Combined Multivariate and Pathway Analyses Show That Allergen-Induced Gene Expression Changes in CD4+ T Cells Are Reversed by Glucocorticoids

Background: Glucocorticoids (GCs) play a key role in the treatment of allergy. However, the genome-wide effects of GCs on gene expression in allergen-challenged CD4(+) T cells have not been described. The aim of this study was to perform a genome-wide analysis to investigate whether allergen-induced gene expression changes in CD4(+) T cells could be reversed by GCs. Methodology/Principal Findings: Gene expression microarray analysis was performed to profile gene expression in diluent( D), allergen- (A), and allergen + hydrocortisone- (T) challenged CD4(+) T cells from patients with seasonal allergic rhinitis. Principal component analysis (PCA) showed good separation of the three groups. To identify the correlation between changes in gene expression in allergen-challenged CD4(+) T cells before and after GC treatment, we performed orthogonal partial least squares discriminant analysis (OPLS-DA) followed by Pearson correlation analysis. This revealed that allergen-induced genes were widely reversed by GC treatment (r = -0.77, Pless than0.0001). We extracted 547 genes reversed by GC treatment from OPLS-DA models based on their high contribution to the discrimination and found that those genes belonged to several different inflammatory pathways including TNFR2 Signalling, Interferon Signalling, Glucocorticoid Receptor Signalling and T Helper Cell Differentiation. The results were supported by gene expression microarray analyses of two independent materials. Conclusions/Significance: Allergen-induced gene expression changes in CD4(+) T cells were reversed by treatment with glucocorticoids. The top allergen-induced genes that reversed by GC treatment belonged to several inflammatory pathways and genes of known or potential relevance for allergy

Diminished levels of nasal S100A7 (psoriasin) in seasonal allergic rhinitis: an effect mediated by Th2 cytokines

<p>Abstract</p> <p>Background</p> <p>S100A7 is an antimicrobial peptide involved in several inflammatory diseases. The aim of the present study was to explore the expression and regulation of S100A7 in seasonal allergic rhinitis (SAR).</p> <p>Methods</p> <p>Nasal lavage (NAL) fluid was obtained from healthy controls before and after lipopolysaccharide (LPS) provocation, from SAR patients before and after allergen challenge, and from SAR patients having completed allergen-specific immunotherapy (ASIT). Nasal biopsies, nasal epithelial cells and blood were acquired from healthy donors. The airway epithelial cell line FaDu was used for <it>in vitro </it>experiments. Real-time RT-PCR and immunohistochemistry were used to determine S100A7 expression in nasal tissue and cells. Release of S100A7 in NAL and culture supernatants was measured by ELISA. The function of recombinant S100A7 was explored in epithelial cells, neutrophils and peripheral blood mononuclear cells (PBMC).</p> <p>Results</p> <p>Nasal administration of LPS induced S100A7 release in healthy non-allergic subjects. The level of S100A7 was lower in NAL from SAR patients than from healthy controls, and it was further reduced in the SAR group 6 h post allergen provocation. In contrast, ASIT patients displayed higher levels after completed treatment. S100A7 was expressed in the nasal epithelium and in glands, and it was secreted by cultured epithelial cells. Stimulation with IL-4 and histamine repressed the epithelial S100A7 release. Further, recombinant S100A7 induced activation of neutrophils and PBMC.</p> <p>Conclusions</p> <p>The present study shows an epithelial expression and excretion of S100A7 in the nose after microbial stimulation. The levels are diminished in rhinitis patients and in the presence of an allergic cytokine milieu, suggesting that the antimicrobial defense is compromised in patients with SAR.</p

