Toward Mail-in-Sensors for SARS-CoV-2 Detection: Interfacing Gel Switch Resonators with Cell-Free Toehold Switches.

The COVID-19 pandemic has emphasized the importance of widespread testing to control the spread of infectious diseases. The rapid development, scale-up, and deployment of viral and antibody detection methods since the beginning of the pandemic have greatly increased testing capacity. Desirable attributes of detection methods are low product costs, self-administered protocols, and the ability to be mailed in sealed envelopes for the safe analysis and subsequent logging to public health databases. Herein, such a platform is demonstrated with a screen-printed, inductor-capacitor (LC) resonator as a transducer and a toehold switch coupled with cell-free expression as the biological selective recognition element. In the presence of the N-gene from SARS-CoV-2, the toehold switch relaxes, protease enzyme is expressed, and it degrades a gelatin switch that ultimately shifts the resonant frequency of the planar resonant sensor. The gelatin switch resonator (GSR) can be analyzed through a sealed envelope allowing for assessment without the need for careful sample handling with personal protective equipment or the need for workup with other reagents. The toehold switch used in this sensor demonstrated selectivity to SARS-CoV-2 virus over three seasonal coronaviruses and SARS-CoV-1, with a limit of detection of 100 copies/µL. The functionality of the platform and assessment in a sealed envelope with an automated scanner is shown with overnight shipment, and further improvements are discussed to increase signal stability and further simplify user protocols toward a mail-in platform.

A glucose meter interface for point-of-care gene circuit-based diagnostics.

Recent advances in cell-free synthetic biology have given rise to gene circuit-based sensors with the potential to provide decentralized and low-cost molecular diagnostics. However, it remains a challenge to deliver this sensing capacity into the hands of users in a practical manner. Here, we leverage the glucose meter, one of the most widely available point-of-care sensing devices, to serve as a universal reader for these decentralized diagnostics. We describe a molecular translator that can convert the activation of conventional gene circuit-based sensors into a glucose output that can be read by off-the-shelf glucose meters. We show the development of new glucogenic reporter systems, multiplexed reporter outputs and detection of nucleic acid targets down to the low attomolar range. Using this glucose-meter interface, we demonstrate the detection of a small-molecule analyte; sample-to-result diagnostics for typhoid, paratyphoid A/B; and show the potential for pandemic response with nucleic acid sensors for SARS-CoV-2.

A multiplexed, electrochemical interface for gene-circuit-based sensors.

The field of synthetic biology has used the engineered assembly of synthetic gene networks to create a wide range of functions in biological systems. To date, gene-circuit-based sensors have primarily used optical proteins (for example, fluorescent, colorimetric) as reporter outputs, which has limited the potential to measure multiple distinct signals. Here we present an electrochemical interface that permits expanded multiplexed reporting for cell-free gene-circuit-based sensors. We have engineered a scalable system of reporter enzymes that cleave specific DNA sequences in solution, which results in an electrochemical signal when these newly liberated strands are captured at the surface of a nanostructured microelectrode. We describe the development of this interface and show its utility using a ligand-inducible gene circuit and toehold switch-based sensors by demonstrating the detection of multiple antibiotic resistance genes in parallel. This technology has the potential to expand the field of synthetic biology by providing an interface for materials, hardware and software.

Predictive Modeling of a Batch Filter Mating Process.

Quantitative characterizations of horizontal gene transfer are needed to accurately describe gene transfer processes in natural and engineered systems. A number of approaches to the quantitative description of plasmid conjugation have appeared in the literature. In this study, we seek to extend that work, motivated by the question of whether a mathematical model can accurately predict growth and conjugation dynamics in a batch process. We used flow cytometry to make time-point observations of a filter-associated mating between two E. coli strains, and fit ordinary differential equation models to the data. A model comparison analysis identified the model formulation that is best supported by the data. Identifiability analysis revealed that the parameters were estimated with acceptable accuracy. The predictive power of the model was assessed by comparison with test data that demanded extrapolation from the training experiments. This study represents the first attempt to assess the quality of model predictions for plasmid conjugation. Our successful application of this approach lays a foundation for predictive modeling that can be used both in the study of natural plasmid transmission and in model-based design of engineering approaches that employ conjugation, such as plasmid-mediated bioaugmentation.