Recent advances in large-scale protein interactome mapping

Global Protein Interactome Mapping in Rice Using Barcode-Indexed PCR Coupled with HiFi Long-Read Sequencing

Establishing the protein-protein interaction network sheds light on functional genomics studies by providing insights from known counterparts. However, the rice interactome has barely been studied due to the lack of massive, reliable, and cost-effective methodologies. Here, the development of a barcode-indexed PCR coupled with HiFi long-read sequencing pipeline (BIP-seq) is reported for high throughput Protein Protein Interaction (PPI)identification. BIP-seq is essentially built on the integration of library versus library Y2H mating strategy to facilitate the efficient acquisition of random PPI colonies, semi-mechanized dual barcode-indexed yeast colony PCR for the large-scale indexed amplification of bait and prey cDNAs, and massive pac-bio sequencing of PCR amplicon pools. It is demonstrated that BIP-seq could map over 15 000 high-confidence (≈62.5% could be verified by Bimolecular fluorescence Complementation (BiFC)) rice PPIs within 2 months, outperforming the other reported methods. In addition, the obtained 23 032 rice PPIs, including 22,665 newly identified PPIs, greatly expanded the current rice PPI dataset, provided a comprehensive overview of the rice PPIs networks, and could be a valuable asset in facilitating functional genomics research in rice.

Physical and functional interactome atlas of human receptor tyrosine kinases

Much cell-to-cell communication is facilitated by cell surface receptor tyrosine kinases (RTKs). These proteins phosphorylate their downstream cytoplasmic substrates in response to stimuli such as growth factors. Despite their central roles, the functions of many RTKs are still poorly understood. To resolve the lack of systematic knowledge, we apply three complementary methods to map the molecular context and substrate profiles of RTKs. We use affinity purification coupled to mass spectrometry (AP-MS) to characterize stable binding partners and RTK-protein complexes, proximity-dependent biotin identification (BioID) to identify transient and proximal interactions, and an in vitro kinase assay to identify RTK substrates. To identify how kinase interactions depend on kinase activity, we also use kinase-deficient mutants. Our data represent a comprehensive, systemic mapping of RTK interactions and substrates. This resource adds information regarding well-studied RTKs, offers insights into the functions of less well-studied RTKs, and highlights RTK-RTK interactions and shared signaling pathways.

Proximity proteomics identifies PAK4 as a component of Afadin-Nectin junctions

Human PAK4 is an ubiquitously expressed p21-activated kinase which acts downstream of Cdc42. Since PAK4 is enriched in cell-cell junctions, we probed the local protein environment around the kinase with a view to understanding its location and substrates. We report that U2OS cells expressing PAK4-BirA-GFP identify a subset of 27 PAK4-proximal proteins that are primarily cell-cell junction components. Afadin/AF6 showed the highest relative biotin labelling and links to the nectin family of homophilic junctional proteins. Reciprocally >50% of the PAK4-proximal proteins were identified by Afadin BioID. Co-precipitation experiments failed to identify junctional proteins, emphasizing the advantage of the BioID method. Mechanistically PAK4 depended on Afadin for its junctional localization, which is similar to the situation in Drosophila. A highly ranked PAK4-proximal protein LZTS2 was immuno-localized with Afadin at cell-cell junctions. Though PAK4 and Cdc42 are junctional, BioID analysis did not yield conventional cadherins, indicating their spatial segregation. To identify cellular PAK4 substrates we then assessed rapid changes (12') in phospho-proteome after treatment with two PAK inhibitors. Among the PAK4-proximal junctional proteins seventeen PAK4 sites were identified. We anticipate mammalian group II PAKs are selective for the Afadin/nectin sub-compartment, with a demonstrably distinct localization from tight and cadherin junctions.

