Contribution of Mass Spectrometry-Based Proteomics to the

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Contribution of Mass Spectrometry-Based Proteomics to the Understanding of TNF‑α Signaling Rodolfo Ciuffa,*,† Etienne Caron,† Alexander Leitner,† Federico Uliana,† Matthias Gstaiger,† and Ruedi Aebersold†,‡ †

Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland Faculty of Science, University of Zurich, 8006 Zurich, Switzerland

‡

S Supporting Information *

ABSTRACT: NF-κB is a family of ubiquitous dimeric transcription factors that play a role in a myriad of cellular processes, ranging from differentiation to stress response and immunity. In inflammation, activation of NF-κB is mediated by pro-inflammatory cytokines, in particular the prototypic cytokines IL-1β and TNF-α, which trigger the activation of complex signaling cascades. In spite of decades of research, the system level understanding of TNF-α signaling is still incomplete. This is partially due to the limited knowledge at the proteome level. The objective of this review is to summarize and critically evaluate the current status of the proteomic research on TNF-α signaling. We will discuss the merits and flaws of the existing studies as well as the insights that they have generated into the proteomic landscape and architecture connected to this signaling pathway. Besides delineating past and current trends in TNF-α proteomic research, we will identify research directions and new methodologies that can further contribute to characterize the TNF-α associated proteome in space and time. KEYWORDS: mass spectrometry, proteomics, inflammation, TNF-α, NF-κB, AP-MS, PTM, proteome profiling

1. INTRODUCTION

pro-inflammatory cytokines TNF-α and IL-1β to antigens, growth factors, and oxidative stress.11,15,16 What the target genes are whose transcription is induced depends on the stimulus as well as many other cytoplasmic and nuclear events, including posttranslational modifications and the chromatin state.15,17 There are two main pathways that lead to the activation of NF-κB:18,19 a canonical one, which is stimulated by TNF-α and other receptor ligands, depends on the formation of a cytoplasmic complex requiring IKKβ and NEMO and leads to the nuclear translocation of heterodimers transcription factors containing primarily RELA/p65; and a noncanonical one, activated by CD40L, RANKL, and others ligands, that proceeds independent of IKKβ and NEMO, depends on IKKα and leads to the formation of a p52/NFκB2-RelB heterodimer11,20,21 (see Table 1 of the Supporting Information for mapping of protein names to Uniprot identifiers and gene names). Besides the canonical pathway, TNF-α stimulation also activates three MAPK pathways, the JNK, the p38-MAPK and the ERK signaling cascades,3 which are also important in mediating the inflammatory response. The canonical pathway, illustrated in

1.1. TNF-α and NF-κB

Discovered about 40 years ago,1 TNF-α has emerged as one of the most important and prototypical proinflammatory cytokines. It is one of the 19 ligands of the TNF superfamily, binding 2 of 29 related transmembrane receptors (TNFRI and TNFRII).2 The two receptors TNF-α bind to exhibit markedly different expression patterns, with TNFRI being ubiquitously expressed and TNFRII being mainly restricted to immune system cells.3 For the purpose of this review, we will focus on TNFRI, as this is expressed in most cell types and is the most extensively investigated receptor.3−5 TNF-α, that is present both as a transmembrane type II pro-form and as a soluble, mature form, does assemble into noncovalent functional trimers and binds to a TNFRI homotrimer.6,7 The main effector of TNF-α is the family of dimeric transcription factors NF-κB. In the last 30 years,8 incessant research has helped elucidate its fundamental role in regulating processes as diverse as stress response, apoptosis, and inflammation9−11 and its implication in atherosclerosis, asthma,12 diabetes,13 muscular dystrophy, rheumatoid arthritis (RA), cancer,14 and many other diseases (see ref 15 for a general review). NF-κB can be activated in nerve cells, macrophages, fibroblasts, and countless other cell types by a variety of stimuli ranging from the © XXXX American Chemical Society

Special Issue: The Immune System and the Proteome 2016 Received: August 10, 2016

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DOI: 10.1021/acs.jproteome.6b00728 J. Proteome Res. XXXX, XXX, XXX−XXX

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Journal of Proteome Research

Figure 1. Schematic representation of TNF-α induced activation of NFkB. After ligand binding, a large signaling complex assembles at the plasma membrane. The process results in the translocation of the transcription factor in the nucleus and the transcriptional activation of target genes. Blue bars at the bottom indicate the process intervals upon which interaction studies, PTM studies, and profiling studies, respectively, have primarily focused.

ubiquitin modifying and deubiquitinating enzymes, such as TNFAIP3/A20, CYLD, and OTULIN, play a key role in the modulation of signaling.25 In fact, specific PTM regulation as well as dissociation from the receptor and genetic defects may all lead to a dysfunctional/unstable TNF-RSC. In particular, deubiquitination of RIPK1 or cIAPs inhibition/depletion and failed ubiquitylation of RIPK1 lead to the formation of two secondary complexes, complex IIa and complex IIb/ripoptosome, that induce apoptosis.26,27 In the absence of active caspases, a different complex forms, the necrosome, which induces necrosis. In this review, we will consider only TNF-RSC. To appreciate the complexity of this pathway, three aspects must be stressed. First, the exact assembly dynamics and the role and requirement of some of the identified PTMs are still debated,5,23 and even the function of some of the key players can be context-dependent (e.g., the requirement of RIPK1 for the activation of the classical pathway seems to be cell-type specific28). Second, many of these players partake in several other inflammation-related pathways (e.g., LUBAC is required for NOD2 signaling and NLRP3 inflammasome activation29). Finally, as already mentioned, NF-κB is the main, but not the only, effector of TNF-α stimulation, as three MAPK pathways are activated: the ERK pathway and the stress response p38 and JNK pathways, resulting in the activation of the AP1 transcription factor.30,31 The position of TNF-α at the crossroad between prosurvival and proapoptotic signaling defines its role in the pathogenesis of autoimmune and inflammatory diseases and in the progression of tumors. Even though TNF-α owes its name to its first identified tumoricidal activity, it has been

Figure 1, has been extensively reviewed elsewhere and will be presented here only in broad strokes.5,22,23 Briefly, TNF-α binds to and induces the homotrimerization of its plasma membrane receptor, TNFRI, and its translocation to lipid rafts.24 This leads first to the recruitment of the adaptor protein TRADD and the protein kinase RIPK1 via their DD domains, followed by the association and activation of the ubiquitin ligase TRAF2 (and, secondarily, TRAF5) through the adaptor TRADD. TRAF2 is required for the recruitment of the ubiquitin ligases BIRC2/ cIAP1 and BIRC3/cIAP2, that K-63 ubiquitylates RIPK1 (already K-48 ubiquitylated by TRAF2), an indispensable step for the recruitment of the heterotrimeric LUBAC complex. This, in turn, stabilizes the complex by adding linear ubiquitin chains to some of its members, including RIPK1. The heterotrimeric IKK complex (IKKα, IKKβ, and NEMO) is recruited via RIPK1 ubiquitin chains, and TAB2/TAB3-associated TAK1, which is required for IKK complex activation, is recruited via NEMO as well as RIPK1 ubiquitin chains. This leads to activation of the IKK complex and the phosphorylation and subsequent ubiquitylation and degradation of the inhibitor of NF-κB, IκBα. In this way, NF-κB is free to translocate to the nucleus and activate the transcription of its target genes (the temporal organization of gene expression induction by TNF-α will be described in a later section). We will collectively refer to the large, receptor-proximal complex as TNF-RSC (TNF receptorassociated signaling complex). As partly described, it has become apparent that this signaling cascade crucially depends on posttranslational modifications (PTMs), especially phosphorylation and ubiquitylation (see, for example, ref 25). In accord, B


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Journal of Proteome Research shown that it can have both pro- and antitumor effects.32On the other hand, TNF-α signaling has been directly implicated in the occurrence of rheumatoid arthritis, colitis, inflammatory bowel diseases, septic shock, systemic lupus erythematosus, multiple sclerosis, type 1 diabetes, and other inflammatory, autoimmune, and neurological diseases.33 Its fundamental role in these ailments has prompted the development and commercialization of several anti-TNF-α biologics that block the action of this cytokine.33

quantitatively compared at the MS1 level.43 (2) iTRAQ (isobaric tag for relative and absolute quantification) involves the chemical labeling of peptides with isobaric tags and allows sample multiplexing and quantification at the MS2 level.44 (3) LFQ (label-free quantification) is based on spectral counting or peptide peak area integration and has the advantage of being inexpensive and applicable to an unlimited number of samples.45 As opposed to DDA, in DIA no real-time selection of precursor ions is performed. Instead, all coeluting peptides in the entire or in a predefined m/z range are fragmented, so as to generate highly complex MS2 spectra that can be computationally interrogated in different ways.41 This circumvents the stochasticity and reproducibility issues associated with DDA, but it results in an increased data complexity. The merits and flaws of all these approaches will be discussed in a later section of the review. Overall, the main goal of this review is to offer a critical summary of the current status of MS-based proteomic research in the TNF-α area. By this means, we aim at (1) describing strengths and weaknesses of published studies; (2) identifying relevant knowledge gaps; (3) offer useful indications about MS technologies that can fill them. The review is structured following the temporal order of the signaling events (see Figure 1). We will first review studies that have contributed to the understanding of the assembly (AP-MS studies) and regulation (PTM studies) of the TNF-RSC and then those that have elucidated the identity and activation dynamics of NF-κB (as well as other TF) target genes.

