An atlas of protein-protein interactions across mouse tissues.

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the interactome has therefore been a central objective of high-throughput biology. However, the dynamics of protein interactions across physiological contexts remain poorly understood. Here, we develop a quantitative proteomic approach combining protein correlation profiling with stable isotope labeling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide a proteome-scale survey of interactome rewiring across mammalian tissues, revealing more than 125,000 unique interactions at a quality comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewired proteins are tightly regulated by multiple cellular mechanisms and are implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

Global Interactome Mapping Reveals Pro-tumorigenic Interactions of NF-κB in Breast Cancer.

NF-κB pathway is involved in inflammation; however, recent data shows its role also in cancer development and progression, including metastasis. To understand the role of NF-κB interactome dynamics in cancer, we study the complexity of breast cancer interactome in luminal A breast cancer model and its rearrangement associated with NF-κB modulation. Liquid chromatography-mass spectrometry measurement of 160 size-exclusion chromatography fractions identifies 5460 protein groups. Seven thousand five hundred sixty eight interactions among these proteins have been reconstructed by PrInCE algorithm, of which 2564 have been validated in independent datasets. NF-κB modulation leads to rearrangement of protein complexes involved in NF-κB signaling and immune response, cell cycle regulation, and DNA replication. Central NF-κB transcription regulator RELA co-elutes with interactors of NF-κB activator PRMT5, and these complexes are confirmed by AlphaPulldown prediction. A complementary immunoprecipitation experiment recapitulates RELA interactions with other NF-κB factors, associating NF-κB inhibition with lower binding of NF-κB activators to RELA. This study describes a network of pro-tumorigenic protein interactions and their rearrangement upon NF-κB inhibition with potential therapeutic implications in tumors with high NF-κB activity.

TMEM67, TMEM237, and Embigin in Complex With Monocarboxylate Transporter MCT1 Are Unique Components of the Photoreceptor Outer Segment Plasma Membrane.

The outer segment (OS) organelle of vertebrate photoreceptors is a highly specialized cilium evolved to capture light and initiate light response. The plasma membrane which envelopes the OS plays vital and diverse roles in supporting photoreceptor function and health. However, little is known about the identity of its protein constituents, as this membrane cannot be purified to homogeneity. In this study, we used the technique of protein correlation profiling to identify unique OS plasma membrane proteins. To achieve this, we used label-free quantitative MS to compare relative protein abundances in an enriched preparation of the OS plasma membrane with a preparation of total OS membranes. We have found that only five proteins were enriched at the same level as previously validated OS plasma membrane markers. Two of these proteins, TMEM67 and TMEM237, had not been previously assigned to this membrane, and one, embigin, had not been identified in photoreceptors. We further showed that embigin associates with monocarboxylate transporter MCT1 in the OS plasma membrane, facilitating lactate transport through this cellular compartment.

Analytical Guidelines for co-fractionation Mass Spectrometry Obtained through Global Profiling of Gold Standard Saccharomyces cerevisiae Protein Complexes.

Co-fractionation MS (CF-MS) is a technique with potential to characterize endogenous and unmanipulated protein complexes on an unprecedented scale. However this potential has been offset by a lack of guidelines for best-practice CF-MS data collection and analysis. To obtain such guidelines, this study thoroughly evaluates novel and published Saccharomyces cerevisiae CF-MS data sets using very high proteome coverage libraries of yeast gold standard complexes. A new method for identifying gold standard complexes in CF-MS data, Reference Complex Profiling, and the Extending 'Guilt-by-Association' by Degree (EGAD) R package are used for these evaluations, which are verified with concurrent analyses of published human data. By evaluating data collection designs, which involve fractionation of cell lysates, it is found that near-maximum recall of complexes can be achieved with fewer samples than published studies. Distributing sample collection across orthogonal fractionation methods, rather than a single high resolution data set, leads to particularly efficient recall. By evaluating 17 different similarity scoring metrics, which are central to CF-MS data analysis, it is found that two metrics rarely used in past CF-MS studies - Spearman and Kendall correlations - and the recently introduced Co-apex metric frequently maximize recall, whereas a popular metric-Euclidean distance-delivers poor recall. The common practice of integrating external genomic data into CF-MS data analysis is also evaluated, revealing that this practice may improve the precision and recall of known complexes but is generally unsuitable for predicting novel complexes in model organisms. If studying nonmodel organisms using orthologous genomic data, it is found that particular subsets of fractionation profiles (e.g. the lowest abundance quartile) should be excluded to minimize false discovery. These assessments are summarized in a series of universally applicable guidelines for precise, sensitive and efficient CF-MS studies of known complexes, and effective predictions of novel complexes for orthogonal experimental validation.

Next-generation Interactomics: Considerations for the Use of Co-elution to Measure Protein Interaction Networks.

Understanding how proteins interact is crucial to understanding cellular processes. Among the available interactome mapping methods, co-elution stands out as a method that is simultaneous in nature and capable of identifying interactions between all the proteins detected in a sample. The general workflow in co-elution methods involves the mild extraction of protein complexes and their separation into several fractions, across which proteins bound together in the same complex will show similar co-elution profiles when analyzed appropriately. In this review we discuss the different separation, quantification and bioinformatic strategies used in co-elution studies, and the important considerations in designing these studies. The benefits of co-elution versus other methods makes it a valuable starting point when asking questions that involve the perturbation of the interactome.

