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Stochastic modeling for the expression of a gene regulated by competing transcription factors.

ABSTRACT: It is widely accepted that gene expression regulation is a stochastic event. The common approach for its computer simulation requires detailed information on the interactions of individual molecules, which is often not available for the analyses of biological experiments. As an alternative approach, we employed a more intuitive model to simulate the experimental result, the Markov-chain model, in which a gene is regulated by activators and repressors, which bind the same site in a mutually exclusive manner. Our stochastic simulation in the presence of both activators and repressors predicted a Hill-coefficient of the dose-response curve closer to the experimentally observed value than the calculated value based on the simple additive effects of activators alone and repressors alone. The simulation also reproduced the heterogeneity of gene expression levels among individual cells observed by Fluorescence Activated Cell Sorting analysis. Therefore, our approach may help to apply stochastic simulations to broader experimental data.

SUBMITTER: Yang HT

PROVIDER: S-EPMC3303788 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Stochastic modeling for the expression of a gene regulated by competing transcription factors.

Yang Hsih-Te HT Ko Minoru S H MS

PloS one 20120314 3

It is widely accepted that gene expression regulation is a stochastic event. The common approach for its computer simulation requires detailed information on the interactions of individual molecules, which is often not available for the analyses of biological experiments. As an alternative approach, we employed a more intuitive model to simulate the experimental result, the Markov-chain model, in which a gene is regulated by activators and repressors, which bind the same site in a mutually exclu ...[more]

PMID: 22431973

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Project description:BACKGROUND:Sulfotransferase 1A1 (SULT1A1) gene expression is tissue specific, with little to no expression in normal breast epithelia. Expression in breast tumors has been documented, but the transcriptional regulation of SULT1A1 in human breast tissue is poorly understood. We identified Nuclear Factor I (NFI) as a transcription factor family involved in the regulation of SULT1A1 expression. METHODS:Transcription Factor Activation Profiling Plate Array assay was used to identify the possible transcription factors that regulate the gene expression of SULT1A1in normal breast MCF-10A cells and breast cancer ZR-75-1 cells. Expression levels of NFI-C and SULT1A1 were determined by real-time RT-PCR using total RNA isolated from 84 human liver samples. Expression levels of SULT1A1, NFI-A, NFI-B, NFI-C, and NFI-X were also determined in different human breast cancer cell lines (MCF-7, T-47D, ZR-75-1, and MDA-MB-231), in the transformed human epithelial cell line MCF-10A, and in ZR-75-1 cells that were transfected with siRNAs directed against NFI-A, NFI-B, NFI-C, or NFI-X for 48 h. The copy numbers of SULT1A1 in cell lines ZR-75-1, MCF-7, T-47D, MDA-MB-231, and MCF-10A were determined using a pre-designed Custom Plus TaqMan® Copy Number kit from Life Technologies. RESULTS:In normal human liver samples, SULT1A1 mRNA level was positively associated with NFI-C. In different human breast cancer and normal epithelial cell lines, SULT1A1 expression was positively correlated with NFI-B and NFI-C. SULT1A1 expression was decreased 41% and 61% in ZR-75-1 cells treated with siRNAs against NFI-A and NFI-C respectively. SULT1A1 gene expression was higher in cells containing more than one SULT1A1 copy numbers. CONCLUSIONS:Our data suggests that SULT1A1 expression is regulated by NFI, as well as SULT1A1 copy number variation in human breast cancer cell lines. These data provide a mechanistic basis for the differential expression of SULT1A1 in different tissues and different physiological states of disease.

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Stochastic modeling for the expression of a gene regulated by competing transcription factors.

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Stochastic modeling for the expression of a gene regulated by competing transcription factors.

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