Cells contain a finite set of resources that must be distributed across many processes to ensure survival. Among them, the largest proportion of cellular resources is dedicated to protein translation. Synthetic biology often exploits these resources in executing orthogonal genetic circuits, yet the burden this places on the cell is rarely considered. Here, we develop a minimal model of ribosome allocation dynamics capturing the demands on translation when expressing a synthetic construct together with endogenous genes required for the maintenance of cell physiology. Critically, it contains three key variables related to design parameters of the synthetic construct covering transcript abundance, translation initiation rate, and elongation time. We show that model-predicted changes in ribosome allocation closely match experimental shifts in synthetic protein expression rate and cellular growth. Intriguingly, the model is also able to accurately infer transcript levels and translation times after further exposure to additional ambient stress. Our results demonstrate that a simple model of resource allocation faithfully captures the redistribution of protein synthesis resources when faced with the burden of synthetic gene expression and environmental stress. The tractable nature of the model makes it a versatile tool for exploring the guiding principles of efficient heterologous expression and the indirect interactions that can arise between synthetic circuits and their host chassis because of competition for shared translational resources.

Translation is a central cellular process and is optimized for speed and fidelity. The speed of translation of a single codon depends on the concentration of aminoacyl-tRNAs. Here, we used microarray-based approaches to analyze the charging levels of tRNAs in Escherichia coli growing at different growth rates. Strikingly, we observed a non-uniform aminoacylation of tRNAs in complex media. In contrast, in minimal medium, the level of aminoacyl-tRNAs is more uniform and rises to approximately 60%. Particularly, the charging level of tRNASer, tRNACys, tRNAThr and tRNAHis is below 50% in complex medium and their aminoacylation levels mirror the degree that amino acids inhibit growth when individually added to minimal medium. Serine is among the most toxic amino acids for bacteria and tRNAsSer exhibit the lowest charging levels, below 10%, at high growth rate although intracellular serine concentration is plentiful. As a result some serine codons are among the most slowly translated codons. A large fraction of the serine is most likely degraded by L-serine-deaminase, which competes with the seryl-tRNA-synthetase that charges the tRNAsSer. These results indicate that the level of aminoacylation in complex media might be a competition between charging for translation and degradation of amino acids that inhibit growth.

No abstract

Transfer tRNAs (tRNAs) are small non-coding RNAs that are highly conserved in all kingdoms of life. Originally discovered as the molecules that deliver amino acids to the growing polypeptide chain during protein synthesis, tRNAs have been believed for a long time to play exclusive role in translation. However, recent studies have identified key roles for tRNAs and tRNA-derived small RNAs in multiple other processes, including regulation of transcription and translation, posttranslational modifications, stress response, and disease. These emerging roles suggest that tRNAs may be central players in the complex machinery of biological regulatory pathways. Here we overview these non-canonical roles of tRNA in normal physiology and disease, focusing largely on eukaryotic and mammalian systems.

Summary Post-translational addition of amino acids to proteins by enzymes using aminoacyl-tRNA is an emerging regulatory mechanism. Examples include Arg transfer in eukaryotes, Leu/Phe transfer in bacteria, and tRNA-synthetase-mediated addition of amino acids to Lys side chains. Here, we present a method of purification and use of tRNA for such reactions, focusing on tRNA Arg and its use for arginylation. This method can also be used for other tRNA-mediated reactions. For complete details on the use and execution of this protocol, please refer to Avcilar-Kucukgoze et al. (2020)