An experimentally derived confidence score for binary protein-protein interactions

Enhanced Y1H assays for Arabidopsis

We present an Arabidopsis thaliana full-length transcription factor resource of 92% of root stele-expressed transcription factors and 74.5% of root-expressed transcription factors. We demonstrate its use with enhanced yeast one-hybrid (eY1H) screening for rapid, systematic mapping of plant transcription factor-promoter interactions. We identified 158 interactions with 13 stele-expressed promoters, many of which occur physically or are regulatory in planta.

Enhanced yeast one-hybrid assays for high-throughput gene-centered regulatory network mapping

A major challenge in systems biology is to understand the gene regulatory networks that drive development, physiology and pathology. Interactions between transcription factors and regulatory genomic regions provide the first level of gene control. Gateway-compatible yeast one-hybrid (Y1H) assays present a convenient method to identify and characterize the repertoire of transcription factors that can bind a DNA sequence of interest. To delineate genome-scale regulatory networks, however, large sets of DNA fragments need to be processed at high throughput and high coverage. Here we present enhanced Y1H (eY1H) assays that use a robotic mating platform with a set of improved Y1H reagents and automated readout quantification. We demonstrate that eY1H assays provide excellent coverage and identify interacting transcription factors for multiple DNA fragments in a short time. eY1H assays will be an important tool for mapping gene regulatory networks in Caenorhabditis elegans and other model organisms as well as in humans.

Yeast one-hybrid assays for gene-centered human gene regulatory network mapping

Gateway-compatible yeast one-hybrid (Y1H) assays provide a convenient gene-centered (DNA to protein) approach to identify transcription factors that can bind a DNA sequence of interest. We present Y1H resources, including clones for 988 of 1,434 (69%) predicted human transcription factors, that can be used to detect both known and new interactions between human DNA regions and transcription factors.

Automated protein-DNA interaction screening of Drosophila regulatory elements

Drosophila melanogaster has one of the best characterized metazoan genomes in terms of functionally annotated regulatory elements. To explore how these elements contribute to gene regulation, we need convenient tools to identify the proteins that bind to them. Here we describe the development and validation of a high-throughput yeast one-hybrid platform, which enables screening of DNA elements versus an array of full-length, sequence-verified clones containing over 85% of predicted Drosophila transcription factors. Using six well-characterized regulatory elements, we identified 33 transcription factor-DNA interactions of which 27 were previously unidentified. To simultaneously validate these interactions and locate the binding sites of involved transcription factors, we implemented a powerful microfluidics-based approach that enabled us to retrieve DNA-occupancy data for each transcription factor throughout the respective target DNA elements. Finally, we biologically validated several interactions and identified two new regulators of sine oculis gene expression and hence eye development.

The SARS-coronavirus-host interactome: identification of cyclophilins as target for pan-coronavirus inhibitors

Coronaviruses (CoVs) are important human and animal pathogens that induce fatal respiratory, gastrointestinal and neurological disease. The outbreak of the severe acute respiratory syndrome (SARS) in 2002/2003 has demonstrated human vulnerability to (Coronavirus) CoV epidemics. Neither vaccines nor therapeutics are available against human and animal CoVs. Knowledge of host cell proteins that take part in pivotal virus-host interactions could define broad-spectrum antiviral targets. In this study, we used a systems biology approach employing a genome-wide yeast-two hybrid interaction screen to identify immunopilins (PPIA, PPIB, PPIH, PPIG, FKBP1A, FKBP1B) as interaction partners of the CoV non-structural protein 1 (Nsp1). These molecules modulate the Calcineurin/NFAT pathway that plays an important role in immune cell activation. Overexpression of NSP1 and infection with live SARS-CoV strongly increased signalling through the Calcineurin/NFAT pathway and enhanced the induction of interleukin 2, compatible with late-stage immunopathogenicity and long-term cytokine dysregulation as observed in severe SARS cases. Conversely, inhibition of cyclophilins by cyclosporine A (CspA) blocked the replication of CoVs of all genera, including SARS-CoV, human CoV-229E and -NL-63, feline CoV, as well as avian infectious bronchitis virus. Non-immunosuppressive derivatives of CspA might serve as broad-range CoV inhibitors applicable against emerging CoVs as well as ubiquitous pathogens of humans and livestock.

Varicella zoster virus ORF25 gene product: an essential hub protein linking encapsidation proteins and the nuclear egress complex

Varicella zoster virus (VZV) ORF25 is a 156 amino acid protein belonging to the approximately 40 core proteins that are conserved throughout the Herpesviridae. By analogy to its functional orthologue UL33 in Herpes simplex virus 1 (HSV-1), ORF25 is thought to be a component of the terminase complex. To investigate how cleavage and encapsidation of viral DNA links to the nuclear egress of mature capsids in VZV, we tested 10 VZV proteins that are predicted to be involved in either of the two processes for protein interactions against each other using three independent protein-protein interaction (PPI) detection systems: the yeast-two-hybrid (Y2H) system, a luminescence based MBP pull-down interaction screening assay (LuMPIS), and a bioluminescence resonance energy transfer (BRET) assay. A set of 20 interactions was consistently detected by at least 2 methods and resulted in a dense interaction network between proteins associated in encapsidation and nuclear egress. The results indicate that the terminase complex in VZV consists of ORF25, ORF30, and ORF45/42 and support a model in which both processes are closely linked to each other. Consistent with its role as a central hub for protein interactions, ORF25 is shown to be essential for VZV replication.

