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Mass spectrometry-based proteomics for systems biology
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Mass spectrometry-based proteomics for systems biology Eduard Sabido´1,3,*, Nathalie Selevsek1,* and Ruedi Aebersold1,2,3 Mass spectrometry (MS)-based proteomics has significantly contributed to the development of systems biology, a new paradigm for the life sciences in which biological processes are addressed in terms of dynamic networks of interacting molecules. Because of its advanced analytical capabilities, MSbased proteomics has been used extensively to identify the components of biological systems, and it is the method of choice to consistently quantify the effects of network perturbation in time and space. Herein, we review recent contributions of MS to systems biology and discuss several examples that illustrate the importance of mass spectrometry to elucidate the components and interactions of molecular networks. Addresses 1 Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Wolfgang-Pauli-Strasse 16, 8093 Zurich, Switzerland 2 Faculty of Science, University of Zurich, 8057 Zurich, Switzerland 3 Competence Center for Systems Physiology and Metabolic Disease, ETH Zurich, 8093 Zurich, Switzerland Corresponding author: Aebersold, Ruedi ([email protected]) Equally contributed.
Further, the data sets should be reproducible and acquired at high-throughput so that multiple measurements on perturbed systems can be carried out in a consistent and systematic manner. Mass spectrometry (MS)-based proteomics fulfills most of the requirements for systems biology and, due to the rapid development of new instruments, computational tools and analytical strategies, it has become an essential tool to study molecular and cellular processes in living cells and organisms. For most MS-based proteomic measurements proteins are digested using specific proteases and the peptide mixture is then subjected to mass spectrometric analysis. In general the peptides are fractionated by chromatography in a liquid phase and then fragmented in the gas phase by collisional activation. The resulting fragment-ion spectra are then used to determine the sequence and quantity of a peptide and, by inference, the protein from which the peptide originated.
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Current Opinion in Biotechnology 2012, 23:591–597 This review comes from a themed issue on Systems biology Edited by Jens Nielsen and Sang Yup Lee For a complete overview see the Issue and the Editorial Available online 12th December 2011 0958-1669/$ – see front matter, # 2011 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.copbio.2011.11.014
Over the last few decades experimental biology has focused on the identification and enumeration of molecules. This includes DNA, transcripts, metabolites but more recently also proteins, leading to the field of proteomics. In parallel, a new paradigm emerged for the life sciences in which complex biological processes are studied as dynamic molecular networks. Network analysis contrasts the focus on isolated molecules that has been the mainstay of molecular biology. Network biology is generally referred to as systems biology, an inter-disciplinary field that attempts to study biological systems from an integrative perspective and to capture the emergent properties of the system. Systems biology has experienced considerable growth in recent years but its general implementation remains limited by the available technologies, specifically those tasked with the acquisition of suitable data sets. Such techniques should be able to detect and quantify all components of the system. www.sciencedirect.com
Several new MS-based proteomics approaches have been introduced in recent years, mainly differing according to their analytical performance in terms of reproducibility, dynamic range, limit of detection and resolving power [1]. Shotgun (or discovery) proteomics is the method of choice for the identification of the protein components of a system, when no prior knowledge is available. This technique has demonstrated the capability to identify several thousand proteins in any cell type or organism and thus to deliver a first view of sample complexity and protein dynamic range [2–4]. Moreover, the relative abundance as well the absolute abundance of the identified proteins can be estimated across multiple samples [5–8]. New instrument configurations with shorter cycle times and higher resolving power at both MS and MS/MS levels have contributed to these advances [9]. Equally important has been the development of statistical methods and software tools for the reliable identification and quantification of the resulting data sets [10–12]. However, when multiple samples are compared, the major drawback of the technique is the selection of precursor ions that is biased towards abundant peptides leading to irreproducible replicates of the shotgun experiments and thus incomplete MS data sets [13]. This limitation has been mainly solved using the directedMS approach, where peptides that were not identified in a first MS analysis are compiled into inclusion lists and then sequenced in a second round [14,15]. Both proteomics approaches, shotgun and directed-MS, are referred to as data dependent acquisition (DDA) methods since precursor ions are only selected for sequencing when they are detected at the MS level. Current Opinion in Biotechnology 2012, 23:591–597
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In contrast to DDA methods, a targeted proteomics approach based on selected reaction monitoring (SRM) has recently emerged as a powerful proteomic tool due to its capability to quantify only the proteins of interest (i.e. protein network in central metabolism) with high reproducibility and high accuracy across multiple samples [16,17,18,19]. In a SRM experiment, the instrument isolates the targeted precursor ion and selected fragment ions arising from its collisional dissociation. The corresponding signals are measured over the chromatographic elution time window to provide better sensitivity and higher linearity for quantification, compared with other MS methods [1]. Even in complex sample backgrounds, detecting low attomole (on column) amounts and across a linear dynamic range of approximately 5 orders of magnitude is possible [16,20]. The SRM method is dependent on the a priori selection of optimal sets of precursor– fragment ion pairs, called SRM transitions. Several, typically 3–5 transitions per peptide and 1–5 peptides per protein constitute an assay for the definitive quantification of a protein in a sample and such assays need to be developed once for each protein. Recently, the highthroughput development of SRM assays has been achieved via the generation of MS/MS spectral libraries from the measurements of thousands of synthetic peptides. The resulting assays have been stored in publicly accessible repositories [21]. SRM-based targeted proteomics is also supported by a wide panel of computational tools that enable automated peptide selection, assay development and optimization, and data evaluation [22–24,25,26]. In conclusion, both discovery and targeted proteomics methods are currently used in systems biology to identify the components of a protein network and to explore its dynamic behavior in time and space (Figure 1A).
