TITLE

Preferential Duplication of Intermodular Hub Genes: An Evolutionary Signature in Eukaryotes Genome Networks

AUTHOR(S)
Ferreira, Ricardo M.; Rybarczyk-Filho, José Luiz; Dalmolin, Rodrigo J. S.; Castro, Mauro A. A.; Moreira, José C. F.; Brunnet, Leonardo G.; de Almeida, Rita M. C.
PUB. DATE
February 2013
SOURCE
PLoS ONE;Feb2013, Vol. 8 Issue 2, p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Whole genome protein-protein association networks are not random and their topological properties stem from genome evolution mechanisms. In fact, more connected, but less clustered proteins are related to genes that, in general, present more paralogs as compared to other genes, indicating frequent previous gene duplication episodes. On the other hand, genes related to conserved biological functions present few or no paralogs and yield proteins that are highly connected and clustered. These general network characteristics must have an evolutionary explanation. Considering data from STRING database, we present here experimental evidence that, more than not being scale free, protein degree distributions of organisms present an increased probability for high degree nodes. Furthermore, based on this experimental evidence, we propose a simulation model for genome evolution, where genes in a network are either acquired de novo using a preferential attachment rule, or duplicated with a probability that linearly grows with gene degree and decreases with its clustering coefficient. For the first time a model yields results that simultaneously describe different topological distributions. Also, this model correctly predicts that, to produce protein-protein association networks with number of links and number of nodes in the observed range for Eukaryotes, it is necessary 90% of gene duplication and 10% of de novo gene acquisition. This scenario implies a universal mechanism for genome evolution.
ACCESSION #
87624739

 

Related Articles

  • Hidden Information Revealed by Optimal Community Structure from a Protein-Complex Bipartite Network Improves Protein Function Prediction. Lee, Juyong; Lee, Jooyoung // PLoS ONE;Apr2013, Vol. 8 Issue 4, p1 

    The task of extracting the maximal amount of information from a biological network has drawn much attention from researchers, for example, predicting the function of a protein from a protein-protein interaction (PPI) network. It is well known that biological networks consist of...

  • MCScanX-transposed: detecting transposed gene duplications based on multiple colinearity scans. Wang, Yupeng; Li, Jingping; Paterson, Andrew H. // Bioinformatics;Jun2013, Vol. 29 Issue 11, p1458 

    Summary: Gene duplication occurs via different modes such as segmental and single-gene duplications. Transposed gene duplication, a specific form of single-gene duplication, ‘copies’ a gene from an ancestral chromosomal location to a novel location. MCScanX is a toolkit for...

  • iAlign: a method for the structural comparison of protein–protein interfaces. Mu Gao; Skolnick, Jeffrey // Bioinformatics;Sep2010, Vol. 26 Issue 18, p2259 

    Motivation: Protein--protein interactions play an essential role in many cellular processes. The rapid accumulation of protein--protein complex structures provides an unprecedented opportunity for comparative studies of protein--protein interactions. To facilitate such studies, it is necessary...

  • Exposing the co-adaptive potential of protein–protein interfaces through computational sequence design. Fromer, Menachem; Linial, Michal // Bioinformatics;Sep2010, Vol. 26 Issue 18, p2266 

    Motivation: In nature, protein--protein interactions are constantly evolving under various selective pressures. Nonetheless, it is expected that crucial interactions are maintained through compensatory mutations between interacting proteins. Thus, many studies have used evolutionary sequence...

  • ModuleRole: A Tool for Modulization, Role Determination and Visualization in Protein-Protein Interaction Networks. Li, GuiPeng; Li, Ming; Zhang, YiWei; Wang, Dong; Li, Rong; Guimerà, Roger; Gao, Juntao Tony; Zhang, Michael Q. // PLoS ONE;May2014, Vol. 9 Issue 5, p1 

    : Rapidly increasing amounts of (physical and genetic) protein-protein interaction (PPI) data are produced by various high-throughput techniques, and interpretation of these data remains a major challenge. In order to gain insight into the organization and structure of the resultant large...

  • Integrated Pathway Clusters with Coherent Biological Themes for Target Prioritisation. Chen, Yi-An; Tripathi, Lokesh P.; Dessailly, Benoit H.; Nyström-Persson, Johan; Ahmad, Shandar; Mizuguchi, Kenji // PLoS ONE;Jun2014, Vol. 9 Issue 6, p1 

    Prioritising candidate genes for further experimental characterisation is an essential, yet challenging task in biomedical research. One way of achieving this goal is to identify specific biological themes that are enriched within the gene set of interest to obtain insights into the biological...

  • Probing the Extent of Randomness in Protein Interaction Networks. Ivanic, Joseph; Wallqvist, Anders; Reifman, Jaques // PLoS Computational Biology;Jul2008, Vol. 4 Issue 7, p1 

    Protein-protein interaction (PPI) networks are commonly explored for the identification of distinctive biological traits, such as pathways, modules, and functional motifs. In this respect, understanding the underlying network structure is vital to assess the significance of any discovered...

  • A hypergraph-based learning algorithm for classifying gene expression and arrayCGH data with prior knowledge. Ze Tian; Tae Hyun Hwang; Rui Kuang // Bioinformatics;Nov2009, Vol. 25 Issue 21, p2831 

    Motivation: Incorporating biological prior knowledge into predictive models is a challenging data integration problem in analyzing high-dimensional genomic data. We introduce a hypergraph-based semi-supervised learning algorithm called HyperPrior to classify gene expression and array-based...

  • G-NEST: a gene neighborhood scoring tool to identify co-conserved, co-expressed genes. Lemay, Danielle G.; Martin, William F.; Hinrichs, Angie S.; Rijnkels, Monique; German, J. Bruce; Korf, Ian; Pollard, Katherine S. // BMC Bioinformatics;2012, Vol. 13 Issue 1, p1 

    Background: In previous studies, gene neighborhoods--spatial clusters of co-expressed genes in the genome-have been defined using arbitrary rules such as requiring adjacency, a minimum number of genes, a fixed window size, or a minimum expression level. In the current study, we developed a Gene...

Share

Read the Article

Courtesy of VIRGINIA BEACH PUBLIC LIBRARY AND SYSTEM

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics