Transcription factors
responsive to small molecules are essential
elements in synthetic biology designs. They are often used as genetically
encoded biosensors with applications ranging from the detection of
environmental contaminants and biomarkers to microbial strain engineering.
Despite our efforts to expand the space of compounds that can be detected
using biosensors, the identification and characterization of transcription
factors and their corresponding inducer molecules remain labor- and
time-intensive tasks. Here, we introduce TFBMiner, a new data mining
and analysis pipeline that enables the automated and rapid identification
of putative metabolite-responsive transcription factor-based biosensors
(TFBs). This user-friendly command line tool harnesses a heuristic
rule-based model of gene organization to identify both gene clusters
involved in the catabolism of user-defined molecules and their associated
transcriptional regulators. Ultimately, biosensors are scored based
on how well they fit the model, providing wet-lab scientists with
a ranked list of candidates that can be experimentally tested. We
validated the pipeline using a set of molecules for which TFBs have
been reported previously, including sensors responding to sugars,
amino acids, and aromatic compounds, among others. We further demonstrated
the utility of TFBMiner by identifying a biosensor for S-mandelic
acid, an aromatic compound for which a responsive transcription factor
had not been found previously. Using a combinatorial library of mandelate-producing
microbial strains, the newly identified biosensor was able to distinguish
between low- and high-producing strain candidates. This work will
aid in the unraveling of metabolite-responsive microbial gene regulatory
networks and expand the synthetic biology toolbox to allow for the
construction of more sophisticated self-regulating biosynthetic pathways.