Alexander D. Diehl

The Gene Ontology project in 2008

The Gene Ontology (GO) project (http://www.geneontology.org/) provides a set of structured, controlled vocabularies for community use in annotating genes, gene products and sequences (also see http://www.sequenceontology.org/). The ontologies have been extended and refined for several biological areas, and improvements to the structure of the ontologies have been implemented. To improve the quantity and quality of gene product annotations available from its public repository, the GO Consortium has launched a focused effort to provide comprehensive and detailed annotation of orthologous genes across a number of ‘reference’ genomes, including human and several key model organisms. Software developments include two releases of the ontology-editing tool OBO-Edit, and improvements to the AmiGO browser interface.

Impaired immune responses and altered peptide repertoire in tapasin-deficient mice.

Tapasin is a component of the major histocompatibility complex (MHC) class I antigen-loading complex. Here we show that mice with a disrupted tapasin gene display reduced MHC class I expression. Cytotoxic T cell (CTL) responses to viruses are impaired, and dendritic cells of tapasin-deficient mice do not cross-present protein antigen via the MHC class I pathway, indicating a defect in antigen processing. Natural killer (NK) cells from tapasin-deficient mice have an altered repertoire and are self-tolerant. In addition, the repertoire of class I–bound peptides is altered towards less stably binding ones. Thus tapasin plays a role in CTL and NK immune responses and in optimal peptide selection.

Gateways to the FANTOM5 promoter level mammalian expression atlas

The FANTOM5 project investigates transcription initiation activities in more than 1,000 human and mouse primary cells, cell lines and tissues using CAGE. Based on manual curation of sample information and development of an ontology for sample classification, we assemble the resulting data into a centralized data resource (http://fantom.gsc.riken.jp/5/). This resource contains web-based tools and data-access points for the research community to search and extract data related to samples, genes, promoter activities, transcription factors and enhancers across the FANTOM5 atlas.

Emergence of CD8 + T Cells Expressing NK Cell Receptors in Influenza A Virus-Infected Mice

Both innate and adaptive immune responses play an important role in the recovery of the host from viral infections. In the present report, a subset of cells coexpressing CD8 and NKR-P1C (NK1.1) was found in the lungs of mice infected with influenza A virus. These cells were detected at low numbers in the lungs of uninfected mice, but represented up to 10% of the total CD8+ T cell population at day 10 postinfection. Almost all of the CD8+NK1.1+ cells were CD8αβ+CD3+TCRαβ+ and a proportion of these cells also expressed the NK cell-associated Ly49 receptors. Interestingly, up to 30% of these cells were virus-specific T cells as determined by MHC class I tetramer staining and by intracellular staining of IFN-γ after viral peptide stimulation. Moreover, these cells were distinct from conventional NKT cells as they were also found at increased numbers in influenza-infected CD1−/− mice. These results demonstrate that a significant proportion of CD8+ T cells acquire NK1.1 and other NK cell-associated molecules, and suggests that these receptors may possibly regulate CD8+ T cell effector functions during viral infection.

Logical Development of the Cell Ontology

BackgroundThe Cell Ontology (CL) is an ontology for the representation of in vivo cell types. As biological ontologies such as the CL grow in complexity, they become increasingly difficult to use and maintain. By making the information in the ontology computable, we can use automated reasoners to detect errors and assist with classification. Here we report on the generation of computable definitions for the hematopoietic cell types in the CL.ResultsComputable definitions for over 340 CL classes have been created using a genus-differentia approach. These define cell types according to multiple axes of classification such as the protein complexes found on the surface of a cell type, the biological processes participated in by a cell type, or the phenotypic characteristics associated with a cell type. We employed automated reasoners to verify the ontology and to reveal mistakes in manual curation. The implementation of this process exposed areas in the ontology where new cell type classes were needed to accommodate species-specific expression of cellular markers. Our use of reasoners also inferred new relationships within the CL, and between the CL and the contributing ontologies. This restructured ontology can be used to identify immune cells by flow cytometry, supports sophisticated biological queries involving cells, and helps generate new hypotheses about cell function based on similarities to other cell types.ConclusionUse of computable definitions enhances the development of the CL and supports the interoperability of OBO ontologies.

Selective targeting of habenular, thalamic midline and monoaminergic brainstem neurons by neurotropic influenza A virus in mice.

Infections caused by influenza A virus have been proposed to be associated with neuropsychiatric complications, the mechanisms of which remain to be unravelled. We here report that a neurotropic strain of influenza A virus (A/WSN/33) introduced into the olfactory bulbs of C57BL/6 (B6) mice, selectively attacks habenular, paraventricular thalamic, and brainstem monoaminergic neurons. In the habenular and paraventricular thalamic areas, infection was followed by an almost total loss of neurons within 12 days. In the brain stem monoaminergic areas, viral gene products were eliminated from neurons by 12 days in B6 wildtype mice, but remained for at least 35 days in immunodefective TAP1 (Transporter associated with Antigen Presentation 1) mutant mice. In conclusion, we show that influenza A virus infection in the brain selectively targets regions which have been implicated in neuropsychiatric disturbances, and that this virus can remain for a significant period of time in specific regions of the brain in immunodefective mice.

