Danny Barash

As viruses evolve over time with their host, they adapt to multiple cellular functions to ensure efficient long-term transmission. Their ability to survive and function efficiently depends on optimizing their genetic code to effectively recruit the host's gene expression machinery, particularly the translation machinery. Codon usage bias (CUB) measures the level of adaptation at the codon level, considering multiple factors such as the host's tRNA pool.By estimating the adaptation scores of viruses to their host, we can gain insight into the changes that occur on the genomic level throughout their evolution. In our study, we propose tracking the viral evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using CUB to estimate adaptation. By comparing different strains and analyzing changes over time, we demonstrate an increased adaptation of the Omicron SARS-COV-2 variant. In addition, we observe fluctuations in CUB scores, with periods of decreased and then increased adaptation over time, offering detailed insights at a fine temporal resolution. This analysis further contributes to understanding how different mutations influence viral adaptation, as well as the evolution of other human-infecting viruses.