Reference : Evolutionary conservation and network structure characterize genes of phenotypic rele...
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
http://hdl.handle.net/10993/17703
Evolutionary conservation and network structure characterize genes of phenotypic relevance for mitosis in human
English
Ostaszewski, Marek mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Eifes, Serge mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
del Sol Mesa, Antonio mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
2012
PLoS ONE
Public Library of Science
7
5
e36488
Yes (verified by ORBilu)
1932-6203
San Franscisco
CA
[en] network analysis ; network motifs ; evolutionary conservation
[en] The impact of gene silencing on cellular phenotypes is difficult to establish due to the complexity of interactions in the associated biological processes and pathways. A recent genome-wide RNA knock-down study both identified and phenotypically characterized a set of important genes for the cell cycle in HeLa cells. Here, we combine a molecular interaction network analysis, based on physical and functional protein interactions, in conjunction with evolutionary information, to elucidate the common biological and topological properties of these key genes. Our results show that these genes tend to be conserved with their corresponding protein interactions across several species and are key constituents of the evolutionary conserved molecular interaction network. Moreover, a group of bistable network motifs is found to be conserved within this network, which are likely to influence the network stability and therefore the robustness of cellular functioning. They form a cluster, which displays functional homogeneity and this cluster is significantly enriched in genes phenotypically relevant for mitosis. Additional results reveal a relationship between specific cellular processes and the phenotypic outcomes induced by gene silencing. This study introduces new ideas regarding the relationship between genotype and phenotype in the context of the cell cycle. We show that the analysis of molecular interaction networks can result in the identification of genes relevant to cellular processes, which is a promising avenue for future research.
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Computational Biology (Del Sol Group)
http://hdl.handle.net/10993/17703
10.1371/journal.pone.0036488

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