Macrolide Biosensor Optimization through Cellular Substrate Sequestration

Directed Evolution of a Macrolide-Sensing Transcription Factor Biosensor for the Detection of Macrolactone Aglycones via "Effector Walking" and Efflux Pump Deletion

Glycosylated macrolactones (macrolides) often display broad and potent biological activities and are targets for drug development and discovery. The modular genetic organization of macrolide polyketide synthases (PKSs) and various polyketide tailoring enzymes has inspired the combinatorial biosynthesis of new-to-nature macrolides. However, most engineered PKS and macrolide biosynthetic pathways are ineffective and produce reduced or negligible product titers. Directed evolution could improve the activity of engineered PKSs and associated pathways but critically requires a high-throughput screen to identify active variants from large libraries. Transcription factor-based biosensors can be used for this purpose. However, the effector specificity of the only known macrolide-sensing transcription factor MphR is limited to macrolides modified with the sugar, desosamine. The potential applications of MphR are subsequently limited, ruling out the possibility of leveraging MphR to screen libraries of pathway variants that make macrolactones that lack sugars (i.e., macrolide aglycones) such as the direct products of PKSs. In this study, we aimed to engineer the effector specificity of the MphR biosensor strain for detecting macrolide aglycones. By developing an "effector walking" strategy coupled with efflux pump deletion, the effector profile of MphR was dramatically broadened to include several erythronolide macrolactones. This work sets the stage for applying directed evolution and other high-throughput screening approaches to various PKSs. Our results suggest a broadly applicable approach to developing biosensors that detect ligands that are very different in structure from the native effector.

Design of synthetic bacterial biosensors

Novel whole-cell bacterial biosensor designs require an emphasis on moving toward field deployment. Many current sensors are characterized under specified laboratory conditions, which frequently do not represent actual deployment conditions. To this end, recent developments such as toolkits for probing new host chassis that are more robust to environments of interest, have paved the way for improved designs. Strategies for rational tuning of genetic components or tools such as genetic amplifiers or designs that allow post hoc tuning are essential in optimizing existing biosensors for practical application. Furthermore, recent work has seen a rise in directed evolution techniques, which can be immensely valuable in both tuning existing sensors and developing sensors for new analytes that lack characterized sensors. Combined with advancements in bioinformatics and capabilities in rewiring two-component systems, many new sensors can be established, broadening biosensor use cases. Last, recent work in CRISPR-based dynamic regulation and memory mechanisms, as well as kill-switches for biosafety and innovative output integration concepts, represents promising steps toward designing bacterial biosensors for deployment in dynamic and heterogeneous conditions.

Cells function as a ternary logic gate to decide migration direction under integrated chemical and fluidic cues

Cells sense various environmental cues and subsequently process intracellular signals to decide their migration direction in many physiological and pathological processes. Although several signaling molecules and networks have been identified in these directed migrations, it still remains ambiguous to predict the migration direction under multiple and integrated cues, specifically chemical and fluidic cues. Here, we investigated the cellular signal processing machinery by reverse-engineering directed cell migration under integrated chemical and fluidic cues. We imposed controlled chemical and fluidic cues to cells using a microfluidic platform and analyzed the extracellular coupling of the cues with respect to the cellular detection limit. Then, the cell's migratory behavior was reverse-engineered to build a cellular signal processing system as a logic gate, which is based on a "selection" gate. This framework is further discussed with a minimal intracellular signaling network of a shared pathway model. The proposed framework of the ternary logic gate suggests a systematic view to understand how cells decode multiple cues and make decisions about the migration direction.

The sequestration mechanism as a generalizable approach to improve the sensitivity of biosensors and bioassays

Biosensors and bioassays, both of which employ proteins and nucleic acids to detect specific molecular targets, have seen significant applications in both biomedical research and clinical practice. This success is largely due to the extraordinary versatility, affinity, and specificity of biomolecular recognition. Nevertheless, these receptors suffer from an inherent limitation: single, saturable binding sites exhibit a hyperbolic relationship (the "Langmuir isotherm") between target concentration and receptor occupancy, which in turn limits the sensitivity of these technologies to small variations in target concentration. To overcome this and generate more responsive biosensors and bioassays, here we have used the sequestration mechanism to improve the steepness of the input/output curves of several bioanalytical methods. As our test bed for this we employed sensors and assays against neutrophil gelatinase-associated lipocalin (NGAL), a kidney biomarker for which enhanced sensitivity will improve the monitoring of kidney injury. Specifically, by introducing sequestration we have improved the responsiveness of an electrochemical aptamer based (EAB) biosensor, and two bioassays, a paper-based "dipstick" assay and an enzyme-linked immunosorbent assay (ELISA). Doing so we have narrowed the dynamic range of these sensors and assays several-fold, thus enhancing their ability to measure small changes in target concentration. Given that introducing sequestration requires only the addition of the appropriate concentration of a high-affinity "depletant," the mechanism appears simple and easily adaptable to tuning the binding properties of the receptors employed in a wide range of biosensors and bioassays.

