z. pezeshkian

In recent years, highly pathogenicity avian influenza (HPAI), especially H5N1, has emerged as a major global health concern due to its potential as a zoonotic disease and its devastating impact on poultry populations. Identifying the molecular mechanisms of response to HPAI infection is critical to control, treat, and prevent the risk of a potential pandemic. Microarray technology is becoming a standard technology used in research laboratories all across the world and it is considered as one of the centers of research in cellular processes related to the level and manner of gene expression, including gene function and cell differentiation mechanisms. By using microarray technology, it is possible to obtain a detailed view of the interaction function of genes while simultaneously studying how the genome is expressed. Using microarrays provides the analysis of gene expression in response to viral infections such as influenza, etc., the study of host-pathogen interactions, and also the identification of the effectiveness of drugs and vaccines. This study aimed to analyze the microarray data of H5N1 avian influenza to compare the gene network and analyze the functional pathway in chickens and ducks.

Data mining and searching of microarray data related to Highly Pathogenic Avian Influenza infection was done in the GEO gene expression database (https://www.ncbi.nlm.nih.gov/geo). The microarray data set with accession number GSE33389 based on the GPL3213 platform was selected which contained lung tissue samples challenged with H5N1 virus in chickens and ducks. Normalization of selected microarray data was done using R software, and samples were grouped to compare between infected and control samples. Limma, Biobase, and GEOquery software packages in R software were used to determine the expression level of genes and to investigate the differentially expressed genes (DEGs) between healthy and H5N1 influenza virus-infected lung tissue samples in chickens and ducks. The criterion for selecting significant DEGs was considered as |logFC|>2 and P<0.05. DAVID online tool (https://david.ncifcrf.gov) was used to investigate biological pathways, structural and functional characteristics of genes with different expressions, and functional interpretation of upregulated and downregulated DEGs. It was evaluated and visualized separately based on biological processes (BP), molecular functions (MF), and cellular components (CC). KEGG tool (http://www.genome.jp/kegg) was used to evaluate and study metabolic pathway enrichment. To reveal interactions between proteins and analyze them, STRING database and Cytoscape software were used. While using the Cytohubba plugin to identify and display key genes, the main modules affecting the interaction of genes and proteins were also identified by the MCODE plugin.

Gene expression analysis revealed 2062 and 565 differentially expressed genes between normal and infected tissue in chickens and ducks, respectively (P<0.05 and |logFC|>2). The results of bioinformatics analysis and protein-protein interaction network analysis showed BUB1, NDC80, CDC20, PLK1, PRC1, KIF11, and AURKA genes as hub genes in chicken and also COL6A3, COL3A1, COL4A3, COL18A1, PLOD2, PLOD1, and P4HA2 as highly effective genes in duck (P< 0.05). The results of the ontology comparison of DEGs proved that most of these genes in chickens are involved in the innate immune response and inflammatory resistance of the host, and the most effective genes in ducks play a role in lipid metabolism and energy production to meet the host's resistance to disease. The results of comparative gene network analysis between chickens and ducks are promising to increase our understanding of the host response to H5N1 influenza infection and the factors affecting virus pathogenesis in different avian species. Differentially expressed genes in response to H5N1 infection in chickens and ducks play critical roles in various biological processes, including immune response, inflammation, viral replication, and host-pathogen interactions.In general, gene network analysis showed that chickens and ducks use different genetic strategies to respond to avian influenza virus infection.

The present research was conducted to discover the response to H5N1 HPAI infection in chickens and ducks through comparative gene network analysis. It is important to note that the gene network analysis presented in this research is an initial step towards discovering the response mode of HPAI (H5N1) infection in chickens and ducks, and further functional studies, validation experiments, and integration with other omics data are needed to confirm the role of genes, pathways and hub genes in the host response to H5N1 virus. Therefore, the results of comparative gene network analysis in chickens and ducks obtained from this research can provide valuable insight into the underlying molecular mechanisms of host response to H5N1 influenza infection. Thus, by identifying differentially expressed genes, functional modules, and hub genes in this research, it can be stated that potential targets for future research have been highlighted to some extent. Undoubtedly, further studies in this field will improve our knowledge about the pathogenesis of avian influenza and will help to develop strategies for effective control and prevention of H5N1 influenza outbreaks.

Although Wood function is of great importance to studying lactation curves because of its coefficients with biological interpretation, it has not been used under a random regression models. The objective of this study was to investigate the possibility of fitting Wood function on the test day records of dairy cows under a random regression model. Data was consisted of 743,205 test day records in first lactation of 81,192 Holstein cows from 46 herds in Iran which have been collected during 1991 to 2008. Linear form of Wood function which was a polynomial of 3 has been used. Three forms of Legendre polynomial functions with third order for additive genetic effects were also applied to evaluate the results of Wood function. Heritability estimates for daily milk yield obtained by linear form of Wood function were higher than those obtained by Legendre functions in most days indicating its advantage for genetic evaluations. Correlation between breeding values of animals predicted by linear form of Wood function and Legendre functions was 90%. In conclusion, Wood function can be employed in random regression models causing exploitation its desired characteristics and the advantages of random regression models to explain the genetic variations of dairy cows’ lactation curve.