SMDP: SARS-CoV-2 mutation distribution profiler for rapid estimation of mutational histories of unusual lineages

Pillar 6
Computational Analysis, Modelling and Evolutionary Outcomes (CAMEO)

The COVID-19 pandemic-causing virus SARS-CoV-2 generally changes at a steady rate over time. However, sometimes, certain versions of the virus (called lineages) develop many more mutations than we would expect for how long they have been around. This can happen for a few reasons, including selection pressures imposed by evolution during a chronic infection in a person who has been infected for a long time or exposure to mutation-inducing drugs like molnupiravir.

To help understand these mutations better, researchers from CoVaRR-Net’s Computational Analysis, Modelling and Evolutionary Outcomes (CAMEO) team created a free online tool called the SARS-CoV-2 Mutation Distribution Profiler (SMDP). This tool compares a list of user-submitted lineage-defining mutations with established mutation distributions:

• Mutations observed during the first nine months of the pandemic (pre-VoC) (global pre-VoC distribution)
• Mutations observed during the Omicron era (global Omicron distribution)
• Mutations observed in chronic infections (chronic distribution)
• Mutations observed in zoonotic spillovers from humans to white-tailed deer (deer distribution)

In addition, the application will inform the user if the mutation pattern is:

• Consistent with molnupiravir use (via examination of the transition: transversion ratio which can change the underlying amino acids)
• A mutator lineage (contains a mutation in nsp14/exonuclease that is known to increase the mutation rate of the lineage)

This tool will be helpful for public health officials and researchers by providing quick insights into how different variants of SARS-CoV-2 are evolving. This can assist in assessing risks and shaping public health strategies.

Overall, understanding the recent history of a virus, especially a genetically unusual virus, can help us reduce future risks such as identifying and clearing chronic infections globally, reducing the risks of treatments that are mutagens, and minimizing risks of spillover events. This application attempts to rapidly aid the interpretation of newly-appearing unusual SARS-CoV-2 lineages by quantifying the similarity to signals left by past selection and mutation pressures.

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