Abnormalities in alternative splicing in diabetes: therapeutic targets

Abstract

Diabetes mellitus (DM) is a non-communicable, metabolic disorder that affects 416 million individuals worldwide. Type 2 diabetes contributes to a vast 85-90% of the diabetes incidences while 10-15% of patients suffer from type 1 diabetes. These two predominant forms of DM cause a significant loss of functional pancreatic β-cell mass causing different degrees of insulin deficiency, most likely, due to increased β-cell apoptosis. Treatment options involve the use of insulin sensitisers, α-glucosidase inhibitors, and β-cell secretagogues which are often expensive, limited in efficacy and carry detrimental adverse effects. Cost-effective options for treatment exists in the form of herbal drugs, however, scientific validations of these widely used medicinal plants are still underway. Alternative splicing (AS) is a co-ordinated post-transcriptional process in which a single gene generates multiple mRNA transcripts which results in increased amounts of functionally different protein isoforms and in some cases aberrant splicing leads to metabolic disease. In this review, we explore the association of AS with metabolic alterations in DM and the biological significance of the abnormal splicing of some pathogenic diabetes-related genes. An understanding of the molecular mechanism behind abnormally spliced transcripts will aid in the development of new diagnostic, prognostic and therapeutic tools.

Changes in Splicing Machinery Components Influence, Precede, and Early Predict the Development of Type 2 Diabetes: From the CORDIOPREV Study

“…obesity, insulin resistance, etc.) the splicing machinery is markedly altered in most tissues [ [10] , [11] , [12] ] and associated with the development of several pathologies [ 10 , 13 , 14 ]. Actually, alternative splicing seems to reside at the crossroad between hyperinsulinemia, insulin resistance, obesity and T2DM [ 11 , 12 , 17 ], and, consequently, the correct function of the splicing machinery (spliceosome components and SFs) is essential to maintain whole body homeostasis [ 18 ].…”

“…Therefore, since postprandial alterations are closely related to the phenotypic flexibility, which is strongly linked to T2DM development [ 9 ], our data primarily demonstrate that the alteration in the splicing machinery precedes the instauration of T2DM, thereby suggesting its putative implication as a driving force in the development of this pathology. Based on all the information mentioned above, it is tempting to propose that the splicing machinery could be acting as a biosensor of the whole body metabolism to adapt cell gene expression to the pathophysiological conditions, and that its dysregulation could lead to an unbalance in the landscape of splicing variants present in a given cell at a given moment [ 12 , 28 , 29 ], which may be associated to the instauration of T2DM [ 10 , 30 ]. This idea is further supported by two pieces of evidence presented herein.…”

“…Particularly, there is emerging evidence that under adverse metabolic conditions (obesity, insulin resistance, etc.) the splicing machinery is markedly dysregulated in most tissues [ [10] , [11] , [12] ], including peripheral blood mononuclear cells (PBMCs); and that dysregulations in splicing processes are associated with development of several pathologies [ 10 , 13 , 14 ]. Specifically, splicing process is catalyzed by the minor and major spliceosomes, which act on different types of introns [ 15 ].…”

Alternative Splicing in the Hippo Pathway—Implications for Disease and Potential Therapeutic Targets

“…It is therefore not surprising that AS has been implicated in regulating key signalling pathways such as Ras-MAPK and PI3K-mTOR signalling [ 14 ]. Aberrant AS is a major contributor to several neurological diseases including Duchenne muscular dystrophy (DMD) [ 15 ], spinal muscular atrophy (SMA) [ 16 ], diabetes [ 17 ] and is implicated in the development and progression of many types of cancer [ 18 , 19 ]. As such, there has been a lot of interest in using splice isoforms as disease biomarkers and in developing novel therapeutic strategies aimed at oncogenic splice isoforms [ 20 , 21 ].…”

A deep learning approach to identify new gene targets of a novel therapeutic for human splicing disorders

“…Disruption of splicing regulatory elements can generate aberrant transcripts through complete or partial exon skipping, intron inclusion or mis-regulation of alternative splicing, while mutations in the UTRs may affect transcript localization, stability or efficiency of translation. Mutations that alter mRNA splicing are known to lead to many human monogenic diseases including spinal muscular atrophy (SMA), neurofibromatosis type 1 (NF1), cystic fibrosis (CF), familial dysautonomia (FD), Duchenne muscular dystrophy (DMD) and myotonic dystrophy (DM), as well as contribute to complex diseases such as cancer and diabetes [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] . The emergence of high throughput sequencing of large disease cohorts [19][20][21] , and the remarkable efforts to aggregate and annotate these mutations in an accessible infrastructure such as ClinVar 22 , now provides an unprecedented opportunity to apply novel deep learning approaches to predict mutations that affect pre-mRNA splicing 23 .…”