2018
DOI: 10.1039/c8ra05640d
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High-resolution lipidomics reveals dysregulation of lipid metabolism in respiratory syncytial virus pneumonia mice

Abstract: Respiratory syncytial virus (RSV) is a leading viral pathogen responsible for lower respiratory tract infections, particularly in children under five years worldwide, often resulting in hospitalization.

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Cited by 40 publications
(46 citation statements)
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“…In addition, we have also identified plasmalogen lipids. These lipids can prevent lung disease due to their antioxidant effects, as the lung is a direct target of reactive oxidant species [24]. In this study, we found that the concentration of plasmalogen lipids decreased.…”
Section: Discussionsupporting
confidence: 48%
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“…In addition, we have also identified plasmalogen lipids. These lipids can prevent lung disease due to their antioxidant effects, as the lung is a direct target of reactive oxidant species [24]. In this study, we found that the concentration of plasmalogen lipids decreased.…”
Section: Discussionsupporting
confidence: 48%
“…It is possible to find new targets for the treatment of diseases if a deeper understanding of the relationship between lipid composition and lung disease is acquired [23]. Lipidomics is a rapidly evolving tool that explores the potential lipid biomarkers in diseases and the biological functions of lipids in various life activities by comparing the changes in lipid metabolism networks under different physiological conditions [24].…”
Section: Discussionmentioning
confidence: 99%
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“…This, and the PICRUSt data in our study, suggest that increased sphingosine metabolism following RSV infection may be due to a change in gut microbiota metabolism. Sphingolipid metabolite abundance has also been observed to be altered in the lung metabolome after RSV infection in mice (Shan et al, 2018) and in the airways of children with RSV-bronchiolitis (Stewart et al, 2017). It is interesting to speculate whether altered gut metabolites promote host resistance to the infection or tolerance to the immunopathology (Wang et al, 2016;Trompette et al, 2018).…”
Section: Discussionmentioning
confidence: 99%
“…Data raw files (.wiff) were converted into .abf format in order to perform retention time correction, peak alignment, and identification. The latter was achieved by comparison of experimental spectra with those in the LipidBlast library [24], using both accurate mass and MS/MS fragmentation (Table S1) data (total identification score >70%). Prevalent adducts were previously investigated in our experimental conditions, and thus, the identification was restricted only on them.…”
Section: Lipidomic Data Processingmentioning
confidence: 99%