Comparative interaction mapping resolves protein complexes and signaling pathways on the basis of their conservation across different species or types of interaction network. It is an emerging methodology which, like comparative genomics, provides a powerful tool for understanding cellular function. Network comparison has been used to identify the functional roles of many proteins, and it offers insight into how mutations in the genome contribute to the evolution of new functions and phenotypes. A current hurdle towards these goals is the lack of high-coverage interaction maps at the appropriate evolutionary distances to enable network comparison. To address this shortcoming, the major goal of this project is to obtain matching sets of highdensity physical and genetic interaction maps across the model organisms Schizosaccharomyces pombe and Saccharomyces cerevisiae. Use of these data will ultimately help resolve the following questions: How closely do the architectures of the physical, genetic, and transcriptional interaction networks reflect variation in the underlying genomic sequence?;What relative contributions do changes in the physical interactome, genetic pathways, transcriptional networks, and mutations at the protein sequence level make to the evolution of new cellular functions? Specific hypotheses directly related to the physiologies of S. pombe and S. cerevisiae can also be addressed. Many aspects of S. pombe physiology bear more in common with mammals than does S. cerevisiae, including intron/exon splicing, chromosomal architecture, and RNA interference machinery. In fact, the last common ancestor of S. pombe and S. cerevisiae is quite ancient (420 mya), making the conserved interaction map generalizable to large parts of the eukaryotic lineage. To focus our initial interaction mapping efforts on the regulatory machinery most likely to form the basis for the similarities and differences between S. pombe and S. cerevisiae physiology, we will screen interactions among a targeted set of ~400 kinases and transcriptional regulators. This set will limit our scope sufficiently such that high coverage maps can be obtained at reasonable funding levels within a five-year time frame. Pair-wise genetic interactions will be measured using epistatic phenotyping, protein-protein interactions using affinity purification followed by tandem mass spectrometry, and transcription factor / promoter binding interactions using genome-wide chromatin immunoprecipitation assays. In parallel, we will develop a companion suite of bioinformatic methods to perform integrative and comparative analysis of the yeast interaction networks. Bioinformatic research will address: [1] Models of interplay between quantitative genetic and physical interactions;[2] Alignment of the integrated networks across species;and [3] Prediction and transfer of interactions within and across species, at varying degrees of data integration. Assembling the network of transcriptional regulators and kinases will serve as a pilot for establishing basic principles of network integration and comparison prior to embarking on larger-scale efforts to comprehensively map fission yeast as well as higher eukaryotes. It will involve close coordination among the two principal investigators Krogan and Ideker. It will join two University of California campuses as well as two California institutes, Cal-IT2 and QB3, which are committing space to house the proposed project in the San Diego and San Francisco areas.
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| Patrick, Kristin L; Wojcechowskyj, Jason A; Bell, Samantha L et al. (2018) Quantitative Yeast Genetic Interaction Profiling of Bacterial Effector Proteins Uncovers a Role for the Human Retromer in Salmonella Infection. Cell Syst 7:323-338.e6 |
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| Nissen, Kelly E; Homer, Christina M; Ryan, Colm J et al. (2017) The histone variant H2A.Z promotes splicing of weak introns. Genes Dev 31:688-701 |
| Kramer, Michael H; Farré, Jean-Claude; Mitra, Koyel et al. (2017) Active Interaction Mapping Reveals the Hierarchical Organization of Autophagy. Mol Cell 65:761-774.e5 |
| Du, Dan; Roguev, Assen; Gordon, David E et al. (2017) Genetic interaction mapping in mammalian cells using CRISPR interference. Nat Methods 14:577-580 |
| Jaeger, Philipp A; Lucin, Kurt M; Britschgi, Markus et al. (2016) Network-driven plasma proteomics expose molecular changes in the Alzheimer's brain. Mol Neurodegener 11:31 |
| Srivas, Rohith; Shen, John Paul; Yang, Chih Cheng et al. (2016) A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy. Mol Cell 63:514-25 |
| Janke, Ryan; Kong, Jeremy; Braberg, Hannes et al. (2016) Nonsense-mediated decay regulates key components of homologous recombination. Nucleic Acids Res 44:5218-30 |
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