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Rheumatoid Arthritis (RA) is an autoimmune disease of unknown aetiology involving complex interactions between environmental and genetic factors. Its pathogenesis is suspected to arise from intricate interplays between signalling, gene regulation and metabolism, leading to synovial inflammation, bone erosion and cartilage destruction in the patients’ joints. In addition, the resident synoviocytes of macrophage and fibroblast types can interact with innate and adaptive immune cells and contribute to the disease’s debilitating symptoms. Therefore, a detailed, mechanistic mapping of the molecular pathways and cellular crosstalks is essential to understand the complex biological processes and different disease manifestations. In this regard, we present the RA-Atlas, an SBGN-standardized, interactive, manually curated representation of existing knowledge related to the onset and progression of RA. This state-of-the-art RA-Atlas includes an updated version of the global RA-map covering relevant metabolic pathways and cell-specific molecular interaction maps for CD4+ Th1 cells, fibroblasts, and M1 and M2 macrophages. The molecular interaction maps were built using information extracted from published literature and pathway databases and enriched using omic data. The RA-Atlas is freely accessible on the webserver MINERVA (https://ramap.uni.lu/minerva/), allowing easy navigation using semantic zoom, cell-specific or experimental data overlay, gene set enrichment analysis, pathway export or drug query.

Η χαλάρωση των τεχνητών προθέσεων αρθροπλαστικών γόνατος ή ισχίου, αποτέλεσμα περιπροσθετικής οστεόλυσης που προκαλείται από διάφορους παράγοντες (κυτταροκίνες, ένζυμα κ.ά) που εκκρίνονται από κύτταρα των περιπροσθετικών ιστών λόγω αντίδρασης ξένου σώματος με τα υλικά φθοράς των τεχνικών προθέσεων, είναι μία μη-φλεγμονώδης πάθηση που προσομοιάζει της οστεοαρθρίτιδας. Μεταξύ των παραγόντων που εμπλέκονται στη χαλάρωση των προθέσεων συγκαταλέγονται και οι μεταλλοπρωτεάσες του εξωκυττάριου χώρου (ΜΜΡs). Οι ΜΜΡs παίζουν σημαντικό ρόλο στην αποικοδόμηση του εξωκυττάριου υλικού και της βασικής μεμβράνης. Εμπλέκονται τόσο σε φυσιολογικές (ανάπλαση ιστών, επούλωση τραυμάτων) όσο και σε παθολογικές (οστεόλυση, μετάσταση καρκινικών κυττάρων, περιοδοντίτιδα, ρευματοειδή αρθρίτιδα, κ.ά.) διαδικασίες. Στην παρούσα διατριβή μελετήθηκε ένας διαλυτός τύπος της μεμβρανικής μεταλλοπρωτεάσης-1 (sΜΤ1-ΜΜΡ) σε εκχυλίσματα περιπροσθετικών ιστών, πνευμονικών ιστών από ασθενείς με διάφορες παθήσεις των πνευμόνων καθώς και σε εκχυλίσματα πολύποδα ασθενών με ρινική πολυποδίαση. Χρησιμοποιήθηκαν διάφορες μέθοδοι, όπως ανοσοαποτύπωση western, ELISA, ανοσοκατακρήμνιση, ζυμογράφημα ζελατίνης, φθορισμομετρία, ανασοϊστοχημεία, RT-PCR και αποτύπωση Northern. H sΜΤ1-ΜΜΡ (57 kDa) ανιχνεύθηκε σε όλα τα δείγματα που εξετάστηκαν. Ο διαλυτός αυτός τύπος δεν συμπλοκοποιείται με α2-μακροσφαιρίνη, φέρει την περιοχή του προπεπτιδίου αλλά και ΤΙΜΡ-2, ο οποίος όμως