Mass spectrometry‐based protein–protein interaction networks for the study of human diseases

Review12 January 2021Open Access Mass spectrometry-based protein–protein interaction networks for the study of human diseases Alicia L Richards Alicia L Richards orcid.org/0000-0002-4869-2945 Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA J. David Gladstone Institutes, San Francisco, CA, USA Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA Search for more papers by this author Manon Eckhardt Manon Eckhardt orcid.org/0000-0001-8143-6129 Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA J. David Gladstone Institutes, San Francisco, CA, USA Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA Search for more papers by this author Nevan J Krogan Corresponding Author Nevan J Krogan [email protected] orcid.org/0000-0003-4902-337X Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA J. David Gladstone Institutes, San Francisco, CA, USA Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA Search for more papers by this author Alicia L Richards Alicia L Richards orcid.org/0000-0002-4869-2945 Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA J. David Gladstone Institutes, San Francisco, CA, USA Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA Search for more papers by this author Manon Eckhardt Manon Eckhardt orcid.org/0000-0001-8143-6129 Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA J. David Gladstone Institutes, San Francisco, CA, USA Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA Search for more papers by this author Nevan J Krogan Corresponding Author Nevan J Krogan [email protected] orcid.org/0000-0003-4902-337X Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA J. David Gladstone Institutes, San Francisco, CA, USA Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA Search for more papers by this author Author Information Alicia L Richards1,2,3, Manon Eckhardt1,2,3 and Nevan J Krogan *,1,2,3 1Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA 2J. David Gladstone Institutes, San Francisco, CA, USA 3Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA *Corresponding author. Tel: +1 415 476 2980; E-mail: [email protected] Molecular Systems Biology (2021)17:e8792https://doi.org/10.15252/msb.20188792 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract A better understanding of the molecular mechanisms underlying disease is key for expediting the development of novel therapeutic interventions. Disease mechanisms are often mediated by interactions between proteins. Insights into the physical rewiring of protein–protein interactions in response to mutations, pathological conditions, or pathogen infection can advance our understanding of disease etiology, progression, and pathogenesis and can lead to the identification of potential druggable targets. Advances in quantitative mass spectrometry (MS)-based approaches have allowed unbiased mapping of these disease-mediated changes in protein–protein interactions on a global scale. Here, we review MS techniques that have been instrumental for the identification of protein–protein interactions at a system-level, and we discuss the challenges associated with these methodologies as well as novel MS advancements that aim to address these challenges. An overview of examples from diverse disease contexts illustrates the potential of MS-based protein–protein interaction mapping approaches for revealing disease mechanisms, pinpointing new therapeutic targets, and eventually moving toward personalized applications. Introduction Identifying the principal molecular basis of human diseases is crucial for successful prevention, diagnosis, and treatment. In the past two decades, scientists have placed a lot of hope on large genomic studies for deciphering disease mechanisms. Nevertheless, despite the wealth of genomic information gathered, the molecular mechanism of most diseases remains unknown. This can be explained at least in part by the fact that many human diseases are complex and do not follow a classical genotype to phenotype model. They may result from multiple genetic changes, epigenetic modifications, or infection by a pathogen. The fallacy of expecting simple genetic changes to explain complex disease phenotypes has been demonstrated especially well in the case of cancer, where a distinct collection of mutations is often not exclusive to a given cancer type (Junttila & de Sauvage, 2013; Leiserson et al, 2015). Additionally, mutations of a single gene can lead to multiple different diseases, with the corresponding proteins having several functions in different cellular contexts (Nadeau, 2001). Consequently, extracting useful diagnostic or prognostic information from genetics alone can be difficult. Considering genetic information in the context of disrupted cellular processes and networks can help overcome this challenge. Systems biology approaches, which aim to provide a comprehensive picture of a biological process by quantifying all observable components and their relationships, are well-suited to understand the influence of disease mutations on a complex network of interconnected pathways. Proteins are the key components of these networks. Often, individual proteins do not perform any of their functions in isolation but accomplish the task through direct interactions with other proteins. As such, studying protein–protein interaction (PPI) networks has become a powerful tool for identifying the functional consequences of genetic variation. In this approach, disease-related gene mutations are mapped to vital PPIs of cellular processes. Comparison of disease states with the wild-type reference map—either through the introduction of proteins carrying mutations or exogenous expression of pathogen proteins—promises to reveal how networks change during disease pathogenesis (Krogan et al, 2015; Willsey et al, 2018). Cellular proteins are directly responsible for adaptation to disease-mediated changes. Because of the connectivity between proteins, the impact of a disease-related mutation is not restricted to a specific gene product. Instead, it affects the entire network and can accordingly impact the activity of a whole subset of proteins. Instead of focusing on individual genes or loci implicated in human disease, PPI-based analyses study the parts of pathway connections that are most changed by the disease state, thus offering an alternative to identify a mutation's impact on cellular function. Interacting proteins can be visualized using a network-based approach, with nodes representing the "bait" proteins of interest of a PPI study. Nodes are connected by edges to the interacting proteins identified by Affinity Purification Mass Spectrometry (AP-MS), proximity labeling, Cross-Linking Mass Spectrometry (XL-MS), or other types of experiments. This mapping is performed in both the diseased state and non-diseased or WT states, and variations between the global regulation of PPIs in the networks are monitored. The introduction of disease-related mutations can lead to perturbations in these networks, including a complete loss of interactions, partial loss of specific interactions, or a rewiring or gain in new interactions (Fig 1). This connectivity suggests that small changes to a PPI network, such as the introduction of mutations to a particular gene, can cause significant changes at multiple nodes across the system. Changes in the interaction partners of the disease-related protein, either during disease progression or following an infection, might contribute to a specific disease state, potentially linking genotype and phenotype. Applying a network-based approach to study human diseases has multiple clinical and therapeutic advantages. The finding that a gene or protein is implicated in a given biochemical process or disease suggests that its interacting proteins may also play a role in the same processes, thus providing potential mechanistic explanations and therapeutic implications beyond a single gene or protein. Figure 1. A systems-level approach for converting genetic information into a pathway-level understanding of dataGenetic variants, which may occur rarely across individuals with a specific disease, can be used as the basis of PPI networks. Comparisons of WT PPI networks and PPI networks with disease-related mutations introduced can aid in determining the functional significance of these mutations. Similarly, the introduction of pathogenic proteins can determine which host pathways are hijacked over the course of an infection. Download figure Download PowerPoint Here, we review the current state of research using mass spectrometry (MS)-based global and unbiased PPI networks to study human disease. Throughout, we will highlight current challenges of the field, and how new advances in the mapping of PPI networks address some of them. For a detailed examination of other PPI identification tools not relying on MS for detection, we refer the reader to other reviews (e.g., Snider et al, 2015; Beltran et al, 2017). MS-based methods for global PPI studies Liquid chromatography-MS (LC-MS) is a sensitive, accurate, and selective method to quantify proteins (Richards et al, 2015; Aebersold & Mann, 2016). One of its major benefits to identify PPIs is the global and unbiased nature of MS proteomics. This is in contrast to other methods for identifying PPIs, including yeast-2-hybrid (Y2H), which maps physical, binary interactions of a predetermined set of proteins of interest (Walhout & Vidal, 2001). The general workflow of utilizing discovery MS to develop PPI networks is outlined in Box 1 and