enrich and isolate ubiquitinylated peptides allowing the characterization of hundreds of ubiquitin-modified peptides covering numerous biological processes [29]. Further analysis of the data showed that ubiquitination sites were preferentially found in regions with abundant hydrophobic residues, although to date no agreement in the consensus sequence for ubiquitination sites has been established [30]. Interestingly, more than 20% of the ubiquitinated lysines were found to also be sites of acetylation, suggesting that acetylation of a specific lysine could serve as a means to prevent lysine ubiquitination. The identification and cataloguing studies focused on post-translational modifications and signaling networks were complemented by studies of protein–protein interaction networks. By expressing 75 hemagglutinin epitope-tagged versions of the deubiquitinating enzymes, and using a standard affinity purification and mass spectrometry pipeline, Sowa et al. defined the first interaction landscape of deubiquitinating enzymes, with more than 774 high-confidence interacting proteins [31]. The study related deubiquitinating enzymes without a clear function to specific biological pathways and identified new links to chromatin processing, DNA damage repair, RNA processing, autophagy, and endoplasmic reticulumassociated protein degradation. In another study, Breitkreutz et al. used standard affinity purification followed by mass spectrometry to identify a kinase and phosphatase network of 1844 interactions in budding yeast [32]. Their experiments not only revealed new potential roles for Cdc14 and the target of rapamycin complex 1 (TORC1), but most importantly showed that the kinase and phosphatase interaction networks are highly interconnected, suggesting a distributed organization of information flow rather than a hierarchical transmission.
Identification of network components Several comprehensive MS-based proteomics studies have recently been performed to identify the components of cells, tissues, signaling pathways and protein–protein interaction networks (Figure 1B). Post-translational modifications, including sites of phosphorylation, glycosylation, ubiquitination and acetylation constitute an essential part of the signaling cascades present in the cell. They modulate the activity and localization of proteins, as well as determine their interactions with other macromolecules. As a result of their importance in signaling networks, significant effort has been expended towards the discovery and cataloguing of post-translational modifications. Over six thousand phosphoproteins harboring nearly 36 000 phosphorylation sites and thousands of N-glycosites were identified in two recent large-scale studies performed in mouse tissues [27,28]. The collected data not only revealed new posttranslational modification sites, but also showed specialized, interconnected networks pinpointing subcellular and tissue-specific modifications, and described new glycosylation motifs beyond the canonical N-X-S/T. In a similar direction, Xu et al. generated a monoclonal antibody to Current Opinion in Biotechnology 2012, 23:591–597
Perturbed networks As a further step to dissect biological networks by mass spectrometry, several studies have recently addressed the dynamics of signaling and protein–protein interaction networks upon different types of perturbations. These experiments go beyond the identification of network components in basal conditions and combine highthroughput identification with quantification techniques to compare proteome modifications and interactions during different states. Monetti et al. used a spike-in SILAC method to quantify the effects of insulin on the phosphorylation state of a mouse liver cell line [33] and a radiomimetic treatment was used by Bensimon et al. to study phosphoproteome dynamics upon DNA damage [34]. The dynamics of phosphorylation signaling networks was further studied in the context of cell–cell direct interaction by Jorgensen et al. Their study revealed different cell-specific tyrosine phosphorylation events showing that the signaling process is asymmetric and that distinct cell types use different tyrosine kinases and targets to process signals induced by the same cell–cell www.sciencedirect.com
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Figure 1
(a)
IDENTIFICATION OF NETWORK COMPONENTS
SHOTGUN PROTEOMICS
ANALYSIS OF NETWORK DYNAMICS
SHOTGUN AND TARGETED PROTEOMICS
TIME SPACE
(b)
SIGNALING NETWORKS
PROTEIN INTERACTION NETWORKS
PROTEIN COMPLEX
PHOSPHORYLATION ACETYLATION UBIQUITYLATION ...
