The discovery and utilization of biocatalysts that selectively valorize lignocellulose is critical to the profitability of next-generation biorefineries. Here, we report the development of a refactored, whole-cell, GFP-based biosensor for high-throughput identification of biocatalysts that transform lignin into specialty chemicals from environmental DNA of uncultivable archaea and bacteria. The biosensor comprises the transcriptional regulator and promoter of the emrRAB operon of E. coli, and the configuration of the biosensor was tuned with the aid of mathematical model. The biosensor sensitively and selectively detects vanillin and syringaldehyde, and responds linearly over a wide detection range. We employed the biosensor to screen 42 520 fosmid clones comprising environmental DNA isolated from two coal beds and successfully identified 147 clones that transform hardwood kraft lignin to vanillin and syringaldehyde.

Antibiotics are wonder drugs. Unfortunately, owing to overuse, antibiotic resistance is now a serious problem. Society now finds itself in the post-antibiotic era, and the threat of infectious diseases is on the rise. New antibiotics are sorely needed. There is strong evidence that suggests natural products are an attractive source of new antimicrobials. They posses desirable structural and chemical properties that make them potent thearpeutics. However, steep tehnological challenges associated with screening and manufacturing these molecules has stifled the discovery, development and marketing of new antimicrobials. To this end, two recent scientific developments are poised to redress this situation. The recent development of metagenomics and ancillary high-throughput screening technologies has exponentiated the volume of useful genetic sequence information that can be screened for antimicrobial discovery. These approaches have been instrumental in the discovery of new antibiotics from soil and marine environments. Secondly, a new manufacturing paradigm employing metabolic engineering as its engine has greatly accelerated the path to market for these molecules, in addition to improving the atom and energy economy of antimicrobial manufacturing. We outine these developments in this review, and provide a perspective on integrating next-generation approaches such as metagenomics and metabolic engineering with traditional methodologies for discovering and manufacturing antimicrobial natural products in order to unleash a rennaissance in the discovery and development of antimicrobials.

Monitoring population dynamics in co-culture is necessary in engineering microbial consortia involved in distributed metabolic processes or biosensing applications. However, it remains difficult to measure strain-specific growth dynamics high-throughput formats. This is especially vexing in plate-based functional screens leveraging whole-cell biosensors to detect specific metabolic signals. Here we develop an experimental high-throughput co-culture system to measure and model the relationship between fluorescence and cell abundance, combining chassis-independent recombinase-assisted genome engineering (CRAGE) and whole-cell biosensing with a PemrR-green fluorescent protein (GFP) monoaromatic reporter used in plate-based functional screening. CRAGE was used to construct E. coli EPI300 strains constitutively expressing red fluorescent protein (RFP) and the relationship between RFP expression and optical density (OD600) was determined throughout the EPI300 growth cycle. A linear equation describing the increase of normalized RFP fluorescence during deceleration phase was derived and used to predict biosensor strain dynamics in co-culture. Measured and predicted values were compared using flow cytometric detection methods. Induction of the biosensor lead to increased GFP fluorescence normalized to biosensor cell abundance, as expected, but a significant decrease in relative abundance of the biosensor strain in co-culture and a decrease in bulk GFP fluorescence. Taken together, these results highlight sensitivity of population dynamics to variations in metabolic activity in co-culture and the potential effect of these dynamics on the performance of functional screens in plate-based formats. The engineered strains and model used to evaluate these dynamics provide a framework for optimizing growth of synthetic co-cultures used in screening, testing and pathway engineering applications