The Ontogeny of Antigen-Binding Cells

In order to analyze the development of antigen-specific cells, the binding of a variety of antigens by cells in the fetal, neonatal, and adult mouse was compared. The fiber-binding assay was used in many of these experiments, because it provides a simple and uniform method for studying the specific interactions of cells with any of a wide variety of antigens. To demonstrate the specificity of the assay, cells from the spleens of immune and nonimmune adult mice were isolated and characterized. Specifically, after removal from the fibers, these cells were assayed for their viability, their ability to rebind to fibers of the same specificity, and their in vivo response to antigen after transfer to irradiated syngeneic recipients. These experiments indicated that the fiber method yields highly enriched populations of specific antigen-binding cells that are viable and include antigen-sensitive bone marrow-derived cells capable of undergoing differentiation into antibody secreting cells. This assay was then used to characterize cells specific for each of eleven different hapten and protein antigens. In all cases, specific antigen-binding cells were first detected in the liver, on the 15th day of the 19-day gestation period. These cells disappeared from the liver within a day of birth, but continued to increase in number in the spleen until adulthood. The proportions of antigen-binding cells of different specificities were similar in fetal, neonatal, and adult tissues. The antigen-binding cell populations from fetal livers and spleens were similar to each other and to adult spleen cell populations in the distributions of their relative avidities for several antigens. These results indicate that antigen-binding cells of various specificities arise relatively rapidly and in parallel during development, and therefore that strong positive antigenic selection is not likely to operate during . ontogeny. This has several implications for theories on the origin of antibody diversity, and in particular suggests that positive selection may not be required for somatic diversification to occur. These results also suggest that the sharply restricted ability of the neonatal animal to respond to antigenic stimulation is not due to the lack of antigen specific cells, but rather to the absence of mature cells capable of the interactions necessary for a full immune response. While measurements of the numbers of antigen-binding cells in the spleens of individual outbred fetal mice failed to detect subpopulations of individuals differing systematically from the fetal population as a whole, significantly more variation among individuals was found than would be expected if the actual number of cells binding a specific antigen were constant, or nearly so, among fetuses. To determine the source of this variation more precisely, the numbers of cells specific for each of two antigens in the spleens of individual outbred (Swiss-L) and inbred (Balb/c and CBA/J) fetal mice were measured as a function of spleen size. For outbred Swiss-L fetuses, the ratio of antigen-binding cells to nucleated cells varied significantly more than could be accounted for by sampling fluctuation. For each inbred strain, however, the number of cells specific for a given antigen was a constant proportion of the number of nucleated cells, within sampling error. These proportions varied from antigen to antigen, and from strain to strain. The ratio of the proportions of cells specific for the two antigens, however, differed no more from CBA/J to Balb/c mice than would be expected in repeated samples of cells from the spleen of a single fetus. These results confirm at the level of the individual fetus the uniform pattern of development seen for populations of fetuses. They reveal a surprising precision in the proliferation of specific antigen-binding cell populations and suggest that the development of these cell populations may be subject to strong genetic controls

Biochemical pathways represented by Gene Ontology-Causal Activity Models identify distinct phenotypes resulting from mutations in pathways.

Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a functional network. Computable representations of biological pathways include detailed process descriptions in the Reactome Knowledgebase and causal activity flows between molecular functions in Gene Ontology-Causal Activity Models (GO-CAMs). A computational process has been developed to convert Reactome pathways to GO-CAMs. Laboratory mice are widely used models of normal and pathological human processes. We have converted human Reactome GO-CAMs to orthologous mouse GO-CAMs, as a resource to transfer pathway knowledge between humans and model organisms. These mouse GO-CAMs allowed us to define sets of genes that function in a causally connected way. To demonstrate that individual variant genes from connected pathways result in similar but distinguishable phenotypes, we used the genes in our pathway models to cross-query mouse phenotype annotations in the Mouse Genome Database (MGD). Using GO-CAM representations of 2 related but distinct pathways, gluconeogenesis and glycolysis, we show that individual causal paths in gene networks give rise to discrete phenotypic outcomes resulting from perturbations of glycolytic and gluconeogenic genes. The accurate and detailed descriptions of gene interactions recovered in this analysis of well-studied processes suggest that this strategy can be applied to less well-understood processes in less well-studied model systems to predict phenotypic outcomes of novel gene variants and to identify potential gene targets in altered processes

Integrative annotation and knowledge discovery of kinase post-translational modifications and cancer-associated mutations through federated protein ontologies and resources.