Removing bias against membrane proteins in interaction networks

Background

Cellular interaction networks can be used to analyze the effects on cell signaling and other functional consequences of perturbations to cellular physiology. Thus, several methods have been used to reconstitute interaction networks from multiple published datasets. However, the structure and performance of these networks depends on both the quality and the unbiased nature of the original data. Due to the inherent bias against membrane proteins in protein-protein interaction (PPI) data, interaction networks can be compromised particularly if they are to be used in conjunction with drug screening efforts, since most drug-targets are membrane proteins.

Results

To overcome the experimental bias against PPIs involving membrane-associated proteins we used a probabilistic approach based on a hypergeometric distribution followed by logistic regression to simultaneously optimize the weights of different sources of interaction data. The resulting less biased genome-scale network constructed for the budding yeast Saccharomyces cerevisiae revealed that the starvation pathway is a distinct subnetwork of autophagy and retrieved a more integrated network of unfolded protein response genes. We also observed that the centrality-lethality rule depends on the content of membrane proteins in networks.

Conclusions

We show here that the bias against membrane proteins can and should be corrected in order to have a better representation of the interactions and topological properties of protein interaction networks.

Arabidopsis G-protein interactome reveals connections to cell wall carbohydrates and morphogenesis

Yeast two-hybrid technology is used to build a high-quality protein interaction network centered on Arabidopsis G-protein coupled signaling. The interactions uncovered are without precedent in animals and fungi and help identify new cellular roles for G-protein signaling in plants.

The heterotrimeric G-protein complex is minimally composed of Gα, Gβ, and Gγ subunits. In the classic scenario, the G-protein complex is the nexus in signaling from the plasma membrane, where the heterotrimeric G-protein associates with heptahelical G-protein-coupled receptors (GPCRs), to cytoplasmic target proteins called effectors. Although a number of effectors are known in metazoans and fungi, none of these are predicted to exist in their canonical forms in plants. To identify ab initio plant G-protein effectors and scaffold proteins, we screened a set of proteins from the G-protein complex using two-hybrid complementation in yeast. After deep and exhaustive interrogation, we detected 544 interactions between 434 proteins, of which 68 highly interconnected proteins form the core G-protein interactome. Within this core, over half of the interactions comprising two-thirds of the nodes were retested and validated as genuine in planta. Co-expression analysis in combination with phenotyping of loss-of-function mutations in a set of core interactome genes revealed a novel role for G-proteins in regulating cell wall modification.

A physical interaction network of dengue virus and human proteins

Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection.

Comparative analysis of EspF variants in inhibition of Escherichia coli phagocytosis by macrophages and inhibition of E. coli translocation through human- and bovine-derived M cells

The EspF protein is secreted by the type III secretion system of enteropathogenic and enterohemorrhagic Escherichia coli (EPEC and EHEC, respectively). EspF sequences differ between EHEC O157:H7, EHEC O26:H11, and EPEC O127:H6 in terms of the number of SH3-binding polyproline-rich repeats and specific residues in these regions, as well as residues in the amino domain involved in cellular localization. EspF(O127) is important for the inhibition of phagocytosis by EPEC and also limits EPEC translocation through antigen-sampling cells (M cells). EspF(O127) has been shown to have effects on cellular organelle function and interacts with several host proteins, including N-WASP and sorting nexin 9 (SNX9). In this study, we compared the capacities of different espF alleles to inhibit (i) bacterial phagocytosis by macrophages, (ii) translocation through an M-cell coculture system, and (iii) uptake by and translocation through cultured bovine epithelial cells. The espF gene from E. coli serotype O157 (espF(O157)) allele was significantly less effective at inhibiting phagocytosis and also had reduced capacity to inhibit E. coli translocation through a human-derived in vitro M-cell coculture system in comparison to espF(O127) and espF(O26). In contrast, espF(O157) was the most effective allele at restricting bacterial uptake into and translocation through primary epithelial cells cultured from the bovine terminal rectum, the predominant colonization site of EHEC O157 in cattle and a site containing M-like cells. Although LUMIER binding assays demonstrated differences in the interactions of the EspF variants with SNX9 and N-WASP, we propose that other, as-yet-uncharacterized interactions contribute to the host-based variation in EspF activity demonstrated here.

Virus-host interactomes and global models of virus-infected cells

Novel high-throughput technologies such as yeast two-hybrid and RNA interference (RNAi) screens provide the tools to study interactions between viral proteins and the host on a genomic scale. In this review, we provide an overview of studies in which these technologies were applied and of computational approaches for the analysis of the identified viral interactors in the context of the host cell. The results of these studies illustrate the advantages of integrative systems biology approaches in the investigation of viral pathogens.

Evidence for network evolution in an Arabidopsis interactome map

Plants have unique features that evolved in response to their environments and ecosystems. A full account of the complex cellular networks that underlie plant-specific functions is still missing. We describe a proteome-wide binary protein-protein interaction map for the interactome network of the plant Arabidopsis thaliana containing about 6200 highly reliable interactions between about 2700 proteins. A global organization of plant biological processes emerges from community analyses of the resulting network, together with large numbers of novel hypothetical functional links between proteins and pathways. We observe a dynamic rewiring of interactions following gene duplication events, providing evidence for a model of evolution acting upon interactome networks. This and future plant interactome maps should facilitate systems approaches to better understand plant biology and improve crops.

