microarray

To date, one of the main areas of research is the creation of breeds with inherent resistance to digestive nematodes in sheep. In this context, resistance to digestive nematodes has exhibited significant phenotypic differences within and between sheep breeds, suggesting a genetic basis for these differences. According to the list of effective candidate genes and the identified gene network for parasite resistance in sheep, there are genes involved in the immune system, such as the interferon-γ gene (IFN-γ). Cytokinin is involved in biochemical immune system signaling pathways, parasite resistance, and immunological responses. In addition, certain mutations in this gene impair the ability of specialized cells of the immune system to fight off parasite invasion. One of the most popular approaches to generating gene expression data for genome function studies is DNA microarray technology, which allows the simultaneous expression of thousands of genes. Proteomics and genomics are two application areas of microarray technology. This research aims to identify and categorize some of the genes involved in the relative genetic resistance of sheep to nematode infection using microarray data.

In this context, the GEO-Bank, part of the NCBI, was searched for access to open-access databases. Downloaded microarray data corresponded to infection with parasitic nematodes with the best replication (e.g., data in two resistant and sensitive groups), and the set of genes with differential expression was identified using R -based appropriate software packages (Biobase, GEOquery, limma, affy, Genfilter, Pheatmap, Plyr, Reshape2, and Ggplot2). Raw data were measured on a logarithmic scale, and the P-value fit statistics were used for expression comparisons between gene groups.

The main analysis was performed after precorrection and processing of the raw data because of the high intragroup variance of the data, as evidenced by the observed quality control results of the raw data and the quality control-related results of the integrated data. After pre-processing of the raw data, correlation analysis revealed a strong relationship between genes in the pre-Inf group (Pre-Inf) and the infected group (Inf) compared to the control group. Three heatmap reference charts, a PCA chart, and a volcano plot were used to verify data quality, and by verifying these charts, samples of unfavorable quality were removed from the next stages of analysis. After a bioinformatics analysis, the results showed significantly increased expression patterns (NACA, RPL4, NAGS, CTCF, GBP1, BHLHE, YTHDF3, PDHA1, and MXI1) and diminished expression patterns of PDHA1 and MXI1 genes. According to the results of this study, these genes play a role in the cellular metabolism process, molecular function, the formation of genetic connections, and cell life. Therefore, they were significant (p-value < 0.05) according to the magnitude of the change. The N-acryl glutamate synthetase (NAGS) gene is the cofactor of the first enzyme involved in the urea cycle in mammals. The functional role of this gene has been identified in many neurological diseases. The CTCF gene has a positive effect on the cells of the immune system and plays a special role in defending against viruses and pathogens that invade the host body. The guanylate binding protein 1 (GBP1) gene plays a role in the body's defense against many infectious pathogens. This gene causes oxidative reactions and autophagy of the host immune system as a barrier to invading pathogens. The methyl adenosine RNA binding 3 gene is more effective in antiviral immunity and is closely linked to the GBP1 gene, which plays a role in the body's resistance to many infectious agents. This gene causes oxidative responses and autophagy of the host immune system against invading pathogens. The PDHA1 pyruvate dehydrogenase gene is involved in immune system metabolism. This gene plays a role in allosteric factor-induced regulation, repair of covalent bonds, and relatively rapid changes in the level of expressed proteins, either through altered gene expression or proteolytic degradation. The MXI1 gene helps control the entry of invading pathogens into the host's body and promotes recovery and healing from infections. The results can be explained by several reasons, including the use of different sample breeds and laboratory techniques, the level of parasite contamination, the age of the test material, the variety of tested nematodes, and inconsistent techniques and tools of the current study with those of previous studies. Other reasons include the climatic differences between the test region and its physical location as well as the use of different RNA and microarray methods. However, the results of the study may be helpful under the related circumstances.

The results of microarrays and differential expression patterns can be of great help to molecularly modify livestock to resist internal parasites. Using more advanced tools, such as next-gene sequencing, will provide more accurate and relevant information.

Microarray technology is a powerful technique to measure the expression levels of large numbers of genes simultaneously. Microarray data contains many noise sources; therefore, several preprocessing steps are necessary to convert the raw data to achieve accurate analyzing results. Preprocessing of microarray data includes background correction, data normalization, and summarization steps each can be performed by a large variety of methods. However, the relative impact of these methods on the detection of differentially expressed genes remains to be determined. The aim of this study was to compare the effects of different methods of preprocessing on the results of differentially expressed gene detection. The used data was downloaded from the NCBI GEO database. The series (GSE) accession number, platform (GPL) accession number, and platform name of the data were GSE56589, GPL18534, and Affymetrix Bovine Genome Array, respectively. Two background correction methods (MAS.5 and RMA.2), two normalization methods (Scaling normalization and Quantile normalization), and two summarization methods (Tukey biweight and Medianpolish) were evaluated. The results showed that the number and types of differentially expressed genes could be mainly affected by background correction and normalization methods, but the summarization method showed a small impact.

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.

Achieving technologies that can depict the behaviors of genes at the transcriptome level and the interaction between them is particularly beneficial. In this regard, the microarray technology is based on the previous discoveries of qPCR and hybridization blotting technology, which uses the same rules but on a larger scale to measure the expression of several thousand genes. In fact, it is a method based on hybridization in combination with nanotechnology and the use of labeled fluorescent dyes. This technology is based on a chip and a glass plate, where special identifier sequences are placed together in a two-dimensional order of several tens of thousands of units, and it's responsible for simultaneously identifying the differential expression pattern of several thousand special sequences or coding regions. Prior knowledge of the microarray architecture and backstage details used for this technique has a direct impact on raw data analysis. Data pre-processing methods and their normalization, using the principal component analysis method, Heatmap drawing, clustering, and data correlation test dendrogram drawing, and the advanced TSN method are among the leading methods. Also, the challenge of comparing multiple averages, the existence of type one error, and the concept of fold change are also briefly discussed. In the present study, it has tried to discuss the theoretical foundations, raw data structure, statistical analysis, and interpretation of the results of microarray technology and to be able to independently analyze the path of data to interpretation as well as overcome the existing challenges with the information that is given.