Bioinformatics Methodologies to Identify Interactions Between Type 2 Diabetes and Neurological Comorbidities

Type 2 diabetes (T2D) is a chronic metabolic disorder characterised by high blood sugar and insulin insensitivity which greatly increases the risk of developing neurological diseases (NDs). The co-existence of T2D and comorbidities such as NDs can complicate or even cause the failure of standard treatments for those diseases. Comorbidities can be both causally linked and influence each other’s development through genetic, molecular, environmental or lifestyle-based risk factors that they share. For T2D and NDs, such underlying common molecular mechanisms remain elusive but large amounts of molecular data accumulated on these diseases enable analytical approaches to identify their interconnected pathways. Here, we propose a framework to explore possible comorbidity interactions between a pair of diseases using a bioinformatic examination of the cellular pathways involved and explore this framework for T2D and NDs with analyses of a large number of publicly available gene expression datasets from tissues affected by these diseases. We designed a bioinformatics pipeline to analyse, utilize and combine gene expression, Gene Ontology (GO) and molecular pathway data by incorporating Gene Set Enrichment Analysis and Semantic Similarity. Our bioinformatics methodology was implemented in R, available at https://github.com/HabibUCAS/T2D_Comorbidity. We identified genes with altered expression shared by T2D and NDs as well as GOs and molecular pathways these diseases share. We also computed the proximity between T2D and neurological pathologies using these genes and GO term semantic similarity. Thus, our method has generated new insights into disease mechanisms important for both T2D and NDs that may mediate their interaction. Our bioinformatics pipeline could be applied to other co-morbidities to identify possible interactions and causal relationships between them.