illustrated in Fig 2. Below, we summarize a variety of methods that, when combined with quantitative MS, allow the proteome-level analysis of complex biological systems. Figure 2. Overview of different mass spectrometry techniques(A) Workflow for bottom-up proteomics. Preparing proteomic samples for LC-MS/MS analysis requires protein extraction, proteolysis, and, optionally, peptide-level fractionation. Online LC separation of complex peptide mixtures introduces analytes into the mass spectrometer for precursor and fragment ion mass analysis. Tandem mass spectra are matched to theoretical spectra generated in silico to garner peptide sequences that are used for protein inference. (B) Label-free quantitation. Following protein digestion, for each sample, an equal amount of peptides is separately loaded on the column. Relative quantitation is performed by comparing the extracted peak intensity of a given peptide across runs in the dataset. (C) SILAC. During cell culture, "light" or "heavy" versions of specific amino acids are metabolically incorporated into samples. Following sample preparation, cell lysates are mixed in equal total protein ratios and digested into peptides. Intensities of peptide extracted ion chromatograms from the MS1 scan can be used to quantify relative protein abundances between samples. (D) Isobaric labeling. Each sample is digested into peptides, labeled with a unique isobaric label, and mixed in equal ratios. During MS/MS analysis, each tag yields a fragment with a unique mass that can be used for relative quantitation. (E) Targeted MS. In SRM, each fragment of a protein of interest is individually monitored and quantified. The peptide of interest is first isolated, and its characteristic fragments can be monitored for quantitation. Only the specific peptide and fragment masses selected by the user are monitored over the analysis. Download figure Download PowerPoint Box The general workflow of discovery MS starts with digesting a mixture of proteins into peptides with defined cleavage sites (e.g., using trypsin), which are separated using liquid chromatography and their mass-to-charge (m/z) is measured in a mass spectrometer. In standard tandem MS/MS experiments, the sequence of individual peptides will be determined by collecting a second MS spectrum after induced fragmentation. Taken together, the m/z data of fragments and full peptides are then used to computationally search large databases specific to the organism of interest and thus identify proteins in the original mixture (Fig 2A). To identify candidate interactors in protein–protein interaction studies, data will be "scored" to determine the accuracy of the identified interaction. This is oftentimes done by combining several parameters such as reproducibility, specificity, and abundance of each detected protein. A variety of scoring algorithms exists for this purpose, including MiST, CompPASS, and SAINT (Sowa et al, 2009; Choi et al, 2011; Teo et al, 2014, 2016; Morris et al, 2014; Verschueren et al, 2015). The general methodology of each algorithm differs—for example, SAINT incorporates quality controls and quantitative data for a given prey to determine the probability that an interaction between the prey and bait protein is a true positive, while CompPASS utilizes several scoring parameters that ultimately focus on abundance, uniqueness, and reproducibility to distinguish between true interactors and contaminant background proteins (Christianson et al, 2011). The output of these programs is a table of filtered, scored data that can be imported into network visualization tools such as Cytoscape (Shannon et al, 2003). In addition to computational approaches assessing the specificity of PPIs by comparing to appropriate controls, a variety of different MS methods exists for quantifying changes between different conditions (Fig 2B–E). Label-free quantitation allows comparing the relative abundances of identified proteins in an unlimited number of samples (Fig 2B). However, there are limitations with this approach, one of them being that for comparison purposes, identical amounts of each sample should be injected on the column for analysis. When this is not possible, normalization of the data may be required. Additionally, to reduce instrumental bias, samples being compared should be analyzed in a single acquisition batch on the mass spectrometer. Randomization of run order can also help avoid systematic errors. Metabolic or isobaric labeling approaches such as Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) and tandem mass tag (TMT) or other isobaric labels allow the user to multiplex multiple samples together, increasing experimental throughput. SILAC metabolically incorporates stable heavy amino acids at the protein level (Fig 2C; Ong et al, 2002; Szklarczyk