Many bioinformatics resources with unique perspectives on the protein landscape are currently available. However, generating new knowledge from these resources requires interoperable workflows that support cross-resource queries. In this study, we employ federated queries linking information from the Protein Kinase Ontology, iPTMnet, Protein Ontology, neXtProt, and the Mouse Genome Informatics to identify key knowledge gaps in the functional coverage of the human kinome and prioritize understudied kinases, cancer variants and post-translational modifications (PTMs) for functional studies. We identify 32 functional domains enriched in cancer variants and PTMs and generate mechanistic hypotheses on overlapping variant and PTM sites by aggregating information at the residue, protein, pathway and species level from these resources. We experimentally test the hypothesis that S768 phosphorylation in the C-helix of EGFR is inhibitory by showing that oncogenic variants altering S768 phosphorylation increase basal EGFR activity. In contrast, oncogenic variants altering conserved phosphorylation sites in the \u27hydrophobic motif\u27 of PKCβII (S660F and S660C) are loss-of-function in that they reduce kinase activity and enhance membrane translocation. Our studies provide a framework for integrative, consistent, and reproducible annotation of the cancer kinomes. Sci Rep 2018 Apr 25; 8(1):6518

Annotating Cancer Variants and Anti-Cancer Therapeutics in Reactome

Reactome describes biological pathways as chemical reactions that closely mirror the actual physical interactions that occur in the cell. Recent extensions of our data model accommodate the annotation of cancer and other disease processes. First, we have extended our class of protein modifications to accommodate annotation of changes in amino acid sequence and the formation of fusion proteins to describe the proteins involved in disease processes. Second, we have added a disease attribute to reaction, pathway, and physical entity classes that uses disease ontology terms. To support the graphical representation of “cancer” pathways, we have adapted our Pathway Browser to display disease variants and events in a way that allows comparison with the wild type pathway, and shows connections between perturbations in cancer and other biological pathways. The curation of pathways associated with cancer, coupled with our efforts to create other disease-specific pathways, will interoperate with our existing pathway and network analysis tools. Using the Epidermal Growth Factor Receptor (EGFR) signaling pathway as an example, we show how Reactome annotates and presents the altered biological behavior of EGFR variants due to their altered kinase and ligand-binding properties, and the mode of action and specificity of anti-cancer therapeutics

Modeling biochemical pathways in the gene ontology.

The concept of a biological pathway, an ordered sequence of molecular transformations, is used to collect and represent molecular knowledge for a broad span of organismal biology. Representations of biomedical pathways typically are rich but idiosyncratic presentations of organized knowledge about individual pathways. Meanwhile, biomedical ontologies and associated annotation files are powerful tools that organize molecular information in a logically rigorous form to support computational analysis. The Gene Ontology (GO), representing Molecular Functions, Biological Processes and Cellular Components, incorporates many aspects of biological pathways within its ontological representations. Here we present a methodology for extending and refining the classes in the GO for more comprehensive, consistent and integrated representation of pathways, leveraging knowledge embedded in current pathway representations such as those in the Reactome Knowledgebase and MetaCyc. With carbohydrate metabolic pathways as a use case, we discuss how our representation supports the integration of variant pathway classes into a unified ontological structure that can be used for data comparison and analysis. Database (Oxford) 2016 Sep 1; 2016:baw126

Reactome and the Gene Ontology: Digital convergence of data resources.

MOTIVATION: GO Causal Activity Models (GO-CAMs) assemble individual associations of gene products with cellular components, molecular functions, and biological processes into causally linked activity flow models. Pathway databases such as the Reactome Knowledgebase create detailed molecular process descriptions of reactions and assemble them, based on sharing of entities between individual reactions into pathway descriptions. RESULTS: To convert the rich content of Reactome into GO-CAMs, we have developed a software tool, Pathways2GO, to convert the entire set of normal human Reactome pathways into GO-CAMs. This conversion yields standard GO annotations from Reactome content and supports enhanced quality control for both Reactome and GO, yielding a nearly seamless conversion between these two resources for the bioinformatics community. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online

Guidelines for the functional annotation of microRNAs using the Gene Ontology.

MicroRNA regulation of developmental and cellular processes is a relatively new field of study, and the available research data have not been organized to enable its inclusion in pathway and network analysis tools. The association of gene products with terms from the Gene Ontology is an effective method to analyze functional data, but until recently there has been no substantial effort dedicated to applying Gene Ontology terms to microRNAs. Consequently, when performing functional analysis of microRNA data sets, researchers have had to rely instead on the functional annotations associated with the genes encoding microRNA targets. In consultation with experts in the field of microRNA research, we have created comprehensive recommendations for the Gene Ontology curation of microRNAs. This curation manual will enable provision of a high-quality, reliable set of functional annotations for the advancement of microRNA research. Here we describe the key aspects of the work, including development of the Gene Ontology to represent this data, standards for describing the data, and guidelines to support curators making these annotations. The full microRNA curation guidelines are available on the GO Consortium wiki (http://wiki.geneontology.org/index.php/MicroRNA_GO_annotation_manual). RNA 2016 May; 22(5): 667-76

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\u27s 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\u27s 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. Nucleic Acids Res 2014 Jan 1; 42(1):D415-21

Recent advances in biocuration: Meeting Report from the fifth International Biocuration Conference.

The 5th International Biocuration Conference brought together over 300 scientists to exchange on their work, as well as discuss issues relevant to the International Society for Biocuration\u27s (ISB) mission. Recurring themes this year included the creation and promotion of gold standards, the need for more ontologies, and more formal interactions with journals. The conference is an essential part of the ISB\u27s goal to support exchanges among members of the biocuration community. Next year\u27s conference will be held in Cambridge, UK, from 7 to 10 April 2013. In the meanwhile, the ISB website provides information about the society\u27s activities (http://biocurator.org), as well as related events of interest