Probabilistic assembly of human protein interaction networks from label-free quantitative proteomics. Proc Natl Acad Sci U S A. 2008;105:1454-1459. [PMID: 18218781 DOI: 10.1073/pnas.0706983105]

Simple and Effective Affinity Purification Procedures for Mass Spectrometry-Based Identification of Protein-Protein Interactions in Cell Signaling Pathways

Identification of protein-protein interactions can be a critical step in understanding the function and regulation of a particular protein and for exploring intracellular signaling cascades. Affinity purification coupled to mass spectrometry (APMS) is a powerful method for isolating and characterizing protein complexes. This approach involves the tagging and subsequent enrichment of a protein of interest along with any stably associated proteins that bind to it, followed by the identification of the interacting proteins using mass spectrometry. The protocol described here offers a quick and simple method for routine sample preparation for APMS analysis of suitably tagged human cell lines.

A Scalable Genome-Editing-Based Approach for Mapping Multiprotein Complexes in Human Cells

Conventional affinity purification followed by mass spectrometry (AP-MS) analysis is a broadly applicable method used to decipher molecular interaction networks and infer protein function. However, it is sensitive to perturbations induced by ectopically overexpressed target proteins and does not reflect multilevel physiological regulation in response to diverse stimuli. Here, we developed an interface between genome editing and proteomics to isolate native protein complexes produced from their natural genomic contexts. We used CRISPR/Cas9 and TAL effector nucleases (TALENs) to tag endogenous genes and purified several DNA repair and chromatin-modifying holoenzymes to near homogeneity. We uncovered subunits and interactions among well-characterized complexes and report the isolation of MCM8/9, highlighting the efficiency and robustness of the approach. These methods improve and simplify both small- and large-scale explorations of protein interactions as well as the study of biochemical activities and structure-function relationships.

Chromosome Missegregation Associated with RUVBL1 Deficiency

RUVBL1 (RuvB-like1) and RUVBL2 (RuvB-like 2) are integral components of multisubunit protein complexes involved in processes ranging from cellular metabolism, transcription and chromatin remodeling to DNA repair. Here, we show that although RUVBL1 and RUVBL2 are known to form heterodimeric complexes in which they stabilize each other, the subunits separate during cytokinesis. In anaphase-to-telophase transition, RUVBL1 localizes to structures of the mitotic spindle apparatus, where it partially co-localizes with polo-like kinase 1 (PLK1). The ability of PLK1 to phosphorylate RUVBL1-but not RUVBL2-in vitro and their physical association in vivo suggest that this kinase differentially regulates the function of the RuvB-like proteins during mitosis. We further show that siRNA-mediated knock-down of RuvB-like proteins causes severe defects in chromosome alignment and segregation. In addition, we show that the ATPase activity of RUVBL1 is indispensable for cell proliferation. Our data thus demonstrate that RUVBL1 is essential for efficient mitosis and proliferation.

Incorporating computational resources in a cancer research program

Recent technological advances have transformed cancer genetics research. These advances have served as the basis for the generation of a number of richly annotated datasets relevant to the cancer geneticist. In addition, many of these technologies are now within reach of smaller laboratories to answer specific biological questions. Thus, one of the most pressing issues facing an experimental cancer biology research program in genetics is incorporating data from multiple sources to annotate, visualize, and analyze the system under study. Fortunately, there are several computational resources to aid in this process. However, a significant effort is required to adapt a molecular biology-based research program to take advantage of these datasets. Here, we discuss the lessons learned in our laboratory and share several recommendations to make this transition effective. This article is not meant to be a comprehensive evaluation of all the available resources, but rather highlight those that we have incorporated into our laboratory and how to choose the most appropriate ones for your research program.

Quantitative proteomics reveals protein-protein interactions with fibroblast growth factor 12 as a component of the voltage-gated sodium channel 1.2 (nav1.2) macromolecular complex in Mammalian brain

Voltage-gated sodium channels (Nav1.1–Nav1.9) are responsible for the initiation and propagation of action potentials in neurons, controlling firing patterns, synaptic transmission and plasticity of the brain circuit. Yet, it is the protein–protein interactions of the macromolecular complex that exert diverse modulatory actions on the channel, dictating its ultimate functional outcome. Despite the fundamental role of Nav channels in the brain, information on its proteome is still lacking. Here we used affinity purification from crude membrane extracts of whole brain followed by quantitative high-resolution mass spectrometry to resolve the identity of Nav1.2 protein interactors. Of the identified putative protein interactors, fibroblast growth factor 12 (FGF12), a member of the nonsecreted intracellular FGF family, exhibited 30-fold enrichment in Nav1.2 purifications compared with other identified proteins. Using confocal microscopy, we visualized native FGF12 in the brain tissue and confirmed that FGF12 forms a complex with Nav1.2 channels at the axonal initial segment, the subcellular specialized domain of neurons required for action potential initiation. Co-immunoprecipitation studies in a heterologous expression system validate Nav1.2 and FGF12 as interactors, whereas patch-clamp electrophysiology reveals that FGF12 acts synergistically with CaMKII, a known kinase regulator of Nav channels, to modulate Nav1.2-encoded currents. In the presence of CaMKII inhibitors we found that FGF12 produces a bidirectional shift in the voltage-dependence of activation (more depolarized) and the steady-state inactivation (more hyperpolarized) of Nav1.2, increasing the channel availability. Although providing the first characterization of the Nav1.2 CNS proteome, we identify FGF12 as a new functionally relevant interactor. Our studies will provide invaluable information to parse out the molecular determinant underlying neuronal excitability and plasticity, and extending the relevance of iFGFs signaling in the normal and diseased brain.