(c)
DISEASE-RELATED NETWORKS FIRST NEIGHBORS
INCREASED ABUNDANCE IN THE DISEASE STATE
NETWORK ANALYSIS
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(A) Shotgun (or discovery) mass spectrometry-based proteomics is the method of choice for identifying the network components, that is, both nodes and edges. By contrast, when analyzing network dynamics in time or space, both shotgun and targeted proteomics techniques have been used. Targeted methods are preferred for this purpose because they provide higher reproducibility, accuracy and sensitivity when analyzing large sample sets. (B) There are two main types of protein networks: signaling networks and protein–protein interaction networks. In protein–protein interaction networks nodes represent proteins and the edge denote an existing interaction between the linked proteins. The edge width reflects quantification data, and in these networks proteins can be clustered together into protein complexes. In signaling networks nodes also designate proteins but edges denote an action of one protein to another, for example an enzyme–substrate interaction such as that of a kinase and its substrate. (C) The analysis from a network perspective of proteins with altered abundances in a certain disease, can lead to a better understanding of complex biological processes. To illustrate this, quantified proteins in a disease state can be arranged in a network. Then, additional proteins that have previously been shown to directly interact with the quantified proteins can be added (first neighbor approach). Thus, altered proteins with no apparent relation at the beginning, appear now clearly connected through a common neighbor, which becomes an excellent candidate as drug target, or other follow-up experiments.
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contact [35]. Finally, in a comprehensive study to determine the relationships between 97 kinases, 27 phosphatases, and more than 1000 phosphoproteins, Bodenmiller et al. [36] showed that protein kinases and phosphatases and their respective substrates form a highly connected network that reacts to perturbations in complex, and so far poorly understood ways. In the study, thousands of regulated phosphorylation events were identified and it was shown that the effects of kinase or phosphatase inactivation affected several phosphorylation signaling pathways, and not only immediate downstream targets. Other studies have addressed the dynamics of post-translational modifications other than phosphorylation such as acetylation, ubiquitylation and ubiquitin-like modifiers (SUMOs). For example, Choudhary et al. quantified acetylation changes in more than three thousand acetylation sites in response to several deacetylase inhibitors [37]. Their work revealed that acetylation preferentially targets large macromolecular complexes and demonstrated the broad regulatory scope of lysine acetylation, which might be comparable with that of other major post-translational modifications. Furthermore, proteasome and translational inhibitors were also used to study the dynamics of endogenous ubiquitylation and ubiquitin-like modifiers (SUMOs) in several recent publications [38–40]. The field of protein–protein interaction networks has also moved towards exploring dynamic perturbed networks [41] as revealed by several recently published studies. Bennett et al. developed a quantitative proteomics platform based on multiplexed AQUA peptides to elucidate the composition of the cullin-RING ubiquitin ligase (CRL) complex and its dynamics in the presence of an inhibitor of the NEDD8-activating enzyme [42]. From their study, the authors could define a new alternative model for CRL complex dynamics in which the abundance of the adaptor molecules, rather than other previously suspected components, drives the network organization of the complex. In a second study, Bisson et al. combined an affinity purification strategy with targeted proteomics to study the dynamics of the protein–protein interaction network of Grb2. After an initial screening to map novel Grb2 interacting proteins in basal conditions, SRM assays were developed and targeted proteomics was used to establish the dynamics of the Grb2 interaction network after activation of the growth factor signaling pathway [43]. Finally, Glatter et al. have recently performed a quantitative study to characterize the dynamics of the Drosophila InR/TOR pathway after insulin stimulation and showed that the protein–protein interaction network is substantially affected by the activity state of the InR [44].
Networks in time and space Several studies have addressed the characterization of network dynamics, not only after a perturbation, but Current Opinion in Biotechnology 2012, 23:591–597
dissecting it in time and space. The profiling of proteomes at different times of a certain cellular process has been mainly addressed in combination with the analysis of signaling networks. Keck et al. have investigated the phosphoproteome in the yeast centrosome at different cell cycle stages by shotgun proteomics [45]. The centrosomal complexes were isolated by affinity purification and after metal affinity phosphopeptide enrichment, the phosphorylation profile was compared between centrosomes in cell cycle-arrested cells (G1 phase and mitosis) versus those growing asynchronously. In total, 297 phosphorylation sites were identified, where 54 were G1 specific and 110 mitosis specific. In vitro kinase assays with Cdk1 and Mps1 confirmed several substrates and identified possible new substrates such as Spc72 and Cnm76. Thus, they could demonstrate how phosphorylation controls centrosome organization in yeast. In a larger-scale profiling study, the Mann group has investigated the dynamics of the proteome and its phosphorylations over the complete cell cycle in mammalian cells using shotgun proteomics [46]. After multistep fractionation and phosphopeptide enrichment, the authors quantified thousands of proteins bearing 20 400 phosphorylation sites, showing distinct regulated proteins clusters specific to each cell cycle stage. Furthermore, they investigated the relative and absolute