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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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Fitness costs, resistance factors and selective advantages of mutants arising under IDV therapy.A. Estimated fitness costs (normalized by setting fitness cost of wild type to 0), B. Resistance factors, on a logarithmic scale (normalized by setting resistance factor of wild type to 1), and C. Estimated selective advantages (normalized by setting selective advantage of wild type to 1) of IDV mutants. In A, B and C, 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.
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pcbi-1003886-g005: Fitness costs, resistance factors and selective advantages of mutants arising under IDV therapy.A. Estimated fitness costs (normalized by setting fitness cost of wild type to 0), B. Resistance factors, on a logarithmic scale (normalized by setting resistance factor of wild type to 1), and C. Estimated selective advantages (normalized by setting selective advantage of wild type to 1) of IDV mutants. In A, B and C, 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.

Mentions: The estimated fitness costs, resistance factors and selective advantages (Table 2 and Figure 5) agreed well with reported experimental findings. In general, we observed that early mutations have a high fitness cost, while the accumulation of further mutations succeeded in compensating almost entirely for this loss in fitness (Figure 5A). This is in agreement with clinical observations that mutations selected early during therapy with protease inhibitors cause impaired protease function and that subsequent accumulation of mutations compensates for this fitness cost [27], [53]. A striking behaviour that we observed was the presence of staircases in the fitness landscape, which has also been described earlier [54]. We observed a monotonic increase of the average selective advantages of the mutants with increasing number of mutations (Figure 5C). This observation provides additional reasoning for the accumulation of mutations during IDV therapy. Notably, the high fitness costs for the double and triple mutants (Figure 5A) were not sufficient to deter their occurrence, as the fitness costs were well-offset by resistance (Figure 5B), which facilitated further climbing of the fitness landscape by accumulating mutations (Figure 5C).


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

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

Fitness costs, resistance factors and selective advantages of mutants arising under IDV therapy.A. Estimated fitness costs (normalized by setting fitness cost of wild type to 0), B. Resistance factors, on a logarithmic scale (normalized by setting resistance factor of wild type to 1), and C. Estimated selective advantages (normalized by setting selective advantage of wild type to 1) of IDV mutants. In A, B and C, 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.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003886-g005: Fitness costs, resistance factors and selective advantages of mutants arising under IDV therapy.A. Estimated fitness costs (normalized by setting fitness cost of wild type to 0), B. Resistance factors, on a logarithmic scale (normalized by setting resistance factor of wild type to 1), and C. Estimated selective advantages (normalized by setting selective advantage of wild type to 1) of IDV mutants. In A, B and C, 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.
Mentions: The estimated fitness costs, resistance factors and selective advantages (Table 2 and Figure 5) agreed well with reported experimental findings. In general, we observed that early mutations have a high fitness cost, while the accumulation of further mutations succeeded in compensating almost entirely for this loss in fitness (Figure 5A). This is in agreement with clinical observations that mutations selected early during therapy with protease inhibitors cause impaired protease function and that subsequent accumulation of mutations compensates for this fitness cost [27], [53]. A striking behaviour that we observed was the presence of staircases in the fitness landscape, which has also been described earlier [54]. We observed a monotonic increase of the average selective advantages of the mutants with increasing number of mutations (Figure 5C). This observation provides additional reasoning for the accumulation of mutations during IDV therapy. Notably, the high fitness costs for the double and triple mutants (Figure 5A) were not sufficient to deter their occurrence, as the fitness costs were well-offset by resistance (Figure 5B), which facilitated further climbing of the fitness landscape by accumulating mutations (Figure 5C).

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