Jacques Colinge

The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data

Affinity purification coupled with mass spectrometry (AP-MS) is a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (for example, proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. The standard approach is to identify nonspecific interactions using one or more negative-control purifications, but many small-scale AP-MS studies do not capture a complete, accurate background protein set when available controls are limited. Fortunately, negative controls are largely bait independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the contaminant repository for affinity purification (the CRAPome) and describe its use for scoring protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely accessible at http://www.crapome.org/.

The Btk tyrosine kinase is a major target of the Bcr-Abl inhibitor dasatinib

Dasatinib is a small-molecule kinase inhibitor used for the treatment of imatinib-resistant chronic myelogenous leukemia (CML). We have analyzed the kinases targeted by dasatinib by using an unbiased chemical proteomics approach to detect binding proteins directly from lysates of CML cells. Besides Abl and Src kinases, we have identified the Tec kinases Btk and Tec, but not Itk, as major binders of dasatinib. The kinase activity of Btk and Tec, but not of Itk, was inhibited by nanomolar concentrations of dasatinib in vitro and in cultured cells. We identified the gatekeeper residue as the critical determinant of dasatinib susceptibility. Mutation of Thr-474 in Btk to Ile and Thr-442 in Tec to Ile conferred resistance to dasatinib, whereas mutation of the corresponding residue in Itk (Phe-435) to Thr sensitized the otherwise insensitive Itk to dasatinib. The configuration of this residue may be a predictor for dasatinib sensitivity across the kinome. Analysis of mast cells derived from Btk-deficient mice suggested that inhibition of Btk by dasatinib may be responsible for the observed reduction in histamine release upon dasatinib treatment. Furthermore, dasatinib inhibited histamine release in primary human basophils and secretion of proinflammatory cytokines in immune cells. The observed inhibition of Tec kinases by dasatinib predicts immunosuppressive (side) effects of this drug and may offer therapeutic opportunities for inflammatory and immunological disorders.

A chemical and phosphoproteomic characterization of dasatinib action in lung cancer

We describe a strategy to comprehend signaling pathways active in lung cancer cells and targeted by dasatinib employing chemical proteomics to identify direct interacting proteins combined with immunoaffinity purification of tyrosine phosphorylated peptides corresponding to activated tyrosine kinases. We identified nearly 40 different kinase targets of dasatinib. These include SFK members (LYN, SRC, FYN, LCK, YES), non-receptor tyrosine kinases (FRK, BRK, ACK), and receptor tyrosine kinases (Ephrin receptors, DDR1, EGFR). Using quantitative phosphoproteomics we identified peptides corresponding to autophosphorylation sites of these tyrosine kinases that are inhibited in a concentration-dependent manner by dasatinib. Using drug resistant gatekeeper mutants, we show that SFK kinases, particularly SRC and FYN, as well as EGFR are relevant targets for dasatinib action. The combined mass spectrometry based approach described here provides a system-level view of dasatinib action in cancer cells and suggests both functional targets and rationale combinatorial therapeutic strategies.

Stereospecific targeting of MTH1 by ( S )-crizotinib as an anticancer strategy

Activated RAS GTPase signalling is a critical driver of oncogenic transformation and malignant disease. Cellular models of RAS-dependent cancers have been used to identify experimental small molecules, such as SCH51344, but their molecular mechanism of action remains generally unknown. Here, using a chemical proteomic approach, we identify the target of SCH51344 as the human mutT homologue MTH1 (also known as NUDT1), a nucleotide pool sanitizing enzyme. Loss-of-function of MTH1 impaired growth of KRAS tumour cells, whereas MTH1 overexpression mitigated sensitivity towards SCH51344. Searching for more drug-like inhibitors, we identified the kinase inhibitor crizotinib as a nanomolar suppressor of MTH1 activity. Surprisingly, the clinically used (R)-enantiomer of the drug was inactive, whereas the (S)-enantiomer selectively inhibited MTH1 catalytic activity. Enzymatic assays, chemical proteomic profiling, kinome-wide activity surveys and MTH1 co-crystal structures of both enantiomers provide a rationale for this remarkable stereospecificity. Disruption of nucleotide pool homeostasis via MTH1 inhibition by (S)-crizotinib induced an increase in DNA single-strand breaks, activated DNA repair in human colon carcinoma cells, and effectively suppressed tumour growth in animal models. Our results propose (S)-crizotinib as an attractive chemical entity for further pre-clinical evaluation, and small-molecule inhibitors of MTH1 in general as a promising novel class of anticancer agents.

