Integrating fecal metabolomics and intestinal microbiota to study the mechanism of cannabidiol in the treatment of idiopathic pulmonary fibrosis

Abstract

Introduction: Idiopathic pulmonary fibrosis is a chronic interstitial lung disease characterized by excessive deposition of extracellular matrix. Cannabidiol, a natural component extracted from plant cannabis, has been shown to have therapeutic effects on lung diseases, but its exact mechanism of action is unknown, hindering its therapeutic effectiveness. Methods: To establish a pulmonary fibrosis model, combined with UPLC-Q-TOF/MS metabolomics and 16S rDNA sequencing, to explore cannabidiol's mechanism in treating pulmonary fibrosis. The rats were randomly divided into the control group, pulmonary fibrosis model group, prednisone treatment group, and cannabidiol low, medium, and high dose groups. The expression levels of HYP, SOD, and MDA in lung tissue and the expression levels of TNF-α, IL-1β, and IL-6 in serum were detected. Intestinal microbiota was detected using UPLC-QTOF/MS analysis of metabolomic properties and 16S rDNA sequencing. Results: Pathological studies and biochemical indexes showed that cannabidiol treatment could significantly alleviate IPF symptoms, significantly reduce the levels of TNF-α, IL-1β, IL-6, MDA, and HYP, and increase the expression level of SOD (p < 0.05). CBD-H can regulate Lachnospiraceae_NK4A136_group, Pseudomonas, Clostridia_UCG-014, Collinsella, Prevotella, [Eubacterium]_coprostanoligenes_group, Fusobacterium, Ruminococcus, and Streptococcus, it can restore intestinal microbiota function and reverse fecal metabolism trend. It also plays the role of fibrosis through the metabolism of linoleic acid, glycerol, linolenic acid, and sphingolipid. Discussion: Cannabidiol reverses intestinal microbiota imbalance and attenuates pulmonary fibrosis in rats through anti-inflammatory, antioxidant, and anti-fibrotic effects. This study lays the foundation for future research on the pathological mechanisms of IPF and the development of new drug candidates.

Keywords: cannabidiol; intestinal microbiota; mechanism; metabolomics; pulmonary fibrosis.

FIGURE 1

Research design. After 28 days of administration, samples were obtained from lung tissue and arterial serum stool. See Method for experimental procedure and analysis.

FIGURE 2

Pathological changes in pulmonary tissue: (A) Representative images of H&E staining (×100) and score; (B) Representative images of Masson dyeing(×100). (C) Analysis of methods for determination of biochemical indexes in each group of rats. (a) Results of the organ index. (b) W/D results. (c) Results of serum TNF-α. (d) Tissue SOD results. (e) Results of the organization of MDA. (f) Results of serum IL-1β. (g) Results of the organization of HYP. (h) Results of serum IL-6. Values represent the mean ± SD. * P < 0.05 and ** P < 0.01 compared to the model group. #P < 0.05 and ##P < 0.01 compared to the sham group.

FIGURE 3

PLS-DA score diagram (A, B), corresponding model verification diagram (C, D), and S-plot score chart (E, F) of the control group and model group in positive and negative ion mode.

FIGURE 4

PCA score plot (A, B), PLS-DA score plot (C, D), and S-plot score chart (E, F) of the control group, model group, Positive group, and CBD group in positive and negative ion mode.

FIGURE 5

(A) Heat map of the differentially abundant metabolites in all groups. The degree of color saturation determines a difference in metabolite expression values between groups. Blue and red indicate the down-regulation and upregulation of MOD and CON expression, respectively. (B, C) Metabolic pathway analysis of crucial biomarkers.

FIGURE 6

(A) Venn diagram depicting the distribution of ASVs among different groups. (B) PCA score plot of the control, model, Positive, and CBD groups. (C) Intestinal microbial Alpha diversity of Chao1, Simpson, Shannon and Observed species in all groups. Statistical significance was calculated with ANOVA. *p < 0.05 and **p < 0.01 compared to the model control group. #p < 0.05 and ##p < 0.01 compared to the control group.

FIGURE 7

(A) Top 7 phyla species composition. (B) Heat map of the top7 phyla. (C) Top 30 genera species composition; and (D) heat map of the top 30 genera. Bright green indicates a lower abundance across species, whereas a bright brown indicates a higher abundance across species.

FIGURE 8

Analysis of the LEfSe. (A) Histogram of LDA value distribution. The longer the length, the higher the degree of influence. (B) Cladogram. The circles radiating from the inside out represent the classification level from boundary to genus. The diameter of small circles is proportional to the relative abundance. Yellow indicates the biomarkers with no significant difference, and the biomarkers with a significant difference were colored with the group. (C) (a–c) Analysis of bacterial flora with a significant difference in phylum level. (d–l) Analysis of bacterial community with a significant difference at the genus level. Values represent the mean ± SD. *p < 0.05 and **p < 0.01 compared to the model group. #p < 0.05 and ##p < 0.01 compared to the sham group.

FIGURE 9

(A) Pearson analysis of 7 potential biomarkers and 9 key difference gut microbes. (B) Correlation network maps of 7 potential biomarkers and 9 key differentiating gut microbes are shown.