Devin K. Schweppe

Quantitative Phosphoproteomics Identifies Substrates and Functional Modules of Aurora and Polo-Like Kinase Activities in Mitotic Cells

Combining quantitative phosphoproteomics and selective kinase inhibition yields previously unknown substrates and functions of two families of mitotic kinases and refinement of their recognition motifs. Building the Corpus of Substrates of Mitotic Kinases Mitosis is a complex process involving duplication of DNA, nuclear membrane dissolution, construction of a mitotic spindle, proper segregation of chromosomes, and, finally, creation of two new cells—each with a complete set of genomic material. Protein phosphorylation plays a critical role in this process and is mediated predominantly by three sets of kinases: the cyclin-dependent kinase–cyclin complex Cdk1/cyclinB, the Aurora family (Aurora A and B), and the Polo-like kinase (Plk) family, especially Plk1. To explore the targets of these kinases, Kettenbach et al. combined specific small-molecule kinase inhibitors with large-scale quantitative phosphoproteomics of mitotic mammalian cells. Their data enable refinement of the motifs recognized by these kinases and suggest previously unknown functions for these kinases, as well as serve as a useful resource for future exploration of these essential mitotic regulators. Mitosis is a process involving a complex series of events that require careful coordination. Protein phosphorylation by a small number of kinases, in particular Aurora A, Aurora B, the cyclin-dependent kinase–cyclin complex Cdk1/cyclinB, and Polo-like kinase 1 (Plk1), orchestrates almost every step of cell division, from entry into mitosis to cytokinesis. To discover more about the functions of Aurora A, Aurora B, and kinases of the Plk family, we mapped mitotic phosphorylation sites to these kinases through the combined use of quantitative phosphoproteomics and selective targeting of kinase activities by small-molecule inhibitors. Using this integrated approach, we connected 778 phosphorylation sites on 562 proteins with these enzymes in cells arrested in mitosis. By connecting the kinases to protein complexes, we associated these kinases with functional modules. In addition to predicting previously unknown functions, this work establishes additional substrate-recognition motifs for these kinases and provides an analytical template for further use in dissecting kinase signaling events in other areas of cellular signaling and systems biology.

Architecture of the human interactome defines protein communities and disease networks

The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein–protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidating how genome variation contributes to disease. Here we present BioPlex 2.0 (Biophysical Interactions of ORFeome-derived complexes), which uses robust affinity purification–mass spectrometry methodology to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein-coding genes from the human genome, and constitutes, to our knowledge, the largest such network so far. With more than 56,000 candidate interactions, BioPlex 2.0 contains more than 29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering of interacting proteins identified more than 1,300 protein communities representing diverse cellular activities. Genes essential for cell fitness are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2,000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.

Quantitative Phosphoproteomic Profiling of Human Non-Small Cell Lung Cancer Tumors

UNLABELLED Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths worldwide. Within the molecular scope of NCSLC, a complex landscape of dysregulated cellular signaling has emerged, defined largely by mutations in select mediators of signal transduction, including the epidermal growth factor receptor (EGFR) and anaplastic lymphoma (ALK) kinases. Consequently, these mutant kinases become constitutively activated and targets for chemotherapeutic intervention. Encouragingly, small molecule inhibitors of these pathways have shown promise in clinical trials or are approved for clinical use. However, many protein kinases are dysregulated in NSCLC without genetic mutations. To quantify differences in tumor cell signaling that are transparent to genomic methods, we established a super-SILAC internal standard derived from NSCLC cell lines grown in vitro and labeled with heavy lysine and arginine, and deployed them in a phosphoproteomic workflow. We identified 9019 and 8753 phosphorylation sites in two separate tumors. Relative quantification of phosphopeptide abundance between tumor samples allowed for the determination of specific hubs and pathways differing between each tumor. Sites downstream of Ras showed decreased inhibitory phosphorylation (Raf/Mek) and increased activating phosphorylation (Erk1/2) in one tumor versus another. In this way, we were able to quantitatively access oncogenic kinase signaling in primary human tumors. BIOLOGICAL SIGNIFICANCE Through the use of quantitative proteomics, we demonstrated the feasibility and coverage that large scale mass spectrometry can leverage for understanding kinase networks in cancer. By incorporating Super-SILAC based quantitation into a typical pathology workflow, we were able to access and compare tumors from multiple patients in this analysis with high accuracy and dynamic range. We analyzed tumors from patients diagnosed with non-small cell lung cancer and were able to detect comprehensive phosphorylation networks relaying through known hubs of oncogenesis in lung cancer. We hereby show that it is possible to track changes to phosphorylation networks across multiple tumors, opening up the possibility that drug susceptibility and patient-specific stratification can be implemented downstream of classical pathology.

