1
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Peng J, Ma X, Chen Y, Yan J, Jiang H. C57BL/6J and C57BL/6N mice exhibit different neuro-behaviors and sensitivity to midazolam- and propofol-induced anesthesia. Physiol Behav 2023; 264:114146. [PMID: 36889487 DOI: 10.1016/j.physbeh.2023.114146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 02/18/2023] [Accepted: 03/03/2023] [Indexed: 03/08/2023]
Abstract
Phenotypes of inbred mice are strain-dependent, indicating the important influence of genetic background in biomedical research. C57BL/6 is one of the most commonly used inbred mouse strains, and its two closely related substrains, C57BL/6J and C57BL/6N, have been separated for only about 70 years. These two substrains have accumulated genetic variations and exhibit different phenotypes, but it remains unclear whether they respond to anesthetics differently. In this study, commercially acquired wildtype C57BL/6J or C57BL/6N mice from two different sources were analyzed and compared for their response to a spectrum of anesthetics (midazolam, propofol, esketamine or isoflurane anesthesia) and their performance in a series of behavioral tests associated with neurological functions including open field test (OFT), elevated plus maze (EPM), Y maze, prepulse inhibition (PPI), tail strain test (TST) and forced swimming test (FST). Loss of the righting reflex (LORR) is used to measure the anesthetic effects. Our results suggested that the anesthesia induction time induced by either of the four anesthetics were comparable for the C57BL/6J and C57BL/6N mice. However, C57BL/6J or C57BL/6N mice do exhibit different sensitivity to midazolam and propofol. The anesthesia duration of midazolam of C57BL/6J mice was about 60% shorter than that of the C57BL/6N mice, while the LORR duration induced by propofol in C57BL/6J mice was 51% longer than that of the C57BL/6N. In comparison, the two substrains were anesthetized by esketamine or isoflurane similarly. In the behavioral analysis, the C57BL/6J mice exhibited a lower level of anxiety- and depression-like behaviors in OFT, EPM, FST and TST than the C57BL/6N mice. Locomotor activity and sensorimotor gating of these two substrains remained comparable. Our results stress the point that when selecting inbred mice for allele mutation or behavioral testing, the influence of even subtle differences in genetic background should be fully considered.
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Affiliation(s)
- Jiali Peng
- Department of Anaesthesiology, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaofan Ma
- Department of Anaesthesiology, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yelin Chen
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Jia Yan
- Department of Anaesthesiology, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Hong Jiang
- Department of Anaesthesiology, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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2
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Gaines CH, Snyder AE, Ervin RB, Farrington J, Walsh K, Schoenrock SA, Tarantino LM. Behavioral characterization of a novel Cisd2 mutant mouse. Behav Brain Res 2021; 405:113187. [PMID: 33610659 DOI: 10.1016/j.bbr.2021.113187] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 02/11/2021] [Accepted: 02/12/2021] [Indexed: 12/11/2022]
Abstract
Wolfram syndrome (WFS) is a rare autosomal recessive disorder characterized by diabetes mellitus and insipidus, progressive optic atrophy and sensorineural deafness. An increased incidence of psychiatric disorders has also been reported in WFS patients. There are two subtypes of WFS. Type 1 (WFS1) is caused by mutations in the WFS1 gene and type 2 (WFS2) results from mutations in the CISD2 gene. Existing Wfs1 knockout mice exhibit many WFS1 cardinal symptoms including diabetic nephropathy, metabolic disruptions and optic atrophy. Far fewer studies have examined loss of Cisd2 function in mice. We identified B6.DDY-Cisd2m1Lmt, a mouse model with a spontaneous mutation in the Cisd2 gene. B6.DDY-Cisd2m1Lmt mice were initially identified based on the presence of audible sonic vocalizations as well as decreased body size and weight compared to unaffected wildtype littermates. Although Wfs1 knockout mice have been characterized for numerous behavioral phenotypes, similar studies have been lacking for Cisd2 mutant mice. We tested B6.DDY-Cisd2m1Lmt mice in a battery of behavioral assays that model phenotypes related to neurological and psychiatric disorders including anxiety, sensorimotor gating, stress response, social interaction and learning and memory. B6.DDY-Cisd2m1Lmt mice displayed hypoactivity across several behavioral tests, exhibited increased stress response and had deficits in spatial learning and memory and sensorimotor gating compared to wildtype littermates. Our data indicate that the B6.DDY-Cisd2m1Lmt mouse strain is a useful model to investigate potential mechanisms underlying the neurological and psychiatric symptoms observed in WFS.
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Affiliation(s)
- Christiann H Gaines
- Department of Genetics, University of North Carolina at Chapel Hill, NC, United States; Neuroscience Curriculum, University of North Carolina at Chapel Hill, NC, United States
| | - Angela E Snyder
- Department of Genetics, University of North Carolina at Chapel Hill, NC, United States
| | - Robin B Ervin
- Psychiatry Department, School of Medicine, University of North Carolina at Chapel Hill, NC, United States
| | - Joseph Farrington
- Department of Genetics, University of North Carolina at Chapel Hill, NC, United States
| | - Kenneth Walsh
- Department of Genetics, University of North Carolina at Chapel Hill, NC, United States
| | - Sarah A Schoenrock
- Department of Genetics, University of North Carolina at Chapel Hill, NC, United States; Neuroscience Curriculum, University of North Carolina at Chapel Hill, NC, United States
| | - Lisa M Tarantino
- Department of Genetics, University of North Carolina at Chapel Hill, NC, United States; Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, NC, United States.
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3
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The influence of rare variants in circulating metabolic biomarkers. PLoS Genet 2020; 16:e1008605. [PMID: 32150548 PMCID: PMC7108731 DOI: 10.1371/journal.pgen.1008605] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 03/31/2020] [Accepted: 01/10/2020] [Indexed: 12/19/2022] Open
Abstract
Circulating metabolite levels are biomarkers for cardiovascular disease (CVD). Here we studied, association of rare variants and 226 serum lipoproteins, lipids and amino acids in 7,142 (discovery plus follow-up) healthy participants. We leveraged the information from multiple metabolite measurements on the same participants to improve discovery in rare variant association analyses for gene-based and gene-set tests by incorporating correlated metabolites as covariates in the validation stage. Gene-based analysis corrected for the effective number of tests performed, confirmed established associations at APOB, APOC3, PAH, HAL and PCSK (p<1.32x10-7) and identified novel gene-trait associations at a lower stringency threshold with ACSL1, MYCN, FBXO36 and B4GALNT3 (p<2.5x10-6). Regulation of the pyruvate dehydrogenase (PDH) complex was associated for the first time, in gene-set analyses also corrected for effective number of tests, with IDL and LDL parameters, as well as circulating cholesterol (pMETASKAT<2.41x10-6). In conclusion, using an approach that leverages metabolite measurements obtained in the same participants, we identified novel loci and pathways involved in the regulation of these important metabolic biomarkers. As large-scale biobanks continue to amass sequencing and phenotypic information, analytical approaches such as ours will be useful to fully exploit the copious amounts of biological data generated in these efforts.
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4
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Fernandes JB, Duhamel M, Seguéla-Arnaud M, Froger N, Girard C, Choinard S, Solier V, De Winne N, De Jaeger G, Gevaert K, Andrey P, Grelon M, Guerois R, Kumar R, Mercier R. FIGL1 and its novel partner FLIP form a conserved complex that regulates homologous recombination. PLoS Genet 2018; 14:e1007317. [PMID: 29608566 PMCID: PMC5897033 DOI: 10.1371/journal.pgen.1007317] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 04/12/2018] [Accepted: 03/19/2018] [Indexed: 02/07/2023] Open
Abstract
Homologous recombination is central to repair DNA double-strand breaks, either accidently arising in mitotic cells or in a programed manner at meiosis. Crossovers resulting from the repair of meiotic breaks are essential for proper chromosome segregation and increase genetic diversity of the progeny. However, mechanisms regulating crossover formation remain elusive. Here, we identified through genetic and protein-protein interaction screens FIDGETIN-LIKE-1 INTERACTING PROTEIN (FLIP) as a new partner of the previously characterized anti-crossover factor FIDGETIN-LIKE-1 (FIGL1) in Arabidopsis thaliana. We showed that FLIP limits meiotic crossover together with FIGL1. Further, FLIP and FIGL1 form a protein complex conserved from Arabidopsis to human. FIGL1 interacts with the recombinases RAD51 and DMC1, the enzymes that catalyze the DNA strand exchange step of homologous recombination. Arabidopsis flip mutants recapitulate the figl1 phenotype, with enhanced meiotic recombination associated with change in counts of DMC1 and RAD51 foci. Our data thus suggests that FLIP and FIGL1 form a conserved complex that regulates the crucial step of strand invasion in homologous recombination. Homologous recombination is a DNA repair mechanism that is essential to preserve the integrity of genetic information and thus to prevent cancer formation. Homologous recombination is also used during sexual reproduction to generate genetic diversity in the offspring by shuffling parental chromosomes. Here, we identified a novel protein complex (FLIP-FIGL1) that regulates homologous recombination and is conserved from plants to mammals. This suggests the existence of a novel mode of regulation at a central step of homologous recombination.
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Affiliation(s)
- Joiselle Blanche Fernandes
- Institut Jean-Pierre Bourgin, UMR1318 INRA-AgroParisTech, Université Paris-Saclay, RD10,Versailles, France
- Université Paris-Sud, Université Paris-Saclay, Orsay, France
| | - Marine Duhamel
- Institut Jean-Pierre Bourgin, UMR1318 INRA-AgroParisTech, Université Paris-Saclay, RD10,Versailles, France
| | - Mathilde Seguéla-Arnaud
- Institut Jean-Pierre Bourgin, UMR1318 INRA-AgroParisTech, Université Paris-Saclay, RD10,Versailles, France
| | - Nicole Froger
- Institut Jean-Pierre Bourgin, UMR1318 INRA-AgroParisTech, Université Paris-Saclay, RD10,Versailles, France
| | - Chloé Girard
- Institut Jean-Pierre Bourgin, UMR1318 INRA-AgroParisTech, Université Paris-Saclay, RD10,Versailles, France
| | - Sandrine Choinard
- Institut Jean-Pierre Bourgin, UMR1318 INRA-AgroParisTech, Université Paris-Saclay, RD10,Versailles, France
| | - Victor Solier
- Institut Jean-Pierre Bourgin, UMR1318 INRA-AgroParisTech, Université Paris-Saclay, RD10,Versailles, France
| | - Nancy De Winne
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Geert De Jaeger
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Kris Gevaert
- Department of Biochemistry, Ghent University, Ghent, Belgium
- VIB Center for Medical Biotechnology, Ghent, Belgium
| | - Philippe Andrey
- Institut Jean-Pierre Bourgin, UMR1318 INRA-AgroParisTech, Université Paris-Saclay, RD10,Versailles, France
| | - Mathilde Grelon
- Institut Jean-Pierre Bourgin, UMR1318 INRA-AgroParisTech, Université Paris-Saclay, RD10,Versailles, France
| | - Raphael Guerois
- Institute for Integrative Biology of the Cell (I2BC), Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA), Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud, CEA-Saclay, Gif-sur-Yvette, France
| | - Rajeev Kumar
- Institut Jean-Pierre Bourgin, UMR1318 INRA-AgroParisTech, Université Paris-Saclay, RD10,Versailles, France
- * E-mail: (RK); (RM)
| | - Raphaël Mercier
- Institut Jean-Pierre Bourgin, UMR1318 INRA-AgroParisTech, Université Paris-Saclay, RD10,Versailles, France
- * E-mail: (RK); (RM)
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5
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Kafkafi N, Agassi J, Chesler EJ, Crabbe JC, Crusio WE, Eilam D, Gerlai R, Golani I, Gomez-Marin A, Heller R, Iraqi F, Jaljuli I, Karp NA, Morgan H, Nicholson G, Pfaff DW, Richter SH, Stark PB, Stiedl O, Stodden V, Tarantino LM, Tucci V, Valdar W, Williams RW, Würbel H, Benjamini Y. Reproducibility and replicability of rodent phenotyping in preclinical studies. Neurosci Biobehav Rev 2018; 87:218-232. [PMID: 29357292 PMCID: PMC6071910 DOI: 10.1016/j.neubiorev.2018.01.003] [Citation(s) in RCA: 116] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Revised: 12/13/2017] [Accepted: 01/11/2018] [Indexed: 12/15/2022]
Abstract
The scientific community is increasingly concerned with the proportion of
published “discoveries” that are not replicated in subsequent
studies. The field of rodent behavioral phenotyping was one of the first to
raise this concern, and to relate it to other methodological issues: the complex
interaction between genotype and environment; the definitions of behavioral
constructs; and the use of laboratory mice and rats as model species for
investigating human health and disease mechanisms. In January 2015, researchers
from various disciplines gathered at Tel Aviv University to discuss these
issues. The general consensus was that the issue is prevalent and of concern,
and should be addressed at the statistical, methodological and policy levels,
but is not so severe as to call into question the validity and the usefulness of
model organisms as a whole. Well-organized community efforts, coupled with
improved data and metadata sharing, have a key role in identifying specific
problems and promoting effective solutions. Replicability is closely related to
validity, may affect generalizability and translation of findings, and has
important ethical implications.
