Molecular epidemiology can help clarify the properties and dynamics of HI-1 transmission networks in both global and regional scales. We studied 143 HIV-1-infected individuals recruited from four medical centers of three cities in South Korea between April 2013 and May 2014. HIV-1 env V3 sequence data were generated (337-793 bp) and analyzed using a pairwise distance-based clustering approach to infer putative transmission networks. Participants whose viruses were ≤2.0% divergent according to Tamura-Nei 93 genetic distance were defined as clustering. We collected demographic, risk, and clinical data and analyzed these data in relation to clustering. Among 143 participants, we identified nine putative transmission clusters of different sizes (range 2-4 participants). The reported risk factor of participants were concordant in only one network involving two participants, that is, both individuals reported homosexual sex as their risk factor. The participants in the other eight networks did not report concordant risk factors, although they were phylogenetically linked. About half of the participants refused to report their risk factor. Overall, molecular epidemiology provides more information to understand local transmission networks and the risks associated with these networks.
Bibliographical noteFunding Information:
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2013R1A1A2005412), a Chronic Infectious Disease Cohort grant (4800-4859-304-260) from the Korea Centers for Disease Control and Prevention, BioNano Health-Guard Research Center funded by the Ministry of Science, ICT, and Future Planning of Korea as a Global Frontier Project (grant H-GUARD-2013M3A6B2078953), a grant from the Ministry of Health & Welfare, Republic of Korea (grant number: HI14C1324), a faculty research grant of Yonsei University College of Medicine (6-2015-0153) and NIH (grant P30 AI036214, AI100665, DA034978, AI036214 and K01AI110181).
© 2017 Mary Ann Liebert, Inc.
All Science Journal Classification (ASJC) codes
- Infectious Diseases