Progress in biomedical research and its translation into clinical practice require the integration of data across multiple scales (molecules, cells, organisms), organism types, and fields of research. The need for data integration is especially acute in infectious disease research where organisms interact on all scales, and these interactions result in the emergence of processes and structures specific to these interactions. True data integration, the ability to jointly interpret and analyze data of heterogeneous types, depends on the ability to link data to information about the biological entities to which the data refer. In the face of rapidly growing volumes of data and information, it is imperative that this link from data to information be computable. Automated processing of the links between data and information requires that they be expressed using a common, formalized system for knowledge representation. Efforts at knowledge representation in biology have focused on either ontology development or pathway representation. While the value of both is unquestionable, neither fully supports the data and information integration needs of infectious disease research. We propose an ontology-based approach to pathway representation that extends ontologies beyond single taxonomies and pathway representations to all levels of granularity, thereby allowing the representation of complex biological systems. Our approach builds upon existing ontologies and pathway representations but is grounded in formal ontological and logical principles. Our overall goal is to test empirically the degree to which the ontology-based representation can improve data interpretation and analysis for translational medicine. We will take as our case study Staphylococcus aureus infection, utilizing the invaluable data resources of the Duke Staphylococcus aureus Bacteremia Group. We will achieve our goal through the following three specific aims: 1. Create an ontology-based representation of host-pathogen interactions, focusing on Staphylococcus aureus bacteremia. 2. Empirically test the ability of the ontology-based representation created in Aim 1 to improve data analysis and interpretation by using the representation to predict disease genes associated with Staphylococcus aureus bacteremia. 3. Empirically test the impact of the ontology-based representation created in Aim 1 on understanding of Staphylococcus aureus pathogenesis, on identification of novel therapeutic targets, and on improvement to patient management by testing experimentally the disease gene predictions made under Aim 2. The anticipated outcomes are: an ontology-based method for the representation of complex biological systems and an ontology of host-pathogen interactions, both subjected to tests designed to demonstrate their utility to clinical and translational research; an improved understanding of the immune response to bacterial pathogens; and the identification of genes associated with Staphylococcus aureus bacteremia that can be used to develop novel diagnostics and therapeutics.The resources developed under this proposal will directly improve data integration, retrieval and analysis, will support cross-disciplinary collaborations within infectious disease research, and will provide a foundation from which to develop similar resources for other areas in biomedicine, thus significantly impacting biomedical research and translational medicine.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
3R01AI077706-03S1
Application #
8246147
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Huntley, Clayton C
Project Start
2009-09-22
Project End
2012-01-31
Budget Start
2010-09-01
Budget End
2012-01-31
Support Year
3
Fiscal Year
2009
Total Cost
$100,732
Indirect Cost
Name
University of Texas Sw Medical Center Dallas
Department
Other Clinical Sciences
Type
Schools of Medicine
DUNS #
800771545
City
Dallas
State
TX
Country
United States
Zip Code
75390
Levin, Mikhail K; Cowell, Lindsay G (2015) owlcpp: a C++ library for working with OWL ontologies. J Biomed Semantics 6:35
Nelson, Charlotte L; Pelak, Kimberly; Podgoreanu, Mihai V et al. (2014) A genome-wide association study of variants associated with acquisition of Staphylococcus aureus bacteremia in a healthcare setting. BMC Infect Dis 14:83
Lozano-Fuentes, Saul; Bandyopadhyay, Aritra; Cowell, Lindsay G et al. (2013) Ontology for vector surveillance and management. J Med Entomol 50:1-14
Gordon, Claire L; Pouch, Stephanie; Cowell, Lindsay G et al. (2013) Design and evaluation of a bacterial clinical infectious diseases ontology. AMIA Annu Symp Proc 2013:502-11
Johnson, Nicole V; Ahn, Sun Hee; Deshmukh, Hitesh et al. (2012) Haplotype Association Mapping Identifies a Candidate Gene Region in Mice Infected With Staphylococcus aureus. G3 (Bethesda) 2:693-700
Diehl, Alexander D; Augustine, Alison Deckhut; Blake, Judith A et al. (2011) Hematopoietic cell types: prototype for a revised cell ontology. J Biomed Inform 44:75-9
Meehan, Terrence F; Masci, Anna Maria; Abdulla, Amina et al. (2011) Logical development of the cell ontology. BMC Bioinformatics 12:6
Goldfain, Albert; Smith, Barry; Cowell, Lindsay G (2011) Towards an ontological representation of resistance: the case of MRSA. J Biomed Inform 44:35-41
Ahn, Sun-Hee; Deshmukh, Hitesh; Johnson, Nicole et al. (2010) Two genes on A/J chromosome 18 are associated with susceptibility to Staphylococcus aureus infection by combined microarray and QTL analyses. PLoS Pathog 6:e1001088
Masci, Anna Maria; Arighi, Cecilia N; Diehl, Alexander D et al. (2009) An improved ontological representation of dendritic cells as a paradigm for all cell types. BMC Bioinformatics 10:70