Effects of the Probiotic, Lactobacillus delbrueckii subsp. bulgaricus, as a Substitute for Antibiotics on the Gastrointestinal Tract Microbiota and Metabolomics Profile of Female Growing-Finishing Pigs

Abstract

Lactobacillus delbrueckii subsp. bulgaricus (LDB) is an approved feed additive on the Chinese ‘Approved Feed Additives’ list. However, the possibility of LDB as an antibiotic replacement remains unclear. Particularly, the effect of LDB on microbiota and metabolites in the gastrointestinal tract (GIT) requires further explanation. This study aimed to identify the microbiota and metabolites present in fecal samples and investigate the relationship between the microbiota and metabolites to evaluate the potential of LDB as an antibiotic replacement in pig production. A total of 42 female growing-finishing pigs were randomly allocated into the antibiotic group (basal diet + 75 mg/kg aureomycin) and LDB (basal diet + 3.0 × 109 cfu/kg LDB) groups. Fecal samples were collected on days 0 and 30. Growth performance was recorded and assessed. 16S rRNA sequencing and liquid chromatography-mass spectrometry-based non-targeted metabolomics approaches were used to analyze the differences in microbiota and metabolites. Associations between the differences were calculated using Spearman correlations with the Benjamini−Hochberg adjustment. The LDB diet had no adverse effect on feed efficiency but slightly enhanced the average daily weight gain and average daily feed intake (p > 0.05). The diet supplemented with LDB increased Lactobacillus abundance and decreased that of Prevotellaceae_NK3B31_group spp. Dietary-supplemented LDB enhanced the concentrations of pyridoxine, tyramine, D-(+)-pyroglutamic acid, hypoxanthine, putrescine and 5-hydroxyindole-3-acetic acid and decreased the lithocholic acid concentration. The Lactobacillus networks (Lactobacillus, Peptococcus, Ruminococcaceae_UCG-004, Escherichia-Shigella, acetophenone, tyramine, putrescine, N-methylisopelletierine, N1-acetylspermine) and Prevotellaceae_NK3B31_group networks (Prevotellaceae_NK3B31_group, Treponema_2, monolaurin, penciclovir, N-(5-acetamidopentyl)acetamide, glycerol 3-phosphate) were the most important in the LDB effect on pig GIT health in our study. These findings indicate that LDB may regulate GIT function through the Lactobacillus and Prevotellaceae_NK3B31_group networks. However, our results were restrained to fecal samples of female growing-finishing pigs; gender, growth stages, breeds and other factors should be considered to comprehensively assess LDB as an antibiotic replacement in pig production.

Keywords: Lactobacillus delbrueckii subsp. bulgaricus; co-occurrence network; gastrointestinal tract; metabolites; microbiota.

Figure 1

The α-diversity and β-diversity in different groups. (A): the Shannon index in different groups, (B): the Simpson index in different groups, (C): the Jost index in different groups, (D): the β-diversity in different groups. Different lowercase letters indicate a significant difference.

Figure 2

The plot of species composition and differential microbiota. (A): the histogram of microbiota at the phylum level, (B): the histogram of microbiota at the genus level.

Figure 3

The differential microbiota and differential KEGG pathway. (A) the differential genera between G0T30 and G1T30, (B) the significantly different pathways between G0D30 and G1D30.

Figure 4

The result of BugBase in different groups. (A) The contribution rate in anaerobic. (B) The contribution rate in forms biofilms. (C) The contribution rate in potentially pathogenic. (D) The contribution rate in facultatively anaerobic organisms. (E) The legends of (A–D).

Figure 5

The principal component analysis (PCA) score plot in for different ion modes. (A) PCA score plot for the three groups analyzed in the positive ion mode. (B) PCA score plot for the three groups analyzed in the negative ion mode.

Figure 6

The orthogonal partial least squares discriminant analysis (OPLS-DA) analysis between G0D0 and G1D0. (A): The OPLS-DA score plot for the two groups analyzed in the positive ion mode; (B): The OPLS-DA score plot for the two groups analyzed in the negative ion mode; (C): The OPLS-DA permutation test plot for the two groups analyzed in the positive ion mode; (D): The OPLS-DA permutation test plot for the two groups analyzed in the negative ion mode.

Figure 7

The orthogonal partial least squares discriminant analysis (OPLS-DA) analysis between G0D30 and G1D30. (A): The OPLS-DA score plot for the two groups analyzed in the positive ion mode; (B): The OPLS-DA score plot for the two groups analyzed in the negative ion mode; (C): The OPLS-DA permutation test plot for the two groups analyzed in the positive ion mode; (D): The OPLS-DA permutation test plot for the two groups analyzed in the negative ion mode.

Figure 8

The heatmap of differential metabolites between G0D30 and G1D30.

Figure 9

The enriched KEGG in differential metabolites between G0D30 and G1D30.

Figure 10

The co-occurrence network between differential microbiota and differential metabolites. The circle shapes were the differential microbiota; the diamond shapes were the differential metabolites. The red lines mean the significant positive correlation; the blue lines mean the significant negative correlation. The size of lines means the correlation size; the size of shapes means the number of networks; the p was the positive ion model, and the N was the negative ion model.