Transcriptional regulatory network analysis during epithelial-mesenchymal transformation of retinal pigment epithelium.

PURPOSE: Phenotypic transformation of retinal pigment epithelial (RPE) cells contributes to the onset and progression of ocular proliferative disorders such as proliferative vitreoretinopathy (PVR). The formation of epiretinal membranes in PVR may involve an epithelial-mesenchymal transformation (EMT) of RPE cells as part of an aberrant wound healing response. While the underlying mechanism remains unclear, this likely involves changes in RPE cell gene expression under the control of specific transcription factors (TFs). Thus, the purpose of the present study was to identify TFs that may play a role in this process. METHODS: Regulatory regions of genes that are differentially regulated during phenotypic transformation of ARPE-19 cells, a human RPE cell line, were subjected to computational analysis using the promoter analysis and interaction network toolset (PAINT). The PAINT analysis was used to identify transcription response elements (TREs) statistically overrepresented in the promoter and first intron regions of two reciprocally regulated RPE gene clusters, across four species including the human genome. These TREs were then used to construct transcriptional regulatory network models of the two RPE gene clusters. The validity of these models was then tested using RT-PCR to detect differential expression of the corresponding TF mRNAs during RPE differentiation in both undifferentiated and differentiated ARPE-19 and primary chicken RPE cell cultures. RESULTS: The computational analysis resulted in the successful identification of specific transcription response elements (TREs) and their cognate TFs that are candidates for serving as nodes in a transcriptional regulatory network regulating EMT in RPE cells. The models predicted TFs whose differential expression during RPE EMT was successfully verified by reverse transcriptase polymerase chain reaction (RT-PCR) analysis, including Oct-1, hepatocyte nuclear factor 1 (HNF-1), similar to mothers against decapentaplegic 3 (SMAD3), transcription factor E (TFE), core binding factor, erythroid transcription factor-1 (GATA-1), interferon regulatory factor-1 (IRF), natural killer homeobox 3A (NKX3A), Sterol regulatory element binding protein-1 (SREBP-1), and lymphocyte enhancer factor-1 (LEF-1). CONCLUSIONS: These studies successfully applied computational modeling and biochemical verification to identify biologically relevant transcription factors that are likely to regulate RPE cell phenotype and pathological changes in RPE in response to diseases or trauma. These TFs may provide potential therapeutic targets for the prevention and treatment of ocular proliferative disorders such as PVR.

From promoter analysis to transcriptional regulatory network prediction using PAINT.

Highly parallel gene-expression analysis has led to analysis of gene regulation, in particular coregulation, at a system level. Promoter analysis and interaction network toolset (PAINT) was developed to provide the biologist a computational tool to integrate functional genomics data, for example, from microarray-based gene-expression analysis with genomic sequence data to carry out transcriptional regulatory network analysis (TRNA). TRNA combines bioinformatics, used to identify and analyze gene-regulatory regions, and statistical significance testing, used to rank the likelihood of the involvement of individual transcription factors (TF), with visualization tools to identify TF likely to play a role in the cellular process under investigation. In summary, given a list of gene identifiers PAINT can: (1) fetch potential promoter sequences for the genes in the list, (2) find TF-binding sites on the sequences, (3) analyze the TF-binding site occurrences for over/under-representation compared with a reference, with or without coexpression clustering information, and (4) generate multiple visualizations for these analyses. At present, PAINT supports TRNA of the human, mouse, and rat genomes. PAINT is currently available as an online, web-based service located at: http://www.dbi.tju.edu/dbi/tools/paint.

PAINT: a promoter analysis and interaction network generation tool for gene regulatory network identification.

We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.