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Affiliation(s)
| | | | | | - John C Crabbe
- Oregon Health & Science University, and VA Portland Health Care System, United States
| | | | | | | | | | | | | | | | | | - Natasha A Karp
- Discovery Sciences, IMED Biotech Unit, AstraZeneca, Cambridge, UK
| | | | | | | | | | | | | | | | | | | | - William Valdar
- University of North Carolina at Chapel Hill, United States
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6
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Eppig JT. Mouse Genome Informatics (MGI) Resource: Genetic, Genomic, and Biological Knowledgebase for the Laboratory Mouse. ILAR J 2017; 58:17-41. [PMID: 28838066 PMCID: PMC5886341 DOI: 10.1093/ilar/ilx013] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 03/14/2017] [Accepted: 03/28/2017] [Indexed: 12/13/2022] Open
Abstract
The Mouse Genome Informatics (MGI) Resource supports basic, translational, and computational research by providing high-quality, integrated data on the genetics, genomics, and biology of the laboratory mouse. MGI serves a strategic role for the scientific community in facilitating biomedical, experimental, and computational studies investigating the genetics and processes of diseases and enabling the development and testing of new disease models and therapeutic interventions. This review describes the nexus of the body of growing genetic and biological data and the advances in computer technology in the late 1980s, including the World Wide Web, that together launched the beginnings of MGI. MGI develops and maintains a gold-standard resource that reflects the current state of knowledge, provides semantic and contextual data integration that fosters hypothesis testing, continually develops new and improved tools for searching and analysis, and partners with the scientific community to assure research data needs are met. Here we describe one slice of MGI relating to the development of community-wide large-scale mutagenesis and phenotyping projects and introduce ways to access and use these MGI data. References and links to additional MGI aspects are provided.
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Affiliation(s)
- Janan T. Eppig
- Janan T. Eppig, PhD, is Professor Emeritus at The Jackson Laboratory in Bar Harbor, Maine
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7
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Raess M, de Castro AA, Gailus-Durner V, Fessele S, Hrabě de Angelis M. INFRAFRONTIER: a European resource for studying the functional basis of human disease. Mamm Genome 2016; 27:445-50. [PMID: 27262858 PMCID: PMC4935733 DOI: 10.1007/s00335-016-9642-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Accepted: 04/14/2016] [Indexed: 12/20/2022]
Abstract
Ageing research and more generally the study of the functional basis of human diseases profit enormously from the large-scale approaches and resources in mouse functional genomics: systematic targeted mutation of the mouse genome, systemic phenotyping in mouse clinics, and the archiving and distribution of the mouse resources in public repositories. INFRAFRONTIER, the European research infrastructure for the development, systemic phenotyping, archiving and distribution of mammalian models, offers access to sustainable mouse resources for biomedical research. INFRAFRONTIER promotes the global sharing of high-quality resources and data and thus contributes to data reproducibility and animal welfare. INFRAFRONTIER puts great effort into international standardisation and quality control and into technology development to improve and expand experimental protocols, reduce the use of animals in research and increase the reproducibility of results. In concert with the research community and the International Mouse Phenotyping Consortium (IMPC), INFRAFRONTIER is currently developing new pilot platforms and services for the research on ageing and age-related diseases.
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Affiliation(s)
| | | | - Valérie Gailus-Durner
- Institute of Experimental Genetics & German Mouse Clinic, Helmholtz Zentrum München, 85764, Neuherberg, Germany
| | | | - Martin Hrabě de Angelis
- INFRAFRONTIER GmbH, 85764, Neuherberg, Germany.
- Institute of Experimental Genetics & German Mouse Clinic, Helmholtz Zentrum München, 85764, Neuherberg, Germany.
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8
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Omori H, Ogaki S, Sakano D, Sato M, Umeda K, Takeda N, Nakagata N, Kume S. Changes in expression of C2cd4c in pancreatic endocrine cells during pancreatic development. FEBS Lett 2016; 590:2584-93. [PMID: 27349930 PMCID: PMC5129588 DOI: 10.1002/1873-3468.12271] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Revised: 06/20/2016] [Accepted: 06/21/2016] [Indexed: 11/10/2022]
Abstract
C2cd4c, encoded by a gene belonging to the C2cd4 family, contains a C2 domain conserved across species and is localized to the cytoplasm. To examine the role of C2cd4c in the pancreas, we studied its localization and generated C2cd4c knockout (KO) mice. C2cd4c was expressed in pancreatic endocrine progenitors at early embryonic stages. When endocrine cells arise from their precursors, C2cd4c is gradually confined to the insulin‐ and pancreatic polypeptide‐expressing cells of the endocrine. In the adult pancreas, C2cd4c is restricted to the beta cells. C2cd4c KO mice showed normal embryonic pancreatic development and adult pancreatic function. Thus, our results suggest that C2cd4c is dispensable for pancreatic development.
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Affiliation(s)
- Hisayoshi Omori
- Institute of Molecular Embryology and Genetics, Kumamoto University, Japan
| | - Soichiro Ogaki
- Institute of Molecular Embryology and Genetics, Kumamoto University, Japan.,Department of Life Science and Technology, School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, Japan.,Division of Pharmacology, National Institute of Health Science, Kamiyoga, Setagaya-ku, Tokyo, Japan
| | - Daisuke Sakano
- Institute of Molecular Embryology and Genetics, Kumamoto University, Japan.,Department of Life Science and Technology, School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, Japan
| | - Mutsumi Sato
- Institute of Molecular Embryology and Genetics, Kumamoto University, Japan
| | - Kahoko Umeda
- Institute of Molecular Embryology and Genetics, Kumamoto University, Japan.,HIGO program, Kumamoto University, Japan
| | - Naoki Takeda
- Division of Developmental Genetics, Institute of Resource Development and Analysis, Kumamoto University, Japan
| | - Naomi Nakagata
- Division of Reproductive Engineering, Institute of Resource Development and Analysis, Kumamoto University, Japan
| | - Shoen Kume
- Institute of Molecular Embryology and Genetics, Kumamoto University, Japan.,Department of Life Science and Technology, School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, Japan
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9
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Bult CJ, Eppig JT, Blake JA, Kadin JA, Richardson JE. Mouse genome database 2016. Nucleic Acids Res 2015; 44:D840-7. [PMID: 26578600 PMCID: PMC4702860 DOI: 10.1093/nar/gkv1211] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Accepted: 10/23/2015] [Indexed: 01/09/2023] Open
Abstract
The Mouse Genome Database (MGD; http://www.informatics.jax.org) is the primary community model organism database for the laboratory mouse and serves as the source for key biological reference data related to mouse genes, gene functions, phenotypes and disease models with a strong emphasis on the relationship of these data to human biology and disease. As the cost of genome-scale sequencing continues to decrease and new technologies for genome editing become widely adopted, the laboratory mouse is more important than ever as a model system for understanding the biological significance of human genetic variation and for advancing the basic research needed to support the emergence of genome-guided precision medicine. Recent enhancements to MGD include new graphical summaries of biological annotations for mouse genes, support for mobile access to the database, tools to support the annotation and analysis of sets of genes, and expanded support for comparative biology through the expansion of homology data.
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Affiliation(s)
- Carol J Bult
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Janan T Eppig
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Judith A Blake
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - James A Kadin
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
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10
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A mouse informatics platform for phenotypic and translational discovery. Mamm Genome 2015; 26:413-21. [PMID: 26314589 PMCID: PMC4602054 DOI: 10.1007/s00335-015-9599-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Accepted: 08/17/2015] [Indexed: 01/05/2023]
Abstract
The International Mouse Phenotyping Consortium (IMPC) is providing the world's first functional catalogue of a mammalian genome by characterising a knockout mouse strain for every gene. A robust and highly structured informatics platform has been developed to systematically collate, analyse and disseminate the data produced by the IMPC. As the first phase of the project, in which 5000 new knockout strains are being broadly phenotyped, nears completion, the informatics platform is extending and adapting to support the increasing volume and complexity of the data produced as well as addressing a large volume of users and emerging user groups. An intuitive interface helps researchers explore IMPC data by giving overviews and the ability to find and visualise data that support a phenotype assertion. Dedicated disease pages allow researchers to find new mouse models of human diseases, and novel viewers provide high-resolution images of embryonic and adult dysmorphologies. With each monthly release, the informatics platform will continue to evolve to support the increased data volume and to maintain its position as the primary route of access to IMPC data and as an invaluable resource for clinical and non-clinical researchers.
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11
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Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics. Nat Genet 2015. [PMID: 26214591 PMCID: PMC4564951 DOI: 10.1038/ng.3360] [Citation(s) in RCA: 115] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The function of the majority of genes in the mouse and human genomes remains unknown. The mouse embryonic stem cell knockout resource provides a basis for the characterization of relationships between genes and phenotypes. The EUMODIC consortium developed and validated robust methodologies for the broad-based phenotyping of knockouts through a pipeline comprising 20 disease-oriented platforms. We developed new statistical methods for pipeline design and data analysis aimed at detecting reproducible phenotypes with high power. We acquired phenotype data from 449 mutant alleles, representing 320 unique genes, of which half had no previous functional annotation. We captured data from over 27,000 mice, finding that 83% of the mutant lines are phenodeviant, with 65% demonstrating pleiotropy. Surprisingly, we found significant differences in phenotype annotation according to zygosity. New phenotypes were uncovered for many genes with previously unknown function, providing a powerful basis for hypothesis generation and further investigation in diverse systems.
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12
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Eppig JT, Richardson JE, Kadin JA, Smith CL, Blake JA, Bult CJ. Mouse Genome Database: From sequence to phenotypes and disease models. Genesis 2015; 53:458-73. [PMID: 26150326 PMCID: PMC4545690 DOI: 10.1002/dvg.22874] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Revised: 06/30/2015] [Accepted: 07/02/2015] [Indexed: 12/19/2022]
Abstract
The Mouse Genome Database (MGD, www.informatics.jax.org) is the international scientific database for genetic, genomic, and biological data on the laboratory mouse to support the research requirements of the biomedical community. To accomplish this goal, MGD provides broad data coverage, serves as the authoritative standard for mouse nomenclature for genes, mutants, and strains, and curates and integrates many types of data from literature and electronic sources. Among the key data sets MGD supports are: the complete catalog of mouse genes and genome features, comparative homology data for mouse and vertebrate genes, the authoritative set of Gene Ontology (GO) annotations for mouse gene functions, a comprehensive catalog of mouse mutations and their phenotypes, and a curated compendium of mouse models of human diseases. Here, we describe the data acquisition process, specifics about MGD's key data areas, methods to access and query MGD data, and outreach and user help facilities. genesis 53:458–473, 2015. © 2015 The Authors. Genesis Published by Wiley Periodicals, Inc.
