Liver transcriptomics-metabolomics integration reveals biological pathways associated with fetal programming in beef cattle

Abstract

We investigated the long-term effects of prenatal nutrition on pre-slaughter Nelore bulls using integrative transcriptome and metabolome analyses of liver tissue. Three prenatal nutritional treatments were administered to 126 cows: NP (control, mineral supplementation only), PP (protein-energy supplementation in the third trimester), and FP (protein-energy supplementation throughout pregnancy). Liver samples from 22.5 ± 1-month-old bulls underwent RNA-Seq and targeted metabolomics. Weighted correlation network analysis (WGCNA) identified treatment-associated gene and metabolite co-expression modules, further analyzed using MetaboAnalyst 6.0 (metabolite over-representation analysis and transcriptome-metabolome integrative analysis) and Enrichr (gene over-representation analysis). We identified several significant gene and metabolite modules, as well as hub components associated with energy, protein and oxidative metabolism, regulatory mechanisms, epigenetics, and immune function. The NP transcriptome-metabolome analysis identified key pathways (aminoacyl t-RNA biosynthesis, gluconeogenesis, and PPAR signaling) and hub components (glutamic acid, SLC6A14). PP highlighted pathways (arginine and proline metabolism, TGF-beta signaling, glyoxylate and dicarboxylate metabolism) with arginine and ODC1 as hub components. This study highlights the significant impact of prenatal nutrition on the liver tissue of Nelore bulls, shedding light on critical metabolic pathways and hub components related to energy and protein metabolism, as well as immune system and epigenetics.

Keywords: Maternal nutrition; Metabolites; RNA-seq; Systems biology; WGCNA.

Fig. 1

Graphical abstract illustrating the experimental design and main analyses conducted.

Fig. 2

Gene module–treatment correlations heatmap. Each row corresponds to a 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

Metabolite module–treatment correlations 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. 4

Functional enrichment analysis of metabolites associated with the significant NP group module.

Fig. 5

Functional enrichment analysis of metabolites associated with the significant PP group modules.

Fig. 6

Gene-metabolite network integration of the NP group. Squares represent the metabolites and circles the genes in the network. The top 30 components (selected based on betweenness centrality) are explicitly labeled in the network, whereas the remaining ones are denoted by grey squares or circles.

Fig. 7

Gene-metabolite network integration of the PP group. Squares represent the metabolites and circles the genes in the network. The top components (selected based on betweenness centrality > 0) are explicitly labeled in the network, whereas the remaining ones are denoted by grey squares or circles.