Comprehensive, accurate, and publicly available HIV-1 antiretroviral (ARV) drug resistance data is essential for population-based monitoring of acquired and transmitted drug resistance in resource-limited regions, for guiding salvage ARV therapy in well-resourced regions, and for identifying overall ARV- development needs. However, the variability of viruses that comprise the HIV-1 pandemic and the high mutation rate of HIV-1 make it difficult to quantify transmitted and acquired drug resistance, to optimally interpret HIV-1 genotypic resistance tests, and to identify those ARV-resistant variants most relevant to the development of future ARVs. The Stanford HIV Drug Resistance Database (HIVDB) is the only publicly available source for three data correlations underlying HIV-1 drug resistance knowledge: (1) Correlations between the genetic sequences of the enzymatic targets of ARV therapy - PR, RT, and IN - with the ARV treatments of persons from whom sequenced HIV-1 variants are obtained; (2) Correlations between genotype and in vitro drug susceptibility; and (3) Correlations between genotype and the virological response to a new ARV treatment regimen. By emphasizing the collection, annotation, dissemination, and analysis of three main types of data, HIVDB facilitates meta-analyses in which data from many published studies and clinical trials can be effectively synthesized.
The specific aims of this competing renewal for funding HIVDB are (1) To develop standardized genotypic methods to monitor the extent of transmitted and acquired drug resistance and to determine whether these methods can be applied across all HIV-1 subtypes; (2) To expand HIVDB by collecting, annotating, and disseminating the genotype-treatment, genotype-phenotype, and genotype-virological response data that inform genotypic resistance test interpretation. Coupled with this aim, we will improve the online framework for representing and describing the evidence basis associated with each genotypic resistance interpretation; and (3) To identify and preliminarily characterize representative and novel ARV-resistant variants that may help guide the development of future ARVs. Innovative aspects of this proposal include (1) the development of widely available and widely used online programs to facilitate data sharing and consistent analytic approaches across laboratories in many countries; (2) the development and implementation of regression methods and correlation network analyses to the study of HIV-1 drug resistance mutations; and (3) the use of data recruitment and curation methods adopted from model organism databases to create a sustainable model for the continuation and expansion of HIVDB.

Public Health Relevance

The development of drug regimens and national programs for treating HIV has saved hundreds of thousands of lives and provided hope to millions of others. However, acquired and transmitted HIV drug resistance present continuing obstacles to this success. This proposal addresses ongoing major challenges in HIV drug research by expanding a publicly available HIV drug resistance database through experimental, analytical, and collaborative projects.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
5R01AI068581-09
Application #
9265770
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Petrakova, Eva
Project Start
2006-05-01
Project End
2018-04-30
Budget Start
2017-05-01
Budget End
2018-04-30
Support Year
9
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Stanford University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304
Tzou, Philip L; Ariyaratne, Pramila; Varghese, Vici et al. (2018) Comparison of an In Vitro Diagnostic Next-Generation Sequencing Assay with Sanger Sequencing for HIV-1 Genotypic Resistance Testing. J Clin Microbiol 56:
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
Manasa, Justen; Varghese, Vici; Pond, Sergei L Kosakovsky et al. (2017) Evolution of gag and gp41 in Patients Receiving Ritonavir-Boosted Protease Inhibitors. Sci Rep 7:11559
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

Showing the most recent 10 out of 76 publications