A proteome-scale map of the human interactome network.

Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ?14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ?30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a "broader" human interactome network than currently appreciated. The map also uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality interactome models will help "connect the dots" of the genomic revolution.

A reference map of the human binary protein interactome.

Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships1,2. Here we present a human 'all-by-all' reference interactome map of human binary protein interactions, or 'HuRI'. With approximately 53,000 protein-protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome3, transcriptome4 and proteome5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein-protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes.

The phosphatidylserine receptor TIM1 promotes infection of enveloped hepatitis E virus.

The hepatitis E virus (HEV) is an underestimated RNA virus of which the viral life cycle and pathogenicity remain partially understood and for which specific antivirals are lacking. The virus exists in two forms: nonenveloped HEV that is shed in feces and transmits between hosts; and membrane-associated, quasi-enveloped HEV that circulates in the blood. It is suggested that both forms employ different mechanisms for cellular entry and internalization but little is known about the exact mechanisms. Interestingly, the membrane of enveloped HEV is enriched with phosphatidylserine, a natural ligand for the T-cell immunoglobulin and mucin domain-containing protein 1 (TIM1) during apoptosis and involved in 'apoptotic mimicry', a process by which viruses hijack the apoptosis pathway to promote infection. We here investigated the role of TIM1 in the entry process of HEV. We determined that HEV infection with particles derived from culture supernatant, which are cloaked by host-derived membranes (eHEV), was significantly impaired after knockout of TIM1, whereas infection with intracellular HEV particles (iHEV) was unaffected. eHEV infection was restored upon TIM1 expression; and enhanced after ectopic TIM1 expression. The significance of TIM1 during entry was further confirmed by viral binding assay, and point mutations of the PS-binding pocket diminished eHEV infection. In addition, Annexin V, a PS-binding molecule also significantly reduced infection. Taken together, our findings support a role for TIM1 in eHEV-mediated cell entry, facilitated by the PS present on the viral membrane, a strategy HEV may use to promote viral spread throughout the infected body.

An Unexpected Encounter: Respiratory Syncytial Virus Nonstructural Protein 1 Interacts with Mediator Subunit MED25.

Human respiratory syncytial virus (RSV) is the leading cause of severe acute lower respiratory tract infections in infants worldwide. Nonstructural protein NS1 of RSV modulates the host innate immune response by acting as an antagonist of type I and type III interferon (IFN) production and signaling in multiple ways. Likely, NS1 performs this function by interacting with different host proteins. In order to obtain a comprehensive overview of the NS1 interaction partners, we performed three complementary protein-protein interaction screens, i.e., BioID, MAPPIT, and KISS. To closely mimic a natural infection, the BioID proximity screen was performed using a recombinant RSV in which the NS1 protein is fused to a biotin ligase. Remarkably, MED25, a subunit of the Mediator complex, was identified in all three performed screening methods as a potential NS1-interacting protein. We confirmed the interaction between MED25 and RSV NS1 by coimmunoprecipitation, not only upon overexpression of NS1 but also with endogenous NS1 during RSV infection. We also demonstrate that the replication of RSV can be enhanced in MED25 knockout A549 cells, suggesting a potential antiviral role of MED25 during RSV infection. Mediator subunits function as transcriptional coactivators and are involved in transcriptional regulation of their target genes. Therefore, the interaction between RSV NS1 and cellular MED25 might be beneficial for RSV during infection by affecting host transcription and the host immune response to infection. IMPORTANCE Innate immune responses, including the production of type I and III interferons, play a crucial role in the first line of defense against RSV infection. However, only a poor induction of type I IFNs is observed during RSV infection, suggesting that RSV has evolved mechanisms to prevent type I IFN expression by the infected host cell. A unique RSV protein, NS1, is largely responsible for this effect, probably through interaction with multiple host proteins. A better understanding of the interactions that occur between RSV NS1 and host proteins may help to identify targets for an effective antiviral therapy. We addressed this question by performing three complementary protein-protein interaction screens and identified MED25 as an RSV NS1-interacting protein. We propose a role in innate anti-RSV defense for this Mediator complex subunit.

A protein domain-based interactome network for C. elegans early embryogenesis.

Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or "interactome" networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early-embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms.

