Brian K Erickson, Julian Mintseris, Devin K Schweppe, Jose Navarrete-Perea, Alison R Erickson, David P Nusinow, Joao A Paulo, and Steven P Gygi. 2019. “Active instrument engagement combined with a real-time database search for improved performance of sample multiplexing workflows.” J Proteome Res.Abstract
Quantitative proteomics employing isobaric reagents has been established as a powerful tool for biological discovery. Current workflows often utilize a dedicated quantitative spectrum to improve quantitative accuracy and precision. A consequence of this approach is a dramatic reduction in the spectral acquisition rate, which necessitates the use of additional instrument time to achieve comprehensive proteomic depth. This work assesses the performance and benefits of online and real-time mass spectrum identification in quantitative multiplexed workflows. A Real-Time Search (RTS) algorithm was implemented to identify fragment spectra within milliseconds as they are acquired using a probabilistic score and to trigger quantitative spectra only upon confident peptide identification. The RTS-MS was benchmarked against standard workflows using a complex two-proteome model of interference and a targeted 10-plex comparison of kinase abundance profiles. Application of the RTS-MS method provided the comprehensive characterization of a 10-plex proteome in 50% less acquisition time. These data indicate that the RTS-MS approach provides dramatic performance improvements for quantitative multiplexed experiments.
Devin K Schweppe, Satendra Prasad, Michael W Belford, Jose Navarrete-Perea, Derek Bailey, Romain Huguet, Mark P Jedrychowski, Ramin Rad, Graeme McAlister, Susan E Abbatiello, Eloy R Woulters, Vlad Zabrouskov, Jean-Jacques Dunyach, Joao A Paulo, and Steven P Gygi. 2019. “Characterization and optimization of multiplexed quantitative analyses using high-field asymmetric-waveform ion mobility mass spectrometry.” Anal Chem.Abstract
Multiplexed, isobaric tagging methods are powerful techniques to increase throughput, precision and accuracy in quantitative proteomics. The dynamic range and accuracy of quantitation, however, can be limited by co-isolation of tag-containing peptides that release reporter ions and conflate quantitative measurements across precursors. Methods to alleviate these effects often lead to the loss of protein and peptide identifications through online or offline filtering of interference containing spectra. To alleviate this effect, high-Field Asymmetric-waveform Ion Mobility Spectroscopy (FAIMS) has been proposed as a method to reduce precursor co-isolation and improve the accuracy and dynamic range of multiplex quantitation. Here we tested the use of FAIMS to improve quantitative accuracy using previously established TMT-based interference standards (triple-knockout [TKO] and Human-Yeast Proteomics Resource [HYPER]). We observed that FAIMS robustly improved the quantitative accuracy of both high-resolution MS2 (HRMS2) and synchronous precursor selection MS3 (SPS-MS3) based methods without sacrificing protein identifications. We further optimized and characterized the main factors that enable robust use of FAIMS for multiplexed quantitation. We highlight these factors and provide method recommendations to take advantage of FAIMS technology to improve isobaric-tag-quantification moving forward.
Devin K Schweppe, Edward L Huttlin, Wade J Harper, and Steven P Gygi. 2018. “BioPlex Display: An Interactive Suite for Large-Scale AP-MS Protein-Protein Interaction Data.” J Proteome Res, 17, 1, Pp. 722-726.Abstract
The development of large-scale data sets requires a new means to display and disseminate research studies to large audiences. Knowledge of protein-protein interaction (PPI) networks has become a principle interest of many groups within the field of proteomics. At the confluence of technologies, such as cross-linking mass spectrometry, yeast two-hybrid, protein cofractionation, and affinity purification mass spectrometry (AP-MS), detection of PPIs can uncover novel biological inferences at a high-throughput. Thus new platforms to provide community access to large data sets are necessary. To this end, we have developed a web application that enables exploration and dissemination of the growing BioPlex interaction network. BioPlex is a large-scale interactome data set based on AP-MS of baits from the human ORFeome. The latest BioPlex data set release (BioPlex 2.0) contains 56 553 interactions from 5891 AP-MS experiments. To improve community access to this vast compendium of interactions, we developed BioPlex Display, which integrates individual protein querying, access to empirical data, and on-the-fly annotation of networks within an easy-to-use and mobile web application. BioPlex Display enables rapid acquisition of data from BioPlex and development of hypotheses based on protein interactions.
