Structural and functional analyses of the cellulase transcription regulator CelR

A catalogue of signal molecules that interact with sensor kinases, chemoreceptors and transcriptional regulators

Bacteria have evolved many different signal transduction systems that sense signals and generate a variety of responses. Generally, most abundant are transcriptional regulators, sensor histidine kinases and chemoreceptors. Typically, these systems recognize their signal molecules with dedicated ligand-binding domains (LBDs), which, in turn, generate a molecular stimulus that modulates the activity of the output module. There are an enormous number of different LBDs that recognize a similarly diverse set of signals. To give a global perspective of the signals that interact with transcriptional regulators, sensor kinases and chemoreceptors, we manually retrieved information on the protein-ligand interaction from about 1,200 publications and 3D structures. The resulting 811 proteins were classified according to the Pfam family into 127 groups. These data permit a delineation of the signal profiles of individual LBD families as well as distinguishing between families that recognize signals in a promiscuous manner and those that possess a well-defined ligand range. A major bottleneck in the field is the fact that the signal input of many signaling systems is unknown. The signal repertoire reported here will help the scientific community design experimental strategies to identify the signaling molecules for uncharacterised sensor proteins.

Biosensor-Based Directed Evolution of Methanol Dehydrogenase from Lysinibacillus xylanilyticus

Methanol dehydrogenase (Mdh), is a crucial enzyme for utilizing methane and methanol as carbon and energy sources in methylotrophy and synthetic methylotrophy. Engineering of Mdh, especially NAD-dependent Mdh, has thus been actively investigated to enhance methanol conversion. However, its poor catalytic activity and low methanol affinity limit its wider application. In this study, we applied a transcriptional factor-based biosensor for the direct evolution of Mdh from Lysinibacillus xylanilyticus (Lxmdh), which has a relatively high turnover rate and low K_M value compared to other wild-type NAD-dependent Mdhs. A random mutant library of Lxmdh was constructed in Escherichia coli and was screened using formaldehyde-detectable biosensors by incubation with low methanol concentrations. Positive clones showing higher fluorescence were selected by fluorescence-activated cell sorting (FACS) system, and their catalytic activities toward methanol were evaluated. The successfully isolated mutants E396V, K318N, and K46E showed high activity, particularly at very low methanol concentrations. In kinetic analysis, mutant E396V, K318N, and K46E had superior methanol conversion efficiency, with 79-, 23-, and 3-fold improvements compared to the wild-type, respectively. These mutant enzymes could thus be useful for engineering synthetic methylotrophy and for enhancing methanol conversion to various useful products.

C1 Compound Biosensors: Design, Functional Study, and Applications

The microbial assimilation of one-carbon (C1) gases is a topic of interest, given that products developed using this pathway have the potential to act as promising substrates for the synthesis of valuable chemicals via enzymatic oxidation or C–C bonding. Despite extensive studies on C1 gas assimilation pathways, their key enzymes have yet to be subjected to high-throughput evolution studies on account of the lack of an efficient analytical tool for C1 metabolites. To address this challenging issue, we attempted to establish a fine-tuned single-cell–level biosensor system constituting a combination of transcription factors (TFs) and several C1-converting enzymes that convert target compounds to the ligand of a TF. This enzymatic conversion broadens the detection range of ligands by the genetic biosensor systems. In this study, we presented new genetic enzyme screening systems (GESSs) to detect formate, formaldehyde, and methanol from specific enzyme activities and pathways, named FA-GESS, Frm-GESS, and MeOH-GESS, respectively. All the biosensors displayed linear responses to their respective C1 molecules, namely, formate (1.0–250 mM), formaldehyde (1.0–50 μM), and methanol (5–400 mM), and they did so with high specificity. Consequently, the helper enzymes, including formaldehyde dehydrogenase and methanol dehydrogenase, were successfully combined to constitute new versatile combinations of the C1-biosensors.