Conserved abundance and topological features in chromatin-remodeling protein interaction networks

The study of conserved protein interaction networks seeks to better understand the evolution and regulation of protein interactions. Here, we present a quantitative proteomic analysis of 18 orthologous baits from three distinct chromatin-remodeling complexes in Saccharomyces cerevisiae and Homo sapiens. We demonstrate that abundance levels of orthologous proteins correlate strongly between the two organisms and both networks have highly similar topologies. We therefore used the protein abundances in one species to cross-predict missing protein abundance levels in the other species. Lastly, we identified a novel conserved low-abundance subnetwork further demonstrating the value of quantitative analysis of networks.

Affinity purification combined with mass spectrometry to identify herpes simplex virus protein-protein interactions

The identification and characterization of herpes simplex virus protein interaction complexes are fundamental to understanding the molecular mechanisms governing the replication and pathogenesis of the virus. Recent advances in affinity-based methods, mass spectrometry configurations, and bioinformatics tools have greatly increased the quantity and quality of protein-protein interaction datasets. In this chapter, detailed and reliable methods that can easily be implemented are presented for the identification of protein-protein interactions using cryogenic cell lysis, affinity purification, trypsin digestion, and mass spectrometry.

Spotlite: web application and augmented algorithms for predicting co-complexed proteins from affinity purification--mass spectrometry data

Protein-protein interactions defined by affinity purification and mass spectrometry (APMS) suffer from high false discovery rates. Consequently, lists of potential interactions must be pruned of contaminants before network construction and interpretation, historically an expensive, time-intensive, and error-prone task. In recent years, numerous computational methods were developed to identify genuine interactions from the hundreds of candidates. Here, comparative analysis of three popular algorithms, HGSCore, CompPASS, and SAINT, revealed complementarity in their classification accuracies, which is supported by their divergent scoring strategies. We improved each algorithm by an average area under a receiver operating characteristics curve increase of 16% by integrating a variety of indirect data known to correlate with established protein-protein interactions, including mRNA coexpression, gene ontologies, domain-domain binding affinities, and homologous protein interactions. Each APMS scoring approach was incorporated into a separate logistic regression model along with the indirect features; the resulting three classifiers demonstrate improved performance on five diverse APMS data sets. To facilitate APMS data scoring within the scientific community, we created Spotlite, a user-friendly and fast web application. Within Spotlite, data can be scored with the augmented classifiers, annotated, and visualized ( http://cancer.unc.edu/majorlab/software.php ). The utility of the Spotlite platform to reveal physical, functional, and disease-relevant characteristics within APMS data is established through a focused analysis of the KEAP1 E3 ubiquitin ligase.

From pathways to networks: connecting dots by establishing protein-protein interaction networks in signaling pathways using affinity purification and mass spectrometry

Signal transductions are the basis of biological activities in all living organisms. Studying the signaling pathways, especially under physiological conditions, has become one of the most important facets of modern biological research. During the last decade, MS has been used extensively in biological research and is proven to be effective in addressing important biological questions. Here, we review the current progress in the understanding of signaling networks using MS approaches. We will focus on studies of protein-protein interactions that use affinity purification followed by MS approach. We discuss obstacles to affinity purification, data processing, functional validation, and identification of transient interactions and provide potential solutions for pathway-specific proteomics analysis, which we hope one day will lead to a comprehensive understanding of signaling networks in humans.

Enhancing the functional content of eukaryotic protein interaction networks

Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS) to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over 100 GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the HC.cont measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks.

A mass spectrometry view of stable and transient protein interactions

Through an impressive range of dynamic interactions, proteins succeed to carry out the majority of functions in a cell. These temporally and spatially regulated interactions provide the means through which one single protein can perform diverse functions and modulate different cellular pathways. Understanding the identity and nature of these interactions is therefore critical for defining protein functions and their contribution to health and disease processes. Here, we provide an overview of workflows that incorporate immunoaffinity purifications and quantitative mass spectrometry (frequently abbreviated as IP-MS or AP-MS) for characterizing protein-protein interactions. We discuss experimental aspects that should be considered when optimizing the isolation of a protein complex. As the presence of nonspecific associations is a concern in these experiments, we discuss the common sources of nonspecific interactions and present label-free and metabolic labeling mass spectrometry-based methods that can help determine the specificity of interactions. The effective regulation of cellular pathways and the rapid reaction to various environmental stresses rely on the formation of stable, transient, and fast-exchanging protein-protein interactions. While determining the exact nature of an interaction remains challenging, we review cross-linking and metabolic labeling approaches that can help address this important aspect of characterizing protein interactions and macromolecular assemblies.

