WATER CHEMISTRY ON SURFACE DEFECT SITES - CHEMIDISSOCIATION VERSUS PHYSISORPTION ON MGO(001)

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miR-375 gene dosage in pancreatic β-cells: implications for regulation of β-cell mass and biomarker development

MicroRNAs play a crucial role in the regulation of cell growth and differentiation. Mice with genetic deletion of miR-375 exhibit impaired glycemic control due to decreased β-cell and increased α-cell mass and function. The relative importance of these processes for the overall phenotype of miR-375KO mice is unknown. Here, we show that mice overexpressing miR-375 exhibit normal β-cell mass and function. Selective re-expression of miR-375 in β-cells of miR-375KO mice normalizes both, α- and β-cell phenotypes as well as glucose metabolism. Using this model, we also analyzed the contribution of β-cells to the total plasma miR-375 levels. Only a small proportion (≈1 %) of circulating miR-375 originates from β-cells. Furthermore, acute and profound β-cell destruction is sufficient to detect elevations of miR-375 levels in the blood. These findings are supported by higher miR-375 levels in the circulation of type 1 diabetes (T1D) subjects but not mature onset diabetes of the young (MODY) and type 2 diabetes (T2D) patients. Together, our data support an essential role for miR-375 in the maintenance of β-cell mass and provide in vivo evidence for release of miRNAs from pancreatic β-cells. The small contribution of β-cells to total plasma miR-375 levels make this miRNA an unlikely biomarker for β-cell function but suggests a utility for the detection of acute β-cell death for autoimmune diabetes

Decoy activity through microRNAs : the therapeutic implications

Introduction: microRNAs (miRNAs), small noncoding RNAs, are deregulated in several diseases including cancer. miRNAs regulate gene expression at a posttranscriptional level by binding to 5´UTR, coding regions or 3´UTR of messenger RNAs (mRNA), inhibiting mRNA translation or causing mRNA degradation. The same miRNA can have multiple mRNA targets, and the same mRNA can be regulated by various miRNAs. Areas covered: Recently, seminal contributions by several groups have implicated miRNAs as components of an RNA--RNA language that involves crosstalk between competing endogenous RNAs through a decoy mechanism. We review the studies that described miRNAs as players in a biological decoy activity. miRNAs can either be trapped by competing endogenous RNAs or interact with proteins that have binding sites for mRNAs. Expert opinion: The miRNA decoy functions have implications for the design of therapeutic approaches in human diseases, including specific ways to overcome resistance to drug therapy and future miRNA-based clinical trials design.M.I.A. is supported by a PhD fellowship (SFRH/BD/47031/2008) from Fundacão para a Ciência e Tecnologia, Portugal. Dr. Calin is The Alan M. Gewirtz Leukemia & Lymphoma Society Scholar. He is also supported as a Fellow at The University of Texas MD Anderson Research Trust, as a University of Texas System Regents Research Scholar, and by the CLL Global Research Foundation. Work in Dr. Calin’s laboratory is supported in part by an NIH/NCI grant (CA135444), a Department of Defense Breast Cancer Idea Award, Developmental Research Awards in Breast Cancer, Ovarian Cancer, Brain Cancer, Prostate Cancer, Multiple Myeloma, and Leukemia SPOREs, the Laura and John Arnold Foundation, the RGK Foundation and the Estate of C. G. Johnson, Jr. The authors disclose no conflicts of interests and no funding was received in preparation of this manuscript

Study profile: the Durban Diabetes Study (DDS): a platform for chronic disease research.

The Durban Diabetes Study (DDS) is a population-based cross-sectional survey of an urban black population in the eThekwini Municipality (city of Durban) in South Africa. The survey combines health, lifestyle and socioeconomic questionnaire data with standardised biophysical measurements, biomarkers for non-communicable and infectious diseases, and genetic data. Data collection for the study is currently underway and the target sample size is 10 000 participants. The DDS has an established infrastructure for survey fieldwork, data collection and management, sample processing and storage, managed data sharing and consent for re-approaching participants, which can be utilised for further research studies. As such, the DDS represents a rich platform for investigating the distribution, interrelation and aetiology of chronic diseases and their risk factors, which is critical for developing health care policies for disease management and prevention. For data access enquiries please contact the African Partnership for Chronic Disease Research (APCDR) at [email protected] or the corresponding author.The study was supported by the Wellcome Trust (grant number 098051), the African Partnership for Chronic Disease Research (Medical Research Council UK partnership grant number MR/K013491/1), the National Institute for Health Research Cambridge Biomedical Research Centre (UK), the Gates Cambridge Scholarship programme (UK), Novo-Nordisk (South Africa), Sanofi-Aventis (South Africa), and MSD Pharmaceuticals (Pty) Ltd (Southern Africa).This is the final version of the article. It first appeared from Cambridge University Press via http://dx.doi.org/10.1017/gheg.2015.

