Interactome disassembly during apoptosis occurs independent of caspase cleavage

DNA-binding protein-A promotes kidney ischemia/reperfusion injury and participates in mitochondrial function

DNA-binding protein-A (DbpA; gene: Ybx3) belongs to the cold shock protein family with known functions in cell cycling, transcription, translation, and tight junction communication. In chronic nephritis, DbpA is upregulated. However, its activities in acute injury models, such as kidney ischemia/reperfusion injury (IRI), are unclear. To study this, mice harboring Ybx3+/+, Ybx3+/- or the Ybx3-/- genotype were characterized over 24 months and following experimental kidney IRI. Mitochondrial function, number and integrity were analyzed by mitochondrial stress tests, MitoTracker staining and electron microscopy. Western Blot, immunohistochemistry and flow cytometry were performed to quantify tubular cell damage and immune cell infiltration. DbpA was found to be dispensable for kidney development and tissue homeostasis under healthy conditions. Furthermore, endogenous DbpA protein localizes within mitochondria in primary tubular epithelial cells. Genetic deletion of Ybx3 elevates the mitochondrial membrane potential, lipid uptake and metabolism, oxygen consumption rates and glycolytic activities of tubular epithelial cells. Ybx3-/- mice demonstrated protection from IRI with less immune cell infiltration, endoplasmic reticulum stress and tubular cell damage. A presumed protective mechanism was identified via upregulated antioxidant activities and reduced ferroptosis, when Ybx3 was deleted. Thus, our studies reveal DbpA acts as a mitochondrial protein with profound adverse effects on cell metabolism and highlights a protective effect against IRI when Ybx3 is genetically deleted. Hence, preemptive DbpA targeting in situations with expected IRI, such as kidney transplantation or cardiac surgery, may preserve post-procedure kidney function.

DIP-MS: ultra-deep interaction proteomics for the deconvolution of protein complexes

Most proteins are organized in macromolecular assemblies, which represent key functional units regulating and catalyzing most cellular processes. Affinity purification of the protein of interest combined with liquid chromatography coupled to tandem mass spectrometry (AP-MS) represents the method of choice to identify interacting proteins. The composition of complex isoforms concurrently present in the AP sample can, however, not be resolved from a single AP-MS experiment but requires computational inference from multiple time- and resource-intensive reciprocal AP-MS experiments. Here we introduce deep interactome profiling by mass spectrometry (DIP-MS), which combines AP with blue-native-PAGE separation, data-independent acquisition with mass spectrometry and deep-learning-based signal processing to resolve complex isoforms sharing the same bait protein in a single experiment. We applied DIP-MS to probe the organization of the human prefoldin family of complexes, resolving distinct prefoldin holo- and subcomplex variants, complex-complex interactions and complex isoforms with new subunits that were experimentally validated. Our results demonstrate that DIP-MS can reveal proteome modularity at unprecedented depth and resolution.

Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry

We present CFdb, a harmonized resource of interaction proteomics data from 411 co-fractionation mass spectrometry (CF-MS) datasets spanning 21,703 fractions. Meta-analysis of this resource charts protein abundance, phosphorylation, and interactions throughout the tree of life, including a reference map of the human interactome. We show how large-scale CF-MS data can enhance analyses of individual CF-MS datasets, and exemplify this strategy by mapping the honey bee interactome.

Co-fractionation-mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain

Human mitochondrial (mt) protein assemblies are vital for neuronal and brain function, and their alteration contributes to many human disorders, e.g., neurodegenerative diseases resulting from abnormal protein-protein interactions (PPIs). Knowledge of the composition of mt protein complexes is, however, still limited. Affinity purification mass spectrometry (MS) and proximity-dependent biotinylation MS have defined protein partners of some mt proteins, but are too technically challenging and laborious to be practical for analyzing large numbers of samples at the proteome level, e.g., for the study of neuronal or brain-specific mt assemblies, as well as altered mtPPIs on a proteome-wide scale for a disease of interest in brain regions, disease tissues or neurons derived from patients. To address this challenge, we adapted a co-fractionation-MS platform to survey native mt assemblies in adult mouse brain and in human NTERA-2 embryonal carcinoma stem cells or differentiated neuronal-like cells. The workflow consists of orthogonal separations of mt extracts isolated from chemically cross-linked samples to stabilize PPIs, data-dependent acquisition MS to identify co-eluted mt protein profiles from collected fractions and a computational scoring pipeline to predict mtPPIs, followed by network partitioning to define complexes linked to mt functions as well as those essential for neuronal and brain physiological homeostasis. We developed an R/CRAN software package, Macromolecular Assemblies from Co-elution Profiles for automated scoring of co-fractionation-MS data to define complexes from mtPPI networks. Presently, the co-fractionation-MS procedure takes 1.5-3.5 d of proteomic sample preparation, 31 d of MS data acquisition and 8.5 d of data analyses to produce meaningful biological insights.