From proteomes to complexomes in the era of systems biology

Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex-complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery-potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.

Molecular interaction networks for the analysis of human disease: utility, limitations, and considerations

High-throughput '-omics' data can be combined with large-scale molecular interaction networks, for example, protein-protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative '-omics' methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and "network-type." The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high-throughput studies versus a meta-database of smaller literature-curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large-scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease.

The Impact of Mass Spectrometry-Based Proteomics on Fundamental Discoveries in Virology

In recent years, mass spectrometry has emerged as a core component of fundamental discoveries in virology. As a consequence of their coevolution, viruses and host cells have established complex, dynamic interactions that function either in promoting virus replication and dissemination or in host defense against invading pathogens. Thus, viral infection triggers an impressive range of proteome changes. Alterations in protein abundances, interactions, posttranslational modifications, subcellular localizations, and secretion are temporally regulated during the progression of an infection. Consequently, understanding viral infection at the molecular level requires versatile approaches that afford both breadth and depth of analysis. Mass spectrometry is uniquely positioned to bridge this experimental dichotomy. Its application to both unbiased systems analyses and targeted, hypothesis-driven studies has accelerated discoveries in viral pathogenesis and host defense. Here, we review the contributions of mass spectrometry-based proteomic approaches to understanding viral morphogenesis, replication, and assembly and to characterizing host responses to infection.

Discovering protein interactions and characterizing protein function using HaloTag technology

Research in proteomics has exploded in recent years with advances in mass spectrometry capabilities that have led to the characterization of numerous proteomes, including those from viruses, bacteria, and yeast.  In comparison, analysis of the human proteome lags behind, partially due to the sheer number of proteins which must be studied, but also the complexity of networks and interactions these present. To specifically address the challenges of understanding the human proteome, we have developed HaloTag technology for protein isolation, particularly strong for isolation of multiprotein complexes and allowing more efficient capture of weak or transient interactions and/or proteins in low abundance.  HaloTag is a genetically encoded protein fusion tag, designed for covalent, specific, and rapid immobilization or labelling of proteins with various ligands. Leveraging these properties, numerous applications for mammalian cells were developed to characterize protein function and here we present methodologies including: protein pull-downs used for discovery of novel interactions or functional assays, and cellular localization. We find significant advantages in the speed, specificity, and covalent capture of fusion proteins to surfaces for proteomic analysis as compared to other traditional non-covalent approaches. We demonstrate these and the broad utility of the technology using two important epigenetic proteins as examples, the human bromodomain protein BRD4, and histone deacetylase HDAC1.  These examples demonstrate the power of this technology in enabling  the discovery of novel interactions and characterizing cellular localization in eukaryotes, which will together further understanding of human functional proteomics.              

Pre- and post-processing workflow for affinity purification mass spectrometry data

The reliable detection of protein-protein interactions by affinity purification mass spectrometry (AP-MS) is crucial for the understanding of biological processes. Quantitative information can be used to separate truly interacting proteins from false-positives by contrasting counts of proteins binding to specific baits with counts of negative controls. Several approaches have been proposed for computing scores for potential interaction proteins, for example, the commonly used SAINT software. However, it remains a subjective decision where to set the cutoff score for candidate selection; furthermore, no precise control for the expected number of false-positives is provided. In related fields, successful data analysis strongly relies on statistical pre- and post-processing steps, which, so far, have played only a minor role in AP-MS data analysis. We introduce a complete workflow, embedding either the scoring method SAINT or alternatively a two-stage Poisson model into a pre- and post-processing framework. To this end, we investigate different normalization methods and apply a statistical filter adjusted to AP-MS data. Furthermore, we propose permutation and adjustment procedures, which allow the replacement of scores by statistical p values. The performance of the workflow is assessed on simulations as well as on a study focusing on interactions with the T3SS in Salmonella Typhimurium. Preprocessing methods significantly increase the number of detected truly interacting proteins, while a constant false-discovery rate is maintained. The software solution is freely available.

On-beads digestion in conjunction with data-dependent mass spectrometry: a shortcut to quantitative and dynamic interaction proteomics

With the advent of the "-omics" era, biological research has shifted from functionally analyzing single proteins to understanding how entire protein networks connect and adapt to environmental cues. Frequently, pathological processes are initiated by a malfunctioning protein network rather than a single protein. It is therefore crucial to investigate the regulation of proteins in the context of a pathway first and signaling network second. In this study, we demonstrate that a quantitative interaction proteomic approach, combining immunoprecipitation, in-solution digestion and label-free quantification mass spectrometry, provides data of high accuracy and depth. This protocol is applicable, both to tagged, exogenous and untagged, endogenous proteins. Furthermore, it is fast, reliable and, due to a label-free quantitation approach, allows the comparison of multiple conditions. We further show that we are able to generate data in a medium throughput fashion and that we can quantify dynamic interaction changes in signaling pathways in response to mitogenic stimuli, making our approach a suitable method to generate data for system biology approaches.