Functional Roles of Bromodomain Proteins in Cancer

Histone acetylation is generally associated with an open chromatin configuration that facilitates many cellular processes including gene transcription, DNA repair, and DNA replication. Aberrant levels of histone lysine acetylation are associated with the development of cancer. Bromodomains represent a family of structurally well-characterized effector domains that recognize acetylated lysines in chromatin. As part of their fundamental reader activity, bromodomain-containing proteins play versatile roles in epigenetic regulation, and additional functional modules are often present in the same protein, or through the assembly of larger enzymatic complexes. Dysregulated gene expression, chromosomal translocations, and/or mutations in bromodomain-containing proteins have been correlated with poor patient outcomes in cancer. Thus, bromodomains have emerged as a highly tractable class of epigenetic targets due to their well-defined structural domains, and the increasing ease of designing or screening for molecules that modulate the reading process. Recent developments in pharmacological agents that target specific bromodomains has helped to understand the diverse mechanisms that bromodomains play with their interaction partners in a variety of chromatin processes, and provide the promise of applying bromodomain inhibitors into the clinical field of cancer treatment. In this review, we explore the expression and protein interactome profiles of bromodomain-containing proteins and discuss them in terms of functional groups. Furthermore, we highlight our current understanding of the roles of bromodomain-containing proteins in cancer, as well as emerging strategies to specifically target bromodomains, including combination therapies using bromodomain inhibitors alongside traditional therapeutic approaches designed to re-program tumorigenesis and metastasis.

An Integrative Computational Approach for the Prediction of Human-Plasmodium Protein-Protein Interactions

Host-pathogen molecular cross-talks are critical in determining the pathophysiology of a specific infection. Most of these cross-talks are mediated via protein-protein interactions between the host and the pathogen (HP-PPI). Thus, it is essential to know how some pathogens interact with their hosts to understand the mechanism of infections. Malaria is a life-threatening disease caused by an obligate intracellular parasite belonging to the Plasmodium genus, of which P. falciparum is the most prevalent. Several previous studies predicted human-plasmodium protein-protein interactions using computational methods have demonstrated their utility, accuracy, and efficiency to identify the interacting partners and therefore complementing experimental efforts to characterize host-pathogen interaction networks. To predict potential putative HP-PPIs, we use an integrative computational approach based on the combination of multiple OMICS-based methods including human red blood cells (RBC) and Plasmodium falciparum 3D7 strain expressed proteins, domain-domain based PPI, similarity of gene ontology terms, structure similarity method homology identification, and machine learning prediction. Our results reported a set of 716 protein interactions involving 302 human proteins and 130 Plasmodium proteins. This work provides a list of potential human-Plasmodium interacting proteins. These findings will contribute to better understand the mechanisms underlying the molecular determinism of malaria disease and potentially to identify candidate pharmacological targets.

Quantitative Interactomics of Lck-TurboID in Living Human T Cells Unveils T Cell Receptor Stimulation-Induced Proximal Lck Interactors

While Lck has been widely recognized to play a pivotal role in the initiation of the T cell receptor (TCR) signaling pathway, an understanding of the precise regulation of Lck in T cells upon TCR activation remains elusive. Investigation of protein-protein interaction (PPI) using proximity labeling techniques such as TurboID has the potential to provide valuable molecular insights into Lck regulatory networks. By expressing Lck-TurboID in Jurkat T cells, we have uncovered a dynamic, short-range Lck protein interaction network upon 30 min of TCR stimulation. In this novel application of TurboID, we detected 27 early signaling-induced Lck-proximal interactors in living T cells, including known and novel Lck interactors, validating the discovery power of this tool. Our results revealed previously unappreciated Lck PPI which may be associated with cytoskeletal rearrangement, ubiquitination of TCR signaling proteins, activation of the mitogen-activated protein kinase cascade, coalescence of the LAT signalosome, and formation of the immunological synapse. In this study, we demonstrated for the first time in immune cells and for the kinase Lck that TurboID can be utilized to unveil PPI dynamics in living cells at a time scale consistent with early TCR signaling. Data are available via ProteomeXchange with identifier PXD020759.

