Transcription factor-based screens and synthetic selections for microbial small-molecule biosynthesis

Abstract

Continued advances in metabolic engineering are increasing the number of small molecules being targeted for microbial production. Pathway yields and productivities, however, are often suboptimal, and strain improvement remains a persistent challenge given that the majority of small molecules are difficult to screen for and their biosynthesis does not improve host fitness. In this work, we have developed a generalized approach to screen or select for improved small-molecule biosynthesis using transcription factor-based biosensors. Using a tetracycline resistance gene 3' of a small-molecule inducible promoter, host antibiotic resistance, and hence growth rate, was coupled to either small-molecule concentration in the growth medium or a small-molecule production phenotype. Biosensors were constructed for two important chemical classes, dicarboxylic acids and alcohols, using transcription factor-promoter pairs derived from Pseudomonas putida, Thauera butanivorans, or E. coli. Transcription factors were selected for specific activation by either succinate, adipate, or 1-butanol, and we demonstrate product-dependent growth in E. coli using all three compounds. The 1-butanol biosensor was applied in a proof-of-principle liquid culture screen to optimize 1-butanol biosynthesis in engineered E. coli, identifying a pathway variant yielding a 35% increase in 1-butanol specific productivity through optimization of enzyme expression levels. Lastly, to demonstrate the capacity to select for enzymatic activity, the 1-butanol biosensor was applied as synthetic selection, coupling in vivo 1-butanol biosynthesis to E. coli fitness, and an 120-fold enrichment for a 1-butanol production phenotype was observed following a single round of positive selection.

Figure 1. Coupling 1-butanol-induced TetA expression to E. coli growth rate

E. coli DH1 ΔadhE harboring tetA-gfp under control of P_BMO displayed butanol-dependent changes in growth phenotype. (a) In positive selection mode, tetracycline (Tet) supplementation resulted in a positive correlation between E. coli growth rate and exogenous 1-butanol concentration. 1- butanol concentrations greater than 40 mM overwhelmed the synthetic selective pressure. (b) In negative selection mode, nickel-chloride (NiCl₂) supplementation inverted the correlation between growth rate and exogenous 1-butanol concentration. Data are mean (s.d.) (n=4).

Figure 2. Modifying screening parameters through control of 1-butanol- and tetracycline-dependent E. coli growth

Biosensor transfer functions in a 96-well plate, liquid culture screen were characterized, showing an increasing in half-maximal, ${\mathrm{IC}}_{50}^{\mathrm{Tet}}$ (black line), and maximal, OD_max (grey line), cell density as tetracycline was supplemented to the growth medium. Increasing the tetracycline supplementation in the assay eliminated background E. coli growth and shifted the linear range of detection. ${\mathrm{IC}}_{50}^{\mathrm{Tet}}$ and OD_max curves are mean (s.d.) (n=4). Heat plot of OD₆₀₀ values are an average of 4 replicate cultures (%CV < 10% for all data points).

Figure 3. Biosensor transfer functions show responsiveness to (a) linear alcohols, (b) branched-chain alcohols, and (c) aldehydes or diols

The fluorescent response of E. coli DH1 ΔadhE harboring plasmid pBMO#1 was shown to be highly selective for C4-C6 linear alcohols and C3-C5 branched-chain alcohols. No response was observed for short-chain (C2-C3) linear alcohols or butyraldehyde, and only a slight increase in normalized fluorescence was observed with 1-heptanol. The linear range of detection for 1-butanol was 100 μM to 40 mM, the broadest of all inducers tested. Abbreviations: C2OH, ethanol; C3OH, 1-propanol; C4OH, 1-butanol; C5OH, 1-pentanol; C6OH, 1-hexanol; C7OH, 1-heptanol; 2M-1-C3OH, 2-methyl-1-propanol; 2M-1-C4OH, 2-methyl-1-butanol; 3M-1-C4OH, 3-methyl-1-butanol; 3M-1-C5OH, 3-methyl-1-pentanol; 4M-1-C5OH, 4-methyl-1-pentanol; C4=0, butyraldehyde; BDO, 1,4-butanediol; PDO, 1,5-pentanediol. Data are mean (s.d.) (n=3).

Figure 4. Coupling dicarboxylic acid concentration to E. coli growth

C4-C7 dicarboxylic acid biosensor transfer functions were obtained in liquid culture medium under 25 μg/ml tetracycline selective pressure. The PcaR biosensor produced the highest cell densities using adipate or pimelate; the weakest response was observed using succinate (sample C4Dioic (PcaR)). In the case of adipate, the linear range of detection was between 0.5 – 6 mM, and a 12-fold increase in cell density was observed between the maximally induced and uninduced samples. The E. coli-derived DcuS/DcuR two-component system only enabled growth upon addition of succinate to the growth medium (sample C4Dioic (DcuS/R)). Differences in response decreases at high succinate concentrations were observed comparing the DcuS/R- and PcaR-based biosensors and may be due to the increase in cell stress using the two plasmid PcaR system. Abbreviations: C4Dioic, succinate; C5Dioic, glutarate; C6Dioic, adipate; C7Dioic, pimelate. For succinate, parentheticals are used to indicate use of either the PcaR or DcuS/R based biosensors. Samples are mean (s.d.) (n=3).

Figure 5. BmoR-P_BMO biosensor screen for improved 1-butanol biosynthesis

(a) The mean total mixed alcohol titer in E. coli DH1 ΔadhE harboring pKivD#1 was significantly lower (t-test; unpaired, p=1×10⁻¹¹) as compared to a heterogeneous population containing mutated kivD and ADH6 ribosome binding site (RBS) sequences, a result suggesting the initial RBS was non-optimal. The RBS library population produced a broad range of alcohol titers (n=50; box and whisker plot depicts 10^th, 25^th, median, 75^th and 90^th percentiles) suitable for characterization of the high-throughput screen. (b) The biosensor response (OD₆₀₀) to spent production medium from a 960-member library of mutated kivD and ADH6 RBS sequences was distributed around OD₆₀₀=0.31. Gas chromatography-mass spectrometry was used to confirm 1-butanol titers for 10% of the sample population, demonstrating a positive correlation between biosensor response and 1-butanol titer.

Figure 6. Demonstration of a synthetic selection for in vivo 1-butanol biosynthesis

E. coli JAD#2 co-transformed with the pSelect#2, TetA-based synthetic selection device and either an RFP control plasmid (pRFPS2) or a non-functional alcohol biosynthetic pathway with a Z. mobilis pyruvate decarboxylase (pPDC) displayed poor growth upon addition of tetracycline selective pressure. Use of the L. lactis promiscuous 2-keto acid decarboxylase pathway (pKivD#2) or introduction of a single point mutation into the Z. mobilis pyruvate decarboxylase (pPDCI472A), imparting 2-oxobutanoate decarboxylase activity, resulted in improved fitness relative to the negative control strains. Data are mean (s.d.) (n=3).

Scheme 1. 2-keto acid-derived alcohol production in E. coli

(a) Biosensor-relevant 2-keto acid-derived alcohols (blue) produced in engineered E. coli are rooted in high-flux amino acid biosynthetic pathways (red). Deletion of the ilvDAYC operon yielded a valine, isoleucine, and leucine auxotroph incapable of producing biosensor-inducing alcohols without 2-keto acid supplementation. 2-oxopentanoate, the 2-keto acid precursor to 1-butanol, is not naturally produced in E. coli. (b) Heterologously expressed L. lactis KivD and S. cerevisiae ADH6 were used to produce user-defined alcohols by medium supplementation with the cognate 2-keto acid substrate.