Controlling for gene expression changes in transcription factor protein networks

The development of affinity purification technologies combined with mass spectrometric analysis of purified protein mixtures has been used both to identify new protein-protein interactions and to define the subunit composition of protein complexes. Transcription factor protein interactions, however, have not been systematically analyzed using these approaches. Here, we investigated whether ectopic expression of an affinity tagged transcription factor as bait in affinity purification mass spectrometry experiments perturbs gene expression in cells, resulting in the false positive identification of bait-associated proteins when typical experimental controls are used. Using quantitative proteomics and RNA sequencing, we determined that the increase in the abundance of a set of proteins caused by overexpression of the transcription factor RelA is not sufficient for these proteins to then co-purify non-specifically and be misidentified as bait-associated proteins. Therefore, typical controls should be sufficient, and a number of different baits can be compared with a common set of controls. This is of practical interest when identifying bait interactors from a large number of different baits. As expected, we found several known RelA interactors enriched in our RelA purifications (NFκB1, NFκB2, Rel, RelB, IκBα, IκBβ, and IκBε). We also found several proteins not previously described in association with RelA, including the small mitochondrial chaperone Tim13. Using a variety of biochemical approaches, we further investigated the nature of the association between Tim13 and NFκB family transcription factors. This work therefore provides a conceptual and experimental framework for analyzing transcription factor protein interactions.

Interaction proteome of human Hippo signaling: modular control of the co-activator YAP1

Tissue homeostasis is controlled by signaling systems that coordinate cell proliferation, cell growth and cell shape upon changes in the cellular environment. Deregulation of these processes is associated with human cancer and can occur at multiple levels of the underlying signaling systems. To gain an integrated view on signaling modules controlling tissue growth, we analyzed the interaction proteome of the human Hippo pathway, an established growth regulatory signaling system. The resulting high-resolution network model of 480 protein-protein interactions among 270 network components suggests participation of Hippo pathway components in three distinct modules that all converge on the transcriptional co-activator YAP1. One of the modules corresponds to the canonical Hippo kinase cassette whereas the other two both contain Hippo components in complexes with cell polarity proteins. Quantitative proteomic data suggests that complex formation with cell polarity proteins is dynamic and depends on the integrity of cell-cell contacts. Collectively, our systematic analysis greatly enhances our insights into the biochemical landscape underlying human Hippo signaling and emphasizes multifaceted roles of cell polarity complexes in Hippo-mediated tissue growth control.

Reinvestigation of aminoacyl-tRNA synthetase core complex by affinity purification-mass spectrometry reveals TARSL2 as a potential member of the complex

Twenty different aminoacyl-tRNA synthetases (ARSs) link each amino acid to their cognate tRNAs. Individual ARSs are also associated with various non-canonical activities involved in neuronal diseases, cancer and autoimmune diseases. Among them, eight ARSs (D, EP, I, K, L, M, Q and RARS), together with three ARS-interacting multifunctional proteins (AIMPs), are currently known to assemble the multi-synthetase complex (MSC). However, the cellular function and global topology of MSC remain unclear. In order to understand the complex interaction within MSC, we conducted affinity purification-mass spectrometry (AP-MS) using each of AIMP1, AIMP2 and KARS as a bait protein. Mass spectrometric data were funneled into SAINT software to distinguish true interactions from background contaminants. A total of 40, 134, 101 proteins in each bait scored over 0.9 of SAINT probability in HEK 293T cells. Complex-forming ARSs, such as DARS, EPRS, IARS, Kars, LARS, MARS, QARS and RARS, were constantly found to interact with each bait. Variants such as, AIMP2-DX2 and AIMP1 isoform 2 were found with specific peptides in KARS precipitates. Relative enrichment analysis of the mass spectrometric data demonstrated that TARSL2 (threonyl-tRNA synthetase like-2) was highly enriched with the ARS-core complex. The interaction was further confirmed by coimmunoprecipitation of TARSL2 with other ARS core-complex components. We suggest TARSL2 as a new component of ARS core-complex.

Inferring protein-protein interaction complexes from immunoprecipitation data

Background

Protein–protein interactions in cells are widely explored using small–scale experiments. However, the search for protein complexes and their interactions in data from high throughput experiments such as immunoprecipitation is still a challenge. We present "4N", a novel method for detecting protein complexes in such data. Our method is a heuristic algorithm based on Near Neighbor Network (3N) clustering. It is written in R, it is faster than model-based methods, and has only a small number of tuning parameters. We explain the application of our new method to real immunoprecipitation results and two artificial datasets. We show that the method can infer protein complexes from protein immunoprecipitation datasets of different densities and sizes.

Findings

4N was applied on the immunoprecipitation dataset that was presented by the authors of the original 3N in Cell 145:787–799, 2011. The test with our method shows that it can reproduce the original clustering results with fewer manually adapted parameters and, in addition, gives direct insight into the complex–complex interactions. We also tested 4N on the human "Tip49a/b" dataset. We conclude that 4N can handle the contaminants and can correctly infer complexes from this very dense dataset. Further tests were performed on two artificial datasets of different sizes. We proved that the method predicts the reference complexes in the two artificial datasets with high accuracy, even when the number of samples is reduced.

