The role of GRIP1 and ephrin B3 in blood pressure control and vascular smooth muscle cell contractility

This work was supported by grants from the Canadian Institutes of Health Research to J.W. (MOP57697, MOP69089 and MOP 123389), H.L. (MOP97829), and G.C. (CMI72323). It was also financed by grants from the Natural Sciences and Engineering Research Council of Canada (203906-2012), and the J.-Louis Levesque Foundation to J.W. This study was also made possible by a group grant from the National Sciences Foundation of China (#81361120264) to J.S., S.H. T.W. and J.W. The funders provided support in the form of salaries for authors [Y.W.; Z.W.; H.L.; J.P.; J.R.], and experimental costs, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the “author contributions”. The authors thank Regeneron Pharmaceuticals for generously providing Efnb3 KO mice. The authors thank all the authors of the International Consortium for Blood Pressure Genome-Wide Association Studies for allow us to mine the study dataset

Genetic evidence for a normal-weight "metabolically obese" phenotype linking insulin resistance, hypertension, coronary artery disease, and type 2 diabetes

PublishedJournal ArticleResearch Support, Non-U.S. Gov'tThe mechanisms that predispose to hypertension, coronary artery disease (CAD), and type 2 diabetes (T2D) in individuals of normal weight are poorly understood. In contrast, in monogenic primary lipodystrophy-a reduction in subcutaneous adipose tissue-it is clear that it is adipose dysfunction that causes severe insulin resistance (IR), hypertension, CAD, and T2D. We aimed to test the hypothesis that common alleles associated with IR also influence the wider clinical and biochemical profile of monogenic IR. We selected 19 common genetic variants associated with fasting insulin-based measures of IR. We used hierarchical clustering and results from genome-wide association studies of eight nondisease outcomes of monogenic IR to group these variants. We analyzed genetic risk scores against disease outcomes, including 12,171 T2D cases, 40,365 CAD cases, and 69,828 individuals with blood pressure measurements. Hierarchical clustering identified 11 variants associated with a metabolic profile consistent with a common, subtle form of lipodystrophy. A genetic risk score consisting of these 11 IR risk alleles was associated with higher triglycerides (β = 0.018; P = 4 × 10(-29)), lower HDL cholesterol (β = -0.020; P = 7 × 10(-37)), greater hepatic steatosis (β = 0.021; P = 3 × 10(-4)), higher alanine transaminase (β = 0.002; P = 3 × 10(-5)), lower sex-hormone-binding globulin (β = -0.010; P = 9 × 10(-13)), and lower adiponectin (β = -0.015; P = 2 × 10(-26)). The same risk alleles were associated with lower BMI (per-allele β = -0.008; P = 7 × 10(-8)) and increased visceral-to-subcutaneous adipose tissue ratio (β = -0.015; P = 6 × 10(-7)). Individuals carrying ≥17 fasting insulin-raising alleles (5.5% population) were slimmer (0.30 kg/m(2)) but at increased risk of T2D (odds ratio [OR] 1.46; per-allele P = 5 × 10(-13)), CAD (OR 1.12; per-allele P = 1 × 10(-5)), and increased blood pressure (systolic and diastolic blood pressure of 1.21 mmHg [per-allele P = 2 × 10(-5)] and 0.67 mmHg [per-allele P = 2 × 10(-4)], respectively) compared with individuals carrying ≤9 risk alleles (5.5% population). Our results provide genetic evidence for a link between the three diseases of the "metabolic syndrome" and point to reduced subcutaneous adiposity as a central mechanism

Novel genetic analysis for case-control genome-wide association studies: quantification of power and genomic prediction accuracy

Genome-wide association studies (GWAS) are routinely conducted for both quantitative and binary (disease) traits. We present two analytical tools for use in the experimental design of GWAS. Firstly, we present power calculations quantifying power in a unified framework for a range of scenarios. In this context we consider the utility of quantitative scores (e.g. endophenotypes) that may be available on cases only or both cases and controls. Secondly, we consider, the accuracy of prediction of genetic risk from genome-wide SNPs and derive an expression for genomic prediction accuracy using a liability threshold model for disease traits in a case-control design. The expected values based on our derived equations for both power and prediction accuracy agree well with observed estimates from simulations

MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS

The genome-wide association study (GWAS) approach has discovered hundreds of genetic variants associated with diseases and quantitative traits. However, despite clinical overlap and statistical correlation between many phenotypes, GWAS are generally performed one-phenotype-at-a-time. Here we compare the performance of modelling multiple phenotypes jointly with that of the standard univariate approach. We introduce a new method and software, MultiPhen, that models multiple phenotypes simultaneously in a fast and interpretable way. By performing ordinal regression, MultiPhen tests the linear combination of phenotypes most associated with the genotypes at each SNP, and thus potentially captures effects hidden to single phenotype GWAS. We demonstrate via simulation that this approach provides a dramatic increase in power in many scenarios. There is a boost in power for variants that affect multiple phenotypes and for those that affect only one phenotype. While other multivariate methods have similar power gains, we describe several benefits of MultiPhen over these. In particular, we demonstrate that other multivariate methods that assume the genotypes are normally distributed, such as canonical correlation analysis (CCA) and MANOVA, can have highly inflated type-1 error rates when testing case-control or non-normal continuous phenotypes, while MultiPhen produces no such inflation. To test the performance of MultiPhen on real data we applied it to lipid traits in the Northern Finland Birth Cohort 1966 (NFBC1966). In these data MultiPhen discovers 21% more independent SNPs with known associations than the standard univariate GWAS approach, while applying MultiPhen in addition to the standard approach provides 37% increased discovery. The most associated linear combinations of the lipids estimated by MultiPhen at the leading SNPs accurately reflect the Friedewald Formula, suggesting that MultiPhen could be used to refine the definition of existing phenotypes or uncover novel heritable phenotypes

Genome wide mapping reveals PDE4B as an IL-2 induced STAT5 target gene in activated human PBMCs and lymphoid cancer cells

IL-2 is the primary growth factor for promoting survival and proliferation of activated T cells that occurs following engagement of the Janus Kinase (JAK)1-3/and Signal Transducer and Activator of Transcription (STAT) 5 signaling pathway. STAT5 has two isoforms: STAT5A and STAT5B ( commonly referred to as STAT5) which, in T cells, play redundant roles transcribing cell cycle and survival genes. As such, inhibition of STAT5 by a variety of mechanisms can rapidly induce apoptosis in certain lymphoid tumor cells, suggesting that it and its target genes represent therapeutic targets to control certain lymphoid diseases. To search for these molecules we aligned IL-2 regulated genes detected by Affymetrix gene expression microarrays with the STAT5 cistrome identified by chip-on-ChIP analysis in an IL-2-dependent human leukemia cell line, Kit225. Select overlapping genes were then validated using qRT(2)PCR medium-throughput arrays in human PHA-activated PBMCs. Of 19 putative genes, one key regulator of T cell receptor signaling, PDE4B, was identified as a novel target, which was readily up-regulated at the protein level (3 h) in IL-2 stimulated, activated human PBMCs. Surprisingly, only purified CD8+ primary T-cells expressed PDE4B, but not CD4+ cells. Moreover, PDE4B was found to be highly expressed in CD4+ lymphoid cancer cells, which suggests that it may represent a physiological role unique to the CD8+ and lymphoid cancer cells and thus might represent a target for pharmaceutical intervention for certain lymphoid diseases

Impact of measurement error on testing genetic association with quantitative traits

10.1371/journal.pone.0087044PLoS ONE91-POLN

Accounting for a Quantitative Trait Locus for Plasma Triglyceride Levels: Utilization of Variants in Multiple Genes

For decades, research efforts have tried to uncover the underlying genetic basis of human susceptibility to a variety of diseases. Linkage studies have resulted in highly replicated findings and helped identify quantitative trait loci (QTL) for many complex traits; however identification of specific alleles accounting for linkage remains elusive. The purpose of this study was to determine whether with a sufficient number of variants a linkage signal can be fully explained.We used comprehensive fine-mapping using a dense set of single nucleotide polymorphisms (SNPs) across the entire quantitative trait locus (QTL) on human chromosome 7q36 linked to plasma triglyceride levels. Analyses included measured genotype and combined linkage association analyses.Screening this linkage region, we found an over representation of nominally significant associations in five genes (MLL3, DPP6, PAXIP1, HTR5A, INSIG1). However, no single genetic variant was sufficient to account for the linkage. On the other hand, multiple variants capturing the variation in these five genes did account for the linkage at this locus. Permutation analyses suggested that this reduction in LOD score was unlikely to have occurred by chance (p = 0.008).With recent findings, it has become clear that most complex traits are influenced by a large number of genetic variants each contributing only a small percentage to the overall phenotype. We found that with a sufficient number of variants, the linkage can be fully explained. The results from this analysis suggest that perhaps the failure to identify causal variants for linkage peaks may be due to multiple variants under the linkage peak with small individual effect, rather than a single variant of large effect

