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Ruberte J, Schofield PN, Sundberg JP, Rodriguez-Baeza A, Carretero A, McKerlie C. Bridging mouse and human anatomies; a knowledge-based approach to comparative anatomy for disease model phenotyping. Mamm Genome 2023:10.1007/s00335-023-10005-4. [PMID: 37421464 PMCID: PMC10382392 DOI: 10.1007/s00335-023-10005-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 06/13/2023] [Indexed: 07/10/2023]
Abstract
The laboratory mouse is the foremost mammalian model used for studying human diseases and is closely anatomically related to humans. Whilst knowledge about human anatomy has been collected throughout the history of mankind, the first comprehensive study of the mouse anatomy was published less than 60 years ago. This has been followed by the more recent publication of several books and resources on mouse anatomy. Nevertheless, to date, our understanding and knowledge of mouse anatomy is far from being at the same level as that of humans. In addition, the alignment between current mouse and human anatomy nomenclatures is far from being as developed as those existing between other species, such as domestic animals and humans. To close this gap, more in depth mouse anatomical research is needed and it will be necessary to extent and refine the current vocabulary of mouse anatomical terms.
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Affiliation(s)
- Jesús Ruberte
- Center for Animal Biotechnology and Gene Therapy, Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Animal Health and Anatomy, Universitat Autònoma de Barcelona, Barcelona, Spain.
| | - Paul N Schofield
- The Jackson Laboratory, Bar Harbor, ME, USA
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - John P Sundberg
- The Jackson Laboratory, Bar Harbor, ME, USA
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Ana Carretero
- Center for Animal Biotechnology and Gene Therapy, Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Animal Health and Anatomy, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Colin McKerlie
- The Hospital for Sick Children, Toronto, Canada
- Department of Lab Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, Canada
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2
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Loh CH, van Genesen S, Perino M, Bark MR, Veenstra GJC. Loss of PRC2 subunits primes lineage choice during exit of pluripotency. Nat Commun 2021; 12:6985. [PMID: 34848733 PMCID: PMC8632979 DOI: 10.1038/s41467-021-27314-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 11/09/2021] [Indexed: 12/11/2022] Open
Abstract
Polycomb Repressive Complex 2 (PRC2) is crucial for the coordinated expression of genes during early embryonic development, catalyzing histone H3 lysine 27 trimethylation. Two distinct PRC2 complexes, PRC2.1 and PRC2.2, contain respectively MTF2 and JARID2 in embryonic stem cells (ESCs). In this study, we explored their roles in lineage specification and commitment, using single-cell transcriptomics and mouse embryoid bodies derived from Mtf2 and Jarid2 null ESCs. We observe that the loss of Mtf2 results in enhanced and faster differentiation towards cell fates from all germ layers, while the Jarid2 null cells are predominantly directed towards early differentiating precursors, with reduced efficiency towards mesendodermal lineages. These effects are caused by derepression of developmental regulators that are poised for activation in pluripotent cells and gain H3K4me3 at their promoters in the absence of PRC2 repression. Upon lineage commitment, the differentiation trajectories are relatively similar to those of wild-type cells. Together, our results uncover a major role for MTF2-containing PRC2.1 in balancing poised lineage-specific gene activation, whereas the contribution of JARID2-containing PRC2 is more selective in nature compared to MTF2. These data explain how PRC2 imposes thresholds for lineage choice during the exit of pluripotency.
