Plk1-dependent phosphorylation of optineurin provides a negative feedback mechanism for mitotic progression.

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.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

The DEAD-box helicase DDX3X is a critical component of the TANK-binding kinase 1-dependent innate immune response.

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.

openBIS: a flexible framework for managing and analyzing complex data in biology research.

BACKGROUND: Modern 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. RESULTS: We 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. CONCLUSIONS: openBIS 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.

A physical and functional map of the human TNF-alpha/NF-kappa B signal transduction pathway.

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.

Informing epidemic (research) responses in a timely fashion by knowledge management - a Zika virus use case.

The response of pathophysiological research to emerging epidemics often occurs after the epidemic and, as a consequence, has little to no impact on improving patient outcomes or on developing high-quality evidence to inform clinical management strategies during the epidemic. Rapid and informed guidance of epidemic (research) responses to severe infectious disease outbreaks requires quick compilation and integration of existing pathophysiological knowledge. As a case study we chose the Zika virus (ZIKV) outbreak that started in 2015 to develop a proof-of-concept knowledge repository. To extract data from available sources and build a computationally tractable and comprehensive molecular interaction map we applied generic knowledge management software for literature mining, expert knowledge curation, data integration, reporting and visualization. A multi-disciplinary team of experts, including clinicians, virologists, bioinformaticians and knowledge management specialists, followed a pre-defined workflow for rapid integration and evaluation of available evidence. While conventional approaches usually require months to comb through the existing literature, the initial ZIKV KnowledgeBase (ZIKA KB) was completed within a few weeks. Recently we updated the ZIKA KB with additional curated data from the large amount of literature published since 2016 and made it publicly available through a web interface together with a step-by-step guide to ensure reproducibility of the described use case. In addition, a detailed online user manual is provided to enable the ZIKV research community to generate hypotheses, share knowledge, identify knowledge gaps, and interactively explore and interpret data. A workflow for rapid response during outbreaks was generated, validated and refined and is also made available. The process described here can be used for timely structuring of pathophysiological knowledge for future threats. The resulting structured biological knowledge is a helpful tool for computational data analysis and generation of predictive models and opens new avenues for infectious disease research. ZIKV Knowledgebase is available at www.zikaknowledgebase.eu.

Cytoplasmic Listeria monocytogenes stimulates IFN-beta synthesis without requiring the adapter protein MAVS.

The mitochondria-associated adapter protein MAVS (also called IPS-1, VISA or CARDIF, designated MAVS for reasons of simplicity in our manuscript) relays signals from cytoplasmic sensors of viral RNA to the IRF3 kinase complex and the interferon-beta (IFN-beta) gene. Using siRNA-mediated knock-down in macrophages we show that IFN-beta synthesis in response to transfected, intracellular double-stranded RNA (dsRNA), a pathogen-associated molecular pattern of viruses, is decreased in absence of MAVS. By contrast, the Gram-positive bacterium Listeria monocytogenes targets the IFN-beta gene without detectable MAVS requirement. The data show that MAVS is not a central adapter protein for all cytoplasmic pathogen sensors that stimulate IFN-beta synthesis.

Implementing systems medicine within healthcare.

The cause of a complex disease cannot be pinpointed to a single origin; rather, a highly complex network of many factors that interact on different levels over time and space is disturbed. This complexity requires novel approaches to diagnosis, treatment, and prevention. To foster the necessary shift to a pro-active systems medicine, proof-of-concept studies are needed. Here, we highlight several systems approaches that have been shown to work within the field of respiratory medicine, and we propose the next steps for broader implementation.

Charting protein complexes, signaling pathways, and networks in the immune system.

Systematic deciphering of protein-protein interactions has the potential to generate comprehensive and instructive signaling networks and to fuel new therapeutic and diagnostic strategies. Here, we describe how recent advances in high-throughput proteomic technologies, involving biochemical purification methods and mass spectrometry analysis, can be applied systematically to the characterization of protein complexes and the computation of molecular networks. The networks obtained form the basis for further functional analyses, such as knockdown by RNA interference, ultimately leading to the identification of nodes that represent candidate targets for pharmacological exploitation. No individual experimental approach can accurately elucidate all critical modulatory components and biological aspects of a signaling network. Such functionally annotated protein-protein interaction networks, however, represent an ideal platform for the integration of additional datasets. By providing links between molecules, they also provide links to all previous observations associated with these molecules, be they of genetic, pharmacological, or other origin. As exemplified here by the analysis of the tumor necrosis factor (TNF)-alpha/nuclear factor-kappaB (NF-kappaB) signaling pathway, the approach is applicable to any mammalian cellular signaling pathway in the immune system.

An efficient tandem affinity purification procedure for interaction proteomics in mammalian cells.

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.

Functional organization of the yeast proteome by systematic analysis of protein complexes.

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.