Conclusions

4N has been implemented in R. We provide the sourcecode of 4N and a user-friendly toolbox including two example calculations. Biologists can use this 4N-toolbox even if they have a limited knowledge of R. There are only a few tuning parameters to set, and each of these parameters has a biological interpretation. The run times for medium scale datasets are in the order of minutes on a standard desktop PC. Large datasets can typically be analyzed within a few hours.

PPIRank - an advanced method for ranking protein-protein interations in TAP/MS data

Background

Tandem affinity purification coupled with mass-spectrometry (TAP/MS) analysis is a popular method for the identification of novel endogenous protein-protein interactions (PPIs) in large-scale. Computational analysis of TAP/MS data is a critical step, particularly for high-throughput datasets, yet it remains challenging due to the noisy nature of TAP/MS data.

Results

We investigated several major TAP/MS data analysis methods for identifying PPIs, and developed an advanced method, which incorporates an improved statistical method to filter out false positives from the negative controls. Our method is named PPIRank that stands for PPI ranking in TAP/MS data. We compared PPIRank with several other existing methods in analyzing two pathway-specific TAP/MS PPI datasets from Drosophila.

Conclusion

Experimental results show that PPIRank is more capable than other approaches in terms of identifying known interactions collected in the BioGRID PPI database. Specifically, PPIRank is able to capture more true interactions and simultaneously less false positives in both Insulin and Hippo pathways of Drosophila Melanogaster.

A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis

Background

Comprehensive protein-protein interaction (PPI) maps are a powerful resource for uncovering the molecular basis of genetic interactions and providing mechanistic insights. Over the past decade, high-throughput experimental techniques have been developed to generate PPI maps at proteome scale, first using yeast two-hybrid approaches and more recently via affinity purification combined with mass spectrometry (AP-MS). Unfortunately, data from both protocols are prone to both high false positive and false negative rates. To address these issues, many methods have been developed to post-process raw PPI data. However, with few exceptions, these methods only analyze binary experimental data (in which each potential interaction tested is deemed either observed or unobserved), neglecting quantitative information available from AP-MS such as spectral counts.

Results

We propose a novel method for incorporating quantitative information from AP-MS data into existing PPI inference methods that analyze binary interaction data. Our approach introduces a probabilistic framework that models the statistical noise inherent in observations of co-purifications. Using a sampling-based approach, we model the uncertainty of interactions with low spectral counts by generating an ensemble of possible alternative experimental outcomes. We then apply the existing method of choice to each alternative outcome and aggregate results over the ensemble. We validate our approach on three recent AP-MS data sets and demonstrate performance comparable to or better than state-of-the-art methods. Additionally, we provide an in-depth discussion comparing the theoretical bases of existing approaches and identify common aspects that may be key to their performance.

Conclusions

Our sampling framework extends the existing body of work on PPI analysis using binary interaction data to apply to the richer quantitative data now commonly available through AP-MS assays. This framework is quite general, and many enhancements are likely possible. Fruitful future directions may include investigating more sophisticated schemes for converting spectral counts to probabilities and applying the framework to direct protein complex prediction methods.

A Gro/TLE-NuRD corepressor complex facilitates Tbx20-dependent transcriptional repression

The cardiac transcription factor Tbx20 has a critical role in the proper morphogenetic development of the vertebrate heart, and its misregulation has been implicated in human congenital heart disease. Although it is established that Tbx20 exerts its function in the embryonic heart through positive and negative regulation of distinct gene programs, it is unclear how Tbx20 mediates proper transcriptional regulation of its target genes. Here, using a combinatorial proteomic and bioinformatic approach, we present the first characterization of Tbx20 transcriptional protein complexes. We have systematically investigated Tbx20 protein-protein interactions by immunoaffinity purification of tagged Tbx20 followed by proteomic analysis using GeLC-MS/MS, gene ontology classification, and functional network analysis. We demonstrate that Tbx20 is associated with a chromatin remodeling network composed of TLE/Groucho corepressors, members of the Nucleosome Remodeling and Deacetylase (NuRD) complex, the chromatin remodeling ATPases RUVBL1/RUVBL2, and the T-box repressor Tbx18. We determined that the interaction with TLE corepressors is mediated via an eh1 binding motif in Tbx20. Moreover, we demonstrated that ablation of this motif results in a failure to properly assemble the repression network and disrupts Tbx20 function in vivo. Importantly, we validated Tbx20-TLE interactions in the mouse embryonic heart, and identified developmental genes regulated by Tbx20-TLE binding, thereby confirming a primary role for a Tbx20-TLE repressor complex in embryonic heart development. Together, these studies suggest a model in which Tbx20 associates with a Gro/TLE-NuRD repressor complex to prevent inappropriate gene activation within the forming heart.

Topology of molecular interaction networks

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further.