State-of-the-art computational methods to predict protein-protein interactions with high accuracy and coverage

Prediction of protein-protein interactions (PPIs) commonly involves a significant computational component. Rapid recent advances in the power of computational methods for protein interaction prediction motivate a review of the state-of-the-art. We review the major approaches, organized according to the primary source of data utilized: protein sequence, protein structure, and protein co-abundance. The advent of deep learning (DL) has brought with it significant advances in interaction prediction, and we show how DL is used for each source data type. We review the literature taxonomically, present example case studies in each category, and conclude with observations about the strengths and weaknesses of machine learning methods in the context of the principal sources of data for protein interaction prediction.

The Role of Apoptotic Genes and Protein-Protein Interactions in Triple-negative Breast Cancer

BACKGROUND/AIM

Compared to other breast cancer types, triple-negative breast cancer (TNBC) has historically had few treatment alternatives. Therefore, exploring and pinpointing potentially implicated genes could be used for treating and managing TNBC. By doing this, we will provide essential data to comprehend how the genes are involved in the apoptotic pathways of the cancer cells to identify potential therapeutic targets. Analysis of a single genetic alteration may not reveal the pathogenicity driving TNBC due to the high genomic complexity and heterogeneity of TNBC. Therefore, searching through a large variety of gene interactions enabled the identification of molecular therapeutic genes.

MATERIALS AND METHODS

This study used integrated bioinformatics methods such as UALCAN, TNM plotter, PANTHER, GO-KEEG and PPIs to assess the gene expression, protein-protein interaction (PPI), and transcription factor interaction of apoptosis-regulated genes.

RESULTS

Compared to normal breast tissue, gene expressions of BNIP3, TNFRSF10B, MCL1, and CASP4 were downregulated in UALCAN. At the same time, BIK, AKT1, BAD, FADD, DIABLO, and CASP9 was down-regulated in bc-GeneExMiner v4.5 mRNA expression (BCGM) databases. Based on GO term enrichment analysis, the cellular process (GO:0009987), which has about 21 apoptosis-regulated genes, is the top category in the biological processes (BP), followed by biological regulation (GO:0065007). We identified 29 differentially regulated pathways, including the p53 pathway, angiogenesis, apoptosis signaling pathway, and the Alzheimer's disease presenilin pathway. We examined the PPIs between the genes that regulate apoptosis; CASP3 and CASP9 interact with FADD, MCL1, TNF, TNFRSRF10A, and TNFRSF10; additionally, CASP3 significantly forms PPIs with CASP9, DFFA, and TP53, and CASP9 with DIABLO. In the top 10 transcription factors, the androgen receptor (AR) interacts with five apoptosis-regulated genes (p<0.0001; q<0.01), followed by retinoic acid receptor alpha (RARA) (p<0.0001; q<0.01) and ring finger protein (RNF2) (p<0.0001; q<0.01). Overall, the gene expression profile, PPIs, and the apoptosis-TF interaction findings suggest that the 27 apoptosis-regulated genes might be used as promising targets in treating and managing TNBC. Furthermore, from a total of 27 key genes, CASP2, CASP3, DAPK1, TNF, TRAF2, and TRAF3 were significantly correlated with poor overall survival in TNBC (p-value <0.05); they could play important roles in the progression of TNBC and provide attractive therapeutic targets that may offer new candidate molecules for targeted therapy.

CONCLUSION

Our findings demonstrate that CASP2, CASP3, DAPK1, TNF, TRAF2, and TRAF3 were substantially associated with the overall survival rate (OS) difference of TNBC patients out of a total of 27 specific genes used in this study, which may play crucial roles in the development of TNBC and offer promising therapeutic interventions.

Discovering Protein-Protein Interactions using Co-Fractionation-Mass Spectrometry with Label-Free Quantitation

Proteins generally achieve their functions through interactions with other proteins, so being able to determine which proteins interact with which other proteins underlies much of molecular biology. Co-fractionation (CF) is a mass spectrometry-based method for detecting proteome-wide protein-protein interactions. An attractive feature of CF is that it is not necessary to label or otherwise alter samples. Although we have previously published a widely used protocol for a label-incorporated CF methodology, no published protocols currently exist for the label-free variation. In this chapter, we describe a label-free CF-MS protocol. This protocol takes a minimum of a week, excluding the time for cell/tissue culture. It begins with cell/tissue lysis under non-denaturing conditions, after which intact protein complexes are isolated using size exclusion chromatography (SEC) where they are fractionated according to size. The proteins in each fraction are then prepared for mass spectrometry analysis where the constituent proteins are identified and quantified. Finally, we describe an in-house bioinformatics pipeline, PrInCE, to accurately predict protein complexes. Taken together, co-fractionation methodologies combined with mass spectrometry can identify and quantify thousands of protein-protein interactions in biological systems.