Thom Vreven, Devin K Schweppe, Juan D Chavez, Chad R Weisbrod, Sayaka Shibata, Chunxiang Zheng, James E Bruce, and Zhiping Weng. 2018. “Integrating Cross-Linking Experiments with Ab Initio Protein-Protein Docking.” J Mol Biol, 430, 12, Pp. 1814-1828.Abstract
Ab initio protein-protein docking algorithms often rely on experimental data to identify the most likely complex structure. We integrated protein-protein docking with the experimental data of chemical cross-linking followed by mass spectrometry. We tested our approach using 19 cases that resulted from an exhaustive search of the Protein Data Bank for protein complexes with cross-links identified in our experiments. We implemented cross-links as constraints based on Euclidean distance or void-volume distance. For most test cases, the rank of the top-scoring near-native prediction was improved by at least twofold compared with docking without the cross-link information, and the success rate for the top 5 predictions nearly tripled. Our results demonstrate the delicate balance between retaining correct predictions and eliminating false positives. Several test cases had multiple components with distinct interfaces, and we present an approach for assigning cross-links to the interfaces. Employing the symmetry information for these cases further improved the performance of complex structure prediction.
Ian M Bird, Susie H Kim, Devin K Schweppe, Joana Caetano-Lopes, Alexander G Robling, Julia F Charles, Steven P Gygi, Matthew L Warman, and Patrick J Smits. 2018. “The skeletal phenotype of achondrogenesis type 1A is caused exclusively by cartilage defects.” Development, 145, 1.Abstract
Inactivating mutations in the ubiquitously expressed membrane trafficking component GMAP-210 (encoded by ) cause achondrogenesis type 1A (ACG1A). ACG1A is surprisingly tissue specific, mainly affecting cartilage development. Bone development is also abnormal, but as chondrogenesis and osteogenesis are closely coupled, this could be a secondary consequence of the cartilage defect. A possible explanation for the tissue specificity of ACG1A is that cartilage and bone are highly secretory tissues with a high use of the membrane trafficking machinery. The perinatal lethality of ACG1A prevents investigating this hypothesis. We therefore generated mice with conditional knockout alleles and inactivated in chondrocytes, osteoblasts, osteoclasts and pancreas acinar cells, all highly secretory cell types. We discovered that the ACG1A skeletal phenotype is solely due to absence of GMAP-210 in chondrocytes. Mice lacking GMAP-210 in osteoblasts, osteoclasts and acinar cells were normal. When we inactivated in primary chondrocyte cultures, GMAP-210 deficiency affected trafficking of a subset of chondrocyte-expressed proteins rather than globally impairing membrane trafficking. Thus, GMAP-210 is essential for trafficking specific cargoes in chondrocytes but is dispensable in other highly secretory cells.
Christopher M Rose, Brian K Erickson, Devin K Schweppe, Rosa Viner, Jae Choi, John Rogers, Ryan Bomgarden, Steven P Gygi, and Donald S Kirkpatrick. 2018. “TomahaqCompanion: A tool for the creation and analysis of isobaric label based multiplexed targeted assays.” J Proteome Res.Abstract
Triggered by Offset, Multiplexed, Accurate mass, High resolution, and Absolute Quantitation (TOMAHAQ) is a recently introduced targeted proteomics method that combines peptide and sample multiplexing. TOMAHAQ assays enable sensitive and accurate multiplexed quantification by implementing an intricate data collection scheme that comprises multiple MSn scans, mass inclusion lists, and data-driven filters. Consequently, manual creation of TOMAHAQ methods can be time consuming and error prone while the resulting TOMAHAQ data may not be compatible with common mass spectrometry analysis pipelines. To address these concerns we introduce TomahaqCompanion, an open-source desktop application that enables rapid creation of TOMAHAQ methods and analysis of TOMAHAQ data. Starting from a list of peptide sequences, a user can perform each step of TOMAHAQ assay development including 1) generation of priming run target list, 2) analysis of priming run data, 3) generation of TOMAHAQ method file, and 4) analysis and export of quantitative TOMAHAQ data. We demonstrate the flexibility of TomahaqCompanion by creating a variety of methods testing TOMAHAQ parameters (e.g., number of SPS notches, run length, etc.). Lastly, we analyze an interference sample comprising heavy yeast peptides, a standard human peptide mixture, TMT11-plex, and super heavy TMT (shTMT) isobaric labels to demonstrate ~10-200 attomol limit of quantification within a complex background using TOMAHAQ.
