Mass spectrometry-based signal networks elucidation

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Mass spectrometry-based signal networks elucidation Kun He1, Na Wang1, Wei-Hua Li1 and Xue-Min Zhang Signalling networks regulate essentially all of the biology of cells and organisms under physiological and pathological states. Analysis of signalling networks by classical biochemical approaches such as antibody-based techniques is limited for large-scale and unbiased studies. Proteomics technique based on mass spectrometry now enables the system-wide characterization of signalling events at the levels of posttranslational modifications, protein complex and changes in protein expression. This function can complement the systemside gene expression analysis since the expression of many proteins is regulated by posttranscriptional mechanisms. The application of these technologies provided a quantum leap in our understanding of the molecular properties of signalling networks in recently years. Address National Center of Biomedical Analysis, 27 Tai-Ping Road, Beijing 100850, China Corresponding authors: He, Kun ([email protected]) and Zhang, Xue-Min ([email protected]) 1 These authors contributed equally.

Current Opinion in Biotechnology 2012, 23:120–125 This review comes from a themed issue on Analytical biotechnology Edited by Wei E. Huang and Jizhong Zhou Available online 16th November 2011 0958-1669/$ – see front matter # 2011 Elsevier Ltd. All rights reserved. DOI 10.1016/j.copbio.2011.10.011

Introduction Extracellular stimuli are sensed, integrated and transducted by cell signalling networks, and ultimately, lead to various biological outcomes. Consequently, studying the nature and mechanism of signalling events has been a large and crucial part of biological and medical research. Signal transduction involves changes mainly on protein post-translational modifications (PTMs), protein complex formation and protein expression. All these events are regulated in a highly dynamic and often spatially segregated manner, and may themselves lead to alteration in protein activity and stability, cellular localization, and association with small molecules such as phospholipids. A primary task of signalling research is therefore the measurement of proteome dynamics, PTMs and protein interactions. The studies of cell signalling have been processed using traditional analytical approaches over Current Opinion in Biotechnology 2012, 23:120–125

decades. The classical one is Western blotting analysis to identify proteins and their modifications by specific antibodies. However, antibody sometimes has nonspecific recognition and it is difficult to translate Western blotting data into reliable quantitative data. Most importantly, antibodies are only available to a number of known signalling proteins and PTMs. Mass spectrometric (MS)based proteomics technologies can address these bottlenecks and make it possible to understand these processes in a systematic and unbiased way, especially in the context of identifying protein expression levels, PTMs and complexes on a large scale (Figure 1) [1,2]. In this review, we discuss recent advances in cell signalling studies exploring protein dynamics, PTMs and functional characterization of protein complexes (Figure 2), and highlight the significance of the findings obtained with the MS based strategies in understanding cell signalling processes.

Protein dynamics in cell signalling One of the advantages of MS-based proteomics in cell signalling research is that the highly accurate quantitative information for thousands of proteins and PTMs may be acquired within a run [2,3]. MS is not inherently quantitative, therefore, strategies such as stable isotope labeling by amino acids in cell culture (SILAC) [4,5] or isobaric tagging for relative and absolute quantification (iTRAQ) [6,7], and label-free [8,9] techniques are generally introduced into the quantitative proteins analysis. For example, using SILAC quantitative proteomics, Mestdagh et al. examined the impact of miR-17-92 microRNA cluster activation on globe protein output in neuroblastoma cells [10]. Their results demonstrate that miR-17-92 is a potent inhibitor of TGF-b signalling and how aggressive neuroblastoma tumors might evade the cytostatic TGF-b pathway. With iTRAQ quantification strategy, Rhein et al. have found the molecular connections between Amyloid b and tau protein in AD pathology in vivo [7]. And with label free quantification, Luber et al. have determined the proteome of mouse splenic conventional and plasmacytoid DC subsets and found mutually exclusive expression of pattern recognition pathways not previously known to be different among conventional DC subsets [9]. The quantitative proteomics also provide an unbiased and high throughput approach for measuring the dynamic protein subcellular localizations in response to a wide range of physiological and experimental perturbations [11]. Combined with effective subcellular fractionation methods, protein distribution and dynamics in organelles such as mitochondria [12], centrosome [13] and nucleolus www.sciencedirect.com

