Evolution-guided optimization of biosynthetic pathways

Engineering biosynthetic pathways for chemical production requires extensive optimization of the host cellular metabolic machinery. Because it is challenging to specify a priori an optimal design, metabolic engineers often need to construct and evaluate a large number of variants of the pathway. We report a general strategy that combines targeted genome-wide mutagenesis to generate pathway variants with evolution to enrich for rare high producers. We convert the intracellular presence of the target chemical into a fitness advantage for the cell by using a sensor domain responsive to the chemical to control a reporter gene necessary for survival under selective conditions. Because artificial selection tends to amplify unproductive cheaters, we devised a negative selection scheme to eliminate cheaters while preserving library diversity. This scheme allows us to perform multiple rounds of evolution (addressing ∼10⁹ cells per round) with minimal carryover of cheaters after each round. Based on candidate genes identified by flux balance analysis, we used targeted genome-wide mutagenesis to vary the expression of pathway genes involved in the production of naringenin and glucaric acid. Through up to four rounds of evolution, we increased production of naringenin and glucaric acid by 36- and 22-fold, respectively. Naringenin production (61 mg/L) from glucose was more than double the previous highest titer reported. Whole-genome sequencing of evolved strains revealed additional untargeted mutations that likely benefit production, suggesting new routes for optimization.Microbial production of chemicals presents an alternative to ubiquitous chemical synthesis methods. Biosynthetic production is attractive because it can use a broad assortment of organic feedstocks, proceed under benign physiological conditions, and avoid environmentally deleterious byproducts. Biosynthetic alternatives are being pursued for a wide range of chemicals, from bulk commodity building blocks to specialty chemicals.Natural cells are seldom optimized to produce a desired molecule. To achieve economically viable production, extensive modifications to host cell metabolism are often required to improve metabolite titer, production rate, and yield. The optimizations of biosynthetic pathways for 1,3-propanediol (1), flavonoids (2, 3), l-tyrosine (4), and 1,4-butanediol (5) illustrate this complexity. Fortunately, computational models of cellular metabolism, such as flux balance analysis (FBA), aid in predicting metabolic changes likely to improve the production of a target molecule. Powerful methods including oligonucleotide-directed genome engineering (6) (multiplex automated genome engineering, MAGE) and Cas9-mediated editing can specifically mutate genomic targets predicted by FBA. The combinatorial space of these genomic mutations quickly outstrips the throughput of current analytical methods for evaluating chemical production in individual clones (<10³ samples per machine per day).Biosensors that report on the concentration of a chemical within each individual cell can alleviate this screening bottleneck (10). Such sensor reporters transduce the binding of a target small molecule by a sensory protein or RNA into a gene expression readout (7). The resulting expression of a fluorescent reporter gene or antibiotic resistance gene allows facile identification of mutant cells with increased production of the target chemical.Sensor reporters have been used to screen for increased microbial production of several chemicals, including the isoprenoid precursor mevalonate (8), l-lysine (9, 10), 1-butanol (11), and triacetic acid lactone (12). These studies evaluated a set of variants that altered the expression or coding sequences of one or two key enzyme genes encoded on a plasmid (8, 10−12). Similarly, a lysine-responsive sensor reporter was used to uncover new endogenous enzyme mutants in Corynebacterium glutamicum implicated in higher l-lysine production (9).We sought to expand the scope of sensor-directed metabolic engineering to the directed evolution of whole endogenous pathways. Using FBA as a guide, we simultaneously targeted up to 18 Escherichia coli genomic loci to induce mutations in regulatory or coding sequence of genes implicated in biosynthesis of a target molecule. We established a robust selection, using a sensor protein responsive to the target chemical to regulate the expression of an antibiotic resistance gene. Nearly a billion pathway variants could be evaluated simultaneously, enriching for the best producers when selection pressure was applied.A major challenge faced by this selection approach (and a difficulty for most genetic selections) is the incidence of cheater cells that survive without producing the target molecule. These cheaters evolve to survive selection by mutating the sensor or selection machinery, rather than through higher target molecule synthesis. Lacking a metabolic burden, these “evolutionary escapees” outcompete the top producers during a selection. Multiple selection cycles compound escape, obscuring productive cells and making further pathway evolution infeasible. We therefore devised a selection scheme that, by toggling between negative and positive selection, allows us to remove escapees from the population when they arise. This strategy maintained high selection fidelity, permitting multiple rounds of evolution to progressively enrich for higher-producing cells.For sensor reporter metabolic engineering to be generalizable, sensor domains specific to many different target molecules must be available. Fortunately, natural sensors exist for a wide array of industrially relevant chemicals, including aliphatic hydrocarbons, short-chain alcohols, sugars, amino acids, polymer building blocks, and vitamins. Many more sensor domains are likely to be present among the thousands of additional bacterial regulators known from sequence (13−15) that remain to be characterized. We adapted 10 regulators to our selection system, creating synthetic dependence on their cognate inducer molecules, and demonstrated the utility of two of these for genome-wide metabolic engineering.