Rapid acquisition and model-based analysis of cell-free transcription–translation reactions from nonmodel bacteria

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 115; no. 19; pp. E4340 - E4349
• Main Authors: Moore, Simon J., MacDonald, James T., Wienecke, Sarah, Ishwarbhai, Alka, Tsipa, Argyro, Aw, Rochelle, Kylilis, Nicolas, Bell, David J., McClymont, David W., Jensen, Kirsten, Polizzi, Karen M., Biedendieck, Rebekka, Freemont, Paul S.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 08.05.2018
• Series: PNAS Plus
• Subjects: Amino acids; Bacillus megaterium - chemistry; Bacillus megaterium - genetics; Bacillus megaterium - metabolism; Bacteria; Bayesian analysis; Binding sites; Biological Sciences; Biotechnology; Cell culture; Cell-Free System - chemistry; Cell-Free System - metabolism; Competition; Differential equations; Gene expression; Mathematical models; Microorganisms; Modelling; Models, Biological; Nucleotides; PNAS Plus; Promoters; Protein Biosynthesis; Regulatory sequences; Ribonucleic acid; Ribosomes; RNA; Transcription factors; Transcription, Genetic; Translation; automation; modeling; in vitro transcription–translation; cell-free synthetic biology; Bacillus
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.1715806115

Native cell-free transcription–translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host, Bacillus megaterium, is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated platform for measuring and modeling in vitro cell-free reactions and have applied this to B. megaterium to quantify a range of ribosome binding site variants and previously uncharacterized endogenous constitutive and inducible promoters. To provide quantitative models for cell-free systems, we have also applied a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions. Using this modeling framework, we were able to infer previously unknown transcription factor binding affinities and quantify the sharing of cell-free transcription–translation resources (energy, ribosomes, RNA polymerases, nucleotides, and amino acids) using a promoter competition experiment. This allows insights into resource limiting-factors in batch cell-free synthesis mode. Our combined automated and modeling platform allows for the rapid acquisition and model-based analysis of cell-free transcription–translation data from uncharacterized microbial cell hosts, as well as resource competition within cell-free systems, which potentially can be applied to a range of cell-free synthetic biology and biotechnology applications.