Bioinformatics investigation of expression changes of abomasal gene transcripts to Heamonchus contortus infection in resistant and sensitive sheep

Gastrointestinal nematodes (GIN) represent a major health issue for livestock production systems worldwide. Haemonchus contortus is one of the most pathogenic GIN in small ruminants and causes serious losses to farmers, both in impaired production and in control with anthelmintics. It has long been recognized that differences in host resistance and susceptibility to parasitic infection exist in various sheep breeds and that genetics may play an important role in regulating host resistance, which has spurred efforts to control parasitic infection through selective breeding for naturally resistant sheep. A detailed understanding of the genes and mechanisms involved in expressing a resistant phenotype and the factors that regulate this response would facilitate the identification of candidate genes for selection. Recent advances in high-throughput technology, such as microarrays and RNA sequencing, have made a large number of transcriptome data accessible. As a result, researchers are now able to obtain more reliable results by integrating information from multiple sources. Accordingly, meta-analysis can be used as a useful and powerful tool to identify differential gene expression. It can also find out genes that their products are key molecules in response to the infection and use in animal breeding programs. The purpose of this study was to conduct a systematic review and meta-analysis on data collected from infected sheep with H. contortus and general analysis of changes in abomasal gene transcripts in response to infection using RNA-seq technology and bioinformatics tools. In this study, to identify infection-related genes, pathways, and molecular mechanisms underlying host resistance to this parasite, a meta-analysis was performed by combining two different datasets, including 70 samples of sheep H. contortus infection with Rankprod package of R software. After pre-processing, to remove heterogenicity across studies, batch effect correction was performed on gene expression data. The result of the principal component analysis showed that batch correction reduced the batch variation among the datasets. Meta-analysis was carried out and DEGs selected by meta-analysis were further analyzed and characterized. Enrichment analysis, as an efficient method for functional analysis of massive genetic data, was used to determine the biological process, molecular function, and cellular component of DEGs. Moreover, we searched upstream regions of DEGs for over-represented DNA motifs and functional analysis of discovered motifs. To explore the potential interaction network of the DEGs, the protein-protein interaction network among the DEGs was analyzed using the STRING database, which included direct and indirect associations of proteins. After analyzing the result derived from STRING analysis and expression change information for each DEG, the network figure was drawn for the selected DEGs (connected with one or more DEGs) by using the Cytoscape software and hub genes identified with the CytoHubba plugin of Cytoscape. Results derived from the meta-analysis showed a total of 1388 differentially expressed genes between resistant and susceptible sheep. Among them, 1137 were significantly upregulated, whereas 251 were downregulated across the datasets. In the identified DEGs, DEGs corresponding to ribosomal protein S3A, lysozyme C-1-like (LOC443320), and heterogeneous nuclear ribonucleoprotein K were the most strongly upregulated ones, while tenascin C and fibromodulin were the most strongly downregulated. Results from enrichment analysis showed these differentially expressed genes (DEGs) were involved in different biological processes such as one-carbon metabolism, translation, cell surface receptor signaling pathway, immune response, metabolic pathways, PPAR signaling pathway, etc. Moreover, searching in upstream regions of DEGs to find DNA motifs, were able to identify eight conserved sequence motifs. The functional analysis of these motifs revealed that they were involved in the positive regulation of gene expression, defense response, positive regulation of immune response, cellular calcium ion homeostasis, etc. Using the protein-protein interaction analysis also identified multiple hub genes such as albumin and CD4 which may show that improved immune response, induced by up-regulation different genes affects the creation of resistance. The mechanisms of sheep resistance to GIN infections involve complex immune responses. Our results offered overall insight into changes in the transcriptomes of resistant and susceptible sheep and molecular mechanisms of host resistance induced by H. contortus infection. We propose these DEGs as a useful resource of molecular biomarkers and potential candidate genes for breeding programs which can provide a basis for further research on this topic.