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Can modular analysis identify disease-associated candidate genes for therapeutics?

Tegnér J - J. Biol. (2009)

Bottom Line: Complex diseases such as allergy change gene expression in several cell types and tissues.Benson and colleagues have now shown, in a paper in BMC Systems Biology, that this complexity can be studied effectively using an integrated experimental and computational modular analysis.Their strategy revealed a core of allergy-associated genes of potential therapeutic value.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Medicine, Center for Molecular Medicine, Karolinska University Hospital, 171 76 Solna, Stockholm, Sweden. jesper.tegner@ki.se

ABSTRACT
Complex diseases such as allergy change gene expression in several cell types and tissues. Benson and colleagues have now shown, in a paper in BMC Systems Biology, that this complexity can be studied effectively using an integrated experimental and computational modular analysis. Their strategy revealed a core of allergy-associated genes of potential therapeutic value.

Show MeSH
Flowchart of the modular analysis by Benson and colleagues [1]. Integration of several public gene expression datasets revealed a group of shared (blue) and closely connected clique (red and black) disease-associated genes. A subset of these genes were found to share the T-cell receptor signalling pathway, an observation that was then validated by independent experimentation. To identify a transcription factor (GATA3) regulating one of this subset, the ITK gene, a promoter analysis was performed. The final module of 37 disease-associated genes consisted of genes listed in public databases as having relevant expression patterns and interacting with GATA3.
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Figure 1: Flowchart of the modular analysis by Benson and colleagues [1]. Integration of several public gene expression datasets revealed a group of shared (blue) and closely connected clique (red and black) disease-associated genes. A subset of these genes were found to share the T-cell receptor signalling pathway, an observation that was then validated by independent experimentation. To identify a transcription factor (GATA3) regulating one of this subset, the ITK gene, a promoter analysis was performed. The final module of 37 disease-associated genes consisted of genes listed in public databases as having relevant expression patterns and interacting with GATA3.

Mentions: Benson and colleagues [1] have now contributed to a disease-oriented modular analysis by combining several of the above ideas in a novel manner, as summarized in the flow chart in Figure 1. First, because allergic disease involves multiple cells in different tissues and because no prior characterization of key genes was available, they turned to several different sets of gene expression microarray data in order to find a reference disease-associated gene around which they could construct a module. Using the idea that disease-associated genes tend to interact, they could search for other disease-associated genes that were 'close'. For this purpose, the authors used a graph algorithm that identified a connected clique of 103 disease-associated genes from the microarray data.


Can modular analysis identify disease-associated candidate genes for therapeutics?

Tegnér J - J. Biol. (2009)

Flowchart of the modular analysis by Benson and colleagues [1]. Integration of several public gene expression datasets revealed a group of shared (blue) and closely connected clique (red and black) disease-associated genes. A subset of these genes were found to share the T-cell receptor signalling pathway, an observation that was then validated by independent experimentation. To identify a transcription factor (GATA3) regulating one of this subset, the ITK gene, a promoter analysis was performed. The final module of 37 disease-associated genes consisted of genes listed in public databases as having relevant expression patterns and interacting with GATA3.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2736663&req=5

Figure 1: Flowchart of the modular analysis by Benson and colleagues [1]. Integration of several public gene expression datasets revealed a group of shared (blue) and closely connected clique (red and black) disease-associated genes. A subset of these genes were found to share the T-cell receptor signalling pathway, an observation that was then validated by independent experimentation. To identify a transcription factor (GATA3) regulating one of this subset, the ITK gene, a promoter analysis was performed. The final module of 37 disease-associated genes consisted of genes listed in public databases as having relevant expression patterns and interacting with GATA3.
Mentions: Benson and colleagues [1] have now contributed to a disease-oriented modular analysis by combining several of the above ideas in a novel manner, as summarized in the flow chart in Figure 1. First, because allergic disease involves multiple cells in different tissues and because no prior characterization of key genes was available, they turned to several different sets of gene expression microarray data in order to find a reference disease-associated gene around which they could construct a module. Using the idea that disease-associated genes tend to interact, they could search for other disease-associated genes that were 'close'. For this purpose, the authors used a graph algorithm that identified a connected clique of 103 disease-associated genes from the microarray data.

Bottom Line: Complex diseases such as allergy change gene expression in several cell types and tissues.Benson and colleagues have now shown, in a paper in BMC Systems Biology, that this complexity can be studied effectively using an integrated experimental and computational modular analysis.Their strategy revealed a core of allergy-associated genes of potential therapeutic value.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Medicine, Center for Molecular Medicine, Karolinska University Hospital, 171 76 Solna, Stockholm, Sweden. jesper.tegner@ki.se

ABSTRACT
Complex diseases such as allergy change gene expression in several cell types and tissues. Benson and colleagues have now shown, in a paper in BMC Systems Biology, that this complexity can be studied effectively using an integrated experimental and computational modular analysis. Their strategy revealed a core of allergy-associated genes of potential therapeutic value.

Show MeSH