Olmesartan Attenuates Single-Lung Ventilation Induced Lung Injury via Regulating Pulmonary Microbiota

Abstract

Single-lung ventilation (SLV) associated acute lung injury is similar to ischemia reperfusion (IR) injury which is usually occurred during lung surgery. Olmesartan (Olm), a novel angiotensin receptor blocker (ARB), has been reported to ameliorate organ IR injury. Several recent studies have shown that lung microbiota may be involved in pulmonary diseases, but the effect of pulmonary microbiota in SLV-induced lung injury has not been reported. This study aims to determine the mechanism of how Olm attenuates SLV induced lung injury. Our data showed that 7 days Olm treatment before modeling markedly alleviated SLV-induced lung injury by suppressing inflammation and reactive oxygen species. Bronchoalveolar lavage fluid samples from the injured side were collected for 16S rRNA gene-based sequencing analysis and 53 different bacteria at the genus and species levels were identified. Furthermore, the injured lung samples were collected for metabolomics analysis using liquid chromatography-mass spectrometry analyses to explore differential metabolites. The Kyoto Encyclopedia of Genes and Genomes (KEGG) was applied to analyze the correlation between differential metabolites and lung microbiota. A total of 38 pathways were identified according to differential metabolites and 275 relevant pathways were enriched via analyzing the microbial community, 24 pathways were both identified by analyzing either metabolites or microbiota, including pyrimidine metabolism, purine metabolism, aminoacyl-tRNA biosynthesis and ATP-binding cassette transporter. Besides classical blockage of the renin-angiotensin II system, Olm could also alleviate SLV-induced lung injury by rewiring the interaction between pulmonary microbiota and metabolites.

Keywords: angiotensin receptor blocker; lung injury; metabolite; olmesartan; pulmonary microbiota; single-lung ventilation.

FIGURE 1

Olmesartan (Olm) can attenuate single-lung ventilation (SLV) induced lung injury. Lung histopathological alterations in rats in the S (sham) group; AS (ARB + sham) group, in which the rats given 7 days Olm treatment before the sham surgery; I (injury) group, in which the rats underwent SLV for 1 h (right lung ventilation and left lung collapsed) and double lungs ventilation for 3 h; and the AI (ARB + injury) group (hematoxylin and eosin staining; original magnification, ×400) (n = 6) (A). The lung injury score of HE staining in rats in each group (B). Lung W/D ratio in rats in all groups (C). IL-6 in plasma as shown by ELISA (D). Quantitative analysis of IL-6, IL-1β and TNF-α in HUVECs culture supernatant in different groups by ELISA (E–G) (n = 3). Quantitative analysis of IL-6, IL-1β and TNF-α in A549 cell culture supernatant by ELISA (H–J) (n = 3). Data are presented as the mean ± standard error of the mean. NS: not significantly different. *p < 0.05, **p < 0.005, ***p < 0.001, ****p < 0.0001. SLV: single-lung ventilation; Olm: olmesartan; ARB: angiotensin receptor blocker; S: sham; AS: ARB + sham; I: injury; AI: ARB + injury; Ctr: control; IR: ischemia reperfusion; W/D: wet/dry ratio; IL-6: interleukin-6; IL-1β: interleukin-1β; TNF-α: tumor necrosis factor-α; ELISA: Enzyme-linked immunosorbent assay.

FIGURE 2

Olmesartan (Olm) inhibits SLV induced acute lung injury by inhibiting oxidative stress. MDA level in serum (A) The production of ROS was determined by flow cytometry (B–D) (n = 3). Representative images of HUVECs (B). Representative images of A549 cells (C). The average MFI of HUVECs (D). The average MFI of A549 cells (E). Data are presented as the mean ± standard error of the mean. NS: not significantly different. *p < 0.05, **p < 0.005, ***p < 0.001, ****p < 0.0001. SLV: single-lung ventilation; Olm: olmesartan; S: sham; AS: ARB + sham; I: injury; AI: ARB + injury; Ctr: control; IR: ischemia reperfusion; MDA: malondialdehyde; MFI: mean fluorescence intensity.

FIGURE 3

The composition and variance of the microbial communities in all four groups. PCA of bacterial patterns in the lung microbiota community between group S and AS (A), and between group I and group AI (B). The lung microbial community structures of the four groups (C). Boxplots based on the Unweighted Unifrac index were used to show the differences in the mean value of ranks between groups visually (D–H). The analysis was tested using analysis of similarities (Anosim) PCA: principal component analysis; S: Sham; AS: ARB + Sham; I: injury; AI: ARB + injury.

FIGURE 4

Identification and comparison of the differential bacteria in the lung microbiota from the four groups. The cladograms at both genus and species levels for target differential bacteria between groups S and AS (A,B), and between groups I and AI (C,D). Venn diagrams of shared Operational Taxonomic Units (OTUs) between group I and group AI, and between group S and group AS at both genus and species levels (E,F). Heat maps of both genus and species levels of differential lung microbiota in the different groups (G,H). S: sham; AS: ARB + sham; I: injury; AI: ARB + injury.

FIGURE 5

Screening and identification of the differential metabolites in the different groups. The score plot of the metabolic differences between group S and AS (A), and between group I and AI (B) by Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) in both positive and negative ion modes. The heat maps of different metabolites in each sample from the four different groups in both positive and negative ion mode (C). The ionic strengths of both the positive and negative modes of the different metabolites between group S and AS (D), and between group I and AI (E) by variable importance in projection (VIP) generated after OPLS-DA. The subsequent Venn diagram in the negative ion mode (F). The mean relative abundance of five known differential metabolites in the different groups (G). S: sham; AS: ARB + sham; I: injury; AI: ARB + injury.

FIGURE 6

The correlation between differential metabolites and lung microbiota. Correlation heat maps at both genus and species levels are mapped based on O2PLS analysis (A,B). The connections between the lung microbiota and metabolites at both levels are multiple (C,D). KEGG pathway annotation analysis of metabolism (E). Bubble chart from the top 20 pathways enriching with the most metabolites (F). The heat map of the top 20 most relevant pathways according to analysis of the microbial community (G). Venn diagrams of 24 shared pathways (H). Bubble chart from the 24 shared pathways (I). S: sham; AS: ARB + sham; I: injury; AI: ARB + injury; KEGG: Kyoto Encyclopedia of Genes and Genomes; O2PLS: Two-way Orthogonal Partial Least Squares.

FIGURE 7

Olm plays a positive role in the prevention of SLV-induced lung injury. In addition to its traditional blockage of the renin-angiotensin II system, Olm may play a positive role in the prevention of SLV-induced lung injury through the pulmonary microbiota and metabolites. SLV: single-lung ventilation; Olm: olmesartan.