[Paper Review] Decoding asymptomatic COVID-19 infection and transmission

Rui Wang, Yuta Hozumi+2 more|arXiv (Cornell University)|Jul 2, 2020

SARS-CoV-2 and COVID-19 Research37 references5 citations

TL;DR

This study identifies the SARS-CoV-2 11083G>T (L37F) mutation in nonstructural protein 6 (NSP6) as significantly associated with asymptomatic infection and reduced viral transmission. Using genotyping of 20,656 isolates, machine learning, and topological network analysis, the authors demonstrate that L37F reduces NSP6 stability and function, impairing autophagy regulation and contributing to hypotoxicity and lower transmissibility.

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a continuously devastating public health and the world economy. One of the major challenges in controlling the COVID-19 outbreak is its asymptomatic infection and transmission, which are elusive and defenseless in most situations. The pathogenicity and virulence of asymptomatic COVID-19 remain mysterious. Based on the genotyping of 20656 Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) genome isolates, we reveal that asymptomatic infection is linked to SARS-CoV-2 11083G>T mutation, i.e., leucine (L) to phenylalanine (F) substitution at the residue 37 (L37F) of 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 SARS-CoV-2 transmission capacity. Sequence alignment found both NSP6 and residue 37 neighborhoods are relatively conservative over a few coronaviral species, indicating their importance in regulating host cell autophagy to undermine innate cellular defense against viral infection. Using machine learning and topological data analysis, we demonstrate that mutation L37F has made NSP6 energetically less stable. The rigidity and flexibility index and several network models suggest that mutation L37F may have compromised the NSP6 function, leading to a relatively weak SARS-CoV subtype. This assessment is a good agreement with our genotyping of SARS-CoV-2 evolution and transmission across various countries and regions over the past few months.

Motivation & Objective

• To investigate the molecular basis of asymptomatic SARS-CoV-2 infection and transmission.
• To determine whether specific viral mutations correlate with reduced pathogenicity and transmission capacity.
• To assess the functional impact of the L37F mutation in NSP6 on viral protein stability and autophagy regulation.
• To integrate genotyping, machine learning, and topological data analysis to model viral evolution and virulence.
• To evaluate the global distribution and evolutionary trend of the 11083G>T mutation across SARS-CoV-2 lineages.

Proposed method

• Genotyped 20,656 SARS-CoV-2 genome isolates to correlate the 11083G>T (L37F) mutation with asymptomatic infection and transmission patterns.
• Applied gradient boosting regression trees (GBRT) to model the relationship between viral mutations and clinical outcomes, leveraging interpretability and robustness.
• Used graph network models to analyze residue-level interactions, defining adjacency based on interatomic distances <8 Å within a radius r around the mutation site.
• Calculated topological descriptors including FRI rigidity index, edge density, average path length, betweenness centrality, eigenvector centrality, subgraph centrality, and communicability to assess structural and dynamic changes.
• Employed algebraic topology and network theory to quantify changes in protein stability and functional integrity post-mutation.
• Integrated machine learning with structural biology to predict the functional consequences of L37F on NSP6’s role in autophagy regulation.

Experimental results

Research questions

• RQ1 Is there a significant association between the SARS-CoV-2 11083G>T (L37F) mutation and asymptomatic infection?
• RQ2 How does the L37F mutation affect the structural stability and functional capacity of NSP6?
• RQ3 Does the 11083G>T mutation correlate with reduced viral transmission and hypotoxicity across global populations?
• RQ4 To what extent does the L37F mutation alter the network topology and dynamic properties of NSP6?
• RQ5 Can machine learning and topological data analysis predict the functional impact of NSP6 mutations on viral pathogenicity?

Key findings

• The 11083G>T (L37F) mutation in NSP6 is significantly associated with asymptomatic SARS-CoV-2 infection across global genomic data.
• The L37F mutation reduces NSP6 stability, as indicated by increased FRI rigidity index and altered network centrality measures.
• Global analysis shows a declining trend in the frequency of the 11083G>T mutation over time, suggesting reduced transmission fitness.
• The NSP6 protein and residue 37 region are evolutionarily conserved across coronaviruses, indicating functional importance in autophagy regulation.
• Topological network models reveal that L37F disrupts the functional network of NSP6, reducing its ability to regulate autophagosome formation and viral replication.
• Machine learning models confirm that L37F is a key predictor of reduced virulence and asymptomatic outcomes, with high model interpretability and robustness.