Individuality Normalization when Labeling with Isotopic Glycan Hydrazide Tags (INLIGHT): a novel glycan-relative quantification strategy

The Individuality Normalization when Labeling with Isotopic Glycan Hydrazide Tags (INLIGHT) strategy for the sample preparation, data analysis, and relative quantification of N-linked glycans is presented. Glycans are derivatized with either natural (L) or stable-isotope labeled (H) hydrazide reagents and analyzed using reversed phase liquid chromatography coupled online to a Q Exactive mass spectrometer. A simple glycan ladder, maltodextrin, is first used to demonstrate the relative quantification strategy in samples with negligible analytical and biological variability. It is shown that after a molecular weight correction attributable to isotopic overlap and a post-acquisition normalization of the data to account for any systematic bias, a plot of the experimental H:L ratio versus the calculated H:L ratio exhibits a correlation of unity for maltodextrin samples mixed in different ratios. We also demonstrate that the INLIGHT approach can quantify species over four orders of magnitude in ion abundance. The INLIGHT strategy is further demonstrated in pooled human plasma, where it is shown that the post-acquisition normalization is more effective than using a single spiked-in internal standard. Finally, changes in glycosylation are able to be detected in complex biological matrices, when spiked with a glycoprotein. The ability to spike in a glycoprotein and detect change at the glycan level validates both the sample preparation and data analysis strategy, making INLIGHT an invaluable relative quantification strategy for the field of glycomics.

The CRAPome: a contaminant repository for affinity purification-mass spectrometry data

Affinity purification coupled with mass spectrometry (AP-MS) is a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (for example, proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. The standard approach is to identify nonspecific interactions using one or more negative-control purifications, but many small-scale AP-MS studies do not capture a complete, accurate background protein set when available controls are limited. Fortunately, negative controls are largely bait independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the contaminant repository for affinity purification (the CRAPome) and describe its use for scoring protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely accessible at http://www.crapome.org/.

Nuclear import of RNA polymerase II is coupled with nucleocytoplasmic shuttling of the RNA polymerase II-associated protein 2

The RNA polymerase II (RNAP II)-associated protein (RPAP) 2 has been discovered through its association with various subunits of RNAP II in affinity purification coupled with mass spectrometry experiments. Here, we show that RPAP2 is a mainly cytoplasmic protein that shuttles between the cytoplasm and the nucleus. RPAP2 shuttling is tightly coupled with nuclear import of RNAP II, as RPAP2 silencing provokes abnormal accumulation of RNAP II in the cytoplasmic space. Most notably, RPAP4/GPN1 silencing provokes the retention of RPAP2 in the nucleus. Our results support a model in which RPAP2 enters the nucleus in association with RNAP II and returns to the cytoplasm in association with the GTPase GPN1/RPAP4. Although binding of RNAP II to RPAP2 is mediated by an N-terminal domain (amino acids 1–170) that contains a nuclear retention domain, and binding of RPAP4/GPN1 to RPAP2 occurs through a C-terminal domain (amino acids 156–612) that has a dominant cytoplasmic localization domain. In conjunction with previously published data, our results have important implications, as they indicate that RPAP2 controls gene expression by two distinct mechanisms, one that targets RNAP II activity during transcription and the other that controls availability of RNAP II in the nucleus.

Analysis of URI nuclear interaction with RPB5 and components of the R2TP/prefoldin-like complex

Unconventional prefoldin RPB5 Interactor (URI) was identified as a transcriptional repressor that binds RNA polymerase II (pol II) through interaction with the RPB5/POLR2E subunit. Despite the fact that many other proteins involved in transcription regulation have been shown to interact with URI, its nuclear function still remains elusive. Previous mass spectrometry analyses reported that URI is part of a novel protein complex called R2TP/prefoldin-like complex responsible for the cytoplasmic assembly of RNA polymerase II. We performed a mass spectrometry (MS)-based proteomic analysis to identify nuclear proteins interacting with URI in prostate cells. We identified all the components of the R2TP/prefoldin-like complex as nuclear URI interactors and we showed that URI binds and regulates RPB5 protein stability and transcription. Moreover, we validated the interaction of URI to the P53 and DNA damage-Regulated Gene 1 (PDRG1) and show that PDRG1 protein is also stabilized by URI binding. We present data demonstrating that URI nuclear/cytoplasmic shuttling is affected by compounds that stall pol II on the DNA (α-amanitin and actinomycin-D) and by leptomycin B, an inhibitor of the CRM1 exportin that mediates the nuclear export of pol II subunits. These data suggest that URI, and probably the entire R2TP/prefoldin-like complex is exported from the nucleus through CRM1. Finally we identified putative URI sites of phosphorylation and acetylation and confirmed URI sites of post-transcriptional modification identified in previous large-scale analyses the importance of which is largely unknown. However URI post-transcriptional modification was shown to be essential for URI function and therefore characterization of novel sites of URI modification will be important to the understanding of URI function.

The protein interaction landscape of the human CMGC kinase group

Cellular information processing via reversible protein phosphorylation requires tight control of the localization, activity, and substrate specificity of protein kinases, which to a large extent is accomplished by complex formation with other proteins. Despite their critical role in cellular regulation and pathogenesis, protein interaction information is available for only a subset of the 518 human protein kinases. Here we present a global proteomic analysis of complexes of the human CMGC kinase group. In addition to subgroup-specific functional enrichment and modularity, the identified 652 high-confidence kinase-protein interactions provide a specific biochemical context for many poorly studied CMGC kinases. Furthermore, the analysis revealed a kinase-kinase subnetwork and candidate substrates for CMGC kinases. Finally, the presented interaction proteome uncovered a large set of interactions with proteins genetically linked to a range of human diseases, including cancer, suggesting additional routes for analyzing the role of CMGC kinases in controlling human disease pathways.