Striving for sustainable biosynthesis: discovery, diversification, and production of antimicrobial drugs in Escherichia coli

New antimicrobials need to be discovered to fight the advance of multidrug-resistant pathogens. A promising approach is the screening for antimicrobial agents naturally produced by living organisms. As an alternative to studying the native producer, it is possible to use genetically tractable microbes as heterologous hosts to aid the discovery process, facilitate product diversification through genetic engineering, and ultimately enable environmentally friendly production. In this mini-review, we summarize the literature from 2017 to 2022 on the application of Escherichia coli and E. coli-based platforms as versatile and powerful systems for the discovery, characterization, and sustainable production of antimicrobials. We highlight recent developments in high-throughput screening methods and genetic engineering approaches that build on the strengths of E. coli as an expression host and that led to the production of antimicrobial compounds. In the last section, we briefly discuss new techniques that have not been applied to discover or engineer antimicrobials yet, but that may be useful for this application in the future.

Development of Whole-Cell Biosensors for Screening of Peptidoglycan-Targeting Antibiotics in a Gram-Negative Bacterium

There is an urgent need to develop novel antibiotics since antibiotic resistance is an increasingly serious threat to global public health. Whole-cell biosensors are one of the promising strategies for new antibiotic discovery. The peptidoglycan (PG) of the bacterial cell wall is one of the most important targets for antibiotics. However, the biosensors for the detection of PG-targeting antibiotics in Gram-negative bacteria have not been developed, mainly because of the lack of the regulatory systems that sense and respond to PG stress. Recently, we identified a novel two-component signal transduction system (PghKR) that is responsible for sensing and responding to PG damage in the Gram-negative bacterium Shewanella oneidensis. Based on this system, we developed biosensors for the detection of PG-targeting antibiotics. Using ampicillin as an inducer for PG stress and the bacterial luciferase LuxCDABE as the reporter, we found that the PghKR biosensors are specific to antibiotics targeting PG synthesis, including β-lactams, vancomycin, and d-cycloserine. Deletion of genes encoding PG permease AmpG and β-lactamase BlaA improves the sensitivity of the biosensors substantially. The PghKR biosensor in the background of ΔblaA is also functional on agar plates, providing a simple method for screening bacteria that produce PG-targeting antibiotics. IMPORTANCE The growing problem of antibiotic resistance in Gram-negative bacteria urgently needs new strategies so that researchers can develop novel antibiotics. Microbial whole-cell biosensors are capable of sensing various stimuli with a quantifiable output and show tremendous potential for the discovery of novel antibiotics. As the Achilles' heel of bacteria, the synthesis of the peptidoglycan (PG) is targeted by many antibiotics. However, the regulatory systems that sense and respond to PG-targeting stress in Gram-negative bacteria are reported rarely, restricting the development of biosensors for the detection of PG-targeting antibiotics. In this study, we developed a highly sensitive and specific biosensor based on a novel two-component system in the Gram-negative bacterium Shewanella oneidensis that is responsible for the sensing and responding to PG stress. Our biosensors have great potential for discovering novel antibiotics and determining the mode of action of antibiotics.

Enzyme directed evolution using genetically encodable biosensors

Directed evolution has been remarkably successful in identifying enzyme variants with new or improved properties, such as altered substrate scope or novel reactivity. Genetically encodable biosensors (GEBs), which convert the concentration of a small molecule ligand into an easily detectable output signal, have seen increasing application to enzyme directed evolution in the last decade. GEBs enable the use of high-throughput methods to assess enzyme activity of very large libraries, which can accelerate the search for variants with desirable activity. Here, we review different classes of GEBs and their properties in the context of enzyme evolution, how GEBs have been integrated into directed evolution workflows, and recent examples of enzyme evolution efforts utilizing GEBs. Finally, we discuss the advantages, challenges, and opportunities for using GEBs in the directed evolution of enzymes.