1.2. Mass Spectrometry-Based Studies of TNF-α Signaling

The complex architecture and regulation of the TNF-α pathway as well as its clinical implications have been extensively investigated by mass spectrometry (MS). For the purpose of this review, dedicated MS studies of TNF-α signaling can be classified in at least 5 classes: (1) Studies of the architecture of the TNF-RSC; (2) PTM studies; (3) proteome profiling studies; (4) biophysical/structural studies;34 (5) biomarker studies (especially to predict response to anti-TNF-α therapy35−37). The architecture of signaling complexes has been probed primarily by affinity purification coupled to mass spectrometry (AP-MS), but also by other means. PTMs, especially in recent years, have been systematically dissected both to identify molecularly and clinically relevant modifications for the assembly of the TNF-RSC and to map enzyme−substrate relationships for kinases and ubiquitin ligases. Proteome profiling studies have been primarily concerned with the induction of NF-κB and AP1 target genes downstream of TNF-α stimulation in a variety of cellular systems. Interestingly, TNF-α has been used as a test case for the development and implementation of new mass spectrometry routines, protocols, and methods. In this review, we will focus primarily on studies belonging to classes (1), (2), and (3). We will briefly touch upon method development applications, and will not consider studies in biomarker discovery and metabolomics areas.

2. AP-MS STUDIES 2.1. Introduction to AP-MS

Affinity purification coupled to mass spectrometry (AP-MS) is one of the most popular techniques to determine protein− protein interactions. It usually involves the enrichment of a target protein, the bait, and its associated interactions, the preys, with a ligand immobilized on a stationary phase/support.46 Typically, the bait is captured exploiting either a specific antibody or one of a variety of epitope tags (for a study using both, see, for instance, ref 47), which is either inserted with the gene of interest into a pre-engineered genomic locus or knocked into the endogenous locus via genomic engineering.48 The first AP-MS interactions deposited in the large protein−protein interaction (PPI) database BioGrid date back to 1993.49 In the last 23 years, enormous progress has been made to render AP-MS a robust and reliable technology for PPI determination, both as a standalone tool and as a companion to primarily cell biological and biochemical studies (for relevant examples, see, for instance, refs 50−52). On the one hand, several algorithms and tools have been developed to distinguish between the usually abundant nonspecific interactions and the true interactors, one of the most vexing problems in the field.53−55 On the other, optimized and standardized protocols56−58 have enabled the realization, in recent years, of enormous enterprises probing the interaction space of several thousand baits59,60 (for an early attempt see ref 61). Figure 2A shows a comparison between the number of interactions determined by AP-MS vs all other methods (excluding genetic interactions) as deposited in BioGrid since 1993 (as of July 2016). Overall, AP-MS accounts for a striking 37% of all the entries.

1.3. Introduction to Relevant MS Concepts

From a methodological point of view, this review is centered on bottom-up proteomic approaches exploiting the combination of reversed-phase liquid chromatography and tandem mass spectrometry (LC-MS/MS). In LC-MS/MS, proteins are digested and the resulting peptides are separated on a chromatographic column and ionized before entering the mass spectrometer. Typically, precursor ions are then selected based on their intensity and mass-to-charge ratio (m/z) (MS1), precursor ions are fragmented in a collision cell, and fragment ions are detected by a second mass analyzer (MS2). We will consider two MS acquisition and data analysis strategies: data-dependent acquisition (DDA) and data-independent acquisition (DIA). An additional technique used in many of the cited studies, MALDI-TOF combined with 1- or 2-dimensional electrophoretic separation, has been largely replaced by LC-MS approaches and will not be discussed in detail.38−41 Briefly, in shotgun (DDA) MS, most abundant precursor ions are selected for fragmentation from a survey scan and fragment ion spectra are used for peptide identification, while dynamically excluding in subsequent scans already selected precursor ions to avoid redundant peptide sampling (for a review of shotgun approaches in immunology, see ref 42). Three quantification strategies usually associated with shotgun proteomics will be referred to in the rest of this review: (1) SILAC (stable isotope labeling with amino acids in cell culture) is a method based on the metabolic incorporation of isotopically labeled amino acids. By this means, samples from different conditions can be pooled prior to data acquisition and

2.2. Overview of TNF-α-Related AP-MS Studies

AP-MS studies of TNF-α signaling have centered around the identification of new components of the complex and the determination of their interactomes in both unstimulated and C


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Figure 2. (A and B) The contribution of AP-MS to the discovery of PPI is described. (A) Bar chart of the number of interactions determined by AP-MS vs all other methods (in BioGrid, excluding genetic interactions). (B) Bar chart showing the absolute number and proportion of interactions of TNF-RSC members identified by AP-MS (blue), other methods (orange), or both (gray). (C) Network representation of the TNF-RSC members interactome (baits are shown in orange, their size is proportional to the number of interactions, and the thickness of the edges codes for the number of experimental observations for a specific interaction). (D) Diagram illustrating the features and relationships of interaction proteomics techniques.

e.g. TRAF5, are not included28). Two of the type (2) studies are dedicated to deubiquitinating (DUB) enzymes and type I interferon response. They are mentioned in this context, as they do contain a significant amount of information about TNF-RSC interactors. Overall, high-throughput proteome-wide studies have significantly contributed to the identification of interactors of currently known TNFR members. As shown in Table 1, all of the listed known TNF-RSC members have been used as a bait and/or found among the interactors in the three PW studies considered, amounting to more than 400 identified interactions (no TNF-α stimulation was used in these studies). However, some limitations still may affect very large studies: the amount of material used for each experiment may be limiting, the optimization of each of the baits as well as testing different conditions is impractical, and functional validation and follow-up experiments can rarely be part of the original study. At the other end of the spectrum, several more targeted, single or low-throughput AP-MS studies, as well as pathwayspecific large scale studies, have contributed to the identification of new TNF-RSC members and the description of the

stimulated conditions, primarily up to 15 min after treatments. For our purpose, studies that are providing information about the TNF-RSC members can be classified in 4 categories: (1) high throughput proteome-wide (PW) AP-MS studies; (2) purpose/pathway-specific large-scale studies, targeting as many components as possible of specific signaling pathways/ classes of proteins; (3) low-throughput discovery studies, focusing on the application of AP-MS to one or few baits and exploiting the resulting findings as a starting point for further functional validation; (4) low-throughput validation studies, using AP-MS to validate or expand on results generated chiefly by other means. Table 1 lists known members of the TNF-RSC and the corresponding type (1) and (2) studies either that have identified them as components of the complex or that have contributed to the reconstruction of their interactome. For the purpose of clarity and consistency, the list reported in the study by Wagner et al. in 2016 has been used here as a reference, since it is the most recent AP-MS study on the entire TNF-RSC. However, it must be stressed that this list is not exhaustive (for instance, some partially redundant components, D


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Journal of Proteome Research Table 1. List of TNF-RSC Members and Their Presence in Large-Scale Proteomic Studiesa

a

For each protein in each study, the left cell (N.I. = number of interactors as bait) indicates how many interactors were found when the protein was used as bait; the right cell (N.F. = number of times found as prey) indicates how many times the protein was found among the preys of other baits. Data are based on BioGrid and include the pre-publication deposition of a large dataset by Huttlin et al. (Gygi lab, Harvard University; URL: http:// thebiogrid.org/166968/publication/high-throughput-proteomic-mapping-of-human-interaction-networks-via-affinity-purification-mass-spectrometry. html).

