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. 2018 Aug 17;8(51):29368-29377.
doi: 10.1039/c8ra05640d. eCollection 2018 Aug 14.

High-resolution lipidomics reveals dysregulation of lipid metabolism in respiratory syncytial virus pneumonia mice

Affiliations

High-resolution lipidomics reveals dysregulation of lipid metabolism in respiratory syncytial virus pneumonia mice

Jinjun Shan et al. RSC Adv. .

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. At present, the molecular-level interactions between RSV and its host and the underlying mechanisms of RSV-induced inflammation are poorly understood. Herein, we describe an untargeted high-resolution lipidomics platform based on UHPLC-Q-Exactive-MS to assess the lipid alterations of lung tissues and plasma from a mouse model of RSV pneumonia. Untargeted lipidomics using LC-MS with multivariate analysis was applied to describe the lipidomic profiling of the lung tissues and plasma in RSV pneumonia mice. Lipid identification was conducted via an in silico MS/MS LipidBlast library using the MS-DIAL software. We observed distinct compartmental lipid signatures in the mice lung tissues and plasma and significant lipid profile changes between the systematic and localized host responses to RSV. A total of 87 and 68 differential lipids were captured in the mice lung tissue and plasma, respectively, including phospholipids, sphingolipids, acylcarnitine, and fatty acids. Some of these lipids belong to pulmonary surfactants, illustrating that RSV pneumonia-induced aberrations of the pulmonary surfactant system may play a vital role in the etiology of respiratory inflammation. Our findings reveal that the host responses to RSV and various lipid metabolic pathways were linked to disease pathology. Furthermore, our findings could provide mechanistic insights into RSV pneumonia.

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Conflict of interest statement

There are no conflicts to declare.

Figures

Fig. 1
Fig. 1. RSV-induced body weight loss (A), lung index (B), pulmonary histopathological damage (C) and inflammation cytokines changes (D) in the mice. Values are expressed as mean ± SD. (n = 6), **p < 0.01 vs. normal control mice.
Fig. 2
Fig. 2. PCA score plot based on the lipid profiling of RSV pneumonia mice and the normal control mice. (A) lung ESI(+), (B) lung ESI(−), (C) plasma ESI(+), and (D) plasma ESI(−).
Fig. 3
Fig. 3. Heatmaps illustrating the lipid profiles of the RSV pneumonia mice versus the normal control mice: (A) lung ESI(+), (B) lung ESI(−), (C) plasma ESI(+), and (D) plasma ESI(−). The top 25 lipid features were ranked using the t test, distance was measured using the Pearson correlation, and clustering was determined using the Ward algorithm. C1–C8 represent the normal control mice and R1–R8 represent the RSV pneumonia mice.
Fig. 4
Fig. 4. Volcano plots of lipids of the RSV pneumonia mice versus the normal control mice: (A) lung ESI(+), (B) lung ESI(−), (C) plasma ESI(+), and (D) plasma ESI(−). The x-axis represents the fold change (threshold of 1.5 or 0.67) and the y-axis the adjusted t-test (threshold of 0.05). Both values are median-normalized and log transformed.
Fig. 5
Fig. 5. Metabolic dysregulations and their mapping against the pathway. Each node reflects a significantly altered cluster of metabolites. Enrichment p-values are given by the Kolmogorov–Smirnov-test. Node sizes represent the total number of metabolites in each cluster set. Red-color represents the increased metabolites and the blue-color shows the decreased compounds in the RSV mice compared to the normal control mice. Purple-color nodes have both increased and decreased metabolites.

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