Independently evolved virulence effectors converge onto hubs in a plant immune system network

Plants generate effective responses to infection by recognizing both conserved and variable pathogen-encoded molecules. Pathogens deploy virulence effector proteins into host cells, where they interact physically with host proteins to modulate defense. We generated an interaction network of plant-pathogen effectors from two pathogens spanning the eukaryote-eubacteria divergence, three classes of Arabidopsis immune system proteins, and ~8000 other Arabidopsis proteins. We noted convergence of effectors onto highly interconnected host proteins and indirect, rather than direct, connections between effectors and plant immune receptors. We demonstrated plant immune system functions for 15 of 17 tested host proteins that interact with effectors from both pathogens. Thus, pathogens from different kingdoms deploy independently evolved virulence proteins that interact with a limited set of highly connected cellular hubs to facilitate their diverse life-cycle strategies.

Independently evolved virulence effectors converge onto hubs in a plant immune system network

Plants generate effective responses to infection by recognizing both conserved and variable pathogen-encoded molecules. Pathogens deploy virulence effector proteins into host cells, where they interact physically with host proteins to modulate defense. We generated an interaction network of plant-pathogen effectors from two pathogens spanning the eukaryote-eubacteria divergence, three classes of Arabidopsis immune system proteins, and ~8000 other Arabidopsis proteins. We noted convergence of effectors onto highly interconnected host proteins and indirect, rather than direct, connections between effectors and plant immune receptors. We demonstrated plant immune system functions for 15 of 17 tested host proteins that interact with effectors from both pathogens. Thus, pathogens from different kingdoms deploy independently evolved virulence proteins that interact with a limited set of highly connected cellular hubs to facilitate their diverse life-cycle strategies.

Evidence for network evolution in an Arabidopsis interactome map

Plants have unique features that evolved in response to their environments and ecosystems. A full account of the complex cellular networks that underlie plant-specific functions is still missing. We describe a proteome-wide binary protein-protein interaction map for the interactome network of the plant Arabidopsis thaliana containing about 6200 highly reliable interactions between about 2700 proteins. A global organization of plant biological processes emerges from community analyses of the resulting network, together with large numbers of novel hypothetical functional links between proteins and pathways. We observe a dynamic rewiring of interactions following gene duplication events, providing evidence for a model of evolution acting upon interactome networks. This and future plant interactome maps should facilitate systems approaches to better understand plant biology and improve crops.

Tissue-specific subnetworks and characteristics of publicly available human protein interaction databases

MOTIVATION

Protein-protein interaction (PPI) databases are widely used tools to study cellular pathways and networks; however, there are several databases available that still do not account for cell type-specific differences. Here, we evaluated the characteristics of six interaction databases, incorporated tissue-specific gene expression information and finally, investigated if the most popular proteins of scientific literature are involved in good quality interactions.

RESULTS

We found that the evaluated databases are comparable in terms of node connectivity (i.e. proteins with few interaction partners also have few interaction partners in other databases), but may differ in the identity of interaction partners. We also observed that the incorporation of tissue-specific expression information significantly altered the interaction landscape and finally, we demonstrated that many of the most intensively studied proteins are engaged in interactions associated with low confidence scores. In summary, interaction databases are valuable research tools but may lead to different predictions on interactions or pathways. The accuracy of predictions can be improved by incorporating datasets on organ- and cell type-specific gene expression, and by obtaining additional interaction evidence for the most 'popular' proteins.

CONTACT

kitano@sbi.jp

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An integrated mathematical model of thrombin-, histamine-and VEGF-mediated signalling in endothelial permeability

BACKGROUND

Endothelial permeability is involved in injury, inflammation, diabetes and cancer. It is partly regulated by the thrombin-, histamine-, and VEGF-mediated myosin-light-chain (MLC) activation pathways. While these pathways have been investigated, questions such as temporal effects and the dynamics of multi-mediator regulation remain to be fully studied. Mathematical modeling of these pathways facilitates such studies. Based on the published ordinary differential equation models of the pathway components, we developed an integrated model of thrombin-, histamine-, and VEGF-mediated MLC activation pathways.

RESULTS

Our model was validated against experimental data for calcium release and thrombin-, histamine-, and VEGF-mediated MLC activation. The simulated effects of PAR-1, Rho GTPase, ROCK, VEGF and VEGFR2 over-expression on MLC activation, and the collective modulation by thrombin and histamine are consistent with experimental findings. Our model was used to predict enhanced MLC activation by CPI-17 over-expression and by synergistic action of thrombin and VEGF at low mediator levels. These may have impact in endothelial permeability and metastasis in cancer patients with blood coagulation.

CONCLUSION

Our model was validated against a number of experimental findings and the observed synergistic effects of low concentrations of thrombin and histamine in mediating the activation of MLC. It can be used to predict the effects of altered pathway components, collective actions of multiple mediators and the potential impact to various diseases. Similar to the published models of other pathways, our model can potentially be used to identify important disease genes through sensitivity analysis of signalling components.

Protein interactome reveals converging molecular pathways among autism disorders

To uncover shared pathogenic mechanisms among the highly heterogeneous autism spectrum disorders (ASDs), we developed a protein interaction network that identified hundreds of new interactions among proteins encoded by ASD-associated genes. We discovered unexpectedly high connectivity between SHANK and TSC1, previously implicated in syndromic autism, suggesting that common molecular pathways underlie autistic phenotypes in distinct syndromes. ASD patients were more likely to harbor copy number variations that encompass network genes than were control subjects. We also identified, in patients with idiopathic ASD, three de novo lesions (deletions in 16q23.3 and 15q22 and one duplication in Xq28) that involve three network genes (NECAB2, PKM2, and FLNA). The protein interaction network thus provides a framework for identifying causes of idiopathic autism and for understanding molecular pathways that underpin both syndromic and idiopathic ASDs.

