Research

Overview

Genes encoding serine/threonine phosphatase catalytic subunits are ten times less abundant than genes corresponding to the serine/threonine kinases. Phosphatase specificity is conferred by the association of catalytic subunits with other proteins that provide regulatory or targeting functions. Cataloguing these associating proteins is key to deciphering the function of the enzymes. We are particularly interested in studying the PP2A-type enzymes (PP2A, PP4 and PP6), which have been implicated in many aspects of cell cycle control, and responses to extracellular stimuli or stresses. Misregulation of PP2A enzymes has been linked to several pathological conditions, including Alzheimer's disease and cancer. We also devote a significant portion of our efforts to improving interaction proteomics methodologies, and generating physical and genetic interaction maps.

PP4 and cancer

We have identified a novel binding partner (which we called PP4R3, 2 genes in mammals) for the serine/threonine phosphatase PP4. PP4 forms an apparent trimer with a previously known interactor, PP4R2 and PP4R3 (Gingras et al., Mol Cell Proteomics, 2005). The interactions between PP4-PP4R2-PP4R3 have been preserved from yeast to human. Interestingly, the yeast PP4R3 ortholog was first identified in a screen for genes that play a role in conferring resistance to cisplatin, a frequently used anticancer drug. Intrinsic or acquired resistance to cisplatin significantly limits its usefulness in the clinic, and drugs circumventing this resistance could have an important clinical impact. We demonstrated that the entire yeast PP4 complex is involved in cisplatin-resistance, and that this resistance is conserved in higher eukaryotes. We are pursuing studies to understand how the PP4-PP4R2-PP4R3 complex is assembled and regulated, and identifying the cellular targets of the complex. Notably, we collaborated with the Durocher lab to determine that PP4 and the related phosphatase PP2A both contribute to the dephosphorylation of the histone variant H2AX during recovery from the DNA damage checkpoint (Nakada et al., EMBO Reports, 2008).

PP2A, STRIPAK and cerebral cavernous malformations

In 2008-2009, we reported on the discovery of a novel large protein complex, which we termed STRIPAK, for STRiatin Interacting Phosphatase And Kinase, which contains both the PP2A phosphatase (and regulatory subunit striatin) and a Ste20 kinase. Importantly, we also found that the protein CCM3 is a component of STRIPAK.

Composition of a STRIPAK complex. The previously-identified PP2A holoenzyme is shown in green. Kinases of the GCK-III family of serine/threonine kinases are part of STRIPAK, which also contains CCM3, and several less well-characterized proteins (see Goudreault et al., Mol Cell Proteomics 2009, for details).

CCM3 is one of three genes mutated in familial cases of cerebral cavernous malformations. CCMs are brain "caverns", in which blood accumulates due to leaky capillaries. CCMs can be asymptomatic, or patients may present with a wide array of symptoms, ranging from headaches to seizures and cerebral hemorrhage. While there are treatment options to reduce the symptoms (such as epilepsy), there is currently no known cure for this disease. More information on CCMs can be found at the Angioma Alliance.

Imaging of a patient brain showing a large CCM. Image courtesy of Dr. M. Gunel.

We aim to better understand the molecular function of CCM3 within STRIPAK, and to better characterize the molecular causes of cerebral cavernous malformations. Together with the Sicheri group, we recently characterized the molecular organization of the core component of STRIPAK: the phosphatase PP2A is bridged to the Ste20 kinase via striatin and the CCM3 molecule. Interestingly, we have now shown that striatin and CCM3 exhibit opposite effects on the localization of the Ste20 kinase MST4 to the Golgi, and on the polarization of the Golgi towards the leading edge of a wound. We also found that, while CCM3 was previously thought to homodimerize, the amino terminus of CCM3 bears homology to the C-terminal tail of the Ste20 kinase and that heterodimerization is favored. This may have important implications for how we view the disease. Importantly, we continue to work with several of our Toronto colleagues to better tackle the molecular causes of this disease: Brent Derry uses the worm C. elegans as a genetic model for CCM disease. Frank Sicheri is a structural biologist with expertise in signaling complexes, and is working on understanding how STRIPAK is assembled. Ian Scott uses zebrafish as a model to determine how cardiovascular development is modulated by CCM proteins and STRIPAK.

