Biofuel metabolic engineering with biosensors

Abstract

Metabolic engineering offers the potential to renewably produce important classes of chemicals, particularly biofuels, at an industrial scale. DNA synthesis and editing techniques can generate large pathway libraries, yet identifying the best variants is slow and cumbersome. Traditionally, analytical methods like chromatography and mass spectrometry have been used to evaluate pathway variants, but such techniques cannot be performed with high throughput. Biosensors - genetically encoded components that actuate a cellular output in response to a change in metabolite concentration - are therefore a promising tool for rapid and high-throughput evaluation of candidate pathway variants. Applying biosensors can also dynamically tune pathways in response to metabolic changes, improving balance and productivity. Here, we describe the major classes of biosensors and briefly highlight recent progress in applying them to biofuel-related metabolic pathway engineering.

Figure 1

Biosensors enable rapid engineering of metabolism. Biosensors enable rapid and single-cell quantitation of metabolites allowing for high-throughput evaluation of pathway variants and improving the rate-limiting “test” step of the design-build-test-learn engineering cycle.

Figure 2

Biosensor definition and types. Biological input indicated as orange spheres. Ligand-binding component indicated in light blue. Biosensor output highlighted: Förster resonance energy transfer (FRET), energy-transfer efficiency; single fluorescent protein biosensors (SFPB), emission; RNA, translation product; transcription factor (TF), transcription product; G-protein-coupled receptor (GPCR), signaling pathway product.

Figure 3

Applications of biosensors. (a) A biosensor with an output, such as fluorescence, can be used in a screen. The metabolite of interest (MOI, orange) is detected by a biosensor, which drives the expression of an output signal (green) in proportion to the MOI concentration. The output signal, often fluorescence, is used to isolate high-producing variants through screening (e.g. FACS or plate reader assays). (b) A biosensor with a selectable output. The MOI drives the expression of a protein (purple) that provides a growth advantage to the cell. As depicted, an enzyme that neutralizes an antibiotic (red) is expressed in a ligand-dependent manner. Growing variants under selective pressure (e.g. in the presence of antibiotic) enriches the population with high-producing variants. (c) Biosensors for dynamic regulation of pathways. The MOI (orange) is detected by a biosensor (not depicted – biosensor actions are represented by feedback symbols) which alters expression of enzymes in the MOI pathway. The cartoon illustrates balancing of a pathway intermediate (MOI, orange) by repressing the preceding enzyme and activating the subsequent enzyme in the pathway (similar to [31]). (d) Visualization of pathway balancing. Enzyme activity is represented by tubes (i.e. maximal flux that can be carried). Regulation of enzyme concentration (by TFs or degradation) or activity (allostery) alters the flux capacity between pathway intermediates. When enzyme flux is imbalanced (top two examples), starting materials or intermediates accumulate and product formation is limited. When flux is balanced (bottom two examples), accumulation does not occur at any step. (e) Potential biofuel pathways and biosensors. Binding proteins and transcription factors (or regulated promoters) are shown with colors designating: known binder with no biosensor use published; demonstrated use as a biosensor; or biosensor applied to screening, selection, or pathway-balancing. AlkR [61]; AlkS [62] AraC-mev [63]; BmoR [24]; DesT [64]; εFoF1 [65]; Erg20 [27]; FabT [64]; FadR [30]; FapR [31]; GlnK1 [14]; Idi [27]; IclR (ecocyc.org); LldR [66], mBFP [16••]; MglB [67]; NRI [28]; PdhR [68]; P_gadE [69]; P_GPD2 [70]; P_HXT1 [71]; P_rstA [69]; Rex [15]; rxYFP [72]; SoxR [44]; TTHA0766 [73].

Figure 4

Future challenges and opportunities. (a) If no known Ligand-Binding Domain (LBD) is known for a molecule of interest (orange), techniques are needed to discover or create the necessary biosensor input. Uncharacterized potential LBDs from (meta)genome databases can be mined for the desired binding property. Alternatively, in silico approaches of creating new binding proteins or redesigning characterized LBDs can lead to a desired input domain. (b) Engineering allosteric communication between input and output domains is challenging. Methods that create and test many fusion variants have led to successful biosensor function. Computational analysis of coevolving residues in a protein can predict surface sites for functional allosteric fusions. (c) Many new output functions are available for exploration in biosensors. Enzyme output domains provide opportunities for growth selection as well as direct metabolic pathway feedback control. Detectable outputs that are orthogonal can be used in unique biosensors for multiplex measurements of different molecules of interest.