A collaborative network analysis for the interpretation of transcriptomics data in Huntington's disease

Abstract

Rare diseases may affect the quality of life of patients and be life-threatening. Therapeutic opportunities are often limited, in part because of the lack of understanding of the molecular mechanisms underlying these diseases. 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 pathogenic mechanisms from multiple perspectives.We analyzed a Huntington's disease 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 Huntington's disease and pathways whose direct contribution to disease pathogenesis remains unclear and requires further investigation.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.

Keywords: Collaborative analysis; Huntington’s disease; Network analysis; Rare disease.

Fig. 1

Collaborative network analysis workflow.

Fig. 2

Reactome pathway enrichments from the six network-based methods, summarized by orsum. The top 50 terms are presented.

Fig. 3

The UpSet plot presenting the numbers of unique and shared Immune System related annotation terms identified by each method. In the plots, a single circle points to the unique terms identified by the corresponding method while connected circles point to the terms shared by the corresponding methods. Note that wTO-CoDiNA has not found any Immune System term.

Fig. 4

Network of mRNA splicing proteins identified by MOGAMUN. Image is retrieved from STRING database.