to pull-down the entire complex 5 and 15 min after stimulation.65 Their study recapitulates the results of several other works, and not only were they able to monitor the temporal execution of the signaling complex assembly, but they obtained information about three new interactors/members of the complex. One of them, HTRA2, is the same mitochondrial protein identified as a new CD40 complex member in the abovementioned AP-MS study by Hostager and colleagues,52 but the authors admit that while it has been found in vitro after lysis, it may not be found in vivo (because of the cellular localization). The second, MIB2, is a ubiquitin ligase whose interaction with CYLD had already been unveiled by a recent proteomic study,66 and whose role in type I interferon response had also been established starting from a large interactomic study.67 The third, SPATA2, a protein linked to spermatogenesis, is the focus of the functional validation of the study, indicating that it is required for CYLD recruitment to the TNF-SRC. CYLD recruitment and DUB activity is one of the key events required for the transition to complex II.25 Strikingly, two more papers

remodeling of their interactome upon stimulation. Not surprisingly, many of the new interactions identified by MS relate to the more recently discovered members; as for the older ones, decades of biochemical studies could unveil a large portion of them. For instance, RBCK1/HOIL-1L and RNF31/ HOIP, now known to be part of the LUBAC complex62 and to play a fundamental role in TNF-α signaling,63 were first identified as members of the TNF-RSC in 2009 by Haas and colleagues by pull-down of the entire complex combined with MS analysis.64 Incidentally, HOIP, together with two additional proteins (SMAC and HTRA2), was identified the year later also as part of the CD40 receptor complex by a similar method.52 An additional insight about the HOIP function came from Schaeffer and colleagues in 2014, who found by AP-MS that HOIP interacts with the DUB OTULIN and could demonstrate that it is required for its recruitment to the signaling complex and, in this way, for the modulation of the HOIPdependent activation of the NF-κB pathway.50 In an elegant effort to investigate the dynamic composition of the TNF-RSC, Wagner and colleagues have recently used Flag-tagged TNF-α E


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component of the Mediator complex, has been detected by AP-MS but not BioID in a recent comparative study (Lambert et al. 2015, Suppl. Table 4); several new interactors of TAB1 have been unveiled,77,78 and more than 20% of the SPATA2 interactions deposited in the BioGrid have also been discovered by proximity tagging. However, given the relatively rapid time scale of TNF-RSC assembly, BioID does not seem to be suited for its temporal analysis, as the required incubation time with biotin is typically 24 h. A related method, APEX, would have instead the potential of unveiling transient interactions and vicinal proteins within the required time frame for TNF-RSC analysis.79 APEX was initially designed as an electron microscopy (EM) imaging tag and to survey the proteomic composition of subcellular compartments,80 but it can also be used to chart proximal interactions in interactomic studies. In the APEX method, 28 kD ascorbate peroxidase is fused to a protein of interest. Upon addition of biotin-phenol and H2O2 to the medium, the enzyme catalyzes the oxidation of biotin-phenol to phenol radicals and promotes their reaction with electron-rich amino acids. The reaction takes place in 1 min, and it would be therefore exquisitely suited for the dynamic analysis of TNF-RSC. A disadvantage of APEX is that, at the moment, its applicability to map interactomes has not been extended to nonmembrane proteins, and there are, to the best of our knowledge, no well-established filtering methods, rendering the interpretation of the results difficult. As of July 2016, there are 7332 interactions classified as Proximity Label-MS in BioGrid, accounting for about 1% of all the entries. 2.3.2. Protein Complex Topology Determination. Some of the shortcomings of the current analysis of TNF-RSC could be addressed as well by a combination of AP-MS and cross-linking coupled to mass spectrometry (XL-MS) (for a recent review see ref 81). In this approach, the bait-prey complex is cross-linked with a residue-specific, homobifunctional crosslinking reagent prior to proteolytic digestion. The cross-linking reaction stabilizes transient interactions, and the information contained in the cross-linked peptides allows discriminating direct/primary from indirect/secondary interactions as well as providing spatial information about the specific regions of the interactors that are engaged in the protein−protein contact. Conceptually, the combination of a well-established structural technique with an interactomic one represents an improvement over stand-alone AP-MS studies, bridging interaction and structural studies and thereby converting AP-MS networks into hierarchical and topological maps. Several studies have shown the applicability of this method to both specific protein complexes and cell lysates,82−85 and an early study has also been published in the TNF signaling area.86 Given the rich structural knowledge available for the TNF-RSC and its sheer size, we predict that AP-XL-MS could significantly improve our global understanding of the topology of the complex. There are, however, some caveats to this method that have to be borne in mind: the method is low throughput; chemical requirements for optimal cross-linking are different for different protein complexes (as observed in structural XL-MS studies87,88); and thus far, to our knowledge, there is no generalized consensus on solid statistical methods for the unbiased and automated validation of the identified cross-linked peptides in such a complex background.89 2.3.3. Cofractionation Studies. In addition to proximity tagging and AP-XL-MS, other methods have attempted to extract orthogonal information about PPI and protein complexes. Several laboratories have coupled chromatographic and nonchromatographic methods to MS to catalogue hundreds of

published shortly afterward used MS methods to identify SPATA2 as a new TNF-RSC member acting as a bridging factor between HOIP and CYLD.68,69 Intriguingly, the crucial interaction between CYLD and SPATA2 had already been identified by AP-MS in 200970(see Table 1), but it was not followed-up on until recently. These are important MS-driven discoveries that deeply alter our understanding of TNF-α signaling.71 Finally, it is worth mentioning that the first and only study to systematically chart the interactome of the TNF-α signaling-associated proteins was published in 2004 by the Superti-Furga lab.72 This highly cited work is a paradigmatic example of a comprehensive MS-based exploration of a signaling system. The authors carried out 237 TAP purifications with 32 baits and identified 181 high-confidence interactions, more than 60% of which were new, including 26 stimulusdependent interactions. Although proteomics technology has improved considerably since the publication of this work, this is still a rich resource to understand the TNF-RSC. Overall, AP-MS accounts on average for more than 20% of TNF-RSC related nonredundant interactions (i.e., identified only by one method), and for nearly 30% of all of them. It has served in the identification of at least 5 new members of the TNF-RSC and, in general, as a fundamental discovery and validation tool largely complementary to other techniques (Figure 2B). In Figure 2C the (partial) interactome of TNFRSC members is shown, with the edges being proportional to the number of pieces of experimental evidence available for that interaction and the size of the bait, in orange, proportional to the number of interactions. Improvements and variations on these techniques, discussed in the next section, promise to reveal further details about the regulation of this pathway and to place MS-based methods for PPI determination at the forefront of TNF-α signaling research. 2.3. Interaction Studies of TNF-α: Alternative Strategies

The main limitations of the otherwise exhaustive and (somewhat) integrated works presented are primarily dictated by intrinsic shortcomings of the adopted methodology, AP-MS. In its most typical form, AP-MS suffers from the following deficiencies: (1) it does not detect labile interactions, (2) it does not provide information as to whether an interaction is direct or indirect and as a consequence does not unambiguously define the topology of a network, and (3) it mainly relies on the semistochastic data-dependent acquisition. In the past decade, several families of methods have been developed that define new features of the spatial and biochemical relationship between proteins. These methods, which are illustrated, together with AP-MS, in Figure 2D, bear the potential to further elucidate the dynamics and architecture of TNF-RSC. Their principles and applicability to the TNF-RSC are presented in the following paragraphs. 2.3.1. Proximity Tagging. A family of methods recently developed is called proximity tagging.73 In 2012 Roux et al. introduced a method, called BioID, that allows mapping proximal interactions.74 The method is based on the fusion of the target protein with a promiscuous prokaryotic biotin ligase that biotinylates proteins in the vicinity of the bait with an estimated labeling radius of about ∼10 nm.75 The advantage of BioID over AP-MS is that it is able to detect not only directly or indirectly interacting proteins, but also vicinal, transiently interacting, and possibly lower abundant proteins.74,76 Overall, it has been shown that the two methods are complementary. For instance, the interaction between TAK1 and MED4, a F


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which we alluded to, MS-based technologies for PPI interaction determination are bound to further expand their leading role in this area.

