1- Department of Animal Science, Faculty of Animal Science and Fisheries, Sari Agricultural Sciences and Natural Resources University, SARI-IRAN
Abstract: (29 Views)
Introduction and Objective: Abomasal displacement is one of the most common abdominal disorders in dairy cattle, particularly during early lactation, and has significant economic implications due to decreased milk production, increased culling rates, and substantial treatment costs. Although management factors such as nutrition and husbandry play a critical role in disease occurrence, evidence suggests that genetic predisposition also contributes to susceptibility. Protein–protein interaction (PPI) network analysis is a key systems biology approach that enables the identification of functional modules, protein complexes, and hub genes within biological pathways. Examining network structure and protein interactions can reveal genes involved in similar cellular processes and predict the functions of less-characterized genes. Clustering PPI networks, whether as protein complexes or functional modules, clarifies network architecture and the dominant role of each cluster, and algorithms such as MCODE, through node weighting, cluster prediction, and post-processing refinement, are effective tools for extracting these structures. The aim of this study was to identify genes and biological pathways associated with abomasal displacement in dairy cattle using a multi-step bioinformatics approach and PPI network analysis.
Materials and Methods:
To identify the genes and biological pathways involved in abomasal displacement in dairy cows, a list of 124 previously reported genes was first compiled and standardized based on official gene symbols. Functional enrichment analysis of these genes was conducted using the DAVID Bioinformatics Resources v2023, with Bos taurus selected as the background organism. Enriched terms from the three major Gene Ontology categories—Biological Process, Molecular Function, and Cellular Component—as well as KEGG biological pathways were extracted. The analysis was performed using an EASE score threshold of 0.01, a minimum of three genes per term, and Benjamini–Hochberg FDR correction (Benjamini < 0.05) for multiple testing. To assess protein interactions encoded by the selected genes, a protein–protein interaction (PPI) network was constructed using STRING v12 with a minimum confidence score of 0.4 (medium confidence) and an FDR cutoff of five percent. Core network metrics, including the number of nodes, number of edges, average node degree, clustering coefficient, and PPI enrichment p-value, were obtained. For the identification of functional clusters and subnetworks, the resulting STRING network was imported into Cytoscape v3.10, and the MCODE plugin was applied using the following parameters: minimum node degree = 2, node score cutoff = 0.2, Haircut option enabled, Fluff option disabled, and K-Core = 2, enabling the identification of highly interconnected functional modules. To determine key genes within the network structure, multiple centrality indices—including Degree Centrality, Betweenness Centrality, Closeness Centrality, and Eigenvector Centrality—were calculated using the CentiScaPe plugin. Genes simultaneously exhibiting high degree (hub genes) and high betweenness (bottleneck genes) were considered key regulatory genes. Their positions within MCODE-identified clusters were subsequently evaluated to determine their functional significance within major biological modules.
Results:
Bioinformatics analyses revealed that the genes associated with abomasal displacement in dairy cows were enriched in a range of significant terms related to hormonal activity, protein binding, transcriptional regulation, extracellular processes, metabolic pathways, and stress responses. In the GO enrichment analysis, hormone-related activities and RNA polymerase II–dependent transcriptional regulation were identified within the Molecular Function category, while extracellular matrix, extracellular space, and caveola were among the significant terms in the Cellular Component category. In the Biological Process category, terms such as response to nutrient stimulus, regulation of glucose levels, cellular response to insulin, heat stress response, regulation of insulin secretion, protein stabilization, and regulation of programmed cell death were enriched. KEGG pathway analysis also identified several highly enriched pathways, including hormone signaling, AMPK signaling, insulin resistance, insulin signaling, growth hormone synthesis and secretion, longevity regulation, and type II diabetes mellitus, as well as PPAR signaling, HIF-1 signaling, AGE–RAGE signaling in diabetic complications, and adipocytokine signaling.
The protein–protein interaction network extracted from STRING consisted of 119 genes and 523 interactions, and the PPI enrichment test indicated that the proteins interacted significantly more than expected by chance. The MCODE clustering revealed four subnetworks. The first and largest cluster consisted of key genes such as INS, IGF1, IRS1, AKT1, LEP, ADIPOQ, TNF, IL6, PPARG, LPL, FASN, SREBF1, and JAK2, forming the core of metabolic regulation, energy homeostasis, inflammation, and hormonal signaling. The second cluster included gastrointestinal and neuroendocrine genes (GAST, MLN, CHGA, GRP, SCT), while the third cluster consisted of PLAG1, XKR4, CHCHD7, and TGS1, which are associated with growth pathways and production traits.
In the network centrality analysis, ALB, GAPDH, and INS showed the highest degree centrality, while GAPDH, ALB, INS, and IL6 exhibited the highest betweenness centrality. Finally, GAPDH and ALB were identified as topological hub genes, likely due to their extensive cellular interactions and fundamental biological roles, whereas INS, IL6, JAK2, and IRS1 were recognized as disease-related hub genes, playing central roles in maintaining network integrity and signal transduction, particularly within metabolic and hormonal pathways.
