Dynamic networks reveal key players in aging

MOTIVATION Because susceptibility to diseases increases with age, studying aging gains importance. Analyses of gene expression or sequence data, which have been indispensable for investigating aging, have been limited to studying genes and their protein products in isolation, ignoring their connectivities. However, proteins function by interacting with other proteins, and this is exactly what biological networks (BNs) model. Thus, analyzing the proteins' BN topologies could contribute to the understanding of aging. Current methods for analyzing systems-level BNs deal with their static representations, even though cells are dynamic. For this reason, and because different data types can give complementary biological insights, we integrate current static BNs with aging-related gene expression data to construct dynamic age-specific BNs. Then, we apply sensitive measures of topology to the dynamic BNs to study cellular changes with age. RESULTS While global BN topologies do not significantly change with age, local topologies of a number of genes do. We predict such genes to be aging-related. We demonstrate credibility of our predictions by (i) observing significant overlap between our predicted aging-related genes and 'ground truth' aging-related genes; (ii) observing significant overlap between functions and diseases that are enriched in our aging-related predictions and those that are enriched in 'ground truth' aging-related data; (iii) providing evidence that diseases which are enriched in our aging-related predictions are linked to human aging; and (iv) validating our high-scoring novel predictions in the literature. AVAILABILITY AND IMPLEMENTATION Software executables are available upon request.

[1]  Matthew D. Dyer,et al.  The Landscape of Human Proteins Interacting with Viruses and Other Pathogens , 2008, PLoS pathogens.

[2]  Igor Jurisica,et al.  Inferring the functions of longevity genes with modular subnetwork biomarkers of Caenorhabditis elegans aging , 2010, Genome Biology.

[3]  Aleksandar Stevanovic,et al.  GraphCrunch 2: Software tool for network modeling, alignment and clustering , 2011, BMC Bioinformatics.

[4]  Tijana Milenkoviæ,et al.  Uncovering Biological Network Function via Graphlet Degree Signatures , 2008, Cancer informatics.

[5]  Andres Kriete,et al.  Computational systems biology of aging , 2011, Wiley interdisciplinary reviews. Systems biology and medicine.

[6]  R. Sharan,et al.  Network-based prediction of protein function , 2007, Molecular systems biology.

[7]  Dipanwita Roy Chowdhury,et al.  Human protein reference database as a discovery resource for proteomics , 2004, Nucleic Acids Res..

[8]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[9]  G. A. Whitmore,et al.  Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[10]  C. Brayne,et al.  Microarray analysis of the astrocyte transcriptome in the aging brain: relationship to Alzheimer's pathology and APOE genotype , 2011, Neurobiology of Aging.

[11]  Tijana Milenkovic,et al.  Complementarity of network and sequence information in homologous proteins , 2010, J. Integr. Bioinform..

[12]  A. Venkatakrishnan,et al.  MitoInteractome: Mitochondrial protein interactome database, and its application in 'aging network' analysis , 2009, BMC Genomics.

[13]  Nataša Pržulj,et al.  Protein‐protein interactions: Making sense of networks via graph‐theoretic modeling , 2011, BioEssays : news and reviews in molecular, cellular and developmental biology.

[14]  Paul A. Bates,et al.  Global topological features of cancer proteins in the human interactome , 2006, Bioinform..

[15]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[16]  Carsten Wiuf,et al.  From evidence to inference: Probing the evolution of protein interaction networks , 2009, HFSP journal.

[17]  G. Smyth,et al.  Statistical Applications in Genetics and Molecular Biology Permutation P -values Should Never Be Zero: Calculating Exact P -values When Permutations Are Randomly Drawn , 2011 .

[18]  Roded Sharan,et al.  Associating Genes and Protein Complexes with Disease via Network Propagation , 2010, PLoS Comput. Biol..

[19]  Carl W. Cotman,et al.  Gene expression changes in the course of normal brain aging are sexually dimorphic , 2008, Proceedings of the National Academy of Sciences.

[20]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[21]  Giovanni Scardoni,et al.  Analyzing biological network parameters with CentiScaPe , 2009, Bioinform..

[22]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[23]  R. Sharan,et al.  Protein networks in disease. , 2008, Genome research.

[24]  Dean P. Jones,et al.  A network perspective on metabolism and aging. , 2010, Integrative and comparative biology.

[25]  Aleksandar Stevanovic,et al.  Geometric Evolutionary Dynamics of Protein Interaction Networks , 2010, Pacific Symposium on Biocomputing.

[26]  F. Schreiber,et al.  Centrality Analysis Methods for Biological Networks and Their Application to Gene Regulatory Networks , 2008, Gene regulation and systems biology.

[27]  D. Promislow.  Protein networks, pleiotropy and the evolution of senescence , 2004, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[28]  J. Thornton,et al.  Computational biology for ageing , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[29]  T. Milenković,et al.  Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data , 2010, Journal of The Royal Society Interface.

[30]  P. Stadler,et al.  Centers of complex networks. , 2003, Journal of theoretical biology.

[31]  P. Csermely,et al.  Aging cellular networks: Chaperones as major participants , 2006, Experimental Gerontology.

[32]  Teresa M Przytycka,et al.  Network integration meets network dynamics , 2010, BMC Biology.

[33]  Arie Budovsky,et al.  The Human Ageing Genomic Resources: online databases and tools for biogerontologists , 2009, Aging cell.

[34]  A. Bonato,et al.  Dominating Biological Networks , 2011, PloS one.

[35]  Luca Ferrarini,et al.  A more efficient search strategy for aging genes based on connectivity , 2005, Bioinform..

[36]  Natasa Przulj,et al.  Biological network comparison using graphlet degree distribution , 2007, Bioinform..

[37]  I. Kohane,et al.  Gene regulation and DNA damage in the ageing human brain , 2004, Nature.

[38]  Tijana Milenkovic,et al.  An integrative approach to modeling biological networks , 2010, J. Integr. Bioinform..

[39]  Baldomero Oliva,et al.  Predicting cancer involvement of genes from heterogeneous data , 2008, BMC Bioinformatics.

[40]  Gang Feng,et al.  From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations , 2009, Bioinform..

[41]  Zheng Yan,et al.  Widespread splicing changes in human brain development and aging , 2013 .

[42]  P. Radivojac,et al.  An integrated approach to inferring gene–disease associations in humans , 2008, Proteins.

[43]  Tijana Milenkovic,et al.  GraphCrunch: A tool for large network analyses , 2008, BMC Bioinformatics.

[44]  Kara Dolinski,et al.  The BioGRID Interaction Database: 2008 update , 2008, Nucleic Acids Res..

[45]  Natasa Przulj,et al.  The Core Diseasome. , 2012, Molecular bioSystems.

[46]  Michael Lappe,et al.  Optimized Null Model for Protein Structure Networks , 2009, PloS one.

[47]  Tijana Milenkovic,et al.  Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets , 2010, BMC Systems Biology.