MER41 Repeat Sequences Contain Inducible STAT1 Binding Sites

Chromatin immunoprecipitation combined with massively parallel sequencing methods (ChIP-seq) is becoming the standard approach to study interactions of transcription factors (TF) with genomic sequences. At the example of public STAT1 ChIP-seq data sets, we present novel approaches for the interpretation of ChIP-seq data

Stat3 and c-Myc Genome-Wide Promoter Occupancy in Embryonic Stem Cells

Embryonic stem (ES) cell pluripotency is regulated in part by transcription factor (TF) pathways that maintain self-renewal and inhibit differentiation. Stat3 and c-Myc TFs are essential for maintaining mouse ES cell self-renewal. c-Myc, together with Oct4, Sox2, and Klf4, is a reprogramming factor. While previous studies have investigated core transcriptional circuitry in ES cells, other TF pathways that promote ES cell pluripotency have yet to be investigated. Therefore, to further understand ES cell transcriptional networks, we used genome-wide chromatin immunoprecipitation and microarray analysis (ChIP-chip) to map Stat3 and c-Myc binding targets in ES cells. Our results show that Stat3 and c-Myc occupy a significant number of genes whose expression is highly enriched in ES cells. By comparing Stat3 and c-Myc target genes with gene expression data from undifferentiated ES cells and embryoid bodies (EBs), we found that Stat3 binds active and inactive genes in ES cells, while c-Myc binds predominantly active genes. Moreover, the transcriptional states of Stat3 and c-Myc targets are correlated with co-occupancy of pluripotency-related TFs, polycomb group proteins, and active and repressive histone modifications. We also provide evidence that Stat3 targets are differentially expressed in ES cells following removal of LIF, where culture of ES cells in the absence of LIF resulted in downregulation of Stat3 target genes enriched in ES cells, and upregulation of lineage specific Stat3 target genes. Altogether, we reveal transcriptional targets of two key pluripotency-related genes in ES cells – Stat3 and c-Myc, thus providing further insight into the ES cell transcriptional network

FRA2 is a STAT5 target gene regulated by IL-2 in human CD4 T cells

Signal transducers and activators of transcription 5(STAT5) are cytokine induced signaling proteins, which regulate key immunological processes, such as tolerance induction, maintenance of homeostasis, and CD4 T-effector cell differentiation. In this study, transcriptional targets of STAT5 in CD4 T cells were studied by Chromatin Immunoprecipitation (ChIP). Genomic mapping of the sites cloned and identified in this study revealed the striking observation that the majority of STAT5-binding sites mapped to intergenic (>50 kb upstream) or intronic, rather than promoter proximal regions. Of the 105 STAT5 responsive binding sites identified, 94% contained the canonical (IFN-γ activation site) GAS motifs. A number of putative target genes identified here are associated with tumor biology. Here, we identified Fos-related antigen 2 (FRA2) as a transcriptional target of IL-2 regulated STAT5. FRA2 is a basic -leucine zipper (bZIP) motif 'Fos' family transcription factor that is part of the AP-1 transcription factor complex and is also known to play a critical role in the progression of human tumours and more recently as a determinant of T cell plasticity. The binding site mapped to an internal intron within the FRA2 gene. The epigenetic architecture of FRA2, characterizes a transcriptionally active promoter as indicated by enrichment for histone methylation marks H3K4me1, H3K4me2, H3K4me3, and transcription/elongation associated marks H2BK5me1 and H4K20me1. FRA2 is regulated by IL-2 in activated CD4 T cells. Consistently, STAT5 bound to GAS sequence in the internal intron of FRA2 and reporter gene assays confirmed IL-2 induced STAT5 binding and transcriptional activation. Furthermore, addition of JAK3 inhibitor (R333) or Daclizumab inhibited the induction in TCR stimulated cells. Taken together, our data suggest that FRA2 is a novel STAT5 target gene, regulated by IL-2 in activated CD4 T cells