Proximity Dependent Biotinylation: Key Enzymes and Adaptation to Proteomics Approaches

The study of protein subcellular distribution, their assembly into complexes and the set of proteins with which they interact with is essential to our understanding of fundamental biological processes. Complementary to traditional assays, proximity-dependent biotinylation (PDB) approaches coupled with mass spectrometry (such as BioID or APEX) have emerged as powerful techniques to study proximal protein interactions and the subcellular proteome in the context of living cells and organisms. Since their introduction in 2012, PDB approaches have been used in an increasing number of studies and the enzymes themselves have been subjected to intensive optimization. How these enzymes have been optimized and considerations for their use in proteomics experiments are important questions. Here, we review the structural diversity and mechanisms of the two main classes of PDB enzymes: the biotin protein ligases (BioID) and the peroxidases (APEX). We describe the engineering of these enzymes for PDB and review emerging applications, including the development of PDB for coincidence detection (split-PDB). Lastly, we briefly review enzyme selection and experimental design guidelines and reflect on the labeling chemistries and their implication for data interpretation.

Mitochondrial Interactome: A Focus on Antiviral Signaling Pathways

In the last years, proteomics has represented a valuable approach to elucidate key aspects in the regulation of type I/III interferons (IFNs) and autophagy, two main processes involved in the response to viral infection, to unveil the molecular strategies that viruses have evolved to counteract these processes. Besides their main metabolic roles, mitochondria are well recognized as pivotal organelles in controlling signaling pathways essential to restrain viral infections. In particular, a major role in antiviral defense is played by mitochondrial antiviral signaling (MAVS) protein, an adaptor protein that coordinates the activation of IFN inducing pathways and autophagy at the mitochondrial level. Here, we provide an overview of how mass spectrometry-based studies of protein–protein interactions and post-translational modifications (PTMs) have fostered our understanding of the molecular mechanisms that control the mitochondria-mediated antiviral immunity.

Extended Human G-Protein Coupled Receptor Network: Cell-Type-Specific Analysis of G-Protein Coupled Receptor Signaling Pathways

G-protein coupled receptors (GPCRs) mediate crucial physiological functions in humans, have been implicated in an array of diseases, and are therefore prime drug targets. GPCRs signal via a multitude of pathways, mainly through G-proteins and β-arrestins, to regulate effectors responsible for cellular responses. The limited number of transducers results in different GPCRs exerting control on the same pathway, while the availability of signaling proteins in a cell defines the result of GPCR activation. The aim of this study was to construct the extended human GPCR network (hGPCRnet) and examine the effect that cell-type specificity has on GPCR signaling pathways. To achieve this, protein-protein interaction data between GPCRs, G-protein coupled receptor kinases (GRKs), Gα subunits, β-arrestins, and effectors were combined with protein expression data in cell types. This resulted in the hGPCRnet, a very large interconnected network, and similar cell-type-specific networks in which, distinct GPCR signaling pathways were formed. Finally, a user friendly web application, hGPCRnet ( http://bioinformatics.biol.uoa.gr/hGPCRnet ), was created to allow for the visualization and exploration of these networks and of GPCR signaling pathways. This work, and the resulting application, can be useful in further studies of GPCR function and pharmacology.

The online Tabloid Proteome: an annotated database of protein associations

A complete knowledge of the proteome can only be attained by determining the associations between proteins, along with the nature of these associations (e.g. physical contact in protein–protein interactions, participation in complex formation or different roles in the same pathway). Despite extensive efforts in elucidating direct protein interactions, our knowledge on the complete spectrum of protein associations remains limited. We therefore developed a new approach that detects protein associations from identifications obtained after re-processing of large-scale, public mass spectrometry-based proteomics data. Our approach infers protein association based on the co-occurrence of proteins across many different proteomics experiments, and provides information that is almost completely complementary to traditional direct protein interaction studies. We here present a web interface to query and explore the associations derived from this method, called the online Tabloid Proteome. The online Tabloid Proteome also integrates biological knowledge from several existing resources to annotate our derived protein associations. The online Tabloid Proteome is freely available through a user-friendly web interface, which provides intuitive navigation and data exploration options for the user at http://iomics.ugent.be/tabloidproteome.

