Codon usage modulates the relationship between the burden and yield of protein overexpression.

Abstract

Background: Excess utilization of translational resources is a critical source of burden on cells engineered to overexpress exogenous proteins. To improve translational efficiency, researchers often modify codon usage in an exogenous gene to more closely match the composition of a host organism's highly expressed genes. Despite empirical data showing the benefits of codon optimization, little is known about the quantitative relationships between codon usage, protein yield, and the burden imposed on a host cell by protein overexpression.

Results: We develop and experimentally evaluate a stochastic gene expression model that considers the impact of codon usage bias on the availability of ribosomes and different tRNAs in a cell. In agreement with other studies, our model shows that increasing exogenous protein expression decreases production of native cellular proteins in a linear fashion. We also find that the slope of this relationship is modulated by how well the codon usage bias of the exogenous gene and the host's genes match. Lastly, our model predicts that an overoptimization domain exists where further increasing usage of optimal codons worsens yield and burden. We test our model by expressing sfGFP and mCherry2 from constructs that have a wide range of codon optimization levels in Escherichia coli. The results agree with our model, including for an mCherry2 gene sequence that appears to less efficiently express this gene due to codon overoptimization.

Conclusions: Our model reproduces experimentally observed relationships between codon usage bias, gene expression, and burden for overexpressed proteins. Furthermore, it suggests that more nuanced recoding strategies that seek to match a host's overall codon usage bias are less burdensome and will lead to greater protein yields compared to strategies that simply maximize usage of optimal codons. Increasing the level of mechanistic detail in gene expression models can lead to insights that allow researchers to engineer more optimal cellular systems.