Dataset Information

Network-based comparison of temporal gene expression patterns.

ABSTRACT:

Motivation

In the pursuits of mechanistic understanding of cell differentiation, it is often necessary to compare multiple differentiation processes triggered by different external stimuli and internal perturbations. Available methods for comparing temporal gene expression patterns are limited to a gene-by-gene approach, which ignores co-expression information and thus is sensitive to measurement noise.

Methods

We present a method for co-expression network based comparison of temporal expression patterns (NACEP). NACEP compares the temporal patterns of a gene between two experimental conditions, taking into consideration all of the possible co-expression modules that this gene may participate in. The NACEP program is available at http://biocomp.bioen.uiuc.edu/nacep.

Results

We applied NACEP to analyze retinoid acid (RA)-induced differentiation of embryonic stem (ES) cells. The analysis suggests that RA may facilitate neural differentiation by inducing the shh and insulin receptor pathways. NACEP was also applied to compare the temporal responses of seven RNA inhibition (RNAi) experiments. As proof of concept, we demonstrate that the difference in the temporal responses to RNAi treatments can be used to derive interaction relationships of transcription factors (TFs), and therefore infer regulatory modules within a transcription network. In particular, the analysis suggested a novel regulatory relationship between two pluripotency regulators, Esrrb and Tbx3, which was supported by in vivo binding of Esrrb to the promoter of Tbx3.

Availability

The NACEP program and the supplementary documents are available at http://biocomp.bioen.uiuc.edu/nacep.

Contact

szhong@illinois.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Huang W

PROVIDER: S-EPMC2982154 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Publications

Network-based comparison of temporal gene expression patterns.

Huang Wei W Cao Xiaoyi X Zhong Sheng S

Bioinformatics (Oxford, England) 20100930 23

<h4>Motivation</h4>In the pursuits of mechanistic understanding of cell differentiation, it is often necessary to compare multiple differentiation processes triggered by different external stimuli and internal perturbations. Available methods for comparing temporal gene expression patterns are limited to a gene-by-gene approach, which ignores co-expression information and thus is sensitive to measurement noise.<h4>Methods</h4>We present a method for co-expression network based comparison of temp ...[more]

PMID: 20889495

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Project description:Newly differentiated pancreatic β cells lack proper insulin secretion profiles of mature functional β cells. The global gene expression differences between paired immature and mature β cells have been studied, but the dynamics of transcriptional events, correlating with temporal development of glucose-stimulated insulin secretion (GSIS), remain to be fully defined. This aspect is important to identify which genes and pathways are necessary for β-cell development or for maturation, as defective insulin secretion is linked with diseases such as diabetes. In this study, we assayed through RNA sequencing the global gene expression across six β-cell developmental stages in mice, spanning from β-cell progenitor to mature β cells. A computational pipeline then selected genes differentially expressed with respect to progenitors and clustered them into groups with distinct temporal patterns associated with biological functions and pathways. These patterns were finally correlated with experimental GSIS, calcium influx, and insulin granule formation data. Gene expression temporal profiling revealed the timing of important biological processes across β-cell maturation, such as the deregulation of β-cell developmental pathways and the activation of molecular machineries for vesicle biosynthesis and transport, signal transduction of transmembrane receptors, and glucose-induced Ca2+ influx, which were established over a week before β-cell maturation completes. In particular, β cells developed robust insulin secretion at high glucose several days after birth, coincident with the establishment of glucose-induced calcium influx. Yet the neonatal β cells displayed high basal insulin secretion, which decreased to the low levels found in mature β cells only a week later. Different genes associated with calcium-mediated processes, whose alterations are linked with insulin resistance and deregulation of glucose homeostasis, showed increased expression across β-cell stages, in accordance with the temporal acquisition of proper GSIS. Our temporal gene expression pattern analysis provided a comprehensive database of the underlying molecular components and biological mechanisms driving β-cell maturation at different temporal stages, which are fundamental for better control of the in vitro production of functional β cells from human embryonic stem/induced pluripotent cell for transplantation-based type 1 diabetes therapy.

Project description:BackgroundA huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among different biological entities in the context of personalized medicine and translational research. Recently, network-based computational approaches have gained popularity in investigating such heterogeneous data, proposing novel therapeutic targets and deciphering disease mechanisms. However, little effort has been devoted to investigating associations among drugs, diseases, and genes in an integrative manner.ResultsWe propose a novel network-based computational framework to identify statistically over-expressed subnetwork patterns, called network motifs, in an integrated disease-drug-gene network extracted from Semantic MEDLINE. The framework consists of two steps. The first step is to construct an association network by extracting pair-wise associations between diseases, drugs and genes in Semantic MEDLINE using a domain pattern driven strategy. A Resource Description Framework (RDF)-linked data approach is used to re-organize the data to increase the flexibility of data integration, the interoperability within domain ontologies, and the efficiency of data storage. Unique associations among drugs, diseases, and genes are extracted for downstream network-based analysis. The second step is to apply a network-based approach to mine the local network structure of this heterogeneous network. Significant network motifs are then identified as the backbone of the network. A simplified network based on those significant motifs is then constructed to facilitate discovery. We implemented our computational framework and identified five network motifs, each of which corresponds to specific biological meanings. Three case studies demonstrate that novel associations are derived from the network topology analysis of reconstructed networks of significant network motifs, further validated by expert knowledge and functional enrichment analyses.ConclusionsWe have developed a novel network-based computational approach to investigate the heterogeneous drug-gene-disease network extracted from Semantic MEDLINE. We demonstrate the power of this approach by prioritizing candidate disease genes, inferring potential disease relationships, and proposing novel drug targets, within the context of the entire knowledge. The results indicate that such approach will facilitate the formulization of novel research hypotheses, which is critical for translational medicine research and personalized medicine.

Dataset Information

Network-based comparison of temporal gene expression patterns.

Motivation

Methods

Results

Availability

Contact

Supplementary information

Publications

Network-based comparison of temporal gene expression patterns.

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