Biological interaction networks are conserved at the module level

BackgroundOrthologous genes are highly conserved between closely related species and biological systems often utilize the same genes across different organisms. However, while sequence similarity often implies functional similarity, interaction data is not well conserved even for proteins with high sequence similarity. Several recent studies comparing high throughput data including expression, protein-protein, protein-DNA, and genetic interactions between close species show conservation at a much lower rate than expected.ResultsIn this work we collected comprehensive high-throughput interaction datasets for four model organisms (S. cerevisiae, S. pombe, C. elegans, and D. melanogaster) and carried out systematic analyses in order to explain the apparent lower conservation of interaction data when compared to the conservation of sequence data. We first showed that several previously proposed hypotheses only provide a limited explanation for such lower conservation rates. We combined all interaction evidences into an integrated network for each species and identified functional modules from these integrated networks. We then demonstrate that interactions that are part of functional modules are conserved at much higher rates than previous reports in the literature, while interactions that connect between distinct functional modules are conserved at lower rates.ConclusionsWe show that conservation is maintained between species, but mainly at the module level. Our results indicate that interactions within modules are much more likely to be conserved than interactions between proteins in different modules. This provides a network based explanation to the observed conservation rates that can also help explain why so many biological processes are well conserved despite the lower levels of conservation for the interactions of proteins participating in these processes.Accompanying website: http://www.sb.cs.cmu.edu/CrossSP

[1]  Johannes Berg,et al.  Cross-species analysis of biological networks by Bayesian alignment. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Jacques van Helden,et al.  Evaluation of clustering algorithms for protein-protein interaction networks , 2006, BMC Bioinformatics.

[3]  Yan P. Yuan,et al.  Predicting function: from genes to genomes and back. , 1998, Journal of molecular biology.

[4]  Donna K. Slonim,et al.  High Throughput Interaction Data Reveals Degree Conservation of Hub Proteins , 2008, Pacific Symposium on Biocomputing.

[5]  G. Sumara,et al.  A Probabilistic Functional Network of Yeast Genes , 2004 .

[6]  Berend Snel,et al.  Protein Complex Evolution Does Not Involve Extensive Network Rewiring , 2008, PLoS Comput. Biol..

[7]  Sean R. Collins,et al.  Global landscape of protein complexes in the yeast Saccharomyces cerevisiae , 2006, Nature.

[8]  Mark Gerstein,et al.  Divergence of transcription factor binding sites across related yeast species. , 2007, Science.

[9]  P. Muti,et al.  RIPK1/RIPK3 promotes vascular permeability to allow tumor cell extravasation independent of its necroptotic function , 2017, Cell Death & Disease.

[10]  P. Bork,et al.  Proteome survey reveals modularity of the yeast cell machinery , 2006, Nature.

[11]  Philip S. Yu,et al.  A new method to measure the semantic similarity of GO terms , 2007, Bioinform..

[12]  E. Coccia,et al.  RNAseq Analyses Identify Tumor Necrosis Factor-Mediated Inflammation as a Major Abnormality in ALS Spinal Cord , 2009, Nature Cell Biology.

[13]  D. Durocher,et al.  Significant conservation of synthetic lethal genetic interaction networks between distantly related eukaryotes , 2008, Proceedings of the National Academy of Sciences.

[14]  Joshua M. Stuart,et al.  A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules , 2003, Science.

[15]  Anton J. Enright,et al.  An efficient algorithm for large-scale detection of protein families. , 2002, Nucleic acids research.

[16]  T. Sittler,et al.  The Plasmodium protein network diverges from those of other eukaryotes , 2005, Nature.

[17]  Martijn A. Huynen,et al.  Reconstructing the evolution of the mitochondrial ribosomal proteome , 2007, Nucleic acids research.

[18]  Mike Tyers,et al.  BioGRID: a general repository for interaction datasets , 2005, Nucleic Acids Res..

[19]  Valeria Panebianco,et al.  Supplementary Figure 2 , 2012 .