Systems Biology

The group is performing a number of systems-wide study to better characterize signaling events:
With the Tyers and Nesvizhskii labs, we systematically investigated the physical interaction network established by all kinases and phosphatases in the yeast S. cerevisiae (Breitkreutz et al., Science, 2010). To do so, we developed a sensitive experimental platform, a data management system for interaction proteomics, and a novel algorithm that uses statistical methods to discriminate true protein-protein interacting partners from background contaminants in AP-MS experiments. We systematically expressed and purified each yeast kinase and phosphatase, as well as many of their known binding partners, and generated an interaction network containing 1844 interactions. This new interactome revealed several unsuspected functions for a myriad of kinases, and revealed the functional association of multiple kinases to the Target Of Rapamycin complexes. We are pursing many of these novel findings, and extending our network analysis to mammalian serine/threonine phosphatases.

We are also building systematic interaction maps for all human phosphatases (with an emphasis on quantitative analysis of cell cycle-regulated events) and for components of the acetylation system in human. These studies which combine physical interactions with functional outcomes are performed in collaboration with several groups, including the Pelletier lab and the Pawson lab at the Lunenfeld.

We are also collaborating with local groups to combine physical and genetic interaction maps, as these approaches provide complementary results. For examples, see Costanzo et al., Science 2010, and Li et al., Nature Biotech 2011, from Boone and colleagues, Lawo et al., Curr Biol 2009, by Laurence Pelletier's lab or collaborations with the Daniel Durocher's lab (e.g. Nakada et al., Nature, 2010; O'Donnell et al., Mol. Cell, 2010).

Enabling tools for proteomics

Sample quality is key to interaction proteomics success, and the choice of expression system, epitope tag, and purification protocol can greatly influence outcome. Please visit our "resources" section for more information. We have spent considerable time evaluating different protocols for purification (see, e.g. Chen and Gingras, Methods, 2007), and have recently performed a comparative analysis on the advantages of multidimensional fractionation to analyze AP-MS data (see Dunham et al., Proteomics, 2011). We also reported on the performance of the new AB-SCIEX TripleTOF 5600 (collaboration with AB-SCIEX) for the quantitative analysis of AP-MS data (see Dunhame et al., Proteomics, 2011; Kean et al., J Biol Chem, 2011).

Another key factor in the success of interaction proteomics experiments is the ability to discriminate true interaction partners from background contaminants. Many excellent methods exist which utilize high stringency purification procedures to limit background, or employ isotope-based quantitative proteomics approaches to assist in the identification of true interactors (see Gingras et al., Nat Reviews Mol Cell Biol). However, such approaches may not always be feasible or desirable. In these cases, we perform single affinity purification coupled to mass spectrometry from unlabeled cells. Since background contaminants are generally more abundant in these cases, we are developing experimental design and computational tools to allow us to identify true interaction partners. Together with Mike Tyers, we released the software ProHits (Liu et al., Nature Biotech, 2011; see Resources) to help other groups managing their interaction proteomics data. We also developed with Alexey Nesvizhskii the SAINT (Significance Analysis of INTeractome) tool which provides confidence scores for each detected interaction (Choi et al., Nature Methods, 2011; see Resources).

We are also collaborating with several other research groups interested in protein-protein interactions to develop better frameworks for mapping of the human interactome, and to identify common contaminants (see the websites of Alexey Nesvizhskii, Benoit Coulombe, CCSB, Brian Raught, Stephane Angers, Jeff Wrana, Tony Pawson, Rob Ewing, Ruedi Aebersold).

Sources of support

We are grateful to the following agencies for supporting the projects in the Gingras lab and the SLRI proteomics group:

• Canadian Cancer Society Research Institute
• Canadian Institutes of Health Research
• Leukemia and Lymphoma Society of Canada
• Canada Research Chair Program
• Canadian Foundation for Innovation
• Ontario Ministry of Research and Innovation
• National Institutes of Health Research.
• Mount Sinai Hospital Foundation.