Genome-Scale Analysis of Perturbations in Translation Elongation Based on a Computational Model

Specific codons control cellular resources and fitness

As cellular engineering progresses from simply overexpressing proteins to imparting complex phenotypes through multigene expression, judicious appropriation of cellular resources is essential. Since codon use is degenerate and biased, codons may control cellular resources at a translational level. We investigate how partitioning transfer RNA (tRNA) resources by incorporating dissimilar codon usage can drastically alter interdependence of expression level and burden on the host. By isolating the effect of individual codons' use during translation elongation while eliminating confounding factors, we show that codon choice can trans-regulate fitness of the host and expression of other heterologous or native genes. We correlate specific codon usage patterns with host fitness and derive a coding scheme for multigene expression called the Codon Health Index (CHI, χ). This empirically derived coding scheme (χ) enables the design of multigene expression systems that avoid catastrophic cellular burden and is robust across several proteins and conditions.

Determinants of efficient modulation of ribosomal traffic jams

mRNA translation is the process which consumes most of the cellular energy. Thus, this process is under strong evolutionary selection for its optimization and rational optimization or reduction of the translation efficiency can impact the cell growth rate. Algorithms for modulating cell growth rate can have various applications in biotechnology, medicine, and agriculture. In this study, we demonstrate that the analysis of these algorithms can also be used for understanding translation.

We specifically describe and analyze various generic algorithms, based on comprehensive computational models and whole cell simulations of translation, for introducing silent mutations that can either reduce or increase ribosomal traffic jams along the mRNA. As a result, more or less resources are available, for the cell, promoting improved or reduced cells growth-rate, respectively. We then explore the cost of these algorithms' performance, in terms of their computational time, the number of mutations they introduce, the modified genomic region, the effect on local translation rates, and the properties of the modified genes.

Among others, we show that mRNA levels of a gene are much stronger predictors for the effect of its engineering on the ribosomal pool than the ribosomal density of the gene. We also demonstrate that the mutations at the ends of the coding regions have a stronger effect on the ribosomal pool. Furthermore, we report two optimization algorithms that exhibit a tread-off between the number of mutations they introduce and their executing time.

The reported results here are fundamental both for understanding the biophysics and evolution of translation, as well as for developing efficient approaches for its engineering.

Comparative analysis of the relationship between translation efficiency and sequence features of endogenous proteins in multiple organisms

The translation efficiency of protein genes is known to be affected by sequence features. Previous studies have found that various sequence features based on codon usage and mRNA secondary structure contribute to translation efficiency. However, most studies have focused on a specific organism, usually a model organism such as Escherichia coli or Saccharomyces cerevisiae. Here, we investigate whether the relationship between translation efficiency and sequence features is conserved among multiple organisms using publicly available ribosome profiling data and RNA-Seq data. We analyze nine organisms from various taxa: Staphylococcus aureus, five species of Streptomyces, two strains of E. coli, and S. cerevisiae. We reveal that the relationship between translation efficiency and sequence features differs across organisms, partly reflecting their taxonomy. The codon adaptation index shows high correlation in all analyzed organisms. Our study provides an insight into the diversity and commonality of sequence determinants of protein expression in these organisms.

The Key Parameters that Govern Translation Efficiency

Translation of mRNA into protein is a fundamental yet complex biological process with multiple factors that can potentially affect its efficiency. Here, we study a stochastic model describing the traffic flow of ribosomes along the mRNA and identify the key parameters that govern the overall rate of protein synthesis, sensitivity to initiation rate changes, and efficiency of ribosome usage. By analyzing a continuum limit of the model, we obtain closed-form expressions for stationary currents and ribosomal densities, which agree well with Monte Carlo simulations. Furthermore, we completely characterize the phase transitions in the system, and by applying our theoretical results, we formulate design principles that detail how to tune the key parameters we identified to optimize translation efficiency. Using ribosome profiling data from S. cerevisiae, we show that its translation system is generally consistent with these principles. Our theoretical results have implications for evolutionary biology, as well as for synthetic biology.