Weighted gene co-expression network analysis identifies modules and functionally enriched pathways in the lactation process

Abstract

The exponential growth in knowledge has resulted in a better understanding of the lactation process in a wide variety of animals. However, the underlying genetic mechanisms are not yet clearly known. In order to identify the mechanisms involved in the lactation process, various mehods, including meta-analysis, weighted gene co-express network analysis (WGCNA), hub genes identification, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment at before peak (BP), peak (P), and after peak (AP) stages of the lactation processes have been employed. A total of 104, 85, and 26 differentially expressed genes were identified based on PB vs. P, BP vs. AP, and P vs. AP comparisons, respectively. GO and KEGG pathway enrichment analysis revealed that DEGs were significantly enriched in the “ubiquitin-dependent ERAD” and the “chaperone cofactor-dependent protein refolding” in BP vs. P and P vs. P, respectively. WGCNA identified five significant functional modules related to the lactation process. Moreover, GJA1, AP2A2, and NPAS3 were defined as hub genes in the identified modules, highlighting the importance of their regulatory impacts on the lactation process. The findings of this study provide new insights into the complex regulatory networks of the lactation process at three distinct stages, while suggesting several candidate genes that may be useful for future animal breeding programs. Furthermore, this study supports the notion that in combination with a meta-analysis, the WGCNA represents an opportunity to achieve a higher resolution analysis that can better predict the most important functional genes that might provide a more robust bio-signature for phenotypic traits, thus providing more suitable biomarker candidates for future studies.

Identification of the Real Hub Gene and Construction of a Novel Prognostic Signature for Pancreatic Adenocarcinoma Based on the Weighted Gene Co-expression Network Analysis and Least Absolute Shrinkage and Selection Operator Algorithms

“…WGCNA provides a new approach in performing higher-resolution analysis, which can more accurately predict hub genes in a disease, thus, providing a novel field of vision for the exploration of disease pathophysiology and the construction of disease prognostic signatures ( Panahi and Hejazi, 2021 ). For instance, in 2021, Farhadian et al (2021) highlighted the pivotal role of GJA1, AP2A2, and NPAS3 in the lactation process using WGCNA algorithms. Our previous study also identified TLR7 as a candidate gene for stomach adenocarcinoma (STAD) via the WGCNA algorithm, and this could help predict the progression and prognosis of STAD and shed new light on its immunotherapy ( Yuan et al, 2021b ).…”

Identification of Genomic Regions Influencing N-Metabolism and N-Excretion in Lactating Holstein- Friesians

“…Therefore, it seems conceivable that GJA1 also influences the diffusion of urea across the udder epithelium, and thus the urea content of the milk. Interestingly, connexin 43 was previously shown to play a critical role in the development of the mammary gland epithelia and in lactation processes in mice and cattle (Plante and Laird, 2008;Farhadian et al, 2021). Moreover, GJA1 has been associated with fertility parameters in cows (Ribeiro et al, 2016;Neupane et al, 2017).…”

In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds

“…WGCNA package is designed for clustering genes based on their expression profiles and therefore we used the gene lists generated from Salmon for weighted co-express analysis. Normalization was done by filtering out genes with counts less than 10 in more than 90% of samples since they are not informative and tend to introduce noise (Farhadian et al, 2021). Co-expression networks were generated using WGCNA using the Bioconductor R package v3.5.1 (Langfelder & Horvath, 2008).…”