Search tips
Search criteria

Results 1-10 (10)

Clipboard (0)

Select a Filter Below

Year of Publication
1.  Principles and application of LIMS in mouse clinics 
Mammalian Genome  2015;26(9-10):467-481.
Large-scale systemic mouse phenotyping, as performed by mouse clinics for more than a decade, requires thousands of mice from a multitude of different mutant lines to be bred, individually tracked and subjected to phenotyping procedures according to a standardised schedule. All these efforts are typically organised in overlapping projects, running in parallel. In terms of logistics, data capture, data analysis, result visualisation and reporting, new challenges have emerged from such projects. These challenges could hardly be met with traditional methods such as pen & paper colony management, spreadsheet-based data management and manual data analysis. Hence, different Laboratory Information Management Systems (LIMS) have been developed in mouse clinics to facilitate or even enable mouse and data management in the described order of magnitude. This review shows that general principles of LIMS can be empirically deduced from LIMS used by different mouse clinics, although these have evolved differently. Supported by LIMS descriptions and lessons learned from seven mouse clinics, this review also shows that the unique LIMS environment in a particular facility strongly influences strategic LIMS decisions and LIMS development. As a major conclusion, this review states that there is no universal LIMS for the mouse research domain that fits all requirements. Still, empirically deduced general LIMS principles can serve as a master decision support template, which is provided as a hands-on tool for mouse research facilities looking for a LIMS.
Electronic supplementary material
The online version of this article (doi:10.1007/s00335-015-9586-7) contains supplementary material, which is available to authorized users.
PMCID: PMC4602070  PMID: 26208973
2.  Applying the ARRIVE Guidelines to an In Vivo Database 
PLoS Biology  2015;13(5):e1002151.
The Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines were developed to address the lack of reproducibility in biomedical animal studies and improve the communication of research findings. While intended to guide the preparation of peer-reviewed manuscripts, the principles of transparent reporting are also fundamental for in vivo databases. Here, we describe the benefits and challenges of applying the guidelines for the International Mouse Phenotyping Consortium (IMPC), whose goal is to produce and phenotype 20,000 knockout mouse strains in a reproducible manner across ten research centres. In addition to ensuring the transparency and reproducibility of the IMPC, the solutions to the challenges of applying the ARRIVE guidelines in the context of IMPC will provide a resource to help guide similar initiatives in the future.
Transparent reporting is key to ensuring reproducibility of animal research. This article examines the challenges of applying the ARRIVE guidelines to a large-scale, collaborative, in vivo research initiative—the International Mouse Phenotyping Consortium.
PMCID: PMC4439173  PMID: 25992600
3.  Novel retinoblastoma mutation abrogating the interaction to E2F2/3, but not E2F1, led to selective suppression of thyroid tumors 
Cancer Science  2014;105(10):1360-1368.
Mutant mouse models are indispensable tools for clarifying gene functions and elucidating the pathogenic mechanisms of human diseases. Here, we describe novel cancer models bearing point mutations in the retinoblastoma gene (Rb1) generated by N-ethyl-N-nitrosourea mutagenesis. Two mutations in splice sites reduced Rb1 expression and led to a tumor spectrum and incidence similar to those observed in the conventional Rb1 knockout mice. The missense mutant, Rb1D326V/+, developed pituitary tumors, but thyroid tumors were completely suppressed. Immunohistochemical analyses of thyroid tissue revealed that E2F1, but not E2F2/3, was selectively inactivated, indicating that the mutant Rb protein (pRb) suppressed thyroid tumors by inactivating E2F1. Interestingly, Rb1D326V/+ mice developed pituitary tumors that originated from the intermediate lobe of the pituitary, despite selective inactivation of E2F1. Furthermore, in the anterior lobe of the pituitary, other E2F were also inactivated. These observations show that pRb mediates the inactivation of E2F function and its contribution to tumorigenesis is highly dependent on the cell type. Last, by using a reconstitution assay of synthesized proteins, we showed that the D326V missense pRb bound to E2F1 but failed to interact with E2F2/3. These results reveal the effect of the pRb N-terminal domain on E2F function and the impact of the protein on tumorigenesis. Thus, this mutant mouse model can be used to investigate human Rb family-bearing mutations at the N-terminal region.