phosphorylation-site stoichiometry of thousands of phosphorylation sites along the cell cycle, revealing that particular mitotic kinases and predicted substrates of Cdk1 were highly phosphorylated during mitosis. Using this proteomic phenotyping approach, the authors were led to hypothesize that a high degree of phosphorylation inhibits the activity of these kinases during mitosis. Importantly, such quantitative profiling experiments of phosphorylation occupancy lead to distinguishing functional from nonfunctional phosphorylation sites. Finally, a study by Lage et al. addressed the temporal and spatial analysis of protein interaction networks along many stages of human heart morphogenesis and congenital heart disease to serve as a general model for studying the functional architecture of organ development [47]. After stratification of 255 human proteins into 19 different morphological subgroups, functional networks were constructed for each subgroup using experimental interactome data. The results revealed that the networks consist of relatively few protein modules that are extensively recycled during organ development and that each network consists of a combinatorially unique module pattern. Additionally, the analysis suggested that increased morphological complexity of the heart correlates with increased complexity at the molecular level, which was demonstrated with the transcription regulation modules. Indeed, regulators such as Gata4, Nkx2-5 and Tbx5-5 are displaying different functions during heart development, due to their flexibility in interacting with other proteins at different stages or at different times. www.sciencedirect.com
MS-based proteomics for systems biology Sabido´, Selevsek and Aebersold
Disease-related networks The analysis of molecular networks instead of a classical molecule-by-molecule approach has also been applied to elucidate protein dynamics in the context of human disorders (Figure 1C). Recent studies have focused on the detection of changes in the proteome of animal models carrying disease-causing mutations. In this regard, aiming to detect signatures that reflects the pten status leading to prostate cancer (PCa) progression, Cima et al. have compared by label free shotgun experiments serum N-linked glycoproteome and prostate tissue isolated from wild-type and pten null mice [48]. In a second step, a targeted approach was used to detect the 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. This allowed the extraction of robust patterns predicting tissue pten status and could be used for the diagnosis and grading of prostate cancer. Finally, the Hanash group has recently compared the serum proteomes from mouse models of lung cancer with those of other cancer models by shotgun proteomics to reveal protein signatures in plasma that reflect lung tumor biology [49]. For each model, quantitative proteomics was performed after 2-D protein fractionation and differential cysteine alkylation in intact proteins using isotopically labeled acrylamide. Protein products of more than 2000 unique genes were quantified across 14 plasma samples collected from individual mouse models, revealing distinct regulated protein clusters specific to each organ type. Several proteins showing an increase in abundance were found to be encoded by genes that are known targets of Tif1/Nkx2-1, a master transcription factor of peripheral airway cells. After confirming the expression of Tif1/Nkx2-1 in lung tumors by immunohistochemistry, positive correlation between mRNA levels of protein signature genes and Tif1/Nkx2-1 expression was obtained in human lung cancer and cell lines. These findings provided a new approach to search for cancer protein signatures in plasma based on proteins encoded by genes that are under the control of expressed master development regulators. Overall, as illustrated in this review, mass spectrometry has made significant contributions to the development of the systems biology paradigm, in which complex biological processes are addressed in terms of molecular networks instead of molecule-by-molecule analyses. In this regard, two complementary strategies, discovery proteomics for the identification of the system components and targeting proteomics by SRM for the reproducible quantification of the system components under multiple perturbed conditions have proven particularly valuable. The synergy between MS-based proteomics and systems biology is especially evident in the analysis of perturbed networks and in the recent studies of network dynamics in time and space. Finally, the analysis of disease-related networks, which will certainly be extended in the upcoming years, www.sciencedirect.com
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appears as a promising systematic approach to better understand complex diseases, pinpoint new targets for drug development and identify new biomarkers.
Acknowledgements E.S. is supported by the LiverX program of the Swiss Initiative for Systems Biology (SystemsX.ch), N.S. by the EU FP7 grant ‘Unicellsys’ (grant# 201142), and R.A. by ERC advanced grant ‘Proteomics v3.0’ (grant# 233226) of the European Union.
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Dissecting spatio-temporal protein networks driving human heart development and related disorders. Mol Syst Biol 2010, 6:381. In this study, the authors made a systematic analysis of spatio-temporal protein networks that drives several stages of a developing organ using interactome data. 48. Cima I, Schiess R, Wild P, Kaelin M, Schuffler P, Lange V, Picotti P, Ossola R, Templeton A, Schubert O et al.: Cancer geneticsguided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer. Proc Natl Acad Sci USA 2011, 108:3342-3347. 49. Taguchi A, Politi K, Pitteri SJ, Lockwood WW, Faca VM, Kelly Spratt K, Wong CH, Zhang Q, Chin A, Park KS et al.: Lung cancer signatures in plasma based on proteome profiling of mouse tumor models. Cancer Cell 2011, 20:289-299. In this paper, the authors used shotgun proteomics to compare the serum proteomes from mouse models of lung cancer with those of other cancers in order to reveal protein signatures that reflect lung tumor biology.