Protein Ontology: a controlled structured network of protein entities

The Protein Ontology (PRO; http://proconsortium.org) formally defines protein entities and explicitly represents their major forms and interrelations. Protein entities represented in PRO corresponding to single amino acid chains are categorized by level of specificity into family, gene, sequence and modification metaclasses, and there is a separate metaclass for protein complexes. All metaclasses also have organism-specific derivatives. PRO complements established sequence databases such as UniProtKB, and interoperates with other biomedical and biological ontologies such as the Gene Ontology (GO). PRO relates to UniProtKB in that PRO’s organism-specific classes of proteins encoded by a specific gene correspond to entities documented in UniProtKB entries. PRO relates to the GO in that PRO’s representations of organism-specific protein complexes are subclasses of the organism-agnostic protein complex terms in the GO Cellular Component Ontology. The past few years have seen growth and changes to the PRO, as well as new points of access to the data and new applications of PRO in immunology and proteomics. Here we describe some of these developments.

Ontology development for biological systems

UNLABELLED We recently implemented improvements to the representation of immunology content of the biological process branch of the Gene Ontology (GO). The aims of the revision were to provide a comprehensive representation of immunological processes and to improve the organization of immunology related terms in the GO to match current concepts in the field of immunology. With these improvements, the GO will better reflect current understanding in the field of immunology and thus prove to be a more valuable resource for knowledge representation in gene annotation and analysis in the areas of immunology related to genomics and bioinformatics. AVAILABILITY http://www.geneontology.org.

CLO: The cell line ontology

BackgroundCell lines have been widely used in biomedical research. The community-based Cell Line Ontology (CLO) is a member of the OBO Foundry library that covers the domain of cell lines. Since its publication two years ago, significant updates have been made, including new groups joining the CLO consortium, new cell line cells, upper level alignment with the Cell Ontology (CL) and the Ontology for Biomedical Investigation, and logical extensions.Construction and contentCollaboration among the CLO, CL, and OBI has established consensus definitions of cell line-specific terms such as ‘cell line’, ‘cell line cell’, ‘cell line culturing’, and ‘mortal’ vs. ‘immortal cell line cell’. A cell line is a genetically stable cultured cell population that contains individual cell line cells. The hierarchical structure of the CLO is built based on the hierarchy of the in vivo cell types defined in CL and tissue types (from which cell line cells are derived) defined in the UBERON cross-species anatomy ontology. The new hierarchical structure makes it easier to browse, query, and perform automated classification. We have recently added classes representing more than 2,000 cell line cells from the RIKEN BRC Cell Bank to CLO. Overall, the CLO now contains ~38,000 classes of specific cell line cells derived from over 200 in vivo cell types from various organisms.Utility and discussionThe CLO has been applied to different biomedical research studies. Example case studies include annotation and analysis of EBI ArrayExpress data, bioassays, and host-vaccine/pathogen interaction. CLO’s utility goes beyond a catalogue of cell line types. The alignment of the CLO with related ontologies combined with the use of ontological reasoners will support sophisticated inferencing to advance translational informatics development.

An improved ontological representation of dendritic cells as a paradigm for all cell types

BackgroundRecent increases in the volume and diversity of life science data and information and an increasing emphasis on data sharing and interoperability have resulted in the creation of a large number of biological ontologies, including the Cell Ontology (CL), designed to provide a standardized representation of cell types for data annotation. Ontologies have been shown to have significant benefits for computational analyses of large data sets and for automated reasoning applications, leading to organized attempts to improve the structure and formal rigor of ontologies to better support computation. Currently, the CL employs multiple is_a relations, defining cell types in terms of histological, functional, and lineage properties, and the majority of definitions are written with sufficient generality to hold across multiple species. This approach limits the CLs utility for computation and for cross-species data integration.ResultsTo enhance the CLs utility for computational analyses, we developed a method for the ontological representation of cells and applied this method to develop a dendritic cell ontology (DC-CL). DC-CL subtypes are delineated on the basis of surface protein expression, systematically including both species-general and species-specific types and optimizing DC-CL for the analysis of flow cytometry data. We avoid multiple uses of is_a by linking DC-CL terms to terms in other ontologies via additional, formally defined relations such as has_function.ConclusionThis approach brings benefits in the form of increased accuracy, support for reasoning, and interoperability with other ontology resources. Accordingly, we propose our method as a general strategy for the ontological representation of cells. DC-CL is available from http://www.obofoundry.org.