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.

Interaction landscape of membrane-protein complexes in Saccharomyces cerevisiae.

Macromolecular assemblies involving membrane proteins (MPs) serve vital biological roles and are prime drug targets in a variety of diseases. Large-scale affinity purification studies of soluble-protein complexes have been accomplished for diverse model organisms, but no global characterization of MP-complex membership has been described so far. Here we report a complete survey of 1,590 putative integral, peripheral and lipid-anchored MPs from Saccharomyces cerevisiae, which were affinity purified in the presence of non-denaturing detergents. The identities of the co-purifying proteins were determined by tandem mass spectrometry and subsequently used to derive a high-confidence physical interaction map encompassing 1,726 membrane protein-protein interactions and 501 putative heteromeric complexes associated with the various cellular membrane systems. Our analysis reveals unexpected physical associations underlying the membrane biology of eukaryotes and delineates the global topological landscape of the membrane interactome.

Quantitative genome-wide genetic interaction screens reveal global epistatic relationships of protein complexes in Escherichia coli.

Large-scale proteomic analyses in Escherichia coli have documented the composition and physical relationships of multiprotein complexes, but not their functional organization into biological pathways and processes. Conversely, genetic interaction (GI) screens can provide insights into the biological role(s) of individual gene and higher order associations. Combining the information from both approaches should elucidate how complexes and pathways intersect functionally at a systems level. However, such integrative analysis has been hindered due to the lack of relevant GI data. Here we present a systematic, unbiased, and quantitative synthetic genetic array screen in E. coli describing the genetic dependencies and functional cross-talk among over 600,000 digenic mutant combinations. Combining this epistasis information with putative functional modules derived from previous proteomic data and genomic context-based methods revealed unexpected associations, including new components required for the biogenesis of iron-sulphur and ribosome integrity, and the interplay between molecular chaperones and proteases. We find that functionally-linked genes co-conserved among γ-proteobacteria are far more likely to have correlated GI profiles than genes with divergent patterns of evolution. Overall, examining bacterial GIs in the context of protein complexes provides avenues for a deeper mechanistic understanding of core microbial systems.

Novel function discovery with GeneMANIA: a new integrated resource for gene function prediction in Escherichia coli.

MOTIVATION: The model bacterium Escherichia coli is among the best studied prokaryotes, yet nearly half of its proteins are still of unknown biological function. This is despite a wealth of available large-scale physical and genetic interaction data. To address this, we extended the GeneMANIA function prediction web application developed for model eukaryotes to support E.coli. RESULTS: We integrated 48 distinct E.coli functional interaction datasets and used the GeneMANIA algorithm to produce thousands of novel functional predictions and prioritize genes for further functional assays. Our analysis achieved cross-validation performance comparable to that reported for eukaryotic model organisms, and revealed new functions for previously uncharacterized genes in specific bioprocesses, including components required for cell adhesion, iron-sulphur complex assembly and ribosome biogenesis. The GeneMANIA approach for network-based function prediction provides an innovative new tool for probing mechanisms underlying bacterial bioprocesses. CONTACT: gary.bader@utoronto.ca; mohan.babu@uregina.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mitochondrial targets for pharmacological intervention in human disease.

Over the past several years, mitochondrial dysfunction has been linked to an increasing number of human illnesses, making mitochondrial proteins (MPs) an ever more appealing target for therapeutic intervention. With 20% of the mitochondrial proteome (312 of an estimated 1500 MPs) having known interactions with small molecules, MPs appear to be highly targetable. Yet, despite these targeted proteins functioning in a range of biological processes (including induction of apoptosis, calcium homeostasis, and metabolism), very few of the compounds targeting MPs find clinical use. Recent work has greatly expanded the number of proteins known to localize to the mitochondria and has generated a considerable increase in MP 3D structures available in public databases, allowing experimental screening and in silico prediction of mitochondrial drug targets on an unprecedented scale. Here, we summarize the current literature on clinically active drugs that target MPs, with a focus on how existing drug targets are distributed across biochemical pathways and organelle substructures. Also, we examine current strategies for mitochondrial drug discovery, focusing on genetic, proteomic, and chemogenomic assays, and relevant model systems. As cell models and screening techniques improve, MPs appear poised to emerge as relevant targets for a wide range of complex human diseases, an eventuality that can be expedited through systematic analysis of MP function.

Yeast mitochondrial protein-protein interactions reveal diverse complexes and disease-relevant functional relationships.