Current Opinion in Biotechnology 2012, 23:591–597
Mass spectrometry-based proteomics for systems biology Eduard Sabido´1,3,*, Nathalie Selevsek1,* and Ruedi Aebersold1,2,3 Mass spectrometry (MS)-based proteomics has significantly contributed to the development of systems biology, a new paradigm for the life sciences in which biological processes are addressed in terms of dynamic networks of interacting molecules. Because of its advanced analytical capabilities, MSbased proteomics has been used extensively to identify the components of biological systems, and it is the method of choice to consistently quantify the effects of network perturbation in time and space. Herein, we review recent contributions of MS to systems biology and discuss several examples that illustrate the importance of mass spectrometry to elucidate the components and interactions of molecular networks. Addresses 1 Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Wolfgang-Pauli-Strasse 16, 8093 Zurich, Switzerland 2 Faculty of Science, University of Zurich, 8057 Zurich, Switzerland 3 Competence Center for Systems Physiology and Metabolic Disease, ETH Zurich, 8093 Zurich, Switzerland Corresponding author: Aebersold, Ruedi ([email protected]) Equally contributed.
Further, the data sets should be reproducible and acquired at high-throughput so that multiple measurements on perturbed systems can be carried out in a consistent and systematic manner. Mass spectrometry (MS)-based proteomics fulfills most of the requirements for systems biology and, due to the rapid development of new instruments, computational tools and analytical strategies, it has become an essential tool to study molecular and cellular processes in living cells and organisms. For most MS-based proteomic measurements proteins are digested using specific proteases and the peptide mixture is then subjected to mass spectrometric analysis. In general the peptides are fractionated by chromatography in a liquid phase and then fragmented in the gas phase by collisional activation. The resulting fragment-ion spectra are then used to determine the sequence and quantity of a peptide and, by inference, the protein from which the peptide originated.
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Current Opinion in Biotechnology 2012, 23:591–597 This review comes from a themed issue on Systems biology Edited by Jens Nielsen and Sang Yup Lee For a complete overview see the Issue and the Editorial Available online 12th December 2011 0958-1669/$ – see front matter, # 2011 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.copbio.2011.11.014
Over the last few decades experimental biology has focused on the identification and enumeration of molecules. This includes DNA, transcripts, metabolites but more recently also proteins, leading to the field of proteomics. In parallel, a new paradigm emerged for the life sciences in which complex biological processes are studied as dynamic molecular networks. Network analysis contrasts the focus on isolated molecules that has been the mainstay of molecular biology. Network biology is generally referred to as systems biology, an inter-disciplinary field that attempts to study biological systems from an integrative perspective and to capture the emergent properties of the system. Systems biology has experienced considerable growth in recent years but its general implementation remains limited by the available technologies, specifically those tasked with the acquisition of suitable data sets. Such techniques should be able to detect and quantify all components of the system. www.sciencedirect.com
Several new MS-based proteomics approaches have been introduced in recent years, mainly differing according to their analytical performance in terms of reproducibility, dynamic range, limit of detection and resolving power [1]. Shotgun (or discovery) proteomics is the method of choice for the identification of the protein components of a system, when no prior knowledge is available. This technique has demonstrated the capability to identify several thousand proteins in any cell type or organism and thus to deliver a first view of sample complexity and protein dynamic range [2–4]. Moreover, the relative abundance as well the absolute abundance of the identified proteins can be estimated across multiple samples [5–8]. New instrument configurations with shorter cycle times and higher resolving power at both MS and MS/MS levels have contributed to these advances [9]. Equally important has been the development of statistical methods and software tools for the reliable identification and quantification of the resulting data sets [10–12]. However, when multiple samples are compared, the major drawback of the technique is the selection of precursor ions that is biased towards abundant peptides leading to irreproducible replicates of the shotgun experiments and thus incomplete MS data sets [13]. This limitation has been mainly solved using the directedMS approach, where peptides that were not identified in a first MS analysis are compiled into inclusion lists and then sequenced in a second round [14,15]. Both proteomics approaches, shotgun and directed-MS, are referred to as data dependent acquisition (DDA) methods since precursor ions are only selected for sequencing when they are detected at the MS level. Current Opinion in Biotechnology 2012, 23:591–597
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In contrast to DDA methods, a targeted proteomics approach based on selected reaction monitoring (SRM) has recently emerged as a powerful proteomic tool due to its capability to quantify only the proteins of interest (i.e. protein network in central metabolism) with high reproducibility and high accuracy across multiple samples [16,17,18,19]. In a SRM experiment, the instrument isolates the targeted precursor ion and selected fragment ions arising from its collisional dissociation. The corresponding signals are measured over the chromatographic elution time window to provide better sensitivity and higher linearity for quantification, compared with other MS methods [1]. Even in complex sample backgrounds, detecting low attomole (on column) amounts and across a linear dynamic range of approximately 5 orders of magnitude is possible [16,20]. The SRM method is dependent on the a priori selection of optimal sets of precursor– fragment ion pairs, called SRM transitions. Several, typically 3–5 transitions per peptide and 1–5 peptides per protein constitute an assay for the definitive quantification of a protein in a sample and such assays need to be developed once for each protein. Recently, the highthroughput development of SRM assays has been achieved via the generation of MS/MS spectral libraries from the measurements of thousands of synthetic peptides. The resulting assays have been stored in publicly accessible repositories [21]. SRM-based targeted proteomics is also supported by a wide panel of computational tools that enable automated peptide selection, assay development and optimization, and data evaluation [22–24,25,26]. In conclusion, both discovery and targeted proteomics methods are currently used in systems biology to identify the components of a protein network and to explore its dynamic behavior in time and space (Figure 1A).
