Transcription factor-based biosensors for screening and dynamic regulation

Abstract

Advances in synthetic biology and genetic engineering are bringing into the spotlight a wide range of bio-based applications that demand better sensing and control of biological behaviours. Transcription factor (TF)-based biosensors are promising tools that can be used to detect several types of chemical compounds and elicit a response according to the desired application. However, the wider use of this type of device is still hindered by several challenges, which can be addressed by increasing the current metabolite-activated transcription factor knowledge base, developing better methods to identify new transcription factors, and improving the overall workflow for the design of novel biosensor circuits. These improvements are particularly important in the bioproduction field, where researchers need better biosensor-based approaches for screening production-strains and precise dynamic regulation strategies. In this work, we summarize what is currently known about transcription factor-based biosensors, discuss recent experimental and computational approaches targeted at their modification and improvement, and suggest possible future research directions based on two applications: bioproduction screening and dynamic regulation of genetic circuits.

Keywords: allosteric transcription factors; biosensors; dynamic regulation; metabolic engineering; screening.

FIGURE 1

Broad phylogenetic classification of the ten most common bacterial aTF families. Family name and representative UniProt IDs are used as tree labels. Structure images were obtained using Mol* Viewer (Sehnal et al., 2021) via RCSB PDB. The text boxes detail, in order, the most common aTF-controlled pathway, the species name of the specific aTF, the representative effector molecule and the PDB identifier of the structure.

FIGURE 2

The full stack biosensor development toolbox. (A) Computational tools can be used to determine the allosteric pockets of interaction between ligands and aTF. (B) Using these pockets as reference, docking computations can be carried out to assess the affinity of the aTF towards a library of putative ligand compounds. (C) The allosteric site computation can be validated using directed mutagenesis to evaluate changes in affinity or specificity. (D) ChIP technology allows researchers to determine the TFBSs of a newly discovered aTF. (E) Similarly, EMSA can be used to individually validate single TFBS. (F) SELEX can be used to artificially obtain new TFBS to the aTF. (G) Cell-free assays are a quick prototyping technology to test biosensor circuits once assembled. (H) Whole-cell biosensor experiments allow the characterisation of the biosensor circuit in conditions closer to in vivo applications.

FIGURE 3

Biosensor development by directed evolution of the aTF. (A) Biosensor construct on one plasmid, composed of the sensor component, which, in turn, contains the transcription factor (TF) gene with a ribosome binding site (RBS) and a consecutively active promoter (P_c), and the reporter component, comprised of a TF-inducible promoter (P_TF) and a reporter gene (green fluorescent protein, GFP). (B) Biosensor mutagenesis strategies. Depending on the availability of structural information, a domain of interest of the TF (e.g., the effector binding domain, EBD, or the dimerisation domain, DD) are inspected or modeled and residues or sequence stretches for mutagenesis are selected. To guide mutagenesis, ligands can be placed into the EBD with in silico-docking and side chains can be designed and repacked (e.g., in the DD). (C) Directed evolution strategies. Based on the residues or sequence stretches selected, the domain of interest is addressed (EBD or DD in the current examples) by rational approaches, i.e., site-directed mutagenesis or random/error-prone PCR. Alternatively, the complete aTF gene is targeted with random mutagenesis in the absence of structural information, or if non-intuitive effects should be probed (e.g., those affecting allostery or DNA binding). During site-directed mutagenesis, combinations of amino acids are generated for selected, fixed sequence positions (this is represented in the picture by different colors at identical sequence positions). (D) The library of sensor constructs obtained by directed evolution is transformed into cells, which are then subjected to (usually) multiple rounds of negative selection (non-induced sensor in the absence of effector) and positive selection (induced sensor in the presence of effector), facilitated by fluorescence-activated cell sorting (FACS) [after (Machado and Dixon, 2022)]. The best candidates are further optimised with respect to other genetic elements determining the fidelity of the bio-sensor, such as RBS and promoter sequences.

FIGURE 4

Bioproduction biosensor-based screening and dynamic regulation for bioproduction. New (A) enzyme variants and (B) genetic circuits can easily be screened for production by linking the production levels of the target metabolite to the output of the biosensor reporter. Dynamically regulated strains can be built using biosensors to control the production of the compound by regulating (C) the metabolic pathway and (D) native genes of the host.