Abstract
mRNA translation is a fundamental cellular process consuming most of the intracellular energy; thus, it is under extensive evolutionary selection for optimization, and its efficiency can affect the host's growth rate. We describe a generic approach for improving the growth rate (fitness) of any organism by introducing synonymous mutations based on comprehensive computational models. The algorithms introduce silent mutations that may improve the allocation of ribosomes in the cells via the decreasing of their traffic jams during translation respectively. As a result, resources availability in the cell changes leading to improved growth-rate. We demonstrate experimentally the implementation of the method on Saccharomyces cerevisiae: we show that by introducing a few mutations in two computationally selected genes the mutant's titer increased. Our approach can be employed for improving the growth rate of any organism providing the existence of data for inferring models, and with the relevant genomic engineering tools; thus, it is expected to be extremely useful in biotechnology, medicine, and agriculture.
Highlights
MRNA translation is a fundamental cellular process consuming most of the intracellular energy; it is under extensive evolutionary selection for optimization, and its efficiency can affect the host’s growth rate
Our mRNA translation model is based on a mean field approximation of the TASEP (Totally Asymmetric Simple Exclusion Process) which considers all the fundamental aspects of translation dynamics such as: ribosome movement according to codon decoding times from the 5′ to the 3′ end of the mRNA, and excluded volume interactions[41]
(See Fig. 1 and full details in the Methods section), we determine the optimal mutations across the host genome, according to the following Ribosome Traffic Engineering (RTE) greedy algorithm: Iterate all the host genes, for each gene we look at codons 11–50—a region that we call ’ramp’, and mutate a codon to a slower or faster synonymous codon
Summary
MRNA translation is a fundamental cellular process consuming most of the intracellular energy; it is under extensive evolutionary selection for optimization, and its efficiency can affect the host’s growth rate. It was shown that in both prokaryotes (bacteria and archaea) and eukaryotes, the first ~ 30–50 codons of the ORF tend to be recognized by tRNA species with lower intracellular abundance, resulting in slower ribosomal elongation speed in this r egion[37,38] This provides several physiological benefits, such as assisting in ribosomal allocation, recruiting protein chaperons, co-translational folding, and protein m aturation[37,38]. We propose to exploit this region’s properties via introducing silent engineered mutations to the first 50 codons of the endogenous genes, and thereby modulate the free ribosomal pool, while constraining the limits of translation efficiency (reduction or enhancement) of these genes This is facilitated by the inherent redundancy of the genetic code, where 61 codons encode only 20 amino acids[39,40], such that we are able to change the elongation rate in this region while maintaining the encoded protein. The novel approach suggested in this study can be used for biotechnological objectives such as heterologous gene expression and vaccine development
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