T2DB: A Web Database for Long Non-Coding RNA Genes in Type II Diabetes.
Rebecca DistefanoMirolyuba Simeonova IlievaJens Hedelund MadsenHideshi IshiiMasanori AikawaSarah RennieShziuka UchidaPublished in: Non-coding RNA (2023)
Type II diabetes (T2D) is a growing health problem worldwide due to increased levels of obesity and can lead to other life-threatening diseases, such as cardiovascular and kidney diseases. As the number of individuals diagnosed with T2D rises, there is an urgent need to understand the pathogenesis of the disease in order to prevent further harm to the body caused by elevated blood glucose levels. Recent advances in long non-coding RNA (lncRNA) research may provide insights into the pathogenesis of T2D. Although lncRNAs can be readily detected in RNA sequencing (RNA-seq) data, most published datasets of T2D patients compared to healthy donors focus only on protein-coding genes, leaving lncRNAs to be undiscovered and understudied. To address this knowledge gap, we performed a secondary analysis of published RNA-seq data of T2D patients and of patients with related health complications to systematically analyze the expression changes of lncRNA genes in relation to the protein-coding genes. Since immune cells play important roles in T2D, we conducted loss-of-function experiments to provide functional data on the T2D-related lncRNA USP30-AS1 , using an in vitro model of pro-inflammatory macrophage activation. To facilitate lncRNA research in T2D, we developed a web application, T2DB, to provide a one-stop-shop for expression profiling of protein-coding and lncRNA genes in T2D patients compared to healthy donors or subjects without T2D.
Keyphrases
- long non coding rna
- rna seq
- poor prognosis
- single cell
- end stage renal disease
- healthcare
- blood glucose
- genome wide
- genome wide identification
- type diabetes
- newly diagnosed
- ejection fraction
- chronic kidney disease
- prognostic factors
- glycemic control
- public health
- emergency department
- mental health
- big data
- electronic health record
- metabolic syndrome
- risk assessment
- deep learning
- long noncoding rna
- body mass index
- adipose tissue
- cardiovascular disease
- systematic review
- insulin resistance
- genome wide analysis
- bioinformatics analysis
- artificial intelligence
- amino acid
- gene expression