(27br) Specific Codons Control Cellular Resources and Fitness

A significant amount of recent work has focused on de-coupling non-native gene expression from host resources to improve the outcome of synthetic biology and metabolic engineering efforts. So far, most research has focused on transcription, translation initiation, or global sequence parameters, but fewer studies have investigated the mechanistic underpinnings of resource allocation during translational elongation. The degenerate genetic code provides an opportunity to allocate cellular tRNA and ribosomal resources optimally between host and heterologous protein expression. 18 of the 20 natural amino acids can be encoded with multiple codons, which can result in significant codon usage bias due to translational selection for codons with faster elongation times, or that are tuned to host tRNA supply. Through our research, we aim to improve the predictability and robustness of genetic engineering in microbes by systematically determining optimal codon bias schemes.

We investigate how the partitioning of microbial translational resources, specifically through allocation of tRNA by incorporating dissimilar codon usage bias (CUB), can drastically alter expression of proteins and reduce the burden on gene expression systems. Utilizing nearly identical fluorescent reporters (CFP and YFP), we assayed genetic resource competition both in vitro and in vivo using novel designs that isolate translation elongation from other variables. We find that alternative CUB designs can trans-regulate gene expression of competing heterologous and endogenous genes, yielding profitable or catastrophic design options. By isolating individual codons experimentally, we correlate specific codon usage patterns with genetic burden, and derive novel coding schemes for multi-gene expression. These empirically derived coding schemes based on a new codon adaptation index enable the design of harmonious multi-gene expression systems while avoiding catastrophic cellular burden.

Associated work: Love & Nair (2022) bioRxiv