Protein interactions study through proximity-labeling

Introduction: The proteome is a dynamic system in which protein-protein interactions play a crucial part in shaping the cell phenotype. However, given the current limitations of available technologies to describe the dynamic nature of these interactions, the identification of protein-protein interactions has long been a major challenge in proteomics. In recent years, the development of BioID and APEX, two proximity-tagging technologies, have opened-up new perspectives and have already started to change our conception of protein-protein interactions, and more generally, of the proteome. With a broad range of application encompassing health, these new technologies are currently setting milestones crucial to understand fine cellular mechanisms. Area covered: In this article, we describe both the recent and the more conventional available tools to study protein-protein interactions, compare the advantages and the limitations of these techniques, and discuss the recent advancements led by the proximity tagging techniques to refine our conception of the proteome. Expert opinion: The recent development of proximity labeling techniques emphasizes the growing importance of such technologies to decipher cellular mechanism. Although several challenges still need to be addressed, many fields can benefit from these tools and notably the detection of new therapeutic targets for patient care.

From single drug targets to synergistic network pharmacology in ischemic stroke

Current one drug–one target–one disease approaches in drug discovery have become increasingly inefficient. Network pharmacology defines disease mechanisms as networks best targeted by multiple, synergistic drugs. Using the high unmet medical need indication stroke, we here develop an integrative in silico approach based on a primary target, NADPH oxidase type 4, to identify a mechanistically related cotarget, NO synthase, for network pharmacology. Indeed, we validate both in vivo and in vitro, including humans, that both NOX4 and NOS inhibition is highly synergistic, leading to a significant reduction of infarct volume, direct neuroprotection, and blood–brain-barrier stabilization. This systems medicine approach provides a ground plan to decrease current failure in the field by being implemented in other complex indications.

Drug discovery faces an efficacy crisis to which ineffective mainly single-target and symptom-based rather than mechanistic approaches have contributed. We here explore a mechanism-based disease definition for network pharmacology. Beginning with a primary causal target, we extend this to a second using guilt-by-association analysis. We then validate our prediction and explore synergy using both cellular in vitro and mouse in vivo models. As a disease model we chose ischemic stroke, one of the highest unmet medical need indications in medicine, and reactive oxygen species forming NADPH oxidase type 4 (Nox4) as a primary causal therapeutic target. For network analysis, we use classical protein–protein interactions but also metabolite-dependent interactions. Based on this protein–metabolite network, we conduct a gene ontology-based semantic similarity ranking to find suitable synergistic cotargets for network pharmacology. We identify the nitric oxide synthase (Nos1 to 3) gene family as the closest target to Nox4. Indeed, when combining a NOS and a NOX inhibitor at subthreshold concentrations, we observe pharmacological synergy as evidenced by reduced cell death, reduced infarct size, stabilized blood–brain barrier, reduced reoxygenation-induced leakage, and preserved neuromotor function, all in a supraadditive manner. Thus, protein–metabolite network analysis, for example guilt by association, can predict and pair synergistic mechanistic disease targets for systems medicine-driven network pharmacology. Such approaches may in the future reduce the risk of failure in single-target and symptom-based drug discovery and therapy.

Functional homogeneity and specificity of topological modules in human proteome

BACKGROUND

Functional modules in protein-protein interaction networks (PPIN) are defined by maximal sets of functionally associated proteins and are vital to understanding cellular mechanisms and identifying disease associated proteins. Topological modules of the human proteome have been shown to be related to functional modules of PPIN. However, the effects of the weights of interactions between protein pairs and the integration of physical (direct) interactions with functional (indirect expression-based) interactions have not been investigated in the detection of functional modules of the human proteome.

RESULTS

We investigated functional homogeneity and specificity of topological modules of the human proteome and validated them with known biological and disease pathways. Specifically, we determined the effects on functional homogeneity and heterogeneity of topological modules (i) with both physical and functional protein-protein interactions; and (ii) with incorporation of functional similarities between proteins as weights of interactions. With functional enrichment analyses and a novel measure for functional specificity, we evaluated functional relevance and specificity of topological modules of the human proteome.

CONCLUSIONS

The topological modules ranked using specificity scores show high enrichment with gene sets of known functions. Physical interactions in PPIN contribute to high specificity of the topological modules of the human proteome whereas functional interactions contribute to high homogeneity of the modules. Weighted networks result in more number of topological modules but did not affect their functional propensity. Modules of human proteome are more homogeneous for molecular functions than biological processes.

Protein interactome mapping in Caenorhabditis elegans

The systematic identification of all protein-protein interactions that take place in an organism (the 'interactome') is an important goal in modern biology. The nematode Caenorhabditis elegans was one of the first multicellular models for which a proteome-wide interactome mapping project was initiated. Most Caenorhabditis elegans interactome mapping efforts have utilized the yeast two-hybrid system, yielding an extensive binary interactome, while recent developments in mass spectrometry-based approaches hold great potential for further improving our understanding of protein interactome networks in a multicellular context. For example, methods like co-fractionation, proximity labeling, and tissue-specific protein purification not only identify protein-protein interactions, but have the potential to provide crucial insight into when and where interactions take place. Here we review current standards and recent improvements in protein interaction mapping in C. elegans.

Complex-centric proteome profiling by SEC-SWATH-MS

Proteins are major effectors and regulators of biological processes that can elicit multiple functions depending on their interaction with other proteins. The organization of proteins into macromolecular complexes and their quantitative distribution across these complexes is, therefore, of great biological and clinical significance. In this paper, we describe an integrated experimental and computational technique to quantify hundreds of protein complexes in a single operation. The method consists of size exclusion chromatography (SEC) to fractionate native protein complexes, SWATH/DIA mass spectrometry to precisely quantify the proteins in each SEC fraction, and the computational framework CCprofiler to detect and quantify protein complexes by error‐controlled, complex‐centric analysis using prior information from generic protein interaction maps. Our analysis of the HEK293 cell line proteome delineates 462 complexes composed of 2,127 protein subunits. The technique identifies novel sub‐complexes and assembly intermediates of central regulatory complexes while assessing the quantitative subunit distribution across them. We make the toolset CCprofiler freely accessible and provide a web platform, SECexplorer, for custom exploration of the HEK293 proteome modularity.

Hub Protein Controversy: Taking a Closer Look at Plant Stress Response Hubs

Plant stress responses involve numerous changes at the molecular and cellular level and are regulated by highly complex signaling pathways. Studying protein-protein interactions (PPIs) and the resulting networks is therefore becoming increasingly important in understanding these responses. Crucial in PPI networks are the so-called hubs or hub proteins, commonly defined as the most highly connected central proteins in scale-free PPI networks. However, despite their importance, a growing amount of confusion and controversy seems to exist regarding hub protein identification, characterization and classification. In order to highlight these inconsistencies and stimulate further clarification, this review critically analyses the current knowledge on hub proteins in the plant interactome field. We focus on current hub protein definitions, including the properties generally seen as hub-defining, and the challenges and approaches associated with hub protein identification. Furthermore, we give an overview of the most important large-scale plant PPI studies of the last decade that identified hub proteins, pointing out the lack of overlap between different studies. As such, it appears that although major advances are being made in the plant interactome field, defining hub proteins is still heavily dependent on the quality, origin and interpretation of the acquired PPI data. Nevertheless, many hub proteins seem to have a reported role in the plant stress response, including transcription factors, protein kinases and phosphatases, ubiquitin proteasome system related proteins, (co-)chaperones and redox signaling proteins. A significant number of identified plant stress hubs are however still functionally uncharacterized, making them interesting targets for future research. This review clearly shows the ongoing improvements in the plant interactome field but also calls attention to the need for a more comprehensive and precise identification of hub proteins, allowing a more efficient systems biology driven unraveling of complex processes, including those involved in stress responses.

