Phosphatase activity tunes two-component system sensor detection threshold

Combinatorial Sensor Design in Caulobacter crescentus for Selective Environmental Uranium Detection

The ability to detect uranium (U) through environmental monitoring is of critical importance for informing water resource protection and nonproliferation efforts. While technologies exist for environmental U detection, wide-area environmental monitoring, i.e. sampling coverage over large areas not known to possess U contamination, remains a challenging prospect that necessitates the development of novel detection approaches. Herein, we describe the development of a whole-cell U sensor by integrating two functionally independent, native U-responsive two-component signaling systems (TCS), UzcRS and UrpRS, within an AND gate circuit in the bacterium Caulobacter crescentus. Through leverage of the distinct but imperfect selectivity profiles of both TCS, this combinatorial approach enabled greater selectivity relative to a prior biosensor developed with UzcRS alone; no cross-reactivity was observed with most common environmental metals (e.g, Fe, As, Cu, Ca, Mg, Cd, Cr, Al) or the U decay-chain product Th, and the selectivity against Zn and Pb was significantly improved. In addition, integration of the UzcRS signal amplifier protein UzcY within the AND gate circuit further enhanced overall sensitivity and selectivity for U. The functionality of the sensor in an environmental context was confirmed by detection of U concentrations as low as 1 μM in groundwater samples. The results highlight the value of a combinatorial approach for constructing whole-cell sensors for the selective detection of analytes for which there are no known evolved regulators.

Cascaded amplifying circuits enable ultrasensitive cellular sensors for toxic metals

Cell-based biosensors have great potential to detect various toxic and pathogenic contaminants in aqueous environments. However, frequently they cannot meet practical requirements due to insufficient sensing performance. To address this issue, we investigated a modular, cascaded signal amplifying methodology. We first tuned intracellular sensory receptor densities to increase sensitivity, and then engineered multi-layered transcriptional amplifiers to sequentially boost output expression level. We demonstrated these strategies by engineering ultrasensitive bacterial sensors for arsenic and mercury, and improved detection limit and output up to 5,000-fold and 750-fold, respectively. Coupled by leakage regulation approaches, we developed an encapsulated microbial sensor cell array for low-cost, portable and precise field monitoring, where the analyte can be readily quantified via displaying an easy-to-interpret volume bar-like pattern. The ultrasensitive signal amplifying methodology along with the background regulation and the sensing platform will be widely applicable to many other cell-based sensors, paving the way for their real-world applications.

Dynamic control of endogenous metabolism with combinatorial logic circuits

Controlling gene expression during a bioprocess enables real-time metabolic control, coordinated cellular responses, and staging order-of-operations. Achieving this with small molecule inducers is impractical at scale and dynamic circuits are difficult to design. Here, we show that the same set of sensors can be integrated by different combinatorial logic circuits to vary when genes are turned on and off during growth. Three Escherichia coli sensors that respond to the consumption of feedstock (glucose), dissolved oxygen, and by-product accumulation (acetate) are constructed and optimized. By integrating these sensors, logic circuits implement temporal control over an 18-h period. The circuit outputs are used to regulate endogenous enzymes at the transcriptional and post-translational level using CRISPRi and targeted proteolysis, respectively. As a demonstration, two circuits are designed to control acetate production by matching their dynamics to when endogenous genes are expressed (pta or poxB) and respond by turning off the corresponding gene. This work demonstrates how simple circuits can be implemented to enable customizable dynamic gene regulation.

DNA-based memory devices for recording cellular events

Measuring biological data across time and space is critical for understanding complex biological processes and for various biosurveillance applications. However, such data are often inaccessible or difficult to directly obtain. Less invasive, more robust and higher-throughput biological recording tools are needed to profile cells and their environments. DNA-based cellular recording is an emerging and powerful framework for tracking intracellular and extracellular biological events over time across living cells and populations. Here, we review and assess DNA recorders that utilize CRISPR nucleases, integrases and base-editing strategies, as well as recombinase and polymerase-based methods. Quantitative characterization, modelling and evaluation of these DNA-recording modalities can guide their design and implementation for specific application areas.