δεν είναι δεσμευμένος στο ενεργό κέντρο του ενζύμου. Τόσο το ΑΡΜΑ όσο και η ουροκινάση δεν είχαν την ικανότητα απομάκρυνσης του προπεπτιδίου της sΜΤ1-ΜΜΡ. Η ύπαρξη της sΜΤ1-ΜΜΡ δεν φαίνεται να οφείλεται σε εναλλακτικό μάτισμα. Η ανοσοϊστοχημική ανάλυση έδειξε ότι η μεμβρανική μεταλλοπρωτεάση ΜΤ1-ΜΜΡ εκφράζεται στην κυτταρική μεμβράνη ενεργοποιημένη μόνο στις ινοβλάστες, ενώ σε όλα τα υπόλοιπα κύτταρα, όπως φλεγμονώδη, επιθηλιακά, ενδοθηλιακά και γιγαντοκύτταρα εκφράζεται στη μορφή του προενζύμου με το προπεπτίδιο. Ο τρόπος παραγωγής της sΜΤ1-ΜΜΡ όπως και ο βιολογικός της ρόλος παραμένουν πρός διερεύνηση. Χαλάρωση των προθέσεων παρατηρείται συνήθως μετά από 8-10 χρόνια από την πρώτη αρθροπλαστική και τότε απαιτείται αναθεώρηση της αρθροπλαστικής, επέμβαση επώδυνη για άτομα μεγάλης ηλικίας που είναι η πλειονότητα αυτών που έχουν υποβληθεί σε αρθροπλαστική. Είναι λοιπόν σήμερα ζητούμενο η καθυστέρηση της χαλάρωσης και η παράταση της ζωής των προθέσεων με κάποια φαρμακευτική αγωγή. Οι πρωτεϊνικές και οι ΜΑΡ κινάσες θεωρούνται κομβικά ένζυμα στη κυτταρική σηματοδότηση και στην ενεργοποίηση ποικίλων μεταγραφικών παραγόντων, και εμπλέκονται σε διάφορες παθολογικές καταστάσεις. Αναστολείς των ενζύμων αυτών βρίσκονται σήμερα σε διάφορες κλινικές δοκιμές. Για την προσέγγιση του ερωτήματος κατά πόσο αναστολείς των πρωτεϊνικών και ΜΑΡ κινασών θα μπορούσαν να συμβάλλουν στην επιμήκηνση του χρόνου ζωής των προθέσεων αρθροπλαστικής, περιπροσθετικοί ιστοί καλλιεργήθηκαν παρουσία και απουσία αναστολέων των ενζύμων αυτών και στο μέσο καλλιέργειας προσδιορίσθηκαν τα επίπεδα παραγόντων με σημαντικό ρόλο στη χαλάρωση των προθέσεων, όπως μεταλλοπρωτεασών και των αναστολέων τους (sΜΤ1-ΜΜΡ, MMP-1, ΜΜΡ-2, ΜΜΡ-9, MMP-13 & TIMP-1), IL-6 και PGE2. Χρησιμοποιήθηκαν μέθοδοι όπως, ανοσοαποτύπωση western, ELISA, και ζυμογράφημα ζελατίνης. Τα πειράματα έδειξαν ότι σε γενικές γραμμές οι αναστολείς προκάλεσαν κυρίως μείωση ή δεν επηρέασαν την παραγωγή των παραγόντων που μελετήθηκαν σε κάποιες όμως περιπτώσεις προκάλεσαν και αύξηση της παραγωγής τους. Για τη διατύπωση όμως ασφαλών συμπερασμάτων σχετικά με το κατά πόσο τέτοιοι αναστολείς θα μπορούσαν να συμβάλλουν στην επιμήκυνση του χρόνου ζωής των προθέσεων, απαιτούνται περαιτέρω πειράματα με μεγαλύτερο αριθμό ασθενών.

Nucleic acid sensing is a 3 decades old but still challenging area of application for different biological sub-domains, from pathogen detection to single cell transcriptomics analysis. The many applications of nucleic acid detection and identification are mostly carried out by PCR techniques, sequencing, and their derivatives used at large scale. However, these methods' limitations on speed, cost, complexity and specificity have motivated the development of innovative detection methods among which nucleic acid biosensing technologies seem promising. Toehold switches are a particular class of RNA sensing devices relying on a conformational switch of secondary structure induced by the pairing of the detected trigger RNA with a

