[Trends in interactomics].
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Computational Biology↗
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Mining Alzheimer disease relevant proteins from integrated protein interactome data.
Huge unrealized post-genome opportunities remain in the understanding of detailed molecular mechanisms for Alzheimer Disease (AD). In this work, we developed a computational method to rank-order AD-related proteins, based on an initial list of AD-related genes and public human protein interaction data. In this method, we first collected an initial seed list of 65 AD-related genes from the OMIM database and mapped them to 70 AD seed proteins. We then expanded the seed proteins to an enriched AD set of 765 proteins using protein interactions from the Online Predicated Human Interaction Database (OPHID). We showed that the expanded AD-related proteins form a highly connected and statistically significant protein interaction sub-network. We further analyzed the sub-network to develop an algorithm, which can be used to automatically score and rank-order each protein for its biological relevance to AD pathways(s). Our results show that functionally relevant AD proteins were consistently ranked at the top: among the top 20 of 765 expanded AD proteins, 19 proteins are confirmed to belong to the original 70 AD seed protein set. Our method represents a novel use of protein interaction network data for Alzheimer disease studies and may be generalized for other disease areas in the future.
Algorithms↗
[Network "interactome"].
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Computer Graphics↗
Effect of sampling on topology predictions of protein-protein interaction networks.
Currently available protein-protein interaction (PPI) network or 'interactome' maps, obtained with the yeast two-hybrid (Y2H) assay or by co-affinity purification followed by mass spectrometry (co-AP/MS), only cover a fraction of the complete PPI networks. These partial networks display scale-free topologies--most proteins participate in only a few interactions whereas a few proteins have many interaction partners. Here we analyze whether the scale-free topologies of the partial networks obtained from Y2H assays can be used to accurately infer the topology of complete interactomes. We generated four theoretical interaction networks of different topologies (random, exponential, power law, truncated normal). Partial sampling of these networks resulted in sub-networks with topological characteristics that were virtually indistinguishable from those of currently available Y2H-derived partial interactome maps. We conclude that given the current limited coverage levels, the observed scale-free topology of existing interactome maps cannot be confidently extrapolated to complete interactomes.
Animals↗
Inferring topology from clustering coefficients in protein-protein interaction networks.
BACKGROUND: Although protein-protein interaction networks determined with high-throughput methods are incomplete, they are commonly used to infer the topology of the complete interactome. These partial networks often show a scale-free behavior with only a few proteins having many and the majority having only a few connections. Recently, the possibility was suggested that this scale-free nature may not actually reflect the topology of the complete interactome but could also be due to the error proneness and incompleteness of large-scale experiments. RESULTS: In this paper, we investigate the effect of limited sampling on average clustering coefficients and how this can help to more confidently exclude possible topology models for the complete interactome. Both analytical and simulation results for different network topologies indicate that partial sampling alone lowers the clustering coefficient of all networks tremendously. Furthermore, we extend the original sampling model by also including spurious interactions via a preferential attachment process. Simulations of this extended model show that the effect of wrong interactions on clustering coefficients depends strongly on the skewness of the original topology and on the degree of randomness of clustering coefficients in the corresponding networks. CONCLUSION: Our findings suggest that the complete interactome is either highly skewed such as e.g. in scale-free networks or is at least highly clustered. Although the correct topology of the interactome may not be inferred beyond any reasonable doubt from the interaction networks available, a number of topologies can nevertheless be excluded with high confidence.
Cluster Analysis↗
Interaction network of proteins associated with abiotic stress response and development in wheat.
Wheat is the most widely adapted crop to abiotic stresses and considered an excellent system to study stress tolerance in spite of its genetic complexity. Recent studies indicated that several hundred genes are either up- or down-regulated in response to stress treatment. To elucidate the function of some of these genes, an interactome of proteins associated with abiotic stress response and development in wheat was generated using the yeast two-hybrid GAL4 system and specific protein interaction assays. The interactome is comprised of 73 proteins, generating 97 interactions pairs. Twenty-one interactions were confirmed by bimolecular fluorescent complementation in Nicotiana benthamiana. A confidence-scoring system was elaborated to evaluate the significance of the interactions. The main feature of this interactome is that almost all bait proteins along with their interactors were interconnected, creating a spider web-like structure. The interactome revealed also the presence of a "cluster of proteins involved in flowering control" in three- and four-protein interaction loops. This network provides a novel insight into the complex relationships among transcription factors known to play central roles in vernalization, flower initiation and abscisic acid signaling, as well as associations that tie abiotic stress with other regulatory and signaling proteins. This analysis provides useful information in elucidating the molecular mechanism associated with abiotic stress response in plants.
DNA, Complementary↗
Towards a proteome-scale map of the human protein-protein interaction network.
