Research Article: Phylogenetic transmission clusters among newly diagnosed antiretroviral drug-naïve patients with human immunodeficiency virus-1 in Korea: A study from 1999 to 2012

Date Published: June 5, 2019

Publisher: Public Library of Science

Author(s): Yoon-Seok Chung, Ju-Yeon Choi, Myoung-Su Yoo, Jae Hyun Seong, Byeong-Sun Choi, Chun Kang, Xinli Lu.


Population-level phylogenetic patterns reflect both transmission dynamics and genetic changes, which accumulate because of selection or drift. In this study, we determined whether a longitudinally sampled dataset derived from human immunodeficiency virus (HIV)-1-infected individuals over a 14-year period (1999–2012) could shed light on the transmission processes involved in the initiation of the HIV-1 epidemic in Korea. In total, 927 sequences were acquired from 1999 to 2012; each sequence was acquired from an individual patient who had not received treatment. Sequences were used for drug resistance and phylogenetic analyses. Phylogenetic and other analyses were conducted using MEGA version 6.06 based on the GTR G+I parameter model and SAS. Of the 927 samples, 863 (93.1%) were classified as subtype B and 64 were classified as other subtypes. Phylogenetic analysis demonstrated that 104 of 927 patient samples (11.2%) were grouped into 37 clusters. Being part of a transmission cluster was significantly associated with subtype-B viruses, infection via sexual contact, and the infection of young males. Of all clusters, three (~8.1%) that comprised 10 individual samples (22.2% of 45 individuals) included at least one member with total transmitted drug resistance (TDR). In summary, HIV transmission cluster analyses can integrate laboratory data with behavioral data to enable the identification of key transmission patterns to develop tailored interventions aimed at interrupting transmission chains.

Partial Text

The extremely high diversity of human immunodeficiency virus (HIV) has been attributed to its high replication capacity and the high frequency of errors introduced by reverse transcriptase during replication. HIV-1 is the most common virus types worldwide and has been classified into four groups as follows: M (major), N (non-M, non-O), O (outer), and P (pending the identification of further human cases); group P was identified recently in two Cameroonian patients. HIV-1 group M can be further classified into nine subtypes including A–D, F–H, and K [1]. This extensive diversity has led to frequent recombination between strains, resulting in several circulating recombinant forms (CRFs) and a very high number of unique recombinant forms (URFs) [2–5]. To date, 72 CRFs have been isolated, and this number is expected to increase in the future [6].

In this study, we compared HIV pol sequences from 927 newly-diagnosed patients receiving care at the Division of AIDS, KNIH between 1999 and 2012. We found that 11.2% of these patients grouped into 37 molecularly-defined HIV transmission clusters. We analyzed the structures of the transmission clusters in Korea through the integration of molecular, clinical, and demographic data. Analysis of the HIV-1 pol sequences generated from antiretroviral resistance surveillance programs has proven useful and informative to assess and define transmission clusters within a population of interest [4, 15, 32]. Based on these criteria, substantial clustering (11.2%, 104/927) was observed in the current study, indicating that the majority of HIV-1 subtype B infections in Korea were linked to a cluster that might be associated with local and/or foreign HIV-1 networks.

Analysis of the phylodynamic or evolutionary history of HIV relies significantly on the depth of population-based sampling; a study of this type should be continued and expanded upon to improve the resolution of HIV-1 genomic diversity and transmission dynamics within HIV transmission networks. The continuing transmission of HIV among MSM individuals indicates the need to maximize the use of available bio-epidemiological data. HIV transmission cluster analysis integrates laboratory data with behavioral data to enable the delineation of key transmission patterns to develop tailored interventions aimed at interrupting transmission chains.




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