Summary: Computational models of biological systems can exploit on a broad range of rapidly developing approaches, including novel experimental approaches, bioinformatics data analysis, emerging modelling paradigms, data standards, and algorithms. A discussion about the most recent advances among experts from various domains is crucial to foster data-driven computational modelling and its growing use in assessing and predicting the behaviour of biological systems. Intending to encourage the development of tools, approaches, and predictive models, and to deepen our understanding of biological systems, the Community of Special Interest (COSI) was launched in Computational Modelling of Biological Systems (SysMod) in 2016. SysMod’s main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, which brings together computer scientists, biologists, mathematicians, engineers, computational and systems biologists. In the five years since its inception, SysMod has evolved into a dynamic and expanding community as the increasing number of contributions and participants illustrate. SysMod maintains several online resources to facilitate interaction among the community members, including an online forum, a calendar of relevant meetings, and a YouTube channel with talks and lectures of interest for the modelling community. Since more than half a decade, the growing interest in computational systems modelling and multi-scale data integration has continued to inspire and support the SysMod community. Its members get progressively more involved and actively contribute not only to the annual COSI meeting but also to several related community workshops and meetings, which focus on specific topics, including particular techniques for computational modelling or standardisation efforts. Contact: sysmod-coord@googlegroups.com., This article appeared in the journal Bioinformatics at https://doi.org/10.1093/bioinformatics/btab229.

In December 2019, the Chinese Center for Disease Control reported several cases of severe pneumonia that resisted usual treatments inthe city of Wuhan. This announcement marked the beginning of the COVID19 pandemic, which caused more than 80 million infection casesand 1.7 million deaths worldwide in 2020 alone and is still raging. The pandemic has given rise to global public health responses and international research efforts of unprecedented scope and speed. This scientific mobilization has yielded remarkable results, enabling a great deal of knowledge accumulation in just a few months: from the identification of the virus and its main proteins to the analysis of its origin and mechanisms. This basic biological knowledge is mandatory for medical advances.In this document, one year after the beginning of the spread of the disease, we wish to shed particular light on the contribution of bioinformatics in all this work. This discipline, at the crossroads of computer sciences, mathematics, biology, and physics, has taken oninestimable importance in modern biology and medicine. It provides computational models, algorithms, software, and guidelines to help thescientific community handle biological data and accelerate research. The discovery and study of the SARS-CoV-2 coronavirus is an emblematicexample of these contributions. Bioinformatics methods have been at the heart of several essential milestones: sequencing the virus genome, analyzing its origin and evolutionary dynamics, modeling interacting biological entities at the structural and network scales, and studying host genetic susceptibility. For several of these topics, research on SARS-CoV-2 could benefit from a wide range of off-the-shelf software packages that rely on well-established algorithms developed by the bioinformatics community over the years. For other topics, the analysis of SARS-CoV-2 pushes the limits of knowledge and invites the community to develop new computational models and methods. This work, as a whole, has made it possible to elucidate the nature and the functioning of the novel pathogen. It has contributed to the fight against COVID19, even if much remains to be done to fully understand the disease and control the epidemic.

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. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.; 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. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

MotivationRheumatoid arthritis (RA) is a multifactorial autoimmune disease that causes chronic inflammation of the joints. RA is considered a complex disease as it involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this approach, we integrate three different biological layers (gene expression, signalling cascades, mutations), obtained by bottom-up and top-down methods to build an integrative, disease-specific network. The goal behind this endeavour is to see if we can unravel mechanisms governing the regulation of key genes identified as mutation carriers in RA and derive patient-specific models to gain more insights into the disease heterogeneity. ResultsIn this work, we combine biological data relevant to Rheumatoid Arthritis, in the form of a global, integrative network. We first make use of publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using prior knowledge encoded in a state-of-the-art, RA-specific molecular map. Lastly, a list of RA specific variants highlights key genes associated with known disease mutations. AvailabilityDatasets used for the analysis are publicly available. All scripts used to generate results and the Shiny app will be freely accessible after peer-reviewed publication.

The fast accumulation of biological data calls for their integration, analysis and exploitation through more systematic approaches. The generation of novel, relevant hypotheses from this enormous quantity of data remains challenging. Logical models have long been used to answer a variety of questions regarding the dynamical behaviours of regulatory networks. As the number of published logical models increases, there is a pressing need for systematic model annotation, referencing and curation in community-supported and standardised formats. This article summarises the key topics and future directions of a meeting entitled ‘Annotation and curation of computational models in biology’, organised as part of the 2019 [BC]2 conference. The purpose of the meeting was to develop and drive forward a plan towards the standardised annotation of logical models, review and connect various ongoing projects of experts from different communities involved in the modelling and annotation of molecular biological entities, interactions, pathways and models. This article defines a roadmap towards the annotation and curation of logical models, including milestones for best practices and minimum standard requirements.

