Combined analysis of cecal microbiota and metabolomics reveals the intervention mechanism of Dayuan Yin in acute lung injury

Abstract

The ancient Chinese medicinal formula, Dayuan Yin (DYY), has a long history of use in treating respiratory ailments and is shown to be effective in treating acute infectious diseases. This study aims to explore how DYY may impact intestinal flora and metabolites induced by acute lung injury (ALI). ALI rats were induced with lipopolysaccharide (LPS) to serve as models for assessing the anti-ALI efficacy of DYY through multiple lung injury indices. Changes in intestinal microflora were assessed via 16SrRNA gene sequencing, while cecum contents were analyzed using non-targeted metabonomics. Differential metabolites were identified through data analysis, and correlations between metabolites, microbiota, and inflammatory markers were examined using Pearson's correlation analysis. DYY demonstrated a significant improvement in LPS-induced lung injury and altered the composition of intestinal microorganisms, and especially reduced the potential harmful bacteria and enriched the beneficial bacteria. At the gate level, DYY exhibited a significant impact on the abundance of Bacteroidota and Firmicutes in ALI rats, as well as on the regulation of genera such as Ruminococcus, Lactobacillus, and Romboutsia. Additionally, cecal metabonomics analysis revealed that DYY effectively modulated the abnormal expression of 12 key metabolic biomarkers in ALI rats, thereby promoting intestinal homeostasis through pathways such as purine metabolism. Furthermore, Pearson's analysis indicated a strong correlation between the dysregulation of intestinal microbiota, differential metabolites, and inflammation. These findings preliminarily confirm that ALI is closely related to cecal microbial and metabolic disorders, and DYY can play a protective role by regulating this imbalance, which provides a new understanding of the multi-system linkage mechanism of DYY improving ALI.

Keywords: Dayuan Yin; Pearson analysis; acute lung injury; inflammatory factors; intestinal flora; metabonomics.

FIGURE 1

Trends in change in the Con, LPS, DXM, and DYY groups during the establishment of the ALI model (n = 6). ∗p < 0.05, ∗∗p < 0.01 compared to the Con group; #p < 0.05, ##p < 0.01 compared to the LPS group. (A–D) Changes in W/D ratio, IL-6, IL-1β, and TNF-α; (E) H&E staining results of lung tissue (200×). The black, red, and green arrows in the LPS group indicated an increase in the thickening of the alveolar septum, an infiltration of inflammatory cells, and a smaller alveolar cavity, respectively.

FIGURE 2

(A) Venn diagram depicting the distribution of OTUs among different groups. (B–E) Alpha diversity of intestinal flora and analysis of beta diversity (F, G). (H) LDA effect size analysis of the major biomarker taxa. (I) Cladogram obtained from LEfSe analysis. ∗p < 0.05 compared to the LPS group and #p < 0.05 compared to the Con group.

FIGURE 3

(A) Species composition abundance map at the phylum and the genus levels (B). (C) Abundance clustering heat map of the phylum and the genera (D). In the heat map, the closer to blue, the lower the abundance, and the closer to red, the higher the abundance. The length of the bar of the LDA represents the influence of species abundance on the different effects. (E) KEGG pathways in Con and LPS groups. (F) LPS and DYY groups were enriched. The green mark on the left side was marked as the pathway significantly changed by both groups.

FIGURE 4

(A) PCA score scatter diagram, (B–C) PLS-DA score scatter diagram, and (D, E) OPLS-DA score scatter diagram of Con , LPS , and DYY groups in the total ion mode.

FIGURE 5

(A) Volcano plot map in the total ion mode of Con and LPS, (B) LPS, and DYY. (C) Twelve metabolites with significantly differential abundance.

FIGURE 6

Pearson identified correlations between metabolites and the microbiome and inflammation indicators. (A) Phylum-level correlations between metabolites and microflora. (B) Genus-level correlations between metabolites and microflora. (C) Correlation between microflora and indicators of inflammation. (D) Correlation between metabolites and inflammation indicators.*p < 0.05 and **p < 0.01. Red indicates a positive correlation, blue indicates a negative correlation, bright color indicates a high correlation, and light color indicates a low correlation.

FIGURE 7

Co-expression networks show relationships between differentially expressed genera and metabolites under DYY treatment. Triangles represent genera, and rectangles represent metabolites. Red lines show positive correlations, and blue lines show negative correlations. Thicker lines indicate stronger correlations.