Inhibiting the CB1 receptor in CIH-induced animal model alleviates colon injury

Abstract

Obstructive sleep apnea (OSA) can lead to intestinal injury, endotoxemia, and disturbance of intestinal flora. Additionally, as a crucial component of the endocannabinoid system, some studies have demonstrated that cannabinoid 1 (CB1) receptors are closely linked to the multiple organ dysfunction triggered by OSA. However, the role of the CB1 receptor in alleviating OSA-induced colon injury remains unclear. Here, through the construction of the OSA classic model, we found that the colon tissue of chronic intermittent hypoxia (CIH)-induced mice exhibited an overexpression of the CB1 receptor. The results of hematoxylin-eosin staining and transmission electron microscopy revealed that inhibition of the CB1 receptor could decrease the gap between the mucosa and muscularis mucosae, alleviate mitochondrial swelling, reduce microvilli shedding, and promote the recovery of tight junctions of CIH-induced mice. Furthermore, CB1 receptor inhibition reduced the levels of metabolic endotoxemia and inflammatory responses, exhibiting significant protective effects on the colon injury caused by CIH. At the molecular level, through western blotting and real-time polymerase chain reaction techniques, we found that inhibiting the CB1 receptor can significantly increase the expression of ZO-1 and Occludin proteins, which are closely related to the maintenance of intestinal mucosal barrier function. Through 16S rRNA high-throughput sequencing and short-chain fatty acid (SCFA) determination, we found that inhibition of the CB1 receptor increased the diversity of the microbial flora and controlled the makeup of intestinal flora. Moreover, butyric acid concentration and the amount of SCFA-producing bacteria, such as Ruminococcaceae and Lachnospiraceae, were both markedly elevated by CB1 receptor inhibition. The results of the spearman correlation study indicated that Lachnospiraceae showed a positive association with both ZO-1 and Occludin but was negatively correlated with the colon CB1 receptor, IL-1β, and TNF-α. According to this study, we found that inhibiting CB1 receptor can improve CIH-induced colon injury by regulating gut microbiota, reducing mucosal damage and promoting tight junction recovery. KEY POINTS: •CIH leads to overexpression of CB1 receptor in colon tissue. •CIH causes intestinal flora disorder, intestinal mucosal damage, and disruption of tight junctions. •Inhibition of CB1 receptor can alleviate the colon injury caused by CIH through regulating the gut microbiota, reducing mucosal injury, and promoting tight junction recovery.

Keywords: CB1 receptor; Chronic intermittent hypoxia; Colon injury; Intestinal flora; Metabolic endotoxemia; Obstructive sleep apnea.

Fig. 1

CIH promotes overexpression of CB1 receptor in colon tissue. a-b The expression of CB1 receptor in colon tissues was detected by immunofluorescence. c RT-PCR was used to detect the mRNA expression level of CB1 receptor in colon tissues. d-e The expression level of CB1 receptor protein in colon tissues was detected by western blot. *P < 0.05, **P < 0.01,***P < 0.001, compared with the Control group; ^#P < 0.05, ^##P < 0.01,^###P < 0.001, compared with the CIH group

Fig. 2

Inhibiting CB1 receptor mitigated the pathological damage of colon induced by CIH. Representative image of HE staining (100 µm) and transmission electron microscopy (20 µm) of colon tissue. TEM, transmission electron microscopy

Fig. 3

Effect of inhibiting CB1 receptor on intestinal microbiome of OSA mice. a OTU Wayne chart; b-c Chao1 and Observed_species indicate the richness of the community; d-e Shannon and Simpson indicate the diversity of the community; and f Pielou_e indicate the evenness of the community. g PCoA principal component analysis based on weighted_unfirc distance. h 3D. *P < 0.05, **P < 0.01

Fig. 4

Inhibition of CB1 receptor alters the gut microbiome composition induced by CIH. a Intestinal microbial composition of top 10 phylum level. b The intestinal microbial composition of the top 10 families. c Intestinal microbial composition of top 10 species level. Difference analysis of flora at d–f phylum level, g–j family level, and k–m species level. *P < 0.05, **P < 0.01, compared to the Control group; ^#P < 0.05, ^##P < 0.01, compared to the CIH group

Fig. 5

a LEFse branch diagram. b LEFse histogram analysis of marker species with significant differences between groups (LDA>2). c Random forest analysis: The heat map shows the abundance distribution of these species in each group. From top to bottom, the importance of species to the model decreases in order

Fig. 6

Correlation analysis of intestinal microbiota and metabolic pathways. a The relative abundance of metabolic pathways. b Top 10 components of KEGG metabolic pathways with significant differences among all groups

Fig. 7

CB1 receptor inhibition may ameliorate SCFA metabolic disorders induced by CIH. a Partial least squares discrimination analysis (PLS-DA). b Score plot of the OPLS-DA model. c–j Concentrations of SCFAs in each group. c Total SCFAs. d Acetic acid. e Butyric acid. f Isovaleric acid. g Valeric acid. h Caproic acid. i Propionic acid. j Isobutyric acid. *P < 0.05, **P < 0.01, compared to the Control group; ^#P < 0.05, ^##P < 0.01, compared to the CIH group

Fig. 8

Inhibition of CB1 receptor promotes the production of intestinal tight junction proteins under CIH conditions and reduces intestinal inflammation and metabolic endotoxemia. a–c Western blot to detect the expression of ZO-1 and Occludin in colon. d, e RT-PCR to detect the mRNA expression of ZO-1 and Occludin in colon. f–h RT-PCR to detect IL-1β (f), TNF-α (g), and IL-10 mRNA (h). i Serum LPS level detected by ELISA (eu, ELISA units). *P < 0.05, **P < 0.01, compared to the Control group; ^#P < 0.05, ^##P < 0.01, compared to the CIH group

Fig. 9

The relationship between intestinal flora and physiological indexes. a Heat map analysis showed the correlation between intestinal flora and physiological indexes. b The relationship between SCFA and cytokines. The depth of the color indicates the strength of the correlation: red indicates a positive correlation, blue indicates a negative correlation, and white indicates no correlation. *P < 0.05, **P < 0.01