Inferring viral proteins that act as public goods during coinfection

Abstract

Interactions among individuals in structured populations can alter fitness effects of mutations and reshape evolutionary processes. In many systems, including bacteria, yeast, and viruses, such interactions often result in public goods: gene products that are costly to produce yet exploitable by others. During viral coinfection of the same cell, gene products from one genome may complement deleterious mutations in another, allowing defective genomes to persist. Yet it remains difficult to infer which proteins are shareable from population sequencing data, because mutation, selection, drift, and complementation are intertwined. Here, we developed a quantitative framework to infer protein-specific public goods in the RNA bacteriophage MS2, which encodes only four proteins. We analyzed experimental evolution data generated under two multiplicity-of-infection (MOI) regimes: low MOI, where coinfection is rare, and high MOI, where coinfection is common. We first compared empirical mutation patterns between regimes and then applied a Wright-Fisher model combined with simulation-based Bayesian inference using neural posterior estimation. In a two-stage strategy, gene-specific fitness effects were inferred from low-MOI data and subsequently used to estimate protein sharing under high-MOI conditions. Across two statistical inference frameworks, lysis emerged as the strongest public-good candidate, replicase and coat showed an intermediate signal, and maturation showed the weakest evidence for sharing. Together, our results show that viral proteins differ markedly in their propensity to act as public goods. More broadly, they illustrate how coinfection can generate density-dependent selection, a general feature of social evolution that may shape evolutionary dynamics.