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Affiliation(s)
- Chet H. Loh
- grid.5590.90000000122931605Department of Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, The Netherlands
| | - Siebe van Genesen
- grid.5590.90000000122931605Department of Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, The Netherlands
| | - Matteo Perino
- grid.5590.90000000122931605Department of Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, The Netherlands ,grid.4709.a0000 0004 0495 846XPresent Address: Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Magnus R. Bark
- grid.5590.90000000122931605Department of Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, The Netherlands
| | - Gert Jan C. Veenstra
- grid.5590.90000000122931605Department of Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, The Netherlands
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Ringwald M, Richardson JE, Baldarelli RM, Blake JA, Kadin JA, Smith C, Bult CJ. Mouse Genome Informatics (MGI): latest news from MGD and GXD. Mamm Genome 2021; 33:4-18. [PMID: 34698891 PMCID: PMC8913530 DOI: 10.1007/s00335-021-09921-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 09/21/2021] [Indexed: 12/01/2022]
Abstract
The Mouse Genome Informatics (MGI) database system combines multiple expertly curated community data resources into a shared knowledge management ecosystem united by common metadata annotation standards. MGI's mission is to facilitate the use of the mouse as an experimental model for understanding the genetic and genomic basis of human health and disease. MGI is the authoritative source for mouse gene, allele, and strain nomenclature and is the primary source of mouse phenotype annotations, functional annotations, developmental gene expression information, and annotations of mouse models with human diseases. MGI maintains mouse anatomy and phenotype ontologies and contributes to the development of the Gene Ontology and Disease Ontology and uses these ontologies as standard terminologies for annotation. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are MGI's two major knowledgebases. Here, we highlight some of the recent changes and enhancements to MGD and GXD that have been implemented in response to changing needs of the biomedical research community and to improve the efficiency of expert curation. MGI can be accessed freely at http://www.informatics.jax.org .
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Smith CM, Kadin JA, Baldarelli RM, Beal JS, Blodgett O, Giannatto SC, Richardson JE, Ringwald M. GXD's RNA-Seq and Microarray Experiment Search: using curated metadata to reliably find mouse expression studies of interest. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2020:5756137. [PMID: 32140729 PMCID: PMC7058436 DOI: 10.1093/database/baaa002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 12/09/2019] [Accepted: 01/06/2020] [Indexed: 12/28/2022]
Abstract
The Gene Expression Database (GXD), an extensive community resource of curated expression information for the mouse, has developed an RNA-Seq and Microarray Experiment Search (http://www.informatics.jax.org/gxd/htexp_index). This tool allows users to quickly and reliably find specific experiments in ArrayExpress and the Gene Expression Omnibus (GEO) that study endogenous gene expression in wild-type and mutant mice. Standardized metadata annotations, curated by GXD, allow users to specify the anatomical structure, developmental stage, mutated gene, strain and sex of samples of interest, as well as the study type and key parameters of the experiment. These searches, powered by controlled vocabularies and ontologies, can be combined with free text searching of experiment titles and descriptions. Search result summaries include link-outs to ArrayExpress and GEO, providing easy access to the expression data itself. Links to the PubMed entries for accompanying publications are also included. More information about this tool and GXD can be found at the GXD home page (http://www.informatics.jax.org/expression.shtml). Database URL:http://www.informatics.jax.org/expression.shtml
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Affiliation(s)
| | - James A Kadin
- The Jackson Laboratory 600 Main Street Bar Harbor, ME 04609, USA
| | | | - Jonathan S Beal
- The Jackson Laboratory 600 Main Street Bar Harbor, ME 04609, USA
| | - Olin Blodgett
- The Jackson Laboratory 600 Main Street Bar Harbor, ME 04609, USA
| | | | | | - Martin Ringwald
- The Jackson Laboratory 600 Main Street Bar Harbor, ME 04609, USA
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Smith CM, Hayamizu TF, Finger JH, Bello SM, McCright IJ, Xu J, Baldarelli RM, Beal JS, Campbell J, Corbani LE, Frost PJ, Lewis JR, Giannatto SC, Miers D, Shaw DR, Kadin JA, Richardson JE, Smith CL, Ringwald M. The mouse Gene Expression Database (GXD): 2019 update. Nucleic Acids Res 2020; 47:D774-D779. [PMID: 30335138 PMCID: PMC6324054 DOI: 10.1093/nar/gky922] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Accepted: 10/04/2018] [Indexed: 11/13/2022] Open
Abstract
The mouse Gene Expression Database (GXD) is an extensive, well-curated community resource freely available at www.informatics.jax.org/expression.shtml. Covering all developmental stages, GXD includes data from RNA in situ hybridization, immunohistochemistry, RT-PCR, northern blot and western blot experiments in wild-type and mutant mice. GXD's gene expression information is integrated with the other data in Mouse Genome Informatics and interconnected with other databases, placing these data in the larger biological and biomedical context. Since the last report, the ability of GXD to provide insights into the molecular mechanisms of development and disease has been greatly enhanced by the addition of new data and by the implementation of new web features. These include: improvements to the Differential Gene Expression Data Search, facilitating searches for genes that have been shown to be exclusively expressed in a specified structure and/or developmental stage; an enhanced anatomy browser that now provides access to expression data and phenotype data for a given anatomical structure; direct access to the wild-type gene expression data for the tissues affected in a specific mutant; and a comparison matrix that juxtaposes tissues where a gene is normally expressed against tissues, where mutations in that gene cause abnormalities.
