Investigating the validity of current network analysis on static conglomerate networks by protein network stratification

BackgroundA molecular network perspective forms the foundation of systems biology. A common practice in analyzing protein-protein interaction (PPI) networks is to perform network analysis on a conglomerate network that is an assembly of all available binary interactions in a given organism from diverse data sources. Recent studies on network dynamics suggested that this approach might have ignored the dynamic nature of context-dependent molecular systems.ResultsIn this study, we employed a network stratification strategy to investigate the validity of the current network analysis on conglomerate PPI networks. Using the genome-scale tissue- and condition-specific proteomics data in Arabidopsis thaliana, we present here the first systematic investigation into this question. We stratified a conglomerate A. thaliana PPI network into three levels of context-dependent subnetworks. We then focused on three types of most commonly conducted network analyses, i.e., topological, functional and modular analyses, and compared the results from these network analyses on the conglomerate network and five stratified context-dependent subnetworks corresponding to specific tissues.ConclusionsWe found that the results based on the conglomerate PPI network are often significantly different from those of context-dependent subnetworks corresponding to specific tissues or conditions. This conclusion depends neither on relatively arbitrary cutoffs (such as those defining network hubs or bottlenecks), nor on specific network clustering algorithms for module extraction, nor on the possible high false positive rates of binary interactions in PPI networks. We also found that our conclusions are likely to be valid in human PPI networks. Furthermore, network stratification may help resolve many controversies in current research of systems biology.

[1]  Ian M. Donaldson,et al.  The Biomolecular Interaction Network Database and related tools 2005 update , 2004, Nucleic Acids Res..

[2]  Andre Levchenko,et al.  Dynamic Properties of Network Motifs Contribute to Biological Network Organization , 2005, PLoS biology.

[3]  Prahlad T. Ram,et al.  Formation of Regulatory Patterns During Signal Propagation in a Mammalian Cellular Network , 2005, Science.

[4]  Marc Vidal,et al.  Confirmation of Organized Modularity in the Yeast Interactome , 2007, PLoS biology.

[5]  Ming-Jing Hwang,et al.  Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks , 2009, BMC Systems Biology.

[6]  S. Teichmann,et al.  Gene regulatory network growth by duplication , 2004, Nature Genetics.

[7]  M. Samanta,et al.  Predicting protein functions from redundancies in large-scale protein interaction networks , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Z. N. Oltvai,et al.  Topological units of environmental signal processing in the transcriptional regulatory network of Escherichia coli , 2005, Proceedings of the National Academy of Sciences of the United States of America.

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

[10]  Zhi Wang,et al.  Correction: In Search of the Biological Significance of Modular Structures in Protein Networks , 2007, PLoS Comput. Biol..

[11]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[12]  S. L. Wong,et al.  Towards a proteome-scale map of the human protein–protein interaction network , 2005, Nature.

[13]  S. Wuchty.  Evolution and topology in the yeast protein interaction network. , 2004, Genome research.

[14]  G. Friso,et al.  Quantitative Proteomics of a Chloroplast SRP54 Sorting Mutant and Its Genetic Interactions with CLPC1 in Arabidopsis1[C][W][OA] , 2008, Plant Physiology.

[15]  M. Tyers,et al.  Still Stratus Not Altocumulus: Further Evidence against the Date/Party Hub Distinction , 2007, PLoS biology.

[16]  K. J. Ray Liu,et al.  Dependence network modeling for biomarker identification , 2007, Bioinform..

[17]  A. Hopkins.  Network pharmacology: the next paradigm in drug discovery. , 2008, Nature chemical biology.

[18]  O. Emanuelsson,et al.  Sorting Signals, N-Terminal Modifications and Abundance of the Chloroplast Proteome , 2008, PloS one.

[19]  Luhua Lai,et al.  Dynamic Simulations on the Arachidonic Acid Metabolic Network , 2007, PLoS Comput. Biol..

[20]  M. Gerstein,et al.  Interrelating different types of genomic data, from proteome to secretome: 'oming in on function. , 2001, Genome research.

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

[22]  Hanno Steen,et al.  Development of human protein reference database as an initial platform for approaching systems biology in humans. , 2003, Genome research.

[23]  Tanya Z. Berardini,et al.  The Arabidopsis Information Resource (TAIR): gene structure and function annotation , 2007, Nucleic Acids Res..

[24]  M. Vidal,et al.  Networking metabolites and diseases , 2008, Proceedings of the National Academy of Sciences.

[25]  Mark Gerstein,et al.  The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics , 2007, PLoS Comput. Biol..

[26]  D. Eisenberg,et al.  Protein function in the post-genomic era , 2000, Nature.

[27]  An-Ping Zeng,et al.  The Connectivity Structure, Giant Strong Component and Centrality of Metabolic Networks , 2003, Bioinform..

[28]  I. Goryanin,et al.  Human metabolic network reconstruction and its impact on drug discovery and development. , 2008, Drug discovery today.

[29]  Guang Li,et al.  AtPID: Arabidopsis thaliana protein interactome database—an integrative platform for plant systems biology , 2007, Nucleic Acids Res..