A Label-free Mass Spectrometry Method to Predict Endogenous Protein Complex Composition.

Information on the composition of protein complexes can accelerate mechanistic analyses of cellular systems. Protein complex composition identifies genes that function together and provides clues about regulation within and between cellular pathways. Cytosolic protein complexes control metabolic flux, signal transduction, protein abundance, and the activities of cytoskeletal and endomembrane systems. It has been estimated that one third of all cytosolic proteins in leaves exist in an oligomeric state, yet the composition of nearly all remain unknown. Subunits of stable protein complexes copurify, and combinations of mass-spectrometry-based protein correlation profiling and bioinformatic analyses have been used to predict protein complex subunits. Because of uncertainty regarding the power or availability of bioinformatic data to inform protein complex predictions across diverse species, it would be highly advantageous to predict composition based on elution profile data alone. Here we describe a mass spectrometry-based protein correlation profiling approach to predict the composition of hundreds of protein complexes based on biochemical data. Extracts were obtained from an intact organ and separated in parallel by size and charge under nondenaturing conditions. More than 1000 proteins with reproducible elution profiles across all replicates were subjected to clustering analyses. The resulting dendrograms were used to predict the composition of known and novel protein complexes, including many that are likely to assemble through self-interaction. An array of validation experiments demonstrated that this new method can drive protein complex discovery, guide hypothesis testing, and enable systems-level analyses of protein complex dynamics in any organism with a sequenced genome.

The Origins of Organellar Mapping by Protein Correlation Profiling.

Cells have a rich inner structure that is commonly explored by microscopy. Classical biochemical methods that break apart the cells and fractionate them along a gradient have now gotten a new lease on life through modern methods of mass spectrometry-based proteomics. Their common principle is to comprehensively measure all the proteins in each of the fractions. The resulting quantitative profile then associates thousands of proteins to their cellular homes. Here, the author recounts how protein correlation profiling, the first such technique, was conceived and how it was applied to answer intricate cell biological questions.

Interactome disassembly during apoptosis occurs independent of caspase cleavage.

Protein-protein interaction networks (interactomes) define the functionality of all biological systems. In apoptosis, proteolysis by caspases is thought to initiate disassembly of protein complexes and cell death. Here we used a quantitative proteomics approach, protein correlation profiling (PCP), to explore changes in cytoplasmic and mitochondrial interactomes in response to apoptosis initiation as a function of caspase activity. We measured the response to initiation of Fas-mediated apoptosis in 17,991 interactions among 2,779 proteins, comprising the largest dynamic interactome to date. The majority of interactions were unaffected early in apoptosis, but multiple complexes containing known caspase targets were disassembled. Nonetheless, proteome-wide analysis of proteolytic processing by terminal amine isotopic labeling of substrates (TAILS) revealed little correlation between proteolytic and interactome changes. Our findings show that, in apoptosis, significant interactome alterations occur before and independently of caspase activity. Thus, apoptosis initiation includes a tight program of interactome rearrangement, leading to disassembly of relatively few, select complexes. These early interactome alterations occur independently of cleavage of these protein by caspases.

A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE).

BACKGROUND: An organism's protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome. RESULTS: Here we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, more predicted interactions at the same stringency, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2017a). Source code and standalone executable programs for Windows and Mac OSX are available at https://github.com/fosterlab/PrInCE , where usage instructions can be found. An example dataset and output are also provided for testing purposes. CONCLUSIONS: PrInCE is the first fast and easy-to-use data analysis pipeline that predicts interactomes and protein complexes from co-elution data. PrInCE allows researchers without bioinformatics expertise to analyze high-throughput co-elution datasets.

Global profiling of protein complexes: current approaches and their perspective in biomedical research.

INTRODUCTION: Despite the rapid evolution of proteomic methods, protein interactions and their participation in protein complexes - an important aspect of their function - has rarely been investigated on the proteome-wide level. Disease states, such as muscular dystrophy or viral infection, are induced by interference in protein-protein interactions within complexes. The purpose of this review is to describe the current methods for global complexome analysis and to critically discuss the challenges and opportunities for the application of these methods in biomedical research. Areas covered: We discuss advancements in experimental techniques and computational tools that facilitate profiling of the complexome. The main focus is on the separation of native protein complexes via size exclusion chromatography and gel electrophoresis, which has recently been combined with quantitative mass spectrometry, for a global protein-complex profiling. The development of this approach has been supported by advanced bioinformatics strategies and fast and sensitive mass spectrometers that have allowed the analysis of whole cell lysates. The application of this technique to biomedical research is assessed, and future directions are anticipated. Expert commentary: The methodology is quite new, and has already shown great potential when combined with complementary methods for detection of protein complexes.

Phosphoproteomics and Organelle Proteomics in Pancreatic Islets.

