Browse by author
Lookup NU author(s): Dr Thomas HowardORCiD
This is the final published version of an article that has been published in its final definitive form by American Chemical Society, 2019.
For re-use rights please refer to the publisher's terms and conditions.
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. 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 as compared to the size of the modelled design space.
Author(s): Gilman J, Singleton C, Tennant RK, James PBC, Howard TP, Lux T, Parker DA, Love J
Publication type: Article
Publication status: Published
Journal: ACS Synthetic Biology
Print publication date: 17/05/2019
Online publication date: 17/04/2019
Acceptance date: 17/04/2019
Date deposited: 01/05/2019
ISSN (electronic): 2161-5063
Publisher: American Chemical Society
Altmetrics provided by Altmetric