Devin K Schweppe, Juan D Chavez, Chi Fung Lee, Arianne Caudal, Shane E Kruse, Rudy Stuppard, David J Marcinek, Gerald S Shadel, Rong Tian, and James E Bruce. 1/27/2017. “Mitochondrial protein interactome elucidated by chemical cross-linking mass spectrometry.” Proceedings of the National Academy of Sciences. Publisher's VersionAbstract

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.

Edward L Huttlin, Raphael J Bruckner, Joao A Paulo, Joe R Cannon, Lily Ting, Kurt Baltier, Greg Colby, Fana Gebreab, Melanie P Gygi, Hannah Parzen, John Szpyt, Stanley Tam, Gabriela Zarraga, Laura Pontano-Vaites, Sharan Swarup, Anne E White, Devin K Schweppe, Ramin Rad, Brian K Erickson, Robert A Obar, KG Guruharsha, Kejie Li, Spyros Artavanis-Tsakonas, Steven P Gygi, and Wade J Harper. 2017. “Architecture of the human interactome defines protein communities and disease networks.” Nature, 545, 7655, Pp. 505-509.Abstract
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.
Patrick J Nygren, Sohum Mehta, Devin K Schweppe, Lorene K Langeberg, Jennifer L Whiting, Chad R Weisbrod, James E Bruce, Jin Zhang, David Veesler, and John D Scott. 2017. “Intrinsic disorder within AKAP79 fine-tunes anchored phosphatase activity toward substrates and drug sensitivity.” Elife, 6.Abstract
Scaffolding the calcium/calmodulin-dependent phosphatase 2B (PP2B, calcineurin) focuses and insulates termination of local second messenger responses. Conformational flexibility in regions of intrinsic disorder within A-kinase anchoring protein 79 (AKAP79) delineates PP2B access to phosphoproteins. Structural analysis by negative-stain electron microscopy (EM) reveals an ensemble of dormant AKAP79-PP2B configurations varying in particle length from 160 to 240 Å. A short-linear interaction motif between residues 337-343 of AKAP79 is the sole PP2B-anchoring determinant sustaining these diverse topologies. Activation with Ca2/calmodulin engages additional interactive surfaces and condenses these conformational variants into a uniform population with mean length 178 ± 17 Å. This includes a Leu-Lys-Ile-Pro sequence (residues 125-128 of AKAP79) that occupies a binding pocket on PP2B utilized by the immunosuppressive drug cyclosporin. Live-cell imaging with fluorescent activity-sensors infers that this region fine-tunes calcium responsiveness and drug sensitivity of the anchored phosphatase.
Xuefei Zhong, Arti T Navare, Juan D Chavez, Jimmy K Eng, Devin K Schweppe, and James E Bruce. 2017. “Large-Scale and Targeted Quantitative Cross-Linking MS Using Isotope-Labeled Protein Interaction Reporter (PIR) Cross-Linkers.” J Proteome Res, 16, 2, Pp. 720-727.Abstract
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 MS 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.
