Decoding Asymptomatic COVID-19 Infection and Transmission.
Rui WangJiahui ChenYuta HozumiChangchuan YinGuo-Wei WeiPublished in: The journal of physical chemistry letters (2020)
One of the major challenges in controlling the coronavirus disease 2019 (COVID-19) outbreak is its asymptomatic transmission. The pathogenicity and virulence of asymptomatic COVID-19 remain mysterious. On the basis of the genotyping of 75775 SARS-CoV-2 genome isolates, we reveal that asymptomatic infection is linked to SARS-CoV-2 11083G>T mutation (i.e., L37F at nonstructure protein 6 (NSP6)). By analyzing the distribution of 11083G>T in various countries, we unveil that 11083G>T may correlate with the hypotoxicity of SARS-CoV-2. Moreover, we show a global decaying tendency of the 11083G>T mutation ratio indicating that 11083G>T hinders the SARS-CoV-2 transmission capacity. Artificial intelligence, sequence alignment, and network analysis are applied to show that NSP6 mutation L37F may have compromised the virus's ability to undermine the innate cellular defense against viral infection via autophagy regulation. This assessment is in good agreement with our genotyping of the SARS-CoV-2 evolution and transmission across various countries and regions over the past few months.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- artificial intelligence
- coronavirus disease
- genome wide
- network analysis
- machine learning
- high throughput
- big data
- cell death
- deep learning
- escherichia coli
- genetic diversity
- pseudomonas aeruginosa
- dna methylation
- oxidative stress
- gene expression
- cystic fibrosis
- protein protein
- small molecule
- high density