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Affiliation(s)
| | - Terry F Hayamizu
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | | | - Susan M Bello
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | | | - Jingxia Xu
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | | | - Jonathan S Beal
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Jeffrey Campbell
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Lori E Corbani
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Pete J Frost
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Jill R Lewis
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | | | - Dave Miers
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - David R Shaw
- 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
| | | | - Cynthia L Smith
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Martin Ringwald
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
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A dynamic view of the proteomic landscape during differentiation of ReNcell VM cells, an immortalized human neural progenitor line. Sci Data 2019; 6:190016. [PMID: 30778261 PMCID: PMC6380223 DOI: 10.1038/sdata.2019.16] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Accepted: 12/21/2018] [Indexed: 01/25/2023] Open
Abstract
The immortalized human ReNcell VM cell line represents a reproducible and easy-to-propagate cell culture system for studying the differentiation of neural progenitors. To better characterize the starting line and its subsequent differentiation, we assessed protein and phospho-protein levels and cell morphology over a 15-day period during which ReNcell progenitors differentiated into neurons, astrocytes and oligodendrocytes. Five of the resulting datasets measured protein levels or states of phosphorylation based on tandem-mass-tag (TMT) mass spectrometry and four datasets characterized cellular phenotypes using high-content microscopy. Proteomic analysis revealed reproducible changes in pathways responsible for cytoskeletal rearrangement, cell phase transitions, neuronal migration, glial differentiation, neurotrophic signalling and extracellular matrix regulation. Proteomic and imaging data revealed accelerated differentiation in cells treated with the poly-selective CDK and GSK3 inhibitor kenpaullone or the HMG-CoA reductase inhibitor mevastatin, both of which have previously been reported to promote neural differentiation. These data provide in-depth information on the ReNcell progenitor state and on neural differentiation in the presence and absence of drugs, setting the stage for functional studies.
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Jiang X, Ringwald M, Blake J, Shatkay H. Effective biomedical document classification for identifying publications relevant to the mouse Gene Expression Database (GXD). DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2017; 2017:3084695. [PMID: 28365740 PMCID: PMC5467553 DOI: 10.1093/database/bax017] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 02/13/2017] [Indexed: 12/16/2022]
Abstract
The Gene Expression Database (GXD) is a comprehensive online database within the Mouse Genome Informatics resource, aiming to provide available information about endogenous gene expression during mouse development. The information stems primarily from many thousands of biomedical publications that database curators must go through and read. Given the very large number of biomedical papers published each year, automatic document classification plays an important role in biomedical research. Specifically, an effective and efficient document classifier is needed for supporting the GXD annotation workflow. We present here an effective yet relatively simple classification scheme, which uses readily available tools while employing feature selection, aiming to assist curators in identifying publications relevant to GXD. We examine the performance of our method over a large manually curated dataset, consisting of more than 25 000 PubMed abstracts, of which about half are curated as relevant to GXD while the other half as irrelevant to GXD. In addition to text from title-and-abstract, we also consider image captions, an important information source that we integrate into our method. We apply a captions-based classifier to a subset of about 3300 documents, for which the full text of the curated articles is available. The results demonstrate that our proposed approach is robust and effectively addresses the GXD document classification. Moreover, using information obtained from image captions clearly improves performance, compared to title and abstract alone, affirming the utility of image captions as a substantial evidence source for automatically determining the relevance of biomedical publications to a specific subject area. Database URL:www.informatics.jax.org
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Affiliation(s)
- Xiangying Jiang
- Department of Computer and Information Sciences, University of Delaware, 101 Smith Hall, Newark, DE, USA
| | - Martin Ringwald
- Department of Computer and Information Sciences, The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, USA
| | - Judith Blake
- Department of Computer and Information Sciences, The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, USA
| | - Hagit Shatkay
- Department of Computer and Information Sciences, University of Delaware, 101 Smith Hall, Newark, DE, USA