protein complexes simultaneously and extract information about their molecular weight (MW) and stoichiometry, besides their composition. In a typical application, a cell lysate is loaded on a SEC column and proteins are separated according to their hydrodynamic radii. Depending on the experimental paradigm, the resulting fractions can be subjected to additional orthogonal fractionation steps or directly digested and prepared for LC-MS/MS analysis.90−92 While coelution does not necessarily mean interaction, stringent filtering and correlation procedures can be implemented to ensure identification of bona fide protein complexes.91 If applied to samples that have been subject to different treatments, SEC-MS promises to reveal the plasticity of protein complexes on a proteome-wide scale. Therefore, this technique can be conceivably applied both to the single TNFRSC as well as to the whole cellular system, and it would have the potential to reveal subtle changes in the composition of the TNF-RSC as well as in the cellular “complexome”. Interestingly, all the IKK complex proteins were identified as engaged in binary interactions/part of complexes by Kristensen and colleagues. Of note, a recent study has recently attempted to combine SEC-MS with XL (to stabilize protein complexes).92 2.3.4. Quantitative AP-MS and Stoichiometry Determination. Most of the studies presented in this and in the following sections are based on data-dependent acquisition schemes. The advantages and the potential increase in information content associated with data-independent acquisition (DIA)/targeted MS (for examples, single/multiple reaction monitoring, SRM/MRM, respectively) will be discussed more at length in the next section. In this context, suffice it to say that in the last few years, protocols have been developed to apply both DIA and SRM in the context of AP-MS. Collins et al. demonstrated that AP-SWATH is a tool exquisitely suited for time course analyses of protein complex dynamics.93 Bisson and colleagues have applied AP-SRM to generate a highly sensitive, reproducible, and quantitative analysis of the GRB2 interactome, an important player in RTK (receptor-tyrosine kinase) signaling pathways.94 Given their sensitivity and reproducibility, these approaches are convenient to detect low-abundance interactors and small abundance changes over time, a very desirable feature in the context of studying the dynamic formation of TNF-RSC. Furthermore, SRM can be conceivably used to determine the stoichiometry of the affinity-purified protein complexes. Interestingly, the stoichiometry, and even the exact MW, of some of the TNF-RSC subcomplexes are not known. LUBAC, for example, is typically described as a ∼600 kDa heterotrimeric complex.95,96 However, this claim is based primarily on very broad SEC profiles that show a significant amount of the complex components also in lower MW fractions.97 In this context, it must also be noted that in the past few years several label-free stoichiometry determination methods applied to AP-MS samples have been developed, that would dispense one from labor-intensive assay development associated with SRM.98,99

3. PTMS 3.1. Introduction to PTMs and MS

PTMs affect the physiochemical properties, conformational state, and activity of substrate proteins, and they ultimately govern signal transduction. MS is one of the main tools to systematically identify PTMs. Among the advantages that MS offers over traditional biochemical techniques are the high throughput, the unbiased nature of the investigation, and the possibility of identifying directly the modification sites.100 However, MS-based analysis of PTMs has to face several issues. One of them is the low abundance and low stoichiometry of modified peptides as compared to the nonmodified ones. For a small number of PTMs, several enrichment strategies have been developed that can increase by up to 100-fold the proportion of the desired species.101 In the case of phosphorylated peptides, the two most popular techniques exploit the affinity of phosphates for metals (immobilized metal affinity chromatography (IMAC) and metal oxide affinity chromatography (MOAC)), but others exist that leverage structural recognition (antibodies), charge (e.g., strong cation exchange (SCX)), and polarity (e.g., hydrophilic interaction chromatography (HILIC)).102 The analysis of ubiquitylated species is complicated by the fact that, besides monoubiquitin, also ubiquitin chains with several different linkages (M1, K6, K11, K27, K29, K33, K48, K63), mixed chains, and branched chains are linked to the substrate’s lysine ε-amino group103a particularly relevant problem in the case of TNF-α signaling. Also, for this reason both protein-level and peptide-level enrichment strategies have been developed. The most widely used enrichment strategy in MS uses antidiglycine antibodies, which had a tremendous impact in the field since its inception few years ago.104 These antibodies recognize a signature diglycine motif that is a remnant of the digested ubiquitin C-terminus conjugated to a substrate lysine (K-ε-GG). Another particularly successful development has been the usage of ubiquitin binding domains or tandem arrays thereof (TUBE, tandem ubiquitin binding entities),105 that exhibit high affinity for specific chain linkages, thus circumventing one of the main drawbacks associated with both the di-Gly antibodies usage and expression of tagged ubiquitin.100 These enrichment strategies, combined with technological improvements and some of the quantification approaches already mentioned in the Introduction, allow the identification and quantification of tens of thousands of phosphorylated and ubiquitylated sites on a routine basis.101,106 However, such streamlined and robust enrichment and analytical strategies exist only for few PTMs. 3.2. PTM Studies of TNF-α Signaling

It is not surprising that, out of hundreds of eukaryotic PTMs indexed and used in the Uniprot knowledgebase (http://www. uniprot.org/docs/ptmlist), only a handful of them have been systematically investigated in the context of TNF-α signaling. In particular, of the 20 most abundant experimentally observed categories,107 only phosphorylation, ubiquitylation, and N-linked glycosylation have been the subject of extensive proteomic studies (see Table 2; in Figure 3A the experimental conditions used for PTM and profiling studies are plotted). PhosphoSitePlus (PSP)108 records more than 200 unique PTMs associated with TNF-α treatment, and this by no means represents an exhaustive inventory. The role of phosphorylation and

2.5. Interaction Studies of TNF-α: Outlook

Taken together, the data presented in this section show that AP-MS has propelled the understanding of TNF-α on at least two levels. First, it did provide unbiased lists of new interactors that have been used in follow-up studies. Second, it has revealed the dynamic assembly of the complex with an unprecedented level of comprehensiveness. With the improvement in sensitivity and increase in depth of AP-MS analyses, together with the development of several new methods, some of G


System

Protein identification approach

H

FAT10ylome

Ubiquitylome

Glycoproteome in Microparticles (MP)/ Exosomes Full Proteome Glycoproteome

Phosphoproteome

Phosphoproteome

A549 human lung adenocarcinoma cell line HEK293 human cell line

3T3-L1 adipocyte mouse cell line

FAT10 immunopurification Gel electrophoresis MS

Membrane protein enrichment with ultracentrifugation and chloroform/methanol precipitation Glycoprotein enrichment with HILIC and RP C18 LC-MS/MS (SILAC) Ubiquitylome enrichment with K-GG ab SCX fractionation LC-MS/MS (SILAC)

Microparticles and exosome enrichment with ultracentrifugation Glycoproteome enrichment with TIO2 LC-MS/MS (SILAC)

40

100

30 h

5,15 min

4d

24 h

50

2

15 min

5, 15 min

10 min

30 min, 2, 4 h

20

100

10

MCF-7 (WT IKKβ/K44 M IKKβ) human cell line A549 human lung adenocarcinoma cell line entire organism Schistosoma mansoni NHI 6F Tu28 rat insulinoma cell line

Phosphopeptide enrichment with SCX chromatography and TiO2 beads LC-MS/MS (SILAC) Phosphopeptide enrichment with TiO2 beads SCX fractionation LC-MS/MS (SILAC) 2DE Detection of phosphoproteins with gel stain LC-MS/MS

Phosphoproteome

10

Phosphopeptide enrichment with IMAC and HILIC LC-MS/MS (iTRAQ)

L929 murine fibroblast (WT RIP3/KO RIP3) cell line

24 h

50

Microparticles and exosome enrichment with ultracentrifugation Phosphoproteome enrichment with TIO2 LC-MS/MS (SILAC)

NHI 6F Tu28 rat insulinoma cell line

4h

7 min

10, 30, 60 min

Time points (min/hs/days)

50

20

1

Phosphoproteome in Microparticles (MP)/ Exosomes Phosphoproteome

Phosphopeptide enrichment with IMAC and HILIC LC-MS/MS (SILAC)

IEF gelfree Phosphopeptide enrichment with IMAC LC-MS/MS (SILAC)

2DE immunoblot with antiphosphotyrosine ab 2DE MALDI/TOF

TNF-α (ng/mL)

MEF (WT RIP3/KO RIP3) mouse cell line

L929 fibroblast murine cell line HeLa S3 human cell line

Experimental details

Phosphoproteome

Phosphoproteome

Phosphoproteome

Target

Table 2. List of PTM Profiling Studies Dedicated to TNF-α Signaling

Treatment

nontreated TNF-α + IFN-γ

nontreated TNF-α

nontreated TNF-α

nontreated IL-1-β TNF-α

nontreated TNF-α

nontreated SC-514 TNF-α nontreated TNF-α

nontreated TNF-α

LPS (peritoneal macrophages) TNF-α (MEF) nontreated IL-1-β TNF-α

nontreated TNF-α

TNF-α

43 glycan structures were assigned

175 phosphopeptides (30 min) 171 phosphopeptides (2h) 189 phosphopeptides (4h) (RIP3-dependent) about 1% of 20000 phosphosites about 8% of 8888 phosphosites 32

