Saturation Mutagenesis of E. coli Sigma Factor As a Tool for Strain Engineering

Metabolic engineering efforts have traditionally focused on the level of modulating expression level of individual or a handful of genes. Global Transcription Machinery Engineering (gTME), on the other hand, focuses on modulating the transcriptome of a cell to explore complex phenotypes resulting from changes in the expression level of hundreds of genes. Bacterial sigma factors, a component of the DNA-dependent RNA polymerase (RNAP), are vital for promoter recognition and for the formation of the transcription initiation complex, making it an attractive target for gTME. However, previous gTME experiments targeting the E. coli sigma factor rpoD used a plasmid based approach to introduce mutants into cells that still had the wildtype endogeneous rpoD. Furthermore, random mutagenesis based methods lack the power to identify functionally important mutations from the pool of background mutations usually accompanying isolated clones. Here, we used Multiplex Automated Genome Engineering combined with deep sequencing (MAGE-seq) to systematically mutate the genomic rpoD and to characterize its fitness landscape and phenotype on a codon level.

Engineering through gTME represent a powerful high throughput method to broadly survey the transcriptional landscape. Through mutagenesis of the transcription machinery, expression levels of hundreds of operons are simultaneous modulated. This in turn facilitates the exploration of complex phenotypes that would normally be unavailable through traditional methods of modulating the expression level of individual genes. Mutants generated through gTME can be screened in a parallel under different selection using fitness as a read out. Colonies can be screened, or alternatively, deep sequencing can be utilized to comprehensively explore all gTME mutants. gTME has proven to be a powerful tool for engineering various phenotypes and gTME of primary sigma factor has been demonstrated to be useful in engineering E. coli strains with high tolerance of ethanol and other solvents.

Here, we used saturation mutagenesis of E. coli rpoD, and explored complex phenotypes associated with the mutagenesis of an essential sigma factor through MAGE-seq. First, MAGE oligonucleotides pools consisting of individual oligonucleotide with degenerate codons were used to comprehensively mutagenize every single codon into all other codons in regions 2-4 of rpoD. Then, the resulting mutants were pooled together and competed in a turbidostat, which keeps the cell density consistent and thereby ensures the cells are always in the same growth phase, here exponential. Finally, through sequencing the timepoint samples obtained through the parallel competition experiment, we determined the relative fitness of every single codon mutation. This pipeline was repeated through different selections to identify critical residues that were important for driving adaptive phenotypes. Ultimately, gTME through MAGE-seq represents a comprehensive and systematic survey that can facilitate screening of thousands of variants in parallel to identify functionally important sequences for any given selection pressure.