High Resolution Expression Profiling of Bacterial Genomes

Scott Scholz, Elayne Fivenson and Xiaoxia (Nina) Lin

Cellular and Molecular Biology PhD Program and Department of Chemical Engineering, University of Michigan, Ann Arbor, MI

Synthetic genetic circuits are typically encoded on plasmids. However, to make them more robust and deployable at large scales, these systems will have to be integrated into host chromosomes. Resources relating a gene’s position on the chromosome to its expression level, therefore, are highly desirable, yet currently not established. There have been limited studies examining the model bacterium E. coli, but previous results were not able to fully elucidate the effects of genome position on gene expression. In one work, examination of reporter gene expression level at 12 loci showed that expression is largely dependent on the copy number during the dynamic chromosomal replication process (i.e. distance from the origin). In another work, however, it was observed that expression of a reporter varied widely across 14 sites and the cause of this variation was not understood in most cases. Additionally, some chromosomal regions have very low expression of native and reporter genes and often have unique structural features. 

To elucidate the effect of chromosomal position on gene expression at a high resolution, we have developed a multiplex strategy to construct and analyze several thousand genome-integrated reporters in a single mixed population of E. coli. We are generating normalized high resolution map of reporter transcription and protein expression variation across the bacterial genome. Libraries of mixed strains of E. coli are constructed through millions of simultaneous unique reporter integration events. Each integrated reporter transcribes a unique barcode which specifies the genome integration location. Using these mixed reporter strain libraries, we are mapping transcription and protein expression variation under different conditions at very high resolution onto the genome. These maps will serve as valuable resources for researchers seeking to identify ideal integration sites. In addition, expression variation maps generated across different conditions will advance our fundamental understanding of gene expression variation and regulation.