efficient-sampling-genetically-encoded-biosensor-design-space-enabled

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Genetically encoded biosensors are powerful tools for high-throughput information processing, enabling the transduction of environmental or chemical input ...

University of Manchester

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.

Genetically encoded biosensors for lignocellulose valorization.

Biological degradation of Polyethylene terephthalate (PET) plastic and assimilation of the corresponding monomers ethylene glycol and terephthalate (TPA) into central metabolism offers an attractive route for bio-based molecular recycling and bioremediation applications. A key step is the cellular uptake of the non-permeable TPA into bacterial cells which has been shown to be dependent upon the presence of the key tphC gene. However, little is known from a biochemical and structural perspective about the encoded solute binding protein, TphC. Here, we report the biochemical and structural characterisation of TphC in both open and TPA-bound closed conformations. This analysis demonstrates the narrow ligand specificity of TphC towards aromatic para-substituted dicarboxylates, such as TPA and closely related analogues. Further phylogenetic and genomic context analysis of the tph genes reveals homologous operons as a genetic resource for future biotechnological and metabolic engineering efforts towards circular plastic bio-economy solutions.

Structural basis of terephthalate recognition by solute binding protein TphC.

Transcription factor-based biosensors are genetic tools that aim to predictability link the presence of a specific input stimuli to a tailored gene expression output. The performance characteristics of a biosensor fundamentally determines its potential applications. However, current methods to engineer and optimise tailored biosensor responses are highly nonintuitive, and struggle to investigate multidimensional sequence/design space efficiently. In this study we employ a design of experiments (DoE) approach to build a framework for efficiently engineering activator-based biosensors with tailored performances, and we apply the framework for the development of biosensors for the polyethylene terephthalate (PET) plastic degradation monomer terephthalate (TPA). We simultaneously engineer the core promoter and operator regions of the responsive promoter, and by employing a dual refactoring approach, we were able to explore an enhanced biosensor design space and assign their causative performance effects. The approach employed here serves as a foundational framework for engineering transcriptional biosensors and enabled development of tailored biosensors with enhanced dynamic range and diverse signal output, sensitivity, and steepness. We further demonstrate its applicability on the development of tailored biosensors for primary screening of PET hydrolases and enzyme condition screening, demonstrating the potential of statistical modelling in optimising biosensors for tailored industrial and environmental applications.

Tuning the performance of a TphR-based terephthalate biosensor with a design of experiments approach.

Biogenic waste-derived feedstocks for production of fuels, chemicals, and materials offer great potential supporting the transition to net-zero and greater circularity. However, such feedstocks are heterogeneous and subject to geographical and seasonal variability. Here, we show that, through careful strain selection and metabolic engineering, Pseudomonas putida can be employed to permit efficient co-utilization of highly heterogeneous substrate compositions derived from hydrolyzed mixed municipal-like waste fractions (food, plastic, organic, paper, cardboard, and textiles) for growth and synthesis of exemplar bioproducts. Design of experiments was employed to explore the combinatorial space of nine waste-derived monomers, displaying robust catabolic efficiency regardless of substrate composition. Prospective Life-Cycle Assessment (LCA) and Life-Cycle Costing (LCC) illustrated the climate change (CC) and economic advantages of biomanufacturing compared with conventional waste treatment options, demonstrating a 41-62% potential reduction in CC impact. This work demonstrates the potential for expanding treatment strategies for mixed waste to include engineered microbes.

Article	Year
Genetically encoded biosensors for lignocellulose valorization. Biotechnology for biofuels\| PubMed ID: 31636705	2019
Structural basis of terephthalate recognition by solute binding protein TphC. Nature communications\| PubMed ID: 34716322	2021
Tuning the performance of a TphR-based terephthalate biosensor with a design of experiments approach. Metabolic engineering communications\| PubMed ID: 39618701	2024
Complex waste stream valorization through combined enzymatic hydrolysis and catabolic assimilation by Pseudomonas putida. Trends in biotechnology\| PubMed ID: 39638703	2025

Guadalupe Alvarez-Gonzalez

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