Interleukin-1ß activates a short STAT-3 isoform in clonal insulin-secreting cells

Abstract Interleukin-1ß (IL-1ß) is a potent inflammatory cytokine involved in type 1 diabetes and acts through defined IL-1ß signaling pathways. In the present work we describe induction of DNA binding activity to signal transducer and activator of transcription (STAT) in response to IL-1ß in clonal insulin-secreting cells. Moreover, IL-1ß activates a short isoform of STAT-3 that potently stimulates transcription. Immunopre- cipitation studies reveal an interaction between the activated STAT-3 and the IL-1 receptor accessory protein indicating an association between the two signaling pathways. This may be a novel point of transduction cross talk and an additional mechanism utilised by IL-1ß in the pancreatic ß-cell during the process of type 1 diabetes. z 1999 Federation of European Biochemical Societies

Leptin signalling in pancreatic islets and clonal insulin-secreting cells

Leptin is a cytokine secreted from adipose tissue at a rate commensurate with the size of the body's fat stores. In addition to its anorectic and thermogenic central actions, leptin is known to act on peripheral tissues, including the pancreatic ß-cell where it inhibits insulin secretion and reduces insulin transcript levels. However, the role of leptin signalling through its full-length receptor, OB-Rb, in the ß-cell remains unclear. In the present study, we show that leptin activates a signal transducer and activator of transcription (STAT)3 signalling mechanism in pancreatic islets and in a rat model of the pancreatic ß-cell, RINm5F. Leptin induced DNA binding to a STAT consensus oligonucleotide and resulted in transcriptional activation from STAT reporter constructs in a manner consistent with STAT3 activation. Western blot analysis confirmed activation of STAT3 in RINm5F and isolated rat islets. Conditions that mimic increased metabolic activity resulted in attenuation of leptin-mediated STAT DNA binding but had no significant eVect on STAT3 tyrosine phosphorylation in RINm5F cells. In addition, leptin activated the mitogen activated protein (MAP) kinase pathway in RINm5F cells. The present study provides a framework for OB-Rb signalling mechanisms in the programming of the ß-cell by leptin and suggests that increased metabolic activity may modulate this function

Expression quantitative trait loci are highly sensitive to cellular differentiation state

Blood cell development from multipotent hematopoietic stem cells to specialized blood cells is accompanied by drastic changes in gene expression for which the triggers remain mostly unknown. Genetical genomics is an approach linking natural genetic variation to gene expression variation, thereby allowing the identification of genomic loci containing gene expression modulators (eQTLs). In this paper, we used a genetical genomics approach to analyze gene expression across four developmentally close blood cell types collected from a large number of genetically different but related mouse strains. We found that, while a significant number of eQTLs (365) had a consistent “static” regulatory effect on gene expression, an even larger number were found to be very sensitive to cell stage. As many as 1,283 eQTLs exhibited a “dynamic” behavior across cell types. By looking more closely at these dynamic eQTLs, we show that the sensitivity of eQTLs to cell stage is largely associated with gene expression changes in target genes. These results stress the importance of studying gene expression variation in well-defined cell populations. Only such studies will be able to reveal the important differences in gene regulation between different ce

Genetical genomics: spotlight on QTL hotspots

No abstract available

Genome-Wide Association of Pericardial Fat Identifies a Unique Locus for Ectopic Fat

Pericardial fat is a localized fat depot associated with coronary artery calcium and myocardial infarction. We hypothesized that genetic loci would be associated with pericardial fat independent of other body fat depots. Pericardial fat was quantified in 5,487 individuals of European ancestry from the Framingham Heart Study (FHS) and the Multi-Ethnic Study of Atherosclerosis (MESA). Genotyping was performed using standard arrays and imputed to ∼2.5 million Hapmap SNPs. Each study performed a genome-wide association analysis of pericardial fat adjusted for age, sex, weight, and height. A weighted z-score meta-analysis was conducted, and validation was obtained in an additional 3,602 multi-ethnic individuals from the MESA study. We identified a genome-wide significant signal in our primary meta-analysis at rs10198628 near TRIB2 (MAF 0.49, p = 2.7×10-08). This SNP was not associated with visceral fat (p = 0.17) or body mass index (p = 0.38), although we observed direction-consistent, nominal significance with visceral fat adjusted for BMI (p = 0.01) in the Framingham Heart Study. Our findings were robust among African ancestry (n = 1,442, p = 0.001), Hispanic (n = 1,399, p = 0.004), and Chinese (n = 761, p = 0.007) participants from the MESA study, with a combined p-value of 5.4E-14. We observed TRIB2 gene expression in the pericardial fat of mice. rs10198628 near TRIB2 is associated with pericardial fat but not measures of generalized or visceral adiposity, reinforcing the concept that there are unique genetic underpinnings to ectopic fat distribution

Intra- and inter-individual genetic differences in gene expression

Genetic variation is known to influence the amount of mRNA produced by a gene. Given that the molecular machines control mRNA levels of multiple genes, we expect genetic variation in the components of these machines would influence multiple genes in a similar fashion. In this study we show that this assumption is correct by using correlation of mRNA levels measured independently in the brain, kidney or liver of multiple, genetically typed, mice strains to detect shared genetic influences. These correlating groups of genes (CGG) have collective properties that account for 40-90% of the variability of their constituent genes and in some cases, but not all, contain genes encoding functionally related proteins. Critically, we show that the genetic influences are essentially tissue specific and consequently the same genetic variations in the one animal may up-regulate a CGG in one tissue but down-regulate the same CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. The implication of this study is that this class of genetic variation can result in complex inter- and intra-individual and tissue differences and that this will create substantial challenges to the investigation of phenotypic outcomes, particularly in humans where multiple tissues are not readily available.

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Phenotype Prediction Using Regularized Regression on Genetic Data in the DREAM5 Systems Genetics B Challenge

A major goal of large-scale genomics projects is to enable the use of data from high-throughput experimental methods to predict complex phenotypes such as disease susceptibility. The DREAM5 Systems Genetics B Challenge solicited algorithms to predict soybean plant resistance to the pathogen Phytophthora sojae from training sets including phenotype, genotype, and gene expression data. The challenge test set was divided into three subcategories, one requiring prediction based on only genotype data, another on only gene expression data, and the third on both genotype and gene expression data. Here we present our approach, primarily using regularized regression, which received the best-performer award for subchallenge B2 (gene expression only). We found that despite the availability of 941 genotype markers and 28,395 gene expression features, optimal models determined by cross-validation experiments typically used fewer than ten predictors, underscoring the importance of strong regularization in noisy datasets with far more features than samples. We also present substantial analysis of the training and test setup of the challenge, identifying high variance in performance on the gold standard test sets.National Science Foundation (U.S.). Graduate Research Fellowship ProgramNational Defense Science and Engineering Graduate Fellowshi