Journal Staff

MicroRNAs (miRNAs) play a key role in regulating mRNA expression, and individual miRNAs have been proposed as diagnostic and therapeutic candidates. The identification of such candidates is complicated by the involvement of multiple miRNAs and mRNAs as well as unknown disease topology of the miRNAs. Here, we investigated if disease-associated miRNAs regulate modules of disease-associated mRNAs, if those miRNAs act complementarily or synergistically, and if single or combinations of miRNAs can be targeted to alter module functions. We first analyzed publicly available miRNA and mRNA expression data for five different diseases. Integrated target prediction and network-based analysis showed that the miRNAs regulated modules of disease-relevant genes. Most of the miRNAs acted complementarily to regulate multiple mRNAs. To functionally test these findings, we repeated the analysis using our own miRNA and mRNA expression data from CD4+ T cells from patients with seasonal allergic rhinitis. This is a good model of complex diseases because of its well-defined phenotype and pathogenesis. Combined computational and functional studies confirmed that miRNAs mainly acted complementarily and that a combination of two complementary miRNAs, miR-223 and miR-139-3p, could be targeted to alter disease-relevant module functions, namely, the release of type 2 helper T-cell (Th2) cytokines. Taken together, our findings indicate that miRNAs act complementarily to regulate modules of disease-related mRNAs and can be targeted to alter disease-relevant functions

A systematic comparison of genome-scale clustering algorithms

Background: A wealth of clustering algorithms has been applied to gene co-expression experiments. These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation. Most prior work on comparative clustering evaluation has focused on parametric methods. Graph theoretical methods are recent additions to the tool set for the global analysis and decomposition of microarray co-expression matrices that have not generally been included in earlier methodological comparisons. In the present study, a variety of parametric and graph theoretical clustering algorithms are compared using well-characterized transcriptomic data at a genome scale from Saccharomyces cerevisiae. Methods: For each clustering method under study, a variety of parameters were tested. Jaccard similarity was used to measure each clusters agreement with every GO and KEGG annotation set, and the highest Jaccard score was assigned to the cluster. Clusters were grouped into small, medium, and large bins, and the Jaccard score of the top five scoring clusters in each bin were averaged and reported as the best average top 5 (BAT5) score for the particular method. Results: Clusters produced by each method were evaluated based upon the positive match to known pathways. This produces a readily interpretable ranking of the relative effectiveness of clustering on the genes. Methods were also tested to determine whether they were able to identify clusters consistent with those identified by other clustering methods. Conclusions: Validation of clusters against known gene classifications demonstrate that for this data, graph-based techniques outperform conventional clustering approaches, suggesting that further development and application of combinatorial strategies is warranted

A module-based analytical strategy to identify novel disease-associated genes shows an inhibitory role for interleukin 7 Receptor in allergic inflammation

<p>Abstract</p> <p>Background</p> <p>The identification of novel genes by high-throughput studies of complex diseases is complicated by the large number of potential genes. However, since disease-associated genes tend to interact, one solution is to arrange them in modules based on co-expression data and known gene interactions. The hypothesis of this study was that such a module could be a) found and validated in allergic disease and b) used to find and validate one ore more novel disease-associated genes.</p> <p>Results</p> <p>To test these hypotheses integrated analysis of a large number of gene expression microarray experiments from different forms of allergy was performed. This led to the identification of an experimentally validated reference gene that was used to construct a module of co-expressed and interacting genes. This module was validated in an independent material, by replicating the expression changes in allergen-challenged CD4⁺cells. Moreover, the changes were reversed following treatment with corticosteroids. The module contained several novel disease-associated genes, of which the one with the highest number of interactions with known disease genes, <it>IL7R</it>, was selected for further validation. The expression levels of <it>IL7R </it>in allergen challenged CD4⁺cells decreased following challenge but increased after treatment. This suggested an inhibitory role, which was confirmed by functional studies.</p> <p>Conclusion</p> <p>We propose that a module-based analytical strategy is generally applicable to find novel genes in complex diseases.</p

Bridging the gap between systems biology and medicine

Systems biology has matured considerably as a discipline over the last decade, yet some of the key challenges separating current research efforts in systems biology and clinically useful results are only now becoming apparent. As these gaps are better defined, the new discipline of systems medicine is emerging as a translational extension of systems biology. How is systems medicine defined? What are relevant ontologies for systems medicine? What are the key theoretic and methodologic challenges facing computational disease modeling? How are inaccurate and incomplete data, and uncertain biologic knowledge best synthesized in useful computational models? Does network analysis provide clinically useful insight? We discuss the outstanding difficulties in translating a rapidly growing body of data into knowledge usable at the bedside. Although core-specific challenges are best met by specialized groups, it appears fundamental that such efforts should be guided by a roadmap for systems medicine drafted by a coalition of scientists from the clinical, experimental, computational, and theoretic domains