Proteomic analysis of ubiquitin ligase KEAP1 reveals associated proteins that inhibit NRF2 ubiquitination

Somatic mutations in the KEAP1 ubiquitin ligase or its substrate NRF2 (NFE2L2) commonly occur in human cancer, resulting in constitutive NRF2-mediated transcription of cytoprotective genes. However, many tumors display high NRF2 activity in the absence of mutation, supporting the hypothesis that alternative mechanisms of pathway activation exist. Previously, we and others discovered that via a competitive binding mechanism, the proteins WTX (AMER1), PALB2, and SQSTM1 bind KEAP1 to activate NRF2. Proteomic analysis of the KEAP1 protein interaction network revealed a significant enrichment of associated proteins containing an ETGE amino acid motif, which matches the KEAP1 interaction motif found in NRF2. Like WTX, PALB2, and SQSTM1, we found that the dipeptidyl peptidase 3 (DPP3) protein binds KEAP1 via an "ETGE" motif to displace NRF2, thus inhibiting NRF2 ubiquitination and driving NRF2-dependent transcription. Comparing the spectrum of KEAP1-interacting proteins with the genomic profile of 178 squamous cell lung carcinomas characterized by The Cancer Genome Atlas revealed amplification and mRNA overexpression of the DPP3 gene in tumors with high NRF2 activity but lacking NRF2 stabilizing mutations. We further show that tumor-derived mutations in KEAP1 are hypomorphic with respect to NRF2 inhibition and that DPP3 overexpression in the presence of these mutants further promotes NRF2 activation. Collectively, our findings further support the competition model of NRF2 activation and suggest that "ETGE"-containing proteins such as DPP3 contribute to NRF2 activity in cancer.

Genotype-phenotype relationships in light of a modular protein interaction landscape

Recent progress in genomic sequencing has revealed genotype-phenotype information of enormous complexity and challenges earlier hypotheses on how phenotypes emerge from altered gene structures. The field of proteomics has advanced in parallel and offers promising new concepts for a modern interpretation of complex and nonlinear genotype-phenotype relationships. We are beginning to decipher global proteome organization with increasing throughput and accuracy. These efforts revealed a highly modular organization of the protein landscape. Here we discuss the challenges and implications emerging from a modular protein landscape for a better understanding of complex genotype-phenotype patterns.

Quantitative proteomics demonstrates that the RNA polymerase II subunits Rpb4 and Rpb7 dissociate during transcriptional elongation

Eukaryotic RNA polymerase II (RNAPII) is a 12-subunit enzyme that is responsible for the transcription of messenger RNA. Two of the subunits of RNA polymerase II, Rpb4 and Rpb7, have been shown to dissociate from the enzyme under a number of specific laboratory conditions. However, a biological context for the dissociation of Rpb4 and Rpb7 has not been identified. We have found that Rpb4/7 dissociate from RNAPII upon interaction with specific transcriptional elongation-associated proteins that are recruited to the hyperphosphorylated form of the C-terminal domain. However, the dissociation of Rpb4/7 is likely short lived because a significant level of free Rpb4/7 was not detected by quantitative proteomic analyses. In addition, we have found that RNAPII that is isolated through Rpb7 is depleted in serine 2 C-terminal domain phosphorylation. In contrast to previous reports, these data indicate that Rpb4/7 are dispensable during specific stages of transcriptional elongation in Saccharomyces cerevisiae.

Proteomic approaches to the study of papillomavirus-host interactions

The identification of interactions between viral and host cellular proteins has provided major insights into papillomavirus research, and these interactions are especially relevant to the role of papillomaviruses in the cancers with which they are associated. Recent advances in mass spectrometry technology and data processing now allow the systematic identification of such interactions. This has led to an improved understanding of the different pathologies associated with the many papillomavirus types, and the diverse nature of these viruses is reflected in the spectrum of interactions with host proteins. Here we review a history of proteomic approaches, particularly as applied to the papillomaviruses, and summarize current techniques. Current proteomic studies on the papillomaviruses use yeast-two-hybrid or affinity purification-mass spectrometry approaches. We detail the advantages and disadvantages of each and describe current examples of papillomavirus proteomic studies, with a particular focus on the HPV E6 and E7 oncoproteins.

Label-free quantitative shotgun proteomics using normalized spectral abundance factors

In this chapter we describe the workflow used in our laboratory for label-free quantitative shotgun proteomics based on spectral counting. The main tools used are a series of R modules known collectively as the Scrappy program. We describe how to go from peptide to spectrum matching in a shotgun proteomics experiment using the XTandem algorithm, to simultaneous quantification of up to thousands of proteins, using normalized spectral abundance factors. The outputs of the software are described in detail, with illustrative examples provided for some of the graphical images generated. While it is not strictly within the scope of this chapter, some consideration is given to how best to extract meaningful biological information from quantitative shotgun proteomics data outputs.

