No abstract

Quorum sensing is a common mechanism used by bacteria to coordinate population behavior, and is involved in a variety of biological processes, such as bioluminescence, virulence factor synthesis, antibiotic production, and biofilm formation. To engineer the LuxI enzyme of the LuxI-LuxR quorum-sensing system, we developed a high throughput genetic selection to identify LuxI mutants with improved OHHL (3-oxo-hexanoyl homoserine lactone) synthesis in E. coli. Using this genetic selection, we created LuxI mutants with improved OHHL synthesis rates and yields through directed evolution, identifying three LuxI mutants after two generations. An in vivo semi-quantitative method allowed for verification of the genetic screen and OHHL yields were quantified using HPLC-MS/MS, revealing an 80-fold increase in a mutant culture compared to the wildtype culture. In addition to OHHL, the yields of C6HSL (hexanoyl homoserine lactone) and C8HSL (octanoyl homoserine lactone) were also improved, and a slight change in substrate specificity towards C6HSL production was observed. Based on alignment with the crystal structure of EsaI, a homolog of LuxI, two mutations are most likely involved in enhancing the interactions between the enzyme and the substrates. The high throughput genetic selection and the semi-quantitative method can be conveniently modified for the directed evolution of LuxI homologs. The identification of these LuxI mutants has implications in synthetic biology, where they can be used for the construction of artificial genetic circuits. In addition, development of drugs that specifically target quorum sensing to attenuate the pathogenesis of gram-negative infectious bacteria might also benefit from the insights into the molecular mechanism of quorum sensing revealed by the amino acid substitutions.

In the field of synthetic biology, recent genetic engineering efforts have enabled the construction of novel genetic circuits with diverse functionalities and unique activation mechanisms. Because of these advances, artificial genetic networks are becoming increasingly complex, and are demonstrating more robust behaviors with reduced crosstalk between defined modules. These properties have allowed for the identification of a growing set of design principles that govern genetic networks, and led to an increased number of applications for genetic circuits in the fields of metabolic engineering and biomedical engineering. Such progress indicates that synthetic biology is rapidly evolving into an integrated engineering practice that uses rational and combinatorial design of synthetic gene networks to solve complex problems in biology, medicine, and human health.

Artificial microbial ecosystems have been increasingly used to understand principles of ecology. These systems offer unique capabilities to mimic a variety of ecological interactions that otherwise would be difficult to study experimentally in a reasonable period of time. However, the elucidation of the genetic bases for these interactions remains a daunting challenge. To address this issue, we have designed and analysed a synthetic symbiotic ecosystem in which the genetic nature of the microbial interactions is defined explicitly. A mathematical model of the gene regulatory network in each species and their interaction through quorum sensing mediated intercellular signalling was derived to investigate the effect of system components on cooperative behaviour. Dynamic simulation and bifurcation analysis showed that the designed system admits a stable coexistence steady state for sufficiently large initial cell concentrations of the two species. The steady-state fraction of each species could be altered by varying model parameters associated with gene transcription and signalling molecule synthesis rates. The design also admitted a stable steady state corresponding to extinction of the two species for low initial cell concentrations and stable periodic solutions over certain domains of parameter space. The mathematical analysis was shown to provide insights into natural microbial ecosystems and to allow identification of molecular targets for engineering system behaviour.