[20]  Jianzhi Zhang,et al.  Low rates of expression profile divergence in highly expressed genes and tissue-specific genes during mammalian evolution. , 2006, Molecular biology and evolution.

[21]  Joshua M. Stuart,et al.  A global analysis of genetic interactions in Caenorhabditis elegans , 2007, Journal of biology.

[22]  Gavin Sherlock,et al.  The Stanford Microarray Database: implementation of new analysis tools and open source release of software , 2002, Nucleic Acids Res..

[23]  Rui Luo,et al.  Is My Network Module Preserved and Reproducible? , 2011, PLoS Comput. Biol..

[24]  K. N. Chandrika,et al.  Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets , 2006, Nature Genetics.

[25]  Wendell A. Lim,et al.  Correction: Evolution of Phosphoregulation: Comparison of Phosphorylation Patterns across Yeast Species , 2009, PLoS Biology.

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

[27]  Sean R. Collins,et al.  Conservation and Rewiring of Functional Modules Revealed by an Epistasis Map in Fission Yeast , 2008, Science.

[28]  D. Koller,et al.  From signatures to models: understanding cancer using microarrays , 2005, Nature Genetics.

[29]  M. Gerstein,et al.  Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. , 2004, Genome research.

[30]  P. Lio’,et al.  Periodic gene expression program of the fission yeast cell cycle , 2004, Nature Genetics.

[31]  P. Bork,et al.  Co-evolution of transcriptional and post-translational cell-cycle regulation , 2006, Nature.

[32]  Ziv Bar-Joseph,et al.  Evolutionary divergence in the fungal response to fluconazole revealed by soft clustering , 2010, Genome Biology.

[33]  S. Bergmann,et al.  Similarities and Differences in Genome-Wide Expression Data of Six Organisms , 2003, PLoS biology.

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

[35]  Maria Victoria Schneider,et al.  MINT: a Molecular INTeraction database. , 2002, FEBS letters.

[36]  Michael Hummel,et al.  Supplementary Figure 3 , 2010 .

[37]  Ioannis Xenarios,et al.  DIP: the Database of Interacting Proteins , 2000, Nucleic Acids Res..

[38]  Jianzhi Zhang,et al.  Null mutations in human and mouse orthologs frequently result in different phenotypes , 2008, Proceedings of the National Academy of Sciences.

[39]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..

[40]  Y. Zhang,et al.  IntAct—open source resource for molecular interaction data , 2006, Nucleic Acids Res..

[41]  Nicola J. Rinaldi,et al.  Transcriptional regulatory code of a eukaryotic genome , 2004, Nature.

[42]  Adam P. Arkin,et al.  Orthologous Transcription Factors in Bacteria Have Different Functions and Regulate Different Genes , 2007, PLoS Comput. Biol..

[43]  A. Fraser,et al.  A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans , 2008, Nature Genetics.

[44]  C. Daub,et al.  BMC Systems Biology , 2007 .

[45]  W. Lim,et al.  Evolution of Phosphoregulation: Comparison of Phosphorylation Patterns across Yeast Species , 2009, PLoS biology.

[46]  Matthew Berriman,et al.  GeneDB: a resource for prokaryotic and eukaryotic organisms , 2004, Nucleic Acids Res..

[47]  Mariana Benítez,et al.  Faculty Opinions recommendation of Biological interaction networks are conserved at the module level. , 2012 .

[48]  Peng Jiang,et al.  SPICi: a fast clustering algorithm for large biological networks , 2010, Bioinform..

[49]  Matthias Sipiczki,et al.  Where does fission yeast sit on the tree of life? , 2000, Genome Biology.

[50]  Erik L. L. Sonnhammer,et al.  Inparanoid: a comprehensive database of eukaryotic orthologs , 2004, Nucleic Acids Res..

[51]  D. Gifford,et al.  Tissue-specific transcriptional regulation has diverged significantly between human and mouse , 2007, Nature Genetics.