Expression of genes in microbial hosts forms the foundation of basic research and applied biotechnology. A significant amount of research recently 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, the focus has been primarily on transcription, translation initiation, or non-specific global parameters and few 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 basis due to translational selection for codons with faster elongation times, or that are tuned to tRNA supply. We are investigating how the partitioning of microbial translational resources, specifically through allocation of tRNA by incorporating dissimilar codon usage bias (CUB), can improve expression of proteins and reduce the burden on gene expression systems. Utilizing nearly identical CFP and YFP reporters, we constructed a genetic resource competition assay both in vitro and in vivo that isolates translation elongation from other variables. We find that alternative CUB designs can trans-regulate gene expression of competing heterologous and endogenous genes, yielding productive gene expression or severe genetic burden. By isolating individual codons experimentally, we correlate specific codon usage patterns with genetic burden, and derive novel CUB schemes that result in more harmonious protein expression. Through our research, we aim to improve the predictability and robustness of genetic engineering in microbes so that engineers and researchers can better manipulate living systems. More broadly, this research advances our understanding of how biological systems respond to genetic burden and adapt to accommodate the acquisition of new genes.

Reducing translational resource competition through dissimilar codon bias

ACS Spring 2022

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