Although detailed, focused, and mechanistic analyses of associations among mitochondrial proteins (MPs) have identified their importance in varied biological processes, a systematic understanding of how MPs function in concert both with one another and with extra-mitochondrial proteins remains incomplete. Consequently, many questions regarding the role of mitochondrial dysfunction in the development of human disease remain unanswered. To address this, we compiled all existing mitochondrial physical interaction data for over 1200 experimentally defined yeast MPs and, through bioinformatic analysis, identified hundreds of heteromeric MP complexes having extensive associations both within and outside the mitochondria. We provide support for these complexes through structure prediction analysis, morphological comparisons of deletion strains, and protein co-immunoprecipitation. The integration of these MP complexes with reported genetic interaction data reveals substantial crosstalk between MPs and non-MPs and identifies novel factors in endoplasmic reticulum-mitochondrial organization, membrane structure, and mitochondrial lipid homeostasis. More than one-third of these MP complexes are conserved in humans, with many containing members linked to clinical pathologies, enabling us to identify genes with putative disease function through guilt-by-association. Although still remaining incomplete, existing mitochondrial interaction data suggests that the relevant molecular machinery is modular, yet highly integrated with non-mitochondrial processes.

Quantitative and Systems-Based Approaches for Deciphering Bacterial Membrane Interactome and Gene Function.

High-throughput genomic and proteomic methods provide a concise description of the molecular constituents of a cell, whereas systems biology strives to understand the way these components function as a whole. Recent developments, such as genome editing technologies and protein epitope-tagging coupled with high-sensitivity mass-spectrometry, allow systemic studies to be performed at an unprecedented scale. Available methods can be successfully applied to various goals, both expanding fundamental knowledge and solving applied problems. In this review, we discuss the present state and future of bacterial cell envelope interactomics, with a specific focus on host-pathogen interactions and drug target discovery. Both experimental and computational methods will be outlined together with examples of their practical implementation.

Genetic interaction maps in Escherichia coli reveal functional crosstalk among cell envelope biogenesis pathways.

As the interface between a microbe and its environment, the bacterial cell envelope has broad biological and clinical significance. While numerous biosynthesis genes and pathways have been identified and studied in isolation, how these intersect functionally to ensure envelope integrity during adaptive responses to environmental challenge remains unclear. To this end, we performed high-density synthetic genetic screens to generate quantitative functional association maps encompassing virtually the entire cell envelope biosynthetic machinery of Escherichia coli under both auxotrophic (rich medium) and prototrophic (minimal medium) culture conditions. The differential patterns of genetic interactions detected among > 235,000 digenic mutant combinations tested reveal unexpected condition-specific functional crosstalk and genetic backup mechanisms that ensure stress-resistant envelope assembly and maintenance. These networks also provide insights into the global systems connectivity and dynamic functional reorganization of a universal bacterial structure that is both broadly conserved among eubacteria (including pathogens) and an important target.

OrthoNets: simultaneous visual analysis of orthologs and their interaction neighborhoods across different organisms.

MOTIVATION: Protein interaction networks contain a wealth of biological information, but their large size often hinders cross-organism comparisons. We present OrthoNets, a Cytoscape plugin that displays protein-protein interaction (PPI) networks from two organisms simultaneously, highlighting orthology relationships and aggregating several types of biomedical annotations. OrthoNets also allows PPI networks derived from experiments to be overlaid on networks extracted from public databases, supporting the identification and verification of new interactors. Any newly identified PPIs can be validated by checking whether their orthologs interact in another organism. AVAILABILITY: OrthoNets is freely available at http://wodaklab.org/orthonets/.

Global landscape of protein complexes in the yeast Saccharomyces cerevisiae.

Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein-protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein-protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.

Challenges and rewards of interaction proteomics.

The recent explosion of high throughput experimental technologies for characterizing protein interactions has generated large amounts of data describing interactions between thousands of proteins and producing genome scale views of protein assemblies. The systems level views afforded by these data hold great promise of leading to new knowledge but also involve many challenges. Deriving meaningful biological conclusions from these views crucially depends on our understanding of the approximation and biases that enter into deriving and interpreting the data. The challenges and rewards of interaction proteomics are reviewed here using as an example the latest comprehensive high throughput analyses of protein interactions in yeast.

Markov clustering versus affinity propagation for the partitioning of protein interaction graphs.

