SRA

Well-characterised 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 characterisation 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. 80 of these sequences were characterised in vivo and activities covered a 2-log range of predictable expression levels. 7 sequences were shown to function consistently regardless of the downstream coding sequence. Partition modelling 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 was a simple, forward, quantitative method for in silico prediction of promoter function.