2019
DOI: 10.7554/elife.50524
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Epistasis and entrenchment of drug resistance in HIV-1 subtype B

Abstract: The development of drug resistance in HIV is the result of primary mutations whose effects on viral fitness depend on the entire genetic background, a phenomenon called ‘epistasis’. Based on protein sequences derived from drug-experienced patients in the Stanford HIV database, we use a co-evolutionary (Potts) Hamiltonian model to provide direct confirmation of epistasis involving many simultaneous mutations. Building on earlier work, we show that primary mutations leading to drug resistance can become highly f… Show more

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Cited by 33 publications
(61 citation statements)
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“…the fitness contribution of a specific amino acid substitution becomes more positive if more RAMs have been fixed. Our results are consistent with previous analyses of sequence co-variation in HIV-1 protease [ 65 , 66 ], where inferred epistastic interactions among mutations at PI resistance associated sites lead to entrenchment of primary drug resistance mutations. In this study, we combine the analyses of co-variation (Potts model) with comprehensive experimental fitness data of HIV-1 protease mutants (including a large number of higher-order mutants) to provide direct evidence of positive epistasis among RAMs of second-generation PIs.…”
Section: Resultssupporting
confidence: 92%
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“…the fitness contribution of a specific amino acid substitution becomes more positive if more RAMs have been fixed. Our results are consistent with previous analyses of sequence co-variation in HIV-1 protease [ 65 , 66 ], where inferred epistastic interactions among mutations at PI resistance associated sites lead to entrenchment of primary drug resistance mutations. In this study, we combine the analyses of co-variation (Potts model) with comprehensive experimental fitness data of HIV-1 protease mutants (including a large number of higher-order mutants) to provide direct evidence of positive epistasis among RAMs of second-generation PIs.…”
Section: Resultssupporting
confidence: 92%
“…Statistical models suggest a pervasive negative distribution of fitness effect for single mutations on HIV-1 [ 31 , 88 , 106 ]. Previous models also predicted the entrenchment of deleterious RAMs by positive epistasis [ 65 , 66 ]. This dataset provides a unique chance to experimentally test these statistical hypotheses.…”
Section: Discussionmentioning
confidence: 99%
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“…Statistical models suggest a pervasive negative distribution of fitness effect for single mutations on HIV-1 [31, 80, 95]. Previous models also predicted the entrenchment of deleterious DRAMs by positive epistasis [96, 97]. This dataset provides a unique chance to experimentally test these statistical hypotheses.…”
Section: Discussionmentioning
confidence: 94%
“…Such models have been shown to accurately predict physical contacts in protein structure, 6,[8][9][10] and have been used to significantly improve the prediction of the fitness effect of mutations to a sequence compared to site-independent sequence variation models which do not account for covariation. 11,12 They are "generative" in the sense that they define the probability, p(S), that a protein sequence S results from the evolutionary process. Intriguingly, the probability distribution p(S) can be used to sample unobserved, and yet viable, artificial sequences.…”
Section: Introductionmentioning
confidence: 99%