Lactation Associated Genes Revealed in Holstein Dairy Cows by Weighted Gene Co-Expression Network Analysis (WGCNA)

Abstract

Weighted gene coexpression network analysis (WGCNA) is a novel approach that can quickly analyze the relationships between genes and traits. In this study, the milk yield, lactose, fat, and protein of Holstein dairy cows were detected in a lactation cycle. Meanwhile, a total of 18 gene expression profiles were detected using mammary glands from six lactation stages (day 7 to calving, −7 d; day 30 post-calving, 30 d; day 90 post-calving, 90 d; day 180 post-calving, 180 d; day 270 post-calving, 270 d; day 315 post-calving, 315 d). On the basis of the 18 profiles, WGCNA identified for the first time 10 significant modules that may be related to lactation stage, milk yield, and the main milk composition content. Genes in the 10 significant modules were examined with gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The results revealed that the galactose metabolism pathway was a potential candidate for milk yield and milk lactose synthesis. In −7 d, ion transportation was more frequent and cell proliferation related terms became active. In late lactation, the suppressor of cytokine signaling 3 (SOCS3) might play a role in apoptosis. The sphingolipid signaling pathway was a potential candidate for milk fat synthesis. Dairy cows at 315 d were in a period of cell proliferation. Another notable phenomenon was that nonlactating dairy cows had a more regular circadian rhythm after a cycle of lactation. The results provide an important theoretical basis for the further molecular breeding of dairy cows.

Identification of critical lncRNAs for milk fat metabolism in dairy cows using WGCNA and the construction of a ceRNAs network

“…It was shown that UFM1-specific ligase 1 mediates the expression of genes related to milk fat synthesis through the MAP signaling pathway in mouse mammary epithelial cells (Kuang et al, 2020). Phingolipids (Fan et al, 2021) and the p53 signaling pathway (Li et al, 2020) were also potential candidates for milk fat synthesis; thus, targeting the core_lncRNAs of these target genes in milk fat metabolism is not negligible.…”

“…WGCNA can be used to analyze data on complex processes and plays a crucial role in exploring the characteristics of gene networks associated with complex traits (Bordini et al, 2021; Jia et al, 2020; Yao et al, 2019). In the last 2 years, scholars have used WGCNA to study mammary gland functional characteristics and lactation properties in ruminants (Deng et al, 2019; Fan et al, 2021). However, the application of WGCNA of lncRNAs to milk fat metabolism in dairy cows has not been reported.…”

Identifying key genes in milk fat metabolism by weighted gene co-expression network analysis

“…Breeding researchers have identified a range of potent genes and biomarkers in milk fat metabolism with the widespread use of next-generation sequencing technologies and the dramatic reduction in sequencing costs 7 . Despite the transcriptome determining the characteristic of spatial and temporal specificity, there is less linkage to phenotypic data 8 . Weighted gene co-expression network analysis (WGCNA) can combine gene expression with phenotypic data 9 , 10 and gather genes with similar expression patterns into one module 11 .…”

Identification of a C2H2 Transcription Factor (PsCZF3) Associated with RxLR Effectors and Carbohydrate-Active Enzymes in Phytophthora sojae Based on WGCNA

“…It takes advantage of a graph theoretical approach to understanding correlations amongst genes and grouping genes into modules that typically have coordinated biological functions and regulatory mechanisms [ 14 ]. Currently, WGCNA has been extensively used to construct gene co-expression networks and identify centrally connected hub genes in humans, animals and plants [ 14 , 15 , 16 ]. However, this method is rarely applied to plant pathogens.…”