1- Yasouj University
2- Department of Animal Science, Faculty of Agriculture, Yasouj University, Yasouj, Iran
3- Tabriz University
4- Education and Extension Organization
2- Department of Animal Science, Faculty of Agriculture, Yasouj University, Yasouj, Iran
3- Tabriz University
4- Education and Extension Organization
Abstract: (214 Views)
Background: Despite the extensive focus on HPAI strains like H5N1 and H5N8, the H5N3 subtype represents a compelling research priority owing to its rapid adaptability, moderate-to-severe pathogenicity in avian species, and zoonotic threat. A thorough understanding of its pathogenesis is therefore contingent upon a comprehensive examination of key target organs, specifically the lungs (primary replication site) and the brain (site of neurotropic involvement). The lungs and the brain are two critical tissues in influenza pathogenesis. The lungs serve as the main site of viral entry and activation of innate immunity, while the virus’s spread to the brain indicates systemic dissemination and neurotropism, leading to severe outcomes such as neurological disorders, paralysis, and sudden death in poultry. While extensive research has been conducted on the pathogenesis of avian influenza at the histopathological and immunological levels, investigations based on high-throughput molecular data, such as transcriptomics, are relatively limited in poultry, particularly in Iranian research contexts. Identifying key hub genes and non-coding regulatory elements such as microRNAs (miRNAs) provides important insight into how the host reorganizes its molecular landscape when challenged by a pathogen. In this context, the present study was undertaken to construct gene regulatory networks based on differential gene expression profiles from the brain and lung tissues of chickens infected with two strains of recombinant H5N3 virus, namely rH5N3 Ori and rH5N3 P6. By applying advanced bioinformatics approaches, we sought to identify key genes, regulatory miRNAs, and biological pathways that play central roles in host defense mechanisms and the progression of disease.
Methods: Data retrieval and analysis began with obtaining DNA microarray data from the publicly available GEO database (accession number: GSE96837), which included transcriptional profiles from brain and lung tissues of chickens experimentally infected with the H5N3 virus, along with control samples from uninfected birds used as baselines. Preprocessing and normalization of the data were conducted in R using the limma package, and differential gene expression was determined through a moderated t-test, considering genes with an adjusted p-value less than 0.05 and an absolute log2 fold change greater than 1 as significant. Global transcriptional changes were visualized using volcano plots and heatmaps. To explore protein-protein interaction (PPI) networks, the differentially expressed genes (DEGs) were uploaded into the STRING database (version 11.5) with a high-confidence threshold of 0.7, and the resulting networks were exported into Cytoscape (version 3.9) for visualization. Hub genes were then identified based on network topology, metrics such as degree centrality and betweenness centrality. For functional interpretation, enrichment analyses were performed using g: Profiler and the Kyoto Encyclopedia of Genes and Genomes (KEGG), with emphasis on pathways associated with immunity, protein synthesis, and RNA quality control.
Results: It was observed that infection with both rH5N3 Ori and rH5N3 P6 strains caused a substantial reprogramming of host gene expression in the examined tissues. The lungs mounted a more pronounced immune-related response, whereas the brain displayed broader alterations in metabolic pathways and neuronal stress networks. In total, 1480 differentially expressed genes (DEGs) were identified in the lungs compared to 936 in the brain, underscoring significant tissue-specific differences in the host response. Further network analysis identified 18 hub genes with high connectivity within the protein-protein interaction (PPI) networks. Remarkably, the majority of these hub genes were ribosomal proteins, including RPL21, RPL31, and RPS28, suggesting that influenza infection strongly impacts host translation machinery. Additional hub components such as EEF1A1 and EIF3F, key players in translation elongation and initiation, reinforced the idea that disruption of protein synthesis pathways is central to viral survival and replication. Enrichment analysis using KEGG pathways highlighted five major molecular routes influenced by infection: ribosome biogenesis and GTP hydrolysis, protein targeting to membranes, nonsense-mediated mRNA decay (NMD), innate immune signaling (particularly interferon response), and apoptosis regulation. In lung tissues, enrichment was dominated by immune-related processes such as RIG-I-like receptor signaling and cytokine-mediated interactions, while the brain showed stronger alterations in RNA quality control, mitochondrial activity, and apoptosis regulation, indicating neurodegenerative stress responses. At the tissue level, lung DEGs such as IFIT5 and MX1 demonstrated strong upregulation consistent with interferon-stimulated antiviral defense, whereas the brain showed reduced ribosomal gene expression, implying a translational arrest that may act as a protective mechanism to limit viral protein production.
