Detecting respiratory viral RNA using expanded genetic alphabets and self-avoiding DNA

Area of Science:

• Synthetic biology applications in molecular diagnostics.
• Development of advanced nucleic acid detection technologies.

Background:

• Nucleic acid (NA)-targeted tests, including polymerase chain reaction (PCR), are crucial for viral detection but are often expensive.
• Multiplexing NA tests can reduce costs but may compromise sensitivity, accuracy, and introduce noise, false positives, or false negatives due to primer and probe interactions.

Purpose of the Study:

• To develop a cost-effective and accurate multiplexed assay for detecting a panel of respiratory viruses.
• To overcome the limitations of traditional multiplexing in NA-targeted tests by integrating synthetic biology tools.

Main Methods:

• Utilized self-avoiding molecular recognition systems (SAMRSs) to facilitate multiplexing.
• Employed artificially expanded genetic information systems (AEGISs) for low-noise PCR.
• Incorporated "transliteration" technology to convert standard nucleotides to AEGIS nucleotides, enhancing hybridization.
• Developed a multiplexed Luminex-based respiratory panel.

Main Results:

• The assay potentially differentiates between influenza A and B viruses, respiratory syncytial virus, SARS-coronavirus, and MERS-coronavirus.
• Achieved sensitive detection, identifying as few as 10 MERS virions in a 20-μl sample.
• Mitigated common issues associated with multiplexing, such as reduced sensitivity and accuracy.

Conclusions:

• The novel combination of SAMRSs, AEGISs, and transliteration technology offers a robust solution for multiplexed respiratory virus detection.
• This approach enhances the accuracy and sensitivity of NA-targeted tests, potentially improving diagnostic capabilities and epidemiological surveillance.