Unveil fitness landscapes of newly discovered zoonotic viruses, contributing to preventive medicine
Having this experimental evolution model mentioned above in hand, we can further use this system, especially while dealing with emerging- and newly discovered zoonotic viruses, to in vitro generate mutations by reiterating several times of infection in natural host cells/animals followed by testing viral fitness landscapes of variant strains in human cells, thereby helping us to understand and predict the possible impact of variant strains derived from these emerging- and newly discovered zoonotic viruses on humans. Retrospective studies of constructing evolutionary trajectories based on phylogenetic analyses should be performed; in such a way, we can closely monitor the sequential transition of virus sequence changes reshaped by evolutionary constraints throughout every passage of cells or every filial generation in animal models at a single-sequence level and estimate the evolutionary timescale that requires viruses to reprogram their tropisms. This experimental evolution model is inspired by the evolutionary approach named retrovolution[153], with which the authors studied the human deoxycytidine kinase[153]. Here under the framework of this approach with modifications, we import the idea of incorporating molecular barcodes embedded in our viruses of interest, thereby bringing our investigation down to the resolution of a single virus sequence. Most importantly, in line with the procedure designed in this model we will be able to immediately assay viral fitness enhanced by emerging mutations that can be traced through phylogenetic trees. In other words, this barcoding evolution model enables us to associate genotypic changes occurring in natural host cells/animals with predictive phenotypes in human cells in every single evolutionary event in order to accelerate our understanding of newly discovered zoonotic viruses and benefit to develop antiviral strategies in advance. It is worthy to stress the point that in addition to different experimental approaches can be used for predicting the likelihood of predictive mutations[152,154–156], we also propose to use in silico approaches for this purpose: we suggest that algorithms, like generative adversarial networks[157], an architecture for training generative models, can be for instance applied for predicting evolutionary paths when the number of input sequences is sufficient. Eventually virus sequences with predictive mutations will also be necessary to test for fitness in human cells.