The Development of RNA-KISS, a Mammalian Three-Hybrid Method to Detect RNA-Protein Interactions in Living Mammalian Cells.

RNA-protein interactions are essential for the regulation of mRNA and noncoding RNA functions and are implicated in many diseases, such as cancer and neurodegenerative disorders. A method that can detect RNA-protein interactions in living mammalian cells on a proteome-wide scale will be an important asset to identify and study these interactions. Here we show that a combination of the mammalian two-hybrid protein-protein detection method KISS (kinase substrate sensor) and the yeast RNA three-hybrid method, utilizing the specific interaction between the MS2 RNA and MS2 coat protein, is capable of detecting RNA-protein interactions in living mammalian cells. For conceptional proof we used the subgenomic flavivirus RNA (sfRNA) of the dengue virus (DENV), a highly structured noncoding RNA derived from the DENV genome known to target host cell proteins involved in innate immunity and antiviral defense, as bait. Using RNA-KISS, we could confirm the previously established interaction between the RNA-binding domain of DDX6 and the DENV sfRNA. Finally, we performed a human proteome-wide screen for DENV sfRNA-binding host factors, identifying several known flavivirus host factors such as DDX6 and PACT, further validating the RNA-KISS method as a robust and high-throughput cell-based RNA-protein interaction screening tool.

Discovering cellular protein-protein interactions: Technological strategies and opportunities.

The analysis of protein interaction networks is one of the key challenges in the study of biology. It connects genotypes to phenotypes, and disruption often leads to diseases. Hence, many technologies have been developed to study protein-protein interactions (PPIs) in a cellular context. The expansion of the PPI technology toolbox however complicates the selection of optimal approaches for diverse biological questions. This review gives an overview of the binary and co-complex technologies, with the former evaluating the interaction of two co-expressed genetically tagged proteins, and the latter only needing the expression of a single tagged protein or no tagged proteins at all. Mass spectrometry is crucial for some binary and all co-complex technologies. After the detailed description of the different technologies, the review compares their unique specifications, advantages, disadvantages, and applicability, while highlighting opportunities for further advancements.

The Zinc-Finger Domain Containing Protein ZC4H2 Interacts with TRPV4, Enhancing Channel Activity and Turnover at the Plasma Membrane.

The Ca2+-permeable Transient Receptor Potential channel vanilloid subfamily member 4 (TRPV4) is involved in a broad range of physiological processes, including the regulation of systemic osmotic pressure, bone resorption, vascular tone, and bladder function. Mutations in the TRPV4 gene are the cause of a spectrum of inherited diseases (or TRPV4-pathies), which include skeletal dysplasias, arthropathies, and neuropathies. There is little understanding of the pathophysiological mechanisms underlying these variable disease phenotypes, but it has been hypothesized that disease-causing mutations affect interaction with regulatory proteins. Here, we performed a mammalian protein-protein interaction trap (MAPPIT) screen to identify proteins that interact with the cytosolic N terminus of human TRPV4, a region containing the majority of disease-causing mutations. We discovered the zinc-finger domain-containing protein ZC4H2 as a TRPV4-interacting protein. In heterologous expression experiments, we found that ZC4H2 increases both the basal activity of human TRPV4 as well as Ca2+ responses evoked by ligands or hypotonic cell swelling. Using total internal reflection fluorescence (TIRF) microscopy, we further showed that ZC4H2 accelerates TRPV4 turnover at the plasma membrane. Overall, these data demonstrate that ZC4H2 is a positive modulator of TRPV4, and suggest a link between TRPV4 and ZC4H2-associated rare disorders, which have several neuromuscular symptoms in common with TRPV4-pathies.

MAPPI-DAT: data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platform.