BACKGROUND: Genome scale data on protein interactions are generally represented as large networks, or graphs, where hundreds or thousands of proteins are linked to one another. Since proteins tend to function in groups, or complexes, an important goal has been to reliably identify protein complexes from these graphs. This task is commonly executed using clustering procedures, which aim at detecting densely connected regions within the interaction graphs. There exists a wealth of clustering algorithms, some of which have been applied to this problem. One of the most successful clustering procedures in this context has been the Markov Cluster algorithm (MCL), which was recently shown to outperform a number of other procedures, some of which were specifically designed for partitioning protein interactions graphs. A novel promising clustering procedure termed Affinity Propagation (AP) was recently shown to be particularly effective, and much faster than other methods for a variety of problems, but has not yet been applied to partition protein interaction graphs. RESULTS: In this work we compare the performance of the Affinity Propagation (AP) and Markov Clustering (MCL) procedures. To this end we derive an unweighted network of protein-protein interactions from a set of 408 protein complexes from S. cervisiae hand curated in-house, and evaluate the performance of the two clustering algorithms in recalling the annotated complexes. In doing so the parameter space of each algorithm is sampled in order to select optimal values for these parameters, and the robustness of the algorithms is assessed by quantifying the level of complex recall as interactions are randomly added or removed to the network to simulate noise. To evaluate the performance on a weighted protein interaction graph, we also apply the two algorithms to the consolidated protein interaction network of S. cerevisiae, derived from genome scale purification experiments and to versions of this network in which varying proportions of the links have been randomly shuffled. CONCLUSION: Our analysis shows that the MCL procedure is significantly more tolerant to noise and behaves more robustly than the AP algorithm. The advantage of MCL over AP is dramatic for unweighted protein interaction graphs, as AP displays severe convergence problems on the majority of the unweighted graph versions that we tested, whereas MCL continues to identify meaningful clusters, albeit fewer of them, as the level of noise in the graph increases. MCL thus remains the method of choice for identifying protein complexes from binary interaction networks.

Local coherence in genetic interaction patterns reveals prevalent functional versatility.

MOTIVATION: Epistatic or genetic interactions, representing the effects of mutating one gene on the phenotypes caused by mutations in one or more distinct genes, can be very helpful for uncovering functional relationships between genes. Recently, the epistatic miniarray profiles (E-MAP) method has emerged as a powerful approach for identifying such interactions systematically. For E-MAP data analysis, hierarchical clustering is used to partition genes into groups on the basis of the similarity between their global interaction profiles, and the resulting descriptions assign each gene to only one group, thereby ignoring the multifunctional roles played by most genes. RESULTS: Here, we present the original local coherence detection (LCD) algorithm for identifying groups of functionally related genes from E-MAP data in a manner that allows individual genes to be assigned to more than one functional group. This enables investigation of the pleiotropic nature of gene function. The performance of our algorithm is illustrated by applying it to two E-MAP datasets and an E-MAP-like in silico dataset for the yeast Saccharomyces cerevisiae. In addition to recapitulating the majority of the functional modules and many protein complexes reported previously, our algorithm uncovers many recently documented and novel multifunctional relationships between genes and gene groups. Our algorithm hence represents a valuable tool for uncovering new roles for genes with annotated functions and for mapping groups of genes and proteins into pathways.

Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis.

Mitochondrial protein (MP) assemblies undergo alterations during neurogenesis, a complex process vital in brain homeostasis and disease. Yet which MP assemblies remodel during differentiation remains unclear. Here, using mass spectrometry-based co-fractionation profiles and phosphoproteomics, we generated mitochondrial interaction maps of human pluripotent embryonal carcinoma stem cells and differentiated neuronal-like cells, which presented as two discrete cell populations by single-cell RNA sequencing. The resulting networks, encompassing 6,442 high-quality associations among 600 MPs, revealed widespread changes in mitochondrial interactions and site-specific phosphorylation during neuronal differentiation. By leveraging the networks, we show the orphan C20orf24 as a respirasome assembly factor whose disruption markedly reduces respiratory chain activity in patients deficient in complex IV. We also find that a heme-containing neurotrophic factor, neuron-derived neurotrophic factor [NENF], couples with Parkinson disease-related proteins to promote neurotrophic activity. Our results provide insights into the dynamic reorganization of mitochondrial networks during neuronal differentiation and highlights mechanisms for MPs in respirasome, neuronal function, and mitochondrial diseases.

Global landscape of cell envelope protein complexes in Escherichia coli.

Bacterial cell envelope protein (CEP) complexes mediate a range of processes, including membrane assembly, antibiotic resistance and metabolic coordination. However, only limited characterization of relevant macromolecules has been reported to date. Here we present a proteomic survey of 1,347 CEPs encompassing 90% inner- and outer-membrane and periplasmic proteins of Escherichia coli. After extraction with non-denaturing detergents, we affinity-purified 785 endogenously tagged CEPs and identified stably associated polypeptides by precision mass spectrometry. The resulting high-quality physical interaction network, comprising 77% of targeted CEPs, revealed many previously uncharacterized heteromeric complexes. We found that the secretion of autotransporters requires translocation and the assembly module TamB to nucleate proper folding from periplasm to cell surface through a cooperative mechanism involving the ß-barrel assembly machinery. We also establish that an ABC transporter of unknown function, YadH, together with the Mla system preserves outer membrane lipid asymmetry. This E. coli CEP 'interactome' provides insights into the functional landscape governing CE systems essential to bacterial growth, metabolism and drug resistance.

