Dynamic complex formation during the yeast cell cycle
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Dynamic complex formation during the yeast cell cycle



EMBL white seminar, European Molecular Biology Laboratory, Heidelberg, Germany, February 4, 2005

EMBL white seminar, European Molecular Biology Laboratory, Heidelberg, Germany, February 4, 2005



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    Dynamic complex formation during the yeast cell cycle Dynamic complex formation during the yeast cell cycle Presentation Transcript

    • Lars Juhl Jensen EMBL Heidelberg Dynamic complex formation during the yeast cell cycle
    • A qualitative model of the yeast cell cycle
      • Should be accurate even at the level individual interactions
      • Provides a global overview of temporal complex formation
      © Chen et al., Mol. Biol. Cell, 2004
    • Model generation through data integration Model generation A Parts List
      • Literature
      • Microarray data
      Dynamic data
      • Microarray data
      • Proteomics data
      • PPI data
      • TF-target data
      Connections YER001W YBR088C YOL007C YPL127C YNR009W YDR224C YDL003W YBL003C YDR225W YBR010W YKR013W … YDR097C YBR089W YBR054W YMR215W YBR071W YBL002W YGR189C YNL031C YNL030W YNL283C YGR152C …
    • Extracting a cell cycle interaction network Cell cycle microarray data Physical PPI interactions with confidence scores Expand the set of proteins to include non-periodic proteins that are strongly connected to periodic proteins Raw Data Node selection List of periodically expressed proteins with peak time Interactions Require compatible compartments and high confidence Extract cell cycle network
    • Getting the parts list Yeast culture Microarrays Gene expression Expression profile Cho & Spellman et al. 600 periodically expressed genes (with associated peak times) that encode “dynamic proteins” The parts list New analysis
    • Topology based quality scores
      • We developed scoring schemes for identifying sticky interactions
      • Yeast two-hybrid data:
        • S1 = -log((N 1 +1) · (N 2 +1))
      • Scoring scheme for complex pull-down data:
        • S2 = log[N 12 · N/((N 1 +1) · (N 2 +1))]
      • Score calibration against KEGG
        • A different raw quality score is used for each evidence type
        • Quality differences exist among data sets of the same type
    • Filtering by subcellular localization
      • Proteins cannot interact if they are not in the same place
        • Large-scale subcellular localization screens have been made in yeast
        • A matrix can be constructed that described the compartments between which interactions should be allowed
        • Two proteins cannot interact if no combination of observed subcellular compartments allow for interaction
    • Benchmark of published interaction sets against the MIPS curated yeast complexes
      • Data sets were filtered to remove the most obvious biases by removing ribosomal proteins and interactions obtained from MIPS
      • High specificity is often obtained at the price of low coverage
    • A temporal interaction network
      • Interacting proteins are mostly expressed close in time
      • This visualization does not show this clearly
      G 1 S M G 2
    • Relaxing the temporal interaction network
    • The temporal interaction network
      • For two thirds of the dynamic proteins, no interactions were found
      • These may be missed components of complexes already in the network
      • Some may not participate in any protein interactions
      • The majority probably participate in transient interactions that are often missed by current assays
    • Static proteins play a major role
      • Static proteins comprise a third of the interactions at all times of the cell cycle
      • Their time of action can be predicted from interactions with dynamic proteins
    • Cdc28p and its interaction partners
    • Pho85p and associated cyclins
      • Pcl7p has so far not been considered a cell cycle related cyclin, but we see it periodically expressed
      • We suggest the phosphoprotein Mmr1p to be a Pho85p-Pcl7p substrate
      • The phosphorylation of Swi5p by Pho85p-Pcl2p is correctly reproduced
    • Just-in-time synthesis vs. just-in-time assembly
      • Most dynamic proteins are expressed just before they are needed to carry out their function
      • Most complexes also contain static proteins
      • Just-in-time assembly of complexes appear to be the general principle
      • The time of assembly is controlled synthesizing the last subunits just-in-time
    • Assembly of the pre-replication complex
    • Network as a discovery tools
      • The network enables us to place 30+ uncharacterized proteins in a temporal interaction context
      • Quite detailed hypotheses can be made concerning the their function
      • The network also contains entire novel modules and complexes
    • Nucleosome / bud formation module
    • Rediscovering the “party” hubs and “date” hubs “ Date” hubs: the hub protein interacts with different proteins at different times. “ Party” hubs: the hub protein and its interactors are expressed close in time.
    • Transcription is linked to phosphorylation
      • A genome-wide screen identified 332 Cdc28p targets, which include
        • 6% of all yeast proteins
        • 8% of the static proteins
        • 27% of the dynamic ones
      • A similar correlation was observed with predicted PEST regions
      • This suggests a hitherto undescribed link between transcriptional and post-translational control
    • Conclusions
      • It is possible to construct highly reliable models from microarray data and high-throughput interaction screen
      • Temporal interaction networks can provide an overview of how and when protein complexes are assembled
      • Different mechanism for regulating protein activity appear to be tightly linked to each other
    • Acknowledgments
      • The yeast cell cycle interaction network
        • Ulrik de Lichtenberg
        • Søren Brunak
        • Peer Bork
      • Re-analysis of cell cycle microarray expression data
        • Thomas Skøt Jensen
        • Anders Fausbøll
      • Also thanks to
        • Sean Hooper
        • Christian von Mering
    • Thank you!