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

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

Towards the prediction of protein interaction partners using physical docking

Deciphering the whole network of protein interactions for a given proteome (‘interactome’) is the goal of many experimental and computational efforts in Systems Biology. Separately the prediction of the structure of protein complexes by docking methods is a well‐established scientific area. To date, docking programs have not been used to predict interaction partners. We provide a proof of principle for such an approach. Using a set of protein complexes representing known interactors in their unbound form, we show that a standard docking program can distinguish the true interactors from a background of 922 non‐redundant potential interactors. We additionally show that true interactions can be distinguished from non‐likely interacting proteins within the same structural family. Our approach may be put in the context of the proposed ‘funnel‐energy model’; the docking algorithm may not find the native complex, but it distinguishes binding partners because of the higher probability of favourable models compared with a collection of non‐binders. The potential exists to develop this proof of principle into new approaches for predicting interaction partners and reconstructing biological networks.     

A domain level interaction network of amyloid precursor protein and Aβ of Alzheimer's disease

The primary constituent of the amyloid plaque, β‐amyloid (Aβ), is thought to be the causal “toxic moiety” of Alzheimer's disease. However, despite much work focused on both Aβ and its parent protein, amyloid precursor protein (APP), the functional roles of APP and its cleavage products remain to be fully elucidated. Protein–protein interaction networks can provide insight into protein function, however, high‐throughput data often report false positives and are in frequent disagreement with low‐throughput experiments. Moreover, the complexity of the CNS is likely to be under represented in such databases. Therefore, we curated the published work characterizing both APP and Aβ to create a protein interaction network of APP and its proteolytic cleavage products, with annotation, where possible, to the level of APP binding domain and isoform. This is the first time that an interactome has been refined to domain level, essential for the interpretation of APP due to the presence of multiple isoforms and processed fragments. Gene ontology and network analysis were used to identify potentially novel functional relationships among interacting proteins.     

Lysine methylation modulates the protein–protein interactions of yeast cytochrome C Cyc1p

In recent years, protein methylation has been established as a major intracellular PTM. It has also been proposed to modulate protein‐protein interactions (PPIs) in the interactome. To investigate the effect of PTMs on PPIs, we recently developed the conditional two‐hybrid (C2H) system. With this, we demonstrated that arginine methylation can modulate PPIs in the yeast interactome. Here, we used the C2H system to investigate the effect of lysine methylation. Specifically, we asked whether Ctm1p‐mediated trimethylation of yeast cytochrome c Cyc1p, on lysine 78, modulates its interactions with Erv1p, Ccp1p, Cyc2p and Cyc3p. We show that the interactions between Cyc1p and Erv1p, and between Cyc1p and Cyc3p, are significantly increased upon trimethylation of lysine 78. This increase of interaction helps explain the reported facilitation of Cyc1p import into the mitochondrial intermembrane space upon methylation. This first application of the C2H system to the study of methyllysine‐modulated interactions further confirms its robustness and flexibility.     

Analysis of the interactome of ribosomal protein S19 mutants

Diamond–Blackfan anemia, characterized by defective erythroid progenitor maturation, is caused in one‐fourth of cases by mutations of ribosomal protein S19 (RPS19), which is a component of the ribosomal 40S subunit. Our previous work described proteins interacting with RPS19 with the aim to determine its functions. Here, two RPS19 mutants, R62W and R101H, have been selected to compare their interactomes versus the wild‐type protein one, using the same functional proteomic approach that we employed to characterize RPS19 interactome. Mutations R62W and R101H impair RPS19 ability to associate with the ribosome. Results presented in this paper highlight the striking differences between the interactomes of wild‐type and mutant RPS19 proteins. In particular, mutations abolish interactions with proteins having splicing, translational and helicase activity, thus confirming the role of RPS19 in RNA processing/metabolism and translational control. The data have been deposited to the ProteomeXchange with identifier PXD000640 ( http://proteomecentral.proteomexchange.org/dataset/PXD000640 ).     

Experimental and computational tools useful for (re)construction of dynamic kinase–substrate networks

The explosion of site‐ and context‐specific in vivo phosphorylation events presents a potentially rich source of biological knowledge and calls for novel data analysis and modeling paradigms. Perhaps the most immediate challenge is delineating detected phosphorylation sites to their effector kinases. This is important for (re)constructing transient kinase–substrate interaction networks that are essential for mechanistic understanding of cellular behaviors and therapeutic intervention, but has largely eluded high‐throughput protein‐interaction studies due to their transient nature and strong dependencies on cellular context. Here, we surveyed some of the computational approaches developed to dissect phosphorylation data detected in systematic proteomic experiments and reviewed some experimental and computational approaches used to map phosphorylation sites to their effector kinases in efforts aimed at reconstructing biological signaling networks.     