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13
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Dulneva A, Lee S, Oliver PL, Di Gleria K, Kessler BM, Davies KE, Becker EBE. The mutant Moonwalker TRPC3 channel links calcium signaling to lipid metabolism in the developing cerebellum. Hum Mol Genet 2015; 24:4114-25. [PMID: 25908616 PMCID: PMC4476454 DOI: 10.1093/hmg/ddv150] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2015] [Accepted: 04/20/2015] [Indexed: 12/22/2022] Open
Abstract
The Moonwalker (Mwk) mouse is a model of dominantly inherited cerebellar ataxia caused by a gain-of-function mutation in the transient receptor potential (TRP) channel TRPC3. Here, we report impairments in dendritic growth and synapse formation early on during Purkinje cell development in the Mwk cerebellum that are accompanied by alterations in calcium signaling. To elucidate the molecular effector pathways that regulate Purkinje cell dendritic arborization downstream of mutant TRPC3, we employed transcriptomic analysis of developing Purkinje cells isolated by laser-capture microdissection. We identified significant gene and protein expression changes in molecules involved in lipid metabolism. Consistently, lipid homeostasis in the Mwk cerebellum was found to be disturbed, and treatment of organotypic cerebellar slices with ceramide significantly improved dendritic outgrowth of Mwk Purkinje cells. These findings provide the first mechanistic insights into the TRPC3-dependent mechanisms, by which activated calcium signaling is coupled to lipid metabolism and the regulation of Purkinje cell development in the Mwk cerebellum.
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Affiliation(s)
- Anna Dulneva
- Medical Research Council Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK and
| | - Sheena Lee
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK and
| | - Peter L Oliver
- Medical Research Council Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK and
| | - Katalin Di Gleria
- TDI Mass Spectrometry Laboratory, Target Discovery Institute, University of Oxford, Oxford OX3 7FZ, UK
| | - Benedikt M Kessler
- TDI Mass Spectrometry Laboratory, Target Discovery Institute, University of Oxford, Oxford OX3 7FZ, UK
| | - Kay E Davies
- Medical Research Council Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK and
| | - Esther B E Becker
- Medical Research Council Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK and
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Smith CL, Eppig JT. Expanding the mammalian phenotype ontology to support automated exchange of high throughput mouse phenotyping data generated by large-scale mouse knockout screens. J Biomed Semantics 2015; 6:11. [PMID: 25825651 PMCID: PMC4378007 DOI: 10.1186/s13326-015-0009-1] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Accepted: 03/03/2015] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND A vast array of data is about to emerge from the large scale high-throughput mouse knockout phenotyping projects worldwide. It is critical that this information is captured in a standardized manner, made accessible, and is fully integrated with other phenotype data sets for comprehensive querying and analysis across all phenotype data types. The volume of data generated by the high-throughput phenotyping screens is expected to grow exponentially, thus, automated methods and standards to exchange phenotype data are required. RESULTS The IMPC (International Mouse Phenotyping Consortium) is using the Mammalian Phenotype (MP) ontology in the automated annotation of phenodeviant data from high throughput phenotyping screens. 287 new term additions with additional hierarchy revisions were made in multiple branches of the MP ontology to accurately describe the results generated by these high throughput screens. CONCLUSIONS Because these large scale phenotyping data sets will be reported using the MP as the common data standard for annotation and data exchange, automated importation of these data to MGI (Mouse Genome Informatics) and other resources is possible without curatorial effort. Maximum biomedical value of these mutant mice will come from integrating primary high-throughput phenotyping data with secondary, comprehensive phenotypic analyses combined with published phenotype details on these and related mutants at MGI and other resources.
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Affiliation(s)
- Cynthia L Smith
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - Janan T Eppig
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
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15
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West DB, Pasumarthi RK, Baridon B, Djan E, Trainor A, Griffey SM, Engelhard EK, Rapp J, Li B, de Jong PJ, Lloyd KCK. A lacZ reporter gene expression atlas for 313 adult KOMP mutant mouse lines. Genome Res 2015; 25:598-607. [PMID: 25591789 PMCID: PMC4381530 DOI: 10.1101/gr.184184.114] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2014] [Accepted: 01/05/2015] [Indexed: 02/04/2023]
Abstract
Expression of the bacterial beta-galactosidase reporter gene (lacZ) in the vector used for the Knockout Mouse Project (KOMP) is driven by the endogenous promoter of the target gene. In tissues from KOMP mice, histochemical staining for LacZ enzyme activity can be used to determine gene expression patterns. With this technique, we have produced a comprehensive resource of gene expression using both whole mount (WM) and frozen section (FS) LacZ staining in 313 unique KOMP mutant mouse lines. Of these, ∼80% of mutants showed specific staining in one or more tissues, while ∼20% showed no specific staining, ∼13% had staining in only one tissue, and ∼25% had staining in >6 tissues. The highest frequency of specific staining occurred in the brain (∼50%), male gonads (42%), and kidney (39%). The WM method was useful for rapidly identifying whole organ and some substructure staining, while the FS method often revealed substructure and cellular staining specificity. Both staining methods had >90% repeatability in biological replicates. Nonspecific LacZ staining occurs in some tissues due to the presence of bacteria or endogenous enzyme activity. However, this can be effectively distinguished from reporter gene activity by the combination of the WM and FS methods. After careful annotation, LacZ staining patterns in a high percentage of mutants revealed a unique structure-function not previously reported for many of these genes. The validation of methods for LacZ staining, annotation, and expression analysis reported here provides unique insights into the function of genes for which little is currently known.
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Affiliation(s)
- David B West
- Children's Hospital of Oakland Research Institute (CHORI), Oakland, California 94609, USA;
| | - Ravi K Pasumarthi
- Mouse Biology Program, University of California, Davis, California 95618, USA
| | - Brian Baridon
- Mouse Biology Program, University of California, Davis, California 95618, USA
| | - Esi Djan
- Mouse Biology Program, University of California, Davis, California 95618, USA
| | - Amanda Trainor
- Mouse Biology Program, University of California, Davis, California 95618, USA
| | - Stephen M Griffey
- Mouse Biology Program, University of California, Davis, California 95618, USA
| | - Eric K Engelhard
- Mouse Biology Program, University of California, Davis, California 95618, USA
| | - Jared Rapp
- Mouse Biology Program, University of California, Davis, California 95618, USA
| | - Bowen Li
- Mouse Biology Program, University of California, Davis, California 95618, USA
| | - Pieter J de Jong
- Children's Hospital of Oakland Research Institute (CHORI), Oakland, California 94609, USA
| | - K C Kent Lloyd
- Mouse Biology Program, University of California, Davis, California 95618, USA
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16
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de Magalhães JP. The scientific quest for lasting youth: prospects for curing aging. Rejuvenation Res 2014; 17:458-67. [PMID: 25132068 PMCID: PMC4203147 DOI: 10.1089/rej.2014.1580] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2014] [Accepted: 08/17/2014] [Indexed: 12/22/2022] Open
Abstract
People have always sought eternal life and everlasting youth. Recent technological breakthroughs and our growing understanding of aging have given strength to the idea that a cure for human aging can eventually be developed. As such, it is crucial to debate the long-term goals and potential impact of the field. Here, I discuss the scientific prospect of eradicating human aging. I argue that curing aging is scientifically possible and not even the most challenging enterprise in the biosciences. Developing the means to abolish aging is also an ethical endeavor because the goal of biomedical research is to allow people to be as healthy as possible for as long as possible. There is no evidence, however, that we are near to developing the technologies permitting radical life extension. One major difficulty in aging research is the time and costs it takes to do experiments and test interventions. I argue that unraveling the functioning of the genome and developing predictive computer models of human biology and disease are essential to increase the accuracy of medical interventions, including in the context of life extension, and exponential growth in informatics and genomics capacity might lead to rapid progress. Nonetheless, developing the tools for significantly modifying human biology is crucial to intervening in a complex process like aging. Yet in spite of advances in areas like regenerative medicine and gene therapy, the development of clinical applications has been slow and this remains a key hurdle for achieving radical life extension in the foreseeable future.
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Affiliation(s)
- João Pedro de Magalhães
- Integrative Genomics of Ageing Group, Institute of Integrative Biology, University of Liverpool , Liverpool, United Kingdom
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17
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Kogelman LJA, Cirera S, Zhernakova DV, Fredholm M, Franke L, Kadarmideen HN. Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model. BMC Med Genomics 2014; 7:57. [PMID: 25270054 PMCID: PMC4183073 DOI: 10.1186/1755-8794-7-57] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Accepted: 09/24/2014] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Obesity is a complex metabolic condition in strong association with various diseases, like type 2 diabetes, resulting in major public health and economic implications. Obesity is the result of environmental and genetic factors and their interactions, including genome-wide genetic interactions. Identification of co-expressed and regulatory genes in RNA extracted from relevant tissues representing lean and obese individuals provides an entry point for the identification of genes and pathways of importance to the development of obesity. The pig, an omnivorous animal, is an excellent model for human obesity, offering the possibility to study in-depth organ-level transcriptomic regulations of obesity, unfeasible in humans. Our aim was to reveal adipose tissue co-expression networks, pathways and transcriptional regulations of obesity using RNA Sequencing based systems biology approaches in a porcine model. METHODS We selected 36 animals for RNA Sequencing from a previously created F2 pig population representing three extreme groups based on their predicted genetic risks for obesity. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to detect clusters of highly co-expressed genes (modules). Additionally, regulator genes were detected using Lemon-Tree algorithms. RESULTS WGCNA revealed five modules which were strongly correlated with at least one obesity-related phenotype (correlations ranging from -0.54 to 0.72, P < 0.001). Functional annotation identified pathways enlightening the association between obesity and other diseases, like osteoporosis (osteoclast differentiation, P = 1.4E-7), and immune-related complications (e.g. Natural killer cell mediated cytotoxity, P = 3.8E-5; B cell receptor signaling pathway, P = 7.2E-5). Lemon-Tree identified three potential regulator genes, using confident scores, for the WGCNA module which was associated with osteoclast differentiation: CCR1, MSR1 and SI1 (probability scores respectively 95.30, 62.28, and 34.58). Moreover, detection of differentially connected genes identified various genes previously identified to be associated with obesity in humans and rodents, e.g. CSF1R and MARC2. CONCLUSIONS To our knowledge, this is the first study to apply systems biology approaches using porcine adipose tissue RNA-Sequencing data in a genetically characterized porcine model for obesity. We revealed complex networks, pathways, candidate and regulatory genes related to obesity, confirming the complexity of obesity and its association with immune-related disorders and osteoporosis.
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Affiliation(s)
| | | | | | | | | | - Haja N Kadarmideen
- Department of Veterinary Clinical and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Grønnegårdsvej 7, 1870, Frederiksberg, Denmark.
