RNAVirFitness SIGNED

Project "RNAVirFitness" data sheet

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 Project objective

Mutations are fundamental drivers of evolution. Characterizing how mutations affect fitness is critical across diverse fields: from pathogen biology, to human genetic diseases, and models of population extinction. RNA viruses, notorious for their high mutation rates and rapid generation times, are ideal models for studying the effects of mutations. To date, deleterious mutations (i.e., mutations with a fitness cost) have been understudied as compared to beneficial mutations, mainly since it has been technically unfeasible to sequence each single rare deleterious mutation. Using novel next generation sequencing (NGS) techniques, we and others have recently overcome this gap, and shown that an appreciable proportion of viral genetic diversity is a consequence of a multitude of rare deleterious mutations. Here, we suggest investigating the distribution of fitness effects (DFE) across a diverse array of RNA viruses, spanning representatives of each class of major human pathogens, both in vivo (in patients) and in vitro (in cell culture). Next, we will focus on genetic linkage and context-dependent fitness effects of mutations. We postulate that over- and under-represented sequence contexts may represent signatures of host anti-viral activity. Finally, we will investigate how the DFE changes following an environmental perturbation (physical and metabolic changes, tissue type, and sex of the host). We will explore how the accumulation of deleterious mutations following rapid perturbations may lead to the extinction of the viral population, and how this can be used as a novel strategy to tackle viral epidemics. To this end we will integrate state-of-the-art NGS, population genetics modelling, and reverse genetics validation. Beyond their contribution to evolutionary biology, we anticipate that our results may be harnessed for the design of safe and effective attenuated vaccine strains, and the development of broad-spectrum antiviral therapeutic strategies.