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

Farhadian, Mohammad and Rafat, Seyed Abbas and Panahi, Bahman and Mayack, Christopher (2021) Weighted gene co-expression network analysis identifies modules and functionally enriched pathways in the lactation process. Scientific Reports, 11 (1). ISSN 2045-2322

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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.
Item Type: Article
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Biological Sciences & Bio Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Christopher Mayack
Date Deposited: 28 Aug 2022 20:54
Last Modified: 28 Aug 2022 20:54
URI: https://research.sabanciuniv.edu/id/eprint/43601

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