Biosensors for target metabolites
provide powerful high-throughput
screening tools to obtain high-performing strains. However, well-characterized
metabolite-sensing modules are often unavailable and limit rapid access
to the robust biosensors with successful applications. In this study,
we developed a strategy of transcriptome-assisted metabolite-sensing (TAMES) to identify the target metabolite-sensing
module based on selectively comparative transcriptome analysis between
the target metabolite producing and nonproducing strains and a subsequent
quantative reverse transcription (RT-qPCR) evaluation. The strategy
was applied to identify the sensing module cusR that
responds positively to the metabolite 3-dehydroshikimate (DHS) and
proved it was effective to narrow down the candidates. We further
constructed the cusR-based synthetic biosensor and
established the DHS biosensor-based high-throughput screening (HTS)
platform to screen higher DHS-producing strains and successfully increased
DHS production by more than 90%. This study demonstrated that the
TAMES strategy was effective at exploiting the metabolite-sensing
transcriptional regulator, and this could likely be extended to develop
the biosensor-based HTS platforms for other molecules.
Genetically encoded biosensors are powerful tools used to screen metabolite-producing microbial strains. Traditionally, biosensor-based screening approaches also use fluorescence-activated cell sorting (FACS). However, these approaches are limited by the measurement of intracellular fluorescence signals in single cells, rather than the signals associated with populations comprising multiple cells. This characteristic reduces the accuracy of screening because of the variability in signal levels among individual cells. To overcome this limitation, we introduced an approach that combined biosensors with droplet microfluidics (i.e., fluorescence-activated droplet sorting, FADS) to detect labeled cells at a multi-copy level and in an independent droplet microenvironment. We used our previously reported genetically encoded biosensor, 3-dehydroshikimic acid (3-DHS), as a model with which to establish the biosensor-based FADS screening method. We then characterized and compared the effects of the sorting method on the biosensor-based screening system by subjecting the same mutant library to FACS and FADS. Notably, our developed biosensor-enabled, droplet microfluidics-based FADS screening system yielded an improved positive mutant enrichment rate and increased productivity by the best mutant, compared with the single-cell FACS system. In conclusion, the combination of a biosensor and droplet microfluidics yielded a more efficient screening method that could be applied to the biosensor-based high-throughput screening of other metabolites.
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