PMCID: PMC4462357  PMID: 25088905
E2F transcription factors; endocrine tumors; mice; mutagenesis; retinoblastoma
4.  CLO: The cell line ontology 
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.
PMCID: PMC4387853  PMID: 25852852
Cell line; Cell line cell; Immortal cell line cell; Mortal cell line cell; Cell line cell culturing; Anatomy
5.  PosMed: ranking genes and bioresources based on Semantic Web Association Study 
Nucleic Acids Research  2013;41(Web Server issue):W109-W114.
Positional MEDLINE (PosMed; is a powerful Semantic Web Association Study engine that ranks biomedical resources such as genes, metabolites, diseases and drugs, based on the statistical significance of associations between user-specified phenotypic keywords and resources connected directly or inferentially through a Semantic Web of biological databases such as MEDLINE, OMIM, pathways, co-expressions, molecular interactions and ontology terms. Since 2005, PosMed has long been used for in silico positional cloning studies to infer candidate disease-responsible genes existing within chromosomal intervals. PosMed is redesigned as a workbench to discover possible functional interpretations for numerous genetic variants found from exome sequencing of human disease samples. We also show that the association search engine enhances the value of mouse bioresources because most knockout mouse resources have no phenotypic annotation, but can be associated inferentially to phenotypes via genes and biomedical documents. For this purpose, we established text-mining rules to the biomedical documents by careful human curation work, and created a huge amount of correct linking between genes and documents. PosMed associates any phenotypic keyword to mouse resources with 20 public databases and four original data sets as of May 2013.
PMCID: PMC3692089  PMID: 23761449
6.  The RIKEN integrated database of mammals 
Nucleic Acids Research  2010;39(Database issue):D861-D870.
The RIKEN integrated database of mammals ( is the official undertaking to integrate its mammalian databases produced from multiple large-scale programs that have been promoted by the institute. The database integrates not only RIKEN’s original databases, such as FANTOM, the ENU mutagenesis program, the RIKEN Cerebellar Development Transcriptome Database and the Bioresource Database, but also imported data from public databases, such as Ensembl, MGI and biomedical ontologies. Our integrated database has been implemented on the infrastructure of publication medium for databases, termed SciNetS/SciNeS, or the Scientists’ Networking System, where the data and metadata are structured as a semantic web and are downloadable in various standardized formats. The top-level ontology-based implementation of mammal-related data directly integrates the representative knowledge and individual data records in existing databases to ensure advanced cross-database searches and reduced unevenness of the data management operations. Through the development of this database, we propose a novel methodology for the development of standardized comprehensive management of heterogeneous data sets in multiple databases to improve the sustainability, accessibility, utility and publicity of the data of biomedical information.
PMCID: PMC3013680  PMID: 21076152
7.  SDOP-DB: a comparative standardized-protocol database for mouse phenotypic analyses 
Bioinformatics  2010;26(8):1133-1134.
Summary: This article reports the development of SDOP-DB, which can provide definite, detailed and easy comparison of experimental protocols used in mouse phenotypic analyses among institutes or laboratories. Because SDOP-DB is fully compliant with international standards, it can act as a practical foundation for international sharing and integration of mouse phenotypic information.
Availability: SDOP-DB (
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC2853689  PMID: 20194625
8.  A monogenic dominant mutation in Rom1 generated by N-ethyl-N-nitrosourea mutagenesis causes retinal degeneration in mice 
Molecular Vision  2010;16:378-391.
To characterize an N-ethyl-N-nitrosourea-induced dominant mouse mutant, M-1156, that exhibits progressive retinal degeneration and to investigate the pathogenesis of the retinal phenotype in the mutant.