Summary: Protein-protein interaction (PPI) studies have dramatically expanded our knowledge about cellular behaviour and development in different conditions. A multitude of high-throughput PPI techniques have been developed to achieve proteome-scale coverage for PPI studies, including the microarray based Mammalian Protein-Protein Interaction Trap (MAPPIT) system. Because such high-throughput techniques typically report thousands of interactions, managing and analysing the large amounts of acquired data is a challenge. We have therefore built the MAPPIT cell microArray Protein Protein Interaction-Data management & Analysis Tool (MAPPI-DAT) as an automated data management and analysis tool for MAPPIT cell microarray experiments. MAPPI-DAT stores the experimental data and metadata in a systematic and structured way, automates data analysis and interpretation, and enables the meta-analysis of MAPPIT cell microarray data across all stored experiments. Availability and Implementation: MAPPI-DAT is developed in Python, using R for data analysis and MySQL as data management system. MAPPI-DAT is cross-platform and can be ran on Microsoft Windows, Linux and OS X/macOS. The source code and a Microsoft Windows executable are freely available under the permissive Apache2 open source license at https://github.com/compomics/MAPPI-DAT. Contact: jan.tavernier@vib-ugent.be or lennart.martens@vib-ugent.be. Supplementary information: Supplementary data are available at Bioinformatics online.

Proteome-scale Binary Interactomics in Human Cells.

Because proteins are the main mediators of most cellular processes they are also prime therapeutic targets. Identifying physical links among proteins and between drugs and their protein targets is essential in order to understand the mechanisms through which both proteins themselves and the molecules they are targeted with act. Thus, there is a strong need for sensitive methods that enable mapping out these biomolecular interactions. Here we present a robust and sensitive approach to screen proteome-scale collections of proteins for binding to proteins or small molecules using the well validated MAPPIT (Mammalian Protein-Protein Interaction Trap) and MASPIT (Mammalian Small Molecule-Protein Interaction Trap) assays. Using high-density reverse transfected cell microarrays, a close to proteome-wide collection of human ORF clones can be screened for interactors at high throughput. The versatility of the platform is demonstrated through several examples. With MAPPIT, we screened a 15k ORF library for binding partners of RNF41, an E3 ubiquitin protein ligase implicated in receptor sorting, identifying known and novel interacting proteins. The potential related to the fact that MAPPIT operates in living human cells is illustrated in a screen where the protein collection is scanned for interactions with the glucocorticoid receptor (GR) in its unliganded versus dexamethasone-induced activated state. Several proteins were identified the interaction of which is modulated upon ligand binding to the GR, including a number of previously reported GR interactors. Finally, the screening technology also enables detecting small molecule target proteins, which in many drug discovery programs represents an important hurdle. We show the efficiency of MASPIT-based target profiling through screening with tamoxifen, a first-line breast cancer drug, and reversine, an investigational drug with interesting dedifferentiation and antitumor activity. In both cases, cell microarray screens yielded known and new potential drug targets highlighting the utility of the technology beyond fundamental biology.

Kinase Substrate Sensor (KISS), a mammalian in situ protein interaction sensor.

Probably every cellular process is governed by protein-protein interaction (PPIs), which are often highly dynamic in nature being modulated by in- or external stimuli. Here we present KISS, for KInase Substrate Sensor, a mammalian two-hybrid approach designed to map intracellular PPIs and some of the dynamic features they exhibit. Benchmarking experiments indicate that in terms of sensitivity and specificity KISS is on par with other binary protein interaction technologies while being complementary with regard to the subset of PPIs it is able to detect. We used KISS to evaluate interactions between different types of proteins, including transmembrane proteins, expressed at their native subcellular location. In situ analysis of endoplasmic reticulum stress-induced clustering of the endoplasmic reticulum stress sensor ERN1 and ligand-dependent ß-arrestin recruitment to GPCRs illustrated the method's potential to study functional PPI modulation in complex cellular processes. Exploring its use as a tool for in cell evaluation of pharmacological interference with PPIs, we showed that reported effects of known GPCR antagonists and PPI inhibitors are properly recapitulated. In a three-hybrid setup, KISS was able to map interactions between small molecules and proteins. Taken together, we established KISS as a sensitive approach for in situ analysis of protein interactions and their modulation in a changing cellular context or in response to pharmacological challenges.

Regulation of the ryanodine receptor by anti-apoptotic Bcl-2 is independent of its BH3-domain-binding properties.