Next-generation sequencing to generate interactome datasets

Next-generation sequencing has not been applied to protein-protein interactome network mapping so far because the association between the members of each interacting pair would not be maintained in en masse sequencing. We describe a massively parallel interactome-mapping pipeline, Stitch-seq, that combines PCR stitching with next-generation sequencing and used it to generate a new human interactome dataset. Stitch-seq is applicable to various interaction assays and should help expand interactome network mapping.

Identification of a Brucella spp. secreted effector specifically interacting with human small GTPase Rab2

Bacteria of the Brucella genus are facultative intracellular class III pathogens. These bacteria are able to control the intracellular trafficking of their vacuole, presumably by the use of yet unknown translocated effectors. To identify such effectors, we used a high-throughput yeast two-hybrid screen to identify interactions between putative human phagosomal proteins and predicted Brucella spp. proteins. We identified a specific interaction between the human small GTPase Rab2 and a Brucella spp. protein named RicA. This interaction was confirmed by GST-pull-down with the GDP-bound form of Rab2. A TEM-β-lactamase-RicA fusion was translocated from Brucella abortus to RAW264.7 macrophages during infection. This translocation was not detectable in a strain deleted for the virB operon, coding for the type IV secretion system. However, RicA secretion in a bacteriological culture was still observed in a ΔvirB mutant. In HeLa cells, a ΔricA mutant recruits less GTP-locked myc-Rab2 on its Brucella-containing vacuoles, compared with the wild-type strain. We observed altered kinetics of intracellular trafficking and faster proliferation of the B. abortusΔricA mutant in HeLa cells, compared with the wild-type control. Altogether, the data reported here suggest RicA as the first reported effector with a proposed function for B. abortus.

Diverse protein kinase interactions identified by protein microarrays reveal novel connections between cellular processes

Protein kinases are key regulators of cellular processes. In spite of considerable effort, a full understanding of the pathways they participate in remains elusive. We globally investigated the proteins that interact with the majority of yeast protein kinases using protein microarrays. Eighty-five kinases were purified and used to probe yeast proteome microarrays. One-thousand-twenty-three interactions were identified, and the vast majority were novel. Coimmunoprecipitation experiments indicate that many of these interactions occurred in vivo. Many novel links of kinases to previously distinct cellular pathways were discovered. For example, the well-studied Kss1 filamentous pathway was found to bind components of diverse cellular pathways, such as those of the stress response pathway and the Ccr4-Not transcriptional/translational regulatory complex; genetic tests revealed that these different components operate in the filamentation pathway in vivo. Overall, our results indicate that kinases operate in a highly interconnected network that coordinates many activities of the proteome. Our results further demonstrate that protein microarrays uncover a diverse set of interactions not observed previously.

Building protein-protein interaction networks with proteomics and informatics tools

The systematic characterization of the whole interactomes of different model organisms has revealed that the eukaryotic proteome is highly interconnected. Therefore, biological research is progressively shifting away from classical approaches that focus only on a few proteins toward whole protein interaction networks to describe the relationship of proteins in biological processes. In this minireview, we survey the most common methods for the systematic identification of protein interactions and exemplify different strategies for the generation of protein interaction networks. In particular, we will focus on the recent development of protein interaction networks derived from quantitative proteomics data sets.

Measuring the evolutionary rate of protein-protein interaction

Despite our extensive knowledge about the rate of protein sequence evolution for thousands of genes in hundreds of species, the corresponding rate of protein function evolution is virtually unknown, especially at the genomic scale. This lack of knowledge is primarily because of the huge diversity in protein function and the consequent difficulty in gauging and comparing rates of protein function evolution. Nevertheless, most proteins function through interacting with other proteins, and protein-protein interaction (PPI) can be tested by standard assays. Thus, the rate of protein function evolution may be measured by the rate of PPI evolution. Here, we experimentally examine 87 potential interactions between Kluyveromyces waltii proteins, whose one to one orthologs in the related budding yeast Saccharomyces cerevisiae have been reported to interact. Combining our results with available data from other eukaryotes, we estimate that the evolutionary rate of protein interaction is (2.6 ± 1.6) × 10(-10) per PPI per year, which is three orders of magnitude lower than the rate of protein sequence evolution measured by the number of amino acid substitutions per protein per year. The extremely slow evolution of protein molecular function may account for the remarkable conservation of life at molecular and cellular levels and allow for studying the mechanistic basis of human disease in much simpler organisms.

Integrative computational biology for cancer research

Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed clinical cancer research. They have revealed novel molecular markers of cancer subtypes, metastasis, and drug sensitivity and resistance. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and great volumes of data are accumulating at a rapid pace. Here we discuss these challenges, and show how integrative computational biology can help diminish them by creating new software tools, analytical methods, and data standards.