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8
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Noncoding copy-number variations are associated with congenital limb malformation. Genet Med 2017; 20:599-607. [PMID: 29236091 DOI: 10.1038/gim.2017.154] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Accepted: 07/11/2017] [Indexed: 01/17/2023] Open
Abstract
PurposeCopy-number variants (CNVs) are generally interpreted by linking the effects of gene dosage with phenotypes. The clinical interpretation of noncoding CNVs remains challenging. We investigated the percentage of disease-associated CNVs in patients with congenital limb malformations that affect noncoding cis-regulatory sequences versus genes sensitive to gene dosage effects.MethodsWe applied high-resolution copy-number analysis to 340 unrelated individuals with isolated limb malformation. To investigate novel candidate CNVs, we re-engineered human CNVs in mice using clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing.ResultsOf the individuals studied, 10% harbored CNVs segregating with the phenotype in the affected families. We identified 31 CNVs previously associated with congenital limb malformations and four novel candidate CNVs. Most of the disease-associated CNVs (57%) affected the noncoding cis-regulatory genome, while only 43% included a known disease gene and were likely to result from gene dosage effects. In transgenic mice harboring four novel candidate CNVs, we observed altered gene expression in all cases, indicating that the CNVs had a regulatory effect either by changing the enhancer dosage or altering the topological associating domain architecture of the genome.ConclusionOur findings suggest that CNVs affecting noncoding regulatory elements are a major cause of congenital limb malformations.
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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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10
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Alles J, Karaiskos N, Praktiknjo SD, Grosswendt S, Wahle P, Ruffault PL, Ayoub S, Schreyer L, Boltengagen A, Birchmeier C, Zinzen R, Kocks C, Rajewsky N. Cell fixation and preservation for droplet-based single-cell transcriptomics. BMC Biol 2017; 15:44. [PMID: 28526029 PMCID: PMC5438562 DOI: 10.1186/s12915-017-0383-5] [Citation(s) in RCA: 140] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 05/03/2017] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Recent developments in droplet-based microfluidics allow the transcriptional profiling of thousands of individual cells in a quantitative, highly parallel and cost-effective way. A critical, often limiting step is the preparation of cells in an unperturbed state, not altered by stress or ageing. Other challenges are rare cells that need to be collected over several days or samples prepared at different times or locations. METHODS Here, we used chemical fixation to address these problems. Methanol fixation allowed us to stabilise and preserve dissociated cells for weeks without compromising single-cell RNA sequencing data. RESULTS By using mixtures of fixed, cultured human and mouse cells, we first showed that individual transcriptomes could be confidently assigned to one of the two species. Single-cell gene expression from live and fixed samples correlated well with bulk mRNA-seq data. We then applied methanol fixation to transcriptionally profile primary cells from dissociated, complex tissues. Low RNA content cells from Drosophila embryos, as well as mouse hindbrain and cerebellum cells prepared by fluorescence-activated cell sorting, were successfully analysed after fixation, storage and single-cell droplet RNA-seq. We were able to identify diverse cell populations, including neuronal subtypes. As an additional resource, we provide 'dropbead', an R package for exploratory data analysis, visualization and filtering of Drop-seq data. CONCLUSIONS We expect that the availability of a simple cell fixation method will open up many new opportunities in diverse biological contexts to analyse transcriptional dynamics at single-cell resolution.
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Affiliation(s)
- Jonathan Alles
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Nikos Karaiskos
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Samantha D Praktiknjo
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Stefanie Grosswendt
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Philipp Wahle
- Systems Biology of Neural Tissue Differentiation, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Pierre-Louis Ruffault
- Developmental Biology/Signal Transduction, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Salah Ayoub
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Luisa Schreyer
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Anastasiya Boltengagen
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Carmen Birchmeier
- Developmental Biology/Signal Transduction, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Robert Zinzen
- Systems Biology of Neural Tissue Differentiation, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany
| | - Christine Kocks
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany.
| | - Nikolaus Rajewsky
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany.