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Figure 1

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Strategies for MS-based proteomics. There are two different strategies for analysis of complex protein sample by MS, top–down and bottom–up. In the top–down strategy (blue area), proteins are separated into single proteins or simple protein mixtures, followed MS measurement for intact protein by special technology, such as ECD (electron capture dissociation) or ETD (electron-transfer dissociation). In the bottom-up strategy (green area), protein mixture can be separated before digest into peptides followed by direct PMF (peptide mass fingerprinting) or PST (peptide sequence tags) analysis. Alternatively, the protein sample can be directly digested into peptide mixture without any pre-fractionation. Then these peptides are separated by LC followed by MS/MS analysis. For quantification, stable isotopes label can be used in this workflow, which in the dotted rectangles of this figure. Absolute measurement of targeted protein abundance can be achieved by MRM (multiple reaction monitoring) or SRM (multiple reaction monitoring) methods.

[14] have been studied. To study the proteome of mitotic chromosomes, Ohta et al. developed novel methods to purify mitotic chromosomes and to identify genuine mitotic chromosome-associated proteins by quantitative MS [15]. With genetic knockouts of kinetochore protein Ska3/Rama1, this study reveals that the stable association of the APC/C or RanBP2/RanGAP1 complex with chromosome depend on the Ska complex. Their studies uncovered functional relationships between protein complexes in the context of intact chromosomes.

approaches can be used to acquire the quantitative information of the activity of the kinases. Kubota et al. developed an improved single-reaction strategy to measure the phosphorylation rate of 90 target peptides using highresolution MS in a single run [19]. Combined this strategy with kinase inhibition in different human cell lines, the authors demonstrate the hyperactivation of cAMP-dependent protein kinase pathway in MCF7 cells as well as the cell line-specific cross-talk between the PI3K and MAPK pathways.

Absolute measurement of the abundance of targeted protein in very complex mixtures can currently be achieved by ‘targeted proteomics’ strategies, when isotope-labelled synthetic peptide analogues are added as internal standards [8,16,17]. Using these highly sensitive techniques, researchers have quantified 100 target yeast proteins of widely variable abundance, ranging from 1,300,000 to only 41 molecules per cell [18]. Similar

PTMs in cell signalling

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The measurement of PTMs is a primary task of signalling research, because PTMs usually act as switches during signalling transductions. MS is ideal for studying PTMs because it is a specific, quantitative and unbiased detection method. Olsen et al. applied high-resolution MSbased proteomics to investigate the global proteome and phosphoproteome of the human cell cycle [20]. They Current Opinion in Biotechnology 2012, 23:120–125

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Figure 2

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PTM Modified proteins

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Typical workflow for PTM and protein complex analysis using MS-based technology. (a) Proteins extracted cells are subjected to affinity purification, then purified protein complex were separated by 1D PAGE and ‘in-gel digested’ into peptides, alternatively, direct digested in to peptides before LC– MS/MS analysis. (b) The peptides containing specific post-translational modifications (PTMs) are enriched using different approaches, and subjected to MS and bioinformatics analysis.

quantified 6027 proteins and 20,443 unique phosphorylation sites and their dynamics. In particular, their study shows that nuclear proteins and proteins involved in regulating metabolic processes have high phosphorylation site occupancy in mitosis, suggesting that these proteins may be inactivated by phosphorylation in mitotic cells. Consistent with this notion, Gao and colleagues have shown that human CUEDC2 protein is phosphorylated by cyclindependent kinase CDK1 at S110 during mitosis, which was also identified in Olsen’s large scale investigation, leading to spindle checkpoint inactivation and chromosomal instability [20,21,22]. To explore the mechanisms and evolution of cell cycle control, Holt et al. combined specific chemical inhibition of Cdk1 with quantitative mass spectrometry to analyze the position and conservation of phosphorylation sites by Cdk1 in the budding yeast Saccharomyces cerevisiae, leading to the identification of 547 phosphorylation sites on 308 Cdk1 substrates in vivo [23]. Through the comparison of these phosphorylation sites with orthologs throughout the ascomycete lineage, they conclude that the position of most phosphorylation sites is not conserved in evolution, instead, clusters of sites shift position in rapidly evolving disordered regions. Although most MS-based studies so far have focused on the analysis of phosphorylation events, MS can in principle examine any other PTMs involved in cell signalling Current Opinion in Biotechnology 2012, 23:120–125

