Using Ontology Fingerprints to evaluate genome-wide association study results

We describe an approach to characterize genes or phenotypes via ontology fingerprints which are composed of Gene Ontology (GO) terms overrepresented among those PubMed abstracts linked to the genes or phenotypes. We then quantify the biological relevance between genes and phenotypes by comparing their ontology fingerprints to calculate a similarity score. We validated this approach by correctly identifying genes belong to their biological pathways with high accuracy, and applied this approach to evaluate GWA study by ranking genes associated with the lipid concentrations in plasma as well as to prioritize genes within linkage disequilibrium (LD) block. We found that the genes with highest scores were: ABCA1, LPL, and CETP for HDL; LDLR, APOE and APOB for LDL; and LPL, APOA1 and APOB for triglyceride. In addition, we identified some top ranked genes linking to lipid metabolism from the literature even in cases where such knowledge was not reflected in current annotation of these genes. These results demonstrate that ontology fingerprints can be used effectively to prioritize genes from GWA studies for experimental validation

A partitioned 88-loci psoriasis genetic risk score reveals HLA and non-HLA contributions to clinical phenotypes in a Newfoundland psoriasis cohort

Psoriasis is an immune-mediated inflammatory skin disease typically characterized by erythematous and scaly plaques. It affects 3% of the Newfoundland population while only affecting 1.7% of the general Canadian population. Recent genome-wide association studies (GWAS) in psoriasis have identified more than 63 genetic susceptibility loci that individually have modest effects. Prior studies have shown that a genetic risk score (GRS) combining multiple loci can improve psoriasis disease prediction. However, these prior GRS studies have not fully explored the association of GRS with patient clinical characteristics. In this study, we calculated three types of GRS: one using all known GWAS SNPs (GRS-ALL), one using a subset of SNPs from the HLA region (GRS-HLA), and the last using non-HLA SNPs (GRS-noHLA). We examined the relationship between these GRS and a number of psoriasis features within a well characterized Newfoundland psoriasis cohort. We found that both GRS-ALL and GRS-HLA were significantly associated with early age of psoriasis onset, psoriasis severity, first presentation of psoriasis at the elbow or knee, and the total number of body locations affected, while only GRS-ALL was associated with a positive family history of psoriasis. GRS-noHLA was uniquely associated with genital psoriasis. These findings clarify the relationship of the HLA and non-HLA components of GRS with important clinical features of psoriasis

Consistent Differential Expression Pattern (CDEP) on microarray to identify genes related to metastatic behavior

<p>Abstract</p> <p>Background</p> <p>To utilize the large volume of gene expression information generated from different microarray experiments, several meta-analysis techniques have been developed. Despite these efforts, there remain significant challenges to effectively increasing the statistical power and decreasing the Type I error rate while pooling the heterogeneous datasets from public resources. The objective of this study is to develop a novel meta-analysis approach, Consistent Differential Expression Pattern (CDEP), to identify genes with common differential expression patterns across different datasets.</p> <p>Results</p> <p>We combined False Discovery Rate (FDR) estimation and the non-parametric RankProd approach to estimate the Type I error rate in each microarray dataset of the meta-analysis. These Type I error rates from all datasets were then used to identify genes with common differential expression patterns. Our simulation study showed that CDEP achieved higher statistical power and maintained low Type I error rate when compared with two recently proposed meta-analysis approaches. We applied CDEP to analyze microarray data from different laboratories that compared transcription profiles between metastatic and primary cancer of different types. Many genes identified as differentially expressed consistently across different cancer types are in pathways related to metastatic behavior, such as ECM-receptor interaction, focal adhesion, and blood vessel development. We also identified novel genes such as <it>AMIGO2</it>, <it>Gem</it>, and <it>CXCL11 </it>that have not been shown to associate with, but may play roles in, metastasis.</p> <p>Conclusions</p> <p>CDEP is a flexible approach that borrows information from each dataset in a meta-analysis in order to identify genes being differentially expressed consistently. We have shown that CDEP can gain higher statistical power than other existing approaches under a variety of settings considered in the simulation study, suggesting its robustness and insensitivity to data variation commonly associated with microarray experiments.</p> <p><b>Availability</b>: CDEP is implemented in R and freely available at: <url>http://genomebioinfo.musc.edu/CDEP/</url></p> <p><b>Contact</b>: [email protected]</p

Signaling network prediction by the Ontology Fingerprint enhanced Bayesian network

Abstract Background Despite large amounts of available genomic and proteomic data, predicting the structure and response of signaling networks is still a significant challenge. While statistical method such as Bayesian network has been explored to meet this challenge, employing existing biological knowledge for network prediction is difficult. The objective of this study is to develop a novel approach that integrates prior biological knowledge in the form of the Ontology Fingerprint to infer cell-type-specific signaling networks via data-driven Bayesian network learning; and to further use the trained model to predict cellular responses. Results We applied our novel approach to address the Predictive Signaling Network Modeling challenge of the fourth (2009) Dialog for Reverse Engineering Assessment's and Methods (DREAM4) competition. The challenge results showed that our method accurately captured signal transduction of a network of protein kinases and phosphoproteins in that the predicted protein phosphorylation levels under all experimental conditions were highly correlated (R2 = 0.93) with the observed results. Based on the evaluation of the DREAM4 organizer, our team was ranked as one of the top five best performers in predicting network structure and protein phosphorylation activity under test conditions. Conclusions Bayesian network can be used to simulate the propagation of signals in cellular systems. Incorporating the Ontology Fingerprint as prior biological knowledge allows us to efficiently infer concise signaling network structure and to accurately predict cellular responses.http://deepblue.lib.umich.edu/bitstream/2027.42/109490/1/12918_2012_Article_989.pd

Circadian control of interferon-sensitive gene expression in murine skin.