The regulation of intracellular Ca(2+) signaling is an important aspect of how anti-apoptotic B-cell lymphoma 2 (Bcl-2) proteins regulate cell death and cell survival. At the endoplasmic reticulum (ER) the Bcl-2 homology (BH) 4 domain of Bcl-2 is known to bind to and inhibit both inositol 1,4,5-trisphosphate receptors (IP3Rs) and ryanodine receptors (RyRs). Besides this, drugs that target the hydrophobic cleft of Bcl-2 have been reported to deplete ER Ca(2+) stores in an IP3R- and RyR-dependent way. This suggests that the hydrophobic cleft of Bcl-2 may also be involved in regulating these ER-located Ca(2+)-release channels. However, the contribution of the hydrophobic cleft on the binding and regulatory properties of Bcl-2 to either IP3Rs or RyRs has until now not been studied. Here, the importance of the hydrophobic cleft of Bcl-2 in binding to and inhibiting the RyR was assessed by using a genetic approach based on site-directed mutagenesis of Bcl-2's hydrophobic cleft and a pharmacological approach based on the selective Bcl-2 hydrophobic cleft inhibitor, ABT-199. Both binding assays and single-cell Ca(2+) measurements indicated that RyR binding and the inhibition of RyR-mediated Ca(2+) release by Bcl-2 is independent of its hydrophobic cleft.

Mammalian two-hybrids come of age.

A diverse series of mammalian two-hybrid technologies for the detection of protein-protein interactions have emerged in the past few years, complementing the established yeast two-hybrid approach. Given the mammalian background in which they operate, these assays open new avenues to study the dynamics of mammalian protein interaction networks, i.e. the temporal, spatial and functional modulation of protein-protein associations. In addition, novel assay formats are available that enable high-throughput mammalian two-hybrid applications, facilitating their use in large-scale interactome mapping projects. Finally, as they can be applied in drug discovery and development programs, these techniques also offer exciting new opportunities for biomedical research.

Virus-Host Protein Interaction Network of the Hepatitis E Virus ORF2-4 by Mammalian Two-Hybrid Assays.

Throughout their life cycle, viruses interact with cellular host factors, thereby influencing propagation, host range, cell tropism and pathogenesis. The hepatitis E virus (HEV) is an underestimated RNA virus in which knowledge of the virus-host interaction network to date is limited. Here, two related high-throughput mammalian two-hybrid approaches (MAPPIT and KISS) were used to screen for HEV-interacting host proteins. Promising hits were examined on protein function, involved pathway(s), and their relation to other viruses. We identified 37 ORF2 hits, 187 for ORF3 and 91 for ORF4. Several hits had functions in the life cycle of distinct viruses. We focused on SHARPIN and RNF5 as candidate hits for ORF3, as they are involved in the RLR-MAVS pathway and interferon (IFN) induction during viral infections. Knocking out (KO) SHARPIN and RNF5 resulted in a different IFN response upon ORF3 transfection, compared to wild-type cells. Moreover, infection was increased in SHARPIN KO cells and decreased in RNF5 KO cells. In conclusion, MAPPIT and KISS are valuable tools to study virus-host interactions, providing insights into the poorly understood HEV life cycle. We further provide evidence for two identified hits as new host factors in the HEV life cycle.

Next-generation large-scale binary protein interaction network for Drosophila melanogaster.

Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster. Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the 'FlyBi' dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings (dwg) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function.

Host-pathogen interactome mapping for HTLV-1 and -2 retroviruses.

BACKGROUND: Human T-cell leukemia virus type 1 (HTLV-1) and type 2 both target T lymphocytes, yet induce radically different phenotypic outcomes. HTLV-1 is a causative agent of Adult T-cell leukemia (ATL), whereas HTLV-2, highly similar to HTLV-1, causes no known overt disease. HTLV gene products are engaged in a dynamic struggle of activating and antagonistic interactions with host cells. Investigations focused on one or a few genes have identified several human factors interacting with HTLV viral proteins. Most of the available interaction data concern the highly investigated HTLV-1 Tax protein. Identifying shared and distinct host-pathogen protein interaction profiles for these two viruses would enlighten how they exploit distinctive or common strategies to subvert cellular pathways toward disease progression. RESULTS: We employ a scalable methodology for the systematic mapping and comparison of pathogen-host protein interactions that includes stringent yeast two-hybrid screening and systematic retest, as well as two independent validations through an additional protein interaction detection method and a functional transactivation assay. The final data set contained 166 interactions between 10 viral proteins and 122 human proteins. Among the 166 interactions identified, 87 and 79 involved HTLV-1 and HTLV-2 -encoded proteins, respectively. Targets for HTLV-1 and HTLV-2 proteins implicate a diverse set of cellular processes including the ubiquitin-proteasome system, the apoptosis, different cancer pathways and the Notch signaling pathway. CONCLUSIONS: This study constitutes a first pass, with homogeneous data, at comparative analysis of host targets for HTLV-1 and -2 retroviruses, complements currently existing data for formulation of systems biology models of retroviral induced diseases and presents new insights on biological pathways involved in retroviral infection.

