Anatomical ontologies: names and places in biology

Ontology has long been the preserve of philosophers and logicians. Recently, ideas from this field have been picked up by computer scientists as a basis for encoding knowledge and with the hope of achieving interoperability and intelligent system behavior. In bioinformatics, ontologies might allow hitherto impossible query and data-mining activities. We review the use of anatomy ontologies to represent space in biological organisms, specifically mouse and human.     

Ontologies of developmental anatomy: their current and future roles

A central problem in current biology is elucidating the molecular networks that drive developmental change and physiological function. Such knowledge is needed partly to understand these networks, partly to be able to manipulate them, and partly to understand and help treat those human congenital abnormalities that arise as a result of mutation. Thus far, bioinformatics technology has been of limited use in this enterprise, mainly because its core focus has been on sequence technology and data archiving. For bioinformatics to be of use in this next tier of investigations, genetic and protein data need to be both archived and searchable by tissue since this is the level at which these networks operate. The resulting databases in turn require ontologies of developmental anatomy that can provide the formal infrastructure for handling gene expression, microarray and other tissue-based data. Here, the progress in making such ontologies, particularly for the developing mouse, is reported and the uses to which they are and will be put, together with the resources and tools currently available for investigating molecular networks and the genetic basis of congenital abnormalities, are considered.     

Integrating phenotype ontologies across multiple species

Phenotype ontologies are typically constructed to serve the needs of a particular community, such as annotation of genotype-phenotype associations in mouse or human. Here we demonstrate how these ontologies can be improved through assignment of logical definitions using a core ontology of phenotypic qualities and multiple additional ontologies from the Open Biological Ontologies library. We also show how these logical definitions can be used for data integration when combined with a unified multi-species anatomy ontology.     

Phenotype Ontologies and Cross-Species Analysis for Translational Research

The use of model organisms as tools for the investigation of human genetic variation has significantly and rapidly advanced our understanding of the aetiologies underlying hereditary traits. However, while equivalences in the DNA sequence of two species may be readily inferred through evolutionary models, the identification of equivalence in the phenotypic consequences resulting from comparable genetic variation is far from straightforward, limiting the value of the modelling paradigm. In this review, we provide an overview of the emerging statistical and computational approaches to objectively identify phenotypic equivalence between human and model organisms with examples from the vertebrate models, mouse and zebrafish. Firstly, we discuss enrichment approaches, which deem the most frequent phenotype among the orthologues of a set of genes associated with a common human phenotype as the orthologous phenotype, or phenolog, in the model species. Secondly, we introduce and discuss computational reasoning approaches to identify phenotypic equivalences made possible through the development of intra- and interspecies ontologies. Finally, we consider the particular challenges involved in modelling neuropsychiatric disorders, which illustrate many of the remaining difficulties in developing comprehensive and unequivocal interspecies phenotype mappings.     

Medaka fish stem cells and their applications

Stem cells are present in developing embryos and adult tissues of multicellular organisms. Owing to their unique features, stem cells provide excellent opportunities for experimental analyses of basic developmental processes such as pluripotency control and cell fate decision and for regenerative medicine by stem cell-based therapy. Stem cell cultures have been best studied in 3 vertebrate organisms. These are the mouse, human and a small laboratory fish called medaka. Specifically, medaka has given rise to...     

Pathway landscapes and epigenetic regulation in breast cancer and melanoma cell lines

Understanding how developmental systems evolve over time is a key question in stem cell and developmental biology research. However, due to hurdles of existing experimental techniques, our understanding of these systems as a whole remains partial and coarse. In recent years, we have been constructing in-silico models that synthesize experimental knowledge using software engineering tools. Our approach integrates known isolated mechanisms with simplified assumptions where the knowledge is limited. This has proven to be a powerful, yet underutilized, tool to analyze the developmental process. The models provide a means to study development in-silico by altering the model’s specifications, and thereby predict unforeseen phenomena to guide future experimental trials. To date, three organs from diverse evolutionary organisms have been modeled: the mouse pancreas, the C. elegans gonad, and partial rodent brain development. Analysis and execution of the models recapitulated the development of the organs, anticipated known experimental results and gave rise to novel testable predictions. Some of these results had already been validated experimentally. In this paper, I review our efforts in realistic in-silico modeling of stem cell research and developmental biology and discuss achievements and challenges. I envision that in the future, in-silico models as presented in this paper would become a common and useful technique for research in developmental biology and related research fields, particularly regenerative medicine, tissue engineering and cancer therapeutics.     