Generation Scotland: Donor DNA Databank; A control DNA resource

<p>Abstract</p> <p>Background</p> <p>Many medical disorders of public health importance are complex diseases caused by multiple genetic, environmental and lifestyle factors. Recent technological advances have made it possible to analyse the genetic variants that predispose to complex diseases. Reliable detection of these variants requires genome-wide association studies in sufficiently large numbers of cases and controls. This approach is often hampered by difficulties in collecting appropriate control samples. The Generation Scotland: Donor DNA Databank (GS:3D) aims to help solve this problem by providing a resource of control DNA and plasma samples accessible for research.</p> <p>Methods</p> <p>GS:3D participants were recruited from volunteer blood donors attending Scottish National Blood Transfusion Service (SNBTS) clinics across Scotland. All participants gave full written consent for GS:3D to take spare blood from their normal donation. Participants also supplied demographic data by completing a short questionnaire.</p> <p>Results</p> <p>Over five thousand complete sets of samples, data and consent forms were collected. DNA and plasma were extracted and stored. The data and samples were unlinked from their original SNBTS identifier number. The plasma, DNA and demographic data are available for research. New data obtained from analysis of the resource will be fed back to GS:3D and will be made available to other researchers as appropriate.</p> <p>Conclusions</p> <p>Recruitment of blood donors is an efficient and cost-effective way of collecting thousands of control samples. Because the collection is large, subsets of controls can be selected, based on age range, gender, and ethnic or geographic origin. The GS:3D resource should reduce time and expense for investigators who would otherwise have had to recruit their own controls.</p

An evaluation of different meta-analysis approaches in the presence of allelic heterogeneity

Meta-analysis has proven a useful tool in genetic association studies. Allelic heterogeneity can arise from ethnic background differences across populations being meta-analyzed (for example, in search of common frequency variants through genome-wide association studies), and through the presence of multiple low frequency and rare associated variants in the same functional unit of interest (for example, within a gene or a regulatory region). The latter challenge will be increasingly relevant in whole-genome and whole-exome sequencing studies investigating association with complex traits. Here, we evaluate the performance of different approaches to meta-analysis in the presence of allelic heterogeneity. We simulate allelic heterogeneity scenarios in three populations and examine the performance of current approaches to the analysis of these data. We show that current approaches can detect only a small fraction of common frequency causal variants. We also find that for low-frequency variants with large effects (odds ratios 2–3), single-point tests have high power, but also high false-positive rates. P-value based meta-analysis of summary results from allele-matching locus-wide tests outperforms collapsing approaches. We conclude that current strategies for the combination of genetic association data in the presence of allelic heterogeneity are insufficiently powered

Sequencing PDX1 (insulin promoter factor 1) in 1788 UK individuals found 5% had a low frequency coding variant, but these variants are not associated with Type 2 diabetes

OnlineOpen Article. This is a copy of an article published in Diabetic Medicine. This journal is available online at: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1464-5491Genome-wide association studies have identified >30 common variants associated with Type 2 diabetes (>5% minor allele frequency). These variants have small effects on individual risk and do not account for a large proportion of the heritable component of the disease. Monogenic forms of diabetes are caused by mutations that occur in <1:2000 individuals and follow strict patterns of inheritance. In contrast, the role of low frequency genetic variants (minor allele frequency 0.1-5%) in Type 2 diabetes is not known. The aim of this study was to assess the role of low frequency PDX1 (also called IPF1) variants in Type 2 diabetes

Decreased STARD10 expression is associated with defective insulin secretion in humans and mice