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Armit C, Richardson L, Venkataraman S, Graham L, Burton N, Hill B, Yang Y, Baldock RA. eMouseAtlas: An atlas-based resource for understanding mammalian embryogenesis. Dev Biol 2017; 423:1-11. [PMID: 28161522 PMCID: PMC5442644 DOI: 10.1016/j.ydbio.2017.01.023] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Revised: 01/31/2017] [Accepted: 01/31/2017] [Indexed: 11/23/2022]
Abstract
The eMouseAtlas resource is an online database of 3D digital models of mouse development, an ontology of mouse embryo anatomy and a gene-expression database with about 30K spatially mapped gene-expression patterns. It is closely linked with the MGI/GXD database at the Jackson Laboratory and holds links to almost all available image-based gene-expression data for the mouse embryo. In this resource article we describe the novel web-based tools we have developed for 3D visualisation of embryo anatomy and gene expression. We show how mapping of gene expression data onto spatial models delivers a framework for capturing gene expression that enhances our understanding of development, and we review the exploratory tools utilised by the EMAGE gene expression database as a means of defining co-expression of in situ hybridisation, immunohistochemistry, and lacZ-omic expression patterns. We report on recent developments of the eHistology atlas and our use of web-services to support embedding of the online 'The Atlas of Mouse Development' in the context of other resources such as the DMDD mouse phenotype database. In addition, we discuss new developments including a cellular-resolution placental atlas, third-party atlas models, clonal analysis data and a new interactive eLearning resource for developmental processes.
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Affiliation(s)
- Chris Armit
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road, EH4 2XU, UK
| | - Lorna Richardson
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road, EH4 2XU, UK
| | - Shanmugasundaram Venkataraman
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road, EH4 2XU, UK
| | - Liz Graham
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road, EH4 2XU, UK
| | - Nicholas Burton
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road, EH4 2XU, UK
| | - Bill Hill
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road, EH4 2XU, UK
| | - Yiya Yang
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road, EH4 2XU, UK
| | - Richard A Baldock
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road, EH4 2XU, UK
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Accelerating Adverse Outcome Pathway Development Using Publicly Available Data Sources. Curr Environ Health Rep 2016; 3:53-63. [PMID: 26809562 DOI: 10.1007/s40572-016-0079-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
The adverse outcome pathway (AOP) concept links molecular perturbations with organism and population-level outcomes to support high-throughput toxicity (HTT) testing. International efforts are underway to define AOPs and store the information supporting these AOPs in a central knowledge base; however, this process is currently labor-intensive and time-consuming. Publicly available data sources provide a wealth of information that could be used to define computationally predicted AOPs (cpAOPs), which could serve as a basis for creating expert-derived AOPs in a much more efficient way. Computational tools for mining large datasets provide the means for extracting and organizing the information captured in these public data sources. Using cpAOPs as a starting point for expert-derived AOPs should accelerate AOP development. Coupling this with tools to coordinate and facilitate the expert development efforts will increase the number and quality of AOPs produced, which should play a key role in advancing the adoption of HTT testing, thereby reducing the use of animals in toxicity testing and greatly increasing the number of chemicals that can be tested.
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13
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Blake JA, Eppig JT, Kadin JA, Richardson JE, Smith CL, Bult CJ. Mouse Genome Database (MGD)-2017: community knowledge resource for the laboratory mouse. Nucleic Acids Res 2016; 45:D723-D729. [PMID: 27899570 PMCID: PMC5210536 DOI: 10.1093/nar/gkw1040] [Citation(s) in RCA: 199] [Impact Index Per Article: 24.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 10/28/2016] [Indexed: 11/30/2022] Open
Abstract
The Mouse Genome Database (MGD: http://www.informatics.jax.org) is the primary community data resource for the laboratory mouse. It provides a highly integrated and highly curated system offering a comprehensive view of current knowledge about mouse genes, genetic markers and genomic features as well as the associations of those features with sequence, phenotypes, functional and comparative information, and their relationships to human diseases. MGD continues to enhance access to these data, to extend the scope of data content and visualizations, and to provide infrastructure and user support that ensures effective and efficient use of MGD in the advancement of scientific knowledge. Here, we report on recent enhancements made to the resource and new features.