An Integrative Approach to Virus-Host Protein-Protein Interactions

Since cell regulation and protein expression can be dramatically altered upon infection by viruses, studying the mechanisms by which viruses infect cells and the regulatory networks they disrupt is essential to understanding viral pathogenicity. This line of study can also lead to discoveries about the workings of host cells themselves. Computational methods are rapidly being developed to investigate viral-host interactions, and here we highlight recent methods and the insights that they have revealed so far, with a particular focus on methods that integrate different types of data. We also review the challenges of working with viruses compared with traditional cellular biology, and the limitations of current experimental and informatics methods.

Facilitating identification of minimal protein binding domains by cross-linking mass spectrometry

Characterization of protein interaction domains is crucial for understanding protein functions. Here we combine cross-linking mass spectrometry (XL-MS) with deletion analysis to accurately locate minimal protein interaction domains. As a proof of concept, we investigated in detail the binding interfaces of two protein assemblies: the complex formed by MICAL3, ELKS and Rab8A, which is involved in exocytosis, and the complex of SLAIN2, CLASP2 and ch-TOG, which controls microtubule dynamics. We found that XL-MS provides valuable information to efficiently guide the design of protein fragments that are essential for protein interaction. However, we also observed a number of cross-links between polypeptide regions that were dispensable for complex formation, especially among intrinsically disordered sequences. Collectively, our results indicate that XL-MS, which renders distance restrains of linked residue pairs, accelerates the characterization of protein binding regions in combination with other biochemical approaches.

A simple elution strategy for biotinylated proteins bound to streptavidin conjugated beads using excess biotin and heat

Protein-protein interactions are the molecular basis of cell signaling. Recently, proximity based biotin identification (BioID) has emerged as an alternative approach to traditional co-immunoprecipitation. In this protocol, a mutant biotin ligase promiscuously labels proximal binding partners with biotin, and resulting biotinylated proteins are purified using streptavidin conjugated beads. This approach does not require preservation of protein complexes in vitro, making it an ideal approach to identify transient or weak protein complexes. However, due to the high affinity bond between streptavidin and biotin, elution of biotinylated proteins from streptavidin conjugated beads requires harsh denaturing conditions, which are often incompatible with downstream processing. To effectively release biotinylated proteins bound to streptavidin conjugated beads, we designed a series of experiments to determine optimal binding and elution conditions. Interestingly, the concentrations of SDS and IGEPAL-CA630 during the incubation with streptavidin conjugated beads were the key to effective elution of biotinylated proteins using excess biotin and heating. This protocol provides an alternative method to isolate biotinylated proteins from streptavidin conjugated beads that is suitable for further downstream analysis.

Elucidation of host-pathogen protein-protein interactions to uncover mechanisms of host cell rewiring

Infectious diseases are the result of molecular cross-talks between hosts and their pathogens. These cross-talks are in part mediated by host-pathogen protein-protein interactions (HP-PPI). HP-PPI play crucial roles in infections, as they may tilt the balance either in favor of the pathogens' spread or their clearance. The identification of host proteins targeted by viral or bacterial pathogenic proteins necessary for the infection can provide insights into their underlying molecular mechanisms of pathogenicity, and potentially even single out pharmacological intervention targets. Here, we review the available methods to study HP-PPI, with a focus on recent mass spectrometry based methods to decipher bacterial-human infectious diseases and examine their relevance in uncovering host cell rewiring by pathogens.