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18
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Hancock JM. Commentary on Shimoyama et al. (2012): three ontologies to define phenotype measurement data. Front Genet 2014; 5:93. [PMID: 24795755 PMCID: PMC4006037 DOI: 10.3389/fgene.2014.00093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2014] [Accepted: 04/03/2014] [Indexed: 01/17/2023] Open
Affiliation(s)
- John M Hancock
- Department of Physiology, Development and Neuroscience, University of Cambridge Cambridge, UK
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19
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Meta-analysis identifies gene-by-environment interactions as demonstrated in a study of 4,965 mice. PLoS Genet 2014; 10:e1004022. [PMID: 24415945 PMCID: PMC3886926 DOI: 10.1371/journal.pgen.1004022] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2013] [Accepted: 10/28/2013] [Indexed: 12/30/2022] Open
Abstract
Identifying environmentally-specific genetic effects is a key challenge in understanding the structure of complex traits. Model organisms play a crucial role in the identification of such gene-by-environment interactions, as a result of the unique ability to observe genetically similar individuals across multiple distinct environments. Many model organism studies examine the same traits but under varying environmental conditions. For example, knock-out or diet-controlled studies are often used to examine cholesterol in mice. These studies, when examined in aggregate, provide an opportunity to identify genomic loci exhibiting environmentally-dependent effects. However, the straightforward application of traditional methodologies to aggregate separate studies suffers from several problems. First, environmental conditions are often variable and do not fit the standard univariate model for interactions. Additionally, applying a multivariate model results in increased degrees of freedom and low statistical power. In this paper, we jointly analyze multiple studies with varying environmental conditions using a meta-analytic approach based on a random effects model to identify loci involved in gene-by-environment interactions. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional uni- or multi-variate approaches for discovery of gene-by-environment interactions. We apply our new method to combine 17 mouse studies containing in aggregate 4,965 distinct animals. We identify 26 significant loci involved in High-density lipoprotein (HDL) cholesterol, many of which are consistent with previous findings. Several of these loci show significant evidence of involvement in gene-by-environment interactions. An additional advantage of our meta-analysis approach is that our combined study has significantly higher power and improved resolution compared to any single study thus explaining the large number of loci discovered in the combined study. Identifying gene-by-environment interactions is important for understand the architecture of a complex trait. Discovering gene-by-environment interaction requires the observation of the same phenotype in individuals under different environments. Model organism studies are often conducted under different environments. These studies provide an unprecedented opportunity for researchers to identify the gene-by-environment interactions. A difference in the effect size of a genetic variant between two studies conducted in different environments may suggest the presence of a gene-by-environment interaction. In this paper, we propose to employ a random-effect-based meta-analysis approach to identify gene-by-environment interaction, which assumes different or heterogeneous effect sizes between studies. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional approaches for discovery of gene-by-environment interactions, which treats the gene-by-environment interactions as covariates in the analysis. We provide a intuitive way to visualize the results of the meta-analysis at a locus which allows us to obtain the biological insights of gene-by-environment interactions. We demonstrate our method by searching for gene-by-environment interactions by combining 17 mouse genetic studies totaling 4,965 distinct animals.
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20
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Koscielny G, Yaikhom G, Iyer V, Meehan TF, Morgan H, Atienza-Herrero J, Blake A, Chen CK, Easty R, Di Fenza A, Fiegel T, Grifiths M, Horne A, Karp NA, Kurbatova N, Mason JC, Matthews P, Oakley DJ, Qazi A, Regnart J, Retha A, Santos LA, Sneddon DJ, Warren J, Westerberg H, Wilson RJ, Melvin DG, Smedley D, Brown SDM, Flicek P, Skarnes WC, Mallon AM, Parkinson H. The International Mouse Phenotyping Consortium Web Portal, a unified point of access for knockout mice and related phenotyping data. Nucleic Acids Res 2014; 42:D802-9. [PMID: 24194600 PMCID: PMC3964955 DOI: 10.1093/nar/gkt977] [Citation(s) in RCA: 207] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2013] [Revised: 09/20/2013] [Accepted: 10/01/2013] [Indexed: 12/21/2022] Open
Abstract
The International Mouse Phenotyping Consortium (IMPC) web portal (http://www.mousephenotype.org) provides the biomedical community with a unified point of access to mutant mice and rich collection of related emerging and existing mouse phenotype data. IMPC mouse clinics worldwide follow rigorous highly structured and standardized protocols for the experimentation, collection and dissemination of data. Dedicated 'data wranglers' work with each phenotyping center to collate data and perform quality control of data. An automated statistical analysis pipeline has been developed to identify knockout strains with a significant change in the phenotype parameters. Annotation with biomedical ontologies allows biologists and clinicians to easily find mouse strains with phenotypic traits relevant to their research. Data integration with other resources will provide insights into mammalian gene function and human disease. As phenotype data become available for every gene in the mouse, the IMPC web portal will become an invaluable tool for researchers studying the genetic contributions of genes to human diseases.
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Affiliation(s)
- Gautier Koscielny
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Gagarine Yaikhom
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Vivek Iyer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Terrence F. Meehan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Hugh Morgan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Julian Atienza-Herrero
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Andrew Blake
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Chao-Kung Chen
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Richard Easty
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Armida Di Fenza
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Tanja Fiegel
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Mark Grifiths
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Alan Horne
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Natasha A. Karp
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Natalja Kurbatova
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Jeremy C. Mason
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Peter Matthews
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Darren J. Oakley
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Asfand Qazi
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Jack Regnart
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Ahmad Retha
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Luis A. Santos
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Duncan J. Sneddon
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Jonathan Warren
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Henrik Westerberg
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Robert J. Wilson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - David G. Melvin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Damian Smedley
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Steve D. M. Brown
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - William C. Skarnes
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Ann-Marie Mallon
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
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Drakakis G, Hendry AE, Hanson K, Brewerton SC, Bodkin MJ, Evans DA, Wheeler GN, Bender A. Comparative mode-of-action analysis following manual and automated phenotype detection in Xenopus laevis. MEDCHEMCOMM 2014. [DOI: 10.1039/c3md00313b] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Given the increasing utilization of phenotypic screens in drug discovery also the subsequent mechanism-of-action analysis gains increased attention.
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Affiliation(s)
- Georgios Drakakis
- Unilever Centre for Molecular Science Informatics
- Department of Chemistry
- University of Cambridge
- Cambridge CB2 1EW
- UK
| | - Adam E. Hendry
- School of Biological Sciences
- University of East Anglia
- Norwich
- UK
| | | | | | | | | | | | - Andreas Bender
- Unilever Centre for Molecular Science Informatics
- Department of Chemistry
- University of Cambridge
- Cambridge CB2 1EW
- UK
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22
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Flicek P, Amode MR, Barrell D, Beal K, Billis K, Brent S, Carvalho-Silva D, Clapham P, Coates G, Fitzgerald S, Gil L, Girón CG, Gordon L, Hourlier T, Hunt S, Johnson N, Juettemann T, Kähäri AK, Keenan S, Kulesha E, Martin FJ, Maurel T, McLaren WM, Murphy DN, Nag R, Overduin B, Pignatelli M, Pritchard B, Pritchard E, Riat HS, Ruffier M, Sheppard D, Taylor K, Thormann A, Trevanion SJ, Vullo A, Wilder SP, Wilson M, Zadissa A, Aken BL, Birney E, Cunningham F, Harrow J, Herrero J, Hubbard TJ, Kinsella R, Muffato M, Parker A, Spudich G, Yates A, Zerbino DR, Searle SM. Ensembl 2014. Nucleic Acids Res 2013; 42:D749-55. [PMID: 24316576 PMCID: PMC3964975 DOI: 10.1093/nar/gkt1196] [Citation(s) in RCA: 1059] [Impact Index Per Article: 96.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Ensembl (http://www.ensembl.org) creates tools and data resources to facilitate genomic analysis in chordate species with an emphasis on human, major vertebrate model organisms and farm animals. Over the past year we have increased the number of species that we support to 77 and expanded our genome browser with a new scrollable overview and improved variation and phenotype views. We also report updates to our core datasets and improvements to our gene homology relationships from the addition of new species. Our REST service has been extended with additional support for comparative genomics and ontology information. Finally, we provide updated information about our methods for data access and resources for user training.
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Affiliation(s)
- Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
- *To whom correspondence should be addressed. Tel: +44 1223 492 581; Fax: +44 1223 494 494;
| | - M. Ridwan Amode
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Daniel Barrell
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Kathryn Beal
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Konstantinos Billis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Simon Brent
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Denise Carvalho-Silva
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Peter Clapham
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Guy Coates
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Stephen Fitzgerald
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Laurent Gil
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Carlos García Girón
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Leo Gordon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Thibaut Hourlier
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Sarah Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Nathan Johnson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Thomas Juettemann
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Andreas K. Kähäri
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Stephen Keenan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Eugene Kulesha
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Fergal J. Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Thomas Maurel
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - William M. McLaren
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Daniel N. Murphy
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Rishi Nag
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Bert Overduin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Miguel Pignatelli
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Bethan Pritchard
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Emily Pritchard
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Harpreet S. Riat
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Magali Ruffier
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Daniel Sheppard
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Kieron Taylor
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Anja Thormann
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Stephen J. Trevanion
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Alessandro Vullo
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Steven P. Wilder
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Mark Wilson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Amonida Zadissa
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Bronwen L. Aken
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Fiona Cunningham
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Jennifer Harrow
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Javier Herrero
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Tim J.P. Hubbard
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Rhoda Kinsella
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Matthieu Muffato
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Anne Parker
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Giulietta Spudich
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Andy Yates
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Daniel R. Zerbino
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Stephen M.J. Searle
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
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23
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Blake JA, Bult CJ, Eppig JT, Kadin JA, Richardson JE. The Mouse Genome Database: integration of and access to knowledge about the laboratory mouse. Nucleic Acids Res 2013; 42:D810-7. [PMID: 24285300 PMCID: PMC3964950 DOI: 10.1093/nar/gkt1225] [Citation(s) in RCA: 171] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The Mouse Genome Database (MGD) (http://www.informatics.jax.org) is the community model organism database resource for the laboratory mouse, a premier animal model for the study of genetic and genomic systems relevant to human biology and disease. MGD maintains a comprehensive catalog of genes, functional RNAs and other genome features as well as heritable phenotypes and quantitative trait loci. The genome feature catalog is generated by the integration of computational and manual genome annotations generated by NCBI, Ensembl and Vega/HAVANA. MGD curates and maintains the comprehensive listing of functional annotations for mouse genes using the Gene Ontology, and MGD curates and integrates comprehensive phenotype annotations including associations of mouse models with human diseases. Recent improvements include integration of the latest mouse genome build (GRCm38), improved access to comparative and functional annotations for mouse genes with expanded representation of comparative vertebrate genomes and new loads of phenotype data from high-throughput phenotyping projects. All MGD resources are freely available to the research community.
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Affiliation(s)
- Judith A Blake
- Bioinformatics and Computational Biology, The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
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Suzuki T, Furuse T, Yamada I, Motegi H, Kozawa Y, Masuya H, Wakana S. Pheno-Pub: a total support system for the publication of mouse phenotypic data on the web. Mamm Genome 2013; 24:473-83. [PMID: 24220852 DOI: 10.1007/s00335-013-9482-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2012] [Accepted: 10/14/2013] [Indexed: 02/01/2023]
Abstract
We have developed an open-source database system named "Pheno-Pub" to support a series of data-handling and publication tasks, including statistical analyses, data review, and web site construction, for mouse phenotyping experiments. This system is composed of three applications. "Mou-Stat" provides semiautomatic statistical analyses for a batch of phenotypic data, including a variety of conditions for group comparisons (e.g., different scales of measurement parameters). "Genotype Viewer" and "Strain Viewer" provide representation of genotype-driven and measurement parameter-driven views of phenotypic data; they highlight significant differences in genotypes and between strains, respectively. Direct links from the Strain Viewer web site to the Genotype Viewer web site provide flexible navigation in the exploration of phenotypic data. With these publication tools, phenotypic data can be made available on the Internet by simple operations. This system is expandable for a wide range of uses in phenotypic comparative analyses, including comparisons among different genotypes and strains and comparisons among groups exposed to different environmental conditions. Finally, Pheno-Pub provides advanced usability for both producers of experimental data and consumers of phenotypic information. Therefore, Pheno-Pub contributes significantly to the publication of data in various fields of phenotyping research and to broad data sharing, thereby promoting the understanding of the functions of the entire mouse genome.