A positional candidate gene approach was used to identify the causative gene in the M-1156 mutant. Funduscopic examination, light microscopy, transmission electron microscopy, and electroretinography were performed to analyze the M-1156 phenotype. Real-time quantitative PCR, immunohistochemistry, and western blotting were also performed.
Linkage analysis enabled the mutant gene to be mapped to a region of chromosome 19 containing Rom1, which encodes rod outer segment membrane protein 1. Sequence analysis demonstrated that the mutation consisted of a single base T→C substitution at position 1,195 in Rom1 (M96760, National Center for Biotechnology Information [NCBI]) and that the mutant allele was expressed. A putative missense mutation designated Rom1Rgsc1156 that was identified in the M-1156 mutant mouse causes a Trp to Arg substitution at position 182 in the translated protein. Rom1Rgsc1156 heterozygotes were found to have a mottled retina and narrowed arteries in the fundus oculi. Photomicrographs of the retina revealed significant differences among the genotypes in the thickness of the outer nuclear layer and in the length of the outer segments of the photoreceptors. The alterations were more marked in the homozygotes than in the heterozygotes. Electron micrographs showed that the diameters of the discs varied in the heterozygotes and that the discs were more compactly stacked than in the wild type. There were significant differences among the genotypes in the amplitude of the a-wave in single-flash electroretinograms, but there were no significant differences among the photopic electroretinograms. Real-time quantitative PCR showed that there were no significant differences among the genotypes in Rom1 or peripherin/rds (Prph2) mRNA levels relative to the rhodopsin (Rho) mRNA level. Rom1 and Prph2 immunoreactivity were decreased in the retinas of the Rom1Rgsc1156 mutants. Semiquantitative western blot analysis of retinas from 3-week-old Rom1Rgsc1156 mutants demonstrated significant decreases in Rom1, Prph2, and Rho protein levels in all of the genotypes.
The Trp182Arg substitution in Rom1Rgsc1156 mutants causes retinal degeneration. The results suggested that Trp182Arg mutant Rom1 causes a decrease in the levels of wild-type Prph2 and Rom1, which in turn cause a reduction in the level of Prph2 containing tetramers in the disc rim region and ultimately result in unstable, disorganized outer segments and photoreceptor degeneration.
PMCID: PMC2838736  PMID: 20300562
9.  Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project 
Nature biotechnology  2008;26(8):889-896.
The Minimum Information for Biological and Biomedical Investigations (MIBBI) project provides a resource for those exploring the range of extant minimum information checklists and fosters coordinated development of such checklists.
PMCID: PMC2771753  PMID: 18688244
10.  PosMed (Positional Medline): prioritizing genes with an artificial neural network comprising medical documents to accelerate positional cloning 
Nucleic Acids Research  2009;37(Web Server issue):W147-W152.
PosMed ( prioritizes candidate genes for positional cloning by employing our original database search engine GRASE, which uses an inferential process similar to an artificial neural network comprising documental neurons (or ‘documentrons’) that represent each document contained in databases such as MEDLINE and OMIM. Given a user-specified query, PosMed initially performs a full-text search of each documentron in the first-layer artificial neurons and then calculates the statistical significance of the connections between the hit documentrons and the second-layer artificial neurons representing each gene. When a chromosomal interval(s) is specified, PosMed explores the second-layer and third-layer artificial neurons representing genes within the chromosomal interval by evaluating the combined significance of the connections from the hit documentrons to the genes. PosMed is, therefore, a powerful tool that immediately ranks the candidate genes by connecting phenotypic keywords to the genes through connections representing not only gene–gene interactions but also other biological interactions (e.g. metabolite–gene, mutant mouse–gene, drug–gene, disease–gene and protein–protein interactions) and ortholog data. By utilizing orthologous connections, PosMed facilitates the ranking of human genes based on evidence found in other model species such as mouse. Currently, PosMed, an artificial superbrain that has learned a vast amount of biological knowledge ranging from genomes to phenomes (or ‘omic space’), supports the prioritization of positional candidate genes in humans, mouse, rat and Arabidopsis thaliana.
PMCID: PMC2703941  PMID: 19468046

Results 1-10 (10)