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      Unequal evolutionary conservation of human protein interactions in interologous networks

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      1 , 2 , 1 , 2 , 3 ,
      Genome Biology
      BioMed Central

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          Abstract

          The conservation of protein-protein interaction networks can be examined by mapping human proteins to yeast and other model organisms, revealing that protein complexes are preferentially conserved, and that such conservation can yield biological insights.

          Abstract

          Background

          Protein-protein interaction (PPI) networks have been transferred between organisms using interologs, allowing model organisms to supplement the interactomes of higher eukaryotes. However, the conservation of various network components has not been fully explored. Unequal conservation of certain network components may limit the ability to fully expand the target interactomes using interologs.

          Results

          In this study, we transfer high quality human interactions to lower eukaryotes, and examine the evolutionary conservation of individual network components. When human proteins are mapped to yeast, we find a strong positive correlation (r = 0.50, P = 3.9 × 10 -4) between evolutionary conservation and the number of interacting proteins, which is also found when mapped to other model organisms. Examining overlapping PPI networks, Gene Ontology (GO) terms, and gene expression data, we are able to demonstrate that protein complexes are conserved preferentially, compared to transient interactions in the network. Despite the preferential conservation of complexes, and the fact that the human interactome comprises an abundance of transient interactions, we demonstrate how transferring human PPIs to yeast augments this well-studied protein interaction network, using the coatomer complex and replisome as examples.

          Conclusion

          Human proteins, like yeast proteins, show a correlation between the number of interacting partners and evolutionary conservation. The preferential conservation of proteins with higher degree leads to enrichment in protein complexes when interactions are transferred between organisms using interologs.

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          Most cited references37

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          The protein kinase complement of the human genome.

          G. Manning (2002)
          We have catalogued the protein kinase complement of the human genome (the "kinome") using public and proprietary genomic, complementary DNA, and expressed sequence tag (EST) sequences. This provides a starting point for comprehensive analysis of protein phosphorylation in normal and disease states, as well as a detailed view of the current state of human genome analysis through a focus on one large gene family. We identify 518 putative protein kinase genes, of which 71 have not previously been reported or described as kinases, and we extend or correct the protein sequences of 56 more kinases. New genes include members of well-studied families as well as previously unidentified families, some of which are conserved in model organisms. Classification and comparison with model organism kinomes identified orthologous groups and highlighted expansions specific to human and other lineages. We also identified 106 protein kinase pseudogenes. Chromosomal mapping revealed several small clusters of kinase genes and revealed that 244 kinases map to disease loci or cancer amplicons.
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            Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.

            The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.
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              Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.

              We sought to create a comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle. To this end, we used DNA microarrays and samples from yeast cultures synchronized by three independent methods: alpha factor arrest, elutriation, and arrest of a cdc15 temperature-sensitive mutant. Using periodicity and correlation algorithms, we identified 800 genes that meet an objective minimum criterion for cell cycle regulation. In separate experiments, designed to examine the effects of inducing either the G1 cyclin Cln3p or the B-type cyclin Clb2p, we found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins. Furthermore, we analyzed our set of cell cycle-regulated genes for known and new promoter elements and show that several known elements (or variations thereof) contain information predictive of cell cycle regulation. A full description and complete data sets are available at http://cellcycle-www.stanford.edu
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                Author and article information

                Journal
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1465-6906
                1465-6914
                2007
                29 May 2007
                : 8
                : 5
                : R95
                Affiliations
                [1 ]Department of Medical Biophysics, University of Toronto, Toronto, Canada M5G 1L7
                [2 ]Ontario Cancer Institute, Toronto Medical Discovery Tower, Toronto, Canada M5G 1L7
                [3 ]Department of Computer Science, University of Toronto, Toronto, Canada M5G 1L71
                Article
                gb-2007-8-5-r95
                10.1186/gb-2007-8-5-r95
                1929159
                17535438
                6ccec76c-30f5-452b-8370-05e5ab9fe22e
                Copyright © 2007 Brown and Jurisica; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 16 November 2006
                : 2 March 2007
                : 29 May 2007
                Categories
                Research

                Genetics
                Genetics

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