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Affiliation(s)
- Tomohiro Suzuki
- Technology and Development Team for Mouse Phenotype Analysis, RIKEN BioResource Center, 3-1-1 Koyadai, Tsukuba, Ibaraki, 305-0074, Japan,
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25
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Fuchs H, Gau C, Hans W, Gailus-Durner V, Hrabě de Angelis M. Long-term experiment to study the development, interaction, and influencing factors of DEXA parameters. Mamm Genome 2013; 24:376-88. [PMID: 24096374 DOI: 10.1007/s00335-013-9477-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2013] [Accepted: 08/10/2013] [Indexed: 12/26/2022]
Abstract
Dual-energy X-ray absorption (DEXA) is commonly used to measure bone mineral density (BMD), bone mineral content (BMC), and body composition data (fat mass and lean mass) for phenotype assessment in mice. We were interested in the long-term development of BMD, BMC, lean mass, and fat mass of mice, also taking into account sex and genetic background. The dataset was used to analyze correlations among the different parameters. We analyzed males and females from inbred strains C3HeB/FeJ and C57BL/6J, starting from 42 until 528 days of age. To evaluate the effect of husbandry systems, we repeated a part of the study in a second facility with a different caging system. We also assessed different DEXA settings and repeatability of the scans. The results of this study were used to draw conclusions for the use of DEXA analysis in mouse phenotyping approaches.
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Affiliation(s)
- Helmut Fuchs
- Helmholtz Zentrum München, German Mouse Clinic, Institute of Experimental Genetics, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany,
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26
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Schofield PN, Sundberg JP, Sundberg BA, McKerlie C, Gkoutos GV. The mouse pathology ontology, MPATH; structure and applications. J Biomed Semantics 2013; 4:18. [PMID: 24033988 PMCID: PMC3851164 DOI: 10.1186/2041-1480-4-18] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Accepted: 08/19/2013] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND The capture and use of disease-related anatomic pathology data for both model organism phenotyping and human clinical practice requires a relatively simple nomenclature and coding system that can be integrated into data collection platforms (such as computerized medical record-keeping systems) to enable the pathologist to rapidly screen and accurately record observations. The MPATH ontology was originally constructed in 2,000 by a committee of pathologists for the annotation of rodent histopathology images, but is now widely used for coding and analysis of disease and phenotype data for rodents, humans and zebrafish. CONSTRUCTION AND CONTENT MPATH is divided into two main branches describing pathological processes and structures based on traditional histopathological principles. It does not aim to include definitive diagnoses, which would generally be regarded as disease concepts. It contains 888 core pathology terms in an almost exclusively is_a hierarchy nine layers deep. Currently, 86% of the terms have textual definitions and contain relationships as well as logical axioms to other ontologies such the Gene Ontology. APPLICATION AND UTILITY MPATH was originally devised for the annotation of histopathological images from mice but is now being used much more widely in the recording of diagnostic and phenotypic data from both mice and humans, and in the construction of logical definitions for phenotype and disease ontologies. We discuss the use of MPATH to generate cross-products with qualifiers derived from a subset of the Phenotype and Trait Ontology (PATO) and its application to large-scale high-throughput phenotyping studies. MPATH provides a largely species-agnostic ontology for the descriptions of anatomic pathology, which can be applied to most amniotes and is now finding extensive use in species other than mice. It enables investigators to interrogate large datasets at a variety of depths, use semantic analysis to identify the relations between diseases in different species and integrate pathology data with other data types, such as pharmacogenomics.
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Affiliation(s)
- Paul N Schofield
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, CB2 3EG, Cambridge, UK.
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27
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Simon MM, Greenaway S, White JK, Fuchs H, Gailus-Durner V, Wells S, Sorg T, Wong K, Bedu E, Cartwright EJ, Dacquin R, Djebali S, Estabel J, Graw J, Ingham NJ, Jackson IJ, Lengeling A, Mandillo S, Marvel J, Meziane H, Preitner F, Puk O, Roux M, Adams DJ, Atkins S, Ayadi A, Becker L, Blake A, Brooker D, Cater H, Champy MF, Combe R, Danecek P, di Fenza A, Gates H, Gerdin AK, Golini E, Hancock JM, Hans W, Hölter SM, Hough T, Jurdic P, Keane TM, Morgan H, Müller W, Neff F, Nicholson G, Pasche B, Roberson LA, Rozman J, Sanderson M, Santos L, Selloum M, Shannon C, Southwell A, Tocchini-Valentini GP, Vancollie VE, Westerberg H, Wurst W, Zi M, Yalcin B, Ramirez-Solis R, Steel KP, Mallon AM, de Angelis MH, Herault Y, Brown SDM. A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains. Genome Biol 2013; 14:R82. [PMID: 23902802 PMCID: PMC4053787 DOI: 10.1186/gb-2013-14-7-r82] [Citation(s) in RCA: 359] [Impact Index Per Article: 32.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2013] [Revised: 06/07/2013] [Accepted: 07/31/2013] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The mouse inbred line C57BL/6J is widely used in mouse genetics and its genome has been incorporated into many genetic reference populations. More recently large initiatives such as the International Knockout Mouse Consortium (IKMC) are using the C57BL/6N mouse strain to generate null alleles for all mouse genes. Hence both strains are now widely used in mouse genetics studies. Here we perform a comprehensive genomic and phenotypic analysis of the two strains to identify differences that may influence their underlying genetic mechanisms. RESULTS We undertake genome sequence comparisons of C57BL/6J and C57BL/6N to identify SNPs, indels and structural variants, with a focus on identifying all coding variants. We annotate 34 SNPs and 2 indels that distinguish C57BL/6J and C57BL/6N coding sequences, as well as 15 structural variants that overlap a gene. In parallel we assess the comparative phenotypes of the two inbred lines utilizing the EMPReSSslim phenotyping pipeline, a broad based assessment encompassing diverse biological systems. We perform additional secondary phenotyping assessments to explore other phenotype domains and to elaborate phenotype differences identified in the primary assessment. We uncover significant phenotypic differences between the two lines, replicated across multiple centers, in a number of physiological, biochemical and behavioral systems. CONCLUSIONS Comparison of C57BL/6J and C57BL/6N demonstrates a range of phenotypic differences that have the potential to impact upon penetrance and expressivity of mutational effects in these strains. Moreover, the sequence variants we identify provide a set of candidate genes for the phenotypic differences observed between the two strains.
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Affiliation(s)
- Michelle M Simon
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Simon Greenaway
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Jacqueline K White
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Helmut Fuchs
- Helmholtz Zentrum München, German Research Centre for Environmental Health, Institute of Experimental Genetics and German Mouse Clinic, Ingolstädter Landstraße 1, Neuherberg, D-85764, Germany
| | - Valérie Gailus-Durner
- Helmholtz Zentrum München, German Research Centre for Environmental Health, Institute of Experimental Genetics and German Mouse Clinic, Ingolstädter Landstraße 1, Neuherberg, D-85764, Germany
| | - Sara Wells
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Tania Sorg
- Institut Clinique de la Souris, ICS/MCI, PHENOMIN, GIE CERBM, IGBMC, CNRS, INSERM, 1 Rue Laurent Fries, 67404 Illkirch-Graffenstaden Cedex, France
| | - Kim Wong
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Elodie Bedu
- Institut Clinique de la Souris, ICS/MCI, PHENOMIN, GIE CERBM, IGBMC, CNRS, INSERM, 1 Rue Laurent Fries, 67404 Illkirch-Graffenstaden Cedex, France
| | - Elizabeth J Cartwright
- Faculty of Medical and Human Sciences, University of Manchester, Oxford Road, Manchester, MN13 9PT, UK
| | - Romain Dacquin
- AniRA ImmOs phenotyping facility- SFR Biosciences Lyon Gerland- UMS3444/US8, 21 avenue Tony Garnier F-69007 Lyon, France
| | - Sophia Djebali
- AniRA ImmOs phenotyping facility- SFR Biosciences Lyon Gerland- UMS3444/US8, 21 avenue Tony Garnier F-69007 Lyon, France
| | - Jeanne Estabel
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Jochen Graw
- Helmholtz Zentrum München, German Research Centre for Environmental Health, Institute of Developmental Genetics, Ingolstädter Landstraße 1, Neuherberg, D-85764, Germany
| | - Neil J Ingham
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Ian J Jackson
- Medical Research Council Human Genetics Unit, IGMM, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK
| | - Andreas Lengeling
- Infection and Immunity Division, Roslin Institute, University of Edinburgh, Easter Bush Veterinary Campus, Midlothian, EH25 9RG, UK
| | - Silvia Mandillo
- Consiglio Nazionale delle Ricerche- Cell Biology and Neurobiology Institute, Via E.Ramarini 32, 00015 Monterotondo Scala, Italy
| | - Jacqueline Marvel
- AniRA ImmOs phenotyping facility- SFR Biosciences Lyon Gerland- UMS3444/US8, 21 avenue Tony Garnier F-69007 Lyon, France
| | - Hamid Meziane
- Institut Clinique de la Souris, ICS/MCI, PHENOMIN, GIE CERBM, IGBMC, CNRS, INSERM, 1 Rue Laurent Fries, 67404 Illkirch-Graffenstaden Cedex, France
| | - Frédéric Preitner
- Department of Infection Genetics, Helmholtz Centre for Infection Research, Inhoffenstraße 7, Braunschweig, 38124, Germany
| | - Oliver Puk
- Helmholtz Zentrum München, German Research Centre for Environmental Health, Institute of Developmental Genetics, Ingolstädter Landstraße 1, Neuherberg, D-85764, Germany
| | - Michel Roux
- Institut Clinique de la Souris, ICS/MCI, PHENOMIN, GIE CERBM, IGBMC, CNRS, INSERM, 1 Rue Laurent Fries, 67404 Illkirch-Graffenstaden Cedex, France
| | - David J Adams
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Sarah Atkins
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Abdel Ayadi
- Institut Clinique de la Souris, ICS/MCI, PHENOMIN, GIE CERBM, IGBMC, CNRS, INSERM, 1 Rue Laurent Fries, 67404 Illkirch-Graffenstaden Cedex, France
| | - Lore Becker
- Helmholtz Zentrum München, German Research Centre for Environmental Health, Institute of Experimental Genetics and German Mouse Clinic, Ingolstädter Landstraße 1, Neuherberg, D-85764, Germany
| | - Andrew Blake
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Debra Brooker
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Heather Cater
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Marie-France Champy
- Institut Clinique de la Souris, ICS/MCI, PHENOMIN, GIE CERBM, IGBMC, CNRS, INSERM, 1 Rue Laurent Fries, 67404 Illkirch-Graffenstaden Cedex, France
| | - Roy Combe
- Institut Clinique de la Souris, ICS/MCI, PHENOMIN, GIE CERBM, IGBMC, CNRS, INSERM, 1 Rue Laurent Fries, 67404 Illkirch-Graffenstaden Cedex, France
| | - Petr Danecek
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Armida di Fenza
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Hilary Gates
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Anna-Karin Gerdin
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Elisabetta Golini
- Consiglio Nazionale delle Ricerche- Cell Biology and Neurobiology Institute, Via E.Ramarini 32, 00015 Monterotondo Scala, Italy
| | - John M Hancock
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Wolfgang Hans
