Impacts of prenatal nutrition on metabolic pathways in beef cattle: an integrative approach using metabolomics and metagenomics

Abstract

Background: This study assessed the long-term metabolic effects of prenatal nutrition in Nelore bulls through an integrated analysis of metabolome and microbiome data to elucidate the interconnected host-microbe metabolic pathways. To this end, a total of 126 cows were assigned to three supplementation strategies during pregnancy: NP (control)- only mineral supplementation; PP- protein-energy supplementation during the last trimester; and FP- protein-energy supplementation throughout pregnancy. At the end of the finishing phase, blood, fecal, and ruminal fluid samples were collected from 63 male offspring. The plasma underwent targeted metabolomics analysis, and fecal and ruminal fluid samples were used to perform 16 S rRNA gene sequencing. Metabolite and ASV (amplicon sequence variant) co-abundance networks were constructed for each treatment using the weighted gene correlation network analysis (WGCNA) framework. Significant modules (p ≤ 0.1) were selected for over-representation analyses to assess the metabolic pathways underlying the metabolome (MetaboAnalyst 6.0) and the microbiome (MicrobiomeProfiler). To explore the metabolome-metagenome interplay, correlation analyses between host metabolome and microbiome were performed. Additionally, a holistic integration of metabolic pathways was performed (MicrobiomeAnalyst 2.0).

Results: A total of one and two metabolite modules associated with the NP and FP were identified, respectively. Regarding fecal microbiome, three, one, and two modules for the NP, PP, and FP were identified, respectively. The rumen microbiome demonstrated two modules correlated with each of the groups under study. Metabolite and microbiome enrichment analyses revealed the main metabolic pathways associated with lipid and protein metabolism, and regulatory mechanisms. The correlation analyses performed between the host metabolome and fecal ASVs revealed 13 and 12 significant correlations for NP and FP, respectively. Regarding the rumen, 16 and 17 significant correlations were found for NP and FP, respectively. The NP holistic analysis was mainly associated with amino acid and methane metabolism. Glycerophospholipid and polyunsaturated fatty acid metabolism were over-represented in the FP group.

Conclusions: Prenatal nutrition significantly affected the plasma metabolome, fecal microbiome, and ruminal fluid microbiome of Nelore bulls, providing insights into key pathways in protein, lipid, and methane metabolism. These findings offer novel discoveries about the molecular mechanisms underlying the effects of prenatal nutrition.

Clinical trial number: Not applicable.

Keywords: Maternal nutrition; Metabolites; Methane; Microbiome; PUFAs; Systems biology; WGCNA.

Fig. 1

Experimental design and workflow illustrating sample collection, treatments, and analyses performed

Fig. 2

Plasma metabolome module–treatment correlation heatmap. Each row corresponds to a metabolite module, and each column corresponds to a prenatal nutritional treatment group (NP, PP, and FP). Each cell contains the corresponding correlation and p-value. The table is colour-coded by correlation, according to the colour legend

Fig. 3

Fecal microbiome module–treatment correlations heatmap. Each row corresponds to an ASV module, and each column corresponds to a prenatal nutritional treatment group (NP, PP, and FP). Each cell contains the corresponding correlation and p-value. The table is colour-coded by correlation, according to the colour legend

Fig. 4

Rumen fluid microbiome module–treatment correlations heatmap. Each row corresponds to an ASV module, and each column corresponds to a prenatal nutritional treatment group (NP, PP, and FP). Each cell contains the corresponding correlation and p-value. The table is colour-coded by correlation, according to the colour legend

Fig. 5

Bubble plot illustrating the over-representation analysis of metabolites associated with the yellow module from plasma WGCNA analysis

Fig. 6

Bubble plot illustrating the over-representation analysis of metabolites associated with the green module from plasma WGCNA analysis

Fig. 7

Bar plots illustrating the over-representation analysis of fecal microbiome from the significant modules correlated in the WGCNA. A Significant metabolic pathways associated with ASVs in the cyan module. B Significant metabolic pathways associated with ASVs in the purple module. C Significant metabolic pathways associated with ASVs in the brown module. D Significant metabolic pathways associated with ASVs in the salmon module

Fig. 8

Bar plots illustrating the over-representation analysis of rumen fluid microbiome from the significant modules correlated in the WGCNA. A Significant metabolic pathways associated with ASVs in the pink module. B Significant metabolic pathways associated with ASVs in the purple module. C Significant metabolic pathways associated with ASVs in the blue module. D Significant metabolic pathways associated with ASVs in the magenta module

Fig. 9

Heatmaps demonstrating the significant correlations between the host plasma metabolome and its fecal and rumen fluid microbiome. A NP group correlation between its significant metabolites and its significant fecal ASVs. B NP group correlation between its significant metabolites and its significant rumen fluid ASVs. C FP group correlation between its significant metabolites and its significant fecal ASVs. D FP group correlation between its significant metabolites and its significant rumen fluid ASVs. The heatmaps were reduced to just display components with at least one significant correlation. Significant correlations are numerically represented in the heatmap, and the significant levels are demonstrated according to the p-values threshold (*p-value < 0.01; **p-value < 0.001)

Fig. 10

Diamond plot showing the holistic integration of significant metabolites and ASVs (fecal and rumen fluid) in each prenatal nutrition group (NP and FP). The figure illustrates all the significant exclusive metabolic pathways associated with each group