Sticking together? Falling apart? Exploring the dynamics of the interactome

Advances in techniques for the study of protein-protein interactions have dramatically improved our understanding of the interactome. However, we know little about the dynamics of this complex system. To better understand the dynamics of the interactome, it is important to consider what happens when single proteins are perturbed. Changes in protein abundance and post-translational modification can function as switches in the interactome, affecting protein-complex assembly and function. Changes in protein sequence or a dramatic increase in abundance might cause a promiscuous gain of interactions. These effects are not identical for all proteins and will differ depending on the number and type of interaction partners that a protein has.     

Interactive three-dimensional visualization and contextual analysis of protein interaction networks

To understand the biology of the interactome, the covisualization of protein interactions and other protein-related data is required. In this study, we have adapted a 3-D network visualization platform, GEOMI, to allow the coanalysis of protein-protein interaction networks with proteomic parameters such as protein localization, abundance, physicochemical parameters, post-translational modifications, and gene ontology classification. Working with Saccharomyces cerevisiae data, we show that rich and interactive visualizations, constructed from multidimensional orthogonal data, provide insights on the complexity of the interactome and its role in biological processes and the architecture of the cell. We present the first organelle-specific interaction networks, that provide subinteractomes of high biological interest. We further present some of the first views of the interactome built from a new combination of yeast two-hybrid data and stable protein complexes, which are likely to approximate the true workings of stable and transient aspects of the interactome. The GEOMI tool and all interactome data are freely available by contacting the authors.     

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.     

Assessing Coverage of Protein Interaction Data Using Capture–Recapture Models

Protein interaction networks comprise thousands of individual binary links between distinct proteins. Whilst these data have attracted considerable attention and been the focus of many different studies, the networks, their structure, function, and how they change over time are still not fully known. More importantly, there is still considerable uncertainty regarding their size, and the quality of the available data continues to be questioned. Here, we employ statistical models of the experimental sampling process, in particular capture–recapture methods, in order to assess the false discovery rate and size of protein interaction networks. We uses these methods to gauge the ability of different experimental systems to find the true binary interactome. Our model allows us to obtain estimates for the size and false-discovery rate from simple considerations regarding the number of repeatedly interactions, and provides suggestions as to how we can exploit this information in order to reduce the effects of noise in such data. In particular our approach does not require a reference dataset. We estimate that approximately more than half of the true physical interactome has now been sampled in yeast.     

The protein–protein interaction network of the human Sirtuin family

Protein–protein interaction networks are useful for studying human diseases and to look for possible health care through a holistic approach. Networks are playing an increasing and important role in the understanding of physiological processes such as homeostasis, signaling, spatial and temporal organizations, and pathological conditions. In this article we show the complex system of interactions determined by human Sirtuins (Sirt) largely involved in many metabolic processes as well as in different diseases. The Sirtuin family consists of seven homologous Sirt-s having structurally similar cores but different terminal segments, being rather variable in length and/or intrinsically disordered. Many studies have determined their cellular location as well as biological functions although molecular mechanisms through which they act are actually little known therefore, the aim of this work was to define, explore and understand the Sirtuin-related human interactome. As a first step, we have integrated the experimentally determined protein–protein interactions of the Sirtuin-family as well as their first and second neighbors to a Sirtuin-related sub-interactome. Our data showed that the second-neighbor network of Sirtuins encompasses 25% of the entire human interactome, and exhibits a scale-free degree distribution and interconnectedness among top degree nodes. Moreover, the Sirtuin sub interactome showed a modular structure around the core comprising mixed functions. Finally, we extracted from the Sirtuin sub-interactome subnets related to cancer, aging and post-translational modifications for information on key nodes and topological space of the subnets in the Sirt family network.     

Adding Protein Context to the Human Protein-Protein Interaction Network to Reveal Meaningful Interactions

Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs), which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays) or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer''s disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the annotated human PPI network via a web frontend that allows the construction of context-specific networks in several ways.     

Interactome Analysis of KIN (Kin17) Shows New Functions of This Protein

KIN (Kin17) protein is overexpressed in a number of cancerous cell lines, and is therefore considered a possible cancer biomarker. It is a well-conserved protein across eukaryotes and is ubiquitously expressed in all cell types studied, suggesting an important role in the maintenance of basic cellular function which is yet to be well determined. Early studies on KIN suggested that this nuclear protein plays a role in cellular mechanisms such as DNA replication and/or repair; however, its association with chromatin depends on its methylation state. In order to provide a better understanding of the cellular role of this protein, we investigated its interactome by proximity-dependent biotin identification coupled to mass spectrometry (BioID-MS), used for identification of protein–protein interactions. Our analyses detected interaction with a novel set of proteins and reinforced previous observations linking KIN to factors involved in RNA processing, notably pre-mRNA splicing and ribosome biogenesis. However, little evidence supports that this protein is directly coupled to DNA replication and/or repair processes, as previously suggested. Furthermore, a novel interaction was observed with PRMT7 (protein arginine methyltransferase 7) and we demonstrated that KIN is modified by this enzyme. This interactome analysis indicates that KIN is associated with several cell metabolism functions, and shows for the first time an association with ribosome biogenesis, suggesting that KIN is likely a moonlight protein.     

