Multi-strain probiotics attenuate carbohydrate-lipid metabolic dysregulation in type 2 diabetic rats via gut-liver axis modulation

Abstract

Type 2 diabetes mellitus (T2DM) is a pervasive chronic metabolic disorder characterized by dysregulation of carbohydrate, protein, and lipid metabolism. The objective of this study was to elucidate the impact of multi-strain probiotic supplementation on the metabolism of carbohydrates and lipids and the interplay between the gut and liver in a T2DM rat model. A total of 32 rats were randomly assigned to four experimental groups: a control group, a T2DM model group, a low-dose probiotics group, and a high-dose probiotics group. The impact of probiotic intervention on glycemic and lipid profiles was evaluated, with a specific emphasis on the high-dose cohort. The treatment with multi-strain probiotics, consisting of three Lacticaseibacillus species and one Bifidobacterium species, resulted in a significant improvement in blood glucose and lipid profiles in T2DM rats, with the highest dosage demonstrating the most pronounced effects. Probiotic administration modulated gut microbiota composition and diversity, enriching potentially beneficial bacterial species and altering gut metabolic modules and carbohydrate-active enzyme profiles. Multi-omics analyses indicated that alterations in fecal short-chain fatty acids and serum bile acids may serve as pivotal mediators for crosstalk between hepatic and gut transcriptomic pathways. This study offers novel insights into the role of probiotics in managing T2DM via the gut-liver axis, emphasizing the potential of probiotic therapy in modulating key metabolic pathways.IMPORTANCEType 2 diabetes mellitus (T2DM) is a chronic metabolic disease characterized by hyperglycemia, caused by defects in insulin secretion, insulin action, or both. For individuals diagnosed with T2DM, managing diabetes-related complications is often the most challenging aspect. Exogenous probiotics have the potential to serve as a promising therapeutic strategy to improve diabetes-related symptoms. We conducted a 64-day animal experiment to investigate the effects of probiotics on T2DM-related metabolic disorders and dyslipidemia by feeding four mixed probiotics to T2DM rats. The results showed that probiotics exerted beneficial effects on glucose- and lipid-related homeostasis indices in diabetic rats to some extent and modulated the gut microbiota to manage T2DM via the gut-liver axis.

Keywords: bile acids; gut microbiota; probiotics; short-chain fatty acids; transcriptome; type 2 diabetes.

Fig 1

Experimental design and probiotic effects on metabolic and physiological parameters in T2DM rats. (a) Schematic timeline of probiotic interventions and sample collection. (b) Body weight changes across experimental groups from adaptation phase completion to intervention endpoint. (c) Post-intervention group differences in body composition, exercise capacity, glycemic, and lipid indices. Data are presented as means ± SEM (n = 8). Significance levels: *P < 0.05, **P < 0.01, ***P < 0.001.

Fig 2

Gut microbiota diversity and species-level genome bin (SGB) features in T2DM rats. (a) Shannon diversity indices of gut microbiota in control (Con), model (Mod), low-dose probiotic (ProL), and high-dose probiotic (ProH) groups. (b) PCoA score plots of β-diversity, with Adonis test results shown in the lower left corner. (c) Heatmap of SGBs showing significant differential abundance between Mod and Con groups post-modeling (green: reduced, blue: enriched). (d and e) SGBs with significant differential abundance between the specified groups. Data are presented as means ± SEM (n = 8); *P < 0.05.

Fig 3

Changes in GMM and carbohydrate-active enzyme (CAZyme) profiles in the Con, Mod, ProL, and ProH groups during the trial. (a) Distribution of GMMs in T2DM rats. Different color blocks represent different phyla. (b) Responsive GMMs showing differences after probiotic intervention. (c) PCoA score plot showing changes in the predicted CAZyme profiles of the Con, Mod, ProL, and ProH groups. (d) Bubble plot showing the significant differences in CAZyme subfamilies with higher gene accumulation after probiotic intervention. PL, polysaccharide lyases; GH, glycoside hydrolases; CE, carbohydrate esterases; CBM, carbohydrate-binding modules. Data were presented as the means ± SEM (n = 8). *P < 0.05.

Fig 4

Changes in major bile acids in the serum of T2DM rats. Changes in primary bile acids (a), secondary bile acids (b), and the sum of primary bile acids, free bile acids, and total bile acids (c) in the Con, Mod, ProL, and ProH groups are shown. Data are presented as the means ± SEM (n = 8).

Fig 5

Effects of high-dose probiotics on gut transcriptome. (a) PCA score plot showed the clustering changes of intestinal genes in the Con, Mod, and ProH groups. Volcano plot analysis of significantly different genes in (b) Con versus Mod and (c) Mod versus ProH. Top 20 Kyoto Encyclopedia of Genes and Genomes (KEGG)-enriched pathway analyses (d). Gene set enrichment analysis (GSEA) of gut inflammation pathways (e and f) and mRNA level of related key genes (g). GSEA enrichment of gut pathogen infection pathways (h and i) and mRNA level of related key genes (j). GSEA enrichment of epidermal and tissue repair pathways (k and l) and mRNA level of related key genes (m). Data were presented as means ± SEM (n = 6). *P < 0.05; **P < 0.01.

Fig 6

Changes of rat liver transcriptome after high-dose probiotics intervention. (a) PCA score plot showed the clustering changes of liver genes in the Con, Mod, and ProH groups. (b) Heat map of the correlation between the three groups. (c) Top 30 KEGG-enriched pathway analyses. (d) Heat map analysis of key pathway gene expression. GSEA enrichment of glucose-related pathways (e and f) and mRNA level of related key genes (g). GSEA enrichment of lipid metabolism-related pathways (h and i) and mRNA level of related key genes (j). GSEA enrichment of cholesterol metabolism-related pathways (k and l) and mRNA level of related key genes (m). Data are presented as means ± SEM (n = 6). *P < 0.05; **P < 0.01.

Fig 7

Correlation network and proposed overall mechanism of probiotics on alleviating T2DM. (a and b) Correlation network based on Spearman rank correlation coefficients between SGBs, differential metabolites, glycemic and lipid indices in ProL and ProH. (c) The proposed overall mechanism of probiotics in alleviating T2DM. The size of the nodes is proportional to the average abundance. The color of the line represents the correlation relationship. Blue, significant negative (r < −0.5, P < 0.05); red, significant positive (r > 0.5, P < 0.05).