A nondesensitizing kainate receptor point mutant.

Ionotropic glutamate receptor (iGluR) desensitization can be modulated by mutations that change the stability of a dimer formed by the agonist binding domain. Desensitization of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors can be blocked by a single point mutation (e.g., GluR2 L483Y) that stabilizes this dimer in an active conformation. In contrast, desensitization of kainate receptors can be slowed, but not blocked, by similar dimer interface mutations. Only covalent cross-linking via introduced disulfides has been previously shown to block kainate receptor desensitization completely. We have now identified an apparently nondesensitizing GluR6 point mutant (D776K) located at the apex of the ligand binding (S1S2) domain dimer interface. Asp776 is one of a cluster of four charged residues in this region that together mediate direct dimer interactions and contribute to the binding sites for one chloride and two sodium ions. Despite the localized +4 change in the net charge of the S1S2 dimer, the D776K mutation actually increased the thermodynamic stability of the dimer. Unlike GluR6 wild type, the D776K mutant is insensitive to external cations but retains sensitivity to external anions. We therefore hypothesize that the unexpected phenotype of this charge reversal mutation results from the substitution of the sodium ions bound within the dimer interface by the introduced lysine \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\mathrm{NH}_{3}^{+}\) \end{document} groups. The nondesensitizing D776K mutant provides insights into kainate receptor gating and represents a potentially useful new tool for dissecting kainate receptor function.

Mitochondrial protein interactome elucidated by chemical cross-linking mass spectrometry

Significance Mitochondria meet the majority of living cells’ demand for ATP and, as important regulators of redox homeostasis, metabolite levels, and calcium buffering, are a critical link between cell energetics and signaling. Disruption of these processes can induce adaptive or pathological signaling responses to stress and under severe stress promote cell death. Mitochondria have a complex proteome with conformations and interactions that are not well understood. Mitochondrial dysfunction is a direct cause of rare inherited diseases and is implicated in common metabolic diseases and age-related pathology. This study illuminates protein interactions and conformational features of nearly one-third of the mitochondrial proteome. Network information on this scale will enable groundbreaking insights into mitochondrial function, dysfunction, and potential therapeutic targets for mitochondrial-based pathology. Mitochondrial protein interactions and complexes facilitate mitochondrial function. These complexes range from simple dimers to the respirasome supercomplex consisting of oxidative phosphorylation complexes I, III, and IV. To improve understanding of mitochondrial function, we used chemical cross-linking mass spectrometry to identify 2,427 cross-linked peptide pairs from 327 mitochondrial proteins in whole, respiring murine mitochondria. In situ interactions were observed in proteins throughout the electron transport chain membrane complexes, ATP synthase, and the mitochondrial contact site and cristae organizing system (MICOS) complex. Cross-linked sites showed excellent agreement with empirical protein structures and delivered complementary constraints for in silico protein docking. These data established direct physical evidence of the assembly of the complex I–III respirasome and enabled prediction of in situ interfacial regions of the complexes. Finally, we established a database and tools to harness the cross-linked interactions we observed as molecular probes, allowing quantification of conformation-dependent protein interfaces and dynamic protein complex assembly.

In vivo protein interaction network analysis reveals porin-localized antibiotic inactivation in Acinetobacter baumannii strain AB5075

The nosocomial pathogen Acinetobacter baumannii is a frequent cause of hospital-acquired infections worldwide and is a challenge for treatment due to its evolved resistance to antibiotics, including carbapenems. Here, to gain insight on A. baumannii antibiotic resistance mechanisms, we analyse the protein interaction network of a multidrug-resistant A. baumannii clinical strain (AB5075). Using in vivo chemical cross-linking and mass spectrometry, we identify 2,068 non-redundant cross-linked peptide pairs containing 245 intra- and 398 inter-molecular interactions. Outer membrane proteins OmpA and YiaD, and carbapenemase Oxa-23 are hubs of the identified interaction network. Eighteen novel interactors of Oxa-23 are identified. Interactions of Oxa-23 with outer membrane porins OmpA and CarO are verified with co-immunoprecipitation analysis. Furthermore, transposon mutagenesis of oxa-23 or interactors of Oxa-23 demonstrates changes in meropenem or imipenem sensitivity in strain AB5075. These results provide a view of porin-localized antibiotic inactivation and increase understanding of bacterial antibiotic resistance mechanisms.

XLinkDB 2.0: Integrated, large-scale structural analysis of protein crosslinking data.

