bioRxivBioinformaticsPreprint
The SARS-CoV-2 infection in Thailand: analysis of spike variants complemented by protein structure insights
Sirawit IttisoponpisaniD, Shalip Yahangkiakan, Michael J.E. SternbergiD, Alessia DavidiD
Abstract
Thailand was the first country outside China to officially report COVID-19 cases. Despite the strict regulations for international arrivals, up until February 2021, Thailand had been hit by two major outbreaks. With a large number of SARS-CoV-2 sequences collected from patients, the effects of many genetic variations, especially those unique to Thai strains, are yet to be elucidated. In this study, we analysed 439,197 sequences of the SARS-CoV-2 spike protein collected from NCBI and GISAID databases. 595 sequences were from Thailand and contained 52 variants, of which 6 had not been observed outside Thailand (p.T51N, p.P57T, p.I68R, p.S205T, p.K278T, p.G832C). These variants were not predicted to be of concern. We demonstrate that the p.D614G, although already present during the first Thai outbreak, became the prevalent strain during the second outbreak, similarly to what was described in other countries. Moreover, we show that the most common variants detected in Thailand (p.A829T, p.S459F and p.S939F) do not appear to cause any major structural change to the spike trimer or the spike-ACE2 interaction. Among the variants identified in Thailand was p.N501T. This variant, which involves an asparagine critical for spike-ACE2 binding, was not predicted to increase SARS-CoV-2 binding, thus in contrast to the variant of global concern p.N501Y. In conclusion, novel variants identified in Thailand are unlikely to increase the fitness of SARS-CoV-2. The insights obtained from this study could aid SARS-CoV-2 variants prioritisations and help molecular biologists and virologists working on strain surveillance.
- organism: human
- assay: whole-genome sequencing
- research focus: variant effect prediction
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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