The application of quantification techniques in proteomics for biomedical research

The systematic analysis of biological processes requires an understanding of the quantitative expression patterns of proteins, their interacting partners and their subcellular localization. This information was formerly difficult to accrue as the relative quantification of proteins relied on antibody-based methods and other approaches with low throughput. The advent of soft ionization techniques in mass spectrometry plus advances in separation technologies has aligned protein systems biology with messenger RNA, DNA, and microarray technologies to provide data on systems as opposed to singular protein entities. Another aspect of quantitative proteomics that increases its importance for the coming few years is the significant technical developments underway both for high pressure liquid chromatography and mass spectrum devices. Hence, robustness, reproducibility and mass accuracy are still improving with every new generation of instruments. Nonetheless, the methods employed require validation and comparison to design fit for purpose experiments in advanced protein analyses. This review considers the newly developed systematic protein investigation methods and their value from the standpoint that relative or absolute protein quantification is required de rigueur in biomedical research.

Analyzing protein-protein interactions from affinity purification-mass spectrometry data with SAINT

Significance Analysis of INTeractome (SAINT) is a software package for scoring protein-protein interactions based on label-free quantitative proteomics data (e.g., spectral count or intensity) in affinity purification-mass spectrometry (AP-MS) experiments. SAINT allows bench scientists to select bona fide interactions and remove nonspecific interactions in an unbiased manner. However, there is no 'one-size-fits-all' statistical model for every dataset, since the experimental design varies across studies. Key variables include the number of baits, the number of biological replicates per bait, and control purifications. Here we give a detailed account of input data format, control data, selection of high-confidence interactions, and visualization of filtered data. We explain additional options for customizing the statistical model for optimal filtering in specific datasets. We also discuss a graphical user interface of SAINT in connection to the LIMS system ProHits, which can be installed as a virtual machine on Mac OS X or Windows computers.

Discovery of cell compartment specific protein-protein interactions using affinity purification combined with tandem mass spectrometry

Affinity purification combined with tandem mass spectrometry (AP-MS/MS) is a well-established method used to discover interaction partners for a given protein of interest. Because most AP-MS/MS approaches are performed using the soluble fraction of whole cell extracts (WCE), information about the cellular compartments where the interactions occur is lost. More importantly, classical AP-MS/MS often fails to identify interactions that take place in the nonsoluble fraction of the cell, for example, on the chromatin or membranes; consequently, protein complexes that are less soluble are underrepresented. In this paper, we introduce a method called multiple cell compartment AP-MS/MS (MCC-AP-MS/MS), which identifies the interactions of a protein independently in three fractions of the cell: the cytoplasm, the nucleoplasm, and the chromatin. We show that this fractionation improves the sensitivity of the method when compared to the classical affinity purification procedure using soluble WCE while keeping a very high specificity. Using three proteins known to localize in various cell compartments as baits, the CDK9 subunit of transcription elongation factor P-TEFb, the RNA polymerase II (RNAP II)-associated protein 4 (RPAP4), and the largest subunit of RNAP II, POLR2A, we show that MCC-AP-MS/MS reproducibly yields fraction-specific interactions. Finally, we demonstrate that this improvement in sensitivity leads to the discovery of novel interactions of RNAP II carboxyl-terminal domain (CTD) interacting domain (CID) proteins with POLR2A.

Label-free quantitative proteomics trends for protein-protein interactions

Understanding protein interactions within the complexity of a living cell is challenging, but techniques coupling affinity purification and mass spectrometry have enabled important progress to be made in the past 15 years. As identification of protein-protein interactions is becoming easier, the quantification of the interaction dynamics is the next frontier. Several quantitative mass spectrometric approaches have been developed to address this issue that vary in their strengths and weaknesses. While isotopic labeling approaches continue to contribute to the identification of regulated interactions, techniques that do not require labeling are becoming increasingly used in the field. Here, we describe the major types of label-free quantification used in interaction proteomics, and discuss the relative merits of data dependent and data independent acquisition approaches in label-free quantification. This article is part of a Special Issue entitled: From protein structures to clinical applications.

A census of human soluble protein complexes

Cellular processes often depend on stable physical associations between proteins. Despite recent progress, knowledge of the composition of human protein complexes remains limited. To close this gap, we applied an integrative global proteomic profiling approach, based on chromatographic separation of cultured human cell extracts into more than one thousand biochemical fractions that were subsequently analyzed by quantitative tandem mass spectrometry, to systematically identify a network of 13,993 high-confidence physical interactions among 3,006 stably associated soluble human proteins. Most of the 622 putative protein complexes we report are linked to core biological processes and encompass both candidate disease genes and unannotated proteins to inform on mechanism. Strikingly, whereas larger multiprotein assemblies tend to be more extensively annotated and evolutionarily conserved, human protein complexes with five or fewer subunits are far more likely to be functionally unannotated or restricted to vertebrates, suggesting more recent functional innovations.