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

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 differe...

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Autores principales: Chen, Lamei, Lee, Christopher
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1523337/
https://www.ncbi.nlm.nih.gov/pubmed/16737543
http://dx.doi.org/10.1186/1745-6150-1-14
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author Chen, Lamei
Lee, Christopher
author_facet Chen, Lamei
Lee, Christopher
author_sort Chen, Lamei
collection PubMed
description 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 REVIEWERS: This article was reviewed by Wen-Hsiung Li (nominated by Eugene V. Koonin), Robert Shafer (nominated by Eugene V. Koonin), and Shamil Sunyaev.
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spelling pubmed-15233372006-07-28 Distinguishing HIV-1 drug resistance, accessory, and viral fitness mutations using conditional selection pressure analysis of treated versus untreated patient samples Chen, Lamei Lee, Christopher Biol Direct Research 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 REVIEWERS: This article was reviewed by Wen-Hsiung Li (nominated by Eugene V. Koonin), Robert Shafer (nominated by Eugene V. Koonin), and Shamil Sunyaev. BioMed Central 2006-05-31 /pmc/articles/PMC1523337/ /pubmed/16737543 http://dx.doi.org/10.1186/1745-6150-1-14 Text en Copyright © 2006 Chen and Lee; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Chen, Lamei
Lee, Christopher
Distinguishing HIV-1 drug resistance, accessory, and viral fitness mutations using conditional selection pressure analysis of treated versus untreated patient samples
title Distinguishing HIV-1 drug resistance, accessory, and viral fitness mutations using conditional selection pressure analysis of treated versus untreated patient samples
title_full Distinguishing HIV-1 drug resistance, accessory, and viral fitness mutations using conditional selection pressure analysis of treated versus untreated patient samples
title_fullStr Distinguishing HIV-1 drug resistance, accessory, and viral fitness mutations using conditional selection pressure analysis of treated versus untreated patient samples
title_full_unstemmed Distinguishing HIV-1 drug resistance, accessory, and viral fitness mutations using conditional selection pressure analysis of treated versus untreated patient samples
title_short Distinguishing HIV-1 drug resistance, accessory, and viral fitness mutations using conditional selection pressure analysis of treated versus untreated patient samples
title_sort distinguishing hiv-1 drug resistance, accessory, and viral fitness mutations using conditional selection pressure analysis of treated versus untreated patient samples
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1523337/
https://www.ncbi.nlm.nih.gov/pubmed/16737543
http://dx.doi.org/10.1186/1745-6150-1-14
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