Development of a Method Combining Peptidiscs and Proteomics to Identify, Stabilize, and Purify a Detergent-Sensitive Membrane Protein Assembly

The peptidisc membrane mimetic enables global reconstitution of the bacterial membrane proteome into water-soluble detergent-free particles, termed peptidisc libraries. We present here a method that combines peptidisc libraries and chromosomal-level gene tagging technology with affinity purification and mass spectrometry (AP/MS) to stabilize and identify fragile membrane protein complexes that exist at native expression levels. This method circumvents common artifacts caused by bait protein overproduction and protein complex dissociation due to lengthy exposure to detergents during protein isolation. Using the Escherichia coli Sec system as a case study, we identify an expanded version of the translocon, termed the HMD complex, consisting of nine different integral membrane subunits. This complex is stable in peptidiscs but dissociates in detergents. Guided by this native-level proteomic information, we design and validate a procedure that enables purification of the HMD complex with minimal protein dissociation. These results highlight the utility of peptidiscs and AP/MS to discover and stabilize fragile membrane protein assemblies. Data are available via ProteomeXchange with identifier PXD032315.

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.

Recent progress in mass spectrometry-based strategies for elucidating protein-protein interactions

Protein-protein interactions are fundamental to various aspects of cell biology with many protein complexes participating in numerous fundamental biological processes such as transcription, translation and cell cycle. MS-based proteomics techniques are routinely applied for characterising the interactome, such as affinity purification coupled to mass spectrometry that has been used to selectively enrich and identify interacting partners of a bait protein. In recent years, many orthogonal MS-based techniques and approaches have surfaced including proximity-dependent labelling of neighbouring proteins, chemical cross-linking of two interacting proteins, as well as inferring PPIs from the co-behaviour of proteins such as the co-fractionating profiles and the thermal solubility profiles of proteins. This review discusses the underlying principles, advantages, limitations and experimental considerations of these emerging techniques. In addition, a brief account on how MS-based techniques are used to investigate the structural and functional properties of protein complexes, including their topology, stoichiometry, copy number and dynamics, are discussed.

Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments

Co-fractionation mass spectrometry (CF-MS) has emerged as a powerful technique for interactome mapping. However, there is little consensus on optimal strategies for the design of CF-MS experiments or their computational analysis. Here, we reanalyzed a total of 206 CF-MS experiments to generate a uniformly processed resource containing over 11 million measurements of protein abundance. We used this resource to benchmark experimental designs for CF-MS studies and systematically optimize computational approaches to network inference. We then applied this optimized methodology to reconstruct a draft-quality human interactome by CF-MS and predict over 700,000 protein-protein interactions across 27 eukaryotic species or clades. Our work defines new resources to illuminate proteome organization over evolutionary timescales and establishes best practices for the design and analysis of CF-MS studies.

PCprophet: a framework for protein complex prediction and differential analysis using proteomic data

Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein-protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography-sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein-protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography-MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics.

PrInCE: an R/bioconductor package for protein-protein interaction network inference from co-fractionation mass spectrometry data

SUMMARY

We present PrInCE, an R/Bioconductor package that employs a machine-learning approach to infer protein-protein interaction networks from co-fractionation mass spectrometry (CF-MS) data. Previously distributed as a collection of Matlab scripts, our ground-up rewrite of this software package in an open-source language dramatically improves runtime and memory requirements. We describe several new features in the R implementation, including a test for the detection of co-eluting protein complexes and a method for differential network analysis. PrInCE is extensively documented and fully compatible with Bioconductor classes, ensuring it can fit seamlessly into existing proteomics workflows.

AVAILABILITY AND IMPLEMENTATION

PrInCE is available from Bioconductor (https://www.bioconductor.org/packages/devel/bioc/html/PrInCE.html). Source code is freely available from GitHub under the MIT license (https://github.com/fosterlab/PrInCE). Support is provided via the GitHub issues tracker (https://github.com/fosterlab/PrInCE/issues).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

System-Wide Profiling of Protein Complexes Via Size Exclusion Chromatography-Mass Spectrometry (SEC-MS)

In living cells, most proteins are organized in stable or transient functional assemblies, protein complexes, which control a multitude of vital cellular processes such as cell cycle progression, metabolism, and signal transduction. Over several decades, specific protein complexes have been analyzed by structural biology methods, initially X-ray crystallography and more recently single particle cryoEM. In parallel, mass spectrometry (MS)-based methods including in vitro affinity-purification coupled to MS or in vivo protein proximity-dependent labeling methods have proven particularly effective to detect complexes, thus nominating new assemblies for structural analysis. Those approaches, however, are either of limited in throughput or require specifically engineered protein systems.In this chapter, we present protocols for a workflow that supports the parallel analysis of multiple complexes from the same biological sample with respect to abundance, subunit composition, and stoichiometry. It consists of the separation of native complexes by size-exclusion chromatography (SEC) and the subsequent mass spectrometric analysis of the proteins in consecutive SEC fractions. In particular, we describe (1) optimized conditions to achieve native protein complex separation by SEC, (2) the preparation of the SEC fractions for MS analysis, (3) the acquisition of the MS data at high throughput via SWATH/DIA (data-independent analysis) mass spectrometry and short chromatographic gradients, and (4) a set of bioinformatic tools for the targeted analysis of protein complexes. Altogether, the parallel measurement of a high number of complexes from a single biological sample results in unprecedented system-level insights into the remodeling of cellular protein complexes in response to perturbations of a broad range of cellular systems.

