Characterization of HIV diversity, phylodynamics and drug resistance in Washington, DC

Washington DC has a high burden of HIV with a 2.0% HIV prevalence. The city is a national and international hub potentially containing a broad diversity of HIV variants; yet few sequences from DC are available on GenBank to assess the evolutionary history of HIV in the US capital. Towards this general goal, here we analyze extensive sequence data and investigate HIV diversity, phylodynamics, and drug resistant mutations (DRM) in DC.

Molecular HIV-1 sequences were collected from participants infected through 2015 as part of the DC Cohort, a longitudinal observational study of HIV+ patients receiving care at 13 DC clinics. Sequences were paired with Cohort demographic, risk, and clinical data and analyzed using maximum likelihood, Bayesian and coalescent approaches of phylogenetic, network and population genetic inference. We analyzed 601 sequences from 223 participants for int (~864 bp) and 2,810 sequences from 1,659 participants for PR/RT (~1497 bp).

Ninety-nine and 94% of the int and PR/RT sequences, respectively, were identified as subtype B, with 14 non-B subtypes also detected. Phylodynamic analyses of US born infected individuals showed that HIV population size varied little over time with no significant decline in diversity. Phylogenetic analyses grouped 13.5% of the int sequences into 14 clusters of 2 or 3 sequences, and 39.0% of the PR/RT sequences into 203 clusters of 2-32 sequences. Network analyses grouped 3.6% of the int sequences into 4 clusters of 2 sequences, and 10.6% of the PR/RT sequences into 76 clusters of 2-7 sequences. All network clusters were detected in our phylogenetic analyses. Higher proportions of clustered sequences were found in zip codes where HIV prevalence is highest (r = 0.607; P<0.00001). We detected a high prevalence of DRM for both int (17.1%) and PR/RT (39.1%), but only 8 int and 12 PR/RT amino acids were identified as under adaptive selection. We observed a significant (P<0.0001) association between main risk factors (men who have sex with men and heterosexuals) and genotypes in the five well-supported clusters with sufficient sample size for testing.

Pairing molecular data with clinical and demographic data provided novel insights into HIV population dynamics in Washington, DC. Identification of populations and geographic locations where clustering occurs can inform and complement active surveillance efforts to interrupt HIV transmission.