International audience; Discrete, logic-based models are increasingly used to describe biological mechanisms. Initially introduced to study gene regulation, these models evolved to cover various molecular mechanisms, such as signaling, transcription factor cooperativity, and even metabolic processes. The abstract nature and amenability of discrete models to robust mathematical analyses make them appropriate for addressing a wide range of complex biological problems. Recent technological breakthroughs have generated a wealth of high-throughput data. Novel, literature-based representations of biological processes and emerging algorithms offer new opportunities for model construction. Here, we review up-to-date efforts to address challenging biological questions by incorporating omic data into logic-based models and discuss critical difficulties in constructing and analyzing integrative, large-scale, logicbased models of biological mechanisms.

Simple Summary Fibroblasts are critical regulators of several physiological processes linked to extracellular matrix regulation. Under certain conditions, fibroblasts can also transform into more aggressive phenotypes and contribute to disease pathophysiology. In this review, we highlight metabolic reprogramming as a critical event toward the transition of fibroblasts from quiescent to activated and aggressive cells, in rheumatoid arthritis and cancer. We draw obvious parallels and discuss how systems biology approaches and computational modeling could be employed to highlight targets of metabolic reprogramming and support the discovery of new lines of therapy. Abstract Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a pivotal role in the wound healing process. While they are maintainers of the ECM turnover and regulate several physiological processes, they can also undergo transformations responding to certain stimuli and display aggressive phenotypes that contribute to disease pathophysiology. In this review, we focus on the metabolic pathways of glucose and highlight metabolic reprogramming as a critical event that contributes to the transition of fibroblasts from quiescent to activated and aggressive cells. We also cover the emerging evidence that allows us to draw parallels between fibroblasts in autoimmune disorders and more specifically in rheumatoid arthritis and cancer. We link the metabolic changes of fibroblasts to the toxic environment created by the disease condition and discuss how targeting of metabolic reprogramming could be employed in the treatment of such diseases. Lastly, we discuss Systems Biology approaches, and more specifically, computational modeling, as a means to elucidate pathogenetic mechanisms and accelerate the identification of novel therapeutic targets.

Rheumatoid arthritis (RA) is a multifactorial autoimmune disease that causes chronic inflammation of the joints, with an aetiology still unclear. RA is a complex inflammatory disease that involves an interconnected array of genetic, environmental, and epigenetic factors. Recently, systems biology and network-based approaches have been proposed to study such complex diseases. Combining multiple data types allows us to discover new knowledge as integration can help compensate for missing or unreliable information. Moreover, if multiple sources of evidence point to the same outcome then it is less likely to obtain false positives. Among computational approaches, machine learning stands out as a promising field in bioinformatics, as it allows for integration of multi-omic biomedical datasets. In this work, we present our efforts to integrate different layers of biological data in the form of networks in order to obtain a global view of the disease. To do so, we make use of publicly available transcriptomic datasets (peripheral blood) relative to RA and a variety of bioinformatics analyses such as statistical analysis, differential expression analysis, and machine learning network inference (Nicolle et al., 2015) to infer an RA-specific TF co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using the state of the art RA map based on the functional overlaps between the two networks (Singh et al., 2020). Lastly, a list of RA specific variants available in DisGeNET (Piñero et al., 2020) will be used as an overlap to highlight key genes associated with known disease mutations. We combine three different biological layers (gene expression, signalling cascades, mutations), obtained by bottom-up prior knowledge-based (DisGenNET, RA map) and top-down data-driven (CoRegNet) methods to build an integrative, disease-specific network. The goal behind this endeavor is to unravel mechanisms governing the regulation of key genes identified as mutation carriers in RA and also derive patient-specific models to gain more insights into the disease heterogeneity., Please open the .m4v file with VLC in order to get the sound of the video