Systematic mapping of protein-protein interactions, or 'interactome' mapping, was initiated in model organisms, starting with defined biological processes and then expanding to the scale of the proteome. Although far from complete, such maps have revealed global topological and dynamic features of interactome networks that relate to known biological properties, suggesting that a human interactome map will provide insight into development and disease mechanisms at a systems level. Here we describe an initial version of a proteome-scale map of human binary protein-protein interactions. Using a stringent, high-throughput yeast two-hybrid system, we tested pairwise interactions among the products of approximately 8,100 currently available Gateway-cloned open reading frames and detected approximately 2,800 interactions. This data set, called CCSB-HI1, has a verification rate of approximately 78% as revealed by an independent co-affinity purification assay, and correlates significantly with other biological attributes. The CCSB-HI1 data set increases by approximately 70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins. This work represents an important step towards a systematic and comprehensive human interactome project.
Cloning, Molecular↗
To cleave or not to cleave: a systemic evaluation of DSS versus DSSO for cross-linking mass spectrometry analysis.
Cross-linking mass spectrometry is a powerful method for structural analysis, but choosing between cleavable and non-cleavable cross-linkers remains challenging. We rigorously compared non-cleavable DSS with cleavable DSSO and found that DSS consistently yields more cross-link identifications from isolated protein complexes to bacterial lysates. The advantage of DSS diminishes as sample complexity increases. At the highest complexity tested-human cell lysate-the trend reverses, with DSSO outperforming DSS. The superior performance of DSS in less complex samples is likely explained by its longer and more flexible spacer arm, which interrogates a spatial volume >40% larger than that of DSSO. For both cross-linkers, the number of identified cross-links decreases as the search space expands, but more steeply for DSS. This sharper decline arises from DSS cross-links producing slightly lower fragment ion coverage, not from the absence of signature ions that could reduce search space. Fragment ion coverage is key to interactome mapping: when coverage reaches 85% or above, identification sensitivity hardly decreases as the search space expands, regardless of the cross-linker used. In summary, we recommend DSS for samples no more complex than bacterial lysates. For interactome mapping of mammalian cells, although DSSO outperforms DSS, neither achieves deep interactome coverage.
Cross-Linking Reagents↗
Evaluation of clustering algorithms for protein-protein interaction networks.
BACKGROUND: Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism). In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies). High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Super Paramagnetic Clustering (SPC), and Molecular Complex Detection (MCODE). RESULTS: A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. CONCLUSION: This analysis shows that MCL is remarkably robust to graph alterations. In the tests of robustness, RNSC is more sensitive to edge deletion but less sensitive to the use of suboptimal parameter values. The other two algorithms are clearly weaker under most conditions. The analysis of high-throughput data supports the superiority of MCL for the extraction of complexes from interaction networks.
Algorithms↗
Identification of the proliferation/differentiation switch in the cellular network of multicellular organisms.
The protein-protein interaction networks, or interactome networks, have been shown to have dynamic modular structures, yet the functional connections between and among the modules are less well understood. Here, using a new pipeline to integrate the interactome and the transcriptome, we identified a pair of transcriptionally anticorrelated modules, each consisting of hundreds of genes in multicellular interactome networks across different individuals and populations. The two modules are associated with cellular proliferation and differentiation, respectively. The proliferation module is conserved among eukaryotic organisms, whereas the differentiation module is specific to multicellular organisms. Upon differentiation of various tissues and cell lines from different organisms, the expression of the proliferation module is more uniformly suppressed, while the differentiation module is upregulated in a tissue- and species-specific manner. Our results indicate that even at the tissue and organism levels, proliferation and differentiation modules may correspond to two alternative states of the molecular network and may reflect a universal symbiotic relationship in a multicellular organism. Our analyses further predict that the proteins mediating the interactions between these modules may serve as modulators at the proliferation/differentiation switch.
Animals↗
Proximity labeling puts ZFP36L1 as central hub for posttranscriptional regulation networks in T cells.
Effective T cell responses against pathogens require a rapid yet tightly controlled remodeling of the proteome, and RNA binding proteins (RBPs) are key in this process. For instance, the RBP ZFP36L1 prevents excessive protein production and thereby limits immunopathology. ZFP36L1 is primarily known to mediate mRNA decay, but it can also regulate other processes. How its mode of action relates to its interaction partners is, however, not well-understood. Here, we mapped the ZFP36L1 interactome in primary human T cells. Using proximity labeling, we identified known and new interactors that regulate 3'UTR-mediated RNA degradation, deadenylation, stress granule/p-body formation, as well as 5'UTR-mediated translation repression and mRNA decapping. Snapshot analysis uncovered the ZFP36L1 interactome dynamics and RNA (in)dependency throughout T cell activation. Intriguingly, proximity labeling also uncovered regulators of ZFP36L1 protein expression. This included the helicase UPF1, which not only interacts with ZFP36L1 protein but that may also promote its protein expression. Altogether, this comprehensive interactome map underlines the versatility of interactions with ZFP36L1 and their possible role in cellular function.