In vivo analysis of proteomes and interactomes using Parallel Affinity Capture (iPAC) coupled to mass spectrometry

Affinity purification coupled to mass spectrometry provides a reliable method for identifying proteins and their binding partners. In this study we have used Drosophila melanogaster proteins triple tagged with Flag, Strep II, and Yellow fluorescent protein in vivo within affinity pull-down experiments and isolated these proteins in their native complexes from embryos. We describe a pipeline for determining interactomes by Parallel Affinity Capture (iPAC) and show its use by identifying partners of several protein baits with a range of sizes and subcellular locations. This purification protocol employs the different tags in parallel and involves detailed comparison of resulting mass spectrometry data sets, ensuring the interaction lists achieved are of high confidence. We show that this approach identifies known interactors of bait proteins as well as novel interaction partners by comparing data achieved with published interaction data sets. The high confidence in vivo protein data sets presented here add new data to the currently incomplete D. melanogaster interactome. Additionally we report contaminant proteins that are persistent with affinity purifications irrespective of the tagged bait.

Quantitative maps of genetic interactions in yeast - comparative evaluation and integrative analysis

Background

High-throughput genetic screening approaches have enabled systematic means to study how interactions among gene mutations contribute to quantitative fitness phenotypes, with the aim of providing insights into the functional wiring diagrams of genetic interaction networks on a global scale. However, it is poorly known how well these quantitative interaction measurements agree across the screening approaches, which hinders their integrated use toward improving the coverage and quality of the genetic interaction maps in yeast and other organisms.

Results

Using large-scale data matrices from epistatic miniarray profiling (E-MAP), genetic interaction mapping (GIM), and synthetic genetic array (SGA) approaches, we carried out here a systematic comparative evaluation among these quantitative maps of genetic interactions in yeast. The relatively low association between the original interaction measurements or their customized scores could be improved using a matrix-based modelling framework, which enables the use of single- and double-mutant fitness estimates and measurements, respectively, when scoring genetic interactions. Toward an integrative analysis, we show how the detections from the different screening approaches can be combined to suggest novel positive and negative interactions which are complementary to those obtained using any single screening approach alone. The matrix approximation procedure has been made available to support the design and analysis of the future screening studies.

Conclusions

We have shown here that even if the correlation between the currently available quantitative genetic interaction maps in yeast is relatively low, their comparability can be improved by means of our computational matrix approximation procedure, which will enable integrative analysis and detection of a wider spectrum of genetic interactions using data from the complementary screening approaches.

Transient protein-protein interactions: structural, functional, and network properties

Transient interactions, which involve protein interactions that are formed and broken easily, are important in many aspects of cellular function. Here we describe structural and functional properties of transient interactions between globular domains and between globular domains, short peptides, and disordered regions. The importance of posttranslational modifications in transient interactions is also considered. We review techniques used in the detection of the different types of transient protein-protein interactions. We also look at the role of transient interactions within protein-protein interaction networks and consider their contribution to different aspects of these networks.

Association of Rex-1 to target genes supports its interaction with Polycomb function

Rex-1/Zfp42 displays a remarkably restricted pattern of expression in preimplantation embryos, primary spermatocytes, and undifferentiated mouse embryonic stem (ES) cells and is frequently used as a marker gene for pluripotent stem cells. To understand the role of Rex-1 in selfrenewal and pluripotency, we used Rex-1 association as a measure to identify potential target genes, and carried out chromatin-immunoprecipitation assays in combination with gene specific primers to identify genomic targets Rex-1 associates with. We find association of Rex-1 to several genes described previously as bivalently marked regulators of differentiation and development, whose repression in mouse embryonic stem (ES) cells is Polycomb Group-mediated, and controlled directly by Ring1A/B. To substantiate the hypothesis that Rex-1 contributes to gene regulation by PcG, we demonstrate interactions of Rex-1 and YY2 (a close relative of YY1) with Ring1 proteins and the PcG-associated proteins RYBP and YAF2, in line with interactions reported previously for YY1. We also demonstrate the presence of Rex-1 protein in both trophectoderm and Inner Cell Mass of the mouse blastocyst and in both ES and in trophectoderm stem (TS) cells. In TS cells, we were unable to demonstrate association of Rex-1 to the genes it associates with in ES cells, suggesting that association may be cell-type specific. Rex-1 might fine-tune pluripotency in ES cells by modulating Polycomb-mediated gene regulation.

The emergence of modularity in biological systems

In this review, we discuss modularity and hierarchy in biological systems. We review examples from protein structure, genetics, and biological networks of modular partitioning of the geometry of biological space. We review theories to explain modular organization of biology, with a focus on explaining how biology may spontaneously organize to a structured form. That is, we seek to explain how biology nucleated from among the many possibilities in chemistry. The emergence of modular organization of biological structure will be described as a symmetry-breaking phase transition, with modularity as the order parameter. Experimental support for this description will be reviewed. Examples will be presented from pathogen structure, metabolic networks, gene networks, and protein-protein interaction networks. Additional examples will be presented from ecological food networks, developmental pathways, physiology, and social networks.

Subunit organization and Rab interactions of Vps-C protein complexes that control endolysosomal membrane traffic

The Vps-C complexes, CORVET and HOPS, are key regulators of membrane traffic through late endosomes and lysosomes. In this study Vps-C intersubunit contacts, domain architecture, and interactions with Rab G proteins are systematically dissected using genetic and biochemical approaches.