Function of the cytoplasmic tail of human calcitonin receptor-like receptor in complex with receptor activity-modifying protein 2

Receptor activity-modifying protein 2 (RAMP2) enables calcitonin receptor-like receptor (CRLR) to form an adrenomedullin (AM)-specific receptor. Here we investigated the function of the cytoplasmic C-terminal tail (C-tail) of human (h)CRLR by co-transfecting its C-terminal mutants into HEK-293 cells stably expressing hRAMP2. Deleting the C-tail from CRLR disrupted AM-evoked cAMP production or receptor internalization, but did not affect [¹²⁵I]AM binding. We found that CRLR residues 428-439 are required for AM-evoked cAMP production, though deleting this region had little effect on receptor internalization. Moreover, pretreatment with pertussis toxin (100 ng/mL) led to significant increases in AM-induced cAMP production via wild-type CRLR/RAMP2 complexes. This effect was canceled by deleting CRLR residues 454-457, suggesting Gi couples to this region. Flow cytometric analysis revealed that CRLR truncation mutants lacking residues in the Ser/Thr-rich region extending from Ser⁴⁴⁹ to Ser⁴⁶⁷ were unable to undergo AM-induced receptor internalization and, in contrast to the effect on wild-type CRLR, overexpression of GPCR kinases-2, -3 and -4 failed to promote internalization of CRLR mutants lacking residues 449-467. Thus, the hCRLR C-tail is crucial for AM-evoked cAMP production and internalization of the CRLR/RAMP2, while the receptor internalization is dependent on the aforementioned GPCR kinases, but not Gs coupling.     

The structural analysis of protein–protein interactions by NMR spectroscopy

A comprehensive understanding of protein–protein interactions is an important next step in our quest to understand how the information contained in a genome is put into action. Although a number of experimental techniques can report on the existence of a protein– protein interaction, very few can provide detailed structural information. NMR spectroscopy is one of these, and in recent years several complementary NMR approaches, including residual dipolar couplings and the use of paramagnetic effects, have been developed that can provide insight into the structure of protein–protein complexes. In this article, we review these approaches and comment on their strengths and weaknesses.     

Proteomic analysis of interactors for yeast protein arginine methyltransferase Hmt1 reveals novel substrate and insights into additional biological roles

Protein arginine methylation is a PTM catalyzed by an evolutionarily conserved family of enzymes called protein arginine methyltransferases (PRMTs), with PRMT1 being the most conserved member of this enzyme family. This modification has emerged to be an important regulator of protein functions. To better understand the role of PRMTs in cellular pathways and functions, we have carried out a proteomic profiling experiment to comprehensively identify the physical interactors of Hmt1, the budding yeast homolog for human PRMT1. Using a dual‐enzymatic digestion linear trap quadrupole/Orbitrap proteomic strategy, we identified a total of 108 proteins that specifically copurify with Hmt1 by tandem affinity purification. A reverse coimmunoprecipitation experiment was used to confirm Hmt1's physical association with Bre5, Mtr4, Snf2, Sum1, and Ssd1, five proteins that were identified as Hmt1‐specific interactors in multiple biological replicates. To determine whether the identified Hmt1‐interactors had the potential to act as an Hmt1 substrate, we used published bioinformatics algorithms that predict the presence and location of potential methylarginines for each identified interactor. One of the top hits from this analysis, Snf2, was experimentally confirmed as a robust substrate of Hmt1 in vitro. Overall, our data provide a feasible proteomic approach that aid in the better understanding of PRMT1's roles within a cell.     

The Interactorium: Visualising proteins,complexes and interaction networks in a virtual 3‐D cell

Here, we describe the Interactorium, a tool in which a Virtual Cell is used as the context for the seamless visualisation of the yeast protein interaction network, protein complexes and protein 3‐D structures. The tool has been designed to display very complex networks of up to 40 000 proteins or 6000 multiprotein complexes and has a series of toolboxes and menus to allow real‐time data manipulation and control the manner in which data are displayed. It incorporates new algorithms that reduce the complexity of the visualisation by the generation of putative new complexes from existing data and by the reduction of edges through the use of protein “twins” when they occur in multiple locations. Since the Interactorium permits multi‐level viewing of the molecular biology of the cell, it is a considerable advance over existing approaches. We illustrate its use for Saccharomyces cerevisiae but note that it will also be useful for the analysis of data from simpler prokaryotes and higher eukaryotes, including humans. The Interactorium is available for download at http://www.interactorium.net .     

The Cartographers toolbox: building bigger and better human protein interaction networks

One of the greatest challenges of the post-genomic era is theconstruction of a more comprehensive human protein interactionmap. While this process may take many years to complete, thedevelopment of stringent high throughput techniques and theemergence of complementary assays mean that the aim of buildinga detailed binary map of the human interactome is now a veryrealistic goal. In particular, methods which facilitate theanalysis of large numbers of membrane-protein interactions meanthat it will be possible to construct more extensive networks,which in turn provide new insights into the functional connectivitybetween intra- and extra-cellular processes. This is importantas many therapeutic strategies are designed to elicit effectsvia ‘tractable’ cell-surface proteins. Therefore,the construction of maps depicting the complexity of trans-cellularcommunication networks will not only improve our understandingof physiological processes, it will also aid the design of rationaltherapeutic strategies, with fewer potential side effects. Thisreview aims to provide a basic insight into the approaches currentlybeing used to construct binary human protein interaction networks,with particular reference to newer techniques, which have thepotential to extend network coverage and aid the conditionalannotation of interactome-scale protein interaction maps.      

