Fig. 7: L. reuteri decreases capric acid and alleviates dyslipidemia via GALR1 signaling in darkness rats.

a Multiomics correlation networks of all variables for the faecal microbiome (green), faecal metabolome (purple) and serum metabolome (yellow) within control rats (left), darkness rats (middle), and DL.reuteri rats (right). Vertices indicate omics variables, and lines indicate a significant Spearman’s rank correlation coefficient at |ρ | > 0.8 and P < 0.05. Red connections indicate positive correlation, and blue connections show negative correlations. b Procrustes analyses of faecal microbiome versus faecal metabolome (above) and of faecal metabolome versus serum metabolome (below). c Spearman correlation network between target faecal metabolites (circle) and target serum metabolites (square) (P < 0.05). The color of each metabolite was determined by their correlations with target genera (P < 0.05): Pink, Lactobacillus; Blue, Clostridium sensu stricto 1; Green, Ruminococcaceae UCG-010; Yellow, Family XIII AD3011 group. d Spearman rank correlations between target serum metabolites and mRNA expression of Galr1, Nr1d1, and Srebf1 in rat liver (*P < 0.05, **P < 0.01, ***P < 0.001). e Timeline depicting the treatments of darkness, L. reuteri, and capric acid in different groups of the capric acid-treated rat model (n = 6 per group). f Body weight changes. g Left to right, serum concentrations of TG, CHOL, HDL-C, LDL-C, and NEFA detected by ELISA. h Representative Oil Red O staining of liver. Scale bar: 50 μm and 25 μm. i mRNA abundances of Galr1, Nr1d1, and Srebf1 in rat liver. β-Actin was used as a loading control for qPCR analyses. Statistical analysis (g, i) was performed with one-way ANOVA followed by Newman–Keuls multiple comparison test. Data present means ± SEM. *P < 0.05, **P < 0.01.