Large-Scale Analysis of the Prevalence and Geographic Distribution of HIV-1 Non-B Variants in the United States

ARUP Institute for Clinical and Experimental Pathology®, 500 Chipeta Way, Salt Lake City, Utah 84108, USA.
Journal of clinical microbiology (Impact Factor: 3.99). 06/2013; 51(8). DOI: 10.1128/JCM.00880-13
Source: PubMed


Genetic diversity of human immunodeficiency virus type 1 (HIV-1) has significant implications for diagnosis, vaccine development, and clinical management of patients. Although HIV-1 subtype B is predominant in the United States, factors such as global travel, immigration, and military deployment have the potential to increase the proportion of non-subtype B infections. Limited data is available on the prevalence and distribution of non-B HIV-1 strains in the U.S. We sought to retrospectively examine the prevalence, geographic distribution, diversity, and temporal trends of HIV-1 non-B infections in samples obtained by ARUP Laboratories, a national reference laboratory, from all regions of the United States. HIV-1 pol sequences from 24,386 specimens collected from 46 states between 2004 and September 2011 for drug resistance genotyping were analyzed using the REGA HIV-1 Subtyping Tool, Version 2.0. Sequences refractory to subtype determination or reported as non-subtype B by this tool were analyzed by PHYLIP v3.5 and Simplot v3.5.1. Non-subtype B strains accounted for 3.27% (798/24,386) of specimens. The 798 non-B specimens were received from 37 states and included: 5 subtypes, 23 different circulating recombinant forms (CRFs) and 39 unique recombinant forms (URFs). Non-subtype B prevalence varied from 0% in 2004 (0/54) to 4.12% in 2011 (201/4884). This large-scale analysis reveals that diversity of HIV-1 in the United States is high with multiple subtypes, CRFs and URFs circulating. Moreover, the geographic distribution of non-B variants is widespread. Data from HIV-1 drug resistance testing have the potential to significantly enhance surveillance of HIV-1 variants in the United States.