Traffic through late endolysosomal compartments is regulated by sequential signaling of small G proteins of the Rab5 and Rab7 families. The Saccharomyces cerevisiae Vps-C protein complexes CORVET (class C core vacuole/endosome tethering complex) and HOPS (homotypic fusion and protein transport) interact with endolysosomal Rabs to coordinate their signaling activities. To better understand these large and intricate complexes, we performed interaction surveys to assemble domain-level interaction topologies for the eight Vps-C subunits. We identified numerous intersubunit interactions and up to six Rab-binding sites. Functional modules coordinate the major Rab interactions within CORVET and HOPS. The CORVET-specific subunits, Vps3 and Vps8, form a subcomplex and physically and genetically interact with the Rab5 orthologue Vps21. The HOPS-specific subunits, Vps39 and Vps41, also form a subcomplex. Both subunits bind the Rab7 orthologue Ypt7, but with distinct nucleotide specificities. The in vivo functions of four RING-like domains within Vps-C subunits were analyzed and shown to have distinct functions in endolysosomal transport. Finally, we show that the CORVET- and HOPS-specific subunits Vps3 and Vps39 bind the Vps-C core through a common region within the Vps11 C-terminal domain (CTD). Biochemical and genetic experiments demonstrate the importance of these regions, revealing the Vps11 CTD as a key integrator of Vps-C complex assembly, Rab signaling, and endosomal and lysosomal traffic.

Large-scale protein interactome mapping: strategies and opportunities

Interactions between proteins are central to any cellular process, and mapping these into a protein network is informative both for the function of individual proteins and the functional organization of the cell as a whole. Many strategies have been developed that are up to this task, and the last 10 years have seen the high-throughput application of a number of those in large-scale, sometimes proteome-wide, interactome mapping efforts. Although initially the quality of the data produced in these screening campaigns has been questioned, quality standards and empirical validation schemes are now in place to ensure high-quality data generation. Through their integration with other 'omics' data, interactomics datasets have proven highly valuable towards applications in different areas of clinical importance.

Gene-centered regulatory network mapping

The Caenorhabditis elegans hermaphrodite is a complex multicellular animal that is composed of 959 somatic cells. The C. elegans genome contains ∼20,000 protein-coding genes, 940 of which encode regulatory transcription factors (TFs). In addition, the worm genome encodes more than 100 microRNAs and many other regulatory RNA and protein molecules. Most C. elegans genes are subject to regulatory control, most likely by multiple regulators, and combined, this dictates the activation or repression of the gene and corresponding protein in the relevant cells and under the appropriate conditions. A major goal in C. elegans research is to determine the spatiotemporal expression pattern of each gene throughout development and in response to different signals, and to determine how this expression pattern is accomplished. Gene regulatory networks describe physical and/or functional interactions between genes and their regulators that result in specific spatiotemporal gene expression. Such regulators can act at transcriptional or post-transcriptional levels. Here, I will discuss the methods that can be used to delineate gene regulatory networks in C. elegans. I will mostly focus on gene-centered yeast one-hybrid (Y1H) assays that are used to map interactions between non-coding genic regions, such as promoters, and regulatory TFs. The approaches discussed here are not only relevant to C. elegans biology, but can also be applied to other model organisms and humans.

Quality control methodology for high-throughput protein-protein interaction screening

Protein-protein interactions are key to many aspects of the cell, including its cytoskeletal structure, the signaling processes in which it is involved, or its metabolism. Failure to form protein complexes or signaling cascades may sometimes translate into pathologic conditions such as cancer or neurodegenerative diseases. The set of all protein interactions between the proteins encoded by an organism constitutes its protein interaction network, representing a scaffold for biological function. Knowing the protein interaction network of an organism, combined with other sources of biological information, can unravel fundamental biological circuits and may help better understand the molecular basics of human diseases. The protein interaction network of an organism can be mapped by combining data obtained from both low-throughput screens, i.e., "one gene at a time" experiments and high-throughput screens, i.e., screens designed to interrogate large sets of proteins at once. In either case, quality controls are required to deal with the inherent imperfect nature of experimental assays. In this chapter, we discuss experimental and statistical methodologies to quantify error rates in high-throughput protein-protein interactions screens.

New insights into protein-protein interaction data lead to increased estimates of the S. cerevisiae interactome size

Background

As protein interactions mediate most cellular mechanisms, protein-protein interaction networks are essential in the study of cellular processes. Consequently, several large-scale interactome mapping projects have been undertaken, and protein-protein interactions are being distilled into databases through literature curation; yet protein-protein interaction data are still far from comprehensive, even in the model organism Saccharomyces cerevisiae. Estimating the interactome size is important for evaluating the completeness of current datasets, in order to measure the remaining efforts that are required.

Results

We examined the yeast interactome from a new perspective, by taking into account how thoroughly proteins have been studied. We discovered that the set of literature-curated protein-protein interactions is qualitatively different when restricted to proteins that have received extensive attention from the scientific community. In particular, these interactions are less often supported by yeast two-hybrid, and more often by more complex experiments such as biochemical activity assays. Our analysis showed that high-throughput and literature-curated interactome datasets are more correlated than commonly assumed, but that this bias can be corrected for by focusing on well-studied proteins. We thus propose a simple and reliable method to estimate the size of an interactome, combining literature-curated data involving well-studied proteins with high-throughput data. It yields an estimate of at least 37, 600 direct physical protein-protein interactions in S. cerevisiae.

Conclusions

Our method leads to higher and more accurate estimates of the interactome size, as it accounts for interactions that are genuine yet difficult to detect with commonly-used experimental assays. This shows that we are even further from completing the yeast interactome map than previously expected.

