Contribution of syndecans to cellular uptake and fibrillation of alpha-synuclein and tau

Scientific evidence suggests that alpha-synuclein and tau have prion-like properties and that prionlike spreading and seeding of misfolded protein aggregates constitutes a central mechanism for neurodegeneration. Heparan sulfate proteoglycans (HSPGs) in the plasma membrane support this process by attaching misfolded protein fibrils. Despite of intense studies, contribution of specific HSPGs to seeding and spreading of alpha-synuclein and tau has not been explored yet. Here we report that members of the syndecan family of HSPGs mediate cellular uptake of alpha-synuclein and tau fibrils via a lipid-raft dependent and clathrin-independent endocytic route. Among syndecans, the neuron predominant syndecan-3 exhibits the highest affinity for both alpha-synuclein and tau. Syndecan-mediated internalization of alpha-synuclein and tau depends heavily on conformation as uptake via syndecans start to dominate once fibrils are formed. Overexpression of syndecans, on the other hand, reduces cellular uptake of monomeric alpha-synuclein and tau, yet exerts a fibril forming effect on both proteins. Data obtained from syndecan overexpressing cellular models presents syndecans, especially the neuron predominant syndecan-3, as important mediators of seeding and spreading of alpha-synuclein and tau and reveal how syndecans contribute to fundamental molecular events of a-synuclein and tau pathology

CTO: A Community-Based Clinical Trial Ontology and Its Applications in PubChemRDF and SCAIViewH

Driven by the use cases of PubChemRDF and SCAIView, we have developed a first community-based clinical trial ontology (CTO) by following the OBO Foundry principles. CTO uses the Basic Formal Ontology (BFO) as the top level ontology and reuses many terms from existing ontologies. CTO has also defined many clinical trial-specific terms. The general CTO design pattern is based on the PICO framework together with two applications. First, the PubChemRDF use case demonstrates how a drug Gleevec is linked to multiple clinical trials investigating Gleevec’s related chemical compounds. Second, the SCAIView text mining engine shows how the use of CTO terms in its search algorithm can identify publications referring to COVID-19-related clinical trials. Future opportunities and challenges are discussed

Machine learning based prediction of COVID-19 mortality suggests repositioning of anticancer drug for treating severe cases

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center ‘Lean European Open Survey on SARS-CoV-2-infected patients’ (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19

Reasoning over genetic variance information in cause-and-effect models of neurodegenerative diseases

The work we present here is based on the recent extension of the syntax of the Biological Expression Language (BEL), which now allows for the representation of genetic variation information in cause-and-effect models. In our article, we describe, how genetic variation information can be used to identify candidate disease mechanisms in diseases with complex aetiology such as Alzheimer’s disease and Parkinson’s disease. In those diseases, we have to assume that many genetic variants contribute moderately to the overall dysregulation that in the case of neurodegenerative diseases has such a long incubation time until the first clinical symptoms are detectable. Owing to the multilevel nature of dysregulation events, systems biomedicine modelling approaches need to combine mechanistic information from various levels, including gene expression, microRNA (miRNA) expression, protein–protein interaction, genetic variation and pathway. OpenBEL, the open source version of BEL, has recently been extended to match this requirement, and we demonstrate in our article, how candidate mechanisms for early dysregulation events in Alzheimer’s disease can be identified based on an integrative mining approach that identifies ‘chains of causation’ that include single nucleotide polymorphism information in BEL models

Computational Modelling Approaches on Epigenetic Factors in Neurodegenerative and Autoimmune Diseases and Their Mechanistic Analysis

Neurodegenerative as well as autoimmune diseases have unclear aetiologies, but an increasing number of evidences report for a combination of genetic and epigenetic alterations that predispose for the development of disease. This review examines the major milestones in epigenetics research in the context of diseases and various computational approaches developed in the last decades to unravel new epigenetic modifications. However, there are limited studies that systematically link genetic and epigenetic alterations of DNA to the aetiology of diseases. In this work, we demonstrate how disease-related epigenetic knowledge can be systematically captured and integrated with heterogeneous information into a functional context using Biological Expression Language (BEL). This novel methodology, based on BEL, enables us to integrate epigenetic modifications such as DNA methylation or acetylation of histones into a specific disease network. As an example, we depict the integration of epigenetic and genetic factors in a functional context specific to Parkinson’s disease (PD) and Multiple Sclerosis (MS)

ProtSTonKGs: A Sophisticated Transformer Trained on Protein Sequences, Text, and Knowledge Graphs

While most approaches individually exploit unstructured data from the biomedical literature or structured data from biomedical knowledge graphs, their union can better exploit the advantages of such approaches, ultimately improving representations of biology. Using multimodal transformers for such purposes can improve performance on context dependent classication tasks, as demonstrated by our previous model, the Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs (STonKGs). In this work, we introduce ProtSTonKGs, a transformer aimed at learning all-encompassing representations of protein-protein interactions. ProtSTonKGs presents an extension to our previous work by adding textual protein descriptions and amino acid sequences (i.e., structural information) to the text- and knowledge graph-based input sequence used in STonKGs. We benchmark ProtSTonKGs against STonKGs, resulting in improved F1 scores by up to 0.066 (i.e., from 0.204 to 0.270) in several tasks such as predicting protein interactions in several contexts. Our work demonstrates how multimodal transformers can be used to integrate heterogeneous sources of information, paving the foundation for future approaches that use multiple modalities for biomedical applications

Multimodal mechanistic signatures for neurodegenerative diseases (NeuroMMSig): A web server for mechanism enrichment

Motivation The concept of a ‘mechanism-based taxonomy of human disease’ is currently replacing the outdated paradigm of diseases classified by clinical appearance. We have tackled the paradigm of mechanism-based patient subgroup identification in the challenging area of research on neurodegenerative diseases. Results We have developed a knowledge base representing essential pathophysiology mechanisms of neurodegenerative diseases. Together with dedicated algorithms, this knowledge base forms the basis for a ‘mechanism-enrichment server’ that supports the mechanistic interpretation of multiscale, multimodal clinical data. Availability and implementation NeuroMMSig is available at http://neurommsig.scai.fraunhofer.de