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

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. thermodenitrif icans, 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.

Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb

“…With higher nucleotide differences of 35%, 200 samples provided reasonable prediction qualities (g). Libraries including multiple sigma factors (e, f) (Gilman et al, 2019) resulted in lower prediction qualities, which parallels studies on heterogenous data in E. coli (Cambray et al, 2018) and yeast (Liya et al, 2021). More libraries are necessary to narrow the required sample size over the whole sequence diversity spectrum.…”

“…A comprehensive table with numerical values of Figure4in the Supplementary Data. Multiple transcription factors are responsible for gene expression in the data set ofGilman et al (2019), hence is not applicable (N.A. ).…”

Importance of the 5′ regulatory region to bacterial synthetic biology applications

“…One disadvantage of this and related models is that the majority, if not all, of the data used to train the computational models comes from a model organism. This scarcity of host-specific information leads to poor model performance in non-model species (Umarov and Solovyev, 2017;Bharanikumar et al, 2018;Gilman et al, 2019). However, even in model organisms, computational models can give erroneous predictions.…”

Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization

“…Currently, a commonly used method for extracting sequence features is position weight matrix [34], but the approach may not be transferable to different species [33]. Another problem with promoter strength prediction is the relative lack of data, particularly in cases where machine learning is applied to experimentally characterized promoters [35,36]. But, use of genome-wide RNA-seq data may provide sufficient data that significantly improves machine learning based predictions of promoter strength.…”