Integrated multi-omics reveals the roles of cecal microbiota and its derived bacterial consortium in promoting chicken growth

Abstract

The improvement of chicken growth performance is one of the major concerns for the poultry industry. Gut microbes are increasingly evidenced to be associated with chicken physiology and metabolism, thereby influencing chicken growth and development. Here, through integrated multi-omics analyses, we showed that chickens from the same line differing in their body weight were very different in their gut microbiota structure and host-microbiota crosstalk; microbes in high body weight (HBW) chickens contributed to chicken growth by regulating the gut function and homeostasis. We also verified that a specific bacterial consortium consisting of isolates from the HBW chickens has the potential to be used as chicken growth promoters. These findings provide new insights into the potential links between gut microbiota and chicken phenotypes, shedding light on future manipulation of chicken gut microbiota to improve chicken growth performance.

Keywords: cecal microbiota; chicken; growth performance; metabolomics; targeted culturomics; transcriptomics.

Fig 1

Study design for the whole experiment.

Fig 2

Different cecal microbiota composition between the HBW and LBW chickens. (A) Body weight of the HBW and LBW chickens. Data were presented as means ± SEM. Statistical significance was determined using a t-test. (*P-value < 0.05, **P-value < 0.01, and ***P-value < 0.001). (B) Principal coordinate analysis (PCoA) plot based on Bray-Curtis distance of cecal microbiota composition between the HBW and LBW chickens. Values of PCoA axis1 and PCoA axis2 were box plotted on the top and the right, respectively. (C) Relative abundances of cecal microbiota at the genus level in the HBW and LBW chickens. (D) Differentially abundant taxa were tested by linear discriminant analysis effect size, with linear discriminant analysis (LDA) score > 2 and P-value < 0.05. (E) Random forest analysis of cecal microbiota at the genus level between the HBW and LBW chickens. (F) ROC curve based on the top 25 marker genera for the HBW and LBW chickens. (G) ROC curve analysis of combinatorial taxa between Lactobacillus and Bacillus.

Fig 3

Changes in the cecal metabolite profiles of the HBW and LBW chickens. (A) OPLS-DA of cecal metabolites between the HBW (red) and LBW (blue) chickens. Intergroup variation is reflected by the T-score, and intragroup variations are reflected by orthogonal the T-score. (B) SCMs in the HBW and LBW chickens. Top panel: density of the VIP of the metabolites and right panel: density of the adjusted P-value. (C) Classification of SCMs at the superclass level in the HBW and LBW chickens. (D) KEGG enrichment analysis was performed using the SCMs between the HBW and LBW chickens (P-value < 0.05). (E) The abundance of the key SCMs in the HBW and LBW chickens. (F) Clustered heatmaps show the correlations between differentially abundant bacterial genera and the key SCMs. Significant correlations were picked with P-value < 0.05 (*P-value < 0.05 and **P-value < 0.01). The color scale bar indicates the correlation coefficient (r value).

Fig 4

Cecal tissue transcriptomic profiles of the HBW and LBW chickens. (A) PCA plots of expression levels from all genes between the HBW and LBW chickens. (B) DEGs in the HBW and LBW chickens. The upregulated (n = 92) and downregulated genes (n = 141) in HBW chickens are shown in red (bottom right) and blue (top left), respectively. (C) Clustered heatmaps show the correlations between the key SCMs and DEGs. Significant correlations were picked with P-value < 0.05. The color scale bar indicates the correlation coefficient (r value). KEGG enrichment analysis was performed using the DEGs in cluster 1 (D), cluster 2 (E), cluster 3 (F), and cluster 4 (G) between the HBW and LBW chickens (P-value < 0.05).

Fig 5

Effects of FMT on growth performance, serum antioxidant, relative mRNA expression, and cecal microbiota of chickens. (A) Average weight gain from day 21 to day 42. The concentrations of T-AOC (B), CAT (C), T-SOD (D), GSH-PX (E), and MDA (F) in the serum of chickens fed with different fecal suspensions. (G) The relative mRNA expression of sugar transporter proteins in the jejunum. (H) The relative mRNA expression of tight junction proteins in the jejunum. (I) The relative mRNA expression of immunity-related genes in the jejunum (*P-value < 0.05, **P-value < 0.01, and ***P-value < 0.001). (J) Venn diagram of core microbiome membership among the Oral-CON, Oral-HBW, and Oral-LBW groups.

Fig 6

Effects of dietary supplementation with L. reuteri CML393 and B. velezensis CML396 on chicken growth performance and gut health. (A) Body weight on the 42nd day. (B) Feed conversion ratio from day 0 to day 42. (C) Abdominal fat rate on the 42nd day. (D) The difference in jejunum villus height and crypt depth between CON and CML393 + CML396 chickens. (E) Jejunum epithelial morphology of CON and CML393 + CML396 chickens. Scale bars = 200 µm. (F) Concentrations of major SCFAs in the cecum of CON and CML393 + CML396 chickens. (G) Concentrations of minor SCFAs in the cecum of CON and CML393 + CML396 chickens. (H) The relative mRNA expression of sugar transporter proteins in the jejunum. (I) The relative mRNA expression of tight junction proteins in the jejunum. (J) The relative mRNA expression of immunity-related genes in the jejunum. (K) Effects of L. reuteri CML393 and B. velezensis CML396 supplementation on serum antioxidant of chickens (*P-value < 0.05 and ** P-value < 0.01).

Fig 7

Effects of dietary supplementation with L. reuteri CML393 and B. velezensis CML396 on cecal metabolites and gene expression of chickens. (A) Classification of SCMs at the superclass level in the CON and CML393 + CML396 chickens. (B) KEGG enrichment analysis was performed using the SCMs between the CON and CML393 + CML396 chickens (P-value < 0.05). (C) DEGs in the CON and CML393 + CML396 chickens. Compared with the CON group, the 43 upregulated genes are shown in red (bottom right) and 42 downregulated genes in blue (top left). (D) KEGG enrichment analysis of DEGs between the CON and CML393 + CML396 chickens (P-value < 0.05).

Fig 8

Competitive analysis and metabolic properties of the four strains in pair-wise combinations. (A) Monoculture growth in SM resulted in mostly decreased AUC values in comparison to fresh MIX medium, which was analyzed by calculating the inhibition factor d_AUC. d_AUC = (AUC_SM − AUC_MIX)/AUC_MIX. (B) The d_AUC of the four strains in pair-wise combinations. (C) Venn diagram of the metabolic pathway of CML391 and CML399. (D) Venn diagram of the metabolic pathway of CML393 and CML396. (E) Venn diagram of the metabolic pathway of CML391 and CML396. (F) Common and unique metabolic pathways among different combinations. Green rectangles mean the presence of the pathway; the pathway categories were ordered by consensus functional classification.