Oral Presentation 25th Annual Lorne Proteomics Symposium 2020

The Hitchhiker’s Guide to the Yeast Interactome (#42)

Ignatius Pang 1 , Aidan P. Tay 1 2 , Apurv Goel 1 , Sara Ballouz 3 , Daniel L. Winter 1 , Daniel Weissberger 1 , Loïc M. Thibaut 4 , Joshua J. Hamey 1 , Jesse Gillis 3 , Gene Hart-Smith 5 , Marc R. Wilkins 1
  1. School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, New South Wales, Australia
  2. Transformational Bioinformatics, The Commonwealth Scientific and Industrial Research Organization (CSIRO), Sydney, NSW, Australia
  3. Stanley Center for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, The United States of America
  4. Computational Genomics, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
  5. Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia

The baker’s yeast (Saccharomyces cerevisiae) is a well-characterised model organism with the most comprehensively mapped eukaryotic interactome networks to date. This talk will outline several studies in which the interactome was used as a scaffold for the co-analysis of -omics datasets (e.g. transcriptomics, proteomics) to gain insights on how these interactions are dynamically regulated within the cell. First, we developed a Cytoscape app, 'PTMOracle' [1, 2], that facilitates the co-visualization of PTMs in the context of PPI networks. Additional types of protein data, including protein sequence and sequence features such as domains, motifs and disordered region could be co-analysed with PPI networks, which enable users to assess the potential impacts of PTMs on protein-protein interactions. Second, the negative genetic interactions in yeast have been systematically screened to near-completeness, but the biological basis of these interactions remains poorly understood. To investigate this, we analysed negative genetic interactions within an integrated biological network, being the union of protein-protein, signalling and regulatory interactions. Network triplet motifs, which contain two genes / proteins that show negative genetic interaction and a third protein from the network, were analysed [3]. Strikingly, only six out of 15 possible triplet types were present in the cell, unlike random networks. Negative genetic interactions among the six triplet motifs showed strong dosage constraints and motifs containing multiple negative genetic interactions highlight regions of ‘network vulnerability’; these could be targeted in fungal species for the regulation of cell growth. Third, protein correlation profiling (PCP) enables many intact protein complexes to be identified in single experiments. We co-analysed yeast PCP data with orthogonal gene co-expression data using EGAD [4] and found that the addition of gene co-expression to PCP data contributed mainly to confident identification of known complexes. In summary, providing that the -omics and interactome data are available, the analytical techniques described above are broadly applicable and could be used to analyse the interactomes of other eukaryotic model organisms including human.

  1. Tay AP, Liang A, Wilkins MR, Pang CNI. (2019) Visualizing Post-Translational Modifications in Protein Interaction Networks Using PTMOracle. Curr Protoc Bioinformatics. 66(1):e71.
  2. Tay AP, Pang CNI, Winter DL, Wilkins MR. (2017) PTMOracle: A Cytoscape App for Covisualizing and Coanalyzing Post-Translational Modifications in Protein Interaction Networks. J Proteome Res.16(5):1988-2003.
  3. Pang CNI, Goel A, Wilkins MR. (2018) Investigating the Network Basis of Negative Genetic Interactions in Saccharomyces cerevisiae with Integrated Biological Networks and Triplet Motif Analysis. J Proteome Res. 17(3):1014-1030.
  4. Ballouz S, Weber M, Pavlidis P, Gillis J. (2017) EGAD: ultra-fast functional analysis of gene networks. Bioinformatics. 33(4):612-614.