[5,24,25]. For example, MS analysis of enriched peptides containing acetylated Lys residues has revealed a surprisingly large number of acetylation sites on mitochondrial proteins [26]. Recently, using high resolution MS in combination with SIlAC, Choudhary et al. have profiled over 3600 acetylation sites on 1750 proteins in cells treated with two different Lys deacetylase inhibitors (MS-275 and SAHA, both in clinic use), thus identifying their in vivo targets [27]. This analysis reveals that Lys acetylation is a widespread modification targeting large macro molecular complexes involved in the regulation of diverse cellular processes. In subsequent studies, Wang et al. determined the overall acetylation status of S. enterica proteins under either fermentable glucose-based glycolysis or under oxidative citrate-based gluconeogenesis [28]. Their study demonstrates that central metabolism enzymes in Salmonella are acetylated extensively and differentially in response to different carbon sources, correlated with changes in cell growth and metabolic flux. Zhao et al. also shows that Lys acetylation plays important roles in regulation of metabolic enzymes in human liver through various mechanisms [29]. Therefore, these studies represent a metabolic regulatory mechanism conserved from bacteria to mammals [28,29]. MS is also increasingly used for measuring more complex PTMs such as ubiquitylation [30] and sumoylation [31]. www.sciencedirect.com

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Xu et al. generated a monoclonal antibody that can enrich peptides that contain lysine residues modified by ubiquitin remanent, which would facilitate the highthroughput identification of ubiquitination sites [32]. In addition to identifying ubiquitination sites, MS is also used to study polyubiquitin topology. By using targeted proteomics method with stable isotope labelling and synthesized specific ubiquitinated GG peptides as internal standards, Xu et al. has quantified the abundance of K6, K11, K27, K29, K33 as well as conventional K48, K63 Ub linkages in yeast cell. Their data show that the unconventional linkages are abundant in vivo and that all non-K63 linkages may target proteins for degradation [33]. In addition to the unconventional polyubiquitin topology mentioned above, linear ubiquitination attracts attention because of its role in immune signalling. Linear ubiquitination of NEMO by ligase complex LUBAC, first identified by Fuminori Tokunaga with MS, is involved in the physiological regulation of the canonical NF-kB activation pathway [34]. More recently, Gerlach et al. have found that RIP1, another important participant of NF-kB signalling can be linear polyubiquitylated during TNF signalling [35]. SHARPIN has been identified as a third component of the linear ubiquitin chain assembly complex responsible for ubiquitination in the TNF receptor signalling complex. SHARPIN interferes with TNFmediated cell death, and thus prevents inflammation. These results provide evidence that linear ubiquitination regulates immune signalling in vivo. While powerful in various PTM studies, there are several issues remained to be addressed in studying ubiquitination and sumoylation. It is difficult for MS to distinguish modification by ubiquitin from other ubiquitin-like molecules, such as neural precursor cell-expressed developmentally down regulated protein 8 (NEDD8) and interferon-induced 17 kDa protein (ISG15), because all modifications leave the same diGly tag at substrate site after tryptic digestion of the modified peptide. Sumoylated peptides contain a large modification after digestion (32 amino acids for human small ubiquitin-related modifier 2 (SuMO2) and SuMO3), leading to large cross-linked peptides. These peptides result in complex fragmentation spectra and require specialized MS techniques for efficient detection.