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.

On the Robustness of Graph-Based Clustering to Random Network Alterations

Biological functions emerge from complex and dynamic networks of protein-protein interactions. Because these protein-protein interaction networks, or interactomes, represent pairwise connections within a hierarchically organized system, it is often useful to identify higher-order associations embedded within them, such as multimember protein complexes. Graph-based clustering techniques are widely used to accomplish this goal, and dozens of field-specific and general clustering algorithms exist. However, interactomes can be prone to errors, especially when inferred from high-throughput biochemical assays. Therefore, robustness to network-level noise is an important criterion. Here, we tested the robustness of a range of graph-based clustering algorithms in the presence of noise, including algorithms common across domains and those specific to protein networks. Strikingly, we found that all of the clustering algorithms tested here markedly amplified network-level noise. Randomly rewiring only 1% of network edges yielded more than a 50% change in clustering results. Moreover, we found the impact of network noise on individual clusters was not uniform: some clusters were consistently robust to injected noise, whereas others were not. Therefore we developed the clust.perturb R package and Shiny web application to measure the reproducibility of clusters by randomly perturbing the network. We show that clust.perturb results are predictive of real-world cluster stability: poorly reproducible clusters as identified by clust.perturb are significantly less likely to be reclustered across experiments. We conclude that graph-based clustering amplifies noise in protein interaction networks, but quantifying the robustness of a cluster to network noise can separate stable protein complexes from spurious associations.

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.

Complex-centric proteome profiling by SEC-SWATH-MS for the parallel detection of hundreds of protein complexes

Most catalytic, structural and regulatory functions of the cell are carried out by functional modules, typically complexes containing or consisting of proteins. The composition and abundance of these complexes and the quantitative distribution of specific proteins across different modules are therefore of major significance in basic and translational biology. However, detection and quantification of protein complexes on a proteome-wide scale is technically challenging. We have recently extended the targeted proteomics rationale to the level of native protein complex analysis (complex-centric proteome profiling). The complex-centric workflow described herein consists of size exclusion chromatography (SEC) to fractionate native protein complexes, data-independent acquisition mass spectrometry to precisely quantify the proteins in each SEC fraction based on a set of proteotypic peptides and targeted, complex-centric analysis where prior information from generic protein interaction maps is used to detect and quantify protein complexes with high selectivity and statistical error control via the computational framework CCprofiler (https://github.com/CCprofiler/CCprofiler). Complex-centric proteome profiling captures most proteins in complex-assembled state and reveals their organization into hundreds of complexes and complex variants observable in a given cellular state. The protocol is applicable to cultured cells and can potentially also be adapted to primary tissue and does not require any genetic engineering of the respective sample sources. At present, it requires ~8 d of wet-laboratory work, 15 d of mass spectrometry measurement time and 7 d of computational analysis.

Predicting Functions of Uncharacterized Human Proteins: From Canonical to Proteoforms

Despite tremendous efforts in genomics, transcriptomics, and proteomics communities, there is still no comprehensive data about the exact number of protein-coding genes, translated proteoforms, and their function. In addition, by now, we lack functional annotation for 1193 genes, where expression was confirmed at the proteomic level (uPE1 proteins). We re-analyzed results of AP-MS experiments from the BioPlex 2.0 database to predict functions of uPE1 proteins and their splice forms. By building a protein-protein interaction network for 12 ths. identified proteins encoded by 11 ths. genes, we were able to predict Gene Ontology categories for a total of 387 uPE1 genes. We predicted different functions for canonical and alternatively spliced forms for four uPE1 genes. In total, functional differences were revealed for 62 proteoforms encoded by 31 genes. Based on these results, it can be carefully concluded that the dynamics and versatility of the interactome is ensured by changing the dominant splice form. Overall, we propose that analysis of large-scale AP-MS experiments performed for various cell lines and under various conditions is a key to understanding the full potential of genes role in cellular processes.

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.