Dynamic remodeling of Escherichia coli interactome in response to environmental perturbations

Proteins play an essential role in the vital biological processes governing cellular functions. Most proteins function as members of macromolecular machines, with the network of interacting proteins revealing the molecular mechanisms driving the formation of these complexes. Profiling the physiology-driven remodeling of these interactions within different contexts constitutes a crucial component to achieving a comprehensive systems-level understanding of interactome dynamics. Here, we apply co-fractionation mass spectrometry and computational modeling to quantify and profile the interactions of ∼2000 proteins in the bacterium Escherichia coli cultured under 10 distinct culture conditions. The resulting quantitative co-elution patterns revealed large-scale condition-dependent interaction remodeling among protein complexes involved in diverse biochemical pathways in response to the unique environmental challenges. The network-level analysis highlighted interactome-wide biophysical properties and structural patterns governing interaction remodeling. Our results provide evidence of the local and global plasticity of the E. coli interactome along with a rigorous generalizable framework to define protein interaction specificity. We provide an accompanying interactive web application to facilitate the exploration of these rewired networks.     

Interconnection of the mycobacterial heparin-binding hemagglutinin with cholesterol degradation and heme/iron pathways identified by proximity-dependent biotin identification in Mycobacterium smegmatis

Deciphering protein–protein interactions is a critical step in the identification and the understanding of biological mechanisms deployed by pathogenic bacteria. The development of in vivo technologies to characterize these interactions is still in its infancy, especially for bacteria whose subcellular organization is particularly complex, such as mycobacteria. In this work, we used the proximity-dependent biotin identification (BioID) to define the mycobacterial heparin-binding hemagglutinin (HbhA) interactome in the saprophytic bacterium Mycobacterium smegmatis. M. smegmatis is a commonly used model to study and characterize the physiology of pathogenic mycobacteria, such as Mycobacterium tuberculosis. Here, we adapted the BioID technology to in vivo protein–protein interactions studies in M. smegmatis, which presents several advantages, such as maintaining the complex organization of the mycomembrane, offering the possibility to study membrane or cell wall-associated proteins, including HbhA, in the presence of cofactors and post-translational modifications, such as the complex methylation pattern of HbhA. Using this technology, we found that HbhA is interconnected with cholesterol degradation and heme/iron pathways. These results are in line with previous studies showing the dual localization of HbhA, associated with the cell wall and intracytoplasmic lipid inclusions, and its induction under high iron growth conditions.     

Visualization of the interactome: What are we looking at?

Network visualization of the interactome has been become routine in systems biology research. Not only does it serve as an illustration on the cellular organization of protein-protein interactions, it also serves as a biological context for gaining insights from high-throughput data. However, the challenges to produce an effective visualization have been great owing to the fact that the scale, biological context and dynamics of any given interactome are too large and complex to be captured by a single visualization. Visualization design therefore requires a pragmatic trade-off between capturing biological concept and being comprehensible. In this review, we focus on the biological interpretation of different network visualizations. We will draw on examples predominantly from our experiences but elaborate them in the context of the broader field. A rich variety of networks will be introduced including interactomes and the complexome in 2D, interactomes in 2.5D and 3D and dynamic networks.     

Interactome networks and human disease

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.     

Vascular Endothelial Growth Factor (VEGF) Promotes Assembly of the p130Cas Interactome to Drive Endothelial Chemotactic Signaling and Angiogenesis

p130Cas is a polyvalent adapter protein essential for cardiovascular development, and with a key role in cell movement. In order to identify the pathways by which p130Cas exerts its biological functions in endothelial cells we mapped the p130Cas interactome and its dynamic changes in response to VEGF using high-resolution mass spectrometry and reconstruction of protein interaction (PPI) networks with the aid of multiple PPI databases. VEGF enriched the p130Cas interactome in proteins involved in actin cytoskeletal dynamics and cell movement, including actin-binding proteins, small GTPases and regulators or binders of GTPases. Detailed studies showed that p130Cas association of the GTPase-binding scaffold protein, IQGAP1, plays a key role in VEGF chemotactic signaling, endothelial polarization, VEGF-induced cell migration, and endothelial tube formation. These findings indicate a cardinal role for assembly of the p130Cas interactome in mediating the cell migratory response to VEGF in angiogenesis, and provide a basis for further studies of p130Cas in cell movement.     

The role of structural disorder in the rewiring of protein interactions through evolution

Structurally disordered regions play a key role in protein-protein interaction networks and the evolution of highly connected proteins, enabling the molecular mechanisms for multiple binding. However, the role of protein disorder in the evolution of interaction networks has only been investigated through the analysis of individual proteins, making it impossible to distinguish its specific impact in the (re)shaping of their interaction environments. Now, the availability of large interactomes for several model organisms permits exploration of the role of disorder in protein interaction networks not only at the level of the interacting proteins but of the interactions themselves. By comparing the interactomes of human, fly, and yeast, we discovered that, despite being much more abundant, disordered interactions are significantly less conserved than their ordered counterparts. Furthermore, our analyses provide evidence that this happens not only because disordered proteins are less conserved but also because they display a higher capacity to rewire their interaction neighborhood through evolution. Overall, our results support the hypothesis that conservation of disorder gives a clear evolutionary advantage, facilitating the change of interaction partners during evolution. Moreover, this mechanism is not exclusive of a few anecdotal cases but a global feature present in the interactome networks of entire organisms.