Genetically encoded biosensors for lignocellulose valorization

Abstract

Modern society is hugely dependent on finite oil reserves for the supply of fuels and chemicals. Moving our dependence away from these unsustainable oil-based feedstocks to renewable ones is, therefore, a critical factor towards the development of a low carbon bioeconomy. Lignin derived from biomass feedstocks offers great potential as a renewable source of aromatic compounds if methods for its effective valorization can be developed. Synthetic biology and metabolic engineering offer the potential to synergistically enable the development of cell factories with novel biosynthetic routes to valuable chemicals from these sustainable sources. Pathway design and optimization is, however, a major bottleneck due to the lack of high-throughput methods capable of screening large libraries of genetic variants and the metabolic burden associated with bioproduction. Genetically encoded biosensors can provide a solution by transducing the target metabolite concentration into detectable signals to provide high-throughput phenotypic read-outs and allow dynamic pathway regulation. The development and application of biosensors in the discovery and engineering of efficient biocatalytic processes for the degradation, conversion, and valorization of lignin are paving the way towards a sustainable and economically viable biorefinery.

Keywords: Biorefinery; Genetically encoded biosensors; Lignocellulose valorization; Sustainable chemical production.

Fig. 1

Applications of genetically encoded biosensors in lignin valorization. Transcriptional biosensors allow the coupling of aromatic monomer or sugar release detection (input) with the expression of a distinct measurable output. Reporter-based outputs can be applied in real-time metabolite monitoring and high-throughput screening of large mutant libraries using FACS. Inputs coupled to an actuator output can afford the dynamic control of enzymatic pathways according to the input level or provide an adaptive advantage under selective conditions through adaptive laboratory evolution, allowing the enrichment of high-producing strains

Fig. 2

Design and optimization strategies for transcriptional biosensors. The performance of transcriptional biosensors can be altered by fine-tuning the expression levels of the allosteric transcription factor (1), engineering the cognate promoter and operator sequences (2), and/or ribosome binding site (3). The biosensor substrate recognition scope can be altered by engineering the specificity of the transcription factor (4)

Fig. 3

Genetically encoded biosensors and their applications in lignin valorization: a functional metagenomic screening method based on an EmrR-based biosensor for identifying lignin-degrading and transforming enzymes from metagenomic samples. A multicopper oxidase (CopA) enzyme capable of degrading lignin to vanillin and syringaldehyde was identified from a recovered metagenomic fosmid DNA library using this system [67]. b Transcriptional biosensors as screening methods to allow enzymatic characterization and evolution. A FerC-based biosensor was applied to identify optimal feruloyl esterase (CE1) activity by detecting ferulic acid release [69]. c Dynamic pathway regulation system for vanillic acid production. A ferulic acid biosensor was utilized to activate and regulate the biosynthetic pathway only when high cell density was reached and enough substrate was present, diminishing metabolic stress and increasing productivity [68]