Juan D Chavez, Jimmy K Eng, Devin K Schweppe, Michelle Cilia, Keith Rivera, Xuefei Zhong, Xia Wu, Terrence Allen, Moshe Khurgel, Akhilesh Kumar, Athanasios Lampropoulos, Mårten Larsson, Shuvadeep Maity, Yaroslav Morozov, Wimal Pathmasiri, Mathew Perez-Neut, Coriness Pineyro-Ruiz, Elizabeth Polina, Stephanie Post, Mark Rider, Dorota Tokmina-Roszyk, Katherine Tyson, Debora Vieira Parrine Sant'Ana, and James E Bruce. 2016. “A General Method for Targeted Quantitative Cross-Linking Mass Spectrometry.” PLoS One, 11, 12, Pp. e0167547.Abstract
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.
Juan D Chavez, Devin K Schweppe, Jimmy K Eng, and James E Bruce. 2016. “In Vivo Conformational Dynamics of Hsp90 and Its Interactors.” Cell Chem Biol, 23, 6, Pp. 716-26.Abstract
Hsp90 belongs to a family of some of the most highly expressed heat shock proteins that function as molecular chaperones to protect the proteome not only from the heat shock but also from other misfolding events. As many client proteins of Hsp90 are involved in oncogenesis, this chaperone has been the focus of intense research efforts. Yet, we lack structural information for how Hsp90 interacts with co-chaperones and client proteins. Here, we developed a mass-spectrometry-based approach that allowed quantitative measurements of in vitro and in vivo effects of small-molecule inhibitors on Hsp90 conformation, and interaction with co-chaperones and client proteins. From this analysis, we were able to derive structural models for how Hsp90 engages its interaction partners in vivo, and how different drugs affect these structures. In addition, the methodology described here offers a new approach to probe the effects of virtually any inhibitor treatment on the proteome level.
Xia Wu, Juan D Chavez, Devin K Schweppe, Chunxiang Zheng, Chad R Weisbrod, Jimmy K Eng, Ananya Murali, Samuel A Lee, Elizabeth Ramage, Larry A Gallagher, Hemantha D Kulasekara, Mauna E Edrozo, Cassandra N Kamischke, Mitchell J Brittnacher, Samuel I Miller, Pradeep K Singh, Colin Manoil, and James E Bruce. 2016. “In vivo protein interaction network analysis reveals porin-localized antibiotic inactivation in Acinetobacter baumannii strain AB5075.” Nat Commun, 7, Pp. 13414.Abstract
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.
Devin K Schweppe, Juan D Chavez, Arti T Navare, Xia Wu, Bianca Ruiz, Jimmy K Eng, Henry Lam, and James E Bruce. 2016. “Spectral Library Searching To Identify Cross-Linked Peptides.” J Proteome Res, 15, 5, Pp. 1725-31.Abstract
Methods harnessing protein cross-linking and mass spectrometry (XL-MS) offer high-throughput means to identify protein-protein interactions (PPIs) and structural interfaces of protein complexes. Yet, specialized data dependent methods and search algorithms are often required to confidently assign peptide identifications to spectra. To improve the efficiency of matching high confidence spectra, we developed a spectral library based approach to search cross-linked peptide data derived from Protein Interaction Reporter (PIR) methods using the spectral library search algorithm, SpectraST. Spectral library matching of cross-linked peptide data from query spectra increased the absolute number of confident peptide relationships matched to spectra and thereby the number of PPIs identified. By matching library spectra from bona fide, previously established PIR-cross-linked peptide relationships, spectral library searching reduces the need for continued, complex mass spectrometric methods to identify peptide relationships, increases coverage of relationship identifications, and improves the accessibility of XL-MS technologies.
Michael E Johnson, Andrew V Grassetti, Jaclyn N Taroni, Shawn M Lyons, Devin Schweppe, Jessica K Gordon, Robert F Spiera, Robert Lafyatis, Paul J Anderson, Scott A Gerber, and Michael L Whitfield. 2016. “Stress granules and RNA processing bodies are novel autoantibody targets in systemic sclerosis.” Arthritis Res Ther, 18, Pp. 27.Abstract
BACKGROUND: Autoantibody 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. METHODS: HeLa 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. RESULTS: A 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. CONCLUSIONS: While 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.