et al, 2019), while isobaric tagging methods utilize NHS-activated molecules that label free amines with chemical tags in vitro following digestion (Fig 2D). All labeling methods rely on the inclusion of additional control samples to which a mass label is added, so that in a mixture of control and experimental sample the origin of a respective protein interactor can be traced (Ong et al, 2002; Thompson et al, 2003; Mann, 2014). Together, these methods allow comparison across different conditions or timepoints or to discriminate between specific and non-specific interactions (Wiese et al, 2007; Virreira Winter et al, 2018). Additionally, targeted MS strategies, such as parallel reaction monitoring (PRM) or multiple/selective reaction monitoring (MRM/SRM), can also be used to validate interactions with greater consistency, sensitivity, and accuracy (Lange et al, 2008; Gallien et al, 2012; Peterson et al, 2012). Briefly, unique peptides of the target protein are selected during assay development. These are then monitored through their signature fragment ions for precise quantitation in the final experiment (Fig 2E). Among the identified proteins in MS-based interaction studies, numerous non-specific interactors or contaminants are copurified together with the protein of interest. Therefore, it is necessary to analyze PPI studies in a way that separates true interactors from artifacts. This can be done, in part, through careful experimental design and suitable controls. Importantly, appropriate controls such as an unrelated protein carrying the same tag, or the tag alone, need to be included to determine the specificity of interaction (Jäger et al, 2011b). For example, GFP can be used as a bait in control experiments. It is unlikely for GFP to form interactions with many proteins, and identified interactors are presumably false positives due to the epitope tags or the affinity capture method (Morris et al, 2014). Additionally, each type of affinity tag can capture specific background contaminations. These contaminants can be accessed via the CRAPome database (Mellacheruvu et al, 2013), a public repository of interactions generated from negative control data, and filtered out of experiments. Contamination can also result from carryover of overexpressed proteins, with residual amount of protein identified in subsequent MS experiments despite not actually being present as an interactor. Strict wash steps between experimental conditions may be required to alleviate this problem. Affinity purification mass spectrometry (AP-MS) AP-MS experiments (Fig 3A) utilize epitope tagging, where short peptide or protein tags (for example, FLAG-, TAP-, Strep-Tag, or c-myc (Chang, 2006)) are fused to the protein of interest—either in the context of an exogenous expression construct or under the gene's endogenous promoter using gene editing technologies like CRISPR-Cas9. The resulting bait protein functions as an affinity capture probe for interacting, or "prey" proteins, eliminating the need for specific antibodies to proteins of interest, as would be the case in lower throughput immunoprecipitation (IP) experiments. The affinity tag can easily be purified on a matrix recognizing the epitope. After washing steps to eliminate non-specific interactors, interacting proteins can be identified via MS. Figure 3. Overview of MS-based methods to determine protein–protein interaction networks(A) General workflow for identifying interacting proteins using AP-MS. Bait proteins are endogenously tagged and expressed in cells, followed by cell lysis and affinity purification of bait proteins and interacting prey proteins. The mixture is digested and analyzed by LC-MS/MS. Following data processing to determine true interactors (BOX), bait and prey proteins can be incorporated into PPI networks. (B) Identification of proximal proteins using proximity labeling. The protein of interest is fused with a promiscuous ligase and expressed in cells. Following the addition of biotin, proteins interacting within the fusion protein's labeling radius are tagged and can be subsequently lysed and captured using an affinity matrix. The mixture is digested and analyzed by LC-MS/MS. Following data processing to determine true proximal proteins (BOX), bait and proximal proteins can be incorporated into PPI networks. (C) Direct interactions via cross-linked peptides using XL-MS. Following cross-linking with the appropriate reagent, cells are lysed and digested, and the mixture is enriched for peptides tagged with the cross-linker. Following LC-MS/MS, data interpretation is performed to identify cross-linked peptides and build PPI networks of directly interacting proteins. Download figure Download PowerPoint Advances in high-throughput AP-MS methodologies have enabled the identification of 1,000s