79 phosphopeptides 97 phosphosites

21 phosphoproteins

differentially expressed/ modulated proteins by TNF-α

Results

ref

121

65

122

164

194

65

110

112

164

111

109

193

Journal of Proteome Research Reviews


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Figure 3. (A) Bubble chart illustrating the frequency of different treatment conditions (cytokine concentration vs incubation time) and the percentage of proteome or PTM studies falling in the early, intermediate, or late phase (bar charts, bottom; these phases are not directly related to the transcriptional phases described in section 4.3.1). Note that the axes of the bubble chart are not linear. Treatments from two publications (a microarray study191 and a 7-day treatment192) are not plotted. (B) Venn diagrams comparing the number of PTM sites identified on TNF-RSC members by high-throughput (HTM) MS methods vs low-throughput methods (PTM; left), in ref 65 vs ref110 (right), and in PhosphoSitePlus vs ref 65 and ref 110 (bottom).

analyzed the phosphoproteome of a control sample vs samples treated with TNF-α alone or in combination with IKKβ inhibitors, IKKβ kinase-dead mutant, or WT overexpression. Besides identifying hundreds of regulated sites, they also predicted IKKβ substrates using a random forest approach. Some of the more relevant predictions, including a functionally noncharacterized phosphorylation on the central CC region of RIPK1 (S320), remain yet to be explored. Two more studies, that we will not discuss in depth here, have taken a similar approach by comparing the phosphoproteome of TNF-α stimulated MEF and L929 cells WT or RIPK3 deletion mutant.111,112 In a more recent effort, Wagner and colleagues combined analysis of phosphorylation, ubiquitylation, and APMS to quantitatively monitor the dynamic assembly of TNFRSC at two different time points using SILAC.65 Of the 8888 phosphopeptides identified from approximately 3000 proteins, about 8% were regulated after stimulation with the cytokine. Besides several already known identified sites, they discovered many new ones whose functional significance remains to be evaluated. Overall, these two studies report approximately 40 phosphorylation sites related to the TNF-RSC members, about 20% of which are new (as assessed by PSP and Uniprot), plus several others that had not previously been identified in the context of TNF-α signaling.71 The overlap between the two studies and their overlap with the PSP knowledge base is illustrated in Figure 3B (right and bottom, respectively). To our knowledge, Wagner and colleagues provide also the first study to systematically dissect ubiquitylation events after TNF-α stimulation, and they obtain important insights about some uncharacterized sites on TNFRI, NEMO, Sharpin, and HOIP.65,71 The fact that two single studies could unveil so many new events and players in such an extensively investigated system highlights once more the importance of MS-based proteomics. Another important contribution to the understanding of the ubiquitin code associated with the TNF-RSC came from the Walczak group in 2011. To identify substrates of linear ubiquitylation by LUBAC, the group used a modified version of

ubiquitylation in TNF-α signaling has already been mentioned in the Introduction, and it is highlighted by the fact that roughly half of the TNF-RSC members are kinases, ubiquitin ligases, or DUB enzymes. These enzymes act in a highly interdependent and concerted fashion. For example, one of the key events in the pathway, the ubiquitin-dependent degradation of IkBα, depends on its phosphorylation by the IKK complex. This is, in turn, regulated by its phosphorylation by the TAK1 kinase, whose recruitment to the TNF-RSC depends on several ubiquitylation events regulated by cIAP1/2 and LUBAC. The enzymatic activity of the DUB CYLD, that removes ubiquitin chains from NEMO, TRAF2, TRAF6, and RIPK1, is itself believed to be regulated by two phosphorylation events. Likewise, it has been suggested that the ubiquitin-editing enzyme A20 would be able to cleave K63 chains upon phosphorylation.25 Furthermore, many of the ubiquitin linkages play a fundamental role in signal transduction and in the stabilization of TNF-RSC, or, conversely, in the formation of complex II and necrosome complex. It is therefore apparent that a system-level understanding of the PTM regulation of the pathway is required. Similar to PPI mapping, the contribution of mass spectrometry to the advancement in the field is substantial. For the over 800 PTM sites identified for the TNF-RSC members and deposited in the PSP repository, more than 80% are based on high-throughput MS experiments (Figure 3B, left). Three main MS studies have been dedicated to the investigation of the TNF-α induced phosphoproteome. In an early work by the Yates lab, TNF-α signaling was used as a model system to test the combination of gel-free isoelectric focusing and immobilized metal affinity chromatography (IMAC). The isolated isotopically labeled phosphopeptides were analyzed, resorting to an LC-MS/MS/MS strategy designed to increase the number of identifications.109 Two much more recent efforts outnumbered this early attempt, with thousands or tens of thousands phosphopeptides and hundreds of differentially regulated phosphopeptides identified. In 2015, Krishnan and colleagues attempted to identify potential substrates of IKKβ.110 To this end, they I


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may be required to define the specific stoichiometry of phosphorylated sites for important members of the TNF-RSC. Finally, while we focused on phosphorylation and ubiquitylation, few other modifications have been explored, including the TNF-α- and IFN-γ-induced ubiquitin-like modifier FAT10121 and N-glycosylation.122 Most importantly, many other modifications remain to be analyzed.

TAP purification (moTAP) and combined it with 2D gel electrophoresis and multiple reaction monitoring (MRM). In this way, they could identify NEMO and RIPK1 as linearly ubiquitylated proteins,113 the former event being a potent IKK activator in vitro,114 and the latter revealing yet another type of chain linkage associated with RIPK1, whose ubiquitylation is considered critical to prevent complex II formation and cell death.25 The interplay between chains of different linkages on the same protein is now a central issue in the field.115 Besides redefining the ubiquitylation landscape of TNF-α signaling, MS has also contributed to the understanding of the deubiquitination process. Ritorto and colleagues116 recently reported the development of a MALDI-TOF DUB assay, whereby DUB activity and chain linkage specificity can be determined by quantifying the amount of monoubiquitin in a sample containing the DUB enzyme and one of the eight ubiquitin dimer topoisomers, plus isotopically labeled monoubiquitin used as a normalization reference for quantification. Remarkably, the authors produced a panoramic view of the specificity and activity of nearly half of the human DUBs at 5 different concentrations and with all 8 ubiquitin linkages dimers, and they included, among the targets, TNF-α-relevant targets such as Cezanne, A20, and the already discussed OTULIN and CYLD.

4. PROTEOME PROFILING STUDIES 4.1. Introduction to Proteome Profiling

While genome sequencing and transcriptomics have progressed at an unprecedented pace in the last decades and have reached single-cell resolution,123−126 the ability of mass spectrometry to map entire proteomes and extract a large amount of information from small quantities of samples is still relatively limited. This is due to both biological and technical reasons. The number of proteoformssplicing variants, truncations, posttranslationally modified polypeptide chainsvastly exceeds the number of genes in the human genome. This poses huge problems at the level of instrumentation and data analysis.127 This issue is compounded by the dynamic range of protein abundances that spans more than 6 orders of magnitude in cells and tissues128 and goes up to more than 10 in human plasma samples.129 However, even if MS-based technology cannot rival other omics technologies in terms of throughput, costs, and speed, many advances have been made (at the level of sample preparation, instrumentation, and data analysis) that have substantially improved the sensitivity, throughput, and accuracy of proteomic analyses.130 Two recent studies131,132 have claimed the mapping of the entire human proteome, and studies using fractionation strategies and reporting 7000− 10000 proteins identified are now not uncommon (for example, see refs 133 and 134). Even if the proteomic coverage were to remain comparatively limited, it would still be irreplaceable. The relationship between transcript levels and protein fold change has been addressed in several large-scale studies and reviews and will not be discussed here.135,136 However, it is worth mentioning that conclusions of different studies are conflicting and that, even in some of the proteomic work that will be presented or cited here, authors have observed mismatches between RNA and protein levels over the course of stimulation experiments.137−139

