HIV drug resistance data are critical for HIV drug resistance surveillance, antiretroviral drug design, and the management of persons infected with drug-resistant HIV. Three fundamental types of correlations form the basis of drug resistance knowledge: (1) Correlations between genotypic data with the treatments of persons from whom sequenced HIV-1 isolates have been obtained (genotype-treatment);(2) Correlations between genotype and in vitro drug susceptibility (genotype-phenotype);and (3) Correlations between genotype and the clinical response to a new treatment regimen (genotype-outcome). Accordingly our three plans are as follows: (1) To use phylogenetically-based statistical analysis of correlations between mutations in the targets of HIV therapy (genotype) and antiretroviral treatment to distinguish between drug- and non-drug- related HIV-1 evolution, to identify temporal and geographic patterns in the prevalence of drug resistance, and to identify the spread of viruses with particular patterns of drug-resistance mutations. (2) To use patterns of mutations in the targets of antiretroviral therapy, antiretroviral treatment data, and in vitro drug susceptibility results to help prioritize the clinical development of experimental antiretroviral compounds. (3) To us correlations between genotype-phenotype and genotype-clinical outcome to identify the treatment regimens most likely to be effective in persons with acquired or transmitted drug resistance. In order to accomplish these aims, we will expand the online publicly available Stanford HIV RT and Protease Sequence Database to represent, store, and analyze the diverse forms of data underlying drug resistance knowledge. Standardized protocols for exchanging data with the broad community of researchers studying HIV-1 drug resistance will be refined and validated. Improved methods for representing published drug resistance data, performing temporal queries and knowledge discovery, and for analyzing genotypic resistance data in a phylogenetic context will be implemented. This proposal is also the start of a three-way collaboration between Stanford University, the University College of London (UCL), and Oxford University. UCL is on the forefront of research into the problem of transmitted drug resistance and Oxford University is on the forefront of research on virus evolution and population genetics.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
5R01AI068581-04
Application #
7609109
Study Section
AIDS Clinical Studies and Epidemiology Study Section (ACE)
Program Officer
Fitzgibbon, Joseph E
Project Start
2006-05-01
Project End
2011-04-30
Budget Start
2009-05-01
Budget End
2010-04-30
Support Year
4
Fiscal Year
2009
Total Cost
$621,795
Indirect Cost
Name
Stanford University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
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Aggeli, Dimitra; Karas, Vlad O; Sinnott-Armstrong, Nicholas A et al. (2018) Diff-seq: A high throughput sequencing-based mismatch detection assay for DNA variant enrichment and discovery. Nucleic Acids Res 46:e42
Wensing, Annemarie M; Calvez, Vincent; Günthard, Huldrych F et al. (2017) 2017 Update of the Drug Resistance Mutations in HIV-1. Top Antivir Med 24:132-133
Clutter, Dana S; Zhou, Shuntai; Varghese, Vici et al. (2017) Prevalence of Drug-Resistant Minority Variants in Untreated HIV-1-Infected Individuals With and Those Without Transmitted Drug Resistance Detected by Sanger Sequencing. J Infect Dis 216:387-391
Shafer, Robert W (2017) Human Immunodeficiency Virus Type 1 Drug Resistance Mutations Update. J Infect Dis 216:S843-S846
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Paredes, Roger; Tzou, Philip L; van Zyl, Gert et al. (2017) Collaborative update of a rule-based expert system for HIV-1 genotypic resistance test interpretation. PLoS One 12:e0181357
Rhee, Soo-Yon; Varghese, Vici; Holmes, Susan P et al. (2017) Mutational Correlates of Virological Failure in Individuals Receiving a WHO-Recommended Tenofovir-Containing First-Line Regimen: An International Collaboration. EBioMedicine 18:225-235
Tzou, Philip L; Huang, Xiaoqiu; Shafer, Robert W (2017) NucAmino: a nucleotide to amino acid alignment optimized for virus gene sequences. BMC Bioinformatics 18:138
Gregson, John; Kaleebu, Pontiano; Marconi, Vincent C et al. (2017) Occult HIV-1 drug resistance to thymidine analogues following failure of first-line tenofovir combined with a cytosine analogue and nevirapine or efavirenz in sub Saharan Africa: a retrospective multi-centre cohort study. Lancet Infect Dis 17:296-304

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