Functional protein complexes in cell signalling It is well known that most proteins exert their function as components of multiprotein complexes in cells. MS instruments have now improved greatly both in the context of the dynamic range and sensitivity of mass analysis, making it easier to identify the components of a purified protein complex [36]. Numerous findings have been accomplished with MS based strategies to dissect interaction networks of proteins that hold essential functions in various cellular processes. www.sciencedirect.com

With an improved affinity protocol using near physiological buffer conditions and very low detergent levels, Debbie and colleagues studied the interaction network of Oct4, an importation transcription factor for maintaining pluripotent cell identity, in mouse embryonic stem cells (ESC) [37]. They identified an Oct4 interactome of 166 proteins, including transcription factors and chromatin modifying complexes with documented roles in ESC self-renewal, but also many factors not previously associated with the ESC interaction network, expanding the circuitry controlling pluripotent cell identity. Bienvenu et al. developed Flag-tagged and haemagglutinin-tagged cyclin D1 knock-in mouse strains that allowed a high throughput MS approach to search for cyclin D1-binding proteins in different mouse organs [38]. In coupled with Genome-wide location analyses (chromatin immunoprecipitation coupled to DNA microarray; ChIP-chip), they found that cyclin D1 in vivo has transcriptional function in mouse development other than its well known roles in cell cycle. In addition to studies focusing on selected proteins of interest [39–41], system-wide protein complex studies provide indispensable data to complement the data on proteome localization and phenotypes. With similar approaches, Hutchins and colleagues analyzed about 100 human protein complexes, many of which had not been fully characterized [42]. Malovannaya and colleagues reported their study of endogenous human coregulator complexom obtained from integrative MSbased analysis of 3290 affinity purifications [43]. By preserving weak protein interactions during complex isolation and utilizing high levels of reciprocity in the large dataset, they have identified many unreported protein associations. Their work revealed a tiered interplay within networks that share common proteins, providing a conceptual organization of a cellular proteome composed of minimal endogenous modules (MEMOs), complex isoforms (uniCOREs), and regulatory complex– complex interaction networks (CCIs). Dynamic reorganization of signalling systems are frequently accompanied by pathway perturbations. Benett and colleagues report the development of a quantitative proteomics platform centered on multiplex absolute quantification (AQUA) technology to elucidate the architecture of the cullinRING ubiquitin ligase (CRL) network and to evaluate current models of dynamic CRL remodeling [44]. These studies suggest an alternative model of CRL dynamicity where the abundance of adaptor modules, rather than cycles of neddylation and CAND1 binding, drives CRL network organization. In addition, protein interactions with other biomolecules, including DNA and RNA, were also studied using MS-based strategies [45,46].

Perspectives Proteomics has surely become a powerful tool for mapping signalling pathways in a system-wide manner with unprecedented depth and accuracy, and provide invaluable resource for mechanistic studies. The main Current Opinion in Biotechnology 2012, 23:120–125

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advantage is that MS-based proteomics allow the endogenous identification of protein interaction and modification in the signalling. The approach makes it possible to understand dynamic and comprehensive network of cell signalling on a large scale, which facilitates the further unraveling of physiological and pathological processes. However, the goal is not actually easy to achieve due to some limitations with the approach. It is still time and labor-consuming, which restrict the throughput of this technology. The breakthrough of the bottleneck will possibly make it a routine part of all phases of signalling research. Another challenge in cell signalling study is that some interactions are signal dependent and transient with weak binding forces. And at same time, the dynamic range of protein expression can vary by as much as 7–12 orders of magnitude in a biological sample. It requires a continuous innovation in the sample preparation and a further improvement in the sensitivity, resolution and dynamic range of MS instrument. In addition, integration of tremendous data in cell signalling is a still big challenge. With the overcome of these disadvantages, we envision that the study on function-based and disease-based signalling will become possible and we expect that more target proteins and bio-markers will be found by the strategy. As a powerful tool to decode the mysteries of cellular signalling, the MS-based technology will lay a solid foundation for the development of system biology.

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