As the global COVID-19 pandemic continues, viral variants have become the latest concern. But variants are complicated. Each one is made up of a collection of mutations, all of which have the potential to change the SARS-CoV-2 virus in unexpected ways.
To understand these rapidly changing virus sequences, DiaCarta, a precision molecular diagnostics company has developed a qPCR testing platform for rapid, efficient, and inexpensive detection of SARS-CoV-2 variant strains. The testing can be easily adopted in laboratories that perform COVID-19 testing. The details about the method were published in MedRxiV on April 22.
DiaCarta’s XNA Technology
The current method for SARS-CoV-2 detection is based on RT-PCR. Briefly, this method uses a DNA probe that binds to the viral sequence, which is then amplified by DNA amplifying enzymes. The amplification is a signal for the presence of the virus.
DiaCarta’s SARS-CoV-2 detection method is also an RT-PCR-based method but instead of using a DNA probe, it uses an XNA probe. XNAs are synthetic nucleic acid analogs that are different from DNA only in their sugar backbone. They can hybridize with DNA and can be used in RT-PCR testing just like a DNA probe. XNAs, however, offer an advantage over conventional DNA probes in the detection of specific mutations.
Conventional RT-PCRs can also detect mutations, however, with low confidence. XNA technology, on the other hand, can detect them with high specificity. This is because the temperature at which XNA and DNA hybridize can drop dramatically if there is a change in DNA sequence such as in the case of a mutation.
This temperature change prevents amplification of DNA molecules without the mutation allowing highly specific amplification of just the mutated sequences. According to the publication, a single base-pair mismatch between XNA/DNA duplex can result in a drop of 10-18C in the melting temperature.
The company tested the functioning of this technology on a total of 278 SARS-CoV-2 positive samples that originated primarily from the San Francisco Bay Area. The group tested for two commonly found mutations, D614G and N501Y in 4 variants of concern- UK variant (B1.1.7), Brazil variant (P.1), South African variant (B.1.351), and CAL.20C variant.
The SARS-CoV-2 spike-gene D614G mutation was detected in 58 of the 139 samples collected in January 2021 and in 78 of the 139 samples collected in February. On the other hand, N501Y mutation appeared only in February samples suggesting the earlier appearance of D614G mutation and later appearance of N501Y mutation. This also indicates that the spread of the UK variant, which has N501Y mutation in Northern California is relatively recent.
Overcoming Mutation Testing Challenges
The RT-PCR-based method is the most commonly used method for monitoring SARS-CoV-2 evolution around the globe. However, these methods still require next generation sequencing to confirm the mutation, which is an expensive and time consuming method. “These issues limit the use of the currently available variant testing methodologies.” said the authors of the study.
XNA technology is particularly helpful in this scenario because XNA sequences can be designed with more flexibility and can detect mutations in samples with low concentrations. It also minimizes wild-type background amplification and can be quickly expanded to other mutations that may arise in the future.
“Next generation sequencing (NGS) has been the standard method of detection for SARS-CoV-2 variants. However, the NGS-based assays are expensive, time consuming and not widely available, thereby limiting their utility in large scale surveillance for SARS-CoV-2 variants,” said Michael Sha, Ph.D., Chief Technology Officer and Senior Vice President of R&D at DiaCarta Inc.
“There has been an urgent need for testing platforms that can detect these variants rapidly and cost-effectively. This study demonstrates that DiaCarta’s XNA technology can do both – accurately detect known and emerging SARS-CoV-2 mutations and provide a rapid, cost-effective solution for SARS-CoV-2 variant surveillance.”
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