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

Abstract

BackgroundType-2 diabetes mellitus (T2DM) is a major health problem with increasing incidence, which severely impacts cardiovascular disease. Because T2DM is associated with altered gene expression and aberrant splicing, we hypothesized that dysregulations in splicing machinery could precede, contribute to, and predict T2DM development.MethodsA cohort of patients with cardiovascular disease (CORDIOPREV study) and without T2DM at baseline (at the inclusion of the study) was used (n = 215). We determined the expression of selected splicing machinery components in fasting and 4 h-postprandial peripheral blood mononuclear cells (PBMCs, obtained at baseline) from all the patients who developed T2DM during 5-years of follow-up (n = 107 incident-T2DM cases) and 108 randomly selected non-T2DM patients (controls). Serum from incident-T2DM and control patients was used to analyze in vitro the modulation of splicing machinery expression in control PBMCs from an independent cohort of healthy subjects.FindingsExpression of key splicing machinery components (e.g. RNU2, RNU4 or RNU12) from fasting and 4 h-postprandial PBMCs of incident-T2DM patients was markedly altered compared to non-T2DM controls. Moreover, in vitro treatment of healthy individuals PBMCs with serum from incident-T2DM patients (compared to non-T2DM controls) reduced the expression of splicing machinery elements found down-regulated in incident-T2DM patients PBMCs. Finally, fasting/postprandial levels of several splicing machinery components in the PBMCs of CORDIOPREV patients were associated to higher risk of T2DM (Odds Ratio > 4) and could accurately predict (AUC > 0.85) T2DM development.InterpretationOur results reveal the existence of splicing machinery alterations that precede and predict T2DM development in patients with cardiovascular disease.FundISCIII, MINECO, CIBERObn.

Dietary Intervention Modulates the Expression of Splicing Machinery in Cardiovascular Patients at High Risk of Type 2 Diabetes Development: From the CORDIOPREV Study

“…Each year, T2DM was diagnosed according to the diagnosis criteria of ADA, specifically, if one or more of the following criteria were present in the study subjects: fasting plasma glucose (FPG) concentration ≥126 mg/dL, FPG ≥200 mg/dL after 2-h of oral glucose test (OGTT), glycated hemoglobin (HbA1c) ≥6.5% (≥48 mmol/mol). In the present study, we included the Incident-T2DM cases after a median follow-up of 60 months ( n = 107) together with 108 matched, randomly selected controls from the remaining 355 subjects who did not develop T2DM (non-T2DM subjects) during the study period, as previously reported [ 10 ] ( Supplementary Figure S1 ). The random selection of the non-T2Dm subjects was performed using stratified sampling from the 462 non-T2DM subjects of the CORDIOPREV study according to the following clinical, anthropometric and biochemical variables: diet, age, gender, fasting plasma glucose, body mass index, low-density lipoprotein (LDL)-cholesterol and high-density lipoprotein (HDL)-cholesterol.…”

“…In this context, early identification of patients at higher risk of T2DM development is critical for prevention of new cardiovascular events [ 8 , 9 ]. Interestingly, in searching for novel associated molecular mechanisms and predictive markers, we recently discovered that the expression pattern of certain splicing machinery elements in the peripheral blood mononuclear cells (PBMCs) of patients is tightly associated with the risk of T2DM and could accurately predict T2DM development in those individuals from the CORDIOPREV study, outperforming the capacity of classical predictors of T2DM development, such as glycated hemoglobin (HbA1c) or predictive scores (FINDRISK) [ 10 ], two established strategies that have limitations and cannot precisely predict an individual’s risk of developing T2DM [ 11 , 12 ].…”

“…In this scenario, it has been proposed that nutrients can influence processes essential for cell homeostasis by altering gene expression and, in particular, modulating the splicing of pre-mRNAs encoding key regulatory proteins (e.g., leptin receptor, insulin receptor) [ 24 ]. Moreover, several studies have shown that the gene expression pattern of PBMCs is severely influenced by the diet [ 25 , 26 , 27 , 28 ] and might reflect metabolic and immune responses of adipocytes or hepatocytes [ 29 , 30 ], thus providing valuable information to advance in the study of diseases such as T2DM and CVDs, using less invasive sampling methods [ 10 , 27 ].…”

Dysregulation of the splicing machinery is directly associated to aggressiveness of prostate cancer

“…Specific primers for human transcripts including components of the major spliceosome (n = 10), minor spliceosome (n = 4), associated SFs (n = 25) and three housekeeping genes (ACTB, GAPDH and HPRT) were specifically designed with the Primer3 software and StepOne TM Real-Time PCR System software v2.3 (Applied Biosystems, Foster City, CA, USA) (Supplemental Table 1). Preamplification, exonuclease treatment and qPCR dynamic array based on microfluidic technology were implemented as recently reported [11,12], following manufacturer's instructions using the Biomark System and the Real-Time PCR Analysis Software (Fluidigm). To control for variations in the efficiency of the retro-transcription reaction, mRNA copy numbers of the different transcripts analysed were adjusted by normalization factor, calculated with the expression levels of ACTB and GAPDH using GeNorm 3.3 [13].…”

“…Significant relation between categorized mRNA expression and biochemical PCa recurrence was studied using the long-rank-p-value method. Predictive models were constructed by Random Forest algorithm (with R language) as classifier as previously reported [11,12]. The rest of the statistical analyses were assessed using GraphPad Prism 7 (GraphPad Software, La Jolla, CA) or SPSS version 17.0.…”