Research

Distinguishing HIV-1 drug resistance, accessory, and viral fitness mutations using conditional selection pressure analysis of treated versus untreated patient samples

Lamei Chen and Christopher Lee

Institute for Genomics & Proteomics, Molecular Biology Institute, Dept. of Chemistry & Biochemistry, UCLA, Los Angeles, CA 90095-1570, USA

author email corresponding author email

Biology Direct 2006, 1:14doi:10.1186/1745-6150-1-14

Published:	31 May 2006

Abstract

Background

HIV can evolve drug resistance rapidly in response to new drug treatments, often through a combination of multiple mutations [1-3]. It would be useful to develop automated analyses of HIV sequence polymorphism that are able to predict drug resistance mutations, and to distinguish different types of functional roles among such mutations, for example, those that directly cause drug resistance, versus those that play an accessory role. Detecting functional interactions between mutations is essential for this classification. We have adapted a well-known measure of evolutionary selection pressure (K_a/K_s) and developed a conditional K_a/K_s approach to detect important interactions.

Results

We have applied this analysis to four independent HIV protease sequencing datasets: 50,000 clinical samples sequenced by Specialty Laboratories, Inc.; 1800 samples from patients treated with protease inhibitors; 2600 samples from untreated patients; 400 samples from untreated African patients. We have identified 428 mutation interactions in Specialty dataset with statistical significance and we were able to distinguish primary vs. accessory mutations for many well-studied examples. Amino acid interactions identified by conditional K_a/K_s matched 80 of 92 pair wise interactions found by a completely independent study of HIV protease (p-value for this match is significant: 10^-70). Furthermore, K_a/K_s selection pressure results were highly reproducible among these independent datasets, both qualitatively and quantitatively, suggesting that they are detecting real drug-resistance and viral fitness mutations in the wild HIV-1 population.

Conclusion

Conditional K_a/K_s analysis can detect mutation interactions and distinguish primary vs. accessory mutations in HIV-1. K_a/K_s analysis of treated vs. untreated patient data can distinguish drug-resistance vs. viral fitness mutations. Verification of these results would require longitudinal studies. The result provides a valuable resource for AIDS research and will be available for open access upon publication at http://www.bioinformatics.ucla.edu/HIV webcite

Reviewers

This article was reviewed by Wen-Hsiung Li (nominated by Eugene V. Koonin), Robert Shafer (nominated by Eugene V. Koonin), and Shamil Sunyaev.