[30]  H. Lehrach,et al.  A Human Protein-Protein Interaction Network: A Resource for Annotating the Proteome , 2005, Cell.

[31]  Gil Alterovitz,et al.  Knowledge-Based Bioinformatics: From analysis to interpretation , 2010 .

[32]  Ney Lemke,et al.  Essentiality and damage in metabolic networks , 2004, Bioinform..

[33]  M. Tyers,et al.  Stratus Not Altocumulus: A New View of the Yeast Protein Interaction Network , 2006, PLoS biology.

[34]  Bo Xu,et al.  In-depth proteomic profiling of the normal human kidney glomerulus using two-dimensional protein prefractionation in combination with liquid chromatography-tandem mass spectrometry. , 2007, Journal of proteome research.

[35]  Lan Chen,et al.  Dynamic Changes in Subgraph Preference Profiles of Crucial Transcription Factors , 2006, PLoS Comput. Biol..

[36]  R. Guimerà,et al.  Functional cartography of complex metabolic networks , 2005, Nature.

[37]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[38]  Ben Lehner,et al.  Tissue specificity and the human protein interaction network , 2009, Molecular systems biology.

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

[40]  Razvan C. Bunescu,et al.  Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome , 2005, Genome Biology.

[41]  P. Bork,et al.  Dynamic Complex Formation During the Yeast Cell Cycle , 2005, Science.

[42]  Lan V. Zhang,et al.  Evidence for dynamically organized modularity in the yeast protein–protein interaction network , 2004, Nature.

[43]  Markus J. Herrgård,et al.  A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology , 2008, Nature Biotechnology.

[44]  T. Ideker,et al.  Systematic interpretation of genetic interactions using protein networks , 2005, Nature Biotechnology.

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

[46]  A. Barabasi,et al.  High-Quality Binary Protein Interaction Map of the Yeast Interactome Network , 2008, Science.

[47]  Lennart Martens,et al.  HUPO Brain Proteome Project: Summary of the pilot phase and introduction of a comprehensive data reprocessing strategy , 2006, Proteomics.

[48]  S. van Nocker,et al.  The VERNALIZATION INDEPENDENCE 4 gene encodes a novel regulator of FLOWERING LOCUS C. , 2002, The Plant journal : for cell and molecular biology.

[49]  Kesheng Liu,et al.  Information Flow Analysis of Interactome Networks , 2009, PLoS Comput. Biol..

[50]  M. Gerstein,et al.  Genomic analysis of regulatory network dynamics reveals large topological changes , 2004, Nature.

[51]  Mark Gerstein,et al.  Analyzing cellular biochemistry in terms of molecular networks. , 2003, Annual review of biochemistry.

[52]  Michelle S. Scott,et al.  Global Survey of Organ and Organelle Protein Expression in Mouse: Combined Proteomic and Transcriptomic Profiling , 2006, Cell.

[53]  Masanori Arita.  The metabolic world of Escherichia coli is not small. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[54]  John D. Storey,et al.  A network-based analysis of systemic inflammation in humans , 2005, Nature.

[55]  J. Hopfield,et al.  From molecular to modular cell biology , 1999, Nature.

[56]  B. Snel,et al.  Comparative assessment of large-scale data sets of protein–protein interactions , 2002, Nature.

[57]  Joel S. Bader,et al.  Precision and recall estimates for two-hybrid screens , 2008, Bioinform..

[58]  S. L. Wong,et al.  A Map of the Interactome Network of the Metazoan C. elegans , 2004, Science.

[59]  Gary D Bader,et al.  Global Mapping of the Yeast Genetic Interaction Network , 2004, Science.

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

[61]  Aidong Zhang,et al.  Semantic integration to identify overlapping functional modules in protein interaction networks , 2007, BMC Bioinformatics.

[62]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[63]  P. Bork,et al.  Functional organization of the yeast proteome by systematic analysis of protein complexes , 2002, Nature.

[64]  P. Zimmermann,et al.  Genome-Scale Proteomics Reveals Arabidopsis thaliana Gene Models and Proteome Dynamics , 2008, Science.

[65]  S. Shen-Orr,et al.  Superfamilies of Evolved and Designed Networks , 2004, Science.

[66]  David Warde-Farley,et al.  Dynamic modularity in protein interaction networks predicts breast cancer outcome , 2009, Nature Biotechnology.

[67]  Mark Gerstein,et al.  Predicting interactions in protein networks by completing defective cliques , 2006, Bioinform..

[68]  Lincoln Stein,et al.  Reactome: a knowledgebase of biological pathways , 2004, Nucleic Acids Res..

[69]  S. Rhee,et al.  Functional Annotation of the Arabidopsis Genome Using Controlled Vocabularies1 , 2004, Plant Physiology.

[70]  P. Bourgine,et al.  Topological and causal structure of the yeast transcriptional regulatory network , 2002, Nature Genetics.

[71]  Mark Gerstein,et al.  The tYNA platform for comparative interactomics: a web tool for managing, comparing and mining multiple networks , 2006, Bioinform..

[72]  T. Ideker,et al.  Network-based classification of breast cancer metastasis , 2007, Molecular systems biology.