SPIKE: a database of highly curated human signaling pathways

The rapid accumulation of knowledge on biological signaling pathways and their regulatory mechanisms has highlighted the need for specific repositories that can store, organize and allow retrieval of pathway information in a way that will be useful for the research community. SPIKE (Signaling Pathways Integrated Knowledge Engine; http://www.cs.tau.ac.il/&~spike/) is a database for achieving this goal, containing highly curated interactions for particular human pathways, along with literature-referenced information on the nature of each interaction. To make database population and pathway comprehension straightforward, a simple yet informative data model is used, and pathways are laid out as maps that reflect the curator’s understanding and make the utilization of the pathways easy. The database currently focuses primarily on pathways describing DNA damage response, cell cycle, programmed cell death and hearing related pathways. Pathways are regularly updated, and additional pathways are gradually added. The complete database and the individual maps are freely exportable in several formats. The database is accompanied by a stand-alone software tool for analysis and dynamic visualization of pathways.

Analysis of protein complexes through model-based biclustering of label-free quantitative AP-MS data

Affinity purification followed by mass spectrometry (AP-MS) has become a common approach for identifying protein–protein interactions (PPIs) and complexes. However, data analysis and visualization often rely on generic approaches that do not take advantage of the quantitative nature of AP-MS. We present a novel computational method, nested clustering, for biclustering of label-free quantitative AP-MS data. Our approach forms bait clusters based on the similarity of quantitative interaction profiles and identifies submatrices of prey proteins showing consistent quantitative association within bait clusters. In doing so, nested clustering effectively addresses the problem of overrepresentation of interactions involving baits proteins as compared with proteins only identified as preys. The method does not require specification of the number of bait clusters, which is an advantage against existing model-based clustering methods. We illustrate the performance of the algorithm using two published intermediate scale human PPI data sets, which are representative of the AP-MS data generated from mammalian cells. We also discuss general challenges of analyzing and interpreting clustering results in the context of AP-MS data.

iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence

We present iRefWeb, a web interface to protein interaction data consolidated from 10 public databases: BIND, BioGRID, CORUM, DIP, IntAct, HPRD, MINT, MPact, MPPI and OPHID. iRefWeb enables users to examine aggregated interactions for a protein of interest, and presents various statistical summaries of the data across databases, such as the number of organism-specific interactions, proteins and cited publications. Through links to source databases and supporting evidence, researchers may gauge the reliability of an interaction using simple criteria, such as the detection methods, the scale of the study (high- or low-throughput) or the number of cited publications. Furthermore, iRefWeb compares the information extracted from the same publication by different databases, and offers means to follow-up possible inconsistencies. We provide an overview of the consolidated protein–protein interaction landscape and show how it can be automatically cropped to aid the generation of meaningful organism-specific interactomes. iRefWeb can be accessed at: http://wodaklab.org/iRefWeb.

Database URL: http://wodaklab.org/iRefWeb/

Recent advances in charting protein-protein interaction: mass spectrometry-based approaches

Cellular functions are the result of the coordinated action of groups of proteins interacting in molecular assemblies or pathways. The systematic and unbiased charting of protein-protein networks in a variety of organisms has become an important challenge in systems biology. These protein-protein interaction networks contribute comprehensive cartographies of key pathways or biological processes relevant to health or disease by providing a molecular frame for the interpretation of genetic links. At a structural level protein-protein networks enabled the identification of the sequences, motifs and structural folds involved in the process of molecular recognition. A rapidly growing choice of technologies is available for the global charting of protein-protein interactions. In this review, we focus on recent developments in a suite of methods that enable the purification of protein complexes under native conditions and, in conjunction with protein mass spectrometry, identification of their constituents.

Physical interactions between tricarboxylic acid cycle enzymes in Bacillus subtilis: evidence for a metabolon

The majority of all proteins of a living cell is active in complexes rather than in an isolated way. These protein-protein interactions are of high relevance for many biological functions. In addition to many well established protein complexes an increasing number of protein-protein interactions, which form rather transient complexes has recently been discovered. The formation of such complexes seems to be a common feature especially for metabolic pathways. In the Gram-positive model organism Bacillus subtilis, we identified a protein complex of three citric acid cycle enzymes. This complex consists of the citrate synthase, the isocitrate dehydrogenase, and the malate dehydrogenase. Moreover, fumarase and aconitase interact with malate dehydrogenase and with each other. These five enzymes catalyze sequential reaction of the TCA cycle. Thus, this interaction might be important for a direct transfer of intermediates of the TCA cycle and thus for elevated metabolic fluxes via substrate channeling. In addition, we discovered a link between the TCA cycle and gluconeogenesis through a flexible interaction of two proteins: the association between the malate dehydrogenase and phosphoenolpyruvate carboxykinase is directly controlled by the metabolic flux. The phosphoenolpyruvate carboxykinase links the TCA cycle with gluconeogenesis and is essential for B. subtilis growing on gluconeogenic carbon sources. Only under gluconeogenic growth conditions an interaction of these two proteins is detectable and disappears under glycolytic growth conditions.