Immune dysregulation was first implicated in the pathogenesis of Rheumatoid Arthritis (RA) by the discovery of anti-immunoglobulin G (IgG) antibodies known as rheumatoid factors. However, concepts of how immune responses contribute to disease have evolved dramatically over the last 50 years. Many cells and their cytokines play critical roles in the development of RA. The synovial compartment is infiltrated by leukocytes and the synovial fluid is inundated with pro-inflammatory mediators that are produced to induce an inflammatory cascade, which is characterized by interactions of fibroblast-like synoviocytes with the cells of the innate immune system, including monocytes, macrophages, mast cells, dendritic cell as well as cells of the adaptive immune system such as T cells and B cells. The fulminant stage contains hyperplastic synovium, cartilage damage, bone erosion, and systemic consequence. The objective of my project is to construct a computational model able to decipher the interplay between cells of the innate and adaptive immunity in RA, that eventually leads to bone and cartilage breakdown. To do so, we will start by creating separate maps for T cells, B cells, macrophages, fibroblasts, osteoblasts and osteoclasts. We will use different data mining tools, appropriate data bases such as KEGG (Kanehisa et al, 2000), REACTOME (Fabregat et al, 2018) as well as internal data generated within Sanofi. We will exploit the graph editor CellDesigner (Funahashi et al, 2003) and the platform Minerva (Gawron et al, 2016) for automatic annotation and reference of the cell-specific maps. We will benefit greatly from a global, fully annotated RA specific map (Singh et al, 2018, Singh et al, 2020). This map features interactions implicated in RA coming from various cell types, but due to the extensive annotations, the user can opt for cell-specific interactions and extract the corresponding network. We are also going to use public datasets of expression data (microarrays, RNAseq, RNAseq single cell), data concerning metabolic pathways from MetaCyc and Sanofi’s proprietary datasets to enrich and expand existing pathway resources. These maps will be used to generate cell-specific dynamic models using the tool CaSQ (Aghamiri et al, 2020). The next step is the creation of a multicellular model to understand how the different cells interact, contributing to the emergent behavior of the system. We will prioritize signature pathways for each cell type and combine them to build a logical model that will represent the intra- and intercellular relationships.

Rheumatoid arthritis (RA) is a systemic autoimmune disease that affects the synovial joints of the body. Rheumatoid arthritis fibroblast-like synoviocytes (RA FLS) are central players in the disease pathogenesis, as they are involved in the secretion of cytokines and proteolytic enzymes, exhibit invasive traits, high rate of self-proliferation and an apoptosis-resistant phenotype. We aim at characterizing transcription factors (TFs) that are master regulators in RA FLS and could potentially explain phenotypic traits. We make use of differentially expressed genes in synovial tissue from patients suffering from RA and osteoarthritis (OA) to infer a TF co-regulatory network, using dedicated software. The co-regulatory network serves as a reference to analyze microarray and single-cell RNA-seq data from isolated RA FLS. We identified five master regulators specific to RA FLS, namely BATF, POU2AF1, STAT1, LEF1 and IRF4. TF activity of the identified master regulators was also estimated with the use of two additional, independent software. The identified TFs contribute to the regulation of inflammation, proliferation and apoptosis, as indicated by the comparison of their differentially expressed target genes with hallmark molecular signatures derived from the Molecular Signatures Database (MSigDB). Our results show that TFs influence could be used to identify putative master regulators of phenotypic traits and suggest novel, druggable targets for experimental validation.

International audience; Following publication, it was found that one of Alexander Mazein's affiliations was missing and the affiliation, Barcelona Supercomputing Center (BSC) was incorrectly spelled. It was also found that a part of the Acknowledgements section and a part of the Competing Interests section were missing. Both the HTML and PDF versions have been updated to reflect these changes.

Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. These regulatory networks can then be used to predict biological and cellular behavior by system perturbations and in silico simulations. Today, broad sets of these interactions are being made available in a variety of biological knowledge resources. Moreover, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. Therefore, data users can find it challenging to efficiently explore resources of causal interaction and to be aware of recorded contextual information that ensures valid use of the data. This manuscript presents a review of public resources collecting causal interactions and the different views they convey, together with a thorough description of the export formats established to store and retrieve these interactions. Our goal is to raise awareness amongst the targeted audience, i.e., logical modelers, but also any scientist interested in molecular causal interactions, about existing data resources and how to get familiar with them.

Researchers around the world join forces to reconstruct the molecular processes of the virus-host interactions aiming to combat the cause of the ongoing pandemic.