3.3. PTM Studies of TNF-α: Discussion and Outlook

In this section, we have explored the important contribution of MS-based proteomics to the deciphering of the PTM code regulating TNF-α signaling. We have presented some state-ofthe-art studies that have produced a remarkable amount of information in a systematic, multilayered fashion, both recapitulating the results of several previous works and adding new exciting findings. It is critical, now, to further interrogate this data and functionally validate the insights generated. However, additional molecular, temporal, and functional proteomic depth can be gained by several means. As of phosphoproteomics, complementary information can be obtained by adopting orthogonal enrichment strategies, or, alternatively, by increasing the depth of coverage. Ultradeep coverage seems to level out differences between enrichment methods,106 but it also requires considerable cost and labor. Furthermore, phosphorylation events have been investigated mostly for the first few minutes after TNF-α stimulation, but important changes in the phosphoproteomic landscape are expected to occur also at a later phase and/or under different conditions, with the activation of transcriptional and translational programs and with the transition from complex I to apoptotic and necroptotic complexes, as confirmed by some proteomic studies dedicated to TNF-α induced necroptosis.111,112 Given the existence of important feedback mechanisms, this may shed light on the regulation of progression of the signaling. Finally, more studies dedicated to the understanding of the kinase−substrate relationship are required. Besides siRNA- and CRISPR-based screens, inhibitors for many of the kinases (e.g., RIP1K,117TAK1,118 IKKβ110) involved in TNF-α signaling exist that can be used to this end. Interestingly, successful inhibitors have been developed also for the cIAP1/2 E3 ligases (SMAC mimetics119), and their use in the context of proteomic studies could be highly beneficial. Tandem arrays of ubiquitin binding domains, which selectively isolate ubiquitin chains with specific linkages, can be used both to profile the substrates of specific ligases (as in the case of LUBAC) and to map ubiquitylation changes over time with chain-specific resolution. Middle-down proteomics approaches could increase our understanding of chain length and branched chains,120 and top-down methods

4.2. Overview of TNF-α-Related Proteome Profiling Studies

To our knowledge, more than 30 studies (listed in Table 3) have been published which compare the proteome of TNF-α treated systems with the proteome of untreated (or otherwise treated) systems. The listed works span more than 15 years, several biological systems, species, mass spectrometry techniques, and experimental designs. This extreme heterogeneity is rendered even more problematic by the lack, until recently, of large, standardized repositories for profiling data, even if this lamentable state has been alleviated by the introduction of large MS data repositories, in particular with the ProteomeXchange project.140 The heterogeneity of these studies is represented in Figures 3A (stimulation conditions), 4A (stimulations used in combination with TNF-α), and 4B (subproteome studies). Condensing all the results of these studies into a single framework is therefore unfeasible and would not be particularly informative. However, we will highlight their general features, some trends, and important results. A global overview will help identify the most important open questions on both the technological and biological side. J


K

3T3-L1 preadipocytes mouse cell line

Mitochondrial proteome Secretome

pancreatic stellate human cells line

Full proteome

Secretome

Secretome Exosomes

Full proteome

Secretome

normal human knee chondrocyte (primary cell line from patients) hASC human adipose tissue-derived mesenchymal cells (primary cell line from patients) KDH-8 hepatoma rat cells (TNF-α resistant and sensitive) L6 skeletal muscle rat cell line HMEC human microvascular endothelial cells THP-1 Human monocytic cell line

Full proteome

cartilage explants from bovine joints

Murine visceral endoderm-like cells primary cultures of murine astrocytes HEK293 human cell line

Secretome Full proteome Nuclear proteome

ARPE-19 human cell line cartilage explants from bovine joints

LC-MS/MS (Label free quantification) Exosome enrichment with ultracentrifugation SCX fractionation LC-MS/MS (iTRAQ) Secretome enrichment with AHA labeling RP C18 fractionation LC-MS/MS (iTRAQ) Antibody microarray

2DE LC-MS/MS

LC-MS/MS (Label free quantification)

2DE MALDI/TOF

Mitochondrial enrichment with sucrose gradient separation 2DE LC-MS/MS SCX fractionation LC-MS/MS (iTRAQ)

2DE LC-MS/MS SCX fractionation LC-MS/MS 2DE MS/MS Nuclear enrichment with extraction kit SCX fractionation LC-MS/MS (SILAC)

SCX fractionation LC-MS/MS 2DE MALDI/TOF Endothelial microparticle enrichment with ultracentrifugation Gel electrophoresis LC-MS/MS Gel electrophoresis LC-MS/MS (SILAC) SDS-PAGE LC-MS/MS (Label free quantification)

HEK293 human cell line human lung fibroblasts cell line HUVEC human cell line

Full proteome Full proteome Endothelial microparticles Secretome Secretome

2DE MALDI/TOF LC-MS/MS

2DE MALDI/TOF

2DE MALDI/TOF Antibody microarray Flow cytometry Kinase assay Immunoblot analysis

VSMC cell line (primary cell line from patients) 3T3-L1 adipocyte mouse cell line

10

10

2 10

10

10

100

10

30 20, 100 20

10 100

10 10 10

5 0.2, 5, 100

17.5

10

25

125

200

TNF-α (ng/mL)


Full proteome H-411E rat cell line Proteome subset HT-29 human colon adenocarcinoma cell line

Full proteome

Full proteome

Full proteome

humam peripheral blood eosinophils cell 2DE 35S methionine labeling line HCEC and HUVEC human cell line 2DE MALDI/TOF LC-MS/MS

Full proteome

2DE MALDI/TOF MS/MS

Protein identification approach

TGG rat trigeminal ganglia

System

Full proteome

Target

Table 3. List of Proteome Profiling Studies

Treatment nontreated TNF-α + IFN-γ + IL1-β TNF-α+ IFN-γ + IL1- β + paracetamol TNF-α GM-CSF

48 h

4h

4d 24 h

48 h

48 h

5d

24 h

24 h 24 h 10, 20, 30 min

24 h 5d

16 h 0,5,15,30,60,90 min; 2,4,8,12,16,20, 25 h 6h 24 h 3h

96 h

nontreated TNF-α other ctytokines

nontreated TNF-α nontreated TNF-α hypoxia high glucose,mannose conc. nontreated TNF-α IL-1α

nontreated TNF-α IL-1β injurious compression nontreated TNF-α IL1-B TNF-α + IL1-β nontreated TNF-α

nontreated TNF-α isoproterenol

nontreated TNF-α nontreated TNF-α IL-1β injurious compression nontreated TNF-α nontreated TNF-α IFNγ nontreated TNF-α

nontreated TNF-α IL-1α nontreated TNF-α nontreated TNF-α PAI-1

nontreated starvation starvation + TNF-α insulin TNF-α TNF-α + Insulin nontreated TNF-α EGF insulin

HUVEC: 4,6,8, 25 nontreated TNF-α h HCEC: 4,7, 25 h 24 h nontreated TNF-α TNF-α + ALA

18 h

22 h


Results

56

15

28 18

32

118

20

12

32 4 235 (10 min) 130 (20 min) 155 (30 min) 35

6 23

16 18 70

7

32

Differentially expressed/ modulated proteins by TNF-α

ref

143

154

167 158

142

137

152

156

173

150 148 163

166 155

147 145 162

172 191

151

160

144

153

159

Journal of Proteome Research Reviews


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269 nontreated TNF-α

146

45 nontreated TNF-α + BMP-2

24 h

4.3. Summary of TNF-α-Related Proteome Profiling Studies

4.3.1. Perturbation Studies. Transcriptomic studies have shown that TNF regulates the induction or repression of hundreds of genes, including TNF itself, Tnfaip3, Nf kbia, Il1b, Icam1, Nf kbie, and many other cytokines, chemokines, adhesion molecules, and regulators of the TNF-α signaling and apoptosis.30,168 This regulation can be transient or sustained, and happens at different phases, with the expression of several genes being dependent on the prior expression of other factors.169,170 This regulation can translate into an early, intermediate, or late phase induction of genes involved in three successive stages of the inflammatory response.16 To our knowledge, it has not yet been exhaustively determined whether the structure of the transcriptional regulation is also mirrored at the translational level. Several proteomic studies have used TNF-α stimulation to address its key connection with insulin. Indeed, TNF-α is known to play a key role in inducing insulin resistance, a hallmark of type 2 diabetes, in obese individuals.171 Solomon and colleagues, for instance, compared TNF-α or TNF-α + insulin treated H411E liver cell proteomes and identified several differentially expressed proteins. Some of them are related to translation, degradation, G-protein signaling, and radical formation, which can be related to insulin signaling transduction. The authors discuss the results but do not provide any functional validation.172 In a later study, Cho et al. induced adipocyte lipolysis by stimulating 3T3L1 cells with TNF-α or isoprotenerol before isolating mitochondria by gradient centrifugation and analyzing their proteome. By this means, they could identify an altered level of proteins involved in energy production and oxidative phosphorylation in both treatments. Furthermore, a specific factor, prohibitin, was differentially regulated under the two conditions, which may account for mitochondrial damage in the TNF-α treatment.173 The same cells had been used in an earlier study to investigate the effect of a combination of starvation and TNF-α treatment.151 Yoon et al. used a different system, L6 GLUT4myc skeletal myoblast cells, again to study the effect of TNF-α-induced insulin resistance, and they identified 28 differentially expressed secreted proteins, several of which were not considered to be regulated before and for some of which they could propose a link with metabolic dysfunctions, without however going into further validation.167 In order to dissect the relative contribution of individual players in a more

