Multiomics analysis reveals that microbiota regulate fat and muscle synthesis in chickens

Abstract

Intestinal microbiota regulates the host metabolism, including fat metabolism and muscle development in mammals; however, studies on the interactions between the gut microbiome and in chickens with respect to fat metabolism and muscle development are still rare. We established a germ-free (GF) chicken model to determine the transcriptomes and metabolomes of GF and specific-pathogen-free (SPF) chickens. Transcriptome analysis showed 1,282 differentially expressed genes (DEGs) in GF and SPF chickens. The expression levels of some genes related to muscle formation were very high in SPF chickens but low in GF chickens, suggesting that GF chickens had poorer muscle development ability. In contrast, the expression levels of some fat synthesis-related genes were very low in SPF chickens but high in GF chickens, suggesting that GF chickens had a more potent fat-synthesizing ability. Metabolome analysis revealed 62 differentially expressed metabolites (DEMs) in GF and SPF chickens, of which 35 were upregulated and 27 were downregulated. Furthermore, the Pearson correlation coefficient (PCC) was calculated, and an interaction network was constructed to visualize the crosstalk between the genes, metabolites, and gut microbiota in GF and SPF chickens. The top 10 gut microbiota were positively correlated with lipid metabolism including13(S)-HpODE and 9(S)-HpOTrE, and genes related to muscle development, while were negatively correlated with genes related to fat synthesis. In conclusion, this study indicated that chicken intestinal microbiota regulate host metabolism, inhibit fat synthesis, and may promote muscle development.

Keywords: GF chicken; fat accumulation; intestinal microbes; metabolism; muscle development.

Figure 1

Basal analysis of the transcriptome. (A) An FPKM boxplot for each sample. (B) The PCA of GF and SPF chicken. (C) Volcano plots of all DEGs between GF and SPF chicken.

Figure 2

GO and KEGG analysis of the transcriptome. GO analysis shows the top 20 pathways associated with the (A) upregulated and (B) downregulated DEGs between GF and SPF chickens. KEGG analysis shows the top 20 pathways of the (C) upregulated and (D) downregulated DEGs (P < 0.05).

Figure 3

Validation of the RNA-Seq results using RT-qPCR. The x-axis represents the genes and the y-axis represents the relative expressions of those genes.

Figure 4

PCA and PLS-DA classification of GF and SPF chickens. MetaboAnalystR R library 3.0, principal component analysis (PCA) and partial least squares discriminant analyses (PLS-DA) were used to classify the samples. PCA score plots of metabolites identified in (A) positive polarity and (B) negative polarity modes. PLS-DA score plots of metabolites identified in (C) positive polarity and (D) negative polarity modes. A t test was used to identify subgroups of differential metabolites, which were declared differential metabolites if differences in expression values between GF and SPF chickens resulted in projective variable importance (VIP) >1 and P < 0.05.

Figure 5

The expression and functional analyses of the DEM in GF and SPF chickens. (A) The volcano plot of DEMs between GF and SPF groups. |log₂ fold change|>1, P < 0.01. The q value was adjusted to the P value by multiple hypothesis testing. (B) Heat maps of the DEGs expressed between the GF and SPF groups. Red and blue indicate high and low levels of gene expression, respectively. (C) KEGG analysis identified the top 30 pathways of the DEGs. (D) Random forest analysis of important DEMs in GF and SPF groups. (E) Heat map of DEMs that correlated with lipid metabolism.

Figure 6

Joint analysis of the transcriptome and metabolome. (A) Correlation heatmap for the integrated analysis of microbiome and metabolome, where red and blue represent positive and negative correlations, respectively. (B) The interaction network constructed from the integrated analysis of the transcriptome and metabolome data, where pink lines and green lines represent positive and negative correlations, respectively. The blue and orange nodes represent metabolites and genes, respectively. The top 3 nodes with the highest betweenness value are highlighted in yellow.

Figure 7

Integrated analysis of the microbiome, metabolome, and transcriptome. (A) The integrated analysis of the correlation among the microbiome, metabolome, and transcriptome, where red and blue represent the negative and positive correlations, respectively. (B) Interaction network for the integrated analysis of the microbiome and metabolome, where pink and green lines represent positive and negative correlations, respectively. The purple, green, and orange nodes represent genes, metabolites, and bacteria species respectively. The top 3 nodes with the highest betweenness value are highlighted in yellow.