Over the recent years, mass spectrometry (MS)-based proteomics has undergone dramatic advances in sample preparation, instrumentation, and computational methods. Here, we describe in detail, how a workflow quantifies global protein phosphorylation in pancreatic islets and characterizes intracellular organelle composition on protein level by MS-based proteomics.

Dynamic rewiring of the human interactome by interferon signaling.

BACKGROUND: The type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes. Comprehensive catalogs of IFN-stimulated genes have been established across species and cell types by transcriptomic and biochemical approaches, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to describe the effects of IFN signaling on the human proteome, and apply protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network. RESULTS: We identify > 26,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer IFN-stimulated gene protein synthesis. CONCLUSIONS: Our map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing IFN-stimulated genes in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.

SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles.

Protein-protein interactions (PPIs) play critical functional and regulatory roles in cellular processes. They are essential for macromolecular complex formation, which in turn constitutes the basis for protein interaction networks that determine the functional state of a cell. We and others have previously shown that chromatographic fractionation of native protein complexes in combination with bottom-up mass spectrometric analysis of consecutive fractions supports the multiplexed characterization and detection of state-specific changes of protein complexes. In this study, we extend co-fractionation and mass spectrometric data analysis to perform quantitative, network-based studies of proteome organization, via the size-exclusion chromatography algorithmic toolkit (SECAT). This framework explicitly accounts for the dynamic nature and rewiring of protein complexes across multiple cell states and samples, thus, elucidating molecular mechanisms that are differentially implemented across different experimental settings. Systematic analysis of multiple datasets shows that SECAT represents a highly scalable and effective methodology to assess condition/state-specific protein-network state. A record of this paper's transparent peer review process is included in the Supplemental Information.

Proteomic analysis of insulin secretory granules in INS-1 cells by protein correlation profiling.

ABSTRACT: Insulin secretory granules (ISGs), a group of distinguishing organelles in pancreatic ß cells, are responsible for the storage and secretion of insulin to maintain blood glucose homeostasis. The molecular mechanisms of ISG biogenesis, maturation, transportation, and exocytosis are still largely unknown because the proteins involved in these distinct steps have not been fully identified. Subcellular fractionation by density gradient centrifugation has been successfully employed to analyze the proteomes of numerous organelles. However, use of this method to elucidate the ISG proteome is limited by co-fractionated contaminants because ISGs are very dynamic and have abundant exchanges or contacts with other organelles, such as the Golgi apparatus, lysosomes, and endosomes. In this study, we developed a new strategy for identifying ISG proteins by protein correlation profiling (PCP)-based proteomics, which included ISG purification by OptiPrep density gradient centrifugation, label-free quantitative proteome, and identification of ISG proteins by correlating fractionation profiles between candidates and known ISG markers. Using this approach, we were able to identify 81 ISG proteins. Among them, TM9SF3, a nine-transmembrane protein, was considered a high confidence ISG candidate protein highlighted in the PCP network. Further biochemical and immunofluorescence assays indicated that TM9SF3 localized in ISGs, suggesting that it is a potential new ISG marker.

Recent advances in large-scale protein interactome mapping.

Protein-protein interactions (PPIs) underlie most, if not all, cellular functions. The comprehensive mapping of these complex networks of stable and transient associations thus remains a key goal, both for systems biology-based initiatives (where it can be combined with other 'omics' data to gain a better understanding of functional pathways and networks) and for focused biological studies. Despite the significant challenges of such an undertaking, major strides have been made over the past few years. They include improvements in the computation prediction of PPIs and the literature curation of low-throughput studies of specific protein complexes, but also an increase in the deposition of high-quality data from non-biased high-throughput experimental PPI mapping strategies into publicly available databases.

Development of a computational framework for the analysis of protein correlation profiling and spatial proteomics experiments.

Standard approaches to studying an interactome do not easily allow conditional experiments but in recent years numerous groups have demonstrated the potential for co-fractionation/co-migration based approaches to assess an interactome at a similar sensitivity and specificity yet significantly lower cost and higher speed than traditional approaches. Unfortunately, there is as yet no implementation of the bioinformatics tools required to robustly analyze co-fractionation data in a way that can also integrate the valuable information contained in biological replicates. Here we have developed a freely available, integrated bioinformatics solution for the analysis of protein correlation profiling SILAC data. This modular solution allows the deconvolution of protein chromatograms into individual Gaussian curves enabling the use of these chromatography features to align replicates and assemble a consensus map of features observed across replicates; the chromatograms and individual curves are then used to quantify changes in protein interactions and construct the interactome. We have applied this workflow to the analysis of HeLa cells infected with a Salmonella enterica serovar Typhimurium infection model where we can identify specific interactions that are affected by the infection. These bioinformatics tools simplify the analysis of co-fractionation/co-migration data to the point where there is no specialized knowledge required to measure an interactome in this way. BIOLOGICAL SIGNIFICANCE: We describe a set of software tools for the bioinformatics analysis of co-migration/co-fractionation data that integrates the results from multiple replicates to generate an interactome, including the impact on individual interactions of any external perturbation. This article is part of a Special Issue entitled: Protein dynamics in health and disease. Guest Editors: Pierre Thibault and Anne-Claude Gingras.