- Helmholtz Zentrum München, German Research Centre for Environmental Health, Institute of Experimental Genetics and German Mouse Clinic, Ingolstädter Landstraße 1, Neuherberg, D-85764, Germany
| | - Sabine M Hölter
- Helmholtz Zentrum München, German Research Centre for Environmental Health, Institute of Experimental Genetics and German Mouse Clinic, Ingolstädter Landstraße 1, Neuherberg, D-85764, Germany
| | - Tertius Hough
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Pierre Jurdic
- AniRA ImmOs phenotyping facility- SFR Biosciences Lyon Gerland- UMS3444/US8, 21 avenue Tony Garnier F-69007 Lyon, France
| | - Thomas M Keane
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Hugh Morgan
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Werner Müller
- Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester, MN13 9PT, UK
| | - Frauke Neff
- Helmholtz Zentrum München, German Research Centre for Environmental Health, Institute of Pathology, Ingolstädter Landstraße 1, Neuherberg, D-85764, Germany
| | - George Nicholson
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Bastian Pasche
- Mouse Metabolic Facility of the Cardiomet Center, University Hospital, and Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
| | - Laura-Anne Roberson
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Jan Rozman
- Helmholtz Zentrum München, German Research Centre for Environmental Health, Institute of Experimental Genetics and German Mouse Clinic, Ingolstädter Landstraße 1, Neuherberg, D-85764, Germany
| | - Mark Sanderson
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Luis Santos
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Mohammed Selloum
- Institut Clinique de la Souris, ICS/MCI, PHENOMIN, GIE CERBM, IGBMC, CNRS, INSERM, 1 Rue Laurent Fries, 67404 Illkirch-Graffenstaden Cedex, France
| | - Carl Shannon
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Anne Southwell
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Glauco P Tocchini-Valentini
- Consiglio Nazionale delle Ricerche- Cell Biology and Neurobiology Institute, Via E.Ramarini 32, 00015 Monterotondo Scala, Italy
| | - Valerie E Vancollie
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Henrik Westerberg
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Wolfgang Wurst
- Helmholtz Zentrum München, German Research Centre for Environmental Health, Institute of Developmental Genetics, Ingolstädter Landstraße 1, Neuherberg, D-85764, Germany
- Chair for Developmental Genetics, Technische Universität München, Arcisstr. 21, Munich, 80333, Germany
- Max Planck Institute of Psychiatry, Kraepelinstrasse 2, Munich, 80804, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen, Schillerstrasse 44, Munich, 80336, Germany
| | - Min Zi
- Faculty of Medical and Human Sciences, University of Manchester, Oxford Road, Manchester, MN13 9PT, UK
| | - Binnaz Yalcin
- Institut Clinique de la Souris, ICS/MCI, PHENOMIN, GIE CERBM, IGBMC, CNRS, INSERM, 1 Rue Laurent Fries, 67404 Illkirch-Graffenstaden Cedex, France
- Center for Integrative Genomics, University of Lausanne, Lausanne, CH-1015, Switzerland
| | - Ramiro Ramirez-Solis
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Karen P Steel
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Ann-Marie Mallon
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
| | - Martin Hrabě de Angelis
- Helmholtz Zentrum München, German Research Centre for Environmental Health, Institute of Experimental Genetics and German Mouse Clinic, Ingolstädter Landstraße 1, Neuherberg, D-85764, Germany
| | - Yann Herault
- Institut Clinique de la Souris, ICS/MCI, PHENOMIN, GIE CERBM, IGBMC, CNRS, INSERM, 1 Rue Laurent Fries, 67404 Illkirch-Graffenstaden Cedex, France
| | - Steve DM Brown
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Science Campus, OX11 0RD, UK
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Chesler EJ, Logan RW. Opportunities for bioinformatics in the classification of behavior and psychiatric disorders. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2013. [PMID: 23195316 DOI: 10.1016/b978-0-12-398323-7.00008-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
A bioinformatics approach to behavioral neuroscience provides both unique opportunities and challenges for research on behavior. A major challenge has been to describe, define, and discriminate among abstract behavioral processes, in large part by distinguishing among the biological mechanisms of unique but not entirely discrete, entities of behavior. Understanding the complexity of neurobiology and behavior requires integration of data across diverse biological systems, types of data, and levels of scale. With the perspective and application of bioinformatics, we can uncover the relationships among these systems and take steps forward in realizing the common and distinct bases of psychiatric disease.
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Smedley D, Oellrich A, Köhler S, Ruef B, Westerfield M, Robinson P, Lewis S, Mungall C. PhenoDigm: analyzing curated annotations to associate animal models with human diseases. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2013; 2013:bat025. [PMID: 23660285 PMCID: PMC3649640 DOI: 10.1093/database/bat025] [Citation(s) in RCA: 96] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
The ultimate goal of studying model organisms is to translate what is learned into useful knowledge about normal human biology and disease to facilitate treatment and early screening for diseases. Recent advances in genomic technologies allow for rapid generation of models with a range of targeted genotypes as well as their characterization by high-throughput phenotyping. As an abundance of phenotype data become available, only systematic analysis will facilitate valid conclusions to be drawn from these data and transferred to human diseases. Owing to the volume of data, automated methods are preferable, allowing for a reliable analysis of the data and providing evidence about possible gene-disease associations. Here, we propose Phenotype comparisons for DIsease Genes and Models (PhenoDigm), as an automated method to provide evidence about gene-disease associations by analysing phenotype information. PhenoDigm integrates data from a variety of model organisms and, at the same time, uses several intermediate scoring methods to identify only strongly data-supported gene candidates for human genetic diseases. We show results of an automated evaluation as well as selected manually assessed examples that support the validity of PhenoDigm. Furthermore, we provide guidance on how to browse the data with PhenoDigm's web interface and illustrate its usefulness in supporting research. Database URL: http://www.sanger.ac.uk/resources/databases/phenodigm
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Affiliation(s)
- Damian Smedley
- Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
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Beck T, Free RC, Thorisson GA, Brookes AJ. Semantically enabling a genome-wide association study database. J Biomed Semantics 2012; 3:9. [PMID: 23244533 PMCID: PMC3579732 DOI: 10.1186/2041-1480-3-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2012] [Accepted: 08/22/2012] [Indexed: 01/03/2023] Open
Abstract
Background The amount of data generated from genome-wide association studies (GWAS) has grown rapidly, but considerations for GWAS phenotype data reuse and interchange have not kept pace. This impacts on the work of GWAS Central – a free and open access resource for the advanced querying and comparison of summary-level genetic association data. The benefits of employing ontologies for standardising and structuring data are widely accepted. The complex spectrum of observed human phenotypes (and traits), and the requirement for cross-species phenotype comparisons, calls for reflection on the most appropriate solution for the organisation of human phenotype data. The Semantic Web provides standards for the possibility of further integration of GWAS data and the ability to contribute to the web of Linked Data. Results A pragmatic consideration when applying phenotype ontologies to GWAS data is the ability to retrieve all data, at the most granular level possible, from querying a single ontology graph. We found the Medical Subject Headings (MeSH) terminology suitable for describing all traits (diseases and medical signs and symptoms) at various levels of granularity and the Human Phenotype Ontology (HPO) most suitable for describing phenotypic abnormalities (medical signs and symptoms) at the most granular level. Diseases within MeSH are mapped to HPO to infer the phenotypic abnormalities associated with diseases. Building on the rich semantic phenotype annotation layer, we are able to make cross-species phenotype comparisons and publish a core subset of GWAS data as RDF nanopublications. Conclusions We present a methodology for applying phenotype annotations to a comprehensive genome-wide association dataset and for ensuring compatibility with the Semantic Web. The annotations are used to assist with cross-species genotype and phenotype comparisons. However, further processing and deconstructions of terms may be required to facilitate automatic phenotype comparisons. The provision of GWAS nanopublications enables a new dimension for exploring GWAS data, by way of intrinsic links to related data resources within the Linked Data web. The value of such annotation and integration will grow as more biomedical resources adopt the standards of the Semantic Web.
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Affiliation(s)
- Tim Beck
- Department of Genetics, University of Leicester, University Road, Leicester, UK.
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Klymiuk I, Kenner L, Adler T, Busch DH, Boersma A, Irmler M, Gailus-Durner V, Fuchs H, Leitner N, Müller M, Kühn R, Schlederer M, Treise I, de Angelis MH, Beckers J. In vivo functional requirement of the mouse Ifitm1 gene for germ cell development, interferon mediated immune response and somitogenesis. PLoS One 2012; 7:e44609. [PMID: 23115618 PMCID: PMC3480353 DOI: 10.1371/journal.pone.0044609] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2012] [Accepted: 08/03/2012] [Indexed: 01/19/2023] Open
Abstract
The mammalian Interferon induced transmembrane protein 1 (Ifitm1) gene was originally identified as a member of a gene family highly inducible by type I and type II interferons. Based on expression analyses, it was suggested to be required for normal primordial germ cell migration. The knockdown of Ifitm1 in mouse embryos provided evidence for a role in somitogenesis. We generated the first targeted knockin allele of the Ifitm1 gene to systematically reassess all inferred functions. Sperm motility and the fertility of male and female mutant mice are as in wild type littermates. Embryonic somites and the adult vertebral column appear normal in homozygous Ifitm1 knockout mice, demonstrating that Ifitm1 is not essential for normal segmentation of the paraxial mesoderm. Proportions of leucocyte subsets, including granulocytes, monocytes, B-cells, T-cells, NK-cells, and NKT-cells, are unchanged in mutant mice. Based on a normal immune response to Listeria monocytogenes infection, there is no evidence for a dysfunction in downstream IFNγ signaling in Ifitm1 mutant mice. Expression from the Ifitm1 locus from E8.5 to E14.5 is highly dynamic. In contrast, in adult mice, Ifitm1 expression is highly restricted and strong in the bronchial epithelium. Intriguingly, IFITM1 is highly overexpressed in tumor epithelia cells of human squamous cell carcinomas and in adenocarcinomas of NSCLC patients. These analyses underline the general importance of targeted in vivo studies for the functional annotation of the mammalian genome. The first comprehensive description of the Ifitm1 expression pattern provides a rational basis for the further examination of Ifitm1 gene functions. Based on our data, the fact that IFITM1 can function as a negative regulator of cell proliferation, and because the gene maps to chromosome band 11p15.5, previously associated with NSCLC, it is likely that IFITM1 in man has a key role in tumor formation.
