Multi-omics analysis reveals interactions between host and microbes in Bama miniature pigs during weaning

Abstract

Introduction: There are complex interactions between host and gut microbes during weaning, many of the mechanisms are not yet fully understood. Previous research mainly focuses on commercial pigs, whereas limited information has been known about the host and gut microbe interactions in miniature pigs.

Methods: To address the issue in Bama miniature piglets that were weaned 30 days after birth, we collected samples on days 25 and 36 for metabolomics, transcriptomics, and microgenomics analysis.

Results and discussion: The average daily weight gain of piglets during weaning was only 58.1% and 40.6% of that during 0-25 days and 36-60 days. Metabolomic results identified 61 significantly different metabolites (SDMs), of which, the most significantly increased and decreased SDMs after weaning were ectoine and taurocholate, respectively, indicating the occurrence of inflammation. Metagenomic analysis identified 30 significantly different microbes before and after weaning. Bacteria related to decreasing intestinal inflammation, such as Megasphaera, Alistipes and Bifidobacterium, were enriched before weaning. While bacteria related to infection such as Chlamydia, Clostridium, Clostridioides, and Blautia were enriched after weaning. The carbohydrate enzymes CBM91, CBM13, GH51_1, and GH94 increase after weaning, which may contribute to the digestion of complex plant fibers. Furthermore, we found the composition of antibiotic resistance genes (ARGs) changed during weaning. Transcriptomic analysis identified 147 significantly differentially expressed genes (DEGs). The upregulated genes after weaning were enriched in immune response categories, whereas downregulated genes were enriched in protein degradation. Combining multi-omics data, we identified significant positive correlations between gene MZB1, genera Alistipes and metabolite stachydrine, which involve anti-inflammatory functions. The reduced abundance of bacteria Dialister after weaning had strong correlations with the decreased 2-AGPE metabolite and the downregulated expression of RHBDF1 gene. Altogether, the multi-omics study reflects dietary changes and gut inflammation during weaning, highlighting complex interactions between gut microbes, host genes and metabolites."

Keywords: gene expression; gut microbes; interactions; metabolites; multi-omics; weaning.

Figure 1

Metabolomic analysis of Bama miniature pigs before and after weaning. (A) Metabolite superclass. (B) OPLS-DA score plots of serum metabolites before and after weaning. (C) KEGG enrichment results of all DEMs (D) Correlation heatmap between modules and different stages (metabolites). (E) Abundance of hub metabolites before and after weaning.

Figure 2

Analysis of the composition of the gut microbiota. (A) Venn diagram of the number of species before and after weaning. (B) Relative gut microbiota abundance at the phylum, genus, species level before and after weaning. (C) Alpha diversity of gut microbiota in piglets before and after weaning. (D) OPLS-DA plots of fecal metagenomics before and after weaning. (E) LEfSe analysis before and after weaning.

Figure 3

Functional analysis of the gut microbiota. (A) Heatmap of relative abundance clustering of carbohydrate enzymes with significant differences before and after weaning. (B) Diversity of ARGs before and after weaning. (C) Abundence of ARGs before and after weaning. (D) Distribution of antibiotic classes. (E) The antibiotic resistance mechanisms of the ARGs. (F) The abundance of pathway before and after weaning. (G) The abundance of pathway before and after weaning.

Figure 4

Transcriptomics analysis of Bama miniature pigs before and after weaning. (A) PCA plot of gene expression. (B) Venn diagrams of highly expressed genes (TPM ≥ 20) before and after weaning. (C) Volcano plot of DEGs. (D) Heatmap of DEGs. (E) Sankey plot of GO and KEGG enrichment results for DEGs (Top 10).

Figure 5

Multi-omics analysis of interactions. (A) Heatmap summarizing the correlation of SDMs and significantly different microorganisms (*p < 0.05, **p < 0.01, and ***p < 0.001). (B) Network correlation between SDMs, significantly different microorganisms and hub genes.