Deep Device Mining for Lignin-Transforming Enzymes Using Whole-Cell Biosensors

One-component transcriptional regulators and their cognate promoters can be harnessed in the development of transcriptional fusions driving the inducible expression of reporters like GFP or luciferase. These biosensors can in turn be used as search functions to discover biological devices encoded in metagenomic libraries. Despite their adaptability, current biosensors typically have narrow detection ranges, low sensitivity, and non-linear responses to different substrate concentrations. These limitations can restrict the use of biosensors as search functions in metagenomic screens. Indeed, without a sensitive and selective biosensor, encoded activities conferred by environmental DNA cannot accurately be reported in a high-throughput manner. With this in mind, we have made generalizable improvements to a lignin transformation biosensor, PemrR:GFP, previously identified from an E. coliGFP promoter-trap library. We develop a mathematical model describing the behaviour of PemrR:GFP and use this model to optimize and reprogram the biosensor based on expression of the emrR transcriptional regulator. Dynamic control of emrR expression under increasing constitutive promoter strength improved the dynamic range of fluorescence output across different substrate concentrations. This response pattern was directly related to promoter copy number leading to a 5-fold linear increase in PemrR:GFP sensitivity to lignin transformation products including vanillin and syringaldehyde. Using the improved biosensor, we employed microplate-based co-culture screening of lignin transformation activities associated with 24 previously identified fosmid clones grown with hard-wood craft lignin. Our results confirmed the capacity of these clones to activate the improved biosensor with increased sensitivity and improved dynamic range. Biosensor-based detection of lignin transformation products provides a powerful screening paradigm that can be used across a wide range of feedstocks to recover genes or gene cassettes conferring differential lignin transformation profiles. By tuning sensors to different mono-aromatic substrates it becomes possible to detect combinations of environmental clones that synergize in combination to produce specific product profiles within biorefining ecosystems.

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