Person:

Taylor, Noah

Characterization and standardization of inducible transcriptional regulators has transformed how scientists approach biology by allowing precise and tunable control of gene expression. Despite their utility, only a handful of well-characterized regulators exist, limiting the complexity of engineered biological systems. We apply a characterization pipeline to four genetically encoded sensors that respond to acrylate, glucarate, erythromycin and naringenin. We evaluate how the concentration of the inducing chemical relates to protein expression, how the extent of induction affects protein expression kinetics, and how the activation behavior of single cells relates to ensemble measurements. We show that activation of each sensor is orthogonal to the other sensors, and to other common inducible systems. We demonstrate independent control of three fluorescent proteins in a single cell, chemically defining eight unique transcriptional states. To demonstrate biosensor utility in metabolic engineering, we apply the glucarate biosensor to monitor product formation in a heterologous glucarate biosynthesis pathway and identify superior enzyme variants. Doubling the number of well-characterized inducible systems makes more complex synthetic biological circuits accessible. Characterizing sensors that transduce the intracellular concentration of valuable metabolites into fluorescent readouts enables high-throughput screening of biological catalysts and alleviates the primary bottleneck of the metabolic engineering design-build-test cycle.

Allosteric proteins have great potential in synthetic biology, but our limited understanding of the molecular underpinnings of allostery has hindered the development of designer molecules, including transcription factors with new DNA-binding or ligand-binding specificities that respond appropriately to inducers. Such allosteric proteins could function as novel switches in complex circuits, metabolite sensors, or orthogonal regulators for independent, inducible control of multiple genes. Advances in DNA synthesis and next-generation sequencing technologies have enabled the assessment of millions of mutants in a single experiment, providing new opportunities to study allostery. Using the classic LacI protein as an example, we describe a genetic selection system using a bidirectional reporter to capture mutants in both allosteric states, allowing the positions most critical for allostery to be identified. This approach is not limited to bacterial transcription factors, and could reveal new mechanistic insights and facilitate engineering of other major classes of allosteric proteins such as nuclear receptors, two-component systems, G-protein coupled receptors and protein kinases.

Microbial metabolic production is an attractive alternative to traditional chemical synthesis for a wide array of commercially relevant molecules. Coaxing microbes to produce a target chemical efficiently often requires substantial modification of host cell metabolism, which necessitates searching a vast genetic space of enzyme genes and expression levels. Millions of pathway designs can now be built, but identifying the most productive cells remains low throughput. The ability to detect and report on the presence of any arbitrary target molecule within individual cells would transform the field of metabolic engineering. To this end, we developed strains of E. coli that survive an antibiotic challenge only in the presence of a specific small molecule, by regulating resistance gene expression via transcription factors responsive to sugars, alkanes, macrolides, flavonoids, vitamins or other molecules. Using two of these whole cell biosensors, responsive to glucaric acid or naringenin, we evolved respective biosynthetic pathways for each compound toward higher production. We used oligonucleotide-mediated genomic editing to simultaneously target up to 20 enzyme genes for expression modulation or knockout, creating billions of unique strains. Demonstrating the first example of iterative, whole-pathway engineering via a metabolite biosensor, we discovered E. coli strains that had increased production of naringenin by 36 times, or glucaric acid by 22 times. However, for many target molecules, especially those that are synthetic, no natural biosensor may exist. We developed a platform to engineer natural allosteric transcription factors with specificity to new inducer molecules. We computationally design for binding, synthesize and clone in multiplex thousands of specified sequences, and use a bidirectional screen to identify new responsive variants that retain allostery. We demonstrate by generating E. coli LacI variants responsive to gentiobiose, fucose, lactitol and sucralose. We uncovered significant plasticity in the ligand recognition of LacI, which may be a hallmark of allosteric transcription factors. Our method relies only on protein structure and operator DNA sequence, making it applicable to many other proteins. These methods together advance the ability to engineer microbial biosynthesis of any target molecule using evolution. Additionally, designer transcription factors can enable broad applications from dynamic metabolic control to cell biology.