Abstract
Type 1 diabetes (T1D) is an autoimmune disease. Different factors, including genetics and viruses may contribute to T1D, but the causes of T1D are not fully known, and there is currently no cure. The advent of high-throughput technologies has revolutionized the field of medicine and biology, and analysis of multi-source data along with clinical information has brought a better understanding of the mechanisms behind disease pathogenesis. The aim of this work was the development of a data repository linking clinical information and interactome studies in T1D. To address this goal, we analyzed the electronic health records and online databases of genes, proteins, miRNAs, and pathways to have a global view of T1D. There were common comorbid diseases such as anemia, hypertension, vitreous diseases, renal diseases, and atherosclerosis in the phenotypic disease networks. In the protein–protein interaction network, CASP3 and TNF were date-hub proteins involved in several pathways. Moreover, CTNNB1, IGF1R, and STAT3 were hub proteins, whereas miR-155-5p, miR-34a-5p, miR-23-3p, and miR-20a-5p were hub miRNAs in the gene-miRNA interaction network. Multiple levels of information including genetic, protein, miRNA and clinical data resulted in multiple results, which suggests the complementarity of multiple sources. With the integration of multifaceted information, it will shed light on the mechanisms underlying T1D; the provided data and repository has utility in understanding phenotypic disease networks for the potential development of comorbidities in T1D patients as well as the clues for further research on T1D comorbidities.
Highlights
Type 1 diabetes (T1D) is an autoimmune disease
Access to high-throughput data has revolutionized the field of medicine and biology, and analysis of multi-omics data along with clinical information has brought a better understanding of the mechanisms behind disease pathogenesis[9]
We analyzed the hospitalization data in Taiwan from 2002 to 2008 to draw the phenotypic disease network (PDN) of T1D consisting of 566 nodes with 644 links for male inpatients and 577 nodes with 667 links for female inpatients (Fig. 1)
Summary
Type 1 diabetes (T1D) is an autoimmune disease. Different factors, including genetics and viruses may contribute to T1D, but the causes of T1D are not fully known, and there is currently no cure. The aim of this work was the development of a data repository linking clinical information and interactome studies in T1D To address this goal, we analyzed the electronic health records and online databases of genes, proteins, miRNAs, and pathways to have a global view of T1D. The analysis of genotype–phenotype associations at multiple scales can provide a comprehensive view of novel insights into the cause and effect of diseases, and lead to a surprising interest in uncovering the organizing principles that govern the topology and the dynamics of various complex n etworks[8,20] It is an application of network science that offers a suitable framework to describe global relationships between human disorders, associated genes and interactome networks. The PPI network, miRNA information, GO enrichment analysis[21], and KEGG pathways[22,23] were employed to have a global view of T1D
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