The concept of a 'mechanism-based taxonomy of human disease' is currently replacing the outdated paradigm of diseases classified by clinical appearance.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.
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.
NeuroMMSig is available at http://neurommsig.scai.fraunhofer.de/.
martin.hofmann-apitius@scai.fraunhofer.de.
Supplementary data are available at Bioinformatics online.
Perturbance in inflammatory pathways have been identified as one of the major factors which leads to neurodegenerative diseases (NDD). Owing to the limited access of human brain tissues and the ...immense complexity of the brain, animal models, specifically mouse models, play a key role in advancing the NDD field. However, many of these mouse models fail to reproduce the clinical manifestations and end points of the disease. NDD drugs, which passed the efficacy test in mice, were repeatedly not successful in clinical trials. There are numerous studies which are supporting and opposing the applicability of mouse models in neuroinflammation and NDD. In this paper, we assessed to what extend a mouse can mimic the cellular and molecular interactions in humans at a mechanism level. Based on our mechanistic modeling approach, we investigate the failure of a neuroinflammation targeted drug in the late phases of clinical trials based on the comparative analyses between the two species.
Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of ...the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.
Molecular signaling pathways have been long used to demonstrate interactions among upstream causal molecules and downstream biological effects. They show the signal flow between cell compartments, ...the majority of which are represented as cartoons. These are often drawn manually by scanning through the literature, which is time-consuming, static, and non-interoperable. Moreover, these pathways are often devoid of context (condition and tissue) and biased toward certain disease conditions. Mining the scientific literature creates new possibilities to retrieve pathway information at higher contextual resolution and specificity. To address this challenge, we have created a pathway terminology system by combining signaling pathways and biological events to ensure a broad coverage of the entire pathway knowledge domain. This terminology was applied to mining biomedical papers and patents about neurodegenerative diseases with focus on Alzheimer's disease. We demonstrate the power of our approach by mapping literature-derived signaling pathways onto their corresponding anatomical regions in the human brain under healthy and Alzheimer's disease states. We demonstrate how this knowledge resource can be used to identify a putative mechanism explaining the mode-of-action of the approved drug Rasagiline, and show how this resource can be used for fingerprinting patents to support the discovery of pathway knowledge for Alzheimer's disease. Finally, we propose that based on next-generation cause-and-effect pathway models, a dedicated inventory of computer-processable pathway models specific to neurodegenerative diseases can be established, which hopefully accelerates context-specific enrichment analysis of experimental data with higher resolution and richer annotations.
Neurodegenerative diseases are incurable and debilitating indications with huge social and economic impact, where much is still to be learnt about the underlying molecular events. Mechanistic disease ...models could offer a knowledge framework to help decipher the complex interactions that occur at molecular and cellular levels. This motivates the need for the development of an approach integrating highly curated and heterogeneous data into a disease model of different regulatory data layers. Although several disease models exist, they often do not consider the quality of underlying data. Moreover, even with the current advancements in semantic web technology, we still do not have cure for complex diseases like Alzheimer's disease. One of the key reasons accountable for this could be the increasing gap between generated data and the derived knowledge.
In this paper, we describe an approach, called as NeuroRDF, to develop an integrative framework for modeling curated knowledge in the area of complex neurodegenerative diseases. The core of this strategy lies in the usage of well curated and context specific data for integration into one single semantic web-based framework, RDF. This increases the probability of the derived knowledge to be novel and reliable in a specific disease context. This infrastructure integrates highly curated data from databases (Bind, IntAct, etc.), literature (PubMed), and gene expression resources (such as GEO and ArrayExpress). We illustrate the effectiveness of our approach by asking real-world biomedical questions that link these resources to prioritize the plausible biomarker candidates. Among the 13 prioritized candidate genes, we identified MIF to be a potential emerging candidate due to its role as a pro-inflammatory cytokine. We additionally report on the effort and challenges faced during generation of such an indication-specific knowledge base comprising of curated and quality-controlled data.
Although many alternative approaches have been proposed and practiced for modeling diseases, the semantic web technology is a flexible and well established solution for harmonized aggregation. The benefit of this work, to use high quality and context specific data, becomes apparent in speculating previously unattended biomarker candidates around a well-known mechanism, further leveraged for experimental investigations.
Although microbiome signatures have been identified in various contexts (ie, pathogenesis of non-communicable diseases and treatment response), qualified microbiome-based biomarkers are currently not ...in use in clinical practice. The Human Microbiome Action consortium initiated a Delphi survey to establish a consensus on the needs, challenges, and limitations in developing qualified microbiome-based biomarkers. The questionnaire was developed by a scientific committee via literature review and expert interviews. To ensure broad applicability of the results, 307 experts were invited to participate; 114 of them responded to the first round of the survey, 93 of whom completed the second and final round as well. The survey highlighted the experts’ confidence in the potential of microbiome-based biomarkers for several indications or pathologies. The paucity of validated analytical methods appears to be the principal factor hindering the qualification of these biomarkers. The survey also showed that clinical implementation of these biomarkers would only be possible if kitted and validated molecular assays with simple interpretation are developed. This initiative serves as a foundation for designing and implementing public-private collaborative projects to overcome the challenges and promote clinical application of microbiome-based biomarkers.
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