GenePro: a Cytoscape plug-in for advanced visualization and analysis of interaction networks.

MOTIVATION: Analyzing the networks of interactions between genes and proteins has become a central theme in systems biology. Versatile software tools for interactively displaying and analyzing these networks are therefore very much in demand. The public-domain open software environment Cytoscape has been developed with the goal of facilitating the design and development of such software tools by the scientific community. RESULTS: We present GenePro, a plugin to Cytoscape featuring a set of versatile tools that greatly facilitates the visualization and analysis of protein networks derived from high-throughput interactions data and the validation of various methods for parsing these networks into meaningful functional modules. AVAILABILITY: The GenePro plugin is available at the website http://genepro.ccb.sickkids.ca.

Features of the Chaperone Cellular Network Revealed through Systematic Interaction Mapping.

A comprehensive view of molecular chaperone function in the cell was obtained through a systematic global integrative network approach based on physical (protein-protein) and genetic (gene-gene or epistatic) interaction mapping. This allowed us to decipher interactions involving all core chaperones (67) and cochaperones (15) of Saccharomyces cerevisiae. Our analysis revealed the presence of a large chaperone functional supercomplex, which we named the naturally joined (NAJ) chaperone complex, encompassing Hsp40, Hsp70, Hsp90, AAA+, CCT, and small Hsps. We further found that many chaperones interact with proteins that form foci or condensates under stress conditions. Using an in vitro reconstitution approach, we demonstrate condensate formation for the highly conserved AAA+ ATPases Rvb1 and Rvb2, which are part of the R2TP complex that interacts with Hsp90. This expanded view of the chaperone network in the cell clearly demonstrates the distinction between chaperones having broad versus narrow substrate specificities in protein homeostasis.

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.

Conditional Epistatic Interaction Maps Reveal Global Functional Rewiring of Genome Integrity Pathways in Escherichia coli.

As antibiotic resistance is increasingly becoming a public health concern, an improved understanding of the bacterial DNA damage response (DDR), which is commonly targeted by antibiotics, could be of tremendous therapeutic value. Although the genetic components of the bacterial DDR have been studied extensively in isolation, how the underlying biological pathways interact functionally remains unclear. Here, we address this by performing systematic, unbiased, quantitative synthetic genetic interaction (GI) screens and uncover widespread changes in the GI network of the entire genomic integrity apparatus of Escherichia coli under standard and DNA-damaging growth conditions. The GI patterns of untreated cultures implicated two previously uncharacterized proteins (YhbQ and YqgF) as nucleases, whereas reorganization of the GI network after DNA damage revealed DDR roles for both annotated and uncharacterized genes. Analyses of pan-bacterial conservation patterns suggest that DDR mechanisms and functional relationships are near universal, highlighting a modular and highly adaptive genomic stress response.

Extracting high confidence protein interactions from affinity purification data: at the crossroads.

Deriving protein-protein interactions from data generated by affinity-purification and mass spectrometry (AP-MS) techniques requires application of scoring methods to measure the reliability of detected putative interactions. Choosing the appropriate scoring method has become a major challenge. Here we apply six popular scoring methods to the same AP-MS dataset and compare their performance. The comparison was carried out for six distinct datasets from human, fly and yeast, which focus on different biological processes and differ in their coverage of the proteome. Results show that the performance of a given scoring method may vary substantially depending on the dataset. Disturbingly, we find that the high confidence (HC) PPI networks built by applying the six scoring methods to the same raw AP-MS dataset display very poor overlap, with only 1.7-4.1% of the HC interactions present in all the networks built, respectively, from the proteome-wide human, fly or yeast datasets. Various properties of the shared versus unique interactions in each network, including biases in protein abundance, suggest that current scoring methods are able to eliminate only the most obvious contaminants, but still fail to reliably single out specific interactions from the large body of spurious associations detected in the AP-MS experiments. BIOLOGICAL SIGNIFICANCE: The fast progress in AP-MS techniques has prompted the development of a multitude of scoring methods, which are relied upon to remove contaminants and non-specific binders. Choosing the appropriate scoring scheme for a given AP-MS dataset has become a major challenge. The comparative analysis of 6 of the most popular scoring methods, presented here, reveals that overall these methods do not perform as expected. Evidence is provided that this is due to 3 closely related issues: the high 'noise' levels of the raw AP-MS data, the limited capacity of current scoring methods to deal with such high noise levels, and the biases introduced using Gold Standard datasets to benchmark the scoring functions and threshold the networks. For the field to move forward, all three issues will have to be addressed. This article is part of a Special Issue entitled: Protein dynamics in health and disease. Guest Editors: Pierre Thibault and Anne-Claude Gingras.