Abstract
BackgroundGenome-wide gene expression profiling of mammalian cells is becoming a staple of many published biomedical and biological research studies. Such data is deposited into data repositories such as the Gene Expression Omnibus (GEO) for potential reuse. However, these repositories currently do not provide simple interfaces to systematically analyze collections of related studies.ResultsHere we present GENE Expression and Enrichment Vector Analyzer (GEN3VA), a web-based system that enables the integrative analysis of aggregated collections of tagged gene expression signatures identified and extracted from GEO. Each tagged collection of signatures is presented in a report that consists of heatmaps of the differentially expressed genes; principal component analysis of all signatures; enrichment analysis with several gene set libraries across all signatures, which we term enrichment vector analysis; and global mapping of small molecules that are predicted to reverse or mimic each signature in the aggregate. We demonstrate how GEN3VA can be used to identify common molecular mechanisms of aging by analyzing tagged signatures from 244 studies that compared young vs. old tissues in mammalian systems. In a second case study, we collected 86 signatures from treatment of human cells with dexamethasone, a glucocorticoid receptor (GR) agonist. Our analysis confirms consensus GR target genes and predicts potential drug mimickers.ConclusionsGEN3VA can be used to identify, aggregate, and analyze themed collections of gene expression signatures from diverse but related studies. Such integrative analyses can be used to address concerns about data reproducibility, confirm results across labs, and discover new collective knowledge by data reuse. GEN3VA is an open-source web-based system that is freely available at: http://amp.pharm.mssm.edu/gen3va.
Highlights
Genome-wide gene expression profiling of mammalian cells is becoming a staple of many published biomedical and biological research studies
Heatmap of LINCS L1000 small molecule compounds To create the heatmap of the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 small molecule compounds, each gene signature is converted into a list of the top 50 small molecule compounds predicted to reverse or mimic the signature expression pattern using the web application programming interface (API) developed for the L1000 Characteristic Direction Signature Search engine (L1000CDS2)
Many of the gene expression signatures in GENE Expression and Enrichment Vector Analyzer (GEN3VA) were collected by students who participated in two Massive Open Online Courses (MOOCs) on Coursera: Network Analysis in Systems Biology (NASB) [19] and Big Data Science with the Big Data to Knowledge (BD2K)-LINCS Data Coordination and Integration Center (DCIC) [20]
Summary
Developing collections of gene expression signatures from GEO To develop GEN3VA, we collected differentially expressed gene signatures from GEO [18] and tagged these signatures based on their shared themes. The imbalance of more upregulated genes compared with downregulated genes in the genes’ heatmap supports the role of dexamethasone as an activator of GR that induces the expression of its downstream targets This is supported by the global enrichment analysis with the ENCODE gene set library, which shows that the most enriched transcription factors are GR (NR3C1) and polymerase 2 (POLR2A) (Fig. 8), suggesting increased transcription and transcriptional activity through GR. While these drugs are known to exert their anti-inflammatory effects through other molecular mechanisms, the close similarity in expression signature suggests that these drugs may act directly on GR, perhaps when applied in high concentrations To examine this possibility, we applied computational docking experiments to show that both drugs, ketorolac and thalidomide, can potentially fit in the same pocket where dexamethasone is known to bind
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