Metabolic pathways of lung inflammation revealed by high-resolution metabolomics (HRM) of H1N1 influenza virus infection in mice

Abstract

Influenza is a significant health concern worldwide. Viral infection induces local and systemic activation of the immune system causing attendant changes in metabolism. High-resolution metabolomics (HRM) uses advanced mass spectrometry and computational methods to measure thousands of metabolites inclusive of most metabolic pathways. We used HRM to identify metabolic pathways and clusters of association related to inflammatory cytokines in lungs of mice with H1N1 influenza virus infection. Infected mice showed progressive weight loss, decreased lung function, and severe lung inflammation with elevated cytokines [interleukin (IL)-1β, IL-6, IL-10, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ] and increased oxidative stress via cysteine oxidation. HRM showed prominent effects of influenza virus infection on tryptophan and other amino acids, and widespread effects on pathways including purines, pyrimidines, fatty acids, and glycerophospholipids. A metabolome-wide association study (MWAS) of the aforementioned inflammatory cytokines was used to determine the relationship of metabolic responses to inflammation during infection. This cytokine-MWAS (cMWAS) showed that metabolic associations consisted of distinct and shared clusters of 396 metabolites highly correlated with inflammatory cytokines. Strong negative associations of selected glycosphingolipid, linoleate, and tryptophan metabolites with IFN-γ contrasted strong positive associations of glycosphingolipid and bile acid metabolites with IL-1β, TNF-α, and IL-10. Anti-inflammatory cytokine IL-10 had strong positive associations with vitamin D, purine, and vitamin E metabolism. The detailed metabolic interactions with cytokines indicate that targeted metabolic interventions may be useful during life-threatening crises related to severe acute infection and inflammation.

Keywords: metabolic pathway analysis; mouse lung metabolome; pulmonary disease; targeted metabolic intervention.

Fig. 1.

Influenza virus infection of mice induces body weight loss and airway resistance. Groups of mice (n = 11 for mock control, n = 13 for H1N1) were intranasally infected with 2009 H1N1 influenza virus (0.8x LD₅₀). A: body weight loss (% of day 0) at day 10 postinfection [Cont (mean ± SE, day 0, 29.3 ± 0.7 g; day 10, 30.3 ± 0.6 g), H1N1 (mean ± SE, day 0, 29.9 ± 0.8 g; day 10, 23.6 ± 0.7 g)]. B: percentages (%) of PenH values (enhanced pause) at day 10 postinfection as measured by plethysmography of live animals. The data are presented as means ± SE (n = 10–13) and statistical significance was performed by Student’s t-test in GraphPad Prism. *P < 0.001 vs. control group.

Fig. 2.

Pulmonary histopathology around the airways, blood vessels, and interstitial spaces. A: lung histology sections stained with hematoxylin and eosin (H and E) at day 10 postinfection. Scale bars indicate 100 µm. Arrows point to airways, blood vessels, and interstitial spaces. B–D: inflammation scoring. Inflammation response on H and E-stained tissue section was scored in airways, blood vessels, and interstitial spaces on a scale of 0 to 5 according to diagnostic criteria. Results (n = 10–13 per group) are presented as means ± SE, and statistical significance was performed by one-way ANOVA with Tukey’s multiple comparisons posttest in GraphPad Prism; *P < 0.001 vs. control group.

Fig. 3.

Influenza virus infection induces proinflammatory cytokines and cellular oxidation. Amounts of cytokines extracted from lung tissue of mice at day 10 postinfection (A–E) were determined by using each corresponding cytokine ELISA kit. Results are presented as means ± SE (n = 10–13), and statistical significance was performed by Student’s t-test in GraphPad Prism; *P < 0.001 vs. control group. Levels of cellular oxidation status of lung tissues were determined by total thiol amounts (F) and redox potential of Cys/CySS (G, H). *P < 0.05 vs. control group (n = 7–8 per group).

Fig. 4.

High-resolution metabolomics (HRM)-identified lung metabolic characteristics associated with H1N1 influenza virus infection. Lungs from mice infected with H1N1 influenza virus (green, n = 8) for 10 days or treated with saline control (red, n = 7) were analyzed for HRM. Mass spectral data of 4552 metabolic features (metabolic ions, m/z) obtained from HRM were further examined for partial least-squares discriminant analysis (PLS-DA) to compare between two groups (A). Of 4,552 features, 549 (P < 0.05, magenta + blue symbols) and 49 features (q < 0.05, blue circles) that are significantly different between two groups analyzed by limma and Benjamini and Hochberg false discovery rate (FDR) are shown, respectively (B). C: two-way hierarchical cluster analysis of the 49 metabolic features.

Fig. 5.

Mummichog-identified metabolic pathways significantly affected by influenza virus infection. The 549 metabolic features were examined for pathway enrichment analysis using mummichog software. The 25 metabolic pathways significantly affected by influenza virus infection are shown by −log P (P < 0.05) and numbers of matched features in a pathway are indicated in overlap/total size.

Fig. 6.

Kyoto Encyclopedia of Genes and Genomes (KEGG). The 549 features were examined for metabolic pathway analysis using KEGG software (http://www.genome.jp/kegg-bin/show_pathway?map01100). Matched features are labeled by black dots and names. Three major pathways affected by influenza infection (amino acid metabolism, nucleotide metabolism, and lipid metabolism) are highlighted.

Fig. 7.

Influenza virus infection-altered lung levels of metabolites associated with Trp metabolism. Influenza virus infection changed lung levels of metabolites of Trp (A, B), kynurenine (C, D), and indole (E–G) compared with mock control. Mass to charge (m/z) and retention time (RT, s) for respective metabolite are indicated (top), and lung amount of each metabolite calculated from peak intensity is shown in whisker plot comparing between 7 mock control and 8 infected mice.

Fig. 8.

Influenza virus infection-altered metabolites associated with purine/pyrimidine metabolism. Influenza virus infection-affected amounts of lung metabolites regulating purine (A–E) and pyrimidine (F, G) pathways are evaluated to compare with mock control as described in Fig. 7.

Fig. 9.

Influenza virus infection-increased metabolites associated with sphingolipid metabolism. The 4 metabolites including sphingosine (A), sphinganine (B), serine (C), and phosphoethanolamine (PE, D) involved in sphingolipid metabolism are significantly elevated by influenza virus infection compared with mock control, and amounts in lung are calculated as described in Fig. 7.

Fig. 10.

Cytokine-metabolome-wide association study (cMWAS). Relationship between lung metabolites affected by influenza virus infection and five pro- and anti-inflammatory cytokines including interferon (IFN)-γ, IL-6, IL-1β, TNF-α, and IL-10 were examined using partial least squares regression and network function (see materials and methods). Of 396 metabolic features, key metabolites with 6 clusters interacted with cytokines are shown (magenta circle) as follows: A, metabolites correlated with IFN-γ only; B, metabolites correlated with IFN-γ and IL-6; C, metabolites correlated with IL-6, IL-1β, and TNF-α; D, metabolites correlated with IL-1β, TNF-α, and IL-10; E, metabolites correlated with IL-10; F, metabolites correlated with all cytokines. Significant pathways of 6 metabolic clusters analyzed by mummichog pathway enrichment software are also indicated in a box (green). The details of metabolites associated with pathways and cMWAS network are provided in Supplement 3 and Supplement 4, respectively, available with the online version of this article.