Photoactivated In Vivo Proximity Labeling

Identification of molecular interactions is paramount to understanding how cells function. Most available technologies rely on co-purification of a protein of interest and its binding partners. Therefore, they are limited in their ability to detect low-affinity interactions and cannot be applied to proteins that localize to difficult-to-solubilize cellular compartments. In vivo proximity labeling (IPL) overcomes these obstacles by covalently tagging proteins and RNAs based on their proximity in vivo to a protein of interest. In IPL, a heterobifunctional probe comprising a photoactivatable moiety and biotin is recruited by a monomeric streptavidin tag fused to a protein of interest. Following UV irradiation, candidate interacting proteins and RNAs are covalently biotinylated with tight spatial and temporal control and subsequently recovered using biotin as an affinity handle. Here, we describe experimental protocols to discover novel protein-protein and protein-RNA interactions using IPL. © 2017 by John Wiley & Sons, Inc.

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

Fatty acid synthase inhibits the O-GlcNAcase during oxidative stress

The dynamic post-translational modification O-linked β-N-acetylglucosamine (O-GlcNAc) regulates thousands of nuclear, cytoplasmic, and mitochondrial proteins. Cellular stress, including oxidative stress, results in increased O-GlcNAcylation of numerous proteins, and this increase is thought to promote cell survival. The mechanisms by which the O-GlcNAc transferase (OGT) and the O-GlcNAcase (OGA), the enzymes that add and remove O-GlcNAc, respectively, are regulated during oxidative stress to alter O-GlcNAcylation are not fully characterized. Here, we demonstrate that oxidative stress leads to elevated O-GlcNAc levels in U2OS cells but has little impact on the activity of OGT. In contrast, the expression and activity of OGA are enhanced. We hypothesized that this seeming paradox could be explained by proteins that bind to and control the local activity or substrate targeting of OGA, thereby resulting in the observed stress-induced elevations of O-GlcNAc. To identify potential protein partners, we utilized BioID proximity biotinylation in combination with stable isotopic labeling of amino acids in cell culture (SILAC). This analysis revealed 90 OGA-interacting partners, many of which exhibited increased binding to OGA upon stress. The associations of OGA with fatty acid synthase (FAS), filamin-A, heat shock cognate 70-kDa protein, and OGT were confirmed by co-immunoprecipitation. The pool of OGA bound to FAS demonstrated a substantial (∼85%) reduction in specific activity, suggesting that FAS inhibits OGA. Consistent with this observation, FAS overexpression augmented stress-induced O-GlcNAcylation. Although the mechanism by which FAS sequesters OGA remains unknown, these data suggest that FAS fine-tunes the cell's response to stress and injury by remodeling cellular O-GlcNAcylation.

Current Understanding of the TCTP Interactome

Evolutionarily conserved and pleiotropic, the translationally controlled tumor protein (TCTP) is a housekeeping protein present in eukaryotic organisms. It plays an important role in regulating many fundamental processes, such as cell proliferation, cell death, immune responses, and apoptosis. As a result of the pioneer work by Adam Telerman and Robert Amson, the critical role of TCTP in tumor reversion was revealed. Moreover, TCTP has emerged as a regulator of cell fate determination and a promising therapeutic target for cancers. The multifaceted action of TCTP depends on its ability to interact with different proteins. Through this interaction network, TCTP regulates diverse physiological and pathological processes in a context-dependent manner. Complete mapping of the entire sets of TCTP protein interactions (interactome) is essential to understand its various cellular functions and to lay the foundation for the rational design of TCTP-based therapeutic approaches. So far, the global profiling of the interacting partners of TCTP has rarely been performed, but many interactions have been identified in small-scale studies in a specific biological system. This chapter, based on information from protein interaction databases and the literature, illustrates current knowledge of the TCTP interactome.

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.