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Meta-Analysis approach for the identification of molecular networks related to infections of the oral cavity

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50131

Daskalaki,  A.
Systems Biology (Christoph Wierling), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50484

Rasche,  A.
Bioinformatics (Ralf Herwig), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Zitation

Daskalaki, A., & Rasche, A. (2010). Meta-Analysis approach for the identification of molecular networks related to infections of the oral cavity. In A. Daskalaki (Ed.), Informatics in oral medicine: advanced techniques in clinical and diagnostic technologies (pp. 237-250). Hershey, PA 17033, USA: IGI Global.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-7C73-C
Zusammenfassung
Chronic periodontitis is the most common infection of the oral cavity. Understanding how and why bacteria enter host cells, and how barrier cells respond to limit their impact, provides a biological basis of infection in the mixed bacterial-human ecosystem of the oral cavity. In addition, elucidation of the underlying shared pathogenic mechanisms of complex diseases like diabetes and oral infections can lead to new insight into the involvement of genes in increased susceptibility of patients with oral infections to complex systemic diseases and vice versa. Transcriptional profiling, statistical and ontology tools are used to uncover and dissect genes and pathways of human gingival epithelial cells that are modulated upon interaction with the periodontal pathogens. Affymetrix microarrays are applied to search the gene expression underlying infection with oral bacteria and identify distinct classes of up- and down-regulated genes during this process. The developed meta-analysis approach can help to extract sets of genes related to oral infection and interaction networks by integrating and combining quantitative gene expression data using statistical approaches. By means of overrepresentation analysis, the authors discovered molecular networks related to immune systems responses.