Ulrich Stelzl

A human protein-protein interaction network : A resource for annotating the proteome

Protein-protein interaction maps provide a valuable framework for a better understanding of the functional organization of the proteome. To detect interacting pairs of human proteins systematically, a protein matrix of 4456 baits and 5632 preys was screened by automated yeast two-hybrid (Y2H) interaction mating. We identified 3186 mostly novel interactions among 1705 proteins, resulting in a large, highly connected network. Independent pull-down and co-immunoprecipitation assays validated the overall quality of the Y2H interactions. Using topological and GO criteria, a scoring system was developed to define 911 high-confidence interactions among 401 proteins. Furthermore, the network was searched for interactions linking uncharacterized gene products and human disease proteins to regulatory cellular pathways. Two novel Axin-1 interactions were validated experimentally, characterizing ANP32A and CRMP1 as modulators of Wnt signaling. Systematic human protein interaction screens can lead to a more comprehensive understanding of protein function and cellular processes.

The ConsensusPathDB interaction database: 2013 update

Knowledge of the various interactions between molecules in the cell is crucial for understanding cellular processes in health and disease. Currently available interaction databases, being largely complementary to each other, must be integrated to obtain a comprehensive global map of the different types of interactions. We have previously reported the development of an integrative interaction database called ConsensusPathDB (http://ConsensusPathDB.org) that aims to fulfill this task. In this update article, we report its significant progress in terms of interaction content and web interface tools. ConsensusPathDB has grown mainly due to the integration of 12 further databases; it now contains 215 541 unique interactions and 4601 pathways from overall 30 databases. Binary protein interactions are scored with our confidence assessment tool, IntScore. The ConsensusPathDB web interface allows users to take advantage of these integrated interaction and pathway data in different contexts. Recent developments include pathway analysis of metabolite lists, visualization of functional gene/metabolite sets as overlap graphs, gene set analysis based on protein complexes and induced network modules analysis that connects a list of genes through various interaction types. To facilitate the interactive, visual interpretation of interaction and pathway data, we have re-implemented the graph visualization feature of ConsensusPathDB using the Cytoscape.js library.

A Directed Protein Interaction Network for Investigating Intracellular Signal Transduction

Effective prediction of the direction of signal flow in an interaction network enables modeling of signaling dynamics and identification of regulatory proteins. Finding More Pieces to the Signaling Puzzle Even well-studied pathways are likely to be incomplete in terms of our knowledge of all the components and their relationships, and the larger interconnected network that represents the true cellular regulatory landscape remains woefully unknown. Vinayagam et al. used an automated yeast two-hybrid interaction mating assay to identify protein-protein interactions (PPIs) among human proteins and then integrated that PPI data set with previously published data to create an undirected human PPI network connecting 9832 proteins through 39,641 interactions. The authors then applied a Bayesian learning strategy to assign direction to the interactions among the proteins. The resulting directed network enabled them to evaluate growth factor–induced protein phosphorylation dynamics and to identify previously unknown modulators of the extracellular signal–regulated protein kinase pathway, of which 18 were validated with cell-based assays. This strategy should prove useful in completing the puzzle of the cellular regulatory network. Cellular signal transduction is a complex process involving protein-protein interactions (PPIs) that transmit information. For example, signals from the plasma membrane may be transduced to transcription factors to regulate gene expression. To obtain a global view of cellular signaling and to predict potential signal modulators, we searched for protein interaction partners of more than 450 signaling-related proteins by means of automated yeast two-hybrid interaction mating. The resulting PPI network connected 1126 proteins through 2626 PPIs. After expansion of this interaction map with publicly available PPI data, we generated a directed network resembling the signal transduction flow between proteins with a naïve Bayesian classifier. We exploited information on the shortest PPI paths from membrane receptors to transcription factors to predict input and output relationships between interacting proteins. Integration of directed PPI with time-resolved protein phosphorylation data revealed network structures that dynamically conveyed information from the activated epidermal growth factor and extracellular signal–regulated kinase (EGF/ERK) signaling cascade to directly associated proteins and more distant proteins in the network. From the model network, we predicted 18 previously unknown modulators of EGF/ERK signaling, which we validated in mammalian cell-based assays. This generic experimental and computational approach provides a framework for elucidating causal connections between signaling proteins and facilitates the identification of proteins that modulate the flow of information in signaling networks.

Dynamic Protein-Protein Interaction Wiring of the Human Spliceosome

More than 200 proteins copurify with spliceosomes, the compositionally dynamic RNPs catalyzing pre-mRNA splicing. To better understand protein - protein interactions governing splicing, we systematically investigated interactions between human spliceosomal proteins. A comprehensive Y2H interaction matrix screen generated a protein interaction map comprising 632 interactions between 196 proteins. Among these, 242 interactions were found between spliceosomal core proteins and largely validated by coimmunoprecipitation. To reveal dynamic changes in protein interactions, we integrated spliceosomal complex purification information with our interaction data and performed link clustering. These data, together with interaction competition experiments, suggest that during step 1 of splicing, hPRP8 interactions with SF3b proteins are replaced by hSLU7, positioning this second step factor close to the active site, and that the DEAH-box helicases hPRP2 and hPRP16 cooperate through ordered interactions with GPKOW. Our data provide extensive information about the spliceosomal protein interaction network and its dynamics.

[19] Preparation of functional ribosomal complexes and effect of buffer conditions on tRNA positions observed by cryoelectron microscopy

Publisher Summary This chapter discusses the isolation of the ribosomes and the preparation of functional complexes and provides an overview of the possibilities for analyzing ribosomal complexes. It summarizes and discusses the results of recent cryoelectron microscopy studies that reflect the effect of buffer conditions. Studies have established that the ribosome has three transfer RNA (tRNA) binding sites, but 3-D cryo-electron microscopy (EM) has revealed five different tRNA positions on the ribosome, classified as A, P, P/E, E, and E2. The occupancy of some of these positions strongly depends on the buffer conditions used and the charge state of the tRNA. In the presence of the polyamine buffer, mimicking the in vivo conditions, only occupancy of A, P, and E sites are observed in complexes of the initiating and elongating ribosomes. The procedure described in the chapter for the small-scale isolation of tightly coupled ribosomes yields highly active and intact ribosomes, an important prerequisite for the preparation of functional complexes. The chapter describes the isolation of ribosomal subunits that can be used to prepare reassociated ribosomes. Reassociated ribosomes show a more efficient tRNA binding as compared to tightly coupled ribosomes, because the saturation of tRNA binding is reached at molar ratios slightly above stoichiometric ones. This can be attributed to at least two factors: (1) a selective pressure for active particles in the reassociation step and (2) the loss of residual amounts of tRNAs and of mitochondrial RNA (mRNA) fragments.

Interactome mapping suggests new mechanistic details underlying Alzheimer's disease

Recent advances toward the characterization of Alzheimers disease (AD) have permitted the identification of a dozen of genetic risk factors, although many more remain undiscovered. In parallel, works in the field of network biology have shown a strong link between protein connectivity and disease. In this manuscript, we demonstrate that AD-related genes are indeed highly interconnected and, based on this observation, we set up an interaction discovery strategy to unveil novel AD causative and susceptibility genes. In total, we report 200 high-confidence protein-protein interactions between eight confirmed AD-related genes and 66 candidates. Of these, 31 are located in chromosomal regions containing susceptibility loci related to the etiology of late-onset AD, and 17 show dysregulated expression patterns in AD patients, which makes them very good candidates for further functional studies. Interestingly, we also identified four novel direct interactions among well-characterized AD causative/susceptibility genes (i.e., APP, A2M, APOE, PSEN1, and PSEN2), which support the suggested link between plaque formation and inflammatory processes and provide insights into the intracellular regulation of APP cleavage. Finally, we contextualize the discovered relationships, integrating them with all the interaction data reported in the literature, building the most complete interactome associated to AD. This general view facilitates the analyses of global properties of the network, such as its functional modularity, and triggers many hypotheses on the molecular mechanisms implicated in AD. For instance, our analyses suggest a putative role for PDCD4 as a neuronal death regulator and ECSIT as a molecular link between oxidative stress, inflammation, and mitochondrial dysfunction in AD.

