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Estimating HIV-1 fitness characteristics from cross-sectional genotype data.

Gopalakrishnan S, Montazeri H, Menz S, Beerenwinkel N, Huisinga W - PLoS Comput. Biol. (2014)

Bottom Line: Assessing in vivo viral fitness is a challenging task and yet one that has significant clinical relevance.We quantify the complex interplay between fitness costs and resistance by computing selective advantages for different mutants.The combined statistical and dynamical modelling approach may help in dissecting the effects of fitness costs and resistance with the ultimate aim of assisting the choice of salvage therapies after treatment failure.

View Article: PubMed Central - PubMed

Affiliation: Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany; Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modelling, Free University of Berlin and University of Potsdam, Berlin/Potsdam, Germany.

ABSTRACT
Despite the success of highly active antiretroviral therapy (HAART) in the management of human immunodeficiency virus (HIV)-1 infection, virological failure due to drug resistance development remains a major challenge. Resistant mutants display reduced drug susceptibilities, but in the absence of drug, they generally have a lower fitness than the wild type, owing to a mutation-incurred cost. The interaction between these fitness costs and drug resistance dictates the appearance of mutants and influences viral suppression and therapeutic success. Assessing in vivo viral fitness is a challenging task and yet one that has significant clinical relevance. Here, we present a new computational modelling approach for estimating viral fitness that relies on common sparse cross-sectional clinical data by combining statistical approaches to learn drug-specific mutational pathways and resistance factors with viral dynamics models to represent the host-virus interaction and actions of drug mechanistically. We estimate in vivo fitness characteristics of mutant genotypes for two antiretroviral drugs, the reverse transcriptase inhibitor zidovudine (ZDV) and the protease inhibitor indinavir (IDV). Well-known features of HIV-1 fitness landscapes are recovered, both in the absence and presence of drugs. We quantify the complex interplay between fitness costs and resistance by computing selective advantages for different mutants. Our approach extends naturally to multiple drugs and we illustrate this by simulating a dual therapy with ZDV and IDV to assess therapy failure. The combined statistical and dynamical modelling approach may help in dissecting the effects of fitness costs and resistance with the ultimate aim of assisting the choice of salvage therapies after treatment failure.

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Partially ordered set and induced genotype lattice for mutations associated with resistance to IDV.A. Poset of the continuous time conjunctive Bayesian network for resistance development to IDV. B. The genotype lattice of mutants induced by the poset in A. The vertices represent the genotypes that are compatible with the poset in A. The predicted levels of phenotypic resistance are color-coded (green =  fully susceptible, red  =  highly resistant). Please see Supplementary Table S2 in Supporting Information for the waiting times of mutations.
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pcbi-1003886-g004: Partially ordered set and induced genotype lattice for mutations associated with resistance to IDV.A. Poset of the continuous time conjunctive Bayesian network for resistance development to IDV. B. The genotype lattice of mutants induced by the poset in A. The vertices represent the genotypes that are compatible with the poset in A. The predicted levels of phenotypic resistance are color-coded (green =  fully susceptible, red  =  highly resistant). Please see Supplementary Table S2 in Supporting Information for the waiting times of mutations.

Mentions: We again used the Stanford HIV Drug Resistance Database [36] to estimate the poset (Figure 4A) and genotype lattice (Figure 4B) of mutations associated with resistance to indinavir (IDV), a protease inhibitor, and corresponding resistance factors of IDV mutants. As for ZDV, the poset, the genotype lattice, and the rate of fixation for each mutation were determined by the I-CBN and CT-CBN models. The dataset for IDV consists of 2170 observations of HIV reverse transcriptase (RT) genotypes and their paired measurements of phenotypic resistance to IDV. We focussed on the five mutations 46I, 54V, 71V, 82A, and 90M. Four of these (46I, 54V, 82A and 90M) are among the most frequent primary (major) mutations reported in the Stanford HIV Drug Resistance Database under IDV therapy [36]. We chose 71V to represent a common secondary (minor) mutation to study possible compensatory fitness effects. We used a drug efficacy on the wild type to illustrate our results. This value was chosen to match average nadir values in viral load after IDV monotherapy (a drop of 1–1.5 log units within 3–4 weeks of therapy) [51], [52].


