Engineering Transcriptional Interference through RNA Polymerase Processivity Control

To estimate the number and diversity of beneficial mutations, we experimentally evolved 115 populations of Escherichia coli to 42.2°C for 2000 generations and sequenced one genome from each population. We identified 1331 total mutations, affecting more than 600 different sites. Few mutations were shared among replicates, but a strong pattern of convergence emerged at the level of genes, operons, and functional complexes. Our experiment uncovered a set of primary functional targets of high temperature, but we estimate that many other beneficial mutations could contribute to similar adaptive outcomes. We inferred the pervasive presence of epistasis among beneficial mutations, which shaped adaptive trajectories into at least two distinct pathways involving mutations either in the RNA polymerase complex or the termination factor rho.

The term "transcriptional interference" (TI) is widely used but poorly defined in the literature. There are a variety of methods by which one can interfere with the process or the product of transcription but the term TI usually refers to the direct negative impact of one transcriptional activity on a second transcriptional activity in cis. Two recent studies, one examining Saccharomyces cerevisiae and the other Escherichia coli, clearly show TI at one promoter caused by the arrival of a transcribing complex initiating at a distant promoter. TI is potentially widespread throughout biology; therefore, it is timely to assess exactly its nature, significance and operative mechanisms. In this article, we will address the following questions: what is TI, how important and widespread is it, how does it work and where should we focus our future research efforts?

Microbial engineering often requires fine control over protein expression; for example, to connect genetic circuits 1-7 or control flux through a metabolic pathway 8-13. We have developed a predictive design method for synthetic ribosome binding sites that enables the rational control of a protein's production rate on a proportional scale. Experimental validation of over 100 predictions in Escherichia coli shows that the method is accurate to within a factor of 2.3 over a range of 100,000-fold. The design method also correctly predicts that reusing a ribosome binding site sequence in different genetic contexts can result in different protein expression levels. We demonstrate the method's utility by rationally optimizing a protein's expression level to connect a genetic sensor to a synthetic circuit. The proposed forward engineering approach will accelerate the construction and systematic optimization of large genetic systems.