Elucidating genes and gene networks linked to individual susceptibility to milk fat depression in dairy goats

Abstract

Dietary supplementation with marine lipids modulates ruminant milk composition toward a healthier fatty acid profile for consumers, but it also causes milk fat depression (MFD). Because the dairy goat industry is mainly oriented toward cheese manufacturing, MFD can elicit economic losses. There is large individual variation in animal susceptibility with goats more (RESPO+) or less (RESPO-) responsive to diet-induced MFD. Thus, we used RNA-Seq to examine gene expression profiles in mammary cells to elucidate mechanisms underlying MFD in goats and individual variation in the extent of diet-induced MFD. Differentially expression analyses (DEA) and weighted gene co-expression network analysis (WGCNA) of RNA-Seq data were used to study milk somatic cell transcriptome changes in goats consuming a diet supplemented with marine lipids. There were 45 differentially expressed genes (DEGs) between control (no-MFD, before diet-induced MFD) and MFD, and 18 between RESPO+ and RESPO-. Biological processes and pathways such as "RNA transcription" and "Chromatin modifying enzymes" were downregulated in MFD compared with controls. Regarding susceptibility to diet-induced MFD, we identified the "Triglyceride Biosynthesis" pathway upregulated in RESPO- goats. The WGCNA approach identified 9 significant functional modules related to milk fat production and one module to the fat yield decrease in diet-induced MFD. The onset of MFD in dairy goats is influenced by the downregulation of SREBF1, other transcription factors and chromatin-modifying enzymes. A list of DEGs between RESPO+ and RESPO- goats (e.g., DBI and GPD1), and a co-related gene network linked to the decrease in milk fat (ABCD3, FABP3, and PLIN2) was uncovered. Results suggest that alterations in fatty acid transport may play an important role in determining individual variation. These candidate genes should be further investigated.

Keywords: RNA-Seq; WGCNA; goat; individual susceptibility; milk-fat depression; nutrigenomics.

Figure 1

Bioinformatics workflow followed for the analysis of the RNA-Seq data.

Figure 2

Sample Distribution of the RNA-Seq data. (A) Hierarchical clustering of samples based on Euclidean distance. The red line represents the threshold fixed to discard outliers (B) Principal Component Analysis (PCA) plots for PC1, PC2, and PC3 of milk somatic cell transcriptomes from control (blue) and more (RESPO+; green) or less (RESPO–; red) responsive goats to a diet inducing milk fat depression (MFD).

Figure 3

Significant module-trait associations found between the module eigengenes (ME) and the milk-fat traits analyzed [fat percentage (Fat.), fat yield (Fat_yield), variation in fat percentage (Variation.), and variation in fat yield (Variation_yield)]. Each row corresponds to a module eigengene labeled with colors and each column to a milk fat trait. Individual cells contain Pearson's correlation coefficients (outside parentheses) and the P-values of the correlation (within parentheses). The red to green color within the cells represents the positive (red) or negative (green) correlation of the modules with the milk fat traits.

Figure 4

Gene co-expression network for the lightyellow module significantly correlated with milk fat-yield reduction due to the MFD-inducing diet. Yellow squared nodes (yellow genes) represent genes functionally associated with mammary lipid metabolism in ruminants. Red edges indicate the connections of yellow genes in the network. The width of the edges corresponds to the weight in the WGCNA. The border color of the node is related to the number of connections, reddish nodes represent highly connected hub genes, and lighter red border colors of nodes represent genes with fewer connections.