Multiscale statistical physics of the pan-viral interactome unravels the systemic nature of SARS-CoV-2 infections

Arsham Ghavasieh,Nina Verstraete,Manlio De Domenico,Sebastiano Bontorin,Oriol Artime

doi:10.1038/s42005-021-00582-8

Abstract

Protein–protein interaction networks have been used to investigate the influence of SARS-CoV-2 viral proteins on the function of human cells, laying out a deeper understanding of COVID–19 and providing ground for applications, such as drug repurposing. Characterizing molecular (dis)similarities between SARS-CoV-2 and other viral agents allows one to exploit existing information about the alteration of key biological processes due to known viruses for predicting the potential effects of this new virus. Here, we compare the novel coronavirus network against 92 known viruses, from the perspective of statistical physics and computational biology. We show that regulatory spreading patterns, physical features and enriched biological pathways in targeted proteins lead, overall, to meaningful clusters of viruses which, across scales, provide complementary perspectives to better characterize SARS-CoV-2 and its effects on humans. Our results indicate that the virus responsible for COVID–19 exhibits expected similarities, such as to Influenza A and Human Respiratory Syncytial viruses, and unexpected ones with different infection types and from distant viral families, like HIV1 and Human Herpes virus. Taken together, our findings indicate that COVID–19 is a systemic disease with potential effects on the function of multiple organs and human body sub-systems.

Highlights

Protein–protein interaction networks have been used to investigate the influence of Severe Acute Respiratory Syndrome (SARS)-CoV-2 viral proteins on the function of human cells, laying out a deeper understanding of COVID–19 and providing ground for applications, such as drug repurposing
Our findings indicate that SARS-CoV-2 groups with a distinct number of pathogens depending on the physical scale and on the biological information used, providing complementary perspective on its functional effects on organs and human sub-systems
To investigate whether SARS-CoV-2 would cluster with other viruses at a higher distance, we extended the clustering analysis to the human proteins located one node further of the proteins directly targeted by viruses

Summary

Introduction

Protein–protein interaction networks have been used to investigate the influence of SARS-CoV-2 viral proteins on the function of human cells, laying out a deeper understanding of COVID–19 and providing ground for applications, such as drug repurposing. Drugs usually used to treat different infection types, like AIDS caused by Human Immunodeficiency Virus (HIV), are under investigation to treat COVID-192–4, suggesting potentially unexplored parallel between the function of other viruses and SARS-CoV-2. Characterizing these (dis)similarities can result in a deeper understanding of the novel coronavirus and facilitate the search for reliable treatments. PPI network analysis has been used for characterizing the interactions between viral and human proteins in case of SARS-CoV-217–19, providing insights into the structure and function of the virus[20] and identifying, for instance, drug repurposing strategies[21,22,23,24]. The molecular analysis unraveled the potential reason behind the fact that SARS-CoV-2 infections lead to diverse outcomes for COVID-19, the disease being more severe and lethal preferentially for males and for older patients rather than children and young adults[25,26,27]

Methods

Results

Conclusion