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, 111 (50), 17803-8

Evolution-guided Optimization of Biosynthetic Pathways

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Evolution-guided Optimization of Biosynthetic Pathways

Srivatsan Raman et al. Proc Natl Acad Sci U S A.

Abstract

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 ∼10(9) 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.

Keywords: biosynthetic pathways; evolution; metabolic engineering; sensors; synthetic biology.

Conflict of interest statement

Conflict of interest statement: The authors have a pending patent application.

Figures

Fig. 1.
Fig. 1.
Sensor selector design and pathway optimization through toggled selection. (A) Sensor selector genetic architecture. (B) Methods for tuning sensor selectors to reduce escape rate and shift operational range. Escape rate is reduced by (i) adding a degradation tag, (ii) mutating the RBS of the selector, (iii) including multiple orthogonal selectors, or (iv) including an additional copy of the sensor. Activating an exporter shifts the sensor selector operational range. (C) Toggled selection protocol for biosynthetic pathway optimization through multiple rounds of evolution. Negative selection eliminates cheaters; subsequent positive selection identifies higher-producing clones from a diverse library.
Fig. 2.
Fig. 2.
Characterization of sensor selector modifications. (A) Escape rate and operational range of 10 sensors with cognate inducer chemicals and TolC as a selector. Horizontal bars depict the operational range. The lower bound of the range reflects the lowest concentration of exogenously supplied inducer that provides a selective advantage. The upper bound of the range indicates that higher inducer concentration does not increase fitness advantage. (B) Effect of genetic modifications on the TtgR-TolC sensor-selector escape rate and operational range. Escape rate (light blue bars, left axis) is the proportion of cells that evade selection (cfu per cells plated). Escape rate not shown if below the limit of detection (10−10 cfu per cells plated). Escape rate operational range ratio (blue boxes, right axis) is the ratio of the high concentration of the operational range to the low concentration of the operational range. (C) MAGE mutagenesis increases the escape rate (cfu per cells plated) in the CdaR−TolC strain. Treatment with colicin E1 removes escapees in a dose-dependent manner. (D) Tetracycline exporter (tetA) expression shifts the operational range of the TetR−CAT (chloramphenicol acetyltransferase) sensor selector. Growth lag times reported for orthogonal concentration gradients of tetracycline vs. chloramphenicol in the absence of tetA (Top) compared with tetA expression (Bottom). (E) The shift in TetR−CAT operational range is tunable by titration of tetA expression. The minimum tetracycline concentration required for growth (y axis) at a given selection pressure (x axis) for three tetA expression levels: none (diamonds), intermediate (triangles), high (circles). Error bars represent SEM of three biological replicates.
Fig. 3.
Fig. 3.
Optimization of the naringenin biosynthetic pathway. (A) Endogenous E. coli genes targeted by MAGE to increase malonyl-CoA and tyrosine availability for naringenin production; targeted genes are colored: purple, up-regulation; red, down-regulation; green, coding changes; gray, untargeted knocked out genes. (B) Genotype and production phenotype of the top seven producers (in no particular order) from the fourth round of toggled selection. Colored boxes denote the type of genetic modification. Shown are mutations found at targeted genes (Bottom) and those at untargeted genes (Center). Naringenin (green bars) and coumaric acid (blue bars) concentrations for single production measurements are shown above the corresponding genotype (Top). Error bars represent SEM of three biological replicates. (C) Average naringenin production titers for parent and highest producer after each round of evolution (blue bars). Production titer from fed batch bioreactor fermentation of the highest producer and highest producer with accABCD overexpressed (red bars).
Fig. 4.
Fig. 4.
Optimization of the glucaric acid biosynthetic pathway. (A) Glucaric acid biosynthetic pathway showing key intermediate metabolites and enzymes. Heterologous gene names are underlined. Endogenous E. coli genes targeted by MAGE for expression modification: blue, RBS modification; purple, knockout. (B) Lag time in growth reflects time required for the pathway enzymes to produce activating levels of glucaric acid in the sensor selector strain CdaR−TolC. Pathway intermediates are supplied exogenously (blue, 10 mM; green, 1 mM). Error bars represent SEM from three biological replicates C) Glucaric acid titers produced by the parent strain, the postselection mixed population, and the highest-producing clone (bars). Squares indicate titers produced by clones isolated from the postselection population. Error bars represent SEM from three biological replicates.

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