Advances in engineering and optimization of transcription factor-based biosensors for plug-and-play small molecule detection

Transcription factor (TF)-based biosensors have been applied in biotechnology for a variety of functions, including protein engineering, dynamic control, environmental detection, and point-of-care diagnostics. Such biosensors are promising analytical tools due to their wide range of detectable ligands and modular nature. However, designing biosensors tailored for applications of interest with the desired performance parameters, including ligand specificity, remains challenging. Biosensors often require significant engineering and tuning to meet desired specificity, sensitivity, dynamic range, and operating range parameters. Another limitation is the orthogonality of biosensors across hosts, given the role of the cellular context. Here, we describe recent advances and examples in the engineering and optimization of TF-based biosensors for plug-and-play small molecule detection. We highlight novel developments in TF discovery and biosensor design, TF specificity engineering, and biosensor tuning, with emphasis on emerging computational methods.

Enriching intracellular macrolides in Escherichia coli improved the sensitivity of bioluminescent sensing systems

A repressor protein MphR and an enhanced green fluorescent protein (eGFP) were used to construct a bioluminescent sensing system for macrolide analysis in Escherichia coli host cells. We deleted TolC, an efflux pump for macrolides in E. coli, to promote the intracellular accumulation of macrolides. The binding constant (K_1/2) of the sensing system constructed in an E. coli strain was decreased up to 33-fold with deleted TolC, and its sensitivity to the macrolides erythromycin, azithromycin, roxithromycin, and pikromycin was increased. The limit of detection of the bioluminescent sensing system for serum azithromycin was 4.1 nM. The ability to detect serum azithromycin concentrations was confirmed by analyzing photographs using ImageJ software. We also developed a novel sensing system for the immune suppressor FK506, another macrolide that is frequently prescribed. Deleting TolC also significantly improved the sensitivity of this sensing system. Bioluminescent sensing systems constructed in TolC mutants were sensitive to various macrolides, indicating their potential for clinical application with hand-held devices.

Strategies for Improving Small-Molecule Biosensors in Bacteria

In recent years, small-molecule biosensors have become increasingly important in synthetic biology and biochemistry, with numerous new applications continuing to be developed throughout the field. For many biosensors, however, their utility is hindered by poor functionality. Here, we review the known types of mechanisms of biosensors within bacterial cells, and the types of approaches for optimizing different biosensor functional parameters. Discussed approaches for improving biosensor functionality include methods of directly engineering biosensor genes, considerations for choosing genetic reporters, approaches for tuning gene expression, and strategies for incorporating additional genetic modules.

Phage-Assisted Continuous Evolution and Selection of Enzymes for Chemical Synthesis

Ligand-dependent biosensors are valuable tools for coupling the intracellular concentrations of small molecules to easily detectable readouts such as absorbance, fluorescence, or cell growth. While ligand-dependent biosensors are widely used for monitoring the production of small molecules in engineered cells and for controlling or optimizing biosynthetic pathways, their application to directed evolution for biocatalysts remains underexplored. As a consequence, emerging continuous evolution technologies are rarely applied to biocatalyst evolution. Here, we develop a panel of ligand-dependent biosensors that can detect a range of small molecules. We demonstrate that these biosensors can link enzymatic activity to the production of an essential phage protein to enable biocatalyst-dependent phage-assisted continuous evolution (PACE) and phage-assisted continuous selection (PACS). By combining these phage-based evolution and library selection technologies, we demonstrate that we can evolve enzyme variants with improved and expanded catalytic properties. Finally, we show that the genetic diversity resulting from a highly mutated PACS library is enriched for active enzyme variants with altered substrate scope. These results lay the foundation for using phage-based continuous evolution and selection technologies to engineer biocatalysts with novel substrate scope and reactivity.

Stability and Robustness of Unbalanced Genetic Toggle Switches in the Presence of Scarce Resources

While the vision of synthetic biology is to create complex genetic systems in a rational fashion, system-level behaviors are often perplexing due to the context-dependent dynamics of modules. One major source of context-dependence emerges due to the limited availability of shared resources, coupling the behavior of disconnected components. Motivated by the ubiquitous role of toggle switches in genetic circuits ranging from controlling cell fate differentiation to optimizing cellular performance, here we reveal how their fundamental dynamic properties are affected by competition for scarce resources. Combining a mechanistic model with nullcline-based stability analysis and potential landscape-based robustness analysis, we uncover not only the detrimental impacts of resource competition, but also how the unbalancedness of the switch further exacerbates them. While in general both of these factors undermine the performance of the switch (by pushing the dynamics toward monostability and increased sensitivity to noise), we also demonstrate that some of the unwanted effects can be alleviated by strategically optimized resource competition. Our results provide explicit guidelines for the context-aware rational design of toggle switches to mitigate our reliance on lengthy and expensive trial-and-error processes, and can be seamlessly integrated into the computer-aided synthesis of complex genetic systems.