Improved functional overview of protein complexes using inferred epistatic relationships View Full Text


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Article Info

DATE

2011-05-23

AUTHORS

Colm Ryan, Derek Greene, Aude Guénolé, Haico van Attikum, Nevan J Krogan, Pádraig Cunningham, Gerard Cagney

ABSTRACT

BackgroundEpistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions. Epistatic interactions are important for understanding cell biology because they define relationships between individual genes, and between sets of genes involved in biochemical pathways and protein complexes. Each E-MAP screen quantifies the interactions between a logically selected subset of genes (e.g. genes whose products share a common function). Interactions that occur between genes involved in different cellular processes are not as frequently measured, yet these interactions are important for providing an overview of cellular organization.ResultsWe introduce a method for combining overlapping E-MAP screens and inferring new interactions between them. We use this method to infer with high confidence 2,240 new strongly epistatic interactions and 34,469 weakly epistatic or neutral interactions. We show that accuracy of the predicted interactions approaches that of replicate experiments and that, like measured interactions, they are enriched for features such as shared biochemical pathways and knockout phenotypes. We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links. We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus.ConclusionOverall, our data support a modular model of yeast cell protein network organization and show how prediction methods can considerably extend the information that can be extracted from overlapping E-MAP screens. More... »

PAGES

80

References to SciGraph publications

  • 2004-12-12. Modular epistasis in yeast metabolism in NATURE GENETICS
  • 2000-05. Gene Ontology: tool for the unification of biology in NATURE GENETICS
  • 2007-06. Exploring genetic interactions and networks with yeast in NATURE REVIEWS GENETICS
  • 2009-01-12. Predicting genetic interactions with random walks on biological networks in BMC BIOINFORMATICS
  • 2003-10. Global analysis of protein localization in budding yeast in NATURE
  • 2006-03-22. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae in NATURE
  • 2006-07-21. A strategy for extracting and analyzing large-scale quantitative epistatic interaction data in GENOME BIOLOGY
  • 2004-01. The MAPK Hog1 recruits Rpd3 histone deacetylase to activate osmoresponsive genes in NATURE
  • 2007-09. Integrating physical and genetic maps: from genomes to interaction networks in NATURE REVIEWS GENETICS
  • 2007-02-21. Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map in NATURE
  • 2002-07-25. Functional profiling of the Saccharomyces cerevisiae genome in NATURE
  • 2002-10. Analyzing yeast protein–protein interaction data obtained from different sources in NATURE BIOTECHNOLOGY
  • 2003-10. Global analysis of protein expression in yeast in NATURE
  • 2006-01-22. Proteome survey reveals modularity of the yeast cell machinery in NATURE
  • 2010-04-20. Missing value imputation for epistatic MAPs in BMC BIOINFORMATICS
  • 2004-03-12. A new enrichment approach identifies genes that alter cell cycle progression in Saccharomyces cerevisiae in CURRENT GENETICS
  • 2000-02. A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae in NATURE
  • 2006-06-08. Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae in BMC BIOLOGY
  • 2005-05-05. Systematic interpretation of genetic interactions using protein networks in NATURE BIOTECHNOLOGY
  • 2008-10-09. Mining protein networks for synthetic genetic interactions in BMC BIOINFORMATICS
  • 2009-12-10. Towards accurate imputation of quantitative genetic interactions in GENOME BIOLOGY
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    http://scigraph.springernature.com/pub.10.1186/1752-0509-5-80

    DOI

    http://dx.doi.org/10.1186/1752-0509-5-80

    DIMENSIONS

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    PUBMED

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    35 schema:description BackgroundEpistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions. Epistatic interactions are important for understanding cell biology because they define relationships between individual genes, and between sets of genes involved in biochemical pathways and protein complexes. Each E-MAP screen quantifies the interactions between a logically selected subset of genes (e.g. genes whose products share a common function). Interactions that occur between genes involved in different cellular processes are not as frequently measured, yet these interactions are important for providing an overview of cellular organization.ResultsWe introduce a method for combining overlapping E-MAP screens and inferring new interactions between them. We use this method to infer with high confidence 2,240 new strongly epistatic interactions and 34,469 weakly epistatic or neutral interactions. We show that accuracy of the predicted interactions approaches that of replicate experiments and that, like measured interactions, they are enriched for features such as shared biochemical pathways and knockout phenotypes. We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links. We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus.ConclusionOverall, our data support a modular model of yeast cell protein network organization and show how prediction methods can considerably extend the information that can be extracted from overlapping E-MAP screens.
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    44 apparatus
    45 biochemical pathways
    46 biology
    47 cell biology
    48 cellular organization
    49 cellular processes
    50 chromatin
    51 complexes
    52 confidence
    53 connection
    54 data
    55 different cellular processes
    56 disruption
    57 effect
    58 epistatic interactions
    59 epistatic relationship
    60 evidence
    61 experiments
    62 features
    63 functional overview
    64 gene disruption
    65 genes
    66 growth rate
    67 high confidence
    68 individual genes
    69 information
    70 interaction
    71 knockout phenotypes
    72 laboratory
    73 link
    74 maps
    75 method
    76 miniarray
    77 model
    78 modular model
    79 net effect
    80 network organization
    81 neutral interactions
    82 new interactions
    83 new links
    84 nuclear transport apparatus
    85 number
    86 organization
    87 overview
    88 pairs
    89 pairs of genes
    90 pathway
    91 phenotype
    92 prediction method
    93 process
    94 protein complexes
    95 rate
    96 relationship
    97 screen
    98 set
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    100 single gene disruption
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