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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.

Deducing generic causal relations between RNA transcript features and protein expression profiles from endogenous gene expression data remains a major unsolved problem in biology. The analysis of gene expression from heterologous genes contributes significantly to solving this problem, but has been heavily biased toward the study of the effect of 5' transcript regions and to prokaryotes. Here, we employ a synthetic biology driven approach that systematically differentiates the effect of different regions of the transcript on gene expression up to 240 nucleotides into the ORF. This enabled us to discover new causal effects between features in previously unexplored regions of transcripts, and gene expression in natural regimes. We rationally designed, constructed, and analyzed 383 gene variants of the viral HRSVgp04 gene ORF, with multiple synonymous mutations at key positions along the transcript in the eukaryote S. cerevisiae. Our results show that a few silent mutations at the 5'UTR can have a dramatic effect of up to 15 fold change on protein levels, and that even synonymous mutations in positions more than 120 nucleotides downstream from the ORF 5'end can modulate protein levels up to 160%-300%. We demonstrate that the correlation between protein levels and folding energy increases with the significance of the level of selection of the latter in endogenous genes, reinforcing the notion that selection for folding strength in different parts of the ORF is related to translation regulation. Our measured protein abundance correlates notably(correlation up to r = 0.62 (p=0.0013)) with mean relative codon decoding times, based on ribosomal densities (Ribo-Seq) in endogenous genes, supporting the conjecture that translation elongation and adaptation to the tRNA pool can modify protein levels in a causal/direct manner. This report provides an improved understanding of transcript evolution, design principles of gene expression regulation, and suggests simple rules for engineering synthetic gene expression in eukaryotes.

Accurate and efficient gene expression requires that protein translation initiates from mRNA transcripts with high fidelity. At the same time, indiscriminate initiation of translation from multiple ATG start-sites per transcript has been demonstrated, raising fundamental questions regarding the rate and rationale governing alternative translation initiation. We devised a sensitive fluorescent reporter assay for monitoring alternative translation initiation. To demonstrate it, we map the translation initiation landscape of a Saccharomyces cerevisiae gene (RMD1) with a typical ATG sequence context profile. We found that up to 3%-5% of translation initiation events occur from alternative out-of-frame start codons downstream of the main ATG. Initiation from these codons follows the ribosome scanning model: initiation rates from different start sites are determined by ATG order, rather than their context strength. Genomic analysis of S. cerevisiae further supports the scanning model: ATG codons downstream rather than upstream of the main ATG tend to have higher context scores., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Background: Gene expression is a central process in all living organisms. Central questions in the field are related to the way the expression levels of genes are encoded in the transcripts and affect their evolution, and the potential to predict expression levels solely by transcript features. In this study we analyze S. cerevisiae, a model organism with the most abundant relevant cellular and genomic measurements, to evaluate the accuracy in which expression levels can be predicted by different parts of the transcript. To this end, we perform various types of regression analyses based on a total of 5323 features of the transcript. The main advantage of the proposed predictors over previous ones is related to the accurate and comprehensive definitions of the relevant transcript features, which are based on biophysical knowledge of the gene transcription and translation processes, their modeling and evolution., Results: Cross validation analyses of our predictors demonstrate that they achieve a correlation of 0.68/0.68/0.70/0.61/0.81 with mRNA levels, ribosomal density, protein levels, proteins per mRNA molecule (PPR), and ribosomal load (RL) respectively (all p-values <10(-140)). When we consider predictors that are based exclusively on the features related to different parts of the transcript (5'UTR, ORF, 3'UTR), the correlations with protein levels were 0.27/0.71/0.25 (all p-values <10(-5)), suggesting that the information in the UTRs is redundant, and features of the ORF alone yield similar predictions to the ones obtained based on the entire transcript., Conclusions: The reported results demonstrate that in the analyzed model organism the expression levels of a gene are encoded in the transcript. Specifically, the prediction of a large fraction of the variance of the different gene expression steps based on transcript features alone is feasible in S. cerevisiae. We report dozens of novel transcript features related to expression levels predictions, demonstrating how such analyses can aid in understanding the gene expression process and its evolution, and how such predictors can be designed for other organisms in the future.

One of the open questions in regulatory genomics is how the efficiency of gene translation is encoded in the coding sequence. Here we analyse recently generated measurements of folding energy in Saccharomyces cerevisiae, showing that genes with high protein abundance tend to have strong mRNA folding (mF; R=0.68). mF strength also strongly correlates with ribosomal density and mRNA levels, suggesting that this relation at least partially pertains to the efficiency of translation elongation, presumably by preventing aggregation of mRNA molecules.