The metabolic fingerprints of HCV and HBV infections studied by Nuclear Magnetic Resonance Spectroscopy View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Gaia Meoni, Serena Lorini, Monica Monti, Francesco Madia, Giampaolo Corti, Claudio Luchinat, Anna Linda Zignego, Leonardo Tenori, Laura Gragnani

ABSTRACT

Few studies are available on metabolic changes in liver injuries and this is the first metabolomic study evaluating a group of HCV-positive patients, before and after viral eradication via DAA IFN-free regimens, using 1H-NMR to characterize and compare their serum fingerprints to naïve HBV-patients and healthy donors. The investigation clearly shows differences in the metabolomic profile of HCV patients before and after effective DAA treatment. Significant changes in metabolites levels in patients undergoing therapy suggest alterations in several metabolic pathways. It has been shown that 1H-NMR fingerprinting approach is an optimal technique in predicting the specific infection and the healthy status of studied subjects (Monte-Carlo cross validated accuracies: 86% in the HCV vs HBV model, 98.7% in the HCV vs HC model). Metabolite data collected support the hypothesis that the HCV virus induces glycolysis over oxidative phosphorylation in a similar manner to the Warburg effect in cancer, moreover our results have demonstrated a different action of the two viruses on cellular metabolism, corroborating the hypothesis that the metabolic perturbation on patients could be attributed to a direct role in viral infection. This metabolomic study has revealed some alteration in metabolites for the first time (2-oxoglutarate and 3-hydroxybutrate) concerning the HCV-infection model that could explain several extrahepatic manifestations associated with such an infection. More... »

PAGES

4128

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-019-40028-4

DOI

http://dx.doi.org/10.1038/s41598-019-40028-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112675272

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30858406


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