Complex biological systems and cellular networks underlie most genotype to phenotype relationships. In the last decade, basic concepts of network biology have been described, emphasizing why cellular networks are important to consider in biology. Importantly, it is becoming increasingly clear that more high quality empirically derived datasets are needed to better describe biological networks and genotype to phenotype relationships. The interactome of an organism is the network formed by the complete set of interactions that can occur in a physiologically relevant dynamic range between all its macromolecules, including protein-protein, DNA-protein, RNA-protein, and RNA-RNA interactions. In this proposal, we focus on high-throughput (HT), proteome-scale mapping of what we refer to as the REFERENCE human binary protein-protein interactome network map. Major innovations in this application enable to define a clear roadmap for completion of this REFERENCE map by the end of this decade. During this coming cycle, we will expand the human HT binary interactome map from ~15% coverage, which is the milestone of the current cycle, to ~50%. We will also briefly discuss how we foresee further expansion to near completeness thereafter. The accumulation of DNA sequencing data exploded for the Human Genome Sequencing project in the 1990s when four crucial elements were assembled: i) cosmids, BAC, and YAC clone resources covering most of the genome;ii) automated laser-fluorescence sequencing, iii) the PHRED score used to systematically assess sequencing data quality, and iv) the development of """"""""hands-off"""""""" automated experimental steps. We describe below how the human binary interactome mapping project is reaching a similarly exploding phase: i) having significantly contributed to the ORFeome Collaboration (OC) we now have a nearly complete protein-coding ORF clone resource, ii) we developed a new strategy to apply the power of next-generation sequencing to interactome mapping, iii) we have published a new empirical framework that systematically assess interactome mapping data quality, and iv) we will describe new """"""""hands-off"""""""" automated strategies that greatly increase throughput and decrease cost.
Our specific aims are: i) to expand human binary interactome mapping to a full complement of protein-coding genes cloned by OC, ii) to reach ~50% coverage of the REFERENCE human binary interactome network map, and iii) to expand global network analyses of our newly mapped human binary interactome network.
The availability of (nearly) complete genome sequences for several model organisms and for human is changing the way scientists formulate and address biological questions. With large numbers of protein predictions, the traditional one-at-a-time approach can now be complemented by more global strategies that consider all proteins at once. Such approaches, referred to as """"""""systems biology"""""""" have the ultimate goal of providing quantitative and dynamic models to describe biological processes. One major impediment to this prospect however is that most predicted proteins have not yet been experimentally characterized in detail. Interactome maps can be used to formulate functional hypotheses for thousands of uncharacterized genes. In addition, global features of the resulting interactome networks have been proposed that provide worthwhile biological insights. From these insights and hypotheses, a better understanding of disease processes and better strategies for therapeutic intervention are anticipated.
|Yachie, Nozomu; Petsalaki, Evangelia; Mellor, Joseph C et al. (2016) Pooled-matrix protein interaction screens using Barcode Fusion Genetics. Mol Syst Biol 12:863|
|Kitsak, Maksim; Sharma, Amitabh; Menche, JÃ¶rg et al. (2016) Tissue Specificity of Human Disease Module. Sci Rep 6:35241|
|Lievens, Sam; Van der Heyden, JosÃ©; Masschaele, Delphine et al. (2016) Proteome-scale Binary Interactomics in Human Cells. Mol Cell Proteomics 15:3624-3639|
|Vo, Tommy V; Das, Jishnu; Meyer, Michael J et al. (2016) A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human. Cell 164:310-23|
|Yang, Xinping; Coulombe-Huntington, Jasmin; Kang, Shuli et al. (2016) Widespread Expansion of Protein Interaction Capabilities by Alternative Splicing. Cell 164:805-17|
|Neupane, Manish; Clark, Allison P; Landini, Serena et al. (2016) MECP2 Is a Frequently Amplified Oncogene with a Novel Epigenetic Mechanism That Mimics the Role of Activated RAS in Malignancy. Cancer Discov 6:45-58|
|Menche, JÃ¶rg; Sharma, Amitabh; Kitsak, Maksim et al. (2015) Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science 347:1257601|
|TaÅŸan, Murat; Musso, Gabriel; Hao, Tong et al. (2015) Selecting causal genes from genome-wide association studies via functionally coherent subnetworks. Nat Methods 12:154-9|
|Yang, Fan; Petsalaki, Evangelia; Rolland, Thomas et al. (2015) Protein domain-level landscape of cancer-type-specific somatic mutations. PLoS Comput Biol 11:e1004147|
|Petsalaki, Evangelia; Helbig, Andreas O; Gopal, Anjali et al. (2015) SELPHI: correlation-based identification of kinase-associated networks from global phospho-proteomics data sets. Nucleic Acids Res 43:W276-82|
Showing the most recent 10 out of 28 publications