From mouse to man: generating megabase chromosome rearrangements

Experimental approaches for deciphering the function of human genes rely heavily on our ability to generate mutations in model organisms such as the mouse. However, because recessive mutations are masked by the wild-type allele in the diploid context, conventional mutagenesis and screening is often laborious and costly. Chromosome engineering combines the power of gene targeting in embryonic stem (ES) cells with Cre--loxP technology to create mice that are functionally haploid in discrete portions of the genome. Chromosome deletions, duplications and inversions can be tagged with visible markers, facilitating strain maintenance. These approaches allow for more refined mutagenesis screens that will greatly accelerate functional mouse genomics and generate mammalian models for developmental processes and cancer.     

A genome-wide survey on basic helix-loop-helix transcription factors in rat and mouse

The basic helix-loop-helix (bHLH) proteins play essential roles in a wide range of developmental processes in higher organisms. bHLH family members have been identified in over 20 organisms, including nematode, fruit fly, and human. Our study identified 114 rat and 14 additional mouse bHLH members in rat and mouse genomes, respectively. Phylogenetic analyses revealed that both rat and mouse had 49, 26, 15, 4, 12, and 4 bHLH members in groups A, B, C, D, E, and F, respectively. Only the rat Mxi1 gene has two copies in the genome. All other rat bHLH genes and all mouse bHLH genes are single-copy genes. The chromosomal distribution pattern of mouse, rat, and human bHLH genes suggests the emergence of some bHLH genes through gene duplication, which probably happened at least before the divergence of vertebrates from invertebrates. The present study provides useful information for future studies using rat as a model animal for mammalian development. X. Zheng and Y. Wang are jointly first authors. An erratum to this article can be found at     

Craniofacial variability and modularity in macaques and mice

Evolutionary developmental biology of primates will be driven largely by the developmental biology of the house mouse. Inferences from how known developmental perturbations produce phenotypic effects in model organisms, such as mice, to how the same perturbations would affect craniofacial form in primates must be informed by comparisons of phenotypic variation and variability in mice and the primate species of interest. We use morphometric methods to compare patterns of cranial variability in homologous datasets obtained for two strains of laboratory mice and rhesus macaques. C57BL/6J represents a common genetic background for transgenic models. A/WySnJ mice exhibit altered facial morphology which results from reduction in the growth of the maxillary process during formation of the face. This is relevant to evolutionary changes in facial prognathism in nonhuman primate and human evolution. Rhesus macaques represent a nonhuman primate about which a great deal of phenotypic and genetic information is available. We find significant similarities in covariation patterns between the C57BL/6J mice and macaques. Among-trait variation in genetic and phenotypic variances are fairly concordant among the three groups, but among-trait variation in developmental stability is not. Finally, analysis of modularity based on phenotypic and genetic correlations did not reveal a consistent pattern in the three groups. We discuss the implications of these results for the study of evolutionary developmental biology of primates and outline a research strategy for integrating mouse genomics and developmental biology into this emerging field.     

A genome-wide survey on basic helix-loop-helix transcription factors in giant panda

The giant panda (Ailuropoda melanoleuca) is a critically endangered mammalian species. Studies on functions of regulatory proteins involved in developmental processes would facilitate understanding of specific behavior in giant panda. The basic helix-loop-helix (bHLH) proteins play essential roles in a wide range of developmental processes in higher organisms. bHLH family members have been identified in over 20 organisms, including fruit fly, zebrafish, mouse and human. Our present study identified 107 bHLH family members being encoded in giant panda genome. Phylogenetic analyses revealed that they belong to 44 bHLH families with 46, 25, 15, 4, 11 and 3 members in group A, B, C, D, E and F, respectively, while the remaining 3 members were assigned into "orphan". Compared to mouse, the giant panda does not encode seven bHLH proteins namely Beta3a, Mesp2, Sclerax, S-Myc, Hes5 (or Hes6), EBF4 and Orphan 1. These results provide useful background information for future studies on structure and function of bHLH proteins in the regulation of giant panda development.     