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Affiliation(s)
- Judith A Blake
- 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
| | - James A Kadin
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | | | - Cynthia L Smith
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Carol J Bult
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
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14
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Finger JH, Smith CM, Hayamizu TF, McCright IJ, Xu J, Law M, Shaw DR, Baldarelli RM, Beal JS, Blodgett O, Campbell JW, Corbani LE, Lewis JR, Forthofer KL, Frost PJ, Giannatto SC, Hutchins LN, Miers DB, Motenko H, Stone KR, Eppig JT, Kadin JA, Richardson JE, Ringwald M. The mouse Gene Expression Database (GXD): 2017 update. Nucleic Acids Res 2016; 45:D730-D736. [PMID: 27899677 PMCID: PMC5210556 DOI: 10.1093/nar/gkw1073] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Revised: 10/21/2016] [Accepted: 10/28/2016] [Indexed: 12/14/2022] Open
Abstract
The Gene Expression Database (GXD; www.informatics.jax.org/expression.shtml) is an extensive and well-curated community resource of mouse developmental expression information. Through curation of the scientific literature and by collaborations with large-scale expression projects, GXD collects and integrates data from RNA in situ hybridization, immunohistochemistry, RT-PCR, northern blot and western blot experiments. Expression data from both wild-type and mutant mice are included. The expression data are combined with genetic and phenotypic data in Mouse Genome Informatics (MGI) and made readily accessible to many types of database searches. At present, GXD includes over 1.5 million expression results and more than 300 000 images, all annotated with detailed and standardized metadata. Since our last report in 2014, we have added a large amount of data, we have enhanced data and database infrastructure, and we have implemented many new search and display features. Interface enhancements include: a new Mouse Developmental Anatomy Browser; interactive tissue-by-developmental stage and tissue-by-gene matrix views; capabilities to filter and sort expression data summaries; a batch search utility; gene-based expression overviews; and links to expression data from other species.
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Affiliation(s)
| | | | - Terry F Hayamizu
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | | | - Jingxia Xu
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Meiyee Law
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - David R Shaw
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | | | - Jon S Beal
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Olin Blodgett
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Jeff W Campbell
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Lori E Corbani
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Jill R Lewis
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Kim L Forthofer
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Pete J Frost
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | | | - Lucie N Hutchins
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Dave B Miers
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Howie Motenko
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Kevin R Stone
- 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
| | - James A Kadin
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | | | - Martin Ringwald
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
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15
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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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16
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Eppig JT, Richardson JE, Kadin JA, Ringwald M, Blake JA, Bult CJ. Mouse Genome Informatics (MGI): reflecting on 25 years. Mamm Genome 2015; 26:272-84. [PMID: 26238262 PMCID: PMC4534491 DOI: 10.1007/s00335-015-9589-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Accepted: 07/20/2015] [Indexed: 12/02/2022]
Abstract
From its inception in 1989, the mission of the Mouse Genome Informatics (MGI) resource remains to integrate genetic, genomic, and biological data about the laboratory mouse to facilitate the study of human health and disease. This mission is ever more feasible as the revolution in genetics knowledge, the ability to sequence genomes, and the ability to specifically manipulate mammalian genomes are now at our fingertips. Through major paradigm shifts in biological research and computer technologies, MGI has adapted and evolved to become an integral part of the larger global bioinformatics infrastructure and honed its ability to provide authoritative reference datasets used and incorporated by many other established bioinformatics resources. Here, we review some of the major changes in research approaches over that last quarter century, how these changes are reflected in the MGI resource you use today, and what may be around the next corner.
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Affiliation(s)
- Janan T. Eppig
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - Joel E. Richardson
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - James A. Kadin
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - Martin Ringwald
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - Judith A. Blake
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - Carol J. Bult
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
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17
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Dolan ME, Baldarelli RM, Bello SM, Ni L, McAndrews MS, Bult CJ, Kadin JA, Richardson JE, Ringwald M, Eppig JT, Blake JA. Orthology for comparative genomics in the mouse genome database. Mamm Genome 2015. [PMID: 26223881 PMCID: PMC4534493 DOI: 10.1007/s00335-015-9588-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
The mouse genome database (MGