Displaced by Deceivers: Prevention of Biosensor Cross-Talk Is Pivotal for Successful Biosensor-Based High-Throughput Screening Campaigns

Abstract

Transcriptional biosensors emerged as powerful tools for protein and strain engineering as they link inconspicuous production phenotypes to easily measurable output signals such as fluorescence. When combined with fluorescence-activated cell sorting, transcriptional biosensors enable high throughput screening of vast mutant libraries. Interestingly, even though many published manuscripts describe the construction and characterization of transcriptional biosensors, only very few studies report the successful application of transcriptional biosensors in such high-throughput screening campaigns. Here, we describe construction and characterization of the trans -cinnamic acid responsive transcriptional biosensor pSenCA for Escherichia coli and its application in a FACS based screen. In this context, we focus on essential methodological challenges during the development of such biosensor-guided high-throughput screens such as biosensor cross-talk between producing and nonproducing cells, which could be minimized by optimization of expression and cultivation conditions. The optimized conditions were applied in a five-step FACS campaign and proved suitable to isolate phenylalanine ammonia lyase variants with improved activity in E. coli and in vitro . Findings from this study will help researchers who want to profit from the unmatched throughput of fluorescence-activated cell sorting by using transcriptional biosensors for their enzyme and strain engineering campaigns.

Transcription Factor Engineering for High-Throughput Strain Evolution and Organic Acid Bioproduction: A Review

“…The number of studies that have applied this strategy is increasing rapidly (Binder et al, 2012(Binder et al, , 2013Mustafi et al, 2012;Siedler et al, 2014bSiedler et al, , 2017Jha et al, 2016;Wang Y. et al, 2016;Liu Y.F. et al, 2017;Schulte et al, 2017;Flachbart et al, 2019;Kortmann et al, 2019). In practice, cross-talk between cells should be noted, as it may lead to false positive results, decreasing the screening efficiency; this can be FIGURE 2 | Genetically encoded TF-based biosensors and their applications in metabolic engineering.…”

Control strategies to manage trade-offs during microbial production

“…The selection can be achieved by controlling the expression of an antibiotic resistance gene, a toxin-antitoxin pair, or an auxotrophic marker [ 49• , 50 , 55 ]. Sensor-selector has been traditionally used to select for the best genetic variant from large mutagenic libraries [ 56 – 58 ]. It has also been used to eliminate undesirable genetic mutants formed during long-term production, as shown for improved mevalonic acid production [ 49• ].…”

Engineering and application of a biosensor with focused ligand specificity

“…CatM of Acinetobacter baylyi ADP1, involved in the degradation of aromatic compounds, was also altered to accept benzoate 16 . However, only a few of these adapted biosensors were used for larger-scale screening campaigns, probably because biosensor properties, such as weak ligand binding or low reporter gene expression prohibited implementation of FACS-based screenings 9 .…”

“…For such applications, RNA-based and transcription factor-based biosensors have been described in literature 7 . In particular, transcription factor-based biosensors, comprised of a fluorescent protein-encoding reporter gene whose expression is controlled by a ligand-inducible transcriptional regulator (TR), have been successfully used for strain engineering in both, academic and industrial settings [8][9][10] .…”