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Available from: Michael Pyne, Sep 22, 2014
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    • "Despite the limitations of this investigation, our findings were generally consistent with those of other recent investigations involving HIV-1 subtype diversity in the United States, which have suggested that evolving immigration patterns may be contributing substantially to local and regional increases in HIV-1 subtype diversity. The predominant non-B subtypes observed in this study (i.e., C, CRF02, and A1) were consistent with those identified among African-born immigrants in several previous investigations (Carr et al., 2010; Hirigoyen and Cartwright, 2005; Pyne et al., 2013; Sides et al., 2005; Wheeler et al., 2010 "
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    ABSTRACT: Diversity of human immunodeficiency virus type 1 (HIV-1) has important implications for the diagnosis, treatment, and management of HIV-1-infected individuals. HIV-1 pol sequences from 3895 clinical plasma specimens collected in the United States over a 1-year period and submitted for routine HIV-1 genotypic drug resistance testing were retrospectively analyzed for HIV-1 subtype. Of these 3895 HIV-1 sequences, 207 (5.31%) were determined to be non-B subtypes (including recombinant forms). Among individual states, the percentage of non-B subtypes ranged from 0% (12 states) to 28.57% in South Dakota, with 7 states having percentages of >10%. All 4 states with the highest percentages of non-B subtypes were located within the US West North Central region: Minnesota, 11.82%; Iowa, 15.38%; North Dakota, 25.00%; and South Dakota, 28.57%. Reasons for the unexpectedly wide diversity of HIV-1 subtypes present in multiple states located in the West North Central region of the United States remain to be determined. Copyright © 2015 Elsevier Inc. All rights reserved.
    Diagnostic microbiology and infectious disease 07/2015; 83(3). DOI:10.1016/j.diagmicrobio.2015.07.014 · 2.46 Impact Factor
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    ABSTRACT: Individuals infected with HIV-1 non-B subtypes are understudied in the United States. Their characterization may augment prevention and treatment interventions. We examined the regional molecular epidemiology of non-B subtypes using a combined phylogenetic and geospatial approach. HIV-1 pol sequences and clinical data obtained for routine clinical care were aggregated from 2004-2011 at the largest HIV center in Rhode Island. Subtyping was performed by neighbor-joining and maximum-likelihood phylogeny and compared across eight commonly used tools (HIVdb, REGA, RIP, NCBI, Geno2Pheno, EuResist, jpHMM and STAR) using proportional odds ordinal regression. Individuals with non-B subtypes were characterized according to demographics and risk factors for infection, intra-subtype clustering by maximum-likelihood phylogeny, and geospatial hotspot analysis using Getis-Ord Gi∗ statistics. Of 1,277 unique sequences, phylogenetic subtyping demonstrated 8.3% (N=106, 95% CI 6.8%-10%) non-B subtypes and circulating recombinant forms (CRFs): CRF02_AG=46; A=15; C=15; CRF01_AE=6; CRF06_CPX=5; CRF14_BG=5; G=3; CRF43_02G=3; D=3; CRF24_BG=3; CRF11_CPX=1; F1=1. Compared to phylogeny, Geno2Pheno was the most concordant (86% exact match) followed by REGA (85%), EuResist (85%) and STAR (82%). Of 106 individuals with non-B subtypes, 50% were male, 71% acquired infection through heterosexual transmission; 76%, were born in Africa, 6% Southeast Asia, 5% the United States, 3% Central America, 1% Europe, and 9% unknown. Eighty percent of CRF02_AG, 93% of A and 87% of C sequences were from African-born individuals. Twenty-two percent of non-B subtypes formed transmission clusters, including a significant number of younger individuals with perinatally-acquired infection. Geospatial analyses revealed hotspots of B and non-B subtypes in the state capital with a more concentrated focus among non-B subtypes. Molecular examination of regional HIV diversity revealed a larger than expected non-subtype B infected population, mostly born in Africa, with low ongoing regional transmission. Phylogenetic and geospatial characterization of infection clusters is helpful to identify targets for treatment and prevention interventions.
    Infection, genetics and evolution: journal of molecular epidemiology and evolutionary genetics in infectious diseases 04/2014; 28. DOI:10.1016/j.meegid.2014.03.027 · 3.02 Impact Factor
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    ABSTRACT: Background HIV-1 subtype B (HIV-1B) still dominates in resource-rich countries but increased migration contributes to changes in the global subtype distribution. Also, spread of non-B subtypes within such countries occurs. The trend of the subtype distribution from the beginning of the epidemic in the country has earlier not been reported in detail. Thus the primary objective of this study is to describe the temporal trend of the subtype distribution from the beginning of the HIV-1 epidemic in Sweden over three decades. Methods HIV-1 pol sequences from patients (n = 3967) diagnosed in Sweden 1983– 2012, corresponding to >40% of patients ever diagnosed, were re-subtyped using several automated bioinformatics tools. The temporal trends of subtypes and recombinants during three decades were described by a multinomial logistic regression model. Results All eleven group M HIV-1 subtypes and sub-subtypes (78%), 17 circulating recombinant forms (CRFs) (19%) and 32 unique recombinants forms (URF) (3%) were identified. When all patients were analysed, there was an increase of newly diagnosed HIV-1C (RR, 95%CI: 1.10, 1.06–1.14), recombinants (1.20, 1.17–1.24) and other pure subtypes (1.11, 1.07–1.16) over time compared to HIV-1B. The same pattern was found when all patients infected in Sweden (n = 1165) were analysed. Also, for MSM patients infected in Sweden (n = 921), recombinant forms and other pure subtypes increased. Significance Sweden exhibits one of the most diverse subtype epidemics outside Africa. The increase of non-B subtypes is due to migration and to a spread among heterosexually infected patients and MSM within the country. This viral heterogeneity may become a hotspot for development of more diverse and complex recombinant forms if the epidemics converge.
    PLoS ONE 06/2014; 9(6):e99390. DOI:10.1371/journal.pone.0099390 · 3.23 Impact Factor