The Emerging Role and Promise of Circular RNAs in Obesity and Related Metabolic Disorders.
Mohamed ZaiouPublished in: Cells (2020)
Circular RNAs (circRNAs) are genome transcripts that are produced from back-splicing of specific regions of pre-mRNA. These single-stranded RNA molecules are widely expressed across diverse phyla and many of them are stable and evolutionary conserved between species. Growing evidence suggests that many circRNAs function as master regulators of gene expression by influencing both transcription and translation processes. Mechanistically, circRNAs are predicted to act as endogenous microRNA (miRNA) sponges, interact with functional RNA-binding proteins (RBPs), and associate with elements of the transcriptional machinery in the nucleus. Evidence is mounting that dysregulation of circRNAs is closely related to the occurrence of a range of diseases including cancer and metabolic diseases. Indeed, there are several reports implicating circRNAs in cardiovascular diseases (CVD), diabetes, hypertension, and atherosclerosis. However, there is very little research addressing the potential role of these RNA transcripts in the occurrence and development of obesity. Emerging data from in vitro and in vivo studies suggest that circRNAs are novel players in adipogenesis, white adipose browning, obesity, obesity-induced inflammation, and insulin resistance. This study explores the current state of knowledge on circRNAs regulating molecular processes associated with adipogenesis and obesity, highlights some of the challenges encountered while studying circRNAs and suggests some perspectives for future research directions in this exciting field of study.
Keyphrases
- insulin resistance
- high fat diet induced
- metabolic syndrome
- type diabetes
- weight loss
- adipose tissue
- gene expression
- cardiovascular disease
- skeletal muscle
- weight gain
- high fat diet
- polycystic ovary syndrome
- transcription factor
- risk assessment
- glycemic control
- healthcare
- blood pressure
- dna methylation
- machine learning
- coronary artery disease
- oxidative stress
- big data
- high glucose
- deep learning
- emergency department
- squamous cell
- body mass index
- binding protein
- young adults
- current status
- heat stress
- genetic diversity
- single molecule