Dataset Information

Time-series gene expression profiles in AGS cells stimulated with Helicobacter pylori.

ABSTRACT:

Aim

To extend the knowledge of the dynamic interaction between Helicobacter pylori (H. pylori) and host mucosa.

Methods

A time-series cDNA microarray was performed in order to detect the temporal gene expression profiles of human gastric epithelial adenocarcinoma cells infected with H. pylori. Six time points were selected to observe the changes in the model. A differential expression profile at each time point was obtained by comparing the microarray signal value with that of 0 h. Real-time polymerase chain reaction was subsequently performed to evaluate the data quality.

Results

We found a diversity of gene expression patterns at different time points and identified a group of genes whose expression levels were significantly correlated with several important immune response and tumor related pathways.

Conclusion

Early infection may trigger some important pathways and may impact the outcome of the infection.

SUBMITTER: You YH

PROVIDER: S-EPMC2842531 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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Publications

Time-series gene expression profiles in AGS cells stimulated with Helicobacter pylori.

You Yuan-Hai YH Song Yan-Yan YY Meng Fan-Liang FL He Li-Hua LH Zhang Mao-Jun MJ Yan Xiao-Mei XM Zhang Jian-Zhong JZ

World journal of gastroenterology 20100301 11

<h4>Aim</h4>To extend the knowledge of the dynamic interaction between Helicobacter pylori (H. pylori) and host mucosa.<h4>Methods</h4>A time-series cDNA microarray was performed in order to detect the temporal gene expression profiles of human gastric epithelial adenocarcinoma cells infected with H. pylori. Six time points were selected to observe the changes in the model. A differential expression profile at each time point was obtained by comparing the microarray signal value with that of 0 h ...[more]

PMID: 20238406

Similar Datasets

Project description:BackgroundCells dynamically adapt their gene expression patterns in response to various stimuli. This response is orchestrated into a number of gene expression modules consisting of co-regulated genes. A growing pool of publicly available microarray datasets allows the identification of modules by monitoring expression changes over time. These time-series datasets can be searched for gene expression modules by one of the many clustering methods published to date. For an integrative analysis, several time-series datasets can be joined into a three-dimensional gene-condition-time dataset, to which standard clustering or biclustering methods are, however, not applicable. We thus devise a probabilistic clustering algorithm for gene-condition-time datasets.ResultsIn this work, we present the EDISA (Extended Dimension Iterative Signature Algorithm), a novel probabilistic clustering approach for 3D gene-condition-time datasets. Based on mathematical definitions of gene expression modules, the EDISA samples initial modules from the dataset which are then refined by removing genes and conditions until they comply with the module definition. A subsequent extension step ensures gene and condition maximality. We applied the algorithm to a synthetic dataset and were able to successfully recover the implanted modules over a range of background noise intensities. Analysis of microarray datasets has lead us to define three biologically relevant module types: 1) We found modules with independent response profiles to be the most prevalent ones. These modules comprise genes which are co-regulated under several conditions, yet with a different response pattern under each condition. 2) Coherent modules with similar responses under all conditions occurred frequently, too, and were often contained within these modules. 3) A third module type, which covers a response specific to a single condition was also detected, but rarely. All of these modules are essentially different types of biclusters.ConclusionWe successfully applied the EDISA to different 3D datasets. While previous studies were mostly aimed at detecting coherent modules only, our results show that coherent responses are often part of a more general module type with independent response profiles under different conditions. Our approach thus allows for a more comprehensive view of the gene expression response. After subsequent analysis of the resulting modules, the EDISA helped to shed light on the global organization of transcriptional control. An implementation of the algorithm is available at http://www-ra.informatik.uni-tuebingen.de/software/IAGEN/.

Dataset Information

Time-series gene expression profiles in AGS cells stimulated with Helicobacter pylori.

Aim

Methods

Results

Conclusion

Publications

Time-series gene expression profiles in AGS cells stimulated with Helicobacter pylori.

Similar Datasets

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