Psychosocial burden and professional and social support in patients with hereditary transthyretin amyloidosis (ATTRv) and their relatives in Italy

Hereditary transthyretin amyloidosis (hATTR), alias ATTR variant (ATTRv) is a severe and disabling disease causing sensory and motor neuropathy, autonomic dysfunction, and cardiomyopathy. The progressive decline of patient's functional autonomy negatively affects the patient's quality of life and requires increasing involvement of relatives in the patient's daily life. Family caregiving may become particularly demanding when the patient is no longer able to move independently. This study is focused on the psychosocial aspects of ATTRv from the patient and relative perspectives. In particular, it explored: the practical and psychological burdens experienced by symptomatic patients with ATTRv and their key relatives and the professional and social network support they may rely on; whether burden varied in relation to patients' and relatives' socio-demographic variables, patients' clinical variables, and perceived professional and social network support; and, any difference in burden and support between patients and their matched relatives

The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases

IntAct (freely available at http://www.ebi.ac.uk/intact) is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. IntAct has developed a sophisticated web-based curation tool, capable of supporting both IMEx- and MIMIx-level curation. This tool is now utilized by multiple additional curation teams, all of whom annotate data directly into the IntAct database. Members of the IntAct team supply appropriate levels of training, perform quality control on entries and take responsibility for long-term data maintenance. Recently, the MINT and IntAct databases decided to merge their separate efforts to make optimal use of limited developer resources and maximize the curation output. All data manually curated by the MINT curators have been moved into the IntAct database at EMBL-EBI and are merged with the existing IntAct dataset. Both IntAct and MINT are active contributors to the IMEx consortium (http://www.imexconsortium.org

Quantitative Analysis of Protein Phosphorylations and Interactions by Multi-Colour IP-FCM as an Input for Kinetic Modelling of Signalling Networks

BACKGROUND: To understand complex biological signalling mechanisms, mathematical modelling of signal transduction pathways has been applied successfully in last few years. However, precise quantitative measurements of signal transduction events such as activation-dependent phosphorylation of proteins, remains one bottleneck to this success. METHODOLOGY/PRINCIPAL FINDINGS: We use multi-colour immunoprecipitation measured by flow cytometry (IP-FCM) for studying signal transduction events to unrivalled precision. In this method, antibody-coupled latex beads capture the protein of interest from cellular lysates and are then stained with differently fluorescent-labelled antibodies to quantify the amount of the immunoprecipitated protein, of an interaction partner and of phosphorylation sites. The fluorescence signals are measured by FCM. Combining this procedure with beads containing defined amounts of a fluorophore allows retrieving absolute numbers of stained proteins, and not only relative values. Using IP-FCM we derived multidimensional data on the membrane-proximal T-cell antigen receptor (TCR-CD3) signalling network, including the recruitment of the kinase ZAP70 to the TCR-CD3 and subsequent ZAP70 activation by phosphorylation in the murine T-cell hybridoma and primary murine T cells. Counter-intuitively, these data showed that cell stimulation by pervanadate led to a transient decrease of the phospho-ZAP70/ZAP70 ratio at the TCR. A mechanistic mathematical model of the underlying processes demonstrated that an initial massive recruitment of non-phosphorylated ZAP70 was responsible for this behaviour. Further, the model predicted a temporal order of multisite phosphorylation of ZAP70 (with Y319 phosphorylation preceding phosphorylation at Y493) that we subsequently verified experimentally. CONCLUSIONS/SIGNIFICANCE: The quantitative data sets generated by IP-FCM are one order of magnitude more precise than Western blot data. This accuracy allowed us to gain unequalled insight into the dynamics of the TCR-CD3-ZAP70 signalling network

Demonstration of Cross-Protective Vaccine Immunity against an Emerging Pathogenic Ebolavirus Species

A major challenge in developing vaccines for emerging pathogens is their continued evolution and ability to escape human immunity. Therefore, an important goal of vaccine research is to advance vaccine candidates with sufficient breadth to respond to new outbreaks of previously undetected viruses. Ebolavirus (EBOV) vaccines have demonstrated protection against EBOV infection in nonhuman primates (NHP) and show promise in human clinical trials but immune protection occurs only with vaccines whose antigens are matched to the infectious challenge species. A 2007 hemorrhagic fever outbreak in Uganda demonstrated the existence of a new EBOV species, Bundibugyo (BEBOV), that differed from viruses covered by current vaccine candidates by up to 43% in genome sequence. To address the question of whether cross-protective immunity can be generated against this novel species, cynomolgus macaques were immunized with DNA/rAd5 vaccines expressing ZEBOV and SEBOV glycoprotein (GP) prior to lethal challenge with BEBOV. Vaccinated subjects developed robust, antigen-specific humoral and cellular immune responses against the GP from ZEBOV as well as cellular immunity against BEBOV GP, and immunized macaques were uniformly protected against lethal challenge with BEBOV. This report provides the first demonstration of vaccine-induced protective immunity against challenge with a heterologous EBOV species, and shows that Ebola vaccines capable of eliciting potent cellular immunity may provide the best strategy for eliciting cross-protection against newly emerging heterologous EBOV species

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.Peer Reviewe

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

IntroductionThe 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. MethodsExtensive 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.ResultsResults 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. DiscussionThe 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

Cosmic ray oriented performance studies for the JEM-EUSO first level trigger

JEM-EUSO is a space mission designed to investigate Ultra-High Energy Cosmic Rays and Neutrinos (E > 5 ⋅ 1019 eV) from the International Space Station (ISS). Looking down from above its wide angle telescope is able to observe their air showers and collect such data from a very wide area. Highly specific trigger algorithms are needed to drastically reduce the data load in the presence of both atmospheric and human activity related background light, yet retain the rare cosmic ray events recorded in the telescope. We report the performance in offline testing of the first level trigger algorithm on data from JEM-EUSO prototypes and laboratory measurements observing different light sources: data taken during a high altitude balloon flight over Canada, laser pulses observed from the ground traversing the real atmosphere, and model landscapes reproducing realistic aspect ratios and light conditions as would be seen from the ISS itself. The first level trigger logic successfully kept the trigger rate within the permissible bounds when challenged with artificially produced as well as naturally encountered night sky background fluctuations and while retaining events with general air-shower characteristics

Towards new theoretical approaches for the description of excited state properties and reactivity : a density based approach

L'interaction lumière-matière est la force motrice à l’origine de nombreux phénomènes et technologies allant de la photosynthèse artificielle à l'électronique moléculaire. Après absorption lumineuse, la matière peut émettre un rayonnement causé par une série de phénomènes, affectant les propriétés électroniques et structurelles de l'objet irradié. La compréhension de ces processus au niveau moléculaire nécessite de caractériser les états excités et permet la prédiction du comportement photophysique de nouveaux systèmes moléculaires. En fait, le but de mon travail théorique est de développer et appliquer des outils théoriques permettant la description détaillée des surfaces d'énergie potentielle des états excités non exclusivement dans la région de Frank-Condon. À cette fin, des méthodes quantiques basées sur la théorie fonctionnelle de la densité dépendante du temps, en combinaison avec des descripteurs basés sur la densité électronique, sont utilisées. Le but final est l'interprétation du comportement photophysique des systèmes et la rationalisation des caractéristiques structurelles et électroniques des états excités donnant lieu à des voies de décroissance radiatives et non. Cela permet la conception in silico de nouveaux composés permettant de résoudre des problèmes importants, notamment l'emploi de matériaux durables et la crise énergétique mondiale.Light-matter interaction is the driving force behind many phenomena and technologies ranging from artificial photosynthesis to molecular electronics. Upon light absorption, matter can emit radiation as a cause of a series of phenomena, affecting the electronic and structural properties of the irradiated object. Understanding these processes at the molecular level requires the characterization of excited states, thus allowing the prediction of the photophysical behavior of new molecular systems. Indeed, the purpose of my theoretical work is to develop and apply theoretical tools for the detailed description of the potential energy surfaces of excited states not exclusively in the Frank-Condon area. To this end, quantum methods based on time-dependent density functional theory (TD-DFT), in combination with descriptors based on the electronic density are used. The ultimate goal is the interpretation of the photophysical behavior of systems and the rationalization of the structural and electronic characteristics of excited states giving rise to radiative and non-radiative decay pathways. This allows the in silico design of new compounds to solve important problems, including the use of sustainable materials and the global energy crisis