Dataset Information

Viral-mediated noisy gene expression reveals biphasic E2f1 response to MYC.

ABSTRACT: Gene expression mediated by viral vectors is subject to cell-to-cell variability, which limits the accuracy of gene delivery. When coupled with single-cell measurements, however, such variability provides an efficient means to quantify signaling dynamics in mammalian cells. Here, we illustrate the utility of this approach by mapping the E2f1 response to MYC, serum stimulation, or both. Our results revealed an underappreciated mode of gene regulation: E2f1 expression first increased, then decreased as MYC input increased. This biphasic pattern was also reflected in other nodes of the network, including the miR-17-92 microRNA cluster and p19Arf. A mathematical model of the network successfully predicted modulation of the biphasic E2F response by serum and a CDK inhibitor. In addition to demonstrating how noise can be exploited to probe signaling dynamics, our results reveal how coordination of the MYC/RB/E2F pathway enables dynamic discrimination of aberrant and normal levels of growth stimulation.

SUBMITTER: Wong JV

PROVIDER: S-EPMC3044932 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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Viral-mediated noisy gene expression reveals biphasic E2f1 response to MYC.

Wong Jeffrey V JV Yao Guang G Nevins Joseph R JR You Lingchong L

Molecular cell 20110201 3

Gene expression mediated by viral vectors is subject to cell-to-cell variability, which limits the accuracy of gene delivery. When coupled with single-cell measurements, however, such variability provides an efficient means to quantify signaling dynamics in mammalian cells. Here, we illustrate the utility of this approach by mapping the E2f1 response to MYC, serum stimulation, or both. Our results revealed an underappreciated mode of gene regulation: E2f1 expression first increased, then decreas ...[more]

PMID: 21292160

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Project description:BackgroundAn important analysis performed on microarray gene-expression data is to discover biclusters, which denote groups of genes that are coherently expressed for a subset of conditions. Various biclustering algorithms have been proposed to find different types of biclusters from these real-valued gene-expression data sets. However, these algorithms suffer from several limitations such as inability to explicitly handle errors/noise in the data; difficulty in discovering small bicliusters due to their top-down approach; inability of some of the approaches to find overlapping biclusters, which is crucial as many genes participate in multiple biological processes. Association pattern mining also produce biclusters as their result and can naturally address some of these limitations. However, traditional association mining only finds exact biclusters, which limits its applicability in real-life data sets where the biclusters may be fragmented due to random noise/errors. Moreover, as they only work with binary or boolean attributes, their application on gene-expression data require transforming real-valued attributes to binary attributes, which often results in loss of information. Many past approaches have tried to address the issue of noise and handling real-valued attributes independently but there is no systematic approach that addresses both of these issues together.ResultsIn this paper, we first propose a novel error-tolerant biclustering model, 'ET-bicluster', and then propose a bottom-up heuristic-based mining algorithm to sequentially discover error-tolerant biclusters directly from real-valued gene-expression data. The efficacy of our proposed approach is illustrated by comparing it with a recent approach RAP in the context of two biological problems: discovery of functional modules and discovery of biomarkers. For the first problem, two real-valued S.Cerevisiae microarray gene-expression data sets are used to demonstrate that the biclusters obtained from ET-bicluster approach not only recover larger set of genes as compared to those obtained from RAP approach but also have higher functional coherence as evaluated using the GO-based functional enrichment analysis. The statistical significance of the discovered error-tolerant biclusters as estimated by using two randomization tests, reveal that they are indeed biologically meaningful and statistically significant. For the second problem of biomarker discovery, we used four real-valued Breast Cancer microarray gene-expression data sets and evaluate the biomarkers obtained using MSigDB gene sets.ConclusionsThe results obtained for both the problems: functional module discovery and biomarkers discovery, clearly signifies the usefulness of the proposed ET-bicluster approach and illustrate the importance of explicitly incorporating noise/errors in discovering coherent groups of genes from gene-expression data.

Dataset Information

Viral-mediated noisy gene expression reveals biphasic E2f1 response to MYC.

Publications

Viral-mediated noisy gene expression reveals biphasic E2f1 response to MYC.

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