MOTIVATION Large-scale chemical cross-linking with mass spectrometry (XL-MS) analyses are quickly becoming a powerful means for high-throughput determination of protein structural information and protein-protein interactions. Recent studies have garnered thousands of cross-linked interactions, yet the field lacks an effective tool to compile experimental data or access the network and structural knowledge for these large scale analyses. We present XLinkDB 2.0 which integrates tools for network analysis, Protein Databank queries, modeling of predicted protein structures and modeling of docked protein structures. The novel, integrated approach of XLinkDB 2.0 enables the holistic analysis of XL-MS protein interaction data without limitation to the cross-linker or analytical system used for the analysis. AVAILABILITY AND IMPLEMENTATION XLinkDB 2.0 can be found here, including documentation and help: http://xlinkdb.gs.washington.edu/ CONTACT : jimbruce@uw.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

A General Method for Targeted Quantitative Cross-Linking Mass Spectrometry.

Chemical cross-linking mass spectrometry (XL-MS) provides protein structural information by identifying covalently linked proximal amino acid residues on protein surfaces. The information gained by this technique is complementary to other structural biology methods such as x-ray crystallography, NMR and cryo-electron microscopy[1]. The extension of traditional quantitative proteomics methods with chemical cross-linking can provide information on the structural dynamics of protein structures and protein complexes. The identification and quantitation of cross-linked peptides remains challenging for the general community, requiring specialized expertise ultimately limiting more widespread adoption of the technique. We describe a general method for targeted quantitative mass spectrometric analysis of cross-linked peptide pairs. We report the adaptation of the widely used, open source software package Skyline, for the analysis of quantitative XL-MS data as a means for data analysis and sharing of methods. We demonstrate the utility and robustness of the method with a cross-laboratory study and present data that is supported by and validates previously published data on quantified cross-linked peptide pairs. This advance provides an easy to use resource so that any lab with access to a LC-MS system capable of performing targeted quantitative analysis can quickly and accurately measure dynamic changes in protein structure and protein interactions.

Stress granules and RNA processing bodies are novel autoantibody targets in systemic sclerosis

BackgroundAutoantibody profiles represent important patient stratification markers in systemic sclerosis (SSc). Here, we performed serum-immunoprecipitations with patient antibodies followed by mass spectrometry (LC-MS/MS) to obtain an unbiased view of all possible autoantibody targets and their associated molecular complexes recognized by SSc.MethodsHeLa whole cell lysates were immunoprecipitated (IP) using sera of patients with SSc clinically positive for autoantibodies against RNA polymerase III (RNAP3), topoisomerase 1 (TOP1), and centromere proteins (CENP). IP eluates were then analyzed by LC-MS/MS to identify novel proteins and complexes targeted in SSc. Target proteins were examined using a functional interaction network to identify major macromolecular complexes, with direct targets validated by IP-Western blots and immunofluorescence.ResultsA wide range of peptides were detected across patients in each clinical autoantibody group. Each group contained peptides representing a broad spectrum of proteins in large macromolecular complexes, with significant overlap between groups. Network analyses revealed significant enrichment for proteins in RNA processing bodies (PB) and cytosolic stress granules (SG) across all SSc subtypes, which were confirmed by both Western blot and immunofluorescence.ConclusionsWhile strong reactivity was observed against major SSc autoantigens, such as RNAP3 and TOP1, there was overlap between groups with widespread reactivity seen against multiple proteins. Identification of PB and SG as major targets of the humoral immune response represents a novel SSc autoantigen and suggests a model in which a combination of chronic and acute cellular stresses result in aberrant cell death, leading to autoantibody generation directed against macromolecular nucleic acid-protein complexes.

Large-Scale and Targeted Quantitative Cross-Linking MS Using Isotope-Labeled Protein Interaction Reporter (PIR) Cross-Linkers

Quantitative measurement of chemically cross-linked proteins is crucial to reveal dynamic information about protein structures and protein-protein interactions and how these are differentially regulated during stress, aging, drug treatment, and most perturbations. Previously, we demonstrated how quantitative in vivo cross-linking (CL) with stable isotope labeling of amino acids in cell culture (SILAC) enables both heritable and dynamic changes in cells to be visualized. In this work, we demonstrate the technical feasibility of proteome-scale quantitative in vivo CL-MS using isotope-labeled protein interaction reporter (PIR) cross-linkers and E. coli as a model system. This isotope-labeled cross-linkers approach, combined with Real-time Analysis of Cross-linked peptide Technology (ReACT) previously developed in our lab, enables the quantification of 941 nonredundant cross-linked peptide pairs from a total of 1213 fully identified peptide pairs in two biological replicate samples through comparison of MS1 peak intensity of the light and heavy cross-linked peptide pairs. For targeted relative quantification of cross-linked peptide pairs, we further developed a PRM-based assay to accurately probe specific site interaction changes in a complex background. The methodology described in this work provides reliable tools for both large-scale and targeted quantitative CL-MS that is useful for any sample where SILAC labeling may not be practical.