Collaborative network analysis for the interpretation of transcriptomics data in rare diseases, an application to Huntington’s disease

Abstract

Background: Rare diseases may affect the quality of life of patients and in some cases be life-threatening. Therapeutic opportunities are often limited, in part because of the lack of understanding of the molecular mechanisms that can cause disease. This can be ascribed to the low prevalence of rare diseases and therefore the lower sample sizes available for research. A way to overcome this is to integrate experimental rare disease data with prior knowledge using network-based methods. Taking this one step further, we hypothesized that combining and analyzing the results from multiple network-based methods could provide data-driven hypotheses of pathogenicity mechanisms from multiple perspectives. Results: We analyzed a Huntington's disease (HD) transcriptomics dataset using six network-based methods in a collaborative way. These methods either inherently reported enriched annotation terms or their results were fed into enrichment analyses. The resulting significantly enriched Reactome pathways were then summarized using the ontological hierarchy which allowed the integration and interpretation of outputs from multiple methods. Among the resulting enriched pathways, there are pathways that have been shown previously to be involved in HD and pathways whose direct contribution to disease pathogenesis remains unclear and requires further investigation. Conclusions: In summary, our study shows that collaborative network analysis approaches are well-suited to study rare diseases, as they provide hypotheses for pathogenic mechanisms from multiple perspectives. Applying different methods to the same case study can uncover different disease mechanisms that would not be apparent with the application of a single method.