enrich and isolate ubiquitinylated peptides allowing the characterization of hundreds of ubiquitin-modified peptides covering numerous biological processes [29]. Further analysis of the data showed that ubiquitination sites were preferentially found in regions with abundant hydrophobic residues, although to date no agreement in the consensus sequence for ubiquitination sites has been established [30]. Interestingly, more than 20% of the ubiquitinated lysines were found to also be sites of acetylation, suggesting that acetylation of a specific lysine could serve as a means to prevent lysine ubiquitination. The identification and cataloguing studies focused on post-translational modifications and signaling networks were complemented by studies of protein–protein interaction networks. By expressing 75 hemagglutinin epitope-tagged versions of the deubiquitinating enzymes, and using a standard affinity purification and mass spectrometry pipeline, Sowa et al. defined the first interaction landscape of deubiquitinating enzymes, with more than 774 high-confidence interacting proteins [31]. The study related deubiquitinating enzymes without a clear function to specific biological pathways and identified new links to chromatin processing, DNA damage repair, RNA processing, autophagy, and endoplasmic reticulumassociated protein degradation. In another study, Breitkreutz et al. used standard affinity purification followed by mass spectrometry to identify a kinase and phosphatase network of 1844 interactions in budding yeast [32]. Their experiments not only revealed new potential roles for Cdc14 and the target of rapamycin complex 1 (TORC1), but most importantly showed that the kinase and phosphatase interaction networks are highly interconnected, suggesting a distributed organization of information flow rather than a hierarchical transmission.
Identification of network components Several comprehensive MS-based proteomics studies have recently been performed to identify the components of cells, tissues, signaling pathways and protein–protein interaction networks (Figure 1B). Post-translational modifications, including sites of phosphorylation, glycosylation, ubiquitination and acetylation constitute an essential part of the signaling cascades present in the cell. They modulate the activity and localization of proteins, as well as determine their interactions with other macromolecules. As a result of their importance in signaling networks, significant effort has been expended towards the discovery and cataloguing of post-translational modifications. Over six thousand phosphoproteins harboring nearly 36 000 phosphorylation sites and thousands of N-glycosites were identified in two recent large-scale studies performed in mouse tissues [27,28]. The collected data not only revealed new posttranslational modification sites, but also showed specialized, interconnected networks pinpointing subcellular and tissue-specific modifications, and described new glycosylation motifs beyond the canonical N-X-S/T. In a similar direction, Xu et al. generated a monoclonal antibody to Current Opinion in Biotechnology 2012, 23:591–597
Perturbed networks As a further step to dissect biological networks by mass spectrometry, several studies have recently addressed the dynamics of signaling and protein–protein interaction networks upon different types of perturbations. These experiments go beyond the identification of network components in basal conditions and combine highthroughput identification with quantification techniques to compare proteome modifications and interactions during different states. Monetti et al. used a spike-in SILAC method to quantify the effects of insulin on the phosphorylation state of a mouse liver cell line [33] and a radiomimetic treatment was used by Bensimon et al. to study phosphoproteome dynamics upon DNA damage [34]. The dynamics of phosphorylation signaling networks was further studied in the context of cell–cell direct interaction by Jorgensen et al. Their study revealed different cell-specific tyrosine phosphorylation events showing that the signaling process is asymmetric and that distinct cell types use different tyrosine kinases and targets to process signals induced by the same cell–cell www.sciencedirect.com
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Figure 1
(a)
IDENTIFICATION OF NETWORK COMPONENTS
SHOTGUN PROTEOMICS
ANALYSIS OF NETWORK DYNAMICS
SHOTGUN AND TARGETED PROTEOMICS
TIME SPACE
(b)
SIGNALING NETWORKS
PROTEIN INTERACTION NETWORKS
PROTEIN COMPLEX
PHOSPHORYLATION ACETYLATION UBIQUITYLATION ...
(c)
DISEASE-RELATED NETWORKS FIRST NEIGHBORS
INCREASED ABUNDANCE IN THE DISEASE STATE
NETWORK ANALYSIS
Current Opinion in Biotechnology
(A) Shotgun (or discovery) mass spectrometry-based proteomics is the method of choice for identifying the network components, that is, both nodes and edges. By contrast, when analyzing network dynamics in time or space, both shotgun and targeted proteomics techniques have been used. Targeted methods are preferred for this purpose because they provide higher reproducibility, accuracy and sensitivity when analyzing large sample sets. (B) There are two main types of protein networks: signaling networks and protein–protein interaction networks. In protein–protein interaction networks nodes represent proteins and the edge denote an existing interaction between the linked proteins. The edge width reflects quantification data, and in these networks proteins can be clustered together into protein complexes. In signaling networks nodes also designate proteins but edges denote an action of one protein to another, for example an enzyme–substrate interaction such as that of a kinase and its substrate. (C) The analysis from a network perspective of proteins with altered abundances in a certain disease, can lead to a better understanding of complex biological processes. To illustrate this, quantified proteins in a disease state can be arranged in a network. Then, additional proteins that have previously been shown to directly interact with the quantified proteins can be added (first neighbor approach). Thus, altered proteins with no apparent relation at the beginning, appear now clearly connected through a common neighbor, which becomes an excellent candidate as drug target, or other follow-up experiments.