Rheumatoid arthritis (RA) is a progressive, inflammatory autoimmune disease of unknown aetiology. The complex mechanism of aetiopathogenesis, progress and chronicity of the disease involves genetic, epigenetic and environmental factors. To understand the molecular mechanisms underlying disease phenotypes, one has to place implicated factors in their functional context. However, integration and organization of such data in a systematic manner remains a challenging task. Molecular maps are widely used in biology to provide a useful and intuitive way of depicting a variety of biological processes and disease mechanisms. Recent large-scale collaborative efforts such as the Disease Maps Project demonstrate the utility of such maps as versatile tools to organize and formalize disease-specific knowledge in a comprehensive way, both human and machine-readable. We present a systematic effort to construct a fully annotated, expert validated, state-of-the-art knowledge base for RA in the form of a molecular map. The RA map illustrates molecular and signalling pathways implicated in the disease. Signal transduction is depicted from receptors to the nucleus using the Systems Biology Graphical Notation (SBGN) standard representation. High-quality manual curation, use of only human-specific studies and focus on small-scale experiments aim to limit false positives in the map. The state-of-the-art molecular map for RA, using information from 353 peer-reviewed scientific publications, comprises 506 species, 446 reactions and 8 phenotypes. The species in the map are classified to 303 proteins, 61 complexes, 106 genes, 106 RNA entities, 2 ions and 7 simple molecules. The RA map is available online at ramap.elixir-luxembourg.org as an open-access knowledge base allowing for easy navigation and search of molecular pathways implicated in the disease. Furthermore, the RA map can serve as a template for omics data visualization.

Objective: Aseptic loosening is a major problem in total joint replacement. Implant wear debris provokes a foreign body host response and activates cells to produce a variety of mediators and ROS, leading to periprosthetic osteolysis. Elevated ROS levels can harm proteasome function. Proteasome inhibitors have been reported to alter the secretory profile of cells involved in inflammation and also to induce ROS production. In this work, we aimed to document the effects of proteasome inhibitors MG-132 and Epoxomicin, on the production of factors involved in aseptic loosening, in periprosthetic tissues and fibroblasts, and investigate the role of proteasome impairment in periprosthetic osteolysis. Materials and methods: IL-6 levels in tissue cultures were determined by sandwich ELISA. MMP-1, -3, -13, -14 and TIMP-1 levels in tissue or cell cultures were determined by indirect ELISA. Results for MMP-1 and TIMP-1 in tissue cultures were confirmed by Western blotting. MMP-2 and MMP-9 levels were determined by gelatin zymography. Gene expression of IL-6, MMP-1,-3,-14, TIMP-1 and collagen type-I was determined by RT-PCR. Results: Results show that proteasome inhibition induces the expression of ΜΜΡ-1, -2, -3, -9 and suppresses that of IL-6, MMP-14, -13, TIMP-1 and collagen type I, enhancing the collagenolytic and gelatinolytic activity already present in periprosthetic tissues, as documented in various studies. Conclusions: These findings suggest that proteasome impairment could be a contributing factor to aseptic loosening. Protection and enhancement of proteasome efficacy could thus be considered as an alternative strategy toward disease treatment.

International audience; The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share informa-tion and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platformfor disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regu-latory network pathways represented in standard formats. The involvement of domain experts ensures that the key dis-ease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computerreadable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation orserve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease MapsCommunity meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the fu-ture development of disease maps, including creating and maintaining standardized disease maps; sharing parts of mapsthat encode common human disease mechanisms; providing technical solutions for complexity management of maps;and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modelingapproaches, as one of the main goals of disease map development is the generation of mathematically interpretablerepresentations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supportingclinical decisions.

International audience; In this work we present a systematic effort to summarize current biological pathway knowledge concerning Rheumatoid Arthritis (RA). We are constructing a detailed molecular map based on exhaustive literature scanning, strict curation criteria, re-evaluation of previously published attempts and most importantly experts' advice. The RA map will be web-published in the coming months in the form of an interactive map, using the MINERVA platform, allowing for easy access, navigation and search of all molecular pathways implicated in RA, serving thus, as an on line knowledgebase for the disease. Moreover the map could be used as a template for Omics data visualization offering a first insight about the pathways affected in different experimental datasets. The second goal of the project is a dynamical study focused on synovial fibroblasts' behavior under different initial conditions specific to RA, as recent studies have shown that synovial fibroblasts play a crucial role in driving the persistent, destructive characteristics of the disease. Leaning on the RA knowledgebase and using the web platform Cell Collective, we are currently building a Boolean large scale dynamical model for the study of RA fibroblasts' activation.

