Development of an integrated disease ontology knowledgebase and its application to study mechanisms of neuropsychiatric disorders

Production and distribution of scientific information has grown exponentially in the recent years. PubMed, a service of the U.S. National Library of Medicine that includes over 18 million Medline citations to journal articles, has been extending its coverage to some 40.000 abstracts in life sciences and biomedical literature every month. The information age allowed storage and dissemination of huge amount of data but our ability to extract and process knowledge remained constant. We make inferences on uncharacterised observations by recording and using natural language, which unfortunately is rarely adequate. Furthermore, biomedical research is characterised by highly specialised disciplines with limited communication among them and poorly shared resources. These many aspects draw attention to the real need of integration, a general concept with many definitions. In the context of my PhD, integration is intended as the process by which data from one source can be exchanged, interpreted or manipulated by another, in a way that make sense to the users in their interaction with the system. Biomedical ontologies (OBO) in general and the Gene Ontology (GO) in particular, have been fundamental components of an important information integration effort started in year 2000 with the ambitious goal to build a tool for the unification of biology and beyond. My PhD project, standing on the shoulders of those initiatives, has been focused on the development of a human-readable knowledgebase system that hopefully would facilitate exploitation of biological experimental data. This resource relies on information extracted from many databases, mostly manually curated, and uses an ontology of human diseases (i.e. the ‘Disease Ontology’) as a backbone of the system. The objective is providing some support to the scientific biomedical community in the interpretation of data on human diseases and their correlated genes, possibly delivering information on available interacting drugs. To test the system meanwhile evaluating its value, real research case was investigated in the second part of my PhD work. Functional analysis of inherently complex high-throughput data sources for systems biology (e.g. microarray) is a fundamental step to understand mechanisms regulating molecular processes modulated in diseases and pathological states. Nonetheless, advances at any level relevant to disease understanding and drug discovery for psychiatric disorders in recent years have been relatively unsuccessful compared with other areas. Therefore, a suitable computational strategy sustained by the newly developed resource was designed to allow investigation of the involvement in dendritic plasticity of specific disease genes, their mechanisms of action and the available drugs they are known to interact with. Dendritic plasticity, an important component of the central nervous system function during development, has been recently postulated to be strongly involved in pathogenesis of psychiatric diseases. The concept of plasticity spans a broad spectrum from describing clinical features of behavior/learning and memory down to the molecular mechanisms by which neurons create and lose synapse connections between one another. The chosen approach allowed the semi-automated identification of a great number of genes involved in plasticity mechanism at the molecular level. At the same time it also allowed preliminary validation of the newly developed Disease Ontology Knowledgebase and an evaluation of its potentialities.