Dataset Information

Functional cis-regulatory genomics for systems biology.

ABSTRACT: Gene expression is controlled by interactions between trans-regulatory factors and cis-regulatory DNA sequences, and these interactions constitute the essential functional linkages of gene regulatory networks (GRNs). Validation of GRN models requires experimental cis-regulatory tests of predicted linkages to authenticate their identities and proposed functions. However, cis-regulatory analysis is, at present, at a severe bottleneck in genomic system biology because of the demanding experimental methodologies currently in use for discovering cis-regulatory modules (CRMs), in the genome, and for measuring their activities. Here we demonstrate a high-throughput approach to both discovery and quantitative characterization of CRMs. The unique aspect is use of DNA sequence tags to "barcode" CRM expression constructs, which can then be mixed, injected together into sea urchin eggs, and subsequently deconvolved. This method has increased the rate of cis-regulatory analysis by >100-fold compared with conventional one-by-one reporter assays. The utility of the DNA-tag reporters was demonstrated by the rapid discovery of 81 active CRMs from 37 previously unexplored sea urchin genes. We then obtained simultaneous high-resolution temporal characterization of the regulatory activities of more than 80 CRMs. On average 2-3 CRMs were discovered per gene. Comparison of endogenous gene expression profiles with those of the CRMs recovered from each gene showed that, for most cases, at least one CRM is active in each phase of endogenous expression, suggesting that CRM recovery was comprehensive. This approach will qualitatively alter the practice of GRN construction as well as validation, and will impact many additional areas of regulatory system biology.

SUBMITTER: Nam J

PROVIDER: S-EPMC2840491 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Functional cis-regulatory genomics for systems biology.

Nam Jongmin J Dong Ping P Tarpine Ryan R Istrail Sorin S Davidson Eric H EH

Proceedings of the National Academy of Sciences of the United States of America 20100208 8

Gene expression is controlled by interactions between trans-regulatory factors and cis-regulatory DNA sequences, and these interactions constitute the essential functional linkages of gene regulatory networks (GRNs). Validation of GRN models requires experimental cis-regulatory tests of predicted linkages to authenticate their identities and proposed functions. However, cis-regulatory analysis is, at present, at a severe bottleneck in genomic system biology because of the demanding experimental ...[more]

PMID: 20142491

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Project description:BackgroundOvarian cancer is one of the rarest lethal oncologic diseases that have hardly any specific biomarkers. The availability of high-throughput genomic data and advancement in bioinformatics tools allow us to predict gene biomarkers and apply systems biology approaches to get better diagnosis, and prognosis of the disease with a tentative drug that may be repurposed.ObjectiveTo perform genome-wide association studies using microarray gene expression of ovarian cancer and identify gene biomarkers, construction and analyze networks, perform survival analysis, and drug interaction studies for better diagnosis, prognosis, and treatment of ovarian cancer.MethodThe gene expression profiles of both healthy and serous ovarian cancer epithelial samples were considered. We applied a series of bioinformatics methods and tools, including fold-change statistics for differential expression analysis, DisGeNET and NCBI-Gene databases for gene-disease association mapping, DAVID 6.8 for GO enrichment analysis, GeneMANIA for network construction, Cytoscape 3.8 with its plugins for network visualization, analysis, and module detection, the UALCAN for patient survival analysis, and PubChem, DrugBank and DGIdb for gene-drug interaction.ResultsWe identified 8 seed genes that were subjected for drug-gene interaction studies. Because of over-expression in all the four stages of ovarian cancer, we discern that genes HMGA1 and PSAT1 are potential therapeutic biomarkers for its diagnosis at an early stage (stage I). Our analysis suggests that there are 11 drugs common in the seed genes. However, hypermethylated seed genes HMGA1 and PSAT1 showcased a good interaction affinity with drugs cisplatin, cyclosporin, bisphenol A, progesterone, and sunitinib, and are crucial in the proliferation of ovarian cancer.ConclusionOur study reveals that HMGA1 and PSAT1 can be deployed for initial screening of ovarian cancer and drugs cisplatin, bisphenol A, cyclosporin, progesterone, and sunitinib are effective in curbing the epigenetic alteration.

Dataset Information

Functional cis-regulatory genomics for systems biology.

Publications

Functional cis-regulatory genomics for systems biology.

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