Co-localization of GSTP1 and JNK in transitional cell carcinoma of urinary bladder

Transitional cell carcinoma (TCC) of urinary bladder belongs to glutathione S-transferase P1 (GSTP1) overexpressing tumors. Upregulated GSTP1 in TCC is related to apoptosis inhibition. This antiapoptotic effects of GSTP1 might be mediated through protein:protein interaction with c-Jun NH2 -terminal kinase (JNK). Herein, we analyzed whether a direct link between GSTP1 and JNK exists in TCC. The presence of GSTP1/JNK complexes was analyzed by immunoprecipitation and Western blotting in 20 TCC specimens, obtained after surgery. Co-localization of GSTP1 and JNK was also investigated in the 5637 TCC cell line by immunofluorescence confocal microscopy. By means of immunoprecipitation we show for the first time the presence of GSTP1/JNK complexes in all TCC samples studied. A co-localization of GSTP1 and JNK was also demonstrated in the 5637 TCC cell line by means of confocal microscopy. Protein-protein interactions, together with co-localization between GSTP1 and JNK provide evidence that GSTP1 most probably inhibits apoptosis in TCC cells by non-covalent binding to JNK

Early metabolic markers identify potential targets for the prevention of type 2 diabetes

Aims/hypothesis The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers. Methods We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 nondiabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study. Results Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong's p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as alpha-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and alpha-hydroxybutyrate and routinely used clinical risk factors. Conclusions/interpretation This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.Peer reviewe

Use of Vascular Assessments and Novel Biomarkers to Predict Cardiovascular Events in Type 2 Diabetes:The SUMMIT VIP Study

Cardiovascular disease (CVD) risk prediction represents an increasing clinical challenge in the treatment of diabetes. We used a panel of vascular imaging, functional assessments, and biomarkers reflecting different disease mechanisms to identify clinically useful markers of risk for cardiovascular (CV) events in subjects with type 2 diabetes (T2D) with or without manifest CVD

Loss-of-function mutations in SLC30A8 protect against type 2 diabetes.

Neðst á síðunni er hægt að nálgast greinina í heild sinni með því að smella á hlekkinn View/OpenLoss-of-function mutations protective against human disease provide in vivo validation of therapeutic targets, but none have yet been described for type 2 diabetes (T2D). Through sequencing or genotyping of ~150,000 individuals across 5 ancestry groups, we identified 12 rare protein-truncating variants in SLC30A8, which encodes an islet zinc transporter (ZnT8) and harbors a common variant (p.Trp325Arg) associated with T2D risk and glucose and proinsulin levels. Collectively, carriers of protein-truncating variants had 65% reduced T2D risk (P = 1.7 × 10(-6)), and non-diabetic Icelandic carriers of a frameshift variant (p.Lys34Serfs*50) demonstrated reduced glucose levels (-0.17 s.d., P = 4.6 × 10(-4)). The two most common protein-truncating variants (p.Arg138* and p.Lys34Serfs*50) individually associate with T2D protection and encode unstable ZnT8 proteins. Previous functional study of SLC30A8 suggested that reduced zinc transport increases T2D risk, and phenotypic heterogeneity was observed in mouse Slc30a8 knockouts. In contrast, loss-of-function mutations in humans provide strong evidence that SLC30A8 haploinsufficiency protects against T2D, suggesting ZnT8 inhibition as a therapeutic strategy in T2D prevention.US National Institutes of Health (NIH) Training 5-T32-GM007748-33 Doris Duke Charitable Foundation 2006087 Fulbright Diabetes UK Fellowship BDA 11/0004348 Broad Institute from Pfizer, Inc. NIH U01 DK085501 U01 DK085524 U01 DK085545 U01 DK085584 Swedish Research Council Dnr 521-2010-3490 Dnr 349-2006-237 European Research Council (ERC) GENETARGET T2D GA269045 ENGAGE 2007-201413 CEED3 2008-223211 Sigrid Juselius Foundation Folkh lsan Research Foundation ERC AdG 293574 Research Council of Norway 197064/V50 KG Jebsen Foundation University of Bergen Western Norway Health Authority Lundbeck Foundation Novo Nordisk Foundation Wellcome Trust WT098017 WT064890 WT090532 WT090367 WT098381 Uppsala University Swedish Research Council and the Swedish Heart- Lung Foundation Academy of Finland 124243 102318 123885 139635 Finnish Heart Foundation Finnish Diabetes Foundation, Tekes 1510/31/06 Commission of the European Community HEALTH-F2-2007-201681 Ministry of Education and Culture of Finland European Commission Framework Programme 6 Integrated Project LSHM-CT-2004-005272 City of Kuopio and Social Insurance Institution of Finland Finnish Foundation for Cardiovascular Disease NIH/NIDDK U01-DK085545 National Heart, Lung, and Blood Institute (NHLBI) National Institute on Minority Health and Health Disparities N01 HC-95170 N01 HC-95171 N01 HC-95172 European Union Seventh Framework Programme, DIAPREPP Swedish Child Diabetes Foundation (Barndiabetesfonden) 5U01DK085526 DK088389 U54HG003067 R01DK072193 R01DK062370 Z01HG000024info:eu-repo/grantAgreement/EC/FP7/20201

Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci.

We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes

Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes

To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis