Genome Scale-Differential Flux Analysis reveals deregulation of lung cell metabolism on SARS-CoV-2 infection

Abstract

The COVID-19 pandemic is posing an unprecedented threat to the whole world. In this regard, it is absolutely imperative to understand the mechanism of metabolic reprogramming of host human cells by SARS-CoV-2. A better understanding of the metabolic alterations would aid in design of better therapeutics to deal with COVID-19 pandemic. We developed an integrated genome-scale metabolic model of normal human bronchial epithelial cells (NHBE) infected with SARS-CoV-2 using gene-expression and macromolecular make-up of the virus. The reconstructed model predicts growth rates of the virus in high agreement with the experimental measured values. Furthermore, we report a method for conducting genome-scale differential flux analysis (GS-DFA) in context-specific metabolic models. We apply the method to the context-specific model and identify severely affected metabolic modules predominantly comprising of lipid metabolism. We conduct an integrated analysis of the flux-altered reactions, host-virus protein-protein interaction network and phospho-proteomics data to understand the mechanism of flux alteration in host cells. We show that several enzymes driving the altered reactions inferred by our method to be directly interacting with viral proteins and also undergoing differential phosphorylation under diseased state. In case of SARS-CoV-2 infection, lipid metabolism particularly fatty acid oxidation, cholesterol biosynthesis and beta-oxidation cycle along with arachidonic acid metabolism are predicted to be most affected which confirms with clinical metabolomics studies. GS-DFA can be applied to existing repertoire of high-throughput proteomic or transcriptomic data in diseased condition to understand metabolic deregulation at the level of flux.

Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models

“…We used the Human-GEM model version 1.6.0 [ 13 ] as a scaffold model for the extraction process. 1 Before reconstructing infected models, the model was augmented with the virus biomass objective function (VBOF) as described by Nanda and Ghosh [ 11 ]. Different model extraction methods (MEMs) were used in the reconstruction, namely iMAT [ 14 ], INIT, tINIT [ 16 ], GIMME [ 17 ].…”

“…One of the main problems is that different MEMs produce variable, sometimes contradictory results [ 10 ]. Different datasets gathered either from COVID-19 patients or cell lines have been applied to the analysis of COVID-19 metabolic signatures by genome-scale metabolic modelling and automatically generated context-specific GEMs [ 11 , 12 ]. However, only selected MEM approaches have been applied in the present studies.…”

“…However, only selected MEM approaches have been applied in the present studies. Namely, Nanda and Ghosh [ 11 ] applied iMAT and Cheng et al [ 12 ] applied tINIT extraction method to variable scope of COVID-19 datasets.…”

“…[ 38 ]). Such analyses have also been conducted in the domain of the analysis of COVID-19 metabolic signatures [ 11 , 12 ]. However, present studies have only applied a single MEM to the analysed datasets.…”

The Role of Cytochrome P450 Enzymes in COVID-19 Pathogenesis and Therapy

“…AA-derived lipid autacoids, including prostaglandins (PGs), thromboxanes, and leukotrienes, are critical mediators in inflammation, and tissue homeostasis (Hoxha, 2020;Ripon et al, 2021). An integrated genomic-scale metabolic model of normal human bronchial epithelial cells (NHBE) infected with SARS-CoV-2, shows that AA metabolism was one of the most affected lipid metabolic pathways in SARS-CoV-2 infection (Nanda and Ghosh, 2021). One in vitro experiment revealed that AA metabolism was markedly perturbed by human coronavirus 229E (HCoV-229E) infection, and the exogenous supplement of AA in HCoV-229E-infected cells significantly suppressed HCoV-229E virus replication (Yan et al, 2019).…”