Genetic variants near ARAP1 (CENTD2) and STARD10 influence type 2 diabetes (T2D) risk. The risk alleles impair glucose-induced insulin secretion and, paradoxically but characteristically, are associated with decreased proinsulin:insulin ratios, indicating improved proinsulin conversion. Neither the identity of the causal variants nor the gene(s) through which risk is conferred have been firmly established. Whereas ARAP1 encodes a GTPase activating protein, STARD10 is a member of the steroidogenic acute regulatory protein (StAR)-related lipid transfer protein family. By integrating genetic fine-mapping and epigenomic annotation data and performing promoter-reporter and chromatin conformational capture (3C) studies in β cell lines, we localize the causal variant(s) at this locus to a 5 kb region that overlaps a stretch-enhancer active in islets. This region contains several highly correlated T2D-risk variants, including the rs140130268 indel. Expression QTL analysis of islet transcriptomes from three independent subject groups demonstrated that T2D-risk allele carriers displayed reduced levels of STARD10 mRNA, with no concomitant change in ARAP1 mRNA levels. Correspondingly, β-cell-selective deletion of StarD10 in mice led to impaired glucose-stimulated Ca2+ dynamics and insulin secretion and recapitulated the pattern of improved proinsulin processing observed at the human GWAS signal. Conversely, overexpression of StarD10 in the adult β cell improved glucose tolerance in high fat-fed animals. In contrast, manipulation of Arap1 in β cells had no impact on insulin secretion or proinsulin conversion in mice. This convergence of human and murine data provides compelling evidence that the T2D risk associated with variation at this locus is mediated through reduction in STARD10 expression in the β cell

Variation in CHI3LI in Relation to Type 2 Diabetes and Related Quantitative Traits

CHI3LI encoding the inflammatory glycoprotein YKL-40 is located on chromosome 1q32.1. YKL-40 is involved in inflammatory processes and patients with Type 2 Diabetes (T2D) have elevated circulating YKL-40 levels which correlate with their level of insulin resistance. Interestingly, it has been reported that rs10399931 (-329 G/A) of CHI3LI contributes to the inter-individual plasma YKL-40 levels in patients with sarcoidosis, and that rs4950928 (-131 C/G) is a susceptibility polymorphism for asthma and a decline in lung function. We hypothesized that single nucleotide polymorphisms (SNPs) or haplotypes thereof the CHI3LI locus might influence risk of T2D. The aim of the present study was to investigate the putative association between SNPs and haplotype blocks of CHI3LI and T2D and T2D related quantitative traits.Eleven SNPs of CHI3LI were genotyped in 6514 individuals from the Inter99 cohort and 2924 individuals from the outpatient clinic at Steno Diabetes Center. In cas-control studies a total of 2345 T2D patients and 5302 individuals with a normal glucose tolerance test were examined. We found no association between rs10399931 (OR, 0.98 (CI, 0.88-1.10), p = 0.76), rs4950928 (0.98 (0.87-1.10), p = 0.68) or any of the other SNPs with T2D. Similarly, we found no significant association between any of the 11 tgSNPs and T2D related quantitative traits, all p>0.14. None of the identified haplotype blocks of CHI3LI showed any association with T2D, all p>0.16.None of the examined SNPs or haplotype blocks of CHI3LI showed any association with T2D or T2D related quantitative traits. Estimates of insulin resistance and dysregulated glucose homeostasis in T2D do not seem to be accounted for by the examined variations of CHI3LI

The Impact of Imputation on Meta-Analysis of Genome-Wide Association Studies

Genotype imputation is often used in the meta-analysis of genome-wide association studies (GWAS), for combining data from different studies and/or genotyping platforms, in order to improve the ability for detecting disease variants with small to moderate effects. However, how genotype imputation affects the performance of the meta-analysis of GWAS is largely unknown. In this study, we investigated the effects of genotype imputation on the performance of meta-analysis through simulations based on empirical data from the Framingham Heart Study. We found that when fix-effects models were used, considerable between-study heterogeneity was detected when causal variants were typed in only some but not all individual studies, resulting in up to ∼25% reduction of detection power. For certain situations, the power of the meta-analysis can be even less than that of individual studies. Additional analyses showed that the detection power was slightly improved when between-study heterogeneity was partially controlled through the random-effects model, relative to that of the fixed-effects model. Our study may aid in the planning, data analysis, and interpretation of GWAS meta-analysis results when genotype imputation is necessary