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

Lookup NU author(s): Dr Thomas Howard

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This is the final published version of an article that has been published in its final definitive form by American Chemical Society, 2019.

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Abstract

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.


Publication metadata

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

Year: 2019

Volume: 8

Issue: 5

Pages: 1175-1186

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

URL: https://doi.org/10.1021/acssynbio.9b00061

DOI: 10.1021/acssynbio.9b00061


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