Rapid, Heuristic Discovery and Design of Promoter Collections in Non-Model Microbes for Industrial Applications

James Gilman, Chloe Singleton, Richard K Tennant, Paul James, Thomas P Howard, Thomas Lux, David A Parker, John Love

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
5 Downloads (Pure)

Abstract

Well-characterized promoter collections for synthetic biology applications are not always available in industrially relevant hosts. We developed a broadly applicable method for promoter identification in atypical microbial hosts that requires no a priori understanding of cis-regulatory element structure. This novel approach combines bioinformatic filtering with rapid empirical characterization to expand the promoter toolkit and uses machine learning to improve the understanding of the relationship between DNA sequence and function. Here, we apply the method in Geobacillus thermoglucosidasius, a thermophilic organism with high potential as a synthetic biology chassis for industrial applications. Bioinformatic screening of G. kaustophilus, G. stearothermophilus, G. thermodenitrificans, and G. thermoglucosidasius resulted in the identification of 636 100 bp putative promoters, encompassing the genome-wide design space and lacking known transcription factor binding sites. Eighty of these sequences were characterized in vivo, and activities covered a 2-log range of predictable expression levels. Seven sequences were shown to function consistently regardless of the downstream coding sequence. Partition modeling identified sequence positions upstream of the canonical -35 and -10 consensus motifs that were predicted to strongly influence regulatory activity in Geobacillus, and artificial neural network and partial least squares regression models were derived to assess if there were a simple, forward, quantitative method for in silico prediction of promoter function. However, the models were insufficiently general to predict pre hoc promoter activity in vivo, most probably as a result of the relatively small size of the training data set compared to the size of the modeled design space.

Original languageEnglish
Pages (from-to)1175-1186
Number of pages12
JournalACS Synthetic Biology
Volume8
Issue number5
Early online date17 Apr 2019
DOIs
Publication statusPublished - 17 May 2019
Externally publishedYes

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