Master Sculptor at Work: Enteropathogenic Escherichia coli Infection Uniquely Modifies Mitochondrial Proteolysis during Its Control of Human Cell Death

Enteropathogenic Escherichia coli (EPEC) causes severe diarrheal disease and is present globally. EPEC virulence requires a bacterial type III secretion system to inject >20 effector proteins into human intestinal cells. Three effectors travel to mitochondria and modulate apoptosis; however, the mechanisms by which effectors control apoptosis from within mitochondria are unknown. To identify and quantify global changes in mitochondrial proteolysis during infection, we applied the mitochondrial terminal proteomics technique mitochondrial stable isotope labeling by amino acids in cell culture-terminal amine isotopic labeling of substrates (MS-TAILS). MS-TAILS identified 1,695 amino N-terminal peptides from 1,060 unique proteins and 390 N-terminal peptides from 215 mitochondrial proteins at a false discovery rate of 0.01. Infection modified 230 cellular and 40 mitochondrial proteins, generating 27 cleaved mitochondrial neo-N termini, demonstrating altered proteolytic processing within mitochondria. To distinguish proteolytic events specific to EPEC from those of canonical apoptosis, we compared mitochondrial changes during infection with those reported from chemically induced apoptosis. During infection, fewer than half of all mitochondrial cleavages were previously described for canonical apoptosis, and we identified nine mitochondrial proteolytic sites not previously reported, including several in proteins with an annotated role in apoptosis, although none occurred at canonical Asp-Glu-Val-Asp (DEVD) sites associated with caspase cleavage. The identification and quantification of novel neo-N termini evidences the involvement of noncaspase human or EPEC protease(s) resulting from mitochondrial-targeting effectors that modulate cell death upon infection. All proteomics data are available via ProteomeXchange with identifier PXD016994.

IMPORTANCE To our knowledge, this is the first study of the mitochondrial proteome or N-terminome during bacterial infection. Identified cleavage sites that had not been previously reported in the mitochondrial N-terminome and that were not generated in canonical apoptosis revealed a pathogen-specific strategy to control human cell apoptosis. These data inform new mechanisms of virulence factors targeting mitochondria and apoptosis during infection and highlight how enteropathogenic Escherichia coli (EPEC) manipulates human cell death pathways during infection, including candidate substrates of an EPEC protease within mitochondria. This understanding informs the development of new antivirulence strategies against the many human pathogens that target mitochondria during infection. Therefore, mitochondrial stable isotope labeling by amino acids in cell culture-terminal amine isotopic labeling of substrates (MS-TAILS) is useful for studying other pathogens targeting human cell compartments.

Proteomic and interactomic insights into the molecular basis of cell functional diversity

The ability of living systems to adapt to changing conditions originates from their capacity to change their molecular constitution. This is achieved by multiple mechanisms that modulate the quantitative composition and the diversity of the molecular inventory. Molecular diversification is particularly pronounced on the proteome level, at which multiple proteoforms derived from the same gene can in turn combinatorially form different protein complexes, thus expanding the repertoire of functional modules in the cell. The study of molecular and modular diversity and their involvement in responses to changing conditions has only recently become possible through the development of new 'omics'-based screening technologies. This Review explores our current knowledge of the mechanisms regulating functional diversification along the axis of gene expression, with a focus on the proteome and interactome. We explore the interdependence between different molecular levels and how this contributes to functional diversity. Finally, we highlight several recent techniques for studying molecular diversity, with specific focus on mass spectrometry-based analysis of the proteome and its organization into functional modules, and examine future directions for this rapidly growing field.

Biogenesis of the Spacious Coxiella-Containing Vacuole Depends on Host Transcription Factors TFEB and TFE3

Coxiella burnetii is an obligate intracellular bacterial pathogen that replicates inside the lysosome-derived Coxiella-containing vacuole (CCV). To establish this unique niche, C. burnetii requires the Dot/Icm type IV secretion system (T4SS) to translocate a cohort of effector proteins into the host cell, which modulate multiple cellular processes. To characterize the host-pathogen interactions that occur during C. burnetii infection, stable-isotope labeling by amino acids in cell culture (SILAC)-based proteomics was used to identify changes in the host proteome during infection of a human-derived macrophage cell line. These data revealed that the abundances of many proteins involved in host cell autophagy and lysosome biogenesis were increased in infected cells. Thus, the role of the host transcription factors TFEB and TFE3, which regulate the expression of a network of genes involved in autophagy and lysosomal biogenesis, were examined in the context of C. burnetii infection. During infection with C. burnetii, both TFEB and TFE3 were activated, as demonstrated by the transport of these proteins from the cytoplasm into the nucleus. The nuclear translocation of these transcription factors was shown to be dependent on the T4SS, as a Dot/Icm mutant showed reduced nuclear translocation of TFEB and TFE3. This was supported by the observation that blocking bacterial translation with chloramphenicol resulted in the movement of TFEB and TFE3 back into the cytoplasm. Silencing of the TFEB and TFE3 genes, alone or in combination, significantly reduced the size of the CCV, which indicates that these host transcription factors facilitate the expansion and maintenance of the organelle that supports C. burnetii intracellular replication.

