Differential abundance of lipids and metabolites related to SARS-CoV-2 infection and susceptibility

Reinforcing the Evidence of Mitochondrial Dysfunction in Long COVID Patients Using a Multiplatform Mass Spectrometry-Based Metabolomics Approach

Despite the recent and increasing knowledge surrounding COVID-19 infection, the underlying mechanisms of the persistence of symptoms for a long time after the acute infection are still not completely understood. Here, a multiplatform mass spectrometry-based approach was used for metabolomic and lipidomic profiling of human plasma samples from Long COVID patients (n = 40) to reveal mitochondrial dysfunction when compared with individuals fully recovered from acute mild COVID-19 (n = 40). Untargeted metabolomic analysis using CE-ESI(+/-)-TOF-MS and GC-Q-MS was performed. Additionally, a lipidomic analysis using LC-ESI(+/-)-QTOF-MS based on an in-house library revealed 447 lipid species identified with a high confidence annotation level. The integration of complementary analytical platforms has allowed a comprehensive metabolic and lipidomic characterization of plasma alterations in Long COVID disease that found 46 relevant metabolites which allowed to discriminate between Long COVID and fully recovered patients. We report specific metabolites altered in Long COVID, mainly related to a decrease in the amino acid metabolism and ceramide plasma levels and an increase in the tricarboxylic acid (TCA) cycle, reinforcing the evidence of an impaired mitochondrial function. The most relevant alterations shown in this study will help to better understand the insights of Long COVID syndrome by providing a deeper knowledge of the metabolomic basis of the pathology.

Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC-MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, L-aspartic, L-glutamic, L-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.

Lipidomics reveals the significance and mechanism of the cellular ceramide metabolism for rotavirus replication

As one of the most important causative agents of severe gastroenteritis in children, piglets, and other young animals, species A rotaviruses have adversely impacted both human health and the global swine industry. Vaccines against rotaviruses (RVs) are insufficiently effective, and no specific treatment is available. To understand the relationships between porcine RV (PoRV) infection and enterocytes in terms of the cellular lipid metabolism, we performed an untargeted liquid chromatography mass spectrometry (LC-MS) lipidomics analysis of PoRV-infected IPEC-J2 cells. Herein, a total of 451 lipids (263 upregulated lipids and 188 downregulated lipids), spanning sphingolipid, glycerolipid, and glycerophospholipids, were significantly altered compared with the mock-infected group. Interestingly, almost all the ceramides among these lipids were upregulated during PoRV infection. LC-MS analysis was used to validated the lipidomics data and demonstrated that PoRV replication increased the levels of long-chain ceramides (C16-ceramide, C18-ceramide, and C24-ceramide) in cells. Furthermore, we found that these long-chain ceramides markedly inhibited PoRV infection and that their antiviral actions were exerted in the replication stage of PoRV infection. Moreover, downregulation of endogenous ceramides with the ceramide metabolic inhibitors enhanced PoRV propagation. Increasing the levels of ceramides by the addition of C6-ceramide strikingly suppressed the replication of diverse RV strains. We further found that the treatment with an apoptotic inhibitor could reverse the antiviral activity of ceramide against PoRV replication, demonstrating that ceramide restricted RV infection by inducing apoptosis. Altogether, this study revealed that ceramides played an antiviral role against RV infection, providing potential approaches for the development of antiviral therapies.IMPORTANCERotaviruses (RVs) are among the most important zoonosis viruses, which mainly infected enterocytes of the intestinal epithelium causing diarrhea in children and the young of many mammalian and avian species. Lipids play an essential role in viral infection. A comprehensive understanding of the interaction between RV and lipid metabolism in the enterocytes will be helpful to control RV infection. Here, we mapped changes in enterocyte lipids following porcine RV (PoRV) infection using an untargeted lipidomics approach. We found that PoRV infection altered the metabolism of various lipid species, especially ceramides (derivatives of the sphingosine). We further demonstrated that PoRV infection increased the accumulation of ceramides and that ceramides exerted antiviral effects on RV replication by inducing apoptosis. Our findings fill a gap in understanding the alterations of lipid metabolism in RV-infected enterocytes and highlight the antiviral effects of ceramides on RV infection, suggesting potential approaches to control RV infection.

The metabolic footprint of Vero E6 cells highlights the key metabolic routes associated with SARS-CoV-2 infection and response to drug combinations

SARS-CoV-2 burdens healthcare systems worldwide, yet specific drug-based treatments are still unavailable. Understanding the effects of SARS-CoV-2 on host molecular pathways is critical for providing full descriptions and optimizing therapeutic targets. The present study used Nuclear Magnetic Resonance-based metabolic footprinting to characterize the secreted cellular metabolite levels (exometabolomes) of Vero E6 cells in response to SARS-CoV-2 infection and to two candidate drugs (Remdesivir, RDV, and Azithromycin, AZI), either alone or in combination. SARS-CoV-2 infection appears to force VE6 cells to have increased glucose concentrations from extra-cellular medium and altered energetic metabolism. RDV and AZI, either alone or in combination, can modify the glycolic-gluconeogenesis pathway in the host cell, thus impairing the mitochondrial oxidative damage caused by the SARS-CoV-2 in the primary phase. RDV treatment appears to be associated with a metabolic shift toward the TCA cycle. Our findings reveal a metabolic reprogramming produced by studied pharmacological treatments that protects host cells against virus-induced metabolic damage, with an emphasis on the glycolytic-gluconeogenetic pathway. These findings may help researchers better understand the relevant biological mechanisms involved in viral infection, as well as the creation of mechanistic hypotheses for such candidate drugs, thereby opening up new possibilities for SARS-CoV-2 pharmacological therapy.