of protein complexes and PPIs in large-scale interaction networks, both in models of healthy and disease states. The largest assembly of such PPI networks is the BioPlex database, which has, to date, compiled over 56,533 interactions with 10,961 proteins in HEK293T cells (Huttlin et al, 2015, 2017). Publicly available data sets like these, including hu.MAP 2.0 (Drew et al, 2017; preprint: Drew et al, 2020), represent important resources for biomedical research efforts and have spurred a multitude of discoveries of molecular mechanisms underlying disease, some of which we discuss further below. A limitation of AP-MS is the need for milder lysis conditions than those typically employed in MS experiments. Membrane proteins can be hard to capture using this approach due to problems in protein extraction (Sastry et al, 2009; Pankow et al, 2016). Weaker or more transient interactions are also prone to loss during extraction or washing steps. Tandem affinity purification (TAP) tagging affixes two separate proteins or peptide tags to a fusion protein of interest (Rigaut et al, 1999), and using one tag that can endure harsher lysis or washing conditions (e.g., His-tag) can increase the recovery rate of proteins that are lost in regular AP-MS experiments (Puig et al, 2001). However, this comes at the disadvantage of more laborious sample preparation and purification, as well as potential artifacts due to the addition of large tags to the protein of interest. Irrespective of the number of tags employed, non-specific interactors that remain after washing can cause background issues, requiring careful selection of negative controls. Another limitation of AP-MS is the lysis-induced mixing of cellular compartments that do not normally interact, which can result in false positive PPI identifications. Possible solutions to deconvolute the effects of compartment mixing are currently being explored and will be discussed in the section New Methodology. It is possible that introducing a tag to the N- or C-terminus may disrupt normal protein function, making it advantageous to test tagging both termini. It is also important to note that AP-MS does not readily differentiate direct interactors from indirect interactors. On the other hand, AP-MS offers many advantages over earlier strategies for determining interactions (e.g. Y2H), including high sensitivity and the quantification of multiple interactors at the same time (non-binary). AP-MS also allows detecting post-translational modifications (PTMs) on interacting proteins (Matsuura et al, 2008). Following data generation, label-free quantification can provide an intensity value for a given protein. This quantitative information can be used to perform comparative analyses and can thus help determine whether an interaction is specific to the protein of interest. Proximity labeling Proximity labeling represents a complementary strategy to traditional AP-MS experiments (Han et al, 2018). In this case, proximal proteins are monitored by expressing in cells a bait protein of interest fused to a promiscuous labeling enzyme (Fig 3B). The addition of a small molecule substrate, such as biotin, allows the covalent tagging of endogenous proteins within a 10–20 nm range, capturing the protein's surrounding environment, including potential interactors. After cell lysis, proteins are denatured and solubilized, followed by selective enrichment of biotinylated proteins, commonly through streptavidin binding, and identification by MS. Because of the strong binding affinity between biotin and streptavidin, proximity labeling permits more efficient protein extraction, lysis methods and harsher washing conditions than AP-MS, allowing the identification of weak or transient interactions that might be lost with other methodologies. The procedure includes the use of detergents during lysis, as complexes are not required to remain intact during lysis and purification. Various proximity labeling methodologies have been established. BioID utilizes BirA, a biotin ligase with specific mutations rendering the enzyme promiscuous. BirA catalyzes the transformation of biotin to a more reactive form, and the resultant biotin cloud reacts with primary amines of proteins in its vicinity, resulting in their covalent biotinylation (Roux et al, 2018). Subcellular compartments that have been targeted by BioID include the nuclear envelope (Kim et al, 2016b), centrosome (Antonicka et al, 2020), nucleus (preprint: Go et al, 2019), cytoplasm (Redwine et al, 2017), Golgi apparatus (Liu et al, 2018), ER (Hoffman et al, 2019), endosome, lysosome, mitochondrial matrix (Antonicka et al, 2020), cell–cell junctions (Fredriksson et al, 2015), and flagella (Kelly et al, 2020), with labeling efficiency limited in the ER (Roux et al, 2018; preprint: Go et al, 