Estimating heritability and genetic correlations from large health datasets in the absence of genetic data

Typically, estimating genetic parameters, such as disease heritability and between-disease genetic correlations, demands large datasets containing all relevant phenotypic measures and detailed knowledge of family relationships or, alternatively, genotypic and phenotypic data for numerous unrelated individuals. Here, we suggest an alternative, efficient estimation approach through the construction of two disease metrics from large health datasets: temporal disease prevalence curves and low-dimensional disease embeddings. We present eleven thousand heritability estimates corresponding to five study types: twins, traditional family studies, health records-based family studies, single nucleotide polymorphisms, and polygenic risk scores. We also compute over six hundred thousand estimates of genetic, environmental and phenotypic correlations. Furthermore, we find that: (1) disease curve shapes cluster into five general patterns; (2) early-onset diseases tend to have lower prevalence than late-onset diseases (Spearmans rho = 0.32, p amp;lt; 10(-16)); and (3) the disease onset age and heritability are negatively correlated (rho = -0.46, p amp;lt; 10(-16)).Funding Agencies|DARPA Big Mechanism program under ARO [W911NF1410333]; National Institutes of HealthUnited States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USA [R01HL122712, 1P50MH094267, U01HL108634-01]; King Abdullah University of Science and Technology (KAUST)King Abdullah University of Science &amp; Technology [FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, FCS/1/4102-02-01]</p

Estimating heritability and genetic correlations from large health datasets in the absence of genetic data.

Typically, estimating genetic parameters, such as disease heritability and between-disease genetic correlations, demands large datasets containing all relevant phenotypic measures and detailed knowledge of family relationships or, alternatively, genotypic and phenotypic data for numerous unrelated individuals. Here, we suggest an alternative, efficient estimation approach through the construction of two disease metrics from large health datasets: temporal disease prevalence curves and low-dimensional disease embeddings. We present eleven thousand heritability estimates corresponding to five study types: twins, traditional family studies, health records-based family studies, single nucleotide polymorphisms, and polygenic risk scores. We also compute over six hundred thousand estimates of genetic, environmental and phenotypic correlations. Furthermore, we find that: (1) disease curve shapes cluster into five general patterns; (2) early-onset diseases tend to have lower prevalence than late-onset diseases (Spearman\u27s ρ = 0.32, p \u3c 1

Integrated genomic and prospective clinical studies show the importance of modular pleiotropy for disease susceptibility, diagnosis and treatment

Background: Translational research typically aims to identify and functionally validate individual, disease-specific genes. However, reaching this aim is complicated by the involvement of thousands of genes in common diseases, and that many of those genes are pleiotropic, that is, shared by several diseases. Methods: We integrated genomic meta-analyses with prospective clinical studies to systematically investigate the pathogenic, diagnostic and therapeutic roles of pleiotropic genes. In a novel approach, we first used pathway analysis of all published genome-wide association studies (GWAS) to find a cell type common to many diseases. Results: The analysis showed over-representation of the T helper cell differentiation pathway, which is expressed in T cells. This led us to focus on expression profiling of CD4(+) T cells from highly diverse inflammatory and malignant diseases. We found that pleiotropic genes were highly interconnected and formed a pleiotropic module, which was enriched for inflammatory, metabolic and proliferative pathways. The general relevance of this module was supported by highly significant enrichment of genetic variants identified by all GWAS and cancer studies, as well as known diagnostic and therapeutic targets. Prospective clinical studies of multiple sclerosis and allergy showed the importance of both pleiotropic and disease specific modules for clinical stratification. Conclusions: In summary, this translational genomics study identified a pleiotropic module, which has key pathogenic, diagnostic and therapeutic roles