A Global Screen for Assembly State Changes of the Mitotic Proteome by SEC-SWATH-MS

Living systems integrate biochemical reactions that determine the functional state of each cell. Reactions are primarily mediated by proteins. In proteomic studies, these have been treated as independent entities, disregarding their higher-level organization into complexes that affects their activity and/or function and is thus of great interest for biological research. Here, we describe the implementation of an integrated technique to quantify cell-state-specific changes in the physical arrangement of protein complexes concurrently for thousands of proteins and hundreds of complexes. Applying this technique to a comparison of human cells in interphase and mitosis, we provide a systematic overview of mitotic proteome reorganization. The results recall key hallmarks of mitotic complex remodeling and suggest a model of nuclear pore complex disassembly, which we validate by orthogonal methods. To support the interpretation of quantitative SEC-SWATH-MS datasets, we extend the software CCprofiler and provide an interactive exploration tool, SECexplorer-cc.

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Global quantification of assembly state changes in the mitotic proteome

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Improved performance over thermostability measurement of proteome states

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Discovery of a mitotic disassembly intermediate of the nuclear pore complex

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Introduction of SECexplorer-cc, a publicly available online platform

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.

Profiling the Escherichia coli membrane protein interactome captured in Peptidisc libraries

Protein-correlation-profiling (PCP), in combination with quantitative proteomics, has emerged as a high-throughput method for the rapid identification of dynamic protein complexes in native conditions. While PCP has been successfully applied to soluble proteomes, characterization of the membrane interactome has lagged, partly due to the necessary use of detergents to maintain protein solubility. Here, we apply the peptidisc, a ‘one-size fits all’ membrane mimetic, for the capture of the Escherichia coli cell envelope proteome and its high-resolution fractionation in the absence of detergent. Analysis of the SILAC-labeled peptidisc library via PCP allows generation of over 4900 possible binary interactions out of >700,000 random associations. Using well-characterized membrane protein systems such as the SecY translocon, the Bam complex and the MetNI transporter, we demonstrate that our dataset is a useful resource for identifying transient and surprisingly novel protein interactions. For example, we discover a trans-periplasmic supercomplex comprising subunits of the Bam and Sec machineries, including membrane-bound chaperones YfgM and PpiD. We identify RcsF and OmpA as bone fide interactors of BamA, and we show that MetQ association with the ABC transporter MetNI depends on its N-terminal lipid anchor. We also discover NlpA as a novel interactor of MetNI complex. Most of these interactions are largely undetected by standard detergent-based purification. Together, the peptidisc workflow applied to the proteomic field is emerging as a promising novel approach to characterize membrane protein interactions under native expression conditions and without genetic manipulation.

Mitoproteomics: Tackling Mitochondrial Dysfunction in Human Disease

Mitochondria are highly dynamic and regulated organelles that historically have been defined based on their crucial role in cell metabolism. However, they are implicated in a variety of other important functions, making mitochondrial dysfunction an important axis in several pathological contexts. Despite that conventional biochemical and molecular biology approaches have provided significant insight into mitochondrial functionality, innovative techniques that provide a global view of the mitochondrion are still necessary. Proteomics fulfils this need by enabling accurate, systems-wide quantitative analysis of protein abundance. More importantly, redox proteomics approaches offer unique opportunities to tackle oxidative stress, a phenomenon that is intimately linked to aging, cardiovascular disease, and cancer. In addition, cutting-edge proteomics approaches reveal how proteins exert their functions in complex interaction networks where even subtle alterations stemming from early pathological states can be monitored. Here, we describe the proteomics approaches that will help to deepen the role of mitochondria in health and disease by assessing not only changes to mitochondrial protein composition but also alterations to their redox state and how protein interaction networks regulate mitochondrial function and dynamics. This review is aimed at showing the reader how the application of proteomics approaches during the last 20 years has revealed crucial mitochondrial roles in the context of aging, neurodegenerative disorders, metabolic disease, and cancer.

Global Profiling of Proteolysis from the Mitochondrial Amino Terminome during Early Intrinsic Apoptosis Prior to Caspase-3 Activation

The human genome encodes ∼20 mitochondrial proteases, yet we know little of how they sculpt the mitochondrial proteome, particularly during important mitochondrial events such as the initiation of apoptosis. To characterize global mitochondrial proteolysis we refined our technique, terminal amine isotopic labeling of substrates, for mitochondrial SILAC (MS-TAILS) to identify proteolysis across mitochondria and parent cells in parallel. Our MS-TAILS analyses identified 45% of the mitochondrial proteome and identified protein amino (N)-termini from 26% of mitochondrial proteins, the highest reported coverage of the human mitochondrial N-terminome. MS-TAILS revealed 97 previously unknown proteolytic sites. MS-TAILS also identified mitochondrial targeting sequence (MTS) removal by proteolysis during protein import, confirming 101 MTS sites and identifying 135 new MTS sites, revealing a wobbly requirement for the MTS cleavage motif. To examine the relatively unknown initial cleavage events occurring before the well-studied activation of caspase-3 in intrinsic apoptosis, we quantitatively compared N-terminomes of mitochondria and their parent cells before and after initiation of apoptosis at very early time points. By identifying altered levels of >400 N-termini, MS-TAILS analyses implicated specific mitochondrial pathways including protein import, fission, and iron homeostasis in apoptosis initiation. Notably, both staurosporine and Bax activator molecule-7 triggered in common 7 mitochondrial and 85 cellular cleavage events that are potentially part of an essential core of apoptosis-initiating events. All mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD009054.

