Host metabolic reprogramming in response to SARS-CoV-2 infection: A systems biology approach

Abstract

Understanding the pathogenesis of SARS-CoV-2 is essential for developing effective treatment strategies. Viruses hijack the host metabolism to redirect the resources for their replication and survival. The influence of SARS-CoV-2 on host metabolism is yet to be fully understood. In this study, we analyzed the transcriptomic data obtained from different human respiratory cell lines and patient samples (nasopharyngeal swab, peripheral blood mononuclear cells, lung biopsy, bronchoalveolar lavage fluid) to understand metabolic alterations in response to SARS-CoV-2 infection. We explored the expression pattern of metabolic genes in the comprehensive genome-scale network model of human metabolism, Recon3D, to extract key metabolic genes, pathways, and reporter metabolites under each SARS-CoV-2-infected condition. A SARS-CoV-2 core metabolic interactome was constructed for network-based drug repurposing. Our analysis revealed the host-dependent dysregulation of glycolysis, mitochondrial metabolism, amino acid metabolism, nucleotide metabolism, glutathione metabolism, polyamine synthesis, and lipid metabolism. We observed different pro- and antiviral metabolic changes and generated hypotheses on how the host metabolism can be targeted for reducing viral titers and immunomodulation. These findings warrant further exploration with more samples and in vitro studies to test predictions.

Keywords: COVID-19; Host-pathogen interaction; Polyamine metabolism; Redox homeostasis; Transcriptomics; Warburg effect.

Fig. 1

The workflow to study the host metabolic response and drug targeting.

Fig. 2

Overlap of differentially expressed metabolic genes in different cell lines and patient samples with SARS-CoV-2 infection.

Fig. 3

KEGG pathways associated with upregulated and downregulated metabolic genes in different cell lines with SARS-CoV-2 infection. A549 and A549-ACE2 is shown for MOI of 0.2 (A549_0.2, ACE2_0.2) and 2 (A549_2, ACE2_2). Up and down represent the upregulated and downregulated metabolic DEGs.

Fig. 4

Downregulated and upregulated reporter metabolites in at least two different cell lines with SARS-CoV-2 infection. A549 and ACE2 is shown for MOI of 0.2 (A549_0.2, ACE2_0.2) and 2 (A549_2, ACE2_2). Up and down represent the upregulated and downregulated metabolic DEGs.

Fig. 5

Feedback loop regulation of (A) Polyamine metabolism and (B) Glycolysis controls viral replication.

Fig. 6

KEGG pathways associated with upregulated and downregulated metabolic genes in different patient samples with SARS-CoV-2 infection. Up and down represent the upregulated and downregulated metabolic DEGs.

Fig. 7

Downregulated and upregulated reporter metabolites in different patient samples with SARS-CoV-2 infection. Up and down represent the upregulated and downregulated metabolic DEGs.

Fig. 8

Protein-protein interaction of viral proteins M^Pro (left) and Orf8 (right) with the host metabolic proteins. Viral proteins are shown in red color. Metabolic DEGs interacting directly and indirectly with viral proteins are shown in orange and yellow colors, respectively. Non-metabolic genes are shown in the grey color.