Dataset Information

Evaluation of the psoriasis transcriptome across different studies by gene set enrichment analysis (GSEA).

ABSTRACT: BACKGROUND:Our objective was to develop a consistent molecular definition of psoriasis. There have been several published microarray studies of psoriasis, and we compared disease-related genes identified across these different studies of psoriasis with our own in order to establish a consensus. METHODOLOGY/PRINCIPAL FINDINGS:We present a psoriasis transcriptome from a group of 15 patients enrolled in a clinical study, and assessed its biological validity using a set of important pathways known to be involved in psoriasis. We also identified a key set of cytokines that are now strongly implicated in driving disease-related pathology, but which are not detected well on gene array platforms and require more sensitive methods to measure mRNA levels in skin tissues. Comparison of our transcriptome with three other published lists of psoriasis genes showed apparent inconsistencies based on the number of overlapping genes. We extended the well-established approach of Gene Set Enrichment Analysis (GSEA) to compare a new study with these other published list of differentially expressed genes (DEG) in a more comprehensive manner. We applied our method to these three published psoriasis transcriptomes and found them to be in good agreement with our study. CONCLUSIONS/SIGNIFICANCE:Due to wide variability in clinical protocols, platform and sample handling, and subtle disease-related signals, intersection of published DEG lists was unable to establish consensus between studies. In order to leverage the power of multiple transcriptomes reported by several laboratories using different patients and protocols, more sophisticated methods like the extension of GSEA presented here, should be used in order to overcome the shortcomings of overlapping individual DEG approach.

SUBMITTER: Suarez-Farinas M

PROVIDER: S-EPMC2857878 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Evaluation of the psoriasis transcriptome across different studies by gene set enrichment analysis (GSEA).

Suárez-Fariñas Mayte M Lowes Michelle A MA Zaba Lisa C LC Krueger James G JG

PloS one 20100420 4

<h4>Background</h4>Our objective was to develop a consistent molecular definition of psoriasis. There have been several published microarray studies of psoriasis, and we compared disease-related genes identified across these different studies of psoriasis with our own in order to establish a consensus.<h4>Methodology/principal findings</h4>We present a psoriasis transcriptome from a group of 15 patients enrolled in a clinical study, and assessed its biological validity using a set of important p ...[more]

PMID: 20422035

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Project description:BackgroundLarge amounts of microarray expression data have been generated for the Apicomplexan parasite Toxoplasma gondii in an effort to identify genes critical for virulence or developmental transitions. However, researchers' ability to analyze this data is limited by the large number of unannotated genes, including many that appear to be conserved hypothetical proteins restricted to Apicomplexa. Further, differential expression of individual genes is not always informative and often relies on investigators to draw big-picture inferences without the benefit of context. We hypothesized that customization of gene set enrichment analysis (GSEA) to T. gondii would enable us to rigorously test whether groups of genes serving a common biological function are co-regulated during the developmental transition to the latent bradyzoite form.ResultsUsing publicly available T. gondii expression microarray data, we created Toxoplasma gene sets related to bradyzoite differentiation, oocyst sporulation, and the cell cycle. We supplemented these with lists of genes derived from community annotation efforts that identified contents of the parasite-specific organelles, rhoptries, micronemes, dense granules, and the apicoplast. Finally, we created gene sets based on metabolic pathways annotated in the KEGG database and Gene Ontology terms associated with gene annotations available at http://www.toxodb.org. These gene sets were used to perform GSEA analysis using two sets of published T. gondii expression data that characterized T. gondii stress response and differentiation to the latent bradyzoite form.ConclusionsGSEA provides evidence that cell cycle regulation and bradyzoite differentiation are coupled. ?gcn5A mutants unable to induce bradyzoite-associated genes in response to alkaline stress have different patterns of cell cycle and bradyzoite gene expression from stressed wild-type parasites. Extracellular tachyzoites resemble a transitional state that differs in gene expression from both replicating intracellular tachyzoites and in vitro bradyzoites by expressing genes that are enriched in bradyzoites as well as genes that are associated with the G1 phase of the cell cycle. The gene sets we have created are readily modified to reflect ongoing research and will aid researchers' ability to use a knowledge-based approach to data analysis facilitating the development of new insights into the intricate biology of Toxoplasma gondii.

Project description:MotivationMuch research effort has been devoted to the identification of enriched gene sets for microarray experiments. However, identified gene sets are often found to be inconsistent among independent studies. This is probably owing to the noisy data of microarray experiments coupled with small sample sizes of individual studies. Therefore, combining information from multiple studies is likely to improve the detection of truly enriched gene classes. As more and more data become available, it calls for statistical methods to integrate information from multiple studies, also known as meta-analysis, to improve the power of identifying enriched gene sets.ResultsWe propose a Bayesian model that provides a coherent framework for joint modeling of both gene set information and gene expression data from multiple studies, to improve the detection of enriched gene sets by leveraging information from different sources available. One distinct feature of our method is that it directly models the gene expression data, instead of using summary statistics, when synthesizing studies. Besides, the proposed model is flexible and offers an appropriate treatment of between-study heterogeneities that frequently arise in the meta-analysis of microarray experiments. We show that under our Bayesian model, the full posterior conditionals all have known distributions, which greatly facilitates the MCMC computation. Simulation results show that the proposed method can improve the power of gene set enrichment meta-analysis, as opposed to existing methods developed by Shen and Tseng (2010, Bioinformatics, 26, 1316-1323), and it is not sensitive to mild or moderate deviations from the distributional assumption for gene expression data. We illustrate the proposed method through an application of combining eight lung cancer datasets for gene set enrichment analysis, which demonstrates the usefulness of the method.Availabilityhttp://qbrc.swmed.edu/software/.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Evaluation of the psoriasis transcriptome across different studies by gene set enrichment analysis (GSEA).

Publications

Evaluation of the psoriasis transcriptome across different studies by gene set enrichment analysis (GSEA).

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