An experimentally derived confidence score for binary protein-protein interactions.

Information on protein-protein interactions is of central importance for many areas of biomedical research. At present no method exists to systematically and experimentally assess the quality of individual interactions reported in interaction mapping experiments. To provide a standardized confidence-scoring method that can be applied to tens of thousands of protein interactions, we have developed an interaction tool kit consisting of four complementary, high-throughput protein interaction assays. We benchmarked these assays against positive and random reference sets consisting of well documented pairs of interacting human proteins and randomly chosen protein pairs, respectively. A logistic regression model was trained using the data from these reference sets to combine the assay outputs and calculate the probability that any newly identified interaction pair is a true biophysical interaction once it has been tested in the tool kit. This general approach will allow a systematic and empirical assignment of confidence scores to all individual protein-protein interactions in interactome networks.

An empirical framework for binary interactome mapping.

Several attempts have been made to systematically map protein-protein interaction, or 'interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains approximately 130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and interactome size are crucial to establish the magnitude of the task of comprehensive human interactome mapping and to elucidate a path toward this goal.

Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network.

To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards.We present an expanded C. elegans protein-protein interaction network, or 'interactome' map, derived from testing a matrix of approximately 10,000 x approximately 10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm interactome at approximately 116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins

Bcl-xL acts as an inhibitor of IP3R channels, thereby antagonizing Ca2+-driven apoptosis.

Anti-apoptotic Bcl-2-family members not only act at mitochondria but also at the endoplasmic reticulum, where they impact Ca2+ dynamics by controlling IP3 receptor (IP3R) function. Current models propose distinct roles for Bcl-2 vs. Bcl-xL, with Bcl-2 inhibiting IP3Rs and preventing pro-apoptotic Ca2+ release and Bcl-xL sensitizing IP3Rs to low [IP3] and promoting pro-survival Ca2+ oscillations. We here demonstrate that Bcl-xL too inhibits IP3R-mediated Ca2+ release by interacting with the same IP3R regions as Bcl-2. Via in silico superposition, we previously found that the residue K87 of Bcl-xL spatially resembled K17 of Bcl-2, a residue critical for Bcl-2's IP3R-inhibitory properties. Mutagenesis of K87 in Bcl-xL impaired its binding to IP3R and abrogated Bcl-xL's inhibitory effect on IP3Rs. Single-channel recordings demonstrate that purified Bcl-xL, but not Bcl-xLK87D, suppressed IP3R single-channel openings stimulated by sub-maximal and threshold [IP3]. Moreover, we demonstrate that Bcl-xL-mediated inhibition of IP3Rs contributes to its anti-apoptotic properties against Ca2+-driven apoptosis. Staurosporine (STS) elicits long-lasting Ca2+ elevations in wild-type but not in IP3R-knockout HeLa cells, sensitizing the former to STS treatment. Overexpression of Bcl-xL in wild-type HeLa cells suppressed STS-induced Ca2+ signals and cell death, while Bcl-xLK87D was much less effective in doing so. In the absence of IP3Rs, Bcl-xL and Bcl-xLK87D were equally effective in suppressing STS-induced cell death. Finally, we demonstrate that endogenous Bcl-xL also suppress IP3R activity in MDA-MB-231 breast cancer cells, whereby Bcl-xL knockdown augmented IP3R-mediated Ca2+ release and increased the sensitivity towards STS, without altering the ER Ca2+ content. Hence, this study challenges the current paradigm of divergent functions for Bcl-2 and Bcl-xL in Ca2+-signaling modulation and reveals that, similarly to Bcl-2, Bcl-xL inhibits IP3R-mediated Ca2+ release and IP3R-driven cell death. Our work further underpins that IP3R inhibition is an integral part of Bcl-xL's anti-apoptotic function.