The Open Medical Informatics Journal

2010, 4 : 225-232
Published online 2010 December 3. DOI: 10.2174/1874431101004010225
Publisher ID: TOMINFOJ-4-225

RESEARCH ARTICLE
 Validation of a Bioinformatics Based Tool to Identify Reduced Replication Capacity in HIV-1

Christina M.R Kitchen, *,1,2 , Paul Krogstad 2,3 and Scott G Kitchen 2,4
1 Department of Biostatistics, UCLA School of Public Health, Los Angeles, California 90095, USA
2 UCLA AIDS Institute, Los Angeles, California 90095, USA
3 Departments of Pediatrics and Molecular and Medical Pharmacology, Los Angeles, California 90095, USA
4 Division of Hematology/Oncology, Department of Medicine, The David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA

* Address correspondence to this author at the Department of Biostatistics, UCLA School of Public Health, BOX 951772, 21-257 CHS, Los Angeles, CA 90095-1772, USA; Tel: 310-825-7332; Fax: 310-267-2113; E-mail: cr@ucla.edu

ABSTRACT

Although antiretroviral drug resistance is common in treated HIV infected individuals, it is not a consistent indicator of HIV morbidity and mortality. To the contrary, HIV resistance-associated mutations may lead to changes in viral fitness that are beneficial to infected individuals. Using a bioinformatics-based model to assess the effects of numerous drug resistance mutations, we determined that the D30N mutation in HIV-1 protease had the largest decrease in replication capacity among known protease resistance mutations. To test this in silico result in an in vivo environment, we constructed several drug-resistant mutant HIV-1 strains and compared their relative fitness utilizing the SCID-hu mouse model. We found HIV-1 containing the D30N mutation had a significant defect in vivo, showing impaired replication kinetics and a decreased ability to deplete CD4+ thymocytes, compared to the wild-type or virus without the D30N mutation. In comparison, virus containing the M184V mutation in reverse transcriptase, which shows decreased replication capacity in vitro, did not have an effect on viral fitness in vivo. Thus, in this study we have verified an in silico bioinformatics result with a biological assessment to identify a unique mutation in HIV-1 that has a significant fitness defect in vivo.

Keywords:

HIV-1, replication capacity, bioinformatics, Bayesian, variable selection, exchangeable on subsets, prior model selection, validation.