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

Abstract

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.

Construction of a broad-host-range Anderson promoter series and particulate methane monooxygenase promoter variants expand the methanotroph genetic toolbox

“…The difference in the activity observed here compared to prior analyses could be due to E. coli strain variations since we measured fluorescence in DH10B while others have used DH5α. Recently, BBa_J23119 was demonstrated to display high activity in the Gammaproteobacterial methanotroph Methylotuvimicrobium buryatense 5GB1C, but it was suggested that other promoters in the series may not function in methanotrophs due to a lack of measured BBa_J23112 and BBa_J23117 activity in M. buryatense [ 39 ]. Notably, these two promoters have limited activity in E. coli , and we observed low activity of BBa_J23117 in M. capsulatus Bath and M. trichosporium OB3b ( Fig.…”

Characterization of a Panel of Constitutive Promoters from Lactococcus cremoris for Fine-Tuning Gene Expression

“…In recent years, with the rapid development of genomics technology, the genomic library screening method has become a common method for screening promoters, and researchers have screened a series of constitutive or inducible promoters from a variety of bacteria using RNA-seq methods. ,− Wherein a constitutive promoter is a class of promoters that are not controlled by any regulator or growth conditions and where the expression level of the gene is usually constant at a certain level. Kong et al validated a series of constitutive promoters by screening genes that were stably expressed during different growth periods under different culture conditions via RNA-seq.…”

Transcription regulation strategies in methylotrophs: progress and challenges

“…Besides, promoter-predicted platforms will help select optimal promoter sequences, and hybridizations are contributed to building a mutated library (Vogl et al 2018 , Cazier et al 2021 ). A computational framework was developed to predict promoter regions according to RNA-seq data sets in M. buryatense 5GB1 (Wilson et al 2021 ). Given this, metabolic networks based on promoter regulation can be easily predicted and established.…”