Validation of Rapid and Economic Colorimetric Nanoparticle Assay for SARS-CoV-2 RNA Detection in Saliva and Nasopharyngeal Swabs.

Even with the widespread uptake of vaccines, the SARS-CoV-2-induced COVID-19 pandemic continues to overwhelm many healthcare systems worldwide. Consequently, massive scale molecular diagnostic testing remains a key strategy to control the ongoing pandemic, and the need for instrument-free, economic and easy-to-use molecular diagnostic alternatives to PCR remains a goal of many healthcare providers, including WHO. We developed a test (Repvit) based on gold nanoparticles that can detect SARS-CoV-2 RNA directly from nasopharyngeal swab or saliva samples with a limit of detection (LOD) of 2.1 × 105 copies mL-1 by the naked eye (or 8 × 104 copies mL-1 by spectrophotometer) in less than 20 min, without the need for any instrumentation, and with a manufacturing price of <$1. We tested this technology on 1143 clinical samples from RNA extracted from nasopharyngeal swabs (n = 188), directly from saliva samples (n = 635; assayed by spectrophotometer) and nasopharyngeal swabs (n = 320) from multiple centers and obtained sensitivity values of 92.86%, 93.75% and 94.57% and specificities of 93.22%, 97.96% and 94.76%, respectively. To our knowledge, this is the first description of a colloidal nanoparticle assay that allows for rapid nucleic acid detection at clinically relevant sensitivity without the need for external instrumentation that could be used in resource-limited settings or for self-testing.

A Bayesian Approach to Extracting Kinetic Information from Artificial Enzymatic Networks.

In order to create artificial enzymatic networks capable of increasingly complex behavior, an improved methodology in understanding and controlling the kinetics of these networks is needed. Here, we introduce a Bayesian analysis method allowing for the accurate inference of enzyme kinetic parameters and determination of most likely reaction mechanisms, by combining data from different experiments and network topologies in a single probabilistic analysis framework. This Bayesian approach explicitly allows us to continuously improve our parameter estimates and behavior predictions by iteratively adding new data to our models, while automatically taking into account uncertainties introduced by the experimental setups or the chemical processes in general. We demonstrate the potential of this approach by characterizing systems of enzymes compartmentalized in beads inside flow reactors. The methods we introduce here provide a new approach to the design of increasingly complex artificial enzymatic networks, making the design of such networks more efficient, and robust against the accumulation of experimental errors.

Modular Design of Small Enzymatic Reaction Networks Based on Reversible and Cleavable Inhibitors.

Systems chemistry aims to mimic the functional behavior of living systems by constructing chemical reaction networks with well-defined dynamic properties. Enzymes can play a key role in such networks, but there is currently no general and scalable route to the design and construction of enzymatic reaction networks. Here, we introduce reversible, cleavable peptide inhibitors that can link proteolytic enzymatic activity into simple network motifs. As a proof-of-principle, we show auto-activation topologies producing sigmoidal responses in enzymatic activity, explore cross-talk in minimal systems, design a simple enzymatic cascade, and introduce non-inhibiting phosphorylated peptides that can be activated using a phosphatase.