Lactobacillus plantarum HNU082-derived improvements in the intestinal microbiome prevent the development of hyperlipidaemia

Abstract

Restricted by research techniques, the probiotic-derived changes in the microbiome and microbial metabolites correlated with the potential prevention of hyperlipidaemia have remained undiscovered. In the present research, a metagenomic approach was applied to describe Lactobacillus plantarum HNU082 consumption-derived changes in the intestinal microbiome and their correlation with the occurrence and development of hyperlipidaemia. Principal coordinate analysis based on UniFrac distances indicated that the intestinal microbiota was profoundly altered in the hyperlipidaemia group, and probiotic consumption regulated the bias in the intestinal microbial structure in hyperlipidaemia. Bifidobacterium, Lactobacillus, Akkermansia and Faecalibacterium were significantly increased in the probiotic group, and the genera Clostridium, Natranaerovirga and Odoribacter were significantly increased in the hyperlipidaemia group. Further analysis based on metabolic pathways revealed that pyruvate metabolism, glycerolipid metabolism, propanoate metabolism, and fatty acid biosynthesis were enriched in the probiotic and control groups. In contrast, the pathways of secondary bile acid and lipopolysaccharide biosynthesis were enriched in the hyperlipidaemia group. Finally, we constructed a network to better explain the potential mechanism of hyperlipidaemia prevention. The present basic research will promote our understanding of the probiotic action mechanism in hyperlipidaemia therapy and provide new insight into the design and application of probiotic-containing functional foods.

Consumption of Goats’ Milk Protects Mice From Carbon Tetrachloride-Induced Acute Hepatic Injury and Improves the Associated Gut Microbiota Imbalance

“…Extraction and analysis of metagenomic DNA from the microbiome present in colon samples was performed using our previously described methods ( 19 ). The V3–V4 regions of the 16S rRNA gene were amplified using specific primers (forward primer 338F and reverse primer 806R; Table 1 ) ( 20 ).…”

“…The 250 bp paired-end reads were generated through sequencing; the bioinformatics analysis we used for these sequence data has been fully described previously ( 8 , 19 , 20 ). Briefly, Qiime pipeline (v1.7.0) was used to filter out low-quality tags.…”

“…Alpha diversity was determined as a measure of the diversity of the microbial community within a sample; Shannon index was calculated by Qiime to measure the alpha diversity of the gut microbiota ( 8 , 19 , 20 ). Beta diversity is a comparative analysis of microbial community composition and complexity between sample pairs ( 8 , 19 , 20 ); Weighted UniFrac distances between microbial communities from the colon samples were calculated by Qiime to study beta diversity metrics ( 8 , 19 , 20 ).…”

“…Alpha diversity was determined as a measure of the diversity of the microbial community within a sample; Shannon index was calculated by Qiime to measure the alpha diversity of the gut microbiota ( 8 , 19 , 20 ). Beta diversity is a comparative analysis of microbial community composition and complexity between sample pairs ( 8 , 19 , 20 ); Weighted UniFrac distances between microbial communities from the colon samples were calculated by Qiime to study beta diversity metrics ( 8 , 19 , 20 ). Based on the UniFrac distances between gut microbial communities, the samples located in the first principal coordinates analysis at different time points were used to fit the curve of changes in microbial community structure and to dynamically analyze differences between the treatment groups in their microbial community structure; this allowed us to identify groups with community structures closest to the untreated control group and thus reveal the treatments providing the best prevention and mitigation effects in the CCl 4 -induced mice.…”

Residual feed intake divergence during the preweaning period is associated with unique hindgut microbiome and metabolome profiles in neonatal Holstein heifer calves

“…The fact that hindgut in M-eff heifers at birth had lower abundance of pathogenic bacteria such as Odoribacter, Cyanobacteria, Ruminiclostridium 9, Prevotellaceae_ UCG-001, and Eubacterium nodatum typically associated with several diseases, is surprising. For example, Odoribacter and Cyanobacteria are positively associated with stress in mice and pigs [30,31]. Ruminiclostridium 9 increased in obese rodents [32].…”