Estimating HIV-1 Fitness Characteristics from Cross-Sectional Genotype Data

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.ISSN:1553-734XISSN:1553-735

Estimating HIV-1 Fitness Characteristics from Cross-Sectional Genotype Data

<div><p>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 <i>in vivo</i> 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 <i>in vivo</i> 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.</p></div

Estimated fitness costs for IDV mutants.

<p>Estimated resistance factors (on a logarithmic scale, log RF, column 2) and fitness costs (column 3) of mutants arising during IDV therapy. In parentheses, are the 95% confidence intervals for the estimates obtained from 200 bootstrap samples (where we resampled with replacement from the list of statistical waiting times and re-estimated fitness costs). Mutant types (column 4) are encoded by one ‘M’ for each major mutation and one ‘m’ for each minor mutation in the genotype.</p><p>Estimated fitness costs for IDV mutants.</p

Estimated fitness costs for ZDV mutants.

<p>Estimated resistance factors (on a logarithmic scale, log RF, column 2) and fitness costs (column 3) of TAM-1 and TAM-2 mutants and two mixed mutants arising during ZDV therapy. In parentheses, are the 95% confidence intervals for the estimates obtained from 200 bootstrap samples (where we resampled with replacement from the list of statistical waiting times and re-estimated fitness costs).</p><p>Estimated fitness costs for ZDV mutants.</p

Two stage mechanistic model of <i>in vivo</i> HIV-1 infection dynamics [6].

<p>Target cells TU (T-cells) and MU (macrophages) can be infected by infective viruses (with effective infection rate constants and ), resulting in early stage infected cells and , respectively. Infection can also be unsuccessful after fusion of the virus, rendering the cell uninfected and thereby eliminating the virus (). and can also possibly return to uninfected states by destruction of essential viral proteins or DNA prior to integration (). cells can enter into a latent state (with probability ) that can get re-activated with a rate constant . Integration of viral DNA in the host genome proceeds with reaction rate constant in the T-cells and in the macrophages, resulting in late stage infected T-cells and macrophages , respectively. The infected cells release new viruses () and non-infective () viruses (with rate constants and , respectively) while the infected cells release new infective and non-infective viruses (with rate constants and , respectively). Target cells TU and MU are produced by the immune system at constant rate with rate constants and , respectively. , , , , and can be cleared by the immune system with reaction rate constants , , , , and , respectively. Viruses are cleared by the immune system with a rate constant . Mutations are modelled to occur at the stage of integration of the viral DNA. The incorporation of the various drug classes is indicated by the inhibition of corresponding processes: EI/FI - entry/fusion inhibitors, NRTI/NNRTI - nucleoside/non-nucleoside reverse transcriptase inhibitors, InI - integrase inhibitors, PI/MI - protease/maturation inhibitors.</p

Fitness costs, resistance factors and selective advantages of mutants arising under IDV therapy.

<p><b>A</b>. Estimated fitness costs (normalized by setting fitness cost of wild type to 0), <b>B</b>. Resistance factors, on a logarithmic scale (normalized by setting resistance factor of wild type to 1), and <b>C</b>. Estimated selective advantages (normalized by setting selective advantage of wild type to 1) of IDV mutants. In <b>A</b>, <b>B</b> and <b>C</b>, the x-axis depicts the number of mutations. Black crosses represent the values for the different mutant genotypes, while the blue solid line represents the average of fitness costs, resistance factors and selective advantages across all mutant genotypes with a given number of mutations.</p

Treatment outcome with ZDV+IDV dual therapy.

<p><b>A</b>. Genotypic reasons of treatment failure were assessed in terms of mutations present at point of virological failure. For different combinations of drug efficacies and , the different genotypic reasons of failure are shown in different colours. The treatment outcome could be a) failure with mutations resistant to both ZDV and IDV, (b) failure with mutations resistant only to ZDV, (c) failure with mutations resistant only to IDV, (d) failure with wild type, and (e) no detection of failure. <b>B</b>. Viral load (in copies RNA/ml) under ZDV+IDV therapy with  = 0.75 and  = 0.90. The blue line shows the total viral load, while the red dashed line depicts the wild type. The horizontal black dashed line represents the detection threshold used (500 copies/ml).</p

Partially ordered set (poset) and induced genotype lattice for mutations associated with resistance to ZDV.

<p><b>A</b>. Poset of resistance development to ZDV. Vertices represent mutations and directed edges represent the order constraints of mutation accumulation. We observed the clustering of thymidine analog mutations (TAMs) along the two classical TAM-1 and TAM-2 pathways that is well-known under ZDV therapy <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003886#pcbi.1003886-Yahi1" target="_blank">[37]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003886#pcbi.1003886-Hanna1" target="_blank">[38]</a>. The left arm of the poset (mutations 41L, 215Y and 210W) is the TAM-1 pathway, while the right arm (mutations 67N, 70R and 219Q) is the TAM-2 pathway. <b>B</b>. Genotype lattice of mutants induced by the poset of mutations in <b>A</b>. The vertices represent the genotypes that are compatible with the poset in <b>A</b>. Predicted levels of phenotypic resistance are color-coded (green, fully susceptible; red, highly resistant). Please see Supplementary <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003886#pcbi.1003886.s008" target="_blank">Table S2</a> in Supporting Information for the waiting times of mutations.</p

Abundance of the 70R mutation and mutant genotypes with 70R under ZDV therapy.

<p><b>A</b>. Absolute abundance (in numbers) of the 70R mutation. <b>B</b>. Relative abundance of the 70R mutation in the viral population. The transient appearance and eventual fixation of the mutation 70R can be seen. <b>C</b>. Absolute abundance (in numbers) of mutant genotypes containing the mutation 70R. The absolute abundance of a certain mutation is calculated by adding all mutant genotypes containing the mutation.</p

Partially ordered set and induced genotype lattice for mutations associated with resistance to IDV.

<p><b>A</b>. Poset of the continuous time conjunctive Bayesian network for resistance development to IDV. <b>B</b>. The genotype lattice of mutants induced by the poset in <b>A</b>. The vertices represent the genotypes that are compatible with the poset in <b>A</b>. The predicted levels of phenotypic resistance are color-coded (green =  fully susceptible, red  =  highly resistant). Please see Supplementary <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003886#pcbi.1003886.s008" target="_blank">Table S2</a> in Supporting Information for the waiting times of mutations.</p