Ribosomal protein L2 is involved in the association of the ribosomal subunits, tRNA binding to A and P sites and peptidyl transfer

Ribosomal proteins L2, L3 and L4, together with the 23S RNA, are the main candidates for catalyzing peptide bond formation on the 50S subunit. That L2 is evolutionarily highly conserved led us to perform a thorough functional analysis with reconstituted 50S particles either lacking L2 or harboring a mutated L2. L2 does not play a dominant role in the assembly of the 50S subunit or in the fixation of the 3′‐ends of the tRNAs at the peptidyl‐transferase center. However, it is absolutely required for the association of 30S and 50S subunits and is strongly involved in tRNA binding to both A and P sites, possibly at the elbow region of the tRNAs. Furthermore, while the conserved histidyl residue 229 is extremely important for peptidyl‐transferase activity, it is apparently not involved in other measured functions. None of the other mutagenized amino acids (H14, D83, S177, D228, H231) showed this strong and exclusive participation in peptide bond formation. These results are used to examine critically the proposed direct involvement of His229 in catalysis of peptide synthesis.

Detection of Alpha-Rod Protein Repeats Using a Neural Network and Application to Huntingtin

A growing number of solved protein structures display an elongated structural domain, denoted here as alpha-rod, composed of stacked pairs of anti-parallel alpha-helices. Alpha-rods are flexible and expose a large surface, which makes them suitable for protein interaction. Although most likely originating by tandem duplication of a two-helix unit, their detection using sequence similarity between repeats is poor. Here, we show that alpha-rod repeats can be detected using a neural network. The network detects more repeats than are identified by domain databases using multiple profiles, with a low level of false positives (<10%). We identify alpha-rod repeats in approximately 0.4% of proteins in eukaryotic genomes. We then investigate the results for all human proteins, identifying alpha-rod repeats for the first time in six protein families, including proteins STAG1-3, SERAC1, and PSMD1-2 & 5. We also characterize a short version of these repeats in eight protein families of Archaeal, Bacterial, and Fungal species. Finally, we demonstrate the utility of these predictions in directing experimental work to demarcate three alpha-rods in huntingtin, a protein mutated in Huntingtons disease. Using yeast two hybrid analysis and an immunoprecipitation technique, we show that the huntingtin fragments containing alpha-rods associate with each other. This is the first definition of domains in huntingtin and the first validation of predicted interactions between fragments of huntingtin, which sets up directions toward functional characterization of this protein. An implementation of the repeat detection algorithm is available as a Web server with a simple graphical output: http://www.ogic.ca/projects/ard. This can be further visualized using BiasViz, a graphic tool for representation of multiple sequence alignments.

Dual coordination of post translational modifications in human protein networks.

Post-translational modifications (PTMs) regulate protein activity, stability and interaction profiles and are critical for cellular functioning. Further regulation is gained through PTM interplay whereby modifications modulate the occurrence of other PTMs or act in combination. Integration of global acetylation, ubiquitination and tyrosine or serine/threonine phosphorylation datasets with protein interaction data identified hundreds of protein complexes that selectively accumulate each PTM, indicating coordinated targeting of specific molecular functions. A second layer of PTM coordination exists in these complexes, mediated by PTM integration (PTMi) spots. PTMi spots represent very dense modification patterns in disordered protein regions and showed an equally high mutation rate as functional protein domains in cancer, inferring equivocal importance for cellular functioning. Systematic PTMi spot identification highlighted more than 300 candidate proteins for combinatorial PTM regulation. This study reveals two global PTM coordination mechanisms and emphasizes dataset integration as requisite in proteomic PTM studies to better predict modification impact on cellular signaling.

A Y2H-seq approach defines the human protein methyltransferase interactome

To accelerate high-density interactome mapping, we developed a yeast two-hybrid interaction screening approach involving short-read second-generation sequencing (Y2H-seq) with improved sensitivity and a quantitative scoring readout allowing rapid interaction validation. We applied Y2H-seq to investigate enzymes involved in protein methylation, a largely unexplored post-translational modification. The reported network of 523 interactions involving 22 methyltransferases or demethylases is comprehensively annotated and validated through coimmunoprecipitation experiments and defines previously undiscovered cellular roles of nonhistone protein methylation.