In silico assessment of 2019-nCoV genomic variation on RT-qPCR assays

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In silico assessment of the impact of 2019 novel coronavirus genomic variation on the efficiency of published real-time quantitative polymerase chain reaction detection assays

When the 2019 novel coronavirus (2019-nCoV) outbreak began in Wuhan, China, in late 2019, real-time quantitative polymerase chain reaction (RT-qPCR) emerged as the critical tool for diagnosing infections. This test works by using short DNA sequences—primers (to start copying the virus’s genome) and probes (to signal the virus’s presence)—to target specific parts of the virus’s genetic code. But viruses mutate, and if those mutations occur where primers or probes bind, the test can fail to detect the virus, leading to false negatives. These missed cases threaten both patient care and outbreak control. To address this risk, researchers from the Beijing Institute of Microbiology and Epidemiology analyzed how 2019-nCoV’s genetic changes might be affecting RT-qPCR accuracy.

How They Did It

The team studied 77 full-length 2019-nCoV genome sequences shared publicly on GISAID, a global database for viral genetic data. They used MAFFT (a leading tool for aligning DNA sequences) to compare these genomes to a reference sequence (IVDC-HB-01) and identify mutations.

What They Found: Viral Mutations Are Common

Across the 77 sequences, the team found 85 single nucleotide variants (SNVs)—small, one-letter changes in the virus’s genome. Seven of these SNVs were present in two different virus samples, and nine were found in three or more samples—signs that some mutations were spreading within the outbreak.

Most RT-qPCR Primers Target Key Genes—But Some Have Flaws

Next, the researchers evaluated 13 RT-qPCR primer-probe sets from eight institutions. These sets were designed to target four viral genes:

  • ORF1ab: A non-structural gene involved in viral replication.
  • Nucleocapsid (N): A structural protein that coats the virus’s RNA.
  • Envelope (E): A protein in the virus’s outer layer.
  • Spike (S): The protein that allows the virus to enter human cells.

Two sets (3 and 6) had a mismatch in their reverse primers—meaning the primer sequence didn’t fully match the 2019-nCoV genome in any of the tested samples. Worse, three SNVs were found exactly where primers or probes bind:

  1. Position 28291: In the forward primer of set 7 (targeting the N gene), found in a sequence from Shenzhen.
  2. Position 28688: In the forward primer of set 9 (targeting the N gene), found in a sequence from Shandong.
  3. Position 29200: In the probe of set 8 (targeting the N gene), found in a sequence from Chongqing.

Mutations in probe regions are particularly dangerous. A 2009 study showed that even one mismatch in a probe can sharply reduce RT-qPCR efficiency—meaning the test might not detect the virus even if it’s present.

What This Means for Testing

Five of the 13 primer sets could potentially produce false negatives:

  • Two sets (3 and 6) had mismatches from the start.
  • Three sets (7, 8, 9) had mutations in critical primer/probe regions.

Notably, all three mutation-affected sets target the N gene—suggesting this gene is more prone to changes than others.

Recommendations to Improve Testing

The researchers offer three key steps to reduce false negatives:

  1. Target Conservative Genes: Use regions of the virus’s genome that rarely mutate, like the nsp12 (RdRp) gene (part of ORF1ab). This gene is essential for viral replication, so mutations here are less likely to survive.
  2. Use Multiple Targets: Design RT-qPCR tests to detect several genes at once. If one target has a mutation, another might still work.
  3. Monitor Mutations Continuously: As the outbreak progresses, track how the virus evolves and update primers/probes as needed.

Acknowledgments

The study relied on data shared by researchers worldwide via GISAID. Funding came from China’s National Key Research & Development Program (2020YFC0840900) and the Beijing Municipal Science and Technology Project (Z201100001020004).

References

  1. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 2013;30:772–780.
  2. Süss B, Flekna G, Wagner M, Hein I. Studying the effect of single mismatches in primer and probe binding regions on amplification curves and quantification in real-time PCR. J Microbiol Methods 2009;76:316–319.

doi: 10.1097/CM9.0000000000000817

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