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contact [35]. Finally, in a comprehensive study to determine the relationships between 97 kinases, 27 phosphatases, and more than 1000 phosphoproteins, Bodenmiller et al. [36] showed that protein kinases and phosphatases and their respective substrates form a highly connected network that reacts to perturbations in complex, and so far poorly understood ways. In the study, thousands of regulated phosphorylation events were identified and it was shown that the effects of kinase or phosphatase inactivation affected several phosphorylation signaling pathways, and not only immediate downstream targets. Other studies have addressed the dynamics of post-translational modifications other than phosphorylation such as acetylation, ubiquitylation and ubiquitin-like modifiers (SUMOs). For example, Choudhary et al. quantified acetylation changes in more than three thousand acetylation sites in response to several deacetylase inhibitors [37]. Their work revealed that acetylation preferentially targets large macromolecular complexes and demonstrated the broad regulatory scope of lysine acetylation, which might be comparable with that of other major post-translational modifications. Furthermore, proteasome and translational inhibitors were also used to study the dynamics of endogenous ubiquitylation and ubiquitin-like modifiers (SUMOs) in several recent publications [38–40]. The field of protein–protein interaction networks has also moved towards exploring dynamic perturbed networks [41] as revealed by several recently published studies. Bennett et al. developed a quantitative proteomics platform based on multiplexed AQUA peptides to elucidate the composition of the cullin-RING ubiquitin ligase (CRL) complex and its dynamics in the presence of an inhibitor of the NEDD8-activating enzyme [42]. From their study, the authors could define a new alternative model for CRL complex dynamics in which the abundance of the adaptor molecules, rather than other previously suspected components, drives the network organization of the complex. In a second study, Bisson et al. combined an affinity purification strategy with targeted proteomics to study the dynamics of the protein–protein interaction network of Grb2. After an initial screening to map novel Grb2 interacting proteins in basal conditions, SRM assays were developed and targeted proteomics was used to establish the dynamics of the Grb2 interaction network after activation of the growth factor signaling pathway [43]. Finally, Glatter et al. have recently performed a quantitative study to characterize the dynamics of the Drosophila InR/TOR pathway after insulin stimulation and showed that the protein–protein interaction network is substantially affected by the activity state of the InR [44].
Networks in time and space Several studies have addressed the characterization of network dynamics, not only after a perturbation, but Current Opinion in Biotechnology 2012, 23:591–597
dissecting it in time and space. The profiling of proteomes at different times of a certain cellular process has been mainly addressed in combination with the analysis of signaling networks. Keck et al. have investigated the phosphoproteome in the yeast centrosome at different cell cycle stages by shotgun proteomics [45]. The centrosomal complexes were isolated by affinity purification and after metal affinity phosphopeptide enrichment, the phosphorylation profile was compared between centrosomes in cell cycle-arrested cells (G1 phase and mitosis) versus those growing asynchronously. In total, 297 phosphorylation sites were identified, where 54 were G1 specific and 110 mitosis specific. In vitro kinase assays with Cdk1 and Mps1 confirmed several substrates and identified possible new substrates such as Spc72 and Cnm76. Thus, they could demonstrate how phosphorylation controls centrosome organization in yeast. In a larger-scale profiling study, the Mann group has investigated the dynamics of the proteome and its phosphorylations over the complete cell cycle in mammalian cells using shotgun proteomics [46]. After multistep fractionation and phosphopeptide enrichment, the authors quantified thousands of proteins bearing 20 400 phosphorylation sites, showing distinct regulated proteins clusters specific to each cell cycle stage. Furthermore, they investigated the relative and absolute phosphorylation-site stoichiometry of thousands of phosphorylation sites along the cell cycle, revealing that particular mitotic kinases and predicted substrates of Cdk1 were highly phosphorylated during mitosis. Using this proteomic phenotyping approach, the authors were led to hypothesize that a high degree of phosphorylation inhibits the activity of these kinases during mitosis. Importantly, such quantitative profiling experiments of phosphorylation occupancy lead to distinguishing functional from nonfunctional phosphorylation sites. Finally, a study by Lage et al. addressed the temporal and spatial analysis of protein interaction networks along many stages of human heart morphogenesis and congenital heart disease to serve as a general model for studying the functional architecture of organ development [47]. After stratification of 255 human proteins into 19 different morphological subgroups, functional networks were constructed for each subgroup using experimental interactome data. The results revealed that the networks consist of relatively few protein modules that are extensively recycled during organ development and that each network consists of a combinatorially unique module pattern. Additionally, the analysis suggested that increased morphological complexity of the heart correlates with increased complexity at the molecular level, which was demonstrated with the transcription regulation modules. Indeed, regulators such as Gata4, Nkx2-5 and Tbx5-5 are displaying different functions during heart development, due to their flexibility in interacting with other proteins at different stages or at different times. www.sciencedirect.com
