Intracellular interactions shape antiviral resistance outcomes in poliovirus via eco-evolutionary feedback

Figures

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  Figure 1:. Genotype-phenotype associations can limit resistance evolution. In bacterial populations, resistance-conferring genotypes exist at low frequencies prior to treatment (A). Because genotypes are directly associated with the phenotypes they encode (B), drug application that selectively favors the survival of resistant phenotypes drives the expansion of resistant genotypes (C). In viruses, multiple genotypes (i.e., resistant and susceptible) may co-infect the same cell via mutation or superinfection and the protein products they encode can mix intracellularly (D). The resulting chimeric phenotypes can be associated with either genotype (E) and even those phenotypes that are partially composed of resistant proteins may be susceptible to drug neutralization (F). Throughout the figure, red and blue represent resistant and susceptible variants, respectively, and yellow triangles represent antimicrobials that can bind to susceptible proteins.

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  Figure 2:. Pocapavir-treated poliovirus outcomes diverged between cell culture and clinical trial settings. (A) In vitro experiments by Tanner et al. [6] demonstrated that coinfection of drug-resistant (res.) and susceptible (sus.) poliovirus strains suppresses the yield of resistant virus under pocapavir treatment (MOI—multiplicity of infection, the ratio of viruses to host cells, PFU– plaque forming units). (B) In the clinical trial reported by Collett et al. [8], pocapavir failed to significantly reduce time to infection clearance compared to a placebo in three of four matched groups of participants administered the live attenuated poliovirus vaccine and resistance was enriched in the pocapavir group. Points represent the clearance dates of individual trial participants and are colored by resistance status (resistance in red, susceptible in blue) and gray boxes indicate dates that were not sampled during the trial (DPI—days post infection).

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  Figure 3:. Discrete-time model of poliovirus replication, mutation and survival under pocapavir treatment. (A) We simulate intracellular poliovirus dynamics in four stages: (1) viral entry into host cells, (2) genome replication with mutation, (3) production of capsid subunits, and (4) assembly and packaging of progeny virions. (B) Capsid-mediated survival is modeled by culling progeny virions according to their capsid composition. Capsid survival probability as a function of the number of resistant subunits (line) was fit from cell culture experimental data from Tanner et al. [6] (points). Resistant viral yield under different intensities of susceptible virus coinfection shows a density-dependent effect in vitro [6] (C) and in silico under our model (D). Throughout the figure, resistant variants are illustrated in red and susceptible variants are illustrated in blue.

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  Figure 4:. Resistance suppression is MOI-dependent and resistance emerges if bottlenecks do not lead to extinction. (A) Change in resistance frequency in a single generation (ΔfRes) depends on the MOI (initial fRes=1×10−4). (B) At high MOI, rare resistant genomes are encapsidated by phenotypically susceptible capsids, muting genotypic selection. Although nearly all capsids are phenotypically susceptible, some survive pocapavir administration (a fraction greatly exaggerated for this cartoon), as observed in cell culture. (C) At low MOI, viruses singly infect cells, and rare resistant genomes are encapsidated by phenotypically resistant capsids, enabling selection for resistance. (D) Over multiple generations, pocapavir treatment transiently reduces viral population size for both resistant and susceptible genomes but leads to viral rebound of a primarily genetically resistant population following low population density. (E) Discrete step phase diagram shows the joint change in genotypic resistance frequency and MOI from different initial conditions. Arrows are shortened by 80% to increase legibility. The trajectory from part (D) is overlaid in grey. (F) Clearance dates from the observed…

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  Figure 5:. Drugs less potent than pocapavir can better suppress resistance and maintain lower viral loads. (A) We modeled hypothetical drugs 10×, 100× and 1,000× weaker than pocapavir and plot the probability of virion survival as a function of the number of resistant capsid subunits under these drug conditions. (B) In deterministic serial passage experiments, the 100× weaker drug maintained a lower total viral population than pocapavir (resistant genomes, red; susceptible genomes, blue). We simulated clinical trials of 100 individuals treated with drugs 1×, 10×, 100×, and 1,000× weaker than pocapavir. Reducing drug potency delayed mean clearance time measured by days post infection (C) but had a non-monotonic effect on the sum viral load over the course of the infection (D). In both (C) and (D), dots represent individual simulations and are colored based on the frequency of resistance in the population over the course of the infection (scale in (D)). Error bars show mean (red triangle) ± variance. For an expanded range of drug potencies, see Figure S8 B&C.

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  Figure 6:. Targeting viral traits affected by multiple intracellular viral genomes requires an understanding of eco-evolutionary feedback. Viral density determines the degree of intracellular interactions, which determines realized phenotypes, which determine absolute fitness, which feed back into viral density in the next generation.