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Affiliation(s)
- Ingeborg Klymiuk
- Institute of Experimental Genetics and German Mouse Clinic, Helmholtz Zentrum München GmbH, Neuherberg, Germany
- * E-mail: (IK); (JB)
| | - Lukas Kenner
- Ludwig Boltzmann Institute for Cancer Research and Institute for Clinical Pathology, Medical University Vienna, Vienna, Austria
| | - Thure Adler
- Institute of Experimental Genetics and German Mouse Clinic, Helmholtz Zentrum München GmbH, Neuherberg, Germany
- Institute for Medical Microbiology, Immunology and Hygiene, Technische Universität München, Munich, Germany
| | - Dirk H. Busch
- Institute for Medical Microbiology, Immunology and Hygiene, Technische Universität München, Munich, Germany
| | - Auke Boersma
- Institute of Experimental Genetics and German Mouse Clinic, Helmholtz Zentrum München GmbH, Neuherberg, Germany
- Institute of Laboratory Animal Science and Biomodels Austria, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Martin Irmler
- Institute of Experimental Genetics and German Mouse Clinic, Helmholtz Zentrum München GmbH, Neuherberg, Germany
| | - Valérie Gailus-Durner
- Institute of Experimental Genetics and German Mouse Clinic, Helmholtz Zentrum München GmbH, Neuherberg, Germany
| | - Helmut Fuchs
- Institute of Experimental Genetics and German Mouse Clinic, Helmholtz Zentrum München GmbH, Neuherberg, Germany
| | - Nicole Leitner
- Institute of Animal Breeding and Genetics, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Mathias Müller
- Institute of Animal Breeding and Genetics, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Ralf Kühn
- Institute of Developmental Genetics, Helmholtz Zentrum München GmbH, Neuherberg, Germany
| | - Michaela Schlederer
- Ludwig Boltzmann Institute for Cancer Research, Ludwig Boltzmann Gesellschaft, Vienna, Austria
| | - Irina Treise
- Institute of Experimental Genetics and German Mouse Clinic, Helmholtz Zentrum München GmbH, Neuherberg, Germany
| | - Martin Hrabě de Angelis
- Institute of Experimental Genetics and German Mouse Clinic, Helmholtz Zentrum München GmbH, Neuherberg, Germany
- Experimental Genetics, Technische Universität München, Freising-Weihenstephan, Germany
| | - Johannes Beckers
- Institute of Experimental Genetics and German Mouse Clinic, Helmholtz Zentrum München GmbH, Neuherberg, Germany
- Experimental Genetics, Technische Universität München, Freising-Weihenstephan, Germany
- * E-mail: (IK); (JB)
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van Dam S, Cordeiro R, Craig T, van Dam J, Wood SH, de Magalhães JP. GeneFriends: an online co-expression analysis tool to identify novel gene targets for aging and complex diseases. BMC Genomics 2012; 13:535. [PMID: 23039964 PMCID: PMC3495651 DOI: 10.1186/1471-2164-13-535] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2012] [Accepted: 08/22/2012] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Although many diseases have been well characterized at the molecular level, the underlying mechanisms are often unknown. Nearly half of all human genes remain poorly studied, yet these genes may contribute to a number of disease processes. Genes involved in common biological processes and diseases are often co-expressed. Using known disease-associated genes in a co-expression analysis may help identify and prioritize novel candidate genes for further study. RESULTS We have created an online tool, called GeneFriends, which identifies co-expressed genes in over 1,000 mouse microarray datasets. GeneFriends can be used to assign putative functions to poorly studied genes. Using a seed list of disease-associated genes and a guilt-by-association method, GeneFriends allows users to quickly identify novel genes and transcription factors associated with a disease or process. We tested GeneFriends using seed lists for aging, cancer, and mitochondrial complex I disease. We identified several candidate genes that have previously been predicted as relevant targets. Some of the genes identified are already being tested in clinical trials, indicating the effectiveness of this approach. Co-expressed transcription factors were investigated, identifying C/ebp genes as candidate regulators of aging. Furthermore, several novel candidate genes, that may be suitable for experimental or clinical follow-up, were identified. Two of the novel candidates of unknown function that were co-expressed with cancer-associated genes were selected for experimental validation. Knock-down of their human homologs (C1ORF112 and C12ORF48) in HeLa cells slowed growth, indicating that these genes of unknown function, identified by GeneFriends, may be involved in cancer. CONCLUSIONS GeneFriends is a resource for biologists to identify and prioritize novel candidate genes involved in biological processes and complex diseases. It is an intuitive online resource that will help drive experimentation. GeneFriends is available online at: http://genefriends.org/.
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Affiliation(s)
- Sipko van Dam
- Integrative Genomics of Ageing Group, Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
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Ayadi A, Birling MC, Bottomley J, Bussell J, Fuchs H, Fray M, Gailus-Durner V, Greenaway S, Houghton R, Karp N, Leblanc S, Lengger C, Maier H, Mallon AM, Marschall S, Melvin D, Morgan H, Pavlovic G, Ryder E, Skarnes WC, Selloum M, Ramirez-Solis R, Sorg T, Teboul L, Vasseur L, Walling A, Weaver T, Wells S, White JK, Bradley A, Adams DJ, Steel KP, Hrabě de Angelis M, Brown SD, Herault Y. Mouse large-scale phenotyping initiatives: overview of the European Mouse Disease Clinic (EUMODIC) and of the Wellcome Trust Sanger Institute Mouse Genetics Project. Mamm Genome 2012; 23:600-10. [PMID: 22961258 PMCID: PMC3463797 DOI: 10.1007/s00335-012-9418-y] [Citation(s) in RCA: 103] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2012] [Accepted: 07/23/2012] [Indexed: 12/17/2022]
Abstract
Two large-scale phenotyping efforts, the European Mouse Disease Clinic (EUMODIC) and the Wellcome Trust Sanger Institute Mouse Genetics Project (SANGER-MGP), started during the late 2000s with the aim to deliver a comprehensive assessment of phenotypes or to screen for robust indicators of diseases in mouse mutants. They both took advantage of available mouse mutant lines but predominantly of the embryonic stem (ES) cells resources derived from the European Conditional Mouse Mutagenesis programme (EUCOMM) and the Knockout Mouse Project (KOMP) to produce and study 799 mouse models that were systematically analysed with a comprehensive set of physiological and behavioural paradigms. They captured more than 400 variables and an additional panel of metadata describing the conditions of the tests. All the data are now available through EuroPhenome database (www.europhenome.org) and the WTSI mouse portal (http://www.sanger.ac.uk/mouseportal/), and the corresponding mouse lines are available through the European Mouse Mutant Archive (EMMA), the International Knockout Mouse Consortium (IKMC), or the Knockout Mouse Project (KOMP) Repository. Overall conclusions from both studies converged, with at least one phenotype scored in at least 80% of the mutant lines. In addition, 57% of the lines were viable, 13% subviable, 30% embryonic lethal, and 7% displayed fertility impairments. These efforts provide an important underpinning for a future global programme that will undertake the complete functional annotation of the mammalian genome in the mouse model.
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Affiliation(s)
- Abdel Ayadi
- Institut Clinique de la Souris, PHENOMIN, IGBMC/ICS-MCI, CNRS, INSERM, Université de Strasbourg, UMR7104, UMR964, 1 rue Laurent Fries, 67404 Illkirch, France
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Smith CL, Eppig JT. The Mammalian Phenotype Ontology as a unifying standard for experimental and high-throughput phenotyping data. Mamm Genome 2012; 23:653-68. [PMID: 22961259 PMCID: PMC3463787 DOI: 10.1007/s00335-012-9421-3] [Citation(s) in RCA: 126] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2012] [Accepted: 07/24/2012] [Indexed: 01/16/2023]
Abstract
The Mammalian Phenotype Ontology (MP) is a structured vocabulary for describing mammalian phenotypes and serves as a critical tool for efficient annotation and comprehensive retrieval of phenotype data. Importantly, the ontology contains broad and specific terms, facilitating annotation of data from initial observations or screens and detailed data from subsequent experimental research. Using the ontology structure, data are retrieved inclusively, i.e., data annotated to chosen terms and to terms subordinate in the hierarchy. Thus, searching for "abnormal craniofacial morphology" also returns annotations to "megacephaly" and "microcephaly," more specific terms in the hierarchy path. The development and refinement of the MP is ongoing, with new terms and modifications to its organization undergoing continuous assessment as users and expert reviewers propose expansions and revisions. A wealth of phenotype data on mouse mutations and variants annotated to the MP already exists in the Mouse Genome Informatics database. These data, along with data curated to the MP by many mouse mutagenesis programs and mouse repositories, provide a platform for comparative analyses and correlative discoveries. The MP provides a standard underpinning to mouse phenotype descriptions for existing and future experimental and large-scale phenotyping projects. In this review we describe the MP as it presently exists, its application to phenotype annotations, the relationship of the MP to other ontologies, and the integration of the MP within large-scale phenotyping projects. Finally we discuss future application of the MP in providing standard descriptors of the phenotype pipeline test results from the International Mouse Phenotype Consortium projects.
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Mallon AM, Iyer V, Melvin D, Morgan H, Parkinson H, Brown SDM, Flicek P, Skarnes WC. Accessing data from the International Mouse Phenotyping Consortium: state of the art and future plans. Mamm Genome 2012; 23:641-52. [PMID: 22991088 DOI: 10.1007/s00335-012-9428-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2012] [Accepted: 08/06/2012] [Indexed: 01/25/2023]
Abstract
The International Mouse Phenotyping Consortium (IMPC) (http://www.mousephenotype.org) will reveal the pleiotropic functions of every gene in the mouse genome and uncover the wider role of genetic loci within diverse biological systems. Comprehensive informatics solutions are vital to ensuring that this vast array of data is captured in a standardised manner and made accessible to the scientific community for interrogation and analysis. Here we review the existing EuroPhenome and WTSI phenotype informatics systems and the IKMC portal, and present plans for extending these systems and lessons learned to the development of a robust IMPC informatics infrastructure.
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Affiliation(s)
- Ann-Marie Mallon
- Mammalian Genetics Unit, Medical Research Council Harwell, Harwell, Oxfordshire OX11 0RD, UK.
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Abstract
The ciliopathies are an apparently disparate group of human diseases that all result from defects in the formation and/or function of cilia. They include disorders such as Meckel-Grüber syndrome (MKS), Joubert syndrome (JBTS), Bardet-Biedl syndrome (BBS) and Alström syndrome (ALS). Reflecting the manifold requirements for cilia in signalling, sensation and motility, different ciliopathies exhibit common elements. The mouse has been used widely as a model organism for the study of ciliopathies. Although many mutant alleles have proved lethal, continued investigations have led to the development of better models. Here, we review current mouse models of a core set of ciliopathies, their utility and future prospects.
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Affiliation(s)
- Dominic P Norris
- Mammalian Genetics Unit, MRC Harwell, Harwell Science and Innovation Campus, Oxfordshire, OX11 0RD, UK.
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38
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Brown SDM, Moore MW. The International Mouse Phenotyping Consortium: past and future perspectives on mouse phenotyping. Mamm Genome 2012; 23:632-40. [PMID: 22940749 DOI: 10.1007/s00335-012-9427-x] [Citation(s) in RCA: 216] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2012] [Accepted: 08/05/2012] [Indexed: 11/24/2022]
Abstract
Determining the function of all mammalian genes remains a major challenge for the biomedical science community in the 21st century. The goal of the International Mouse Phenotyping Consortium (IMPC) over the next 10 years is to undertake broad-based phenotyping of 20,000 mouse genes, providing an unprecedented insight into mammalian gene function. This short article explores the drivers for large-scale mouse phenotyping and provides an overview of the aims and processes involved in IMPC mouse production and phenotyping.
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Affiliation(s)
- Steve D M Brown
- MRC Mammalian Genetics Unit, MRC Harwell, Oxfordshire OX11 0RD, UK.
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Ramírez-Solis R, Ryder E, Houghton R, White JK, Bottomley J. Large-scale mouse knockouts and phenotypes. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2012; 4:547-63. [PMID: 22899600 DOI: 10.1002/wsbm.1183] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Standardized phenotypic analysis of mutant forms of every gene in the mouse genome will provide fundamental insights into mammalian gene function and advance human and animal health. The availability of the human and mouse genome sequences, the development of embryonic stem cell mutagenesis technology, the standardization of phenotypic analysis pipelines, and the paradigm-shifting industrialization of these processes have made this a realistic and achievable goal. The size of this enterprise will require global coordination to ensure economies of scale in both the generation and primary phenotypic analysis of the mutant strains, and to minimize unnecessary duplication of effort. To provide more depth to the functional annotation of the genome, effective mechanisms will also need to be developed to disseminate the information and resources produced to the wider community. Better models of disease, potential new drug targets with novel mechanisms of action, and completely unsuspected genotype-phenotype relationships covering broad aspects of biology will become apparent. To reach these goals, solutions to challenges in mouse production and distribution, as well as development of novel, ever more powerful phenotypic analysis modalities will be necessary. It is a challenging and exciting time to work in mouse genetics.
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Masuya H. Roles and applications of biomedical ontologies in experimental animal science. Exp Anim 2012; 61:365-73. [PMID: 22850636 DOI: 10.1538/expanim.61.365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
A huge amount of experimental data from past studies has played a vital role in the development of new knowledge and technologies in biomedical science. The importance of computational technologies for the reuse of data, data integration, and knowledge discoveries has also increased, providing means of processing large amounts of data. In recent years, information technologies related to "ontologies" have played more significant roles in the standardization, integration, and knowledge representation of biomedical information. This review paper outlines the history of data integration in biomedical science and its recent trends in relation to the field of experimental animal science.
