Most cellular processes are carried out by multiprotein complexes. The identification and analysis of their components provides insight into how the ensemble of expressed proteins (proteome) is organized into functional units. We used tandem-affinity purification (TAP) and mass spectrometry in a large-scale approach to characterize multiprotein complexes in Saccharomyces cerevisiae. We processed 1,739 genes, including 1,143 human orthologues of relevance to human biology, and purified 589 protein assemblies. Bioinformatic analysis of these assemblies defined 232 distinct multiprotein complexes and proposed new cellular roles for 344 proteins, including 231 proteins with no previous functional annotation. Comparison of yeast and human complexes showed that conservation across species extends from single proteins to their molecular environment. Our analysis provides an outline of the eukaryotic proteome as a network of protein complexes at a level of organization beyond binary interactions. This higher-order map contains fundamental biological information and offers the context for a more reasoned and informed approach to drug discovery.

Signal transduction pathways are modular composites of functionally interdependent sets of proteins that act in a coordinated fashion to transform environmental information into a phenotypic response. The pro-inflammatory cytokine tumour necrosis factor (TNF)-alpha triggers a signalling cascade, converging on the activation of the transcription factor NF-kappa B, which forms the basis for numerous physiological and pathological processes. Here we report the mapping of a protein interaction network around 32 known and candidate TNF-alpha/NF-kappa B pathway components by using an integrated approach comprising tandem affinity purification, liquid-chromatography tandem mass spectrometry, network analysis and directed functional perturbation studies using RNA interference. We identified 221 molecular associations and 80 previously unknown interactors, including 10 new functional modulators of the pathway. This systems approach provides significant insight into the logic of the TNF-alpha/NF-kappa B pathway and is generally applicable to other pathways relevant to human disease.

TANK-binding kinase 1 (TBK1) is of central importance for the induction of type-I interferon (IFN) in response to pathogens. We identified the DEAD-box helicase DDX3X as an interaction partner of TBK1. TBK1 and DDX3X acted synergistically in their ability to stimulate the IFN promoter, whereas RNAi-mediated reduction of DDX3X expression led to an impairment of IFN production. Chromatin immunoprecipitation indicated that DDX3X is recruited to the IFN promoter upon infection with Listeria monocytogenes, suggesting a transcriptional mechanism of action. DDX3X was found to be a TBK1 substrate in vitro and in vivo. Phosphorylation-deficient mutants of DDX3X failed to synergize with TBK1 in their ability to stimulate the IFN promoter. Overall, our data imply that DDX3X is a critical effector of TBK1 that is necessary for type I IFN induction.

Tandem affinity purification (TAP) is a generic two-step affinity purification protocol that enables the isolation of protein complexes under close-to-physiological conditions for subsequent analysis by mass spectrometry. Although TAP was instrumental in elucidating the yeast cellular machinery, in mammalian cells the method suffers from a low overall yield. We designed several dual-affinity tags optimized for use in mammalian cells and compared the efficiency of each tag to the conventional TAP tag. A tag based on protein G and the streptavidin-binding peptide (GS-TAP) resulted in a tenfold increase in protein-complex yield and improved the specificity of the procedure. This allows purification of protein complexes that were hitherto not amenable to TAP and use of less starting material, leading to higher success rates and enabling systematic interaction proteomics projects. Using the well-characterized Ku70-Ku80 protein complex as an example, we identified both core elements as well as new candidate effectors.

BackgroundModern data generation techniques used in distributed systems biology research projects often create datasets of enormous size and diversity. We argue that in order to overcome the challenge of managing those large quantitative datasets and maximise the biological information extracted from them, a sound information system is required. Ease of integration with data analysis pipelines and other computational tools is a key requirement for it.ResultsWe have developed openBIS, an open source software framework for constructing user-friendly, scalable and powerful information systems for data and metadata acquired in biological experiments. openBIS enables users to collect, integrate, share, publish data and to connect to data processing pipelines. This framework can be extended and has been customized for different data types acquired by a range of technologies.ConclusionsopenBIS is currently being used by several SystemsX.ch and EU projects applying mass spectrometric measurements of metabolites and proteins, High Content Screening, or Next Generation Sequencing technologies. The attributes that make it interesting to a large research community involved in systems biology projects include versatility, simplicity in deployment, scalability to very large data, flexibility to handle any biological data type and extensibility to the needs of any research domain.

During the interaction of a T cell with an antigen-presenting cell (APC), several receptor ligand pairs, including the T cell receptor (TCR)͞major histocompatibility complex (MHC), accumulate at the T cell͞APC interface in defined geometrical patterns. This accumulation depends on a movement of the T cell cortical actin cytoskeleton toward the interface. Here we study the involvement of the guanine nucleotide exchange factor vav in this process. We crossed 129 vav ؊/؊ mice with B10͞BR 5C.C7 TCR transgenic mice and used peptide-loaded APCs to stimulate T cells from the offspring. We found that the accumulation of TCR͞MHC at the T cell͞APC interface and the T cell actin cytoskeleton rearrangement were clearly defective in these vav ؉/؊ mice. A comparable defect in superantigen-mediated T cell activation of T cells from non-TCR transgenic 129 mice was also observed, although in this case it was more apparent in vav ؊/؊ mice. These data indicate that vav is an essential regulator of cytoskeletal rearrangements during T cell activation.haploinsufficiency ͉ superantigen ͉ costimulation ͉ receptor accumulation ͉ T cell activation

Plk1 activation is required for progression through mitotic entry to cytokinesis. Here we show that at mitotic entry, Plk1 phosphorylates Optineurin (Optn) at serine 177 and that this dissociates Optn from the Golgi-localized GTPase Rab8, inducing its translocation into the nucleus. Mass spectrometry analysis revealed that Optn is associated with a myosin phosphatase complex (MP), which antagonizes the mitotic function of Plk1. Our data also indicate that Optn functionally connects this complex to Plk1 by promoting phosphorylation of the myosin phosphatase targeting subunit 1 (MYPT1). Accordingly, silencing Optn expression increases Plk1 activity and induces abscission failure and multinucleation, which were rescued upon expression of wild-type (WT) Optn, but not a phospho-deficient mutant (S177A) that cannot translocate into the nucleus during mitosis. Overall, these results highlight an important role of Optn in the spatial and temporal coordination of Plk1 activity.

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.