Potential Role of Nuclear Factor κB in Diabetic Cardiomyopathy

Diabetic cardiomyopathy entails the cardiac injury induced by diabetes independently of any vascular disease or hypertension. Some transcription factors have been proposed to control the gene program involved in the setting and development of related processes. Nuclear factor-kappa B is a pleiotropic transcription factor associated to the regulation of many heart diseases. However, the nuclear factor-kappa B role in diabetic cardiomyopathy is under investigation. In this paper, we review the nuclear factor-kappa B pathway and its role in several processes that have been linked to diabetic cardiomyopathy, such as oxidative stress, inflammation, endothelial dysfunction, fibrosis, hypertrophy and apoptosis

A proteomic approach to the myocardium of hypertensive-diabetic rats

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Sitagliptin reduces cardiac apoptosis, hypertrophy and fibrosis primarily by insulin-dependent mechanisms in experimental type-II diabetes. Potential roles of GLP-1 isoforms

Background:Myocardial fibrosis is a key process in diabetic cardiomyopathy. However, their underlying mechanisms have not been elucidated, leading to a lack of therapy. The glucagon-like peptide-1 (GLP-1) enhancer, sitagliptin, reduces hyperglycemia but may also trigger direct effects on the heart.Methods:Goto-Kakizaki (GK) rats developed type-II diabetes and received sitagliptin, an anti-hyperglycemic drug (metformin) or vehicle (n=10, each). After cardiac structure and function assessment, plasma and left ventricles were isolated for biochemical studies. Cultured cardiomyocytes and fibroblasts were used for in vitro assays.Results:Untreated GK rats exhibited hyperglycemia, hyperlipidemia, plasma GLP-1 decrease, and cardiac cell-death, hypertrophy, fibrosis and prolonged deceleration time. Moreover, cardiac pro-apoptotic/necrotic, hypertrophic and fibrotic factors were up-regulated. Importantly, both sitagliptin and metformin lessened all these parameters. In cultured cardiomyocytes and cardiac fibroblasts, high-concentration of palmitate or glucose induced cell-death, hypertrophy and fibrosis. Interestingly, GLP-1 and its insulinotropic-inactive metabolite, GLP-1(9-36), alleviated these responses. In addition, despite a specific GLP-1 receptor was only detected in cardiomyocytes, GLP-1 isoforms attenuated the pro-fibrotic expression in cardiomyocytes and fibroblasts. In addition, GLP-1 receptor signalling may be linked to PPARδ activation, and metformin may also exhibit anti-apoptotic/necrotic and anti-fibrotic direct effects in cardiac cells.Conclusions:Sitagliptin, via GLP-1 stabilization, promoted cardioprotection in type-II diabetic hearts primarily by limiting hyperglycemia e hyperlipidemia. However, GLP-1 and GLP-1(9-36) promoted survival and anti-hypertrophic/fibrotic effects on cultured cardiac cells, suggesting cell-autonomous cardioprotective actionsThis work was supported by national funding from Ministerio de Educación y Ciencia (SAF2009-08367), Comunidad de Madrid (CCG10-UAM/ BIO-5289), and a unrestricted grant from by Merck/MS

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

Abstract The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared to information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known non-pathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification. This article is protected by copyright. All rights reserved.Peer reviewe