Genetic Circuits for Feedback Control of Gamma-Aminobutyric Acid Biosynthesis in Probiotic Escherichia coli Nissle 1917.

Engineered microorganisms such as the probiotic strain Escherichia coli Nissle 1917 (EcN) offer a strategy to sense and modulate the concentration of metabolites or therapeutics in the gastrointestinal tract. Here, we present an approach to regulate the production of the depression-associated metabolite gamma-aminobutyric acid (GABA) in EcN using genetic circuits that implement negative feedback. We engineered EcN to produce GABA by overexpressing glutamate decarboxylase and applied an intracellular GABA biosensor to identify growth conditions that improve GABA biosynthesis. We next employed characterized genetically encoded NOT gates to construct genetic circuits with layered feedback to control the rate of GABA biosynthesis and the concentration of GABA produced. Looking ahead, this approach may be utilized to design feedback control of microbial metabolite biosynthesis to achieve designable smart microbes that act as living therapeutics.

Algorithmic Programming of Sequential Logic and Genetic Circuits for Recording Biochemical Concentration in a Probiotic Bacterium.

Through the implementation of designable genetic circuits, engineered probiotic microorganisms could be used as noninvasive diagnostic tools for the gastrointestinal tract. For these living cells to report detected biomarkers or signals after exiting the gut, the genetic circuits must be able to record these signals by using genetically encoded memory. Complex memory register circuits could enable multiplex interrogation of biomarkers and signals. A theory-based approach to create genetic circuits containing memory, known as sequential logic circuits, was previously established for a model laboratory strain of Escherichia coli, yet how circuit component performance varies for nonmodel and clinically relevant bacterial strains is poorly understood. Here, we develop a scalable computational approach to design robust sequential logic circuits in probiotic strain Escherichia coli Nissle 1917 (EcN). In this work, we used TetR-family transcriptional repressors to build genetic logic gates that can be composed into sequential logic circuits, along with a set of engineered sensors relevant for use in the gut environment. Using standard methods, 16 genetic NOT gates and nine sensors were experimentally characterized in EcN. These data were used to design and predict the performance of circuit designs. We present a set of genetic circuits encoding both combinational logic and sequential logic and show that the circuit outputs are in close agreement with our quantitative predictions from the design algorithm. Furthermore, we demonstrate an analog-like concentration recording circuit that detects and reports three input concentration ranges of a biochemical signal using sequential logic.

Genetic circuits for feedback control of gamma-aminobutyric acid biosynthesis in probiotic Escherichia coli Nissle 1917.

Engineered microorganisms such as the probiotic strain Escherichia coli Nissle 1917 (EcN) offer a strategy to sense and modulate the concentration of metabolites or therapeutics in the gastrointestinal tract. Here, we present an approach to regulate production of the depression-associated metabolite gamma-aminobutyric acid (GABA) in EcN using genetic circuits that implement negative feedback. We engineered EcN to produce GABA by overexpressing glutamate decarboxylase (GadB) from E. coli and applied an intracellular GABA biosensor to identify growth conditions that improve GABA biosynthesis. We next employed characterized genetically-encoded NOT gates to construct genetic circuits with layered feedback to control the rate of GABA biosynthesis and the concentration of GABA produced. Looking ahead, this approach may be utilized to design feedback control of microbial metabolite biosynthesis to achieve designable smart microbes that act as living therapeutics.

Surveying the Genetic Design Space for Transcription Factor-Based Metabolite Biosensors: Synthetic Gamma-Aminobutyric Acid and Propionate Biosensors in E. coli Nissle 1917.

Engineered probiotic bacteria have been proposed as a next-generation strategy for noninvasively detecting biomarkers in the gastrointestinal tract and interrogating the gut-brain axis. A major challenge impeding the implementation of this strategy has been the difficulty to engineer the necessary whole-cell biosensors. Creation of transcription factor-based biosensors in a clinically-relevant strain often requires significant tuning of the genetic parts and gene expression to achieve the dynamic range and sensitivity required. Here, we propose an approach to efficiently engineer transcription-factor based metabolite biosensors that uses a design prototyping construct to quickly assay the gene expression design space and identify an optimal genetic design. We demonstrate this approach using the probiotic bacterium Escherichia coli Nissle 1917 (EcN) and two neuroactive gut metabolites: the neurotransmitter gamma-aminobutyric acid (GABA) and the short-chain fatty acid propionate. The EcN propionate sensor, utilizing the PrpR transcriptional activator from E. coli, has a large 59-fold dynamic range and >500-fold increased sensitivity that matches biologically-relevant concentrations. Our EcN GABA biosensor uses the GabR transcriptional repressor from Bacillus subtilis and a synthetic GabR-regulated promoter created in this study. This work reports the first known synthetic microbial whole-cell biosensor for GABA, which has an observed 138-fold activation in EcN at biologically-relevant concentrations. Using this rapid design prototyping approach, we engineer highly functional biosensors for specified in vivo metabolite concentrations that achieve a large dynamic range and high output promoter activity upon activation. This strategy may be broadly useful for accelerating the engineering of metabolite biosensors for living diagnostics and therapeutics.

ePathOptimize: A Combinatorial Approach for Transcriptional Balancing of Metabolic Pathways.

The ability to fine tune gene expression has created the field of metabolic pathway optimization and balancing where a variety of factors affecting flux balance are carefully modulated to improve product titers, yields, and productivity. Using a library of isopropyl ß-D-1-thiogalactopyranoside (IPTG)-inducible mutant T7 promoters of varied strength a combinatorial method was developed for transcriptional balancing of the violacein pathway. Violacein biosynthesis involves a complex five-gene pathway that is an excellent model for exploratory metabolic engineering efforts into pathway regulation and control due to many colorful intermediates and side products allowing for easy analysis and strain comparison. Upon screening approximately 4% of the total initial library, several high-titer mutants were discovered that resulted in up to a 63-fold improvement over the control strain. With further fermentation optimization, titers were improved to 1829 ± 46 mg/L; a 2.6-fold improvement in titer and a 30-fold improvement in productivity from previous literature reports.

CRISPathBrick: Modular Combinatorial Assembly of Type II-A CRISPR Arrays for dCas9-Mediated Multiplex Transcriptional Repression in E. coli.

Programmable control over an addressable global regulator would enable simultaneous repression of multiple genes and would have tremendous impact on the field of synthetic biology. It has recently been established that CRISPR/Cas systems can be engineered to repress gene transcription at nearly any desired location in a sequence-specific manner, but there remain only a handful of applications described to date. In this work, we report development of a vector possessing a CRISPathBrick feature, enabling rapid modular assembly of natural type II-A CRISPR arrays capable of simultaneously repressing multiple target genes in Escherichia coli. Iterative incorporation of spacers into this CRISPathBrick feature facilitates the combinatorial construction of arrays, from a small number of DNA parts, which can be utilized to generate a suite of complex phenotypes corresponding to an encoded genetic program. We show that CRISPathBrick can be used to tune expression of plasmid-based genes and repress chromosomal targets in probiotic, virulent, and commonly engineered E. coli strains. Furthermore, we describe development of pCRISPReporter, a fluorescent reporter plasmid utilized to quantify dCas9-mediated repression from endogenous promoters. Finally, we demonstrate that dCas9-mediated repression can be harnessed to assess the effect of downregulating both novel and computationally predicted metabolic engineering targets, improving the yield of a heterologous phytochemical through repression of endogenous genes. These tools provide a platform for rapid evaluation of multiplex metabolic engineering interventions.