The effect of various types of nanoliposomes (associated with curcumin, phosphatidic acid, cardiolipin, or GM1 ganglioside) on the aggregation of the amyloid-β(1-42) (Aβ(1-42)) peptide was investigated. Nanoliposomes incorporating curcumin (curcumin-liposomes) were prepared by adding curcumin in the lipid phase during liposome preparation, whereas curcumin surface-decorated liposomes were prepared by using a curcumin-lipid conjugate (lipid-S-curcumin liposomes) or by attaching a curcumin derivative on preformed liposomes by click chemistry (click-curcumin liposomes). The lipid ligands (phosphatidic acid, cardiolipin, or GM1) were also incorporated into nanoliposomes during their formation. All nanoliposomes with curcumin, or the curcumin derivative, were able to inhibit the formation of fibrillar and/or oligomeric Aβ in vitro. Of the three forms of curcumin liposomes tested, the click-curcumin type was by far the most effective. Liposomes with lipid ligands only inhibited Aβ fibril and oligomer formation at a very high ratio of liposome to peptide. Curcumin-based liposomes could be further developed as a novel treatment for Alzheimer's disease.

International audience

A global and rigorous understanding of the signaling pathways and cross-regulatory processes involved in mast cell activation requires the integration of published information with novel functional datasets into a comprehensive computational model. Based on an exhaustive curation of the existing literature and using the software CellDesigner, we have built and annotated a comprehensive molecular map for the FcεRI signaling network. This map can be used to visualize and interpret high-throughput expression data. Furthermore, leaning on this map and using the logical modeling software GINsim, we have derived a qualitative dynamical model, which recapitulates the most salient features of mast cell activation. The resulting logical model can be used to explore the dynamical properties of the system and its responses to different stimuli, in normal or mutant conditions.

A global and rigorous understanding of the signaling pathways and cross-regulatory processes involved in mast cell activation requires the integration of published information with novel functional datasets into a comprehensive computational model. Based on an exhaustive curation of the existing literature and using the software CellDesigner, we have built and annotated a comprehensive molecular map for the FceRI signaling network. This map can be used to visualize and interpret high-throughput expression data. Furthermore, leaning on this map and using the logical modeling software GINsim, we have derived a qualitative dynamical model, which recapitulates the most salient features of mast cell activation. The resulting logical model can be used to explore the dynamical properties of the system and its responses to different stimuli, in normal or mutant conditions.

We report the first proteomic analysis of the SLP76 interactome in resting and activated primary mouse mast cells. This was made possible by a novel genetic approach used for the first time here. It consists in generating knock-in mice that express signaling molecules bearing a C-terminal tag that has a high affinity for a streptavidin analog. Tagged molecules can be used as molecular baits to affinity-purify the molecular complex in which they are engaged, which can then be studied by mass spectrometry. We examined first SLP76 because, although this cytosolic adapter is critical for both T cell and mast cell activation, its role is well known in T cells but not in mast cells. Tagged SLP76 was expressed in physiological amounts and fully functional in mast cells. We unexpectedly found that SLP76 is exquisitely sensitive to mast cell granular proteases, that Zn(2+)-dependent metalloproteases are especially abundant in mast cells and that they were responsible for SLP76 degradation. Adding a Zn(2+) chelator fully protected SLP76 in mast cell lysates, thereby enabling an efficient affinity-purification of this adapter with its partners. Label-free quantitative mass spectrometry analysis of affinity-purified SLP76 interactomes uncovered both partners already described in T cells and novel partners seen in mast cells only. Noticeably, molecules inducibly recruited in both cell types primarily concur to activation signals, whereas molecules recruited in activated mast cells only are mostly associated with inhibition signals. The transmembrane adapter LAT2, and the serine/threonine kinase with an exchange factor activity Bcr were the most recruited molecules. Biochemical and functional validations established the unexpected finding that Bcr is recruited by SLP76 and positively regulates antigen-induced mast cell activation. Knock-in mice expressing tagged molecules with a normal tissue distribution and expression therefore provide potent novel tools to investigate signalosomes and to uncover novel signaling molecules in mast cells.