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.