clusters with ≤3% genetic distance (GD) and ≥90% bootstrap support were identified using ClusterPicker. We conducted Bayesian analysis using BEAST to confirm transmission clusters. The proportion of nucleotides with ambiguity ≤0.5% was considered indicative of early infection. Descriptive statistics were applied to characterize clusters and group comparisons were performed using chi-square or t-test. Results: Among 2775 adults with data from 2014−2015, 2589 (93%) had subtype B HIV-1, mean age was 44 years (SD 12.7), 66.4% were male, and 25% had nucleotide ambiguity ≤0.5. There were 456 individuals in 193 clusters: 149 dyads, 32 triads, and 12 groups with ≥ four individuals per cluster. More commonly in clusters were males than females, 349 (76.5%) vs. 107 (23.5%), p < 0.0001; younger individuals, 35.3 years (SD 12.1) vs. 44.7 (SD 12.3), p < 0.0001; and those with early HIV-1 infection by nucleotide ambiguity, 202/456 (44.3%) vs. 442/2133 (20.7%), p < 0.0001. Members of 43/193 (22.3%) of clusters included individuals in different jurisdictions. Clusters ≥ four individuals were similarly found using BEAST. HIV-1 viral load (VL) ≥3.0 log10 c/mL was most common among individuals in clusters ≥ four, 18/21, (85.7%) compared to 137/208 (65.8%) in clusters sized 2−3, and 927/1169 (79.3%) who were not in a cluster (p < 0.0001). Discussion: HIV sequence data obtained for HIV clinical management provide insights into regional transmission dynamics. Our findings demonstrate the additional utility of HIV-1 VL data in combination with phylogenetic inferences as an enhanced contact tracing tool to direct HIV treatment and prevention services. Trans-jurisdictional approaches are needed to optimize efforts to end the HIV epidemic.

Keywords: HIV drug resistance; clinical and phylogenetic data combined; contact tracing tool; molecular epidemiology; phylogenetic analysis; regional transmission dynamics; transmission networks.

Figure 1

Distribution of individuals over clustered, non-clustered and sex by age group, genetic distance 3%. The bar graph demonstrates the frequency and age distribution of individuals in transmission clusters. The proportion of those in clusters (shown in gray) compared with those not in clusters (shown in yellow) is higher among younger individuals.

Figure 2

Phylogenetic trees of sequences in clusters with ≥4 members. (a). Study population sequences and closest related sequences from GenBank. Bayesian phylogenetic tree of 62 subtype B HIV-1 sequences that were identified in clusters size four or more based on GD 3% using the maximal likelihood method were included alongside the most closely related sequences from the GenBank HIV Sequence database at Los Alamos (labeled “LA”). Bayesian evolutionary analysis sampling trees [29] analysis was performed using the GTR+I+G substitution model, uncorrelated log-normal relaxed molecular clock, tree coalescent assumption of the Gaussian Markov random field (GMRF) skyride [31,32,33], and run of 135 million states to achieve an effective sample size of 302. The cluster relationships were maintained with high posterior probability of ≥95% when analyzed using this BEAST approach. Male participants are indicated in purple and females indicated in green, and sequences from Los Alamos indicated in black. (b). Cluster characteristics: HIV-1 drug resistance mutations and viral load. HIV-1 viral load data are written in black with the corresponding sequence when available. Among the 21 individuals with HIV-1 VL data available, 18 (85.7%) had levels ≥3 log10 c/mL. Four HIV-1 drug resistance associated mutations were identified in two clusters including the resistance mutations Y188H, K101E, K103N/S, and G190S. One cluster had multiple cluster members with drug resistance mutations.

Figure 3

HIV-TRACE Transmission Networks by Genetic Distance. Cluster networks configured in HIV-TRACE from HIV pol sequences are inferred using genetic distances between sequences. Cluster networks demonstrate decreasing linkages with lower genetic distance cutoffs ((a) ≤3%; (b) ≤2%; (c) ≤1.5%; and (d) ≤0.5%), although multijurisdictional networks are identified at all genetic distance cutoffs. Using the less restrictive genetic distance cutoff of 3% provides greater detail and the resulting network visualization provides context and highlights connections that more completely represent potentially important HIV-1 transmission dynamics.