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. 2017 Sep 29;12(9):e0185644.
doi: 10.1371/journal.pone.0185644. eCollection 2017.

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

Marcos Pérez-Losada  1   2   3 , Amanda D Castel  3 , Brittany Lewis  3 , Michael Kharfen  4 , Charles P Cartwright  5 , Bruce Huang  1 , Taylor Maxwell  1 , Alan E Greenberg  3 , Keith A Crandall  1 ; DC Cohort Executive Committee
Affiliations

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

Marcos Pérez-Losada et al. PLoS One. .

Abstract

Background: 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.

Methods: 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).

Results: 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.

Discussion: 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.

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Conflict of interest statement

Competing Interests: CPC has a commercial affiliation with Laboratory Corporation of America. The Laboratory Corporation of America provided support in the form of salaries for author CPC, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author are articulated in the ‘author contributions’ section.” This does not alter our adherence to PLOS ONE policies on sharing data and materials. The other authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Cladogram of the PR/RT gene showing risk factors, ethnicities, subtypes and sex in four concentric rings.
Main phenotypes within each ring are represented with different colors. Well-supported clades comprised of >10 HIV sequences are also indicated in red.
Fig 2
Fig 2. Bayesian skyride plot of HIV-1 subtype B PR/RT past population dynamics in US born patients.
Black lines show the median estimate and blue lines the 95% high posterior density limits of the relative genetic diversity over time.
Fig 3
Fig 3. Geographic distribution of transmission networks by zip code overlaid on Washington DC map with rates of persons living with diagnosed HIV in DC.
Only zip codes containing ≥4 HIV sequences that fall into a ML cluster (70% bootstrap support) are shown.
Fig 4
Fig 4. PR/RT network (HIV-Trace) of Washington DC HIV-1 isolates showing risk factors and ethnicities.
See this image and copyright information in PMC

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