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.

Tumor-associated myeloid cells regulate tumor growth and metastasis, and their accumulation is a negative prognostic factor for breast cancer. Here we find calcium/calmodulin-dependent kinase kinase (CaMKK2) to be highly expressed within intratumoral myeloid cells in mouse models of breast cancer, and demonstrate that its inhibition within myeloid cells suppresses tumor growth by increasing intratumoral accumulation of effector CD8 + T cells and immune-stimulatory myeloid subsets. Tumor-associated macrophages (TAMs) isolated from Camkk2 −/− mice expressed higher levels of chemokines involved in the recruitment of effector T cells compared to WT. Similarly, in vitro generated Camkk2 −/− macrophages recruit more T cells, and have a reduced capability to suppress T cell proliferation, compared to WT. Treatment with CaMKK2 inhibitors blocks tumor growth in a CD8 + T cell-dependent manner, and facilitates a favorable reprogramming of the immune cell microenvironment. These data, credential CaMKK2 as a myeloid-selective checkpoint, the inhibition of which may have utility in the immunotherapy of breast cancer.

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.