Dynamic change of gene-to-gene regulatory networks in response to SARS-CoV-2 infection

TL;DR

This study investigates how SARS-CoV-2 infection alters gene regulatory networks in human cells over time, revealing a shift from interferon to cytokine signaling and capturing individual patient-specific network variations.

Contribution

The paper introduces a Bayesian network framework to analyze dynamic gene regulatory changes caused by SARS-CoV-2, including patient-specific network characterization.

Findings

Interferon signaling gradually shifts to cytokine signaling during infection.

A COVID-19 patient-specific gene network was successfully constructed.

The approach provides detailed insights into cellular responses at an individual level.

Abstract

The current pandemic of SARS-CoV-2 has caused extensive damage to society. The characterization of SARS-CoV-2 profiles has been addressed by researchers globally with the aim of resolving this disruptive crisis. This investigation process is indispensable for an understanding of how SARS-CoV-2 behaves in human host cells. However, little is known about the systematic molecular mechanisms involved in the effect of SARS-CoV-2 infection on human host cells. Here, we have presented gene-to-gene regulatory networks in response to SARS-CoV-2 using a Bayesian network. We examined the dynamic changes of the SARS-CoV-2-purturbated networks established by our proposed framework for gene network analysis, revealing that interferon signaling gradually switches to the subsequent inflammatory-cytokine signaling cascades. Furthermore, we have succeeded in capturing a COVID-19 patient-specific network in which transduction of these signalings is coincidently induced. This enabled us to explore local regulatory systems influenced by SARS-CoV-2 in host cells more precisely at an individual level. Our panel of network analyses has provided new insight into SARS-CoV-2 research from the perspective of cellular systems.