LuMPIS – A modified luminescence‐based mammalian interactome mapping pull‐down assay for the investigation of protein–protein interactions encoded by GC‐low ORFs

The GC content is highly variable among the genomes of different organisms. It has been shown that recombinant gene expression in mammalian cells is much more efficient when GC‐rich coding sequences of a certain protein are used. In order to study protein–protein interactions in Varicella zoster virus, a GC‐low herpesvirus, we have developed a novel luminescence‐based maltose‐binding protein pull‐down interaction screening system (LuMPIS) that is able to overcome the impaired protein expression levels of GC‐low ORFs in mammalian expression systems.     

Systematic discovery of mutation-directed neo-protein-protein interactions in cancer

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Sphingolipids as modulators of membrane proteins

The diversity of the transmembranome of higher eukaryotes is matched by an enormous diversity of sphingolipid classes and molecular species. The intrinsic properties of sphingolipids are not only suited for orchestrating lateral architectures of biological membranes, but their molecular distinctions also allow for the evolution of protein motifs specifically recognising and interacting with individual lipids. Although various reports suggest a role of sphingolipids in membrane protein function, only a few cases have determined the specificity of these interactions. In this review we discuss examples of specific protein–sphingolipid interactions for which a modulator-like dependency on sphingolipids was assigned to specific proteins. These novel functions of sphingolipids in specific protein–lipid assemblies contribute to the complexity of the sphingolipid classes and other molecular species observed in animal cells. This article is part of a Special Issue entitled New Frontiers in Sphingolipid Biology.     

Cytoskeletal scaffolding proteins interact with Lynch‐Syndrome associated mismatch repair protein MLH1

The involvement of MLH1 in several mismatch repair‐independent cellular processes has been reported. In an attempt to gain further insight into the protein's cellular functions, we screened for novel interacting partners of MLH1 utilizing a bacterial two‐hybrid system. Numerous unknown interacting proteins were identified, suggesting novel biological roles of MLH1. The network of MLH1 and its partner proteins involves a multitude of cellular processes. Integration of our data with the “General Repository for Interaction Datasets” highlighted that MLH1 exhibits relationships to three interacting pairs of proteins involved in cytoskeletal and filament organization: Thymosin β 4 and Actin γ, Cathepsin B and Annexin A2 as well as Spectrin α and Desmin. Coimmunoprecipitation and colocalization experiments validated the interaction of MLH1 with these proteins. Differential mRNA levels of many of the identified proteins, detected by microarray analysis comparing MLH1‐deficient and ‐proficient cell lines, support the assumed interplay of MLH1 and the identified candidate proteins. By siRNA knock down of MLH1, we demonstrated the functional impact of MLH1–Actin interaction on filament organization and propose that dysregulation of MLH1 plays an essential role in cytoskeleton dynamics. Our data suggest novel roles of MLH1 in cellular organization and colorectal cancerogenesis.     

Systematic protein–protein interaction mapping for clinically relevant human GPCRs

G‐protein‐coupled receptors (GPCRs) are the largest family of integral membrane receptors with key roles in regulating signaling pathways targeted by therapeutics, but are difficult to study using existing proteomics technologies due to their complex biochemical features. To obtain a global view of GPCR‐mediated signaling and to identify novel components of their pathways, we used a modified membrane yeast two‐hybrid (MYTH) approach and identified interacting partners for 48 selected full‐length human ligand‐unoccupied GPCRs in their native membrane environment. The resulting GPCR interactome connects 686 proteins by 987 unique interactions, including 299 membrane proteins involved in a diverse range of cellular functions. To demonstrate the biological relevance of the GPCR interactome, we validated novel interactions of the GPR37, serotonin 5‐HT4d, and adenosine ADORA2A receptors. Our data represent the first large‐scale interactome mapping for human GPCRs and provide a valuable resource for the analysis of signaling pathways involving this druggable family of integral membrane proteins.     

Information integration of protein–protein interactions as essential tools for immunomics

After a brief introduction to point out the necessity to advance for a global understanding of the macromolecular interactions occurring during the immune system development and responses, Section 2 will be devoted to analyse the current tools for an automatic location of information on these protein–protein interactions in the web. In the next section (Section 3), we will point out different action lines to improve these tools and, consequently, to increase the efficiency to establish (to understand) the “protein network skeleton” that controls our immune responses. Finally, we will briefly present our current strategy and work to advance towards this goal.