A comparative analysis of gene co-expression networks in the diacylglycerol acyltransferase (DGAT) gene family in cattle and humans

Introduction: The diacylglycerol acyltransferase (DGAT) gene family, particularly DGAT1 and DGAT2, is crucial in determining milk fat content, which affects both the nutritional quality and market value of dairy products. DGAT enzymes catalyze the final step of triglyceride synthesis, a key process in lipid metabolism. Understanding the genetic basis of milk fat synthesis is vital for improving dairy production efficiency and improving milk’s health benefits. However, gaps in cattle-specific gene expression data, especially regarding complex traits like milk fat synthesis and disease resistance, hinder genetic improvement efforts. Mastitis, a common and costly dairy cattle disease, may be genetically linked to milk fat synthesis pathways, though these interactions remain unclear. To address this, our study used a comparative genomics approach, analyzing gene expression data from both cattle and humans to explore the association between DGAT genes and mastitis. This research aimed to reveal genetic networks that could inform breeding strategies to boost milk quality, production efficiency, and animal health.
Materials and methods: To conduct this comparative analysis, two gene expression datasets were retrieved from the GEO/NCBI database: GSE24560 for cattle and GSE51874 for humans. These datasets were selected based on their relevance to mastitis and the availability of high-quality gene expression data. The datasets underwent rigorous preprocessing to ensure data quality and comparability. Differentially expressed genes (DEGs) were identified using the LIMMA package in the R environment, which employs linear models to assess differential expression. Clustering and principal component analysis (PCA) were performed to identify patterns and reduce dimensionality in the gene expression data, facilitating the identification of co-expressed gene modules. Co-expression networks were constructed and visualized using the WGCNA (Weighted Gene Co-expression Network Analysis) package, which is designed to identify modules of highly correlated genes and relate them to external traits. Functional enrichment analysis of the resulting networks was conducted using Enrichr, a comprehensive gene set enrichment analysis tool. This analysis helped identify biological processes and pathways associated with the DEGs, providing insights into the functional roles of the identified gene modules.
Results and discussion: In the human dataset, DGAT1, DGAT2L6, and MOGAT1 were identified as hub genes, indicating their central role in the co-expression networks. These genes are known to be involved in lipid metabolism and triglyceride synthesis, supporting their relevance to milk fat content. In the cattle dataset, IL1B and CXCL6 emerged as hub genes, highlighting their potential role in the inflammatory response associated with mastitis. Gene ontology (GO) analysis revealed a strong association with lipid metabolism, particularly in the synthesis and metabolic processes of acyl glycerols and triglycerides. The most significantly enriched biological processes included the biosynthetic process of acyl glycerol (GO:0046463), the biosynthetic process of monoacyl glycerol (GO:0006640), and the biosynthetic process of triglyceride (GO:0019432), all of which exhibited low P-values, indicating statistical significance. Despite these findings, the study did not establish a direct link between milk fat in humans and mastitis in cattle. This lack of a straightforward correlation highlights the complexity of genetic regulation across different species and biological contexts. While both datasets revealed hub genes and pathways associated with lipid metabolism and inflammation, the specific interactions and regulatory mechanisms appeared to be distinct. The number of modules created varied significantly between the cattle and human datasets. In the cattle dataset, the co-expression network analysis identified several modules that were enriched for genes involved in immune response and inflammation, reflecting the primary focus on mastitis. In contrast, the human dataset modules were more diverse, including pathways related to lipid metabolism, cell signaling, and general metabolic processes. This variability suggests that while there are shared genetic pathways, the organization and regulation of these pathways differ between species. The discussion of extracting conserved modules was inconclusive, further emphasizing the intricate nature of genetic networks. Conserved modules are groups of genes that maintain their co-expression patterns across different species or conditions, indicating fundamental biological processes. However, identifying such modules between cattle and humans proved challenging due to the differences in gene expression profiles and the specific biological contexts of milk fat synthesis and mastitis. This inconclusiveness underscores the need for more nuanced comparative analyses that account for species-specific adaptations and the multifaceted nature of genetic regulation. Moreover, the study's findings suggest that while there are shared genetic pathways involved in lipid metabolism and inflammation, the specific regulatory mechanisms governing these pathways may differ between cattle and humans. For instance, the hub genes identified in the cattle dataset, such as IL1B and CXCL6, are known for their roles in the inflammatory response, which is crucial in the context of mastitis. In contrast, the human dataset highlighted genes like DGAT1, DGAT2L6, and MOGAT1, which are more directly involved in lipid synthesis and metabolism. This difference indicates that the regulatory networks controlling milk fat synthesis and mastitis resistance in cattle may have evolved unique features that are not fully captured by human genetic data.
Conclusions: From a systems biology standpoint, the identification of hub genes provides important insights into the regulatory genetic networks underlying milk production and its association with mastitis. These findings offer a foundation for the development of targeted breeding strategies aimed at improving milk quality and reducing mastitis incidence in cattle. Notably, hub genes such as DGAT1, DGAT2L6, and MOGAT1 in humans, and IL1B and CXCL6 in cattle, represent promising candidates for further functional validation through experimental approaches, including gene knockout and overexpression studies. The co-expression networks and gene modules identified herein establish a framework for future investigations into the broader genetic architecture governing these complex traits, particularly concerning immune response, metabolic regulation, and cell signaling pathways. Future research integrating multi-omics data, such as genomics, transcriptomics, proteomics, and metabolomics, as well as comparative genomics analyses across mammalian species, may uncover additional regulatory mechanisms and evolutionary adaptations relevant to milk production and disease resistance in cattle.