Context-specific interactions in literature-curated protein interaction databases

BACKGROUND

Databases of literature-curated protein-protein interactions (PPIs) are often used to interpret high-throughput interactome mapping studies and estimate error rates. These databases combine interactions across thousands of published studies and experimental techniques. Because the tendency for two proteins to interact depends on the local conditions, this heterogeneity of conditions means that only a subset of database PPIs are interacting during any given experiment. A typical use of these databases as gold standards in interactome mapping projects, however, assumes that PPIs included in the database are indeed interacting under the experimental conditions of the study.

RESULTS

Using raw data from 20 co-fractionation experiments and six published interactomes, we demonstrate that this assumption is often false, with up to 55% of purported gold standard interactions showing no evidence of interaction, on average. We identify a subset of CORUM database complexes that do show consistent evidence of interaction in co-fractionation studies, and we use this subset as gold standards to dramatically improve interactome mapping as judged by the number of predicted interactions at a given error rate.

CONCLUSIONS

We recommend using this CORUM subset as the gold standard set in future co-fractionation studies. More generally, we recommend using the subset of literature-curated PPIs that are specific to the experimental context whenever possible.

Genomic data integration systematically biases interactome mapping

Elucidating the complete network of protein-protein interactions, or interactome, is a fundamental goal of the post-genomic era, yet existing interactome maps are far from complete. To increase the throughput and resolution of interactome mapping, methods for protein-protein interaction discovery by co-migration have been introduced. However, accurate identification of interacting protein pairs within the resulting large-scale proteomic datasets is challenging. Consequently, most computational pipelines for co-migration data analysis incorporate external genomic datasets to distinguish interacting from non-interacting protein pairs. The effect of this procedure on interactome mapping is poorly understood. Here, we conduct a rigorous analysis of genomic data integration for interactome recovery across a large number of co-migration datasets, spanning diverse experimental and computational methods. We find that genomic data integration leads to an increase in the functional coherence of the resulting interactome maps, but this comes at the expense of a decrease in power to discover novel interactions. Importantly, putative novel interactions predicted by genomic data integration are no more likely to later be experimentally discovered than those predicted from co-migration data alone. Our results reveal a widespread and unappreciated limitation in a methodology that has been widely used to map the interactome of humans and model organisms.

Proteolytic Cleavage-Mechanisms, Function, and "Omic" Approaches for a Near-Ubiquitous Posttranslational Modification

Proteases enzymatically hydrolyze peptide bonds in substrate proteins, resulting in a widespread, irreversible posttranslational modification of the protein's structure and biological function. Often regarded as a mere degradative mechanism in destruction of proteins or turnover in maintaining physiological homeostasis, recent research in the field of degradomics has led to the recognition of two main yet unexpected concepts. First, that targeted, limited proteolytic cleavage events by a wide repertoire of proteases are pivotal regulators of most, if not all, physiological and pathological processes. Second, an unexpected in vivo abundance of stable cleaved proteins revealed pervasive, functionally relevant protein processing in normal and diseased tissue-from 40 to 70% of proteins also occur in vivo as distinct stable proteoforms with undocumented N- or C-termini, meaning these proteoforms are stable functional cleavage products, most with unknown functional implications. In this Review, we discuss the structural biology aspects and mechanisms of catalysis by different protease classes. We also provide an overview of biological pathways that utilize specific proteolytic cleavage as a precision control mechanism in protein quality control, stability, localization, and maturation, as well as proteolytic cleavage as a mediator in signaling pathways. Lastly, we provide a comprehensive overview of analytical methods and approaches to study activity and substrates of proteolytic enzymes in relevant biological models, both historical and focusing on state of the art proteomics techniques in the field of degradomics research.

Proteomic characterization of EL4 lymphoma-derived tumors upon chemotherapy treatment reveals potential roles for lysosomes and caspase-6 during tumor cell death in vivo

The murine mouse lymphoblastic lymphoma cell line (EL4) tumor model is an established in vivo apoptosis model for the investigation of novel cancer imaging agents and immunological treatments due to the rapid and significant response of the EL4 tumors to cyclophosphamide and etoposide combination chemotherapy. Despite the utility of this model system in cancer research, little is known regarding the molecular details of in vivo tumor cell death. Here, we report the first in-depth quantitative proteomic analysis of the changes that occur in these tumors upon cyclophosphamide and etoposide treatment in vivo. Using a label-free quantitative proteomic approach a total of 5838 proteins were identified in the treated and untreated tumors, of which 875 were determined to change in abundance with statistical significance. Initial analysis of the data reveals changes that may have been predicted, such as the downregulation of ribosomes, but demonstrates the robustness of the dataset. Analysis of the dataset also reveals the unexpected downregulation of caspase-3 and an upregulation of caspase-6 in addition to a global upregulation of lysosomal proteins in the bulk of the tumor.