MS-based proteomics for systems biology Sabido´, Selevsek and Aebersold
Disease-related networks The analysis of molecular networks instead of a classical molecule-by-molecule approach has also been applied to elucidate protein dynamics in the context of human disorders (Figure 1C). Recent studies have focused on the detection of changes in the proteome of animal models carrying disease-causing mutations. In this regard, aiming to detect signatures that reflects the pten status leading to prostate cancer (PCa) progression, Cima et al. have compared by label free shotgun experiments serum N-linked glycoproteome and prostate tissue isolated from wild-type and pten null mice [48]. In a second step, a targeted approach was used to detect the 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. This allowed the extraction of robust patterns predicting tissue pten status and could be used for the diagnosis and grading of prostate cancer. Finally, the Hanash group has recently compared the serum proteomes from mouse models of lung cancer with those of other cancer models by shotgun proteomics to reveal protein signatures in plasma that reflect lung tumor biology [49]. For each model, quantitative proteomics was performed after 2-D protein fractionation and differential cysteine alkylation in intact proteins using isotopically labeled acrylamide. Protein products of more than 2000 unique genes were quantified across 14 plasma samples collected from individual mouse models, revealing distinct regulated protein clusters specific to each organ type. Several proteins showing an increase in abundance were found to be encoded by genes that are known targets of Tif1/Nkx2-1, a master transcription factor of peripheral airway cells. After confirming the expression of Tif1/Nkx2-1 in lung tumors by immunohistochemistry, positive correlation between mRNA levels of protein signature genes and Tif1/Nkx2-1 expression was obtained in human lung cancer and cell lines. These findings provided a new approach to search for cancer protein signatures in plasma based on proteins encoded by genes that are under the control of expressed master development regulators. Overall, as illustrated in this review, mass spectrometry has made significant contributions to the development of the systems biology paradigm, in which complex biological processes are addressed in terms of molecular networks instead of molecule-by-molecule analyses. In this regard, two complementary strategies, discovery proteomics for the identification of the system components and targeting proteomics by SRM for the reproducible quantification of the system components under multiple perturbed conditions have proven particularly valuable. The synergy between MS-based proteomics and systems biology is especially evident in the analysis of perturbed networks and in the recent studies of network dynamics in time and space. Finally, the analysis of disease-related networks, which will certainly be extended in the upcoming years, www.sciencedirect.com
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appears as a promising systematic approach to better understand complex diseases, pinpoint new targets for drug development and identify new biomarkers.
Acknowledgements E.S. is supported by the LiverX program of the Swiss Initiative for Systems Biology (SystemsX.ch), N.S. by the EU FP7 grant ‘Unicellsys’ (grant# 201142), and R.A. by ERC advanced grant ‘Proteomics v3.0’ (grant# 233226) of the European Union.
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Modularity and hormone sensitivity of the Drosophila melanogaster insulin receptor/target of rapamycin Q1 interaction proteome. Mol Syst Biol 2011, 7:. 45. Keck JM, Jones MH, Wong CC, Binkley J, Chen D, Jaspersen SL, Holinger EP, Xu T, Niepel M, Rout MP et al.: A cell cycle phosphoproteome of the yeast centrosome. Science 2011, 332:1557-1561. In this study, the authors investigated the phosphoproteome in the yeast centrosome by shotgun proteomics and revealed specific examples of phosphorylation control in centrosome function. 46. Olsen JV, Vermeulen M, Santamaria A, Kumar C, Miller ML, Jensen LJ, Gnad F, Cox J, Jensen TS, Nigg EA et al.: Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci Signal 2010, 3:ra3. 47. Lage K, Mollgard K, Greenway S, Wakimoto H, Gorham JM, Workman CT, Bendsen E, Hansen NT, Rigina O, Roque FS et al.:
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Dissecting spatio-temporal protein networks driving human heart development and related disorders. Mol Syst Biol 2010, 6:381. In this study, the authors made a systematic analysis of spatio-temporal protein networks that drives several stages of a developing organ using interactome data. 48. Cima I, Schiess R, Wild P, Kaelin M, Schuffler P, Lange V, Picotti P, Ossola R, Templeton A, Schubert O et al.: Cancer geneticsguided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer. Proc Natl Acad Sci USA 2011, 108:3342-3347. 49. Taguchi A, Politi K, Pitteri SJ, Lockwood WW, Faca VM, Kelly Spratt K, Wong CH, Zhang Q, Chin A, Park KS et al.: Lung cancer signatures in plasma based on proteome profiling of mouse tumor models. Cancer Cell 2011, 20:289-299. In this paper, the authors used shotgun proteomics to compare the serum proteomes from mouse models of lung cancer with those of other cancers in order to reveal protein signatures that reflect lung tumor biology.
Current Opinion in Biotechnology 2012, 23:591–597