A Computational Framework for Identifying Promoter Sequences in Nonmodel Organisms Using RNA-seq Data Sets.

Engineering microorganisms into biological factories that convert renewable feedstocks into valuable materials is a major goal of synthetic biology; however, for many nonmodel organisms, we do not yet have the genetic tools, such as suites of strong promoters, necessary to effectively engineer them. In this work, we developed a computational framework that can leverage standard RNA-seq data sets to identify sets of constitutive, strongly expressed genes and predict strong promoter signals within their upstream regions. The framework was applied to a diverse collection of RNA-seq data measured for the methanotroph Methylotuvimicrobium buryatense 5GB1 and identified 25 genes that were constitutively, strongly expressed across 12 experimental conditions. For each gene, the framework predicted short (27-30 nucleotide) sequences as candidate promoters and derived -35 and -10 consensus promoter motifs (TTGACA and TATAAT, respectively) for strong expression in M. buryatense. This consensus closely matches the canonical E. coli sigma-70 motif and was found to be enriched in promoter regions of the genome. A subset of promoter predictions was experimentally validated in a XylE reporter assay, including the consensus promoter, which showed high expression. The pmoC, pqqA, and ssrA promoter predictions were additionally screened in an experiment that scrambled the -35 and -10 signal sequences, confirming that transcription initiation was disrupted when these specific regions of the predicted sequence were altered. These results indicate that the computational framework can make biologically meaningful promoter predictions and identify key pieces of regulatory systems that can serve as foundational tools for engineering diverse microorganisms for biomolecule production.

Systematic Identification of a Panel of Strong Constitutive Promoters from Streptomyces albus.

Actinomycetes are important organisms for the biosynthesis of valuable natural products. However, only a limited number of well-characterized native constitutive promoters from actinomycetes are available for the construction and engineering of large biochemical pathways. Here, we report the discovery and characterization of 32 candidate promoters identified from Streptomyces albus J1074 by RNA-seq analysis. These 32 promoters were cloned and characterized using a streptomycete reporter gene, xylE, encoding catechol 2,3-dioxygenase. The strengths of the identified strong promoters varied from 200 to 1300% of the strength of the well-known ermE*p in MYG medium, and the strongest of these promoters was by far the strongest actinomycete promoter ever reported in the literature. To further confirm the strengths of these promoters, qPCR was employed to determine the transcriptional levels of the xylE reporter. In total, 10 strong promoters were identified and four constitutive promoters were characterized via a time-course study. These promoters were used in a plug-and-play platform to activate a cryptic gene cluster from Streptomyces griseus, and successful activation of the target pathway was observed in three widely used Streptomyces strains. Therefore, these promoters should be highly useful in current synthetic biology platforms for activation and characterization of silent natural product biosynthetic pathways as well as the optimization of pathways for the synthesis of important natural products in actinomycetes.