Cell-free prediction of protein expression costs for growing cells

• Published in: Nature communications Vol. 9; no. 1; pp. 1457 - 11
• Main Authors: Borkowski, Olivier, Bricio, Carlos, Murgiano, Michela, Rothschild-Mancinelli, Brooke, Stan, Guy-Bart, Ellis, Tom
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.04.2018; Nature Publishing Group; Nature Portfolio
• Subjects: 14; 38; 38/35; 631/553; 631/553/2691; 631/553/2697; 631/553/552; 82/80; Amino acid sequence; beta Carotene - metabolism; Biochemistry, Molecular Biology; Cell-Free System; Computer applications; Computer Simulation; DNA, Bacterial - metabolism; E coli; Escherichia coli - metabolism; Escherichia coli Proteins - metabolism; Gene expression; Gene Library; Green Fluorescent Proteins - metabolism; Humanities and Social Sciences; Life Sciences; Models, Genetic; Molecular biology; multidisciplinary; Operon; Operons; Plasmids - metabolism; Predictions; Protein Biosynthesis; Proteins; Proteomics; Resource consumption; RNA, Messenger - metabolism; Science; Science (multidisciplinary); Software; Translation
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-018-03970-x

Translating heterologous proteins places significant burden on host cells, consuming expression resources leading to slower cell growth and productivity. Yet predicting the cost of protein production for any given gene is a major challenge, as multiple processes and factors combine to determine translation efficiency. To enable prediction of the cost of gene expression in bacteria, we describe here a standard cell-free lysate assay that provides a relative measure of resource consumption when a protein coding sequence is expressed. These lysate measurements can then be used with a computational model of translation to predict the in vivo burden placed on growing E. coli cells for a variety of proteins of different functions and lengths. Using this approach, we can predict the burden of expressing multigene operons of different designs and differentiate between the fraction of burden related to gene expression compared to action of a metabolic pathway. The translation of heterologous proteins places a burden on host cell resources, affecting growth and productivity. Here the authors develop a cell-free assay to measure resource consumption and predict in vivo burden.