Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals

Background The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). Results Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing. Conclusions These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication

Expression Profiling of Major Histocompatibility and Natural Killer Complex Genes Reveals Candidates for Controlling Risk of Graft versus Host Disease

Background: The major histocompatibility complex (MHC) is the most important genomic region that contributes to the risk of graft versus host disease (GVHD) after haematopoietic stem cell transplantation. Matching of MHC class I and II genes is essential for the success of transplantation. However, the MHC contains additional genes that also contribute to the risk of developing acute GVHD. It is difficult to identify these genes by genetic association studies alone due to linkage disequilibrium in this region. Therefore, we aimed to identify MHC genes and other genes involved in the pathophysiology of GVHD by mRNA expression profiling. Methodology/Principal Findings: To reduce the complexity of the task, we used genetically well-defined rat inbred strains and a rat skin explant assay, an in-vitro-model of the graft versus host reaction (GVHR), to analyze the expression of MHC, natural killer complex (NKC), and other genes in cutaneous GVHR. We observed a statistically significant and strong up or down regulation of 11 MHC, 6 NKC, and 168 genes encoded in other genomic regions, i.e. 4.9%, 14.0%, and 2.6% of the tested genes respectively. The regulation of 7 selected MHC and 3 NKC genes was confirmed by quantitative real-time PCR and in independent skin explant assays. In addition, similar regulations of most of the selected genes were observed in GVHD-affected skin lesions of transplanted rats and in human skin explant assays. Conclusions/Significance: We identified rat and human MHC and NKC genes that are regulated during GVHR in skin explant assays and could therefore serve as biomarkers for GVHD. Several of the respective human genes, including HLA-DMB, C2, AIF1, SPR1, UBD, and OLR1, are polymorphic. These candidates may therefore contribute to the genetic risk of GVHD in patients

Inflammatory Gene Regulatory Networks in Amnion Cells Following Cytokine Stimulation: Translational Systems Approach to Modeling Human Parturition

A majority of the studies examining the molecular regulation of human labor have been conducted using single gene approaches. While the technology to produce multi-dimensional datasets is readily available, the means for facile analysis of such data are limited. The objective of this study was to develop a systems approach to infer regulatory mechanisms governing global gene expression in cytokine-challenged cells in vitro, and to apply these methods to predict gene regulatory networks (GRNs) in intrauterine tissues during term parturition. To this end, microarray analysis was applied to human amnion mesenchymal cells (AMCs) stimulated with interleukin-1β, and differentially expressed transcripts were subjected to hierarchical clustering, temporal expression profiling, and motif enrichment analysis, from which a GRN was constructed. These methods were then applied to fetal membrane specimens collected in the absence or presence of spontaneous term labor. Analysis of cytokine-responsive genes in AMCs revealed a sterile immune response signature, with promoters enriched in response elements for several inflammation-associated transcription factors. In comparison to the fetal membrane dataset, there were 34 genes commonly upregulated, many of which were part of an acute inflammation gene expression signature. Binding motifs for nuclear factor-κB were prominent in the gene interaction and regulatory networks for both datasets; however, we found little evidence to support the utilization of pathogen-associated molecular pattern (PAMP) signaling. The tissue specimens were also enriched for transcripts governed by hypoxia-inducible factor. The approach presented here provides an uncomplicated means to infer global relationships among gene clusters involved in cellular responses to labor-associated signals

The Staphylococcus aureus superantigen SElX is a bifunctional toxin that inhibits neutrophil function:SElX Inhibits Neutrophil Function

Bacterial superantigens (SAgs) cause Vβ-dependent T-cell proliferation leading to immune dysregulation associated with the pathogenesis of life-threatening infections such as toxic shock syndrome, and necrotizing pneumonia. Previously, we demonstrated that staphylococcal enterotoxin-like toxin X (SElX) from Staphylococcus aureus is a classical superantigen that exhibits T-cell activation in a Vβ-specific manner, and contributes to the pathogenesis of necrotizing pneumonia. Here, we discovered that SElX can also bind to neutrophils from human and other mammalian species and disrupt IgG-mediated phagocytosis. Site-directed mutagenesis of the conserved sialic acid-binding motif of SElX abolished neutrophil binding and phagocytic killing, and revealed multiple glycosylated neutrophil receptors for SElX binding. Furthermore, the neutrophil binding-deficient mutant of SElX retained its capacity for T-cell activation demonstrating that SElX exhibits mechanistically independent activities on distinct cell populations associated with acquired and innate immunity, respectively. Finally, we demonstrated that the neutrophil-binding activity rather than superantigenicity is responsible for the SElX-dependent virulence observed in a necrotizing pneumonia rabbit model of infection. Taken together, we report the first example of a SAg, that can manipulate both the innate and adaptive arms of the human immune system during S. aureus pathogenesis

Exploring hierarchical concepts: theoretical and application comparisons

Phenomena are usually multidimensional and their complexity cannot be directly explored via observable variables. For this reason, a hierarchical structure of nested latent concepts representing different levels of abstraction of the phenomenon under study may be considered. In this paper, we provide a comparison between a procedure based on hierarchical clustering methods and a novelty model recently proposed, called Ultrametric Correlation Matrix (UCM) model. The latter aims at reconstructing the data correlation matrix via an ultrametric correlation matrix and supplies a parsimonious representation of multidimensional phenomena through a partition of the observable variables defining a reduced number of latent concepts. Moreover, the UCM model highlights two main features related to concepts: the correlation among concepts and the internal consistency of a concept. The performances of the UCM model and the procedure based on hierarchical clustering methods are illustrated by an application to the Holzinger data set which represents a real demonstration of a hierarchical factorial structure. The evaluation of the different methodological approaches—the UCM model and the procedure based on hierarchical clustering methods—is provided in terms of classification of variables and goodness of fit, other than of their suitability to analyse bottom-up latent structures of variables