Conclusion:
The results of this study indicate that the genes associated with abomasal displacement influence an interconnected set of metabolic, hormonal, and inflammatory pathways, forming a gene network with a central core of hub–bottleneck genes that play key roles in energy regulation, insulin signaling, and inflammatory responses. The identification of major functional clusters and enriched biological pathways—including AMPK, PPAR, and hormone signaling pathways—suggests that disturbances in energy balance, insulin resistance, and inflammatory mechanisms may contribute significantly to the onset and progression of abomasal displacement. By highlighting critical genes such as INS, IL6, JAK2, IRS1, PPARG, LEP, and FASN, this study provides a cohesive framework for understanding the molecular mechanisms underlying this condition and offers potential foundations for future research aimed at identifying biomarkers and developing improved management and early diagnostic strategies.
Materials and Methods:
To identify the genes and biological pathways involved in abomasal displacement in dairy cows, a list of 124 previously reported genes was first compiled and standardized based on official gene symbols. Functional enrichment analysis of these genes was conducted using the DAVID Bioinformatics Resources v2023, with Bos taurus selected as the background organism. Enriched terms from the three major Gene Ontology categories—Biological Process, Molecular Function, and Cellular Component—as well as KEGG biological pathways were extracted. The analysis was performed using an EASE score threshold of 0.01, a minimum of three genes per term, and Benjamini–Hochberg FDR correction (Benjamini < 0.05) for multiple testing. To assess protein interactions encoded by the selected genes, a protein–protein interaction (PPI) network was constructed using STRING v12 with a minimum confidence score of 0.4 (medium confidence) and an FDR cutoff of five percent. Core network metrics, including the number of nodes, number of edges, average node degree, clustering coefficient, and PPI enrichment p-value, were obtained. For the identification of functional clusters and subnetworks, the resulting STRING network was imported into Cytoscape v3.10, and the MCODE plugin was applied using the following parameters: minimum node degree = 2, node score cutoff = 0.2, Haircut option enabled, Fluff option disabled, and K-Core = 2, enabling the identification of highly interconnected functional modules. To determine key genes within the network structure, multiple centrality indices—including Degree Centrality, Betweenness Centrality, Closeness Centrality, and Eigenvector Centrality—were calculated using the CentiScaPe plugin. Genes simultaneously exhibiting high degree (hub genes) and high betweenness (bottleneck genes) were considered key regulatory genes. Their positions within MCODE-identified clusters were subsequently evaluated to determine their functional significance within major biological modules.
Results:
Bioinformatics analyses revealed that the genes associated with abomasal displacement in dairy cows were enriched in a range of significant terms related to hormonal activity, protein binding, transcriptional regulation, extracellular processes, metabolic pathways, and stress responses. In the GO enrichment analysis, hormone-related activities and RNA polymerase II–dependent transcriptional regulation were identified within the Molecular Function category, while extracellular matrix, extracellular space, and caveola were among the significant terms in the Cellular Component category. In the Biological Process category, terms such as response to nutrient stimulus, regulation of glucose levels, cellular response to insulin, heat stress response, regulation of insulin secretion, protein stabilization, and regulation of programmed cell death were enriched. KEGG pathway analysis also identified several highly enriched pathways, including hormone signaling, AMPK signaling, insulin resistance, insulin signaling, growth hormone synthesis and secretion, longevity regulation, and type II diabetes mellitus, as well as PPAR signaling, HIF-1 signaling, AGE–RAGE signaling in diabetic complications, and adipocytokine signaling.
The protein–protein interaction network extracted from STRING consisted of 119 genes and 523 interactions, and the PPI enrichment test indicated that the proteins interacted significantly more than expected by chance. The MCODE clustering revealed four subnetworks. The first and largest cluster consisted of key genes such as INS, IGF1, IRS1, AKT1, LEP, ADIPOQ, TNF, IL6, PPARG, LPL, FASN, SREBF1, and JAK2, forming the core of metabolic regulation, energy homeostasis, inflammation, and hormonal signaling. The second cluster included gastrointestinal and neuroendocrine genes (GAST, MLN, CHGA, GRP, SCT), while the third cluster consisted of PLAG1, XKR4, CHCHD7, and TGS1, which are associated with growth pathways and production traits.
In the network centrality analysis, ALB, GAPDH, and INS showed the highest degree centrality, while GAPDH, ALB, INS, and IL6 exhibited the highest betweenness centrality. Finally, GAPDH and ALB were identified as topological hub genes, likely due to their extensive cellular interactions and fundamental biological roles, whereas INS, IL6, JAK2, and IRS1 were recognized as disease-related hub genes, playing central roles in maintaining network integrity and signal transduction, particularly within metabolic and hormonal pathways.
Conclusion:
The results of this study indicate that the genes associated with abomasal displacement influence an interconnected set of metabolic, hormonal, and inflammatory pathways, forming a gene network with a central core of hub–bottleneck genes that play key roles in energy regulation, insulin signaling, and inflammatory responses. The identification of major functional clusters and enriched biological pathways—including AMPK, PPAR, and hormone signaling pathways—suggests that disturbances in energy balance, insulin resistance, and inflammatory mechanisms may contribute significantly to the onset and progression of abomasal displacement. By highlighting critical genes such as INS, IL6, JAK2, IRS1, PPARG, LEP, and FASN, this study provides a cohesive framework for understanding the molecular mechanisms underlying this condition and offers potential foundations for future research aimed at identifying biomarkers and developing improved management and early diagnostic strategies.
Keywords: Abomasal displacement, Protein–protein interaction network, Gene enrichment, Network centrality measures
Type of Study: Research |
Subject:
ژنتیک و اصلاح نژاد طیور
Received: 2025/11/25 | Accepted: 2026/05/10
Received: 2025/11/25 | Accepted: 2026/05/10
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