Human milk proteins: an interactomics and updated functional overview

Milk and milk fractions are characterized by a wide array of proteins, whose concentration spans across several orders of magnitude. By exploiting a combined approach based on functional gene ontology enrichment (FatiGO/Babelomics), hierarchical clustering, and pathway and network analyses, we merged data from literature dealing with protein-oriented studies on human milk. A total of 285 entries defined a nonredundant list upon comparison with the Ingenuity Knowledge Base from the Ingenuity Pathway Analysis software. Results were compared with an inventory of bovine milk proteins gathered from dedicated proteomic studies. A protein core of 106 proteins was found, with most of the entries associated to three main biological functions, namely nutrient transport/lipid metabolism, concretization of the immune system response and cellular proliferation processes. Our analyses confirm and emphasize that the biological role of the human milk proteins is not only limited to the provision of external nutrients and defense molecules against pathogens to the suckling but also to the direct stimulation of the growth of neonate tissues/organs and to the development of a proper independent immune system, both through the induction of a number of molecular cascades associated with cell proliferation/differentiation. The latter aspects were previously investigated by single-molecule dedicated studies, missing the holistic view that results from our analysis.

Construction of a large extracellular protein interaction network and its resolution by spatiotemporal expression profiling

Extracellular interactions involving both secreted and membrane-tethered receptor proteins are essential to initiate signaling pathways that orchestrate cellular behaviors within biological systems. Because of the biochemical properties of these proteins and their interactions, identifying novel extracellular interactions remains experimentally challenging. To address this, we have recently developed an assay, AVEXIS (avidity-based extracellular interaction screen) to detect low affinity extracellular interactions on a large scale and have begun to construct interaction networks between zebrafish receptors belonging to the immunoglobulin and leucine-rich repeat protein families to identify novel signaling pathways important for early development. Here, we expanded our zebrafish protein library to include other domain families and many more secreted proteins and performed our largest screen to date totaling 16,544 potential unique interactions. We report 111 interactions of which 96 are novel and include the first documented extracellular ligands for 15 proteins. By including 77 interactions from previous screens, we assembled an expanded network of 188 extracellular interactions between 92 proteins and used it to show that secreted proteins have twice as many interaction partners as membrane-tethered receptors and that the connectivity of the extracellular network behaves as a power law. To try to understand the functional role of these interactions, we determined new expression patterns for 164 genes within our clone library by using whole embryo in situ hybridization at five key stages of zebrafish embryonic development. These expression data were integrated with the binding network to reveal where each interaction was likely to function within the embryo and were used to resolve the static interaction network into dynamic tissue- and stage-specific subnetworks within the developing zebrafish embryo. All these data were organized into a freely accessible on-line database called ARNIE (AVEXIS Receptor Network with Integrated Expression; www.sanger.ac.uk/arnie) and provide a valuable resource of new extracellular signaling interactions for developmental biology.

Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods

We apply our recently developed information-theoretic measures for the characterisation and comparison of protein–protein interaction networks. These measures are used to quantify topological network features via macroscopic statistical properties. Network differences are assessed based on these macroscopic properties as opposed to microscopic overlap, homology information or motif occurrences. We present the results of a large–scale analysis of protein–protein interaction networks. Precise null models are used in our analyses, allowing for reliable interpretation of the results. By quantifying the methodological biases of the experimental data, we can define an information threshold above which networks may be deemed to comprise consistent macroscopic topological properties, despite their small microscopic overlaps. Based on this rationale, data from yeast–two–hybrid methods are sufficiently consistent to allow for intra–species comparisons (between different experiments) and inter–species comparisons, while data from affinity–purification mass–spectrometry methods show large differences even within intra–species comparisons.

A genome-wide gene function prediction resource for Drosophila melanogaster

Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions. We trained function-specific classifiers to optimize the influence of different biological datasets for each functional category. Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens. The resulting resource of prioritized associations between Drosophila genes and their potential functions offers a guide for experimental investigations.

High-throughput identification of transient extracellular protein interactions

Protein interactions are highly diverse in their biochemical nature, varying in affinity and are often dependent on the surrounding biochemical environment. Given this heterogeneity, it seems unlikely that any one method, and particularly those capable of screening for many protein interactions in parallel, will be able to detect all functionally relevant interactions that occur within a living cell. One major class of interactions that are not detected by current popular high-throughput methods are those that occur in the extracellular environment, especially those made by membrane-embedded receptor proteins. In the present article, we discuss some of our recent research in the development of a scalable assay to identify this class of protein interaction and some of the findings from its application in the construction of extracellular protein interaction networks.

In silico models of cancer

Cancer is a complex disease that involves multiple types of biological interactions across diverse physical, temporal, and biological scales. This complexity presents substantial challenges for the characterization of cancer biology, and motivates the study of cancer in the context of molecular, cellular, and physiological systems. Computational models of cancer are being developed to aid both biological discovery and clinical medicine. The development of these in silico models is facilitated by rapidly advancing experimental and analytical tools that generate information-rich, high-throughput biological data. Statistical models of cancer at the genomic, transcriptomic, and pathway levels have proven effective in developing diagnostic and prognostic molecular signatures, as well as in identifying perturbed pathways. Statistically inferred network models can prove useful in settings where data overfitting can be avoided, and provide an important means for biological discovery. Mechanistically based signaling and metabolic models that apply a priori knowledge of biochemical processes derived from experiments can also be reconstructed where data are available, and can provide insight and predictive ability regarding the behavior of these systems. At longer length scales, continuum and agent-based models of the tumor microenvironment and other tissue-level interactions enable modeling of cancer cell populations and tumor progression. Even though cancer has been among the most-studied human diseases using systems approaches, significant challenges remain before the enormous potential of in silico cancer biology can be fully realized.