| Abstract |
Multiple diverse subtypes widely distributed around the world cause the HIV pandemic, a global emergency resulting from primate-to-human transmission in Central Africa. An important response to the pandemic is the global scale-up of antiretroviral (ARV) treatment programs, aimed at increasing delivery of drugs to millions of infected individuals in resource limited settings. A barrier to long-term successful treatment is the emergence of drug resistance, caused by selected mutations in viral genes. Most knowledge about drug resistance comes from the Western world, where one HIV-1 variant - subtype B - predominates. However globally, 90% of infections are in resource-limited settings, mostly in Africa, where non-B subtypes predominate. Although data are lacking from various locations, evidence among non-B subtypes suggests genotypic diversity that may shape the success of first-line and the design of subsequent ARV regimens. Information acquisition about diversity and drug resistance requires infrastructure and expertise, enabling consistent regional monitoring and evaluation. Our long-term objectives are to expand knowledge of drug resistance evolution in multiple subtypes from various locations, and to compare it among subtypes.
The focus of this proposal is to provide preliminary data on HIV-1 subtypes in Western Kenya, and to investigate genotypic diversity before and after drug exposure, while building scientific infrastructure and exploring new methods for surveillance and monitoring of drug resistance. We will (1) I dentify circulating HIV-1 subtypes and recombinant forms by sequencing gag , pol and env genes; (2) D etermine pol genotypic background in 50 drug-naïve persons, and compare those from early and late disease stages; and (3) Determine reverse transcriptase genotypic changes in 50 persons failing WHO recommended first-line regimens, and compare those to subtype B. To obtain these data, we will compare two methods that were developed specifically to obtain and ship samples in resource-limited settings: dried plasma samples and SampleTanker. Data analyses will include advanced bioinformatics and phylogenetic sequence analysis tools.
This proposal will expand the existing collaboration between Moi University in Eldoret, Kenya, and Brown University Medical School. We anticipate that the methods explored in this proposal will be used to build and expand local expertise in Kenya, to conduct surveillance and monitoring of drug resistance. The information provided from this initial AIDS research project on diversity and drug resistance will serve as preliminary data for larger studies and for an NIH RO1 application.
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