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. Author summary Metabolic flux analysis in disease biology is opening up new avenues for therapeutic interventions. Numerous diseases lead to disturbance in the metabolic homeostasis and it is becoming increasingly important to be able to quantify the difference in interaction under normal and diseased condition. While genome-scale metabolic models have been used to study those differences, there are limited methods to probe into the differences in flux between these two conditions. Our method of conducting a differential flux analysis can be leveraged to find which reactions are altered between the diseased and normal state. We applied this to study the altered reactions in the case of SARS-CoV-2 infection. We further corroborated our results with other multi-omics studies and found significant agreement.
【초록키워드】 SARS-CoV-2, metabolomics, pandemic, SARS-COV-2 infection, COVID-19 pandemic, Viral proteins, virus, metabolism, Phosphorylation, epithelial cells, cholesterol, transcriptomic data, disease, predict, protein-protein interaction, mechanism, homeostasis, proteomic, Fatty acid, Interaction, Analysis, lipid metabolism, lead, arachidonic acid, growth rates, host cells, growth rate, metabolic, Viral protein, Reactions, normal state, reaction, enzyme, Author, alteration, therapeutic interventions, disturbance, NHBE, deregulation, gene-expression, while, driving, fatty acid oxidation, flux, human cell, probe, Host, predicted, identify, affected, applied, macromolecular, imperative, conditions, reconstructed, bronchial epithelial cell, increasingly, Numerous, infected with SARS-CoV-2, with COVID-19, 【제목키워드】 deregulation, lung cell, reveal,