Humans↗
Multidimensional OMICs reveal ARID1A orchestrated control of DNA damage, splicing, and cell cycle in normal-like and malignant urothelial cells.
Epigenetic regulators, such as the SWI/SNF complex, with important roles in tissue development and homeostasis, are frequently mutated in cancer. ARID1A, a subunit of the SWI/SNF complex, is mutated in approximately 20% of all bladder tumors; however, the consequences of this remain poorly understood. Finding truncations to be the most common mutation, we generated loss- and gain-of-function models to conduct RNA-Seq, interactome analyses, Omni-ATAC-Seq, and functional studies to characterize ARID1A-affected pathways potentially suitable for the treatment of ARID1A-deficient bladder cancers. We observed decreased cell proliferation and deregulation of stress-regulated pathways, including DNA repair, in ARID1A-deficient cells. Furthermore, ARID1A was linked to alternative splicing and translational regulation on RNA and interactome levels. ARID1A deficiency drastically reduced the accessibility of chromatin, especially around introns and distal enhancers, in a functional enrichment analysis. Less accessible chromatin areas were mapped to pathways such as cell proliferation and DNA damage response. Indeed, the G2/M checkpoint appeared impaired after DNA damage in ARID1A-deficient cells. Together, our data highlight the broad impact of ARID1A loss and the possibility of targeting proliferative and DNA repair pathways for treatment.
Transcription Factors↗
Analysis of phosphatase and tensin homolog tumor suppressor interacting proteins by in vitro and in silico proteomics.
The phosphatase and tensin homolog (PTEN) tumor suppressor is a multifunctional protein deregulated in many types of cancer. To date, a comprehensive documentation of PTEN interacting proteins has not been performed. The goal of our study was to characterize the PTEN interactome using affinity pull-down and tandem mass spectrometry (MS/MS). Wild-type PTEN cDNA was inserted into pTRC-His2 vector to create a 6-His tagged protein, which was expressed in Escherichia coli. Lysate from a human lymphoma cell line was used in pull-down assays, utilizing affinity for nickel-agarose beads. Bound proteins were eluted with imidazole, digested and analyzed on an LCQ DecaXP ion trap mass spectrometer. The nickel affinity pull-down efficiency was evaluated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and Western blot analysis. Acquired data were searched against the NCBI nr.fasta nonredundant protein database using the SEQUEST algorithm and screened using INTERACT and ProteinProphet. All experiments were performed in duplicate with 6-His-lacZ serving as control. A total of 79 proteins were identified in the wild-type 6-His-PTEN pull-down by MS/MS. We further validated a subset of the proteins present in the PTEN interactome by performing immunoprecipitation using an anti-PTEN antibody and establishing the presence of the proteins in the immunocomplex by Western blot analysis. A search of published PTEN interactions was also performed using Online Mendelian Inheritance in Man, Human Protein Reference Database, the IntAct Project database, and PubMed. This in silico analysis confirmed 42 out of 79 (53%) of the proteins identified by MS/MS. The remaining 37 proteins represent probable PTEN interactions not previously documented in public databases or reported in the literature. These results highlight the value of combining both in vitro biochemical approaches with in silico analyses for a comprehensive study of protein-protein interactions.
Algorithms↗
The integrated development of network complexity modulates the diverse evolutionary mutation rates of individual proteins.
The rate of evolution-related mutation varies widely among proteins while the unity of the organism implies an integrated evolution of its protein network. Focusing on the yeast interactome, we monitored the structural impact of amino acid substitution on yeast proteins with reported structure. The impact of mutation in creating or deleting structural markers for interactivity varies across proteins and modulates the evolutionary rates, yielding a unified kinetic law of accumulation of connectivities consistent with an integrated evolution of the interactome.
Amino Acid Substitution↗
Molecular tools for metalloprotease sub-proteome generation.
Molecular systems biology, the highly challenging post-genomic research area has many different facets like transcriptomics, proteomics, metabolomics, interactomics, modelling of cell cycles, etc. Among them, functional proteomics and interactomics represent exciting fields of research with high relevance towards biochemistry, medicinal chemistry, therapy, biotechnology and bioinformatics. The number of different proteins expressed by a cell under a set of certain conditions and the high dynamic range of these proteins together with different activation states require methods for sub-proteome generation on a mechanistic basis to reduce the amount of data. This can be achieved by application of tailor-made molecular tools that are based on inhibitors or, more generally, on protein ligands. Immobilised protein ligands proved to be suitable for the generation of sub-proteomes by affinity chromatography or by fishing using magnetic beads. Metalloproteases share a catalytically active metal ion in the active site. They can for example be addressed by hydroxamate type inhibitors like marimastat which are suitable for targeting active metalloproteases on a mechanistic basis aiming at the generation of an activity- and affinity-based sub-proteome. For such purposes, modified hydroxamate type inhibitors can be attached to a solid surface, e.g., chromatography material, magnetic beads, or a surface plasmon resonance sensor chip. The latter technique is a valuable tool for the optimisation of binding and elution conditions of biomolecules in affinity chromatography or on experiments using magnetic beads. Preliminary results are reported on the application of these probes in fishing experiments using magnetic beads.
