Integration of gene expression data with interaction and annotation data reveals patterns of connection between primary Sjögren’s syndrome associated genes and immune processes

Background: The availability of whole genome sequences has spurred a revolution in biological analyses facilitating the development of technologies for the study of genes and proteins on a cell-wide basis. Consequently, a range of biological data, including metabolic and signalling pathways, experimental interaction data, and protein annotations, is available. There is considerable discordance in data from large-scale gene expression studies of primary SS (pSS). Combining these data with other types of genome-wide information can provide a more complete view of the cell in order to identify the key genes and biological processes that are involved in the pathogenesis of pSS. Methods: In this study, a list of genes, found to be differentially expressed between pSS patients and healthy controls in four large-scale microarray studies, was derived from the literature. KEGG metabolic pathway and GO biological process enrichment analyses were then performed for the individual datasets and the combined pSS-associated gene list. Using all the available human interaction data from the BioGRID database, a functional interaction network was produced in which nodes represented genes or gene products, and edges represented any type of interaction between the nodes. The network was then annotated based on the pathway and process enrichment results. Finally, the network was filtered to identify patterns of connectivity between pSS-associated genes and the enriched cellular processes. Results: Despite the overlap of differentially expressed genes in the four datasets being relatively low, there is significant overlap between the GO biological process enrichments of these genes, with the majority being related to immune processes. Significant enrichment of immune-related KEGG pathways was also observed for the pSS-associated gene list. Following network filtering, a cluster of the pSS-associated genes was identified. All four gene expression datasets were represented within this connected component. Several clusters of genes annotated to the processes innate immune response, multi-organism process, response to virus and response to stress were observed in the integrated network, and network filtering revealed patterns of interaction between these clusters and the pSS-associated genes. In particular, several pSS-associated genes were found to lie between clusters of genes annotated to the immune/stress responses and clusters involved in multi-organism processes. Conclusion: Gene enrichment and network analyses of the pSS-associated genes suggest that the innate immune responses, multi-organism processes, and the responses to virus and to stress are likely to be involved in pSS pathogenesis. Integration of multiple types of data in this manner can aid in the interpretation of results since combining diverse data sources reveals global properties not evident from a single data source. Future studies may benefit from incorporating additional detailed clinical data during the analysis of expression data in order to elucidate the relationship between gene expression and clinical phenotype.1 page(s