A Map of Human Mitochondrial Protein Interactions Linked to Neurodegeneration Reveals New Mechanisms of Redox Homeostasis and NF-κB Signaling

Mitochondrial protein (MP) dysfunction has been linked to neurodegenerative disorders (NDs); however, the discovery of the molecular mechanisms underlying NDs has been impeded by the limited characterization of interactions governing MP function. Here, using mass spectrometry (MS)-based analysis of 210 affinity-purified mitochondrial (mt) fractions isolated from 27 epitope-tagged human ND-linked MPs in HEK293 cells, we report a high-confidence MP network including 1,964 interactions among 772 proteins (>90% previously unreported). Nearly three-fourths of these interactions were confirmed in mouse brain and multiple human differentiated neuronal cell lines by primary antibody immunoprecipitation and MS, with many linked to NDs and autism. We show that the SOD1-PRDX5 interaction, critical for mt redox homeostasis, can be perturbed by amyotrophic lateral sclerosis-linked SOD1 allelic variants and establish a functional role for ND-linked factors coupled with IκBɛ in NF-κB activation. Our results identify mechanisms for ND-linked MPs and expand the human mt interaction landscape.

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.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1865-8) contains supplementary material, which is available to authorized users.

The bacterial arginine glycosyltransferase effector NleB preferentially modifies Fas-associated death domain protein (FADD)

The inhibition of host innate immunity pathways is essential for the persistence of attaching and effacing pathogens such as enteropathogenic Escherichia coli (EPEC) and Citrobacter rodentium during mammalian infections. To subvert these pathways and suppress the antimicrobial response, attaching and effacing pathogens use type III secretion systems to introduce effectors targeting key signaling pathways in host cells. One such effector is the arginine glycosyltransferase NleB1 (NleB^CR in C. rodentium) that modifies conserved arginine residues in death domain-containing host proteins with N-acetylglucosamine (GlcNAc), thereby blocking extrinsic apoptosis signaling. Ectopically expressed NleB1 modifies the host proteins Fas-associated via death domain (FADD), TNFRSF1A-associated via death domain (TRADD), and receptor-interacting serine/threonine protein kinase 1 (RIPK1). However, the full repertoire of arginine GlcNAcylation induced by pathogen-delivered NleB1 is unknown. Using an affinity proteomic approach for measuring arginine-GlcNAcylated glycopeptides, we assessed the global profile of arginine GlcNAcylation during ectopic expression of NleB1, EPEC infection in vitro, or C. rodentium infection in vivo NleB overexpression resulted in arginine GlcNAcylation of multiple host proteins. However, NleB delivery during EPEC and C. rodentium infection caused rapid and preferential modification of Arg¹¹⁷ in FADD. This FADD modification was extremely stable and insensitive to physiological temperatures, glycosidases, or host cell degradation. Despite its stability and effect on the inhibition of apoptosis, arginine GlcNAcylation did not elicit any proteomic changes, even in response to prolonged NleB1 expression. We conclude that, at normal levels of expression during bacterial infection, NleB1/NleB^CR antagonizes death receptor-induced apoptosis of infected cells by modifying FADD in an irreversible manner.

Sharpening Host Defenses during Infection: Proteases Cut to the Chase

The human immune system consists of an intricate network of tightly controlled pathways, where proteases are essential instigators and executioners at multiple levels. Invading microbial pathogens also encode proteases that have evolved to manipulate and dysregulate host proteins, including host proteases during the course of disease. The identification of pathogen proteases as well as their substrates and mechanisms of action have empowered significant developments in therapeutics for infectious diseases. Yet for many pathogens, there remains a great deal to be discovered. Recently, proteomic techniques have been developed that can identify proteolytically processed proteins across the proteome. These “degradomics” approaches can identify human substrates of microbial proteases during infection in vivo and expose the molecular-level changes that occur in the human proteome during infection as an operational network to develop hypotheses for further research as well as new therapeutics. This Perspective Article reviews how proteases are utilized during infection by both the human host and invading bacterial pathogens, including archetypal virulence-associated microbial proteases, such as the Clostridia spp. botulinum and tetanus neurotoxins. We highlight the potential knowledge that degradomics studies of host–pathogen interactions would uncover, as well as how degradomics has been successfully applied in similar contexts, including use with a viral protease. We review how microbial proteases have been targeted in current therapeutic approaches and how microbial proteases have shaped and even contributed to human therapeutics beyond infectious disease. Finally, we discuss how, moving forward, degradomics research can greatly contribute to our understanding of how microbial pathogens cause disease in vivo and lead to the identification of novel substrates in vivo, and the development of improved therapeutics to counter these pathogens.

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.