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

Melis et al., Scientific Reports, doi:10.1038/s41598-024-57726-3
Apr 2024  
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In Vitro study showing that remdesivir and azithromycin, either alone or in combination, can modify the glycolic-gluconeogenesis pathway in host cells, inhibiting the mitochondrial oxidative damage caused by SARS-CoV-2. Authors use Nuclear Magnetic Resonance (NMR)-based metabolic footprinting to explore the metabolic impact of SARS-CoV-2 infection on Vero E6 cells, showing that SARS-CoV-2 significantly alters the metabolic pathways, enhancing glucose uptake and modifying the glycolic-gluconeogenesis pathway, thereby leading to altered energy metabolism and oxidative stress.
Gérard, Wu, Zhou show significantly increased risk of acute kidney injury with remdesivir.
Melis et al., 4 Apr 2024, peer-reviewed, 4 authors. Contact: anedda@portocontericerche.it.
In Vitro studies are an important part of preclinical research, however results may be very different in vivo.
This PaperRemdesivirAll
The metabolic footprint of Vero E6 cells highlights the key metabolic routes associated with SARS-CoV-2 infection and response to drug combinations
Riccardo Melis, Angela Braca, Daniela Pagnozzi, Roberto Anedda
Scientific Reports, doi:10.1038/s41598-024-57726-3
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 glycolicgluconeogenesis 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. Keywords SARS-CoV-2
Statistical data analysis NMR data were imported into the Statistical Analysis module included in the web-based software Metabo-Analyst version 5.0 68 for multivariate and univariate data analysis 69 . For multivariate purposes, 1 H NMR data were preliminary constant sum normalized, logarithm-transformed and Pareto scaled. Unsupervised principal component analysis (PCA) was initially carried out to identify outlier samples and possibly observe group clustering. Additionally, the related PCA scores dendrogram was also generated, by using the PCA2Tree tool, to provide a quantitative measure of the significance of similarity/difference between the observed clusters. PCA2Tree computes the dendrograms using Mahalanobis distances and reports p-values for the null hypothesis at all internal branches, where p < 0.05 indicates a statistically significant difference 70 . A more robust supervised multivariate approach, employing partial least square discriminant analysis (PLS-DA), was also carried out to better characterize sample grouping (1. infected vs. uninfected and 2. infected and treated vs. infected VE6 cells exometabolomes). For each PLS-DA model, cross-validation (CV) was also performed, by using the parameter Q2 as indicative of the predictive ability of the model. Good predictions will have high Q 2 computed values 71 . To get proper information on the metabolites responsible for observed sample clustering, the importance feature analysis based on univariate..
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