Integrated metabolomics, network pharmacology and biological verification to reveal the mechanisms of Nauclea officinalis treatment of LPS-induced acute lung injury

Abstract

Background: Acute lung injury (ALI) is a severe inflammatory disease, underscoring the urgent need for novel treatments. Nauclea officinalis Pierre ex Pitard (Danmu in Chinese, DM) is effective in treating inflammatory respiratory diseases. However, there is still no evidence of its protective effect against ALI.

Methods: Metabolomics was applied to identify the potential biomarkers and pathways in ALI treated with DM. Further, network pharmacology was introduced to predict the key targets of DM against ALI. Then, the potential pathways and key targets were further verified by immunohistochemistry and western blot assays.

Results: DM significantly improved lung histopathological characteristics and inflammatory response in LPS-induced ALI. Metabolomics analysis showed that 16 and 19 differential metabolites were identified in plasma and lung tissue, respectively, and most of these metabolites tended to recover after DM treatment. Network pharmacology analysis revealed that the PI3K/Akt pathway may be the main signaling pathway of DM against ALI. The integrated analysis of metabolomics and network pharmacology identified 10 key genes. These genes are closely related to inflammatory response and cell apoptosis of lipopolysaccharide (LPS)-induced ALI in mice. Furthermore, immunohistochemistry and western blot verified that DM could regulate inflammatory response and cell apoptosis by affecting the PI3K/Akt pathway, and expression changes in Bax and Bcl-2 were also triggered.

Conclusion: This study first integrated metabolomics, network pharmacology and biological verification to investigate the potential mechanism of DM in treating ALI, which is related to the regulation of inflammatory response and cell apoptosis. And the integrated analysis can provide new strategies and ideas for the study of traditional Chinese medicines in the treatment of ALI.

Keywords: Acute lung injury; Mechanisms; Metabolomics; Nauclea officinalis; Network pharmacology.

Fig. 1

Effect of DM on histopathology and cytokines. A H&E staining of lung tissue in mice. Original magnification, × 20, × 100, × 200.The capillaries in the alveolar walls were congested (red arrow), and massive inflammatory cells infiltrated (blue arrow). B The levels of TNF-ɑ in BALF. C The levels of IL-6 in BALF. D The levels of IL-1β in BALF

Fig. 2

PCA analysis of plasma and lung tissue in mice. A Plasma positive ion mode PCA diagram; B plasma negative ion mode PCA diagram; C lung tissue positive ion mode PCA diagram; D lung tissue negative ion mode PCA diagram

Fig. 3

OPLS-DA and permutation test analysis of mouse plasma and lung tissue. A Plasma positive ion mode OPLS-DA diagram; B plasma positive ion mode permutation test diagram; C plasma negative ion mode OPLS-DA diagram; D plasma negative ion mode permutation test diagram; E positive lung tissue ion mode OPLS-DA diagram; F lung tissue positive ion mode permutation test diagram; G lung tissue negative ion mode OPLS-DA diagram; H lung tissue negative ion mode permutation test diagram

Fig. 4

Volcano gram analysis of differential metabolites in plasma, A Con vs. LPS; B LPS vs. LPS + DM-M; C LPS vs. LPS + DM-H; D Venn diagram of differential metabolites in plasma samples. Volcano gram analysis of differential metabolites lung tissue, E Con vs. LPS; F LPS vs. LPS + DM-M; G LPS vs. LPS + DM-H; H Venn diagram of differential metabolites in lung tissue samples

Fig. 5

The expression of differential metabolites in mouse plasma (A) and lung tissue (B)

Fig. 6

The ionic strength of potential metabolites in mouse plasma (A–D). Data are displayed as Mean ± SD. # P < 0.05, ## P < 0.01 vs. control group, *P < 0.05, **P < 0.01 vs. LPS group

Fig. 7

The ionic strength of potential metabolites in mouse lung tissues (A–D). Data are displayed as Mean ± SD. #P < 0.05, ##P < 0.01 vs. control group, *P < 0.05, **P < 0.01 vs. LPS group

Fig. 8

Enrichment analysis of potential metabolic pathways in mouse plasma (A) and lung tissue (B)

Fig. 9

Network pharmacological analysis of DM in the treatment of ALI. A Shared targets of DM and ALI. B Core genes for DM treatment of ALI. C “DM- active components -target-ALI” network, orange: DM, blue: active components (31), yellow: ALI, green: shared target (401)

Fig. 10

Enrichment analysis of GO and KEGG pathways. A biological process B molecular function C cell composition D KEGG enrichment analysis

Fig. 11

“Potential metabolite-reaction-enzyme-gene” interaction network. Red hexagons represent metabolites, gray diamonds represent reactions, green rectangles represent enzymes, and purple circles represent genes

Fig. 12

Cell proliferation and apoptosis detection of LPS-induced ALI. A Immunohistochemistry staining of Bcl-2 and Bax proteins (magnification 200 ×). The expression of Bcl-2 (B) and Bax (C) in lung tissue. D Immunohistochemistry staining of p-PI3K and p-Akt proteins (magnification 200 ×). The expression of p-PI3K (E) and p-Akt (F) in lung tissue. Data are displayed as Mean ± SD. #P < 0.05, ##P < 0.01 vs. control group, *P < 0.05, **P < 0.01 vs. LPS group

Fig. 13

The proliferation and apoptosis of ALI cells were detected by western blotting. A Images of Bcl-2 and Bax proteins. The expression of Bcl-2 (B) and Bax (C) in lung tissue. D Images of p-PI3K and p-Akt proteins. The expression of p-PI3K E and p-Akt F in lung tissue. Data are displayed as the mean ± SD. #P < 0.05, ##P < 0.01 vs. control group and *P < 0.05, **P < 0.01 vs. LPS group