Credit: Baylor College of Medicine
Key points:
- Researchers developed a technique—Targeted Accurate RNA Consensus sequencing (tARC-seq)—that measures mutations that occur during RNA replication of the SARS-CoV-2 virus.
- The team found that SARS-CoV-2 has a high mutation rate and takes advantage of template switching mechanisms to produce more variation.
- The observations in this study recapitulated those revealed by worldwide pandemic viral sequencing data.
The SARS-CoV-2 virus causes COVID and has the ability to frequently generate variants. A new study, published in Nature Microbiology, details emerging technology that can identify the genetic mechanisms impacting SARS-CoV-2 divergence. Researchers used this technique to calculate the virus’ mutation rate and move closer to keeping COVID at bay.
Researchers developed a technique called Targeted Accurate RNA Consensus sequencing (tARC-seq) to examine RNA replication and measure errors that occur during the copying process. Using this tool in both cell culture and clinical samples, the team accurately determined the mutation frequency of SARS-CoV-2 and the types of mutations. They found that the mutation rate was higher than anticipated, which explained the frequency of COVID variants.
Additionally, researchers discovered the existence of hotspots in SARS-CoV-2 RNA that are prone to mutation. They identified a hotspot on the RNA region corresponding to the spike protein that allows the virus to invade cells. Their method revealed template switching – when RNA polymerase jumps to another template on a nearby virus – as another mechanism responsible for variation.
Overall, their study shows that SARS-CoV-2 takes advantage of two biological mechanisms, template switching and complex mutations, to evolve quickly and generate variants that adapt and persevere in human populations.
“tARC-seq allowed us to capture in laboratory cells cultures the emergence of new mutations that recapitulate the mutations observed with worldwide pandemic sequencing data,” said study author Christophe Herman, professor at Baylor College of Medicine. “Our new technology captures a snapshot of new mutations in clinical samples from individual patients and can be useful for monitoring viral evolution in the human population.”