Viral immune modulators perturb the human molecular network by common and unique strategies

Viruses must enter host cells to replicate, assemble and propagate. Because of the restricted size of their genomes, viruses have had to evolve efficient ways of exploiting host cell processes to promote their own life cycles and also to escape host immune defence mechanisms. Many viral open reading frames (viORFs) with immune-modulating functions essential for productive viral growth have been identified across a range of viral classes. However, there has been no comprehensive study to identify the host factors with which these viORFs interact for a global perspective of viral perturbation strategies. Here we show that different viral perturbation patterns of the host molecular defence network can be deduced from a mass-spectrometry-based host-factor survey in a defined human cellular system by using 70 innate immune-modulating viORFs from 30 viral species. The 579 host proteins targeted by the viORFs mapped to an unexpectedly large number of signalling pathways and cellular processes, suggesting yet unknown mechanisms of antiviral immunity. We further experimentally verified the targets heterogeneous nuclear ribonucleoprotein U, phosphatidylinositol-3-OH kinase, the WNK (with-no-lysine) kinase family and USP19 (ubiquitin-specific peptidase 19) as vulnerable nodes in the host cellular defence system. Evaluation of the impact of viral immune modulators on the host molecular network revealed perturbation strategies used by individual viruses and by viral classes. Our data are also valuable for the design of broad and specific antiviral therapies.

CD14 is a coreceptor of Toll-like receptors 7 and 9

CD14 interacts with and is essential for the functions of endosomal TLR7 and TLR9 in mice.

Charting the molecular network of the drug target Bcr-Abl

The tyrosine kinase Bcr-Abl causes chronic myeloid leukemia and is the cognate target of tyrosine kinase inhibitors like imatinib. We have charted the protein–protein interaction network of Bcr-Abl by a 2-pronged approach. Using a monoclonal antibody we have first purified endogenous Bcr-Abl protein complexes from the CML K562 cell line and characterized the set of most tightly-associated interactors by MS. Nine interactors were subsequently subjected to tandem affinity purifications/MS analysis to obtain a molecular interaction network of some hundred cellular proteins. The resulting network revealed a high degree of interconnection of 7 “core” components around Bcr-Abl (Grb2, Shc1, Crk-I, c-Cbl, p85, Sts-1, and SHIP-2), and their links to different signaling pathways. Quantitative proteomics analysis showed that tyrosine kinase inhibitors lead to a disruption of this network. Certain components still appear to interact with Bcr-Abl in a phosphotyrosine-independent manner. We propose that Bcr-Abl and other drug targets, rather than being considered as single polypeptides, can be considered as complex protein assemblies that remodel upon drug action.

A chemical-genetic screen reveals a mechanism of resistance to PI3K inhibitors in cancer

Linking the molecular aberrations of cancer to drug responses could guide treatment choice and identify new therapeutic applications. However, there has been no systematic approach for analyzing gene-drug interactions in human cells. We establish a multiplexed assay to study the cellular fitness of a panel of engineered isogenic cancer cells in response to a collection of drugs, enabling the systematic analysis of thousands of gene-drug interactions. Applying this approach to breast cancer revealed various synthetic-lethal interactions and drug resistance mechanisms, some of which were known, thereby validating the method. NOTCH pathway activation, which occurs frequently in breast cancer, unexpectedly conferred resistance to PI3K inhibitors, which are currently undergoing clinical trials in breast cancer patients. NOTCH1 and downstream induction of c-MYC overrode the dependency of cells on the PI3K/mTOR pathway for proliferation. These data reveal a novel mechanism of resistance to PI3K inhibitors with direct clinical implications.

Interlaboratory reproducibility of large-scale human protein-complex analysis by standardized AP-MS

The characterization of all protein complexes of human cells under defined physiological conditions using affinity purification–mass spectrometry (AP-MS) is a highly desirable step in the quest to understand the phenotypic effects of genomic information. However, such a challenging goal has not yet been achieved, as it requires reproducibility of the experimental workflow and high data consistency across different studies and laboratories. We systematically investigated the reproducibility of a standardized AP-MS workflow by performing a rigorous interlaboratory comparative analysis of the interactomes of 32 human kinases. We show that it is possible to achieve high interlaboratory reproducibility of this standardized workflow despite differences in mass spectrometry configurations and subtle sample preparation–related variations and that combination of independent data sets improves the approach sensitivity, resulting in even more-detailed networks. Our analysis demonstrates the feasibility of obtaining a high-quality map of the human protein interactome with a multilaboratory project.

General statistical modeling of data from protein relative expression isobaric tags.

Quantitative comparison of the protein content of biological samples is a fundamental tool of research. The TMT and iTRAQ isobaric labeling technologies allow the comparison of 2, 4, 6, or 8 samples in one mass spectrometric analysis. Sound statistical models that scale with the most advanced mass spectrometry (MS) instruments are essential for their efficient use. Through the application of robust statistical methods, we developed models that capture variability from individual spectra to biological samples. Classical experimental designs with a distinct sample in each channel as well as the use of replicates in multiple channels are integrated into a single statistical framework. We have prepared complex test samples including controlled ratios ranging from 100:1 to 1:100 to characterize the performance of our method. We demonstrate its application to actual biological data sets originating from three different laboratories and MS platforms. Finally, test data and an R package, named isobar, which can read Mascot, Phenyx, and mzIdentML files, are made available. The isobar package can also be used as an independent software that requires very little or no R programming skills.