Chromatography, Affinity↗
Kv11.1 (hERG) Protein Interaction Networks Connect Endocytic Trafficking to Polygenic Influences on Cardiac Repolarization.
Polygenic scores (PGS) capture the combined effect of many common genetic variants on quantitative traits and disease risk, yet their functional consequences at the protein level remain poorly defined. Here, we integrated quantitative and interaction proteomics to resolve how polygenic liability for cardiac repolarization manifests in human cells. We studied human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from donors with extreme PGS for QT interval duration, a clinically relevant electrophysiologic trait associated with arrhythmia risk. Global quantitative proteomics revealed increased abundance of mitochondrial proteins in high-PGS cardiomyocytes. To define protein network-level effects on a key repolarizing ion channel, we performed multiplexed affinity purification-mass spectrometry (AP-MS) of Kv11.1. While mitochondrial changes did not directly explain Kv11.1-associated complexes, interactome analysis revealed increased association of Kv11.1 with myosin motor proteins and endosomal recycling machinery in high-PGS cells. These findings suggest altered channel trafficking dynamics of Kv11.1, distinct from the trafficking defects observed in monogenic Kv11.1 variants. Together, these data show that integrating global and interaction proteomics can resolve how polygenic variation reshapes protein networks. Future work using these methods could connect genomic risk to subcellular remodeling and our work provides a generalizable framework to probe the proteomic basis of complex traits. SIGNIFICANCE STATEMENT: Polygenic scores (PGS) predict disease risk, but how biological pathways are influenced by these common variants remains difficult to define. We generated human induced pluripotent stem cells from individuals with extreme high- and low- PGS for QT interval, a key electrocardiographic measure linked to arrhythmia risk. By combining global proteomics and interactomics for a common ion channel involved in regulating the QT interval (Kv11.1) we found potential mechanisms that are influenced by common genetic traits in patients. Our work provides an approach to connect polygenic scores to pathway-level molecular mechanisms in human cells and a general framework for uncovering how complex genetic architecture drives disease-relevant biology.
AP-MS↗
Identification of protein networks associated with the PAK1-betaPIX-GIT1-paxillin signaling complex by mass spectrometry.
The process of cell motility involves coordinate signaling events among proteins associated in interactive integrin-linked networks. Mass spectrometric analysis of immunoprecipitation-derived protein mixtures have provided efficient means of identifying proteomes. In this study, we investigate strategies to enhance the detection of interactome proteins for the known signaling module: PAK1, betaPIX, GIT1, and paxillin. Our results indicate that near-endogenous expression levels of bait protein enhances the identification of associated proteins, and that phosphatase inhibition augments the detection of specific protein interactions. Following the analysis of a large pool of spectral data, we have identified and mapped clusters of proteins that either share common interactions among the four bait proteins of interest or are exclusive to single bait proteins. Taken together, these data indicate that biochemical manipulations can enhance the ability for LC-MS/MS to identify interactome proteins, and that qualitative screening of multiple samples leads to the compilation of proteins associated with a known plexus.
Adaptor Proteins, Signal Transducing↗
Disorder and sequence repeats in hub proteins and their implications for network evolution.
Protein interaction networks display approximate scale-free topology, in which hub proteins that interact with a large number of other proteins determine the overall organization of the network. In this study, we aim to determine whether hubs are distinguishable from other networked proteins by specific sequence features. Proteins of different connectednesses were compared in the interaction networks of Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, and Homo sapienswith respect to the distribution of predicted structural disorder, sequence repeats, low complexity regions, and chain length. Highly connected proteins ("hub proteins") contained significantly more of, and greater proportion of, these sequence features and tended to be longer overall as compared to less connected proteins. These sequence features provide two different functional means for realizing multiple interactions: (1) extended interaction surface and (2) flexibility and adaptability, providing a mechanism for the same region to bind distinct partners. Our view contradicts the prevailing view that scaling in protein interactomes arose from gene duplication and preferential attachment of equivalent proteins. We propose an alternative evolutionary network specialization process, in which certain components of the protein interactome improved their fitness for binding by becoming longer or accruing regions of disorder and/or internal repeats and have therefore become specialized in network organization.
Amino Acid Sequence↗