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

Abstract

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 coexistence 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.

Diseasome and comorbidities complexities of SARS-CoV-2 infection with common malignant diseases

“…To clarify the significance of the DEGs from transcriptomic datasets, we have performed gene ontologies and pathway analysis. The pathway-based analysis represents how complex diseases associates with other underlying molecular mechanisms [ 27 ]. Moreover, the following framework is provided on the BP involved in each COVID-19 study.…”

“…However, Figures 7 and 8 show KEGG pathway association with selected datasets. This analysis is useful to understand how complex diseases may be related to each other through their underlying molecular mechanisms [ 27 ]. It represents the relationships between KEGG pathways of COVID-19 and associated cancer data sets.…”

“…Molecular pathways are perturbed in diseased conditions and identification of them enriched by the DEGs provides critical signaling pathways and drug targets. We utilized KEGG database [ 27 ] to identify COVID-19 pathways overlapped with different cancers enriched by the DEGs.…”

“…Differential expression can be used for identifying significant genes altered in a particular condition. To identify DEGs, a linear and Bayesian method was applied [ 27 ]. We considered three statistical criteria, namely P -value, adjusted P -value (False Discovery Rate) and absolute logFC values to screen statistically significant DEGs.…”

System biology and bioinformatics pipeline to identify comorbidities risk association: Neurodegenerative disorder case study

“…However, the use of gene expression analyses in the study of comorbidity may offer improved insights into AD disease mechanisms [ 39 ]. The availability of huge public transcriptomics resources such as microarray data and bioinformatics tools has enabled us to perform comorbidity analyses, i.e., identify gene pathways that enable two diseases to influence each other [ 40 , 41 ]. This study aims to take advantage of the transcriptomics data to demonstrate how AD and other NDDs impact each other at the molecular level through a series of bioinformatics and computational approaches.…”