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Affiliation(s)
- Hiroshi Masuya
- Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, 3–1–1 Kouyadai, Tsukuba 305-0074, Japan
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Larina IV, Syed SH, Sudheendran N, Overbeek PA, Dickinson ME, Larin KV. Optical coherence tomography for live phenotypic analysis of embryonic ocular structures in mouse models. JOURNAL OF BIOMEDICAL OPTICS 2012; 17:081410-1. [PMID: 23224171 PMCID: PMC3397804 DOI: 10.1117/1.jbo.17.8.081410] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2012] [Revised: 04/16/2012] [Accepted: 04/18/2012] [Indexed: 05/18/2023]
Abstract
Mouse models of ocular diseases provide a powerful resource for exploration of molecular regulation of eye development and pre-clinical studies. Availability of a live high-resolution imaging method for mouse embryonic eyes would significantly enhance longitudinal analyses and high-throughput morphological screening. We demonstrate that optical coherence tomography (OCT) can be used for live embryonic ocular imaging throughout gestation. At all studied stages, the whole eye is within the imaging distance of the system and there is a good optical contrast between the structures. We also performed OCT eye imaging in the embryonic retinoblastoma mouse model Pax6-SV40 T-antigen, which spontaneously forms lens and retinal lesions, and demonstrate that OCT allows us to clearly differentiate between the mutant and wild type phenotypes. These results demonstrate that OCTin utero imaging is a potentially useful tool to study embryonic ocular diseases in mouse models.
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Affiliation(s)
- Irina V Larina
- Baylor College of Medicine, Department of Molecular Physiology and Biophysics, One Baylor Plaza, Houston, TX 77030, USA.
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Schofield PN, Hoehndorf R, Gkoutos GV. Mouse genetic and phenotypic resources for human genetics. Hum Mutat 2012; 33:826-36. [PMID: 22422677 DOI: 10.1002/humu.22077] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The use of model organisms to provide information on gene function has proved to be a powerful approach to our understanding of both human disease and fundamental mammalian biology. Large-scale community projects using mice, based on forward and reverse genetics, and now the pan-genomic phenotyping efforts of the International Mouse Phenotyping Consortium, are generating resources on an unprecedented scale, which will be extremely valuable to human genetics and medicine. We discuss the nature and availability of data, mice and embryonic stem cells from these large-scale programmes, the use of these resources to help prioritize and validate candidate genes in human genetic association studies, and how they can improve our understanding of the underlying pathobiology of human disease.
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Affiliation(s)
- Paul N Schofield
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom.
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Shi ML, Xu P, Yin XS, Yang WW, Gu ME, Yu LP, Liu GJ, Wu BJ. [Phenotype analysis and mutant gene location of ventral yellow mouse (VY(Slac))]. DONG WU XUE YAN JIU = ZOOLOGICAL RESEARCH 2012; 33:290-7. [PMID: 22653857 DOI: 10.3724/sp.j.1141.2012.03290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The ventri-yellow pigmentation mouse (temporarily named VY(Slac)) arose spontaneously in the C57BL/6J inbred mouse strain, found and bred by Shanghai SLAC Laboratory Animal Co., Ltd. VY(Slac) presented a special phenotype marked by yellow coat on the ventral surface of neck and trunk that was without melanin deposition but maintained a normal structure. The number of melanocytes in epidermis and melanin in hair follicle of the abdominal skin of the mutant mouse were less than that of their background strain, while there was no significant difference between the dorsal skins of the two strains. This mutant phenotype was inherited as single-gene dominant inheritance, confirmed by genetic experiment, and there was no significant difference between VY(Slac) and B(6) for other biological parameters such as weight, anatomic and histological structures of major organs and blood physiology. When the linkage relationship between the genomic DNA samples of F(2) 48 mice (VY(Slac)D(2)F(1)×D(2)) and mutant phenotype were evaluated, the mutant gene was confirmed on chromosome 2 near D2Mit229. New microsatellite and SNP markers were selected to amplify genomic DNA samples of 196 F(2) mice and the mutant gene was narrowed down to 5.3 Mb region between rs13476833 and rs27310903 on chromosome 2. The preliminary results of our phenotype analysis and gene location provides a solid basis for further identification of this mutant gene.
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Affiliation(s)
- Mei-Lian Shi
- Shanghai SLAC Laboratory Animal Co., Ltd., Shanghai,China
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Shimoyama M, Nigam R, McIntosh LS, Nagarajan R, Rice T, Rao DC, Dwinell MR. Three ontologies to define phenotype measurement data. Front Genet 2012; 3:87. [PMID: 22654893 PMCID: PMC3361058 DOI: 10.3389/fgene.2012.00087] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2012] [Accepted: 04/30/2012] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND There is an increasing need to integrate phenotype measurement data across studies for both human studies and those involving model organisms. Current practices allow researchers to access only those data involved in a single experiment or multiple experiments utilizing the same protocol. RESULTS Three ontologies were created: Clinical Measurement Ontology, Measurement Method Ontology and Experimental Condition Ontology. These ontologies provided the framework for integration of rat phenotype data from multiple studies into a single resource as well as facilitated data integration from multiple human epidemiological studies into a centralized repository. CONCLUSION An ontology based framework for phenotype measurement data affords the ability to successfully integrate vital phenotype data into critical resources, regardless of underlying technological structures allowing the user to easily query and retrieve data from multiple studies.
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Affiliation(s)
- Mary Shimoyama
- Human and Molecular Genetics Center, Medical College of Wisconsin Milwaukee, WI, USA.
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Adamusiak T, Parkinson H, Muilu J, Roos E, van der Velde KJ, Thorisson GA, Byrne M, Pang C, Gollapudi S, Ferretti V, Hillege H, Brookes AJ, Swertz MA. Observ-OM and Observ-TAB: Universal syntax solutions for the integration, search, and exchange of phenotype and genotype information. Hum Mutat 2012; 33:867-73. [DOI: 10.1002/humu.22070] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2011] [Accepted: 02/22/2012] [Indexed: 11/12/2022]
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Larina IV, Larin KV, Justice MJ, Dickinson ME. Optical Coherence Tomography for live imaging of mammalian development. Curr Opin Genet Dev 2011; 21:579-84. [PMID: 21962442 DOI: 10.1016/j.gde.2011.09.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2011] [Revised: 08/24/2011] [Accepted: 09/06/2011] [Indexed: 02/08/2023]
Abstract
Understanding the nature and mechanism of congenital defects of the different organ systems in humans has heavily relied on the analysis of the corresponding mutant phenotypes in rodent models. Optical Coherence Tomography (OCT) has recently emerged as a powerful tool to study early embryonic development. This non-invasive optical methodology does not require labeling and allows visualization of embryonic tissues with single cell resolution. Here, we will discuss how OCT can be applied for structural imaging of early mouse and rat embryos in static culture, cardiodynamic and blood flow analysis, and in utero embryonic imaging at later stages of gestation, demonstrating how OCT can be used to assess structural and functional birth defects in mammalian models.
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Affiliation(s)
- Irina V Larina
- Department of Molecular Physiology and Biophysics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, United States.
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Oakley DJ, Iyer V, Skarnes WC, Smedley D. BioMart as an integration solution for the International Knockout Mouse Consortium. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2011; 2011:bar028. [PMID: 21930503 PMCID: PMC3263594 DOI: 10.1093/database/bar028] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
In this article, we describe the use of the BioMart data management system to provide integrated access to International Knockout Mouse Consortium (IKMC) data and other related mouse resources. The IKMC is currently mutating all mouse protein-coding genes in embryonic stem (ES) cells using gene targeting and gene trapping approaches. The BioMart portal allows researchers to identify and obtain IKMC knockout vectors, ES cells and mice for genes of interest. Gene annotation, expression, phenotype and disease data is also integrated from external BioMarts, allowing selection of IKMC products by a wide variety of criteria. These products are invaluable for researchers involved in the elucidation of gene function and the role of individual genes in human disease. Here, we describe these datasets in more detail and illustrate the functionality of the portal using several examples. Database URL: http://www.knockoutmouse.org/mart
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Affiliation(s)
- Darren J Oakley
- The Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1HH
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Travillian RS, Adamusiak T, Burdett T, Gruenberger M, Hancock J, Mallon AM, Malone J, Schofield P, Parkinson H. Anatomy ontologies and potential users: bridging the gap. J Biomed Semantics 2011; 2 Suppl 4:S3. [PMID: 21995944 PMCID: PMC3194170 DOI: 10.1186/2041-1480-2-s4-s3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Motivation To evaluate how well current anatomical ontologies fit the way real-world users apply anatomy terms in their data annotations. Methods Annotations from three diverse multi-species public-domain datasets provided a set of use cases for matching anatomical terms in two major anatomical ontologies (the Foundational Model of Anatomy and Uberon), using two lexical-matching applications (Zooma and Ontology Mapper). Results Approximately 1500 terms were identified; Uberon/Zooma mappings provided 286 matches, compared to the control and Ontology Mapper returned 319 matches. For the Foundational Model of Anatomy, Zooma returned 312 matches, and Ontology Mapper returned 397. Conclusions Our results indicate that for our datasets the anatomical entities or concepts are embedded in user-generated complex terms, and while lexical mapping works, anatomy ontologies do not provide the majority of terms users supply when annotating data. Provision of searchable cross-products for compositional terms is a key requirement for using ontologies.
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A gene-phenotype network for the laboratory mouse and its implications for systematic phenotyping. PLoS One 2011; 6:e19693. [PMID: 21625554 PMCID: PMC3098258 DOI: 10.1371/journal.pone.0019693] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2011] [Accepted: 04/11/2011] [Indexed: 01/22/2023] Open
Abstract
The laboratory mouse is the pre-eminent model organism for the dissection of human disease pathways. With the advent of a comprehensive panel of gene knockouts, projects to characterise the phenotypes of all knockout lines are being initiated. The range of genotype-phenotype associations can be represented using the Mammalian Phenotype ontology. Using publicly available data annotated with this ontology we have constructed gene and phenotype networks representing these associations. These networks show a scale-free, hierarchical and modular character and community structure. They also exhibit enrichment for gene coexpression, protein-protein interactions and Gene Ontology annotation similarity. Close association between gene communities and some high-level ontology terms suggests that systematic phenotyping can provide a direct insight into underlying pathways. However some phenotypes are distributed more diffusely across gene networks, likely reflecting the pleiotropic roles of many genes. Phenotype communities show a many-to-many relationship to human disease communities, but stronger overlap at more granular levels of description. This may suggest that systematic phenotyping projects should aim for high granularity annotations to maximise their relevance to human disease.
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Syed SH, Larin KV, Dickinson ME, Larina IV. Optical coherence tomography for high-resolution imaging of mouse development in utero. JOURNAL OF BIOMEDICAL OPTICS 2011; 16:046004. [PMID: 21529073 PMCID: PMC3081861 DOI: 10.1117/1.3560300] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2010] [Revised: 02/01/2011] [Accepted: 02/09/2011] [Indexed: 05/19/2023]
Abstract
Although the mouse is a superior model to study mammalian embryonic development, high-resolution live dynamic visualization of mouse embryos remain a technical challenge. We present optical coherence tomography as a novel methodology for live imaging of mouse embryos through the uterine wall thereby allowing for time lapse analysis of developmental processes and direct phenotypic analysis of developing embryos. We assessed the capability of the proposed methodology to visualize structures of the living embryo from embryonic stages 12.5 to 18.5 days postcoitus. Repetitive in utero embryonic imaging is demonstrated. Our work opens the door for a wide range of live, in utero embryonic studies to screen for mutations and understand the effects of pharmacological and toxicological agents leading to birth defects.
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Affiliation(s)
- Saba H Syed
- Department of Biomedical Engineering, University of Houston, 4800 Calhoun Road, 3605 Cullen Boulevard, Houston, Texas 77204, USA
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