PhenomeNET: a whole-phenome approach to disease gene discovery

Phenotypes are investigated in model organisms to understand and reveal the molecular mechanisms underlying disease. Phenotype ontologies were developed to capture and compare phenotypes within the context of a single species. Recently, these ontologies were augmented with formal class definitions that may be utilized to integrate phenotypic data and enable the direct comparison of phenotypes between different species. We have developed a method to transform phenotype ontologies into a formal representation, combine phenotype ontologies with anatomy ontologies, and apply a measure of semantic similarity to construct the PhenomeNET cross-species phenotype network. We demonstrate that PhenomeNET can identify orthologous genes, genes involved in the same pathway and gene-disease associations through the comparison of mutant phenotypes. We provide evidence that the Adam19 and Fgf15 genes in mice are involved in the tetralogy of Fallot, and, using zebrafish phenotypes, propose the hypothesis that the mammalian homologs of Cx36.7 and Nkx2.5 lie in a pathway controlling cardiac morphogenesis and electrical conductivity which, when defective, cause the tetralogy of Fallot phenotype. Our method implements a whole-phenome approach toward disease gene discovery and can be applied to prioritize genes for rare and orphan diseases for which the molecular basis is unknown.     

Functional genomics the old-fashioned way: chemical mutagenesis in mice

Genetic studies using mutants have led to a greater understanding of the mechanisms underlying the physiology, biochemistry and development of organisms. The increasing availability of complete genome sequences has stimulated genome-wide mutagenesis approaches in model organisms. In an ideal model system, it would be possible to choose from a series of mutations in any given gene to study its function, regulation and interaction with other genes; flies and worms with their rich mutant resources provide such models. Because the mouse is a powerful vertebrate model for human disease, it would be advantageous to have an equally comprehensive mutant collection. Recently, much to the joy of the mouse community, two papers, describe screens to generate such a collection. In an ongoing screen, the groups of Brown and Balling have generated over 40,000 F1 mutant mice by treating males with the super mutagen N-ethyl-N-nitrosourea. 300-500 mice are being screened each week using various objective tests and paradigms for morphological, developmental, clinical and behavioral abnormalities. In combination, these analyses have produced an unbiased set of about 700 new dominant, semidominant and recessive mutations.     

CRAVE: a database, middleware and visualization system for phenotype ontologies

MOTIVATION: A major challenge in modern biology is to link genome sequence information to organismal function. In many organisms this is being done by characterizing phenotypes resulting from mutations. Efficiently expressing phenotypic information requires combinatorial use of ontologies. However tools are not currently available to visualize combinations of ontologies. Here we describe CRAVE (Concept Relation Assay Value Explorer), a package allowing storage, active updating and visualization of multiple ontologies. RESULTS: CRAVE is a web-accessible JAVA application that accesses an underlying MySQL database of ontologies via a JAVA persistent middleware layer (Chameleon). This maps the database tables into discrete JAVA classes and creates memory resident, interlinked objects corresponding to the ontology data. These JAVA objects are accessed via calls through the middleware's application programming interface. CRAVE allows simultaneous display and linking of multiple ontologies and searching using Boolean and advanced searches.     

Using ontologies to describe mouse phenotypes

The mouse is an important model of human genetic disease. Describing phenotypes of mutant mice in a standard, structured manner that will facilitate data mining is a major challenge for bioinformatics. Here we describe a novel, compositional approach to this problem which combines core ontologies from a variety of sources. This produces a framework with greater flexibility, power and economy than previous approaches. We discuss some of the issues this approach raises.     

The outcomes of pathway database computations depend on pathway ontology

Different biological notions of pathways are used in different pathway databases. Those pathway ontologies significantly impact pathway computations. Computational users of pathway databases will obtain different results depending on the pathway ontology used by the databases they employ, and different pathway ontologies are preferable for different end uses. We explore differences in pathway ontologies by comparing the BioCyc and KEGG ontologies. The BioCyc ontology defines a pathway as a conserved, atomic module of the metabolic network of a single organism, i.e. often regulated as a unit, whose boundaries are defined at high-connectivity stable metabolites. KEGG pathways are on average 4.2 times larger than BioCyc pathways, and combine multiple biological processes from different organisms to produce a substrate-centered reaction mosaic. We compared KEGG and BioCyc pathways using genome context methods, which determine the functional relatedness of pairs of genes. For each method we employed, a pair of genes randomly selected from a BioCyc pathway is more likely to be related by that method than is a pair of genes randomly selected from a KEGG pathway, supporting the conclusion that the BioCyc pathway conceptualization is closer to a single conserved biological process than is that of KEGG.