CLO: The cell line ontology

Abstract Background Cell 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 content Collaboration 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 discussion The 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.http://deepblue.lib.umich.edu/bitstream/2027.42/109554/1/13326_2013_Article_185.pd

Mouse models of oxidative phosphorylation dysfunction and disease

Oxidative phosphorylation (OXPHOS) deficiency results in a number of human diseases, affecting at least one in 5000 of the general population. Altering the function of genes by mutations are central to our understanding their function. Prior to the development of gene targeting, this approach was limited to rare spontaneous mutations that resulted in a phenotype. Since its discovery, targeted mutagenesis of the mouse germline has proved to be a powerful approach to understand the in vivo function of genes. Gene targeting has yielded remarkable understanding of the role of several gene products in the OXPHOS system. We provide a “tool box” of mouse models with OXPHOS defects that could be used to answer diverse scientific questions

Mouse models of oxidative phosphorylation defects: Powerful tools to study the pathobiology of mitochondrial diseases

Defects in the oxidative phosphorylation system (OXPHOS) are responsible for a group of extremely heterogeneous and pleiotropic pathologies commonly known as mitochondrial diseases. Although many mutations have been found to be responsible for OXPHOS defects, their pathogenetic mechanisms are still poorly understood. An important contribution to investigate the in vivo function of several mitochondrial proteins and their role in mitochondrial dysfunction, has been provided by mouse models. Thanks to their genetic and physiologic similarity to humans, mouse models represent a powerful tool to investigate the impact of pathological mutations on metabolic pathways. In this review we discuss the main mouse models of mitochondrial disease developed, focusing on the ones that directly affect the OXPHOS system

Role of cytochrome C in apoptosis: increased sensitivity to tumor necrosis factor alpha is associated with respiratory defects but not with lack of cytochrome C release

Although the role of cytochrome c in apoptosis is well established, details of its participation in signaling pathways in vivo are not completely understood. The knockout for the somatic isoform of cytochrome c caused embryonic lethality in mice, but derived embryonic fibroblasts were shown to be resistant to apoptosis induced by agents known to trigger the intrinsic apoptotic pathway. In contrast, these cells were reported to be hypersensitive to tumor necrosis factor alpha (TNF-alpha)-induced apoptosis, which signals through the extrinsic pathway. Surprisingly, we found that this cell line (CRL 2613) respired at close to normal levels because of an aberrant activation of a testis isoform of cytochrome c, which, albeit expressed at low levels, was able to replace the somatic isoform for respiration and apoptosis. To produce a bona fide cytochrome c knockout, we developed a mouse knockout for both the testis and somatic isoforms of cytochrome c. The mouse was made viable by the introduction of a ubiquitously expressed cytochrome c transgene flanked by loxP sites. Lung fibroblasts in which the transgene was deleted showed no cytochrome c expression, no respiration, and resistance to agents that activate the intrinsic and to a lesser but significant extent also the extrinsic pathways. Comparison of these cells with lines with a defective oxidative phosphorylation system showed that cells with defective respiration have increased sensitivity to TNF-alpha-induced apoptosis, but this process was still amplified by cytochrome c. These studies underscore the importance of oxidative phosphorylation and apoptosome function to both the intrinsic and extrinsic apoptotic pathways

Formalization, annotation and analysis of diverse drug and probe screening assay datasets using the BioAssay Ontology (BAO).

Huge amounts of high-throughput screening (HTS) data for probe and drug development projects are being generated in the pharmaceutical industry and more recently in the public sector. The resulting experimental datasets are increasingly being disseminated via publically accessible repositories. However, existing repositories lack sufficient metadata to describe the experiments and are often difficult to navigate by non-experts. The lack of standardized descriptions and semantics of biological assays and screening results hinder targeted data retrieval, integration, aggregation, and analyses across different HTS datasets, for example to infer mechanisms of action of small molecule perturbagens. To address these limitations, we created the BioAssay Ontology (BAO). BAO has been developed with a focus on data integration and analysis enabling the classification of assays and screening results by concepts that relate to format, assay design, technology, target, and endpoint. Previously, we reported on the higher-level design of BAO and on the semantic querying capabilities offered by the ontology-indexed triple store of HTS data. Here, we report on our detailed design, annotation pipeline, substantially enlarged annotation knowledgebase, and analysis results. We used BAO to annotate assays from the largest public HTS data repository, PubChem, and demonstrate its utility to categorize and analyze diverse HTS results from numerous experiments. BAO is publically available from the NCBO BioPortal at http://bioportal.bioontology.org/ontologies/1533. BAO provides controlled terminology and uniform scope to report probe and drug discovery screening assays and results. BAO leverages description logic to formalize the domain knowledge and facilitate the semantic integration with diverse other resources. As a consequence, BAO offers the potential to infer new knowledge from a corpus of assay results, for example molecular mechanisms of action of perturbagens

Generation of a hierarchy-flattened BAO format.

<p>The hierarchy-flattened format of BAO contains only the most specific leaf nodes. Lead node IDs were maintained in this process. The labels/names in the flattened version of BAO reflect the class hierarchy in BAO.</p

Analysis of bio-ontologies to describe chemical biology HTS assays.

<p>Coverage of biomedical concepts/terms to describe HTS assays (shown as rows) by various existing biomedical ontologies (shown as columns) is depicted. The color codes are as follows: green: the concept is well described by the ontology, pink: the concept is partially described, red: no (or little) information is available in the ontology, yellow: the concept is imported from an external reference/ontology, blue: the ontology only includes a placeholder to an external reference of that concept.</p

Summary of PubChem assay annotations.

<p>Statistics of 944 curated PubChem assay by the BAO main classes ‘assay stage’ (A) ‘assay format’ (B), ‘assay design’ (C) and ‘detection technology’ (D).</p

List of ontologies used in BAO.

<p>List of ontologies used in BAO.</p

Formal BAO annotation of a PubChem screening campaign.

<p>In the campaign to identify inhibitors of Kruppel-like factor 5, the investigators screened compounds, both to identify inhibitors of KLF5 and to eliminate cytotoxic compounds. The compounds tested at each stage are shown in red boxes. The endpoints (result type) from each of the assays are listed to the right. Abbreviations: Comp: compounds, opt: optimization, conc: concentration, inh: inhibition, 1×: compound tested in singlicate, 3×: compound tested in triplicate, and CID: compound ID.</p