The circadian clock coordinates a variety of immune responses with signals from the external environment to promote survival. We investigated the potential reciprocal relationship between the circadian clock and skin inflammation. We treated mice topically with the Toll-like receptor 7 (TLR7) agonist imiquimod (IMQ) to activate IFN-sensitive gene (ISG) pathways and induce psoriasiform inflammation. IMQ transiently altered core clock gene expression, an effect mirrored in human patient psoriatic lesions. In mouse skin 1 d after IMQ treatment, ISGs, including the key ISG transcription factor IFN regulatory factor 7 (Irf7), were more highly induced after treatment during the day than the night. Nuclear localization of phosphorylated-IRF7 was most prominently time-of-day dependent in epidermal leukocytes, suggesting that these cell types play an important role in the diurnal ISG response to IMQ. Mice lacking Bmal1 systemically had exacerbated and arrhythmic ISG/Irf7 expression after IMQ. Furthermore, daytime-restricted feeding, which affects the phase of the skin circadian clock, reverses the diurnal rhythm of IMQ-induced ISG expression in the skin. These results suggest a role for the circadian clock, driven by BMAL1, as a negative regulator of the ISG response, and highlight the finding that feeding time can modulate the skin immune response. Since the IFN response is essential for the antiviral and antitumor effects of TLR activation, these findings are consistent with the time-of-day-dependent variability in the ability to fight microbial pathogens and tumor initiation and offer support for the use of chronotherapy for their treatment

2D Visualization of the Psoriasis Transcriptome Fails to Support the Existence of Dual-Secreting IL-17A/IL-22 Th17 T Cells.

The present paradigm of psoriasis pathogenesis revolves around the IL-23/IL-17A axis. Dual-secreting Th17 T cells presumably are the predominant sources of the psoriasis phenotype-driving cytokines, IL-17A and IL-22. We thus conducted a meta-analysis of independently acquired RNA-seq psoriasis datasets to explore the relationship between the expression of IL17A and IL22. This analysis failed to support the existence of dual secreting IL-17A/IL-22 Th17 cells as a major source of these cytokines. However, variable relationships amongst the expression of psoriasis susceptibility genes and of IL17A, IL22, and IL23A were identified. Additionally, to shed light on gene expression relationships in psoriasis, we applied a machine learning nonlinear dimensionality reduction strategy (t-SNE) to display the entire psoriasis transcriptome as a 2-dimensonal image. This analysis revealed a variety of gene clusters, relevant to psoriasis pathophysiology but failed to support a relationship between IL17A and IL22. These results support existing theories on alternative sources of IL-17A and IL-22 in psoriasis such as a Th22 cells and non-T cell populations

Large-Scale Imputation of KIR Copy Number and HLA Alleles in North American and European Psoriasis Case-Control Cohorts Reveals Association of Inhibitory KIR2DL2 With Psoriasis

Killer cell immunoglobulin-like receptors (KIR) regulate immune responses in NK and CD8+ T cells via interaction with HLA ligands. KIR genes, including KIR2DS1, KIR3DL1, and KIR3DS1 have previously been implicated in psoriasis susceptibility. However, these previous studies were constrained to small sample sizes, in part due to the time and expense required for direct genotyping of KIR genes. Here, we implemented KIR*IMP to impute KIR copy number from single-nucleotide polymorphisms (SNPs) on chromosome 19 in the discovery cohort (n=11,912) from the PAGE consortium, University of California San Francisco, and the University of Dundee, and in a replication cohort (n=66,357) from Kaiser Permanente Northern California. Stratified multivariate logistic regression that accounted for patient ancestry and high-risk HLA alleles revealed that KIR2DL2 copy number was significantly associated with psoriasis in the discovery cohort (p ≤ 0.05). The KIR2DL2 copy number association was replicated in the Kaiser Permanente replication cohort. This is the first reported association of KIR2DL2 copy number with psoriasis and highlights the importance of KIR genetics in the pathogenesis of psoriasis

Novel cytokine and chemokine markers of hidradenitis suppurativa reflect chronic inflammation and itch

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148237/1/all13665-sup-0001-SupInfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148237/2/all13665_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148237/3/all13665.pd

2D Visualization of the Psoriasis Transcriptome Fails to Support the Existence of Dual-Secreting IL-17A/IL-22 Th17 T Cells

The present paradigm of psoriasis pathogenesis revolves around the IL-23/IL-17A axis. Dual-secreting Th17 T cells presumably are the predominant sources of the psoriasis phenotype-driving cytokines, IL-17A and IL-22. We thus conducted a meta-analysis of independently acquired RNA-seq psoriasis datasets to explore the relationship between the expression of IL17A and IL22. This analysis failed to support the existence of dual secreting IL-17A/IL-22 Th17 cells as a major source of these cytokines. However, variable relationships amongst the expression of psoriasis susceptibility genes and of IL17A, IL22, and IL23A were identified. Additionally, to shed light on gene expression relationships in psoriasis, we applied a machine learning nonlinear dimensionality reduction strategy (t-SNE) to display the entire psoriasis transcriptome as a 2-dimensonal image. This analysis revealed a variety of gene clusters, relevant to psoriasis pathophysiology but failed to support a relationship between IL17A and IL22. These results support existing theories on alternative sources of IL-17A and IL-22 in psoriasis such as a Th22 cells and non-T cell populations