Devin K Schweppe, Chunxiang Zheng, Juan D Chavez, Arti T Navare, Xia Wu, Jimmy K Eng, and James E Bruce. 2016. “XLinkDB 2.0: integrated, large-scale structural analysis of protein crosslinking data.” Bioinformatics, 32, 17, Pp. 2716-8.Abstract
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: CONTACT: : SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Devin K Schweppe, Juan D Chavez, and James E Bruce. 2016. “XLmap: an R package to visualize and score protein structure models based on sites of protein cross-linking.” Bioinformatics, 32, 2, Pp. 306-8.Abstract
MOTIVATION: Chemical cross-linking with mass spectrometry (XL-MS) provides structural information for proteins and protein complexes in the form of crosslinked residue proximity and distance constraints between reactive residues. Utilizing spatial information derived from cross-linked residues can therefore assist with structural modeling of proteins. Selection of computationally derived model structures of proteins remains a major challenge in structural biology. The comparison of site interactions resulting from XL-MS with protein structure contact maps can assist the selection of structural models. AVAILABILITY AND IMPLEMENTATION: XLmap was implemented in R and is freely available at: CONTACT: SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Devin K Schweppe, Christopher Harding, Juan D Chavez, Xia Wu, Elizabeth Ramage, Pradeep K Singh, Colin Manoil, and James E Bruce. 2015. “Host-Microbe Protein Interactions during Bacterial Infection.” Chem Biol, 22, 11, Pp. 1521-1530.Abstract
Interspecies protein-protein interactions are essential mediators of infection. While bacterial proteins required for host cell invasion and infection can be identified through bacterial mutant library screens, information about host target proteins and interspecies complex structures has been more difficult to acquire. Using an unbiased chemical crosslinking/mass spectrometry approach, we identified interspecies protein-protein interactions in human lung epithelial cells infected with Acinetobacter baumannii. These efforts resulted in identification of 3,076 crosslinked peptide pairs and 46 interspecies protein-protein interactions. Most notably, the key A. baumannii virulence factor, OmpA, was identified as crosslinked to host proteins involved in desmosomes, specialized structures that mediate host cell-to-cell adhesion. Co-immunoprecipitation and transposon mutant experiments were used to verify these interactions and demonstrate relevance for host cell invasion and acute murine lung infection. These results shed new light on A. baumannii-host protein interactions and their structural features, and the presented approach is generally applicable to other systems.
Juan D Chavez, Devin K Schweppe, Jimmy K Eng, Chunxiang Zheng, Alex Taipale, Yiyi Zhang, Kohji Takara, and James E Bruce. 2015. “Quantitative interactome analysis reveals a chemoresistant edgotype.” Nat Commun, 6, Pp. 7928.Abstract
Chemoresistance is a common mode of therapy failure for many cancers. Tumours develop resistance to chemotherapeutics through a variety of mechanisms, with proteins serving pivotal roles. Changes in protein conformations and interactions affect the cellular response to environmental conditions contributing to the development of new phenotypes. The ability to understand how protein interaction networks adapt to yield new function or alter phenotype is limited by the inability to determine structural and protein interaction changes on a proteomic scale. Here, chemical crosslinking and mass spectrometry were employed to quantify changes in protein structures and interactions in multidrug-resistant human carcinoma cells. Quantitative analysis of the largest crosslinking-derived, protein interaction network comprising 1,391 crosslinked peptides allows for 'edgotype' analysis in a cell model of chemoresistance. We detect consistent changes to protein interactions and structures, including those involving cytokeratins, topoisomerase-2-alpha, and post-translationally modified histones, which correlate with a chemoresistant phenotype.
Devin K Schweppe, James R Rigas, and Scott A Gerber. 2013. “Quantitative phosphoproteomic profiling of human non-small cell lung cancer tumors.” J Proteomics, 91, Pp. 286-96.Abstract
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.