Conclusion: Network analysis pointed to ribosomal protein genes as central regulators, underscoring the role of host translational machinery as a key target of viral interference and manipulation during infection. Moreover, the identification of novel miRNAs associated with H5N3 infection opens promising opportunities for the development of diagnostic biomarkers and potential antiviral therapies. These findings emphasize the necessity of examining multiple tissues in infection studies, as focusing solely on one organ may provide an incomplete understanding of disease progression and host-pathogen interactions. By employing integrated bioinformatics platforms such as STRING, Cytoscape, and KEGG pathway analysis, this research establishes a framework that can be extended to other viral infections in poultry. Importantly, the insights derived here hold practical value for the poultry sector, particularly in guiding genetic improvement strategies aimed at producing birds with enhanced disease resistance. Future directions should include experimental validation of the identified hub genes and miRNAs through techniques like qRT-PCR and functional gene silencing, as well as integrating proteomic and metabolomic analyses with transcriptomics to achieve a more comprehensive understanding of host-virus dynamics. Ultimately, this study contributes valuable knowledge toward protecting poultry health and strengthening food security in the face of ongoing avian influenza threats.
Methods: Data retrieval and analysis began with obtaining DNA microarray data from the publicly available GEO database (accession number: GSE96837), which included transcriptional profiles from brain and lung tissues of chickens experimentally infected with the H5N3 virus, along with control samples from uninfected birds used as baselines. Preprocessing and normalization of the data were conducted in R using the limma package, and differential gene expression was determined through a moderated t-test, considering genes with an adjusted p-value less than 0.05 and an absolute log2 fold change greater than 1 as significant. Global transcriptional changes were visualized using volcano plots and heatmaps. To explore protein-protein interaction (PPI) networks, the differentially expressed genes (DEGs) were uploaded into the STRING database (version 11.5) with a high-confidence threshold of 0.7, and the resulting networks were exported into Cytoscape (version 3.9) for visualization. Hub genes were then identified based on network topology, metrics such as degree centrality and betweenness centrality. For functional interpretation, enrichment analyses were performed using g: Profiler and the Kyoto Encyclopedia of Genes and Genomes (KEGG), with emphasis on pathways associated with immunity, protein synthesis, and RNA quality control.
Results: It was observed that infection with both rH5N3 Ori and rH5N3 P6 strains caused a substantial reprogramming of host gene expression in the examined tissues. The lungs mounted a more pronounced immune-related response, whereas the brain displayed broader alterations in metabolic pathways and neuronal stress networks. In total, 1480 differentially expressed genes (DEGs) were identified in the lungs compared to 936 in the brain, underscoring significant tissue-specific differences in the host response. Further network analysis identified 18 hub genes with high connectivity within the protein-protein interaction (PPI) networks. Remarkably, the majority of these hub genes were ribosomal proteins, including RPL21, RPL31, and RPS28, suggesting that influenza infection strongly impacts host translation machinery. Additional hub components such as EEF1A1 and EIF3F, key players in translation elongation and initiation, reinforced the idea that disruption of protein synthesis pathways is central to viral survival and replication. Enrichment analysis using KEGG pathways highlighted five major molecular routes influenced by infection: ribosome biogenesis and GTP hydrolysis, protein targeting to membranes, nonsense-mediated mRNA decay (NMD), innate immune signaling (particularly interferon response), and apoptosis regulation. In lung tissues, enrichment was dominated by immune-related processes such as RIG-I-like receptor signaling and cytokine-mediated interactions, while the brain showed stronger alterations in RNA quality control, mitochondrial activity, and apoptosis regulation, indicating neurodegenerative stress responses. At the tissue level, lung DEGs such as IFIT5 and MX1 demonstrated strong upregulation consistent with interferon-stimulated antiviral defense, whereas the brain showed reduced ribosomal gene expression, implying a translational arrest that may act as a protective mechanism to limit viral protein production.
Conclusion: Network analysis pointed to ribosomal protein genes as central regulators, underscoring the role of host translational machinery as a key target of viral interference and manipulation during infection. Moreover, the identification of novel miRNAs associated with H5N3 infection opens promising opportunities for the development of diagnostic biomarkers and potential antiviral therapies. These findings emphasize the necessity of examining multiple tissues in infection studies, as focusing solely on one organ may provide an incomplete understanding of disease progression and host-pathogen interactions. By employing integrated bioinformatics platforms such as STRING, Cytoscape, and KEGG pathway analysis, this research establishes a framework that can be extended to other viral infections in poultry. Importantly, the insights derived here hold practical value for the poultry sector, particularly in guiding genetic improvement strategies aimed at producing birds with enhanced disease resistance. Future directions should include experimental validation of the identified hub genes and miRNAs through techniques like qRT-PCR and functional gene silencing, as well as integrating proteomic and metabolomic analyses with transcriptomics to achieve a more comprehensive understanding of host-virus dynamics. Ultimately, this study contributes valuable knowledge toward protecting poultry health and strengthening food security in the face of ongoing avian influenza threats.
Keywords: Gene networks, Gene expression, H5N3 influenza, miRNA, Ribosomal proteins, Host-pathogen interactions
Type of Study: Research |
Subject:
ژنتیک و اصلاح نژاد دام
Received: 2025/09/21 | Accepted: 2026/06/6
Received: 2025/09/21 | Accepted: 2026/06/6
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