100

168 h 5

The presented studies can be classified by (1) goal/specific perturbation, (2) method, or (3) scope. (1) The simplest experimental paradigm is the attempt to determine the effect of TNF-α on the proteome of a specific system with141 or without genetic perturbations,142−150 with a subgroup specifically interested in induction of insulin resistance.151 Many compare and/or combine these effects with other proinflammatory cytokines,143,147,152−154 to mimic a more physiological situation. Along these lines, others have tested physiologically relevant physical/chemical perturbations, 151 such as mechanical strain,155,156 shear force,157 and hypoxia.158 Additional stimulations that do not fall in these classes have also been used159−162 (Figure 4A). (2) Roughly half of the studies use one- or (mostly) two-dimensional gel separation combined with MALDI-TOF, whereas most of the others adopt label-free or quantitative (SILAC, iTRAQ) LC-MS/MS. (3) A considerable portion of the studies (>30%) focus on subcellular compartment(s)/ subproteome, for example, the proteome of the nucleus,163 of exosomes and endothelial microparticles158,162,164,165 and, above all, the secretome137,150,155,156,166,167 (Figure 4B).

Full proteome

MH7A synovial (primary cell line from patients)

SCX fractionation LC-MS/MS (iTRAQ and MRM) LC-MS/MS C2C12 mouse cell line

Endothelial microparticle (EMP) Full proteome

Full proteome

Membrane proteome

192

83 nontreated TNF-α 10, 100, 200

1,3,24 h

165

29 nontreated TNF-α 10, 100, 200

1,3,24 h

165

149 189 10

24 h

161 13

nontreated TNF-α TNF-α + melittin nontreated TNF-α 12 h 10 Full proteome

ref

54 2h 10

THP-1 Human monocytic cell line 2DE MALDI/TOF (JMJD3-kd and JMJD3-sc) VSMC Vascular smooth muscle cells 2DE MALDI/TOF/TOF human cell line RAW264.7 macrophages mouse cell line Membrane protein enrichment: ultracentrifugation, RP C18 and GELFREE electrophoresis SCX fractionation LC-MS/MS (Label Free Quantification) HUVEC human cell line 2DE MALDI/TOF/TOF HUVEC human cell line Endothelial microparticle enrichment with ultracentrifugation LC-MS/MS Full proteome

TNF-α (ng/mL) Target

Table 3. continued

System

Protein identification approach



Treatment TNF-α

Results

Differentially expressed/ modulated proteins by TNF-α

141


L


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Figure 4. (A) Diagram classifying the different types of stimuli used in conjunction with TNF-α. (B) Schematic representation of subproteomes investigated in connection with TNF-α stimulation. The highest number of proteins identified and differentially regulated (in parentheses) from selected studies are shown.

used 547 for quantitative temporal analyses. Of these, they detected >1.5-fold change in protein abundance for 235, 130, and 155 proteins (for each of the time points), and they grouped them into 9 temporal clusters. The authors could reveal several protein abundance changes in the nucleus within a very small time window, suggesting that important differential regulation (of translation as well as translocation) takes place well before the overall average ∼20 h stimulation time used for other studies (see Figure 3A). Second, by performing simple clustering of the resulting profiles (completely missing in most of the other studies), they could propose the function of single proteins and the properties of an entire cluster. For instance, based on the inverse association between TP53 levels and a key phosphorylation of the transcription factor NF-κB, the authors suggest that TP53 is a negative effector of early nuclear signaling. In addition, based on the steadily increasing nuclear abundance of Fanconi anemia group D2 protein (FANCD2), validated by Western blot and immunofluorescence, they suggest that it is an effector of TNF-α/NF-κB. Finally, the authors hypothesize that proteins belonging to a profile that increases up to 10 min and plateaus thereafter may be involved in gene transcription (one of these proteins being GTFII-I). Overall, the study, even if lacking full functional validations, is a potentially information-rich resource. Endothelial Microparticles and Exosomes. Endothelial microparticles are small particles of 100 nm to 1 μm in size shed by vascular cells and released into the blood.175 They have attracted the attention of researchers because of their role and their potential as biomarkers in inflammatory diseases.176 Together with exosomes, their proteome has been analyzed in several publications. Peterson and colleagues reported the identification from isolated microparticles of 783 proteins from the control and of 643 from TNF-α treated samples, with about ∼150 exclusively found in the latter.162 In a later study, Palmisano et al. identified a total of 401 proteins associated with microparticles and 191 with endosomes (135 in common) stimulated with a combination of TNF-α and IL-1β, that is known to initiate apoptosis in the chosen model system (pancreatic β-cells) and map the most pronounced differences between the control and treated samples to increased abundance of signaling molecules and cell death-related molecules (including 3 of the components of the discussed TNF-RSC: TNFR1, TNFAIP3, RIPK1).164

complex, physiological situation where several cytokines act in concert, a number of studies have compared the individual or cumulative effects of different proinflammatory cytokines. In 2007, Ott et al. compared, in this journal, the proteome of TNF-α and IL-1α activated HEK293 cells, and, by resorting to both proteomic and transcriptomic data, identified the activation of cell cycle arrest as the specific signature of TNF-α stimulation, linking it to a lactase-dependent stabilization of HIF1α.147 An interesting comparison between early proteins induced by TNF-α and IL-1β was carried out a year later by applying a combination of iTRAQ and BONCAT (bioorthogonal noncanonical amino acid tagging), with 11 shared out of 16 differentially expressed in the studied system.154 We will refer to this study later. Finally, in ref 143, the authors used an antibody microarray design to detect differential expression of >700 cancer related proteins in pancreatic cells stimulated with a panel of proinflammatory factors, including TNF-α, FGF2, IL-6, and CCL4. Interestingly, the author observed that TNF-α is the only stimulation that is involved in the activation of the pancreas stellate cells and that induces a significant increase in ROS production.143 Finally, in an attempt to dissect rheumatoid arthritis (RA), Shibasaki and colleagues have recently analyzed the aberrant response of MH7A synovial cells from an RA patient to TNF-α by performing a functional classification of 296 identified differentially produced proteins. By a simple functional classification, they could unveil misregulated pathways, such as apoptosis and autophagy, that are in fact known to be affected in RA.146 4.3.2. Subcellular Fractionation Studies. Subcellular fractionation coupled to MS has the potential to reduce the complexity of the studied system and analyze relevant dynamics in greater depth.42,174 In the context of NF-κB activation and TNF-α signaling, the nucleus (TF regulation), the secretome (auto- and paracrine signaling), and the secretion of vesicles have been the major focuses. Nucleus. To our knowledge, Ma and colleagues authored, in 2009, the only available nuclear proteome study of TNF-α stimulated-cells.163 The authors monitored protein abundance change in nuclei of HEK293 10, 20, and 30 min after stimulation, using a combination of subcellular fractionation and SILAC. Overall, they identified between ∼2300 and ∼2900 proteins, and M


