Reconstructed Genome-Scale Metabolic Model Characterizes Adaptive Metabolic Flux Changes in Peripheral Blood Mononuclear Cells in Severe COVID-19 Patients

Abstract

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) poses a mortal threat to human health. The elucidation of the relationship between peripheral immune cells and the development of inflammation is essential for revealing the pathogenic mechanism of COVID-19 and developing related antiviral drugs. The immune cell metabolism-targeting therapies exhibit a desirable anti-inflammatory effect in some treatment cases. In this study, based on differentially expressed gene (DEG) analysis, a genome-scale metabolic model (GSMM) was reconstructed by integrating transcriptome data to characterize the adaptive metabolic changes in peripheral blood mononuclear cells (PBMCs) in severe COVID-19 patients. Differential flux analysis revealed that metabolic changes such as enhanced aerobic glycolysis, impaired oxidative phosphorylation, fluctuating biogenesis of lipids, vitamins (folate and retinol), and nucleotides played important roles in the inflammation adaptation of PBMCs. Moreover, the main metabolic enzymes such as the solute carrier (SLC) family 2 member 3 (SLC2A3) and fatty acid synthase (FASN), responsible for the reactions with large differential fluxes, were identified as potential therapeutic targets. Our results revealed the inflammation regulation potentials of partial metabolic reactions with differential fluxes and their metabolites. This study provides a reference for developing potential PBMC metabolism-targeting therapy strategies against COVID-19.

Keywords: COVID-19; PBMCs; inflammatory; metabolic model; metabolism; metabolism-directed therapy.

Figure 1

Gene ontology (GO) enrichment analysis of differentially expressed gene (DEGs). Enrichment of up-regulated DEGs related to immune response (A) or metabolism (B). The three layers of the circle indicate GO terms (outer circles), the number of enriched DEGs (middle circles), and the normalized enrichment scores (NES) indicating the enrichment degree of DEGs at both edges of the target gene sets (inner circles).

Figure 2

Workflow of the reconstruction of context-based genome-scale metabolic models (GSMMs). The original metabolic network was pruned and integrated with normalized transcriptome data to generate context-based metabolic network pair. Metabolic targets reflecting the differentially metabolic distribution of peripheral blood mononuclear cells (PBMCs) between severe patients and healthy humans were obtained by flux sampling.

Figure 3

Enrichment of reactions with differential flux. The enrichment of up-regulated (A) and down-regulated (B) metabolic reactions based on subsystem by hypergeometric test. The bigger the bubble size, the larger the number of reactions enriched in subsystems. The darker the color, the higher the reaction enrichment level in subsystems. p < 0.05 was considered as statistically significant. Arg, arginine; Pro, proline; Gly, glycine; Ser, serine; Thr, threonine; Cys, cysteine; Met, methionine; Ala, alanine; Asp, aspartate; Glu, glutamate; Lys, lysine; Tyr, tyrosine; Trp, tryptophan; Phe, phenylalanine; His, histidine; β-Ala, β-alanine; TCA, tricarboxylic acid cycle and glyoxylate/dicarboxylate metabolism; PPP, pentose phosphate pathway.

Figure 4

Visualization of flux differences in peripheral blood mononuclear cell (PBMC) metabolic reactions between severe coronavirus disease 2019 (COVID-19) patients and healthy humans. The main differential metabolic pathways were shown in different block colors. The arrow color and thickness indicated the degree of flux difference. PPP, pentose phosphate pathway; Nucleotide, nucleotide/purine/pyrimidine metabolism; MAS, malate-aspartate shuttle; TCA, tricarboxylic acid cycle; OXPHOS, oxidative phosphorylation; FA, folate metabolism; Retinol, retinol metabolism and transport; LacCer, lactosylceramide metabolism and transport; Gln, glutamine transport.