2019). Due to slow reaction kinetics, BioID requires labeling for 18–24 h to produce sufficient material for identification by MS, which can lead to off-target labeling and high background, and somewhat restricts the type of experiments amenable to BioID. Additionally, due to its timescale, BioID experiments are limited to the generation of static interaction maps. An alternative to BioID, BioID2, was developed by introducing mutations to the biotin ligase of Aquifex aeolicus. This significantly smaller enzyme decreases the disruption to the fusion protein, allowing improved targeting and localization to subcellular compartments (Kim et al, 2016a). However, it still requires over 16 h of labeling. To improve labeling efficiency and speed, Branon et al (2018) performed directed evolution on BirA, which resulted in two faster-acting enzymatic variations: TurboID carrying 15 mutations and miniTurbo carrying 13 mutations and a deletion of the N-terminal domain. The high affinity of these enzymes for biotin allows comparable labeling to BioID in under ten minutes. Another class of proximity labels arose from modifications to peroxidases, enzymes responsible for catalyzing redox reactions. Horseradish peroxidase (HRP) is the best-studied peroxidase and has been employed for proximity labeling. However, it suffers from poor labeling efficiency in reducing environments (Trinkle-Mulcahy, 2019). Engineered ascorbic acid peroxidase (APEX) does not have this drawback, and can be genetically introduced as a tag on bait proteins of interest (Rhee et al, 2013; Hung et al, 2016). Following the timed addition of H2O2, APEX oxidizes phenol derivatives to biotin-phenoxyl radicals that covalently react with electron rich amino acids, providing biotin labeling kinetics on the order of minutes (Martell et al, 2012). The rapid labeling capabilities of APEX offer speed comparable to that of many biological processes and thus make this approach well-suited to investigate transient or dynamically changing protein interactions. APEX labeling can be performed in most subcellular environments, as it retains activity in reducing environments, including the cytosol (Martell et al, 2012). Nevertheless, the need for peroxide has been criticized due to its potentially harmful effect on cells and prevents APEX labeling in living organisms. Newer iterations of proximity labeling methodology seek to avoid potential toxicity issues while requiring short labeling times. The recently introduced, contact-specific SplitID divides the TurboID enzyme in separate, inactive fragments (Cho et al, 2020). These two fragments recombine when in close proximity, as with interacting proteins. This method is well suited for organelle contact sites, where each fragment is targeted to a specific organelle, and subsequently, biotinylation is restricted to their contact sites, eliminating off-target labeling. Similarly, the N- and C-terminal fragments of split APEX are inactive when separated, but when joined through molecular interactions promote peroxidase activity (Han et al, 2019). Experimental design should be carefully considered before undertaking a proximity labeling experiment. With all proximity labeling techniques, proteins neighboring the bait are captured throughout the experiment. Proteins that are not direct interactors but colocalize during the labeling period, simply due to diffusion through the enzymatic labeling region, can lead to high background, making it difficult to distinguish proteins that really reside in the immediate environment (Lobingier et al, 2017). The parallel analysis of the expressed ligase without an attached bait protein can help identify proteins not expected to be interactors. A protein's presence in this control sample can arise from natural interactions with the ligase (Roux et al, 2018) and proteins that attach to the streptavidin used for enrichment. Similar to AP-MS, it is possible that insertion of an enzyme at the N- or C- terminus may alter protein function. Prior to generating an enzyme-expressing stable cell line, enzymatic fusion on both the N- and C-termini of the protein of interest should be tested to ensure there is no disruption to normal localization (Sears et al, 2019). Another possibility is that proteins that are in proximity to the non-labeled terminus fall outside the labeling radius and will therefore not be detected. As such, separate experiments where the N-terminus and C-terminus are labeled may be advantageous. Cross-linking mass spectrometry (XL-MS) Although AP-MS can identify which proteins are within the same complex, it does not provide information on which members of the complex are actually in direct physical contact. XL-MS is an approach that can fill this gap (Fig 3C). It provides structural information by identifying proximat