Reviews


system and on the implementation of diagnostic and therapeutic strategies. Technological limits affect in equal measure the analysis of subproteomes: (1) as of the secretome, current studies are able to identify >1000 putative secreted proteins (after filtering), that is, nearly twice as many (or even more) as in the discussed studies. In two relevant examples, the Mann lab found that about ∼40% of 1073 putative secreted proteins are regulated after lipid-induced insulin resistance,180 and, in a previous study, the authors could monitor the temporal behavior of nearly 800 secreted macrophage proteins upon stimulation with the bacterial endotoxin LPS.138 (2) Insulin-resistance was also the focus of the only mitochondria-specific study dedicated to TNF-α stimulation, which we already discussed. This study fails to produce a full inventory of the identified proteins, and given the comparatively old generation MS utilized and the recently estimated >1100 human mitochondrial proteins,181 it could be updated by adopting more recent instruments and technologies, including APEX (originally used to map mitochondrial proteins79). Finally, analysis of many of the subproteomes as well as subproteome PTMs is entirely missing,174 but discussing the relevance and the opportunity of additional studies in these areas is beyond the scope of this review. Interestingly, DIA approaches are largely absent in the presented studies. Given the importance of low-abundance proteins, unbiased proteome coverage, and reproducibility for comparative proteomics, DIA would seem a valid alternative to DDA. Even within quantitative schemes resorting to chemical or metabolic labeling,182 DDA remains a semistochastic process biased toward highly abundant peptides, and is limited in the signal-to-noise ratio (SNR) by the serial nature of the data acquisition.41,183 Due to its main features, it must also be stressed that DIA is very advantageous for comparing multiple conditions. It must be noted, however, that as the technology progresses and faster and more sensitive instruments are brought to market (i.e., the fraction of sampled precursor ions increases), the marginal advantage using DIA progressively reduces.106,184 Nevertheless, that gap is not yet filled and the benefits that comparative proteome profiling studies would gain from DIA approaches remain noteworthy. As shown clearly in Figure 3A and Table 3, the temporal sampling (as well as the number of chosen time points) varies dramatically from study to study, indicating an additional limit of these investigations. While PTM studies focus on early signaling events (or on prolonged stimulation to induce cell death), proteome profiling studies are more focused on later events, and do typically fall in the 24 h stimulation category. Even though small variations in protein levels may be masked by the high coefficient of variation of the acquired data, several papers, including some in our list, have shown that global or local proteomic changes upon TNF-α and other stimulations are detectable well before the common 24 h treatment.138,139,144,163 Furthermore, it is clear from transcriptomic studies that dozen of genes are induced in an early and intermediate phase. An attractive possibility to study early/intermediate proteomic responses to cytokine treatment is to combine quantitative mass spectrometry with bio-orthogonal noncanonical amino acid tagging (BONCAT). Metabolic incorporation of the unnatural amino acid L-azidohomoalanine (AHA)185 has already been used in combination with iTRAQ to monitor nascent chains after 4 h stimulation with both TNF-α and IL-1β.154 A more comprehensive study in a different system (LPS stimulation) has

This prompted the authors to speculate whether this may be a mechanism to dampen TNF-α signaling and avoid apoptosis. Finally, a quantitative analysis of exosome particles has been carried out by de Jong and colleagues, which revealed the differential expression of some commonly TNF-α differentially induced proteins, such as ICAM-1 and TNFAIP3, confirming also some of the results of a previous study.158 Secretome. Several studies have been dedicated to the proteomic investigation of the secretome. In an attempt to generate insights into the mechanism of age-related macular degeneration (AMD), An et al. analyzed the secretome of ARPE-19 cells and found only 6 differentially secreted proteins after 24 h of treatment.166 Many more proteins were identified later by Lee and colleagues136 in the medium of human adipose tissue-derived mesenchymal stem cells, which are an important potential tool for regeneration of injured and inflamed tissues. Among them, they found well-known repertoires of inflammatory cytokines and chemokines and a number of different proteases and proteins of the complement system. In two related studies, the second resorting to iTRAQ for quantification, Stevens and colleagues used bovine cartilage explants as a model for osteoarthritis, comparing injurious compression to the effect of both TNF-α and IL-β (known to cause cartilage damage). They show a linear relationship of the overall proteomic response between treatments with the two cytokines, a significant increase of several matrix metalloproteinase levels, and lower collagen amounts, with some of the TGF-β and IGF family proteins showing higher levels under all the conditions.155,156 Finally, using a less common approach, Arrell and colleagues150 used differential secretome analysis and network biology to substantiate the priming and contribution of TNF-α to cardiac specification and identifying potential effectors. 4.4. TNF-α-Related Proteome Profiling Studies: New Directions and Outlook

While many of the studies presented have generated insights, besides a considerable amount of data, a significant fraction of them are nevertheless methodologically dated. The limitations they suffer from are, in our opinion, due to technological limitations more than to poor experimental design. Some of the limitations are (1−3) molecular, spatial, and temporal incompleteness; (4) lack of functional validation; and (5) lack of data integration. On a very general level, the amount of data generated by most of these studies is limited by the technology used. Many of the presented studies have been carried out combining 2DE with MALDI-TOF. As already mentioned, 2DE-MALDI-TOF has been largely replaced by LC-MS/MS for proteome profiling. This is due, among others, to the labor-intensiveness of 2DE, to the low reproducibility of the technique, to limitations in detecting medium-to-low abundant as well as very large/small/ acidic/basic/hydrophobic proteins, and to comigrating proteins and multiple spots per protein.40,177 In this sense, it would be desirable to revisit older studies with newer technologies. On the other hand, also the rest of the studies that adopt labelfree or quantitative LC-MS/MS cover mostly about 1000 to 3000 proteins at best, with few exceptions. Comparable studies carried out with state-of-the art technologies (both quadrupoletime-of-flight and Orbitrap-based) combined with fractionation identify regularly between 5000 and 10000 proteins.133,134,178 Considering the importance of low-abundant proteins,179 it is to be expected that a more exhaustive protein coverage could have a profound impact both on our molecular understanding of the N


Journal of Proteome Research been able to detect few differentially abundant proteins after 2 h of treatment.139 Overall, several transcriptomic studies have laid a solid foundation for the understanding of gene induction by TNF-α in a large time window going from less than 1 h to several days. Even though many proteomic studies have attempted to explore how this is mirrored at the translational level, there is no exhaustive depiction of the connection between these two layers. This is also due to the heterogeneity of these studies that, given the exquisite cell-type specificity of TNF-responsive genes,186 cannot be easily condensed into a single framework. Furthermore, global transcriptional profiles are already adopted in clinical studies, whereas proteomic applications in this area are still scarce.29,187

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REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.6b00728. Mapping of selected names to Uniprot accession numbers and gene, protein, and alternative names (XLSX)

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AHA, L-azidohomoalanine; ALA, alipoic acid; APEX, ascorbate peroxidase; ARPE-19, human retinal pigment epithelial cells; BONCAT, bioorthogonal noncanonical amino acid tagging; DDA, data-dependent acquisition; DIA, data-independent acquisition; DUB, deubiquitinating enzymes; ESC, embryonic stem cells; hASC, human adipose tissue-derived mesenchymal stem cells; HCEC, human cerebral endothelial cells; HILIC, hydrophilic interaction chromatography; HUVEC, human umbilical vein endothelial cells; IMAC, immobilized metal affinity chromatography; iTRAQ, isobaric tag for relative and absolute quantification; LC-MS/MS, liquid chromatography coupled to tandem mass spectrometry; LPS, lipopolysaccharide; MALDI, matrix-assisted laser desorption/ionization; MEF, mouse embryonic fibroblasts; MOAC, metal oxide affinity chromatography; NF-κB, nuclear factor kappa B; PPI, protein− protein interaction; PW, proteome-wide; RA, rheumatoid arthritis; ROS, reactive oxygen species; SCX, strong cation exchange; SILAC, stable isotope labeling with amino acids in cell culture; SNR, signal-to-noise ratio; SRM, single reaction monitoring; TAP, tandem affinity purification; TNF-α, tumor necrosis factor α; TNF-RSC, TNF receptor-associated signaling complex; VSMC, vascular smooth muscle cells

5. CONCLUDING REMARKS In this review, we set out to examine to what extent MS-based proteomics has contributed, and will be able to contribute, to the understanding of TNF-α signaling. By surveying the relevant interactomic literature and the works related to PTMs, we found that in these two areas proteomics has fulfilled its role of formidable, data-driven insight generator, by contributing to the identification of new key players in the signaling pathway, by validating results generated by previous methods, and by offering a treasure-trove of data to be mined and interrogated. However, in spite of the extensive research at the basic and translational levels, there is still much to learn about the spatial and temporal organization of this pathway.21 For this reason, we have also discussed several technical advances, including proximity tagging, cross-linking, and protein correlation profiling, that may dramatically further our understanding of the dynamic architecture of TNF-α signaling, as they seem to be practically implementable and suited for this system. A more fragmentary situation emerged from a review of proteome profiling literature. We found that a more exhaustive temporal as well as molecular coverage would be needed, and we discussed what state-of-theart technology may be able to reveal. These three levels will be instrumental in making sense of the many intricacies, redundancies, and inconsistencies across systems that characterize this pathway and that we have alluded to in the Introduction.28 For similar reasons, specific molecular insights should be integrated in a more multilayered understanding of the system, as has been advocated and demonstrated in several publications.188−190

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AUTHOR INFORMATION

Corresponding Author

*E-mail: ciuff[email protected]. Phone: +41 44 633 34 37. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We are grateful to Tencho Tenev for critically reading the manuscript. This work has been in part funded by the Innovative Medicines Initiative through grant agreement #115766. O


Reviews

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Reviews

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