Estimating HIV-1 fitness characteristics from cross-sectional genotype data.

Gopalakrishnan S, Montazeri H, Menz S, Beerenwinkel N, Huisinga W - PLoS Comput. Biol. (2014)

Partially ordered set and induced genotype lattice for mutations associated with resistance to IDV.A. Poset of the continuous time conjunctive Bayesian network for resistance development to IDV. B. The genotype lattice of mutants induced by the poset in A. The vertices represent the genotypes that are compatible with the poset in A. The predicted levels of phenotypic resistance are color-coded (green =  fully susceptible, red  =  highly resistant). Please see Supplementary Table S2 in Supporting Information for the waiting times of mutations.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4222584&req=5

pcbi-1003886-g004: Partially ordered set and induced genotype lattice for mutations associated with resistance to IDV.A. Poset of the continuous time conjunctive Bayesian network for resistance development to IDV. B. The genotype lattice of mutants induced by the poset in A. The vertices represent the genotypes that are compatible with the poset in A. The predicted levels of phenotypic resistance are color-coded (green =  fully susceptible, red  =  highly resistant). Please see Supplementary Table S2 in Supporting Information for the waiting times of mutations.
Mentions: We again used the Stanford HIV Drug Resistance Database [36] to estimate the poset (Figure 4A) and genotype lattice (Figure 4B) of mutations associated with resistance to indinavir (IDV), a protease inhibitor, and corresponding resistance factors of IDV mutants. As for ZDV, the poset, the genotype lattice, and the rate of fixation for each mutation were determined by the I-CBN and CT-CBN models. The dataset for IDV consists of 2170 observations of HIV reverse transcriptase (RT) genotypes and their paired measurements of phenotypic resistance to IDV. We focussed on the five mutations 46I, 54V, 71V, 82A, and 90M. Four of these (46I, 54V, 82A and 90M) are among the most frequent primary (major) mutations reported in the Stanford HIV Drug Resistance Database under IDV therapy [36]. We chose 71V to represent a common secondary (minor) mutation to study possible compensatory fitness effects. We used a drug efficacy on the wild type to illustrate our results. This value was chosen to match average nadir values in viral load after IDV monotherapy (a drop of 1–1.5 log units within 3–4 weeks of therapy) [51], [52].

Bottom Line: Assessing in vivo viral fitness is a challenging task and yet one that has significant clinical relevance.We quantify the complex interplay between fitness costs and resistance by computing selective advantages for different mutants.The combined statistical and dynamical modelling approach may help in dissecting the effects of fitness costs and resistance with the ultimate aim of assisting the choice of salvage therapies after treatment failure.

View Article: PubMed Central - PubMed

Affiliation: Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany; Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modelling, Free University of Berlin and University of Potsdam, Berlin/Potsdam, Germany.

ABSTRACT
Despite the success of highly active antiretroviral therapy (HAART) in the management of human immunodeficiency virus (HIV)-1 infection, virological failure due to drug resistance development remains a major challenge. Resistant mutants display reduced drug susceptibilities, but in the absence of drug, they generally have a lower fitness than the wild type, owing to a mutation-incurred cost. The interaction between these fitness costs and drug resistance dictates the appearance of mutants and influences viral suppression and therapeutic success. Assessing in vivo viral fitness is a challenging task and yet one that has significant clinical relevance. Here, we present a new computational modelling approach for estimating viral fitness that relies on common sparse cross-sectional clinical data by combining statistical approaches to learn drug-specific mutational pathways and resistance factors with viral dynamics models to represent the host-virus interaction and actions of drug mechanistically. We estimate in vivo fitness characteristics of mutant genotypes for two antiretroviral drugs, the reverse transcriptase inhibitor zidovudine (ZDV) and the protease inhibitor indinavir (IDV). Well-known features of HIV-1 fitness landscapes are recovered, both in the absence and presence of drugs. We quantify the complex interplay between fitness costs and resistance by computing selective advantages for different mutants. Our approach extends naturally to multiple drugs and we illustrate this by simulating a dual therapy with ZDV and IDV to assess therapy failure. The combined statistical and dynamical modelling approach may help in dissecting the effects of fitness costs and resistance with the ultimate aim of assisting the choice of salvage therapies after treatment failure.

Show MeSH
Related in: MedlinePlus