Zhenhua Liu1, Jiali Zhao2, Yinyin Tan2, Minglu Tang3, Guanzhen Li3. 1. Department of Neurology and Shandong Sleep Medical Center, Provincial Hospital Affiliated to Shandong University Jinan 250021, China. 2. Department of Neurology, Provincial Hospital Affiliated to Shandong University Jinan 250021, China. 3. Shandong Sleep Medical Center, Provincial Hospital Affiliated to Shandong University Jinan 250021, China.
Abstract
OBJECTIVE: The objective of this work is to identify disrupted pathways in narcolepsy according to systematically tracking the dysregulated modules of reweighted Protein-Protein Interaction (PPI) networks. Here, we performed systematic identification and comparison of modules across normal and narcolepsy conditions by integrating PPI and gene-expression data. METHODS: Firstly, normal and narcolepsy PPI network were inferred and reweighted based on Pearson correlation coefficient (PCC). Then, modules in PPI network were explored by clique-merging algorithm and we identified altered modules using a maximum weight bipartite matching and in non-increasing order. Finally, pathways enrichment analyses of genes in altered modules were carried out based on Expression Analysis Systematic Explored (EASE) test to illuminate the biological pathways in narcolepsy. RESULTS: Our analyses revealed that 235 altered modules were identified by comparing modules in normal and narcolepsy PPI network. Pathway functional enrichment analysis of disrupted module genes showed 59 disrupted pathways within threshold P < 0.001. The most significant five disrupted pathways were: oxidative phosphorylation, T cell receptor signaling pathway, cell cycle, Alzheimer's disease and focal adhesion. CONCLUSIONS: We successfully identified disrupted pathways and these pathways might be potential biological processes for treatment and etiology mechanism in narcolepsy.
OBJECTIVE: The objective of this work is to identify disrupted pathways in narcolepsy according to systematically tracking the dysregulated modules of reweighted Protein-Protein Interaction (PPI) networks. Here, we performed systematic identification and comparison of modules across normal and narcolepsy conditions by integrating PPI and gene-expression data. METHODS: Firstly, normal and narcolepsy PPI network were inferred and reweighted based on Pearson correlation coefficient (PCC). Then, modules in PPI network were explored by clique-merging algorithm and we identified altered modules using a maximum weight bipartite matching and in non-increasing order. Finally, pathways enrichment analyses of genes in altered modules were carried out based on Expression Analysis Systematic Explored (EASE) test to illuminate the biological pathways in narcolepsy. RESULTS: Our analyses revealed that 235 altered modules were identified by comparing modules in normal and narcolepsy PPI network. Pathway functional enrichment analysis of disrupted module genes showed 59 disrupted pathways within threshold P < 0.001. The most significant five disrupted pathways were: oxidative phosphorylation, T cell receptor signaling pathway, cell cycle, Alzheimer's disease and focal adhesion. CONCLUSIONS: We successfully identified disrupted pathways and these pathways might be potential biological processes for treatment and etiology mechanism in narcolepsy.
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Authors: Juliette Faraco; Ling Lin; Birgitte Rahbek Kornum; Eimear E Kenny; Gosia Trynka; Mali Einen; Tom J Rico; Peter Lichtner; Yves Dauvilliers; Isabelle Arnulf; Michel Lecendreux; Sirous Javidi; Peter Geisler; Geert Mayer; Fabio Pizza; Francesca Poli; Giuseppe Plazzi; Sebastiaan Overeem; Gert Jan Lammers; David Kemlink; Karel Sonka; Sona Nevsimalova; Guy Rouleau; Alex Desautels; Jacques Montplaisir; Birgit Frauscher; Laura Ehrmann; Birgit Högl; Poul Jennum; Patrice Bourgin; Rosa Peraita-Adrados; Alex Iranzo; Claudio Bassetti; Wei-Min Chen; Patrick Concannon; Susan D Thompson; Vincent Damotte; Bertrand Fontaine; Maxime Breban; Christian Gieger; Norman Klopp; Panos Deloukas; Cisca Wijmenga; Joachim Hallmayer; Suna Onengut-Gumuscu; Stephen S Rich; Juliane Winkelmann; Emmanuel Mignot Journal: PLoS Genet Date: 2013-02-14 Impact factor: 5.917