Cyclin D1-CDK4 Controls Glucose Metabolism Independently of Cell Cycle Progression

Insulin constitutes a major evolutionarily conserved hormonal axis for maintaining glucose homeostasis1-3; dysregulation of this axis causes diabetes2,4. PGC-1α links insulin signaling to the expression of glucose and lipid metabolic genes5-7. GCN5 acetylates PGC-1α and suppresses its transcriptional activity, whereas SIRT1 deacetylates and activates PGC-1α8,9. Although insulin is a mitogenic signal in proliferative cells10,11, whether components of the cell cycle machinery contribute to insulin’s metabolic action is poorly understood. Herein, we report that insulin activates cyclin D1-CDK4, which, in turn, increases GCN5 acetyltransferase activity and suppresses hepatic glucose production independently of cell cycle progression. Through a cell-based high throughput chemical screen, we identified a CDK4 inhibitor that potently decreases PGC-1α acetylation. Insulin/GSK3β signaling induces cyclin D1 protein stability via sequestering cyclin D1 in the nucleus. In parallel, dietary amino acids increase hepatic cyclin D1 mRNA transcripts. Activated cyclin D1-CDK4 kinase phosphorylates and activates GCN5, which then acetylates and inhibits PGC-1α activity on gluconeogenic genes. Loss of hepatic cyclin D1 results in increased gluconeogenesis and hyperglycemia. In diabetic models, cyclin D1-CDK4 is chronically elevated and refractory to fasting/feeding transitions; nevertheless further activation of this kinase normalizes glycemia. Our findings show that insulin uses components of the cell cycle machinery in post-mitotic cells to control glucose homeostasis independently of cell division

Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure

Background Current risk prediction models in heart failure (HF) including clinical characteristics and biomarkers only have moderate predictive value. The aim of this study was to use matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling to determine if a combination of peptides identified with MALDI-MS will better predict clinical outcomes of patients with HF. Methods A cohort of 100 patients with HF were recruited in the biomarker discovery phase (50 patients who died or had a HF hospital admission vs. 50 patients who did not have an event). The peptide extraction from plasma samples was performed using reversed phase C18. Then samples were analysed using MALDI-MS. A multiple peptide biomarker model was discovered that was able to predict clinical outcomes for patients with HF. Finally, this model was validated in an independent cohort with 100 patients with HF. Results After normalisation and alignment of all the processed spectra, a total of 11,389 peptides (m/z) were detected using MALDI-MS. A multiple biomarker model was developed from 14 plasma peptides that was able to predict clinical outcomes in HF patients with an area under the receiver operating characteristic curve (AUC) of 1.000 (p = 0.0005). This model was validated in an independent cohort with 100 HF patients that yielded an AUC of 0.817 (p = 0.0005) in the biomarker validation phase. Addition of this model to the BIOSTAT risk prediction model increased the predictive probability for clinical outcomes of HF from an AUC value of 0.643 to an AUC of 0.823 (p = 0.0021). Moreover, using the prediction model of fourteen peptides and the composite model of the multiple biomarker of fourteen peptides with the BIOSTAT risk prediction model achieved a better predictive probability of time-to-event in prediction of clinical events in patients with HF (p = 0.0005). Conclusions The results obtained in this study suggest that a cluster of plasma peptides using MALDI-MS can reliably predict clinical outcomes in HF that may help enable precision medicine in HF

Global Array-Based Transcriptomics from Minimal Input RNA Utilising an Optimal RNA Isolation Process Combined with SPIA cDNA Probes

Technical advances in the collection of clinical material, such as laser capture microdissection and cell sorting, provide the advantage of yielding more refined and homogenous populations of cells. However, these attractive advantages are counter balanced by the significant difficultly in obtaining adequate nucleic acid yields to allow transcriptomic analyses. Established technologies are available to carry out global transcriptomics using nanograms of input RNA, however, many clinical samples of low cell content would be expected to yield RNA within the picogram range. To fully exploit these clinical samples the challenge of isolating adequate RNA yield directly and generating sufficient microarray probes for global transcriptional profiling from this low level RNA input has been addressed in the current report. We have established an optimised RNA isolation workflow specifically designed to yield maximal RNA from minimal cell numbers. This procedure obtained RNA yield sufficient for carrying out global transcriptional profiling from vascular endothelial cell biopsies, clinical material not previously amenable to global transcriptomic approaches. In addition, by assessing the performance of two linear isothermal probe generation methods at decreasing input levels of good quality RNA we demonstrated robust detection of a class of low abundance transcripts (GPCRs) at input levels within the picogram range, a lower level of RNA input (50 pg) than previously reported for global transcriptional profiling and report the ability to interrogate the transcriptome from only 10 pg of input RNA. By exploiting an optimal RNA isolation workflow specifically for samples of low cell content, and linear isothermal RNA amplification methods for low level RNA input we were able to perform global transcriptomics on valuable and potentially informative clinically derived vascular endothelial biopsies here for the first time. These workflows provide the ability to robustly exploit ever more common clinical samples yielding extremely low cell numbers and RNA yields for global transcriptomics

Plasma proteomic approach in patients with heart failure:insights into pathogenesis of disease progression and potential novel treatment targets

Aims To provide insights into pathogenesis of disease progression and potential novel treatment targets for patients with heart failure by investigation of the plasma proteome using network analysis. Methods and results The plasma proteome of 50 patients with heart failure who died or were rehospitalised were compared with 50 patients with heart failure, matched for age and sex, who did not have an event. Peptides were analysed on two‐dimensional liquid chromatography coupled to tandem mass spectrometry (2D LC ESI‐MS/MS) in high definition mode (HDMSE). We identified and quantified 3001 proteins, of which 51 were significantly up‐regulated and 46 down‐regulated with more than two‐fold expression changes in those who experienced death or rehospitalisation. Gene ontology enrichment analysis and protein–protein interaction networks of significant differentially expressed proteins discovered the central role of metabolic processes in clinical outcomes of patients with heart failure. The findings revealed that a cluster of proteins related to glutathione metabolism, arginine and proline metabolism, and pyruvate metabolism in the pathogenesis of poor outcome in patients with heart failure who died or were rehospitalised. Conclusions Our findings show that in patients with heart failure who died or were rehospitalised, the glutathione, arginine and proline, and pyruvate pathways were activated. These pathways might be potential targets for therapies to improve poor outcomes in patients with heart failure

Epicardial adipose tissue is related to arterial stiffness and inflammation in patients with cardiovascular disease and type 2 diabetes.

BACKGROUND: Epicardial adipose tissue (EAT) is an emerging cardio-metabolic risk factor and has been shown to correlate with adverse cardiovascular (CV) outcome; however the underlying pathophysiology of this link is not well understood. The aim of this study was to evaluate the relationship between EAT and a comprehensive panel of cardiovascular risk biomarkers and pulse wave velocity (PWV) and indexed left ventricular mass (LVMI) in a cohort of patients with cardiovascular disease (CVD) and diabetes compared to controls. METHODS: One hundred forty-five participants (mean age 63.9 ± 8.1 years; 61% male) were evaluated. All patients underwent cardiovascular magnetic resonance (CMR) examination and PWV. EAT measurements from CMR were performed on the 4-chamber view. Blood samples were taken and a range of CV biomarkers was evaluated. RESULTS: EAT measurements were significantly higher in the groups with CVD, with or without T2DM compared to patients without CVD or T2DM (group 1 EAT 15.9 ± 5.5 cm2 vs. group 4 EAT 11.8 ± 4.1 cm2, p = 0.001; group 3 EAT 15.1 ± 4.3 cm2 vs. group 4 EAT 11.8 ± 4.1 cm2, p = 0.024). EAT was independently associated with IL-6 (beta 0.2, p = 0.019). When added to clinical variables, both EAT (beta 0.16, p = 0.035) and IL-6 (beta 0.26, p = 0.003) were independently associated with PWV. EAT was significantly associated with LVMI in a univariable analysis but not when added to significant clinical variables. CONCLUSIONS: In patients with cardio-metabolic disease, EAT was independently associated with PWV. EAT may be associated with CVD risk due to an increase in systemic vascular inflammation. Whether targeting EAT may reduce inflammation and/or cardiovascular risk should be evaluated in prospective studies

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Abstract: Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies