Alexander Robertson

@alexrob.bsky.social

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PhD candidate at UW Seattle. I study how interactions between viruses shapes their evolution. Also 📷, 🎹, 📽️, 🐦

A cyclic plot depicting how viral density determines the degree of intracellular interactions, which in turn shapes realized phenotypes, influencing absolute fitness, which then feeds back into viral density in the next generation.
We simulated clinical trials of 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 but had a non-monotonic effect on the sum viral load over the course of the infection (n = 100 for each group). In both plots, dots represent individual simulations and are colored based on the frequency of resistance in the population over the course of the infection (scale in right panel)
At low MOI, viruses singly infect cells, and rare resistant genomes are encapsidated by phenotypically resistant capsids, enabling selection for resistance.
Clearance dates from the observed clinical trial and one simulated clinical trial of n = 93 viral populations treated with pocapavir. The dashed line indicates the date of the earliest placebo clearance. As in the clinical trial, we observed a population of early clearers (who cleared before the earliest placebo clearance date) and late clearers, who cleared on par with placebo. Late and early clearers both experienced drops in resistant and susceptible viral population size with diverging outcomes following the population bottleneck. Throughout the figure, resistant variants are illustrated in red and susceptible variants are illustrated in blue.
Resistant viral yield under different intensities of susceptible virus coinfection shows a density-dependent effect in vitro (again, compared to Tanner et al.'s 2014 paper mentioned above) and in silico under our model.
A cartoon schematic of the eco-evolutionary model that we developed. 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. We then apply pocapavir.
In the clinical trial reported by Collett et al., pocapavir failed to significantly reduce time to infection clearance compared with 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 grey boxes indicate dates that were not sampled during the trial (DPI, days post infection).
In vitro experiments by Tanner et al. demonstrated that coinfection of drug-resistant (res.) and susceptible (sus.) poliovirus strains suppresses the yield of resistant virus under pocapavir treatment. Link to their study: https://elifesciences.org/articles/03830
A two-part comparison of how resistance evolves in bacteria versus viruses. The top panels show bacteria: rare resistant cells (red) become common after treatment because each cell’s genes match its traits. The bottom panels show viruses: resistant and susceptible genomes (red and blue) can mix inside the same infected cell, creating viral progeny with "chimeric" capsids. Mixed particles carrying resistant genomes may still get blocked by the drug (yellow triangles), even if their capsid has some number of resistant subunits.