An important aspect of discovering the functionalities of proteins is in trying to understand how they interact with each other, in order to determine their ‘social behavior’. In order to do so, we need to generate global proteomic networks, but more importantly, implement complementary approaches in order to parse these datasets for meaningful phenotypes and disease associations.
Evidently, the demand is for integrated approaches encompassing multiple fields of expertise, in order to reduce complex circuits into meaningful modules. Our scientific vision is to obtain a multi-scale understanding of protein-protein interactions, ranging from discovering high-resolution compositional aspects of individual complexes to modeling global proteomic networks, in order to systematically study sophisticated cellular phenotypes.
Genome-wide yeast two-hybrid human interactome maps have been generated (Rual et al., 2005); however, these networks are limited by the binary output of yeast two-hybrid screening and fail to detect in vivo complexes. Pioneering studies in yeast describe genome-wide proteomic networks by tandem affinity purification (TAP) purification and mass-spectrometry (MS)-based methods (Gavin et al., 2002). However, similar complex datasets need to be generated for the human proteome (Behrends et al., 2010; Jirawatnotai et al., 2011; Malovannaya et al., 2011; Sang et al., 2011; Sowa et al., 2009), and more importantly, these datasets need to be further assessed by complementary approaches in order to parse them for meaningful phenotypes and disease associations.
Description of our general strategy for building functional proteomic networks
TAP-MS-based protein-protein interaction studies provide rapid insights into the function of a protein in the pertinent cellular context. The most important aspect of building proteomic networks is to choose the important cellular “hubs” and doing the tandem affinity purification using appropriate cell lines. Generally, genetic studies in humans or disease phenotypes in model organisms highlight the importance of a gene in a physiological process (see next section).
The candidate genes are double tagged in the N-terminus or C-terminus using the Gateway system and stable clonal cell lines are generated by a high-throughput Flp-In strategy (Torres et al., 2009). Tandem affinity purification eluates are subjected to HPLC and in-line mass spectrometry (MS/MS) to detect interacting proteins. In parallel, we also subject the tandem purification eluates to biochemical and functional studies, and verify reciprocal interactions by further TAP-MS-based proteomics of the protein interactors.
At this stage, these primary networks can be analyzed for emerging graph properties, including the connectivity of the network, and betweenness-centrality/clustering coefficients of the nodes (Seebacher and Gavin, 2011). Furthermore, we carry out cell biological studies on these interactors, which include RNAi-based knockdown studies and sub-cellular localization experiments. To study the role of these proteins in the organismal context, we utilize reverse genetics approaches to generate animal knockout models, and in collaboration with human geneticists carry out homozygosity mapping in human patient pools for discovering novel disease genes.
The combined output of these studies generates high-confidence protein-protein interaction maps, which sets the stage for constructing unbiased global complex protein interaction networks.
A complementary proteomic strategy to solve complex biological problems
This general framework for constructing global protein interaction networks is complemented by a related strategy for deconstructing the protein-protein interaction modules into the smallest functioning units and further doing proteomic studies using these motifs. For example, once we can figure out the minimal motif necessary and sufficient for ciliary localization of signaling molecules, we can generate tagged clonal lines using these motifs, in order to discover protein networks important in ciliary localization of proteins. Similarly, tandem affinity purification of a N-terminal cystin TAP clone, which contains a myristoylation motif, shows Unc119a/b to interact with myristoylated cargos (Wright et al., 2011).
Discovering novel connections important in evolution of biological networks
Biological systems are evolving modules of functioning units. Conceptually, this involves reusing robust functional modules, but this also involves rewiring these ancient modules with new connections in evolving organismal contexts.
The protein interaction maps in higher organisms provide us with cellular network modules that can be compared with similar modules in lower organisms in order to test how these functionalities have evolved. For example, the highly conserved IFT-A complex seems to acquire new effectors in mammals (such as Tulp3, Tubby) in order to achieve new functionalities (such as in neural tube differentiation and neuronal control of obesity) apart from its canonical role in retrograde IFT.
Detailed studies on protein complexes over evolutionary time promises to provide us with important clues regarding evolving protein-protein interactions in sculpting complex organismal networks.
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Gavin, A.C., Bosche, M., Krause, R., Grandi, P., Marzioch, M., Bauer, A., Schultz, J., Rick, J.M., Michon, A.M., Cruciat, C.M., et al. (2002). "Functional organization of the yeast proteome by systematic analysis of protein complexes." Nature 415, 141-147.
Jirawatnotai, S., Hu, Y., Michowski, W., Elias, J.E., Becks, L., Bienvenu, F., Zagozdzon, A., Goswami, T., Wang, Y.E., Clark, A.B., et al. (2011). "A function for cyclin D1 in DNA repair uncovered by protein interactome analyses in human cancers." Nature 474, 230-234.
Malovannaya, A., Lanz, R.B., Jung, S.Y., Bulynko, Y., Le, N.T., Chan, D.W., Ding, C., Shi, Y., Yucer, N., Krenciute, G., et al. (2011). "Analysis of the human endogenous coregulator complexome." Cell 145, 787-799.
Rual, J.F., Venkatesan, K., Hao, T., Hirozane-Kishikawa, T., Dricot, A., Li, N., Berriz, G.F., Gibbons, F.D., Dreze, M., Ayivi-Guedehoussou, N., et al. (2005). "Towards a proteome-scale map of the human protein-protein interaction network." Nature 437, 1173-1178.
Sang, L., Miller, J.J., Corbit, K.C., Giles, R.H., Brauer, M.J., Otto, E.A., Baye, L.M., Wen, X., Scales, S.J., Kwong, M., et al. (2011). "Mapping the NPHP-JBTS-MKS protein network reveals ciliopathy disease genes and pathways." Cell 145, 513-528.
Seebacher, J., and Gavin, A.C. (2011). "SnapShot: Protein-protein interaction networks." Cell 144, 1000, 1000 e1001.
Sowa, M.E., Bennett, E.J., Gygi, S.P., and Harper, J.W. (2009). "Defining the human deubiquitinating enzyme interaction landscape." Cell 138, 389-403.
Torres, J.Z., Miller, J.J., and Jackson, P.K. (2009). "High-throughput generation of tagged stable cell lines for proteomic analysis." Proteomics 9, 2888-2891.
Wright K., Baye L., Olivier-Mason A., Mukhopadhyay S., Sang L., Kwong M., Wang W., Pretorius P., Sheffield V., Sengupta P., Slusarski D., Jackson P. (2011) "An Arl3-Unc119-RP2 GTPase cycle delivers myristoylated NPHP3 to the primary cilium." Genes Dev. 25: 2347-2360.