Systematic engineering and machine learning analysis of intrinsic terminators reveal crucial nucleotides directly upstream of the terminator hairpin

Abstract

Transcriptional termination efficiency is considered an important parameter for finetuning bacterial gene expression. Still, the design principles that determine transcription termination efficiency remain poorly understood. In this study, we aimed to investigate the impact of the 3’ untranslated region (3’UTR) on gene expression in Escherichia coli and other bacteria. First, 3’UTR variant sequences were generated, with randomized 30 bp sequences inserted between the STOP-codon and an intrinsic terminator, consisting of a GC-rich hairpin and a downstream poly(U)-tail. Using three reporter genes, it was found that different 3’UTR sequences resulted in an up to five-fold difference in protein production, independent of the upstream coding sequence. The highest protein production was achieved when an adenosine was present directly upstream of the terminator hairpin. This was consolidated by systematic substitution of key nucleotides of the terminator and assessing their effect on mRNA and protein levels. Subsequently, we developed a predictive random forest machine learning model trained on the termination efficiency of different natural and synthetic terminator sequences, revealing an important role for the nucleotides directly upstream of the terminator hairpin. Altogether, this study showed that an additional adenosine nucleotide upstream of the terminator hairpin leads to improved protein production while reducing terminator read-through. GRAPHICAL ABSTRACT