Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies—data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).
BackgroundDespite the unprecedented and increasing amount of data, relatively little progress has been made in molecular characterization of mechanisms underlying Parkinson’s disease. In the area of Parkinson’s research, there is a pressing need to integrate various pieces of information into a meaningful context of presumed disease mechanism(s). Disease ontologies provide a novel means for organizing, integrating, and standardizing the knowledge domains specific to disease in a compact, formalized and computer-readable form and serve as a reference for knowledge exchange or systems modeling of disease mechanism.MethodsThe Parkinson’s disease ontology was built according to the life cycle of ontology building. Structural, functional, and expert evaluation of the ontology was performed to ensure the quality and usability of the ontology. A novelty metric has been introduced to measure the gain of new knowledge using the ontology. Finally, a cause-and-effect model was built around PINK1 and two gene expression studies from the Gene Expression Omnibus database were re-annotated to demonstrate the usability of the ontology.ResultsThe Parkinson’s disease ontology with a subclass-based taxonomic hierarchy covers the broad spectrum of major biomedical concepts from molecular to clinical features of the disease, and also reflects different views on disease features held by molecular biologists, clinicians and drug developers. The current version of the ontology contains 632 concepts, which are organized under nine views. The structural evaluation showed the balanced dispersion of concept classes throughout the ontology. The functional evaluation demonstrated that the ontology-driven literature search could gain novel knowledge not present in the reference Parkinson’s knowledge map. The ontology was able to answer specific questions related to Parkinson’s when evaluated by experts. Finally, the added value of the Parkinson’s disease ontology is demonstrated by ontology-driven modeling of PINK1 and re-annotation of gene expression datasets relevant to Parkinson’s disease.ConclusionsParkinson’s disease ontology delivers the knowledge domain of Parkinson’s disease in a compact, computer-readable form, which can be further edited and enriched by the scientific community and also to be used to construct, represent and automatically extend Parkinson’s-related computable models. A practical version of the Parkinson’s disease ontology for browsing and editing can be publicly accessed at http://bioportal.bioontology.org/ontologies/PDON.Electronic supplementary materialThe online version of this article (doi:10.1186/s12976-015-0017-y) contains supplementary material, which is available to authorized users.
The stability of therapeutic antibodies is a prime pharmaceutical concern. In this work we examined thermal stability differences between human IgG1 and IgG4 Fab domains containing the same variable regions using the thermofluor assay. It was found that the IgG1 Fab domain is up to 11°C more stable than the IgG4 Fab domain containing the same variable region. We investigated the cause of this difference with the aim of developing a molecule with the enhanced stability of the IgG1 Fab and the biological properties of an IgG4 Fc. We found that replacing the seven residues, which differ between IgG1 C H 1 and IgG4 C H 1 domains, while retaining the native IgG1 light-heavy interchain disulfide (L-H) bond, did not affect thermal stability. Introducing the IgG1 type L-H interchain disulfide bond (DSB) into the IgG4 Fab resulted in an increase in thermal stability to levels observed in the IgG1 Fab with the same variable region. Conversely, replacement of the IgG1 L-H interchain DSB with the IgG4 type L-H interchain DSB reduced the thermal stability. We utilized the increased stability of the IgG1 Fab and designed a hybrid antibody with an IgG1 C H 1 linked to an IgG4 Fc via an IgG1 hinge. This construct has the expected biophysical properties of both the IgG4 Fc and IgG1 Fab domains and may therefore be a pharmaceutically relevant format.
MotivationDetecting novel functional modules in molecular networks is an important step in biological research. In the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as we show, the uneven distribution of functional annotations means that such evaluation methods favor communities of well-studied proteins.ResultsWe propose a novel framework for the evaluation of communities as functional modules. Our proposed framework, CommWalker, takes communities as inputs and evaluates them in their local network environment by performing short random walks. We test CommWalker’s ability to overcome annotation bias using input communities from four community detection methods on two protein interaction networks. We find that modules accepted by CommWalker are similarly co-expressed as those accepted by current methods. Crucially, CommWalker performs well not only in well-annotated regions, but also in regions otherwise obscured by poor annotation. CommWalker community prioritization both faithfully captures well-validated communities and identifies functional modules that may correspond to more novel biology.Availability and implementationThe CommWalker algorithm is freely available at opig.stats.ox.ac.uk/resources or as a docker image on the Docker Hub at hub.docker.com/r/lueckenmd/commwalker/.Supplementary information Supplementary data are available at Bioinformatics online.
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