Glycyrrhizic Acid for COVID-19: Findings of Targeting Pivotal Inflammatory Pathways Triggered by SARS-CoV-2

Abstract

Background: Coronavirus disease 2019 (COVID-19) is now a worldwide public health crisis. The causative pathogen is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Novel therapeutic agents are desperately needed. Because of the frequent mutations in the virus and its ability to cause cytokine storms, targeting the viral proteins has some drawbacks. Targeting cellular factors or pivotal inflammatory pathways triggered by SARS-CoV-2 may produce a broader range of therapies. Glycyrrhizic acid (GA) might be beneficial against SARS-CoV-2 because of its anti-inflammatory and antiviral characteristics and possible ability to regulate crucial host factors. However, the mechanism underlying how GA regulates host factors remains to be determined. Methods: In our report, we conducted a bioinformatics analysis to identify possible GA targets, biological functions, protein-protein interactions, transcription-factor-gene interactions, transcription-factor-miRNA coregulatory networks, and the signaling pathways of GA against COVID-19. Results: Protein-protein interactions and network analysis showed that ICAM1, MMP9, TLR2, and SOCS3 had higher degree values, which may be key targets of GA for COVID-19. GO analysis indicated that the response to reactive oxygen species was significantly enriched. Pathway enrichment analysis showed that the IL-17, IL-6, TNF-α, IFN signals, complement system, and growth factor receptor signaling are the main pathways. The interactions of TF genes and miRNA with common targets and the activity of TFs were also recognized. Conclusions: GA may inhibit COVID-19 through its anti-oxidant, anti-viral, and anti-inflammatory effects, and its ability to activate the immune system, and targeted therapy for those pathways is a predominant strategy to inhibit the cytokine storms triggered by SARS-CoV-2 infection.

Keywords: SARS-CoV-2; coronavirus disease 2019; glycyrrhizic acid; inflammatory pathways; network pharmacology.

FIGURE 1

(A) Venn diagram based on Draw Venn Diagram Tool, showing the overlapping relationship of GA potential targets from five datasets. (B) Venn diagram based on FunRich Tool, showing the constituent ratio of GA potential targets from five datasets.

FIGURE 2

(A) KEGG pathways enrichment analysis of GA targets (p values < 0.05) (B) Wiki pathways enrichment analysis of GA targets (p values < 0.05).

FIGURE 3

(A) Fourteen shared targets were found common from the 117 DEGs of COVID-19 and 1,125 targets of GA. (B) Protein-protein interactions network and network topology analysis. Deeper colors represent higher degree values.

FIGURE 4

Topological result of GA targets on COVID-19. (A) The ordinate indicates the degree, and the abscissa indicates the degree of betweenness (B) Network topology analysis parameter of ICAM1. (C) Network topology analysis information of MMP9.

FIGURE 5

Pathway enrichment analysis from KEGG, WikiPathways, Reactome and BioCarta databases. GO terms enrichment analysis including biological process, molecular function and cellular component aspects. All the results along with COVID-19 related gene sets were identified through the p value.

FIGURE 6

Network for transcription factor gene interplay with shared targets.

FIGURE 7

The network presents the transcription factor-miRNA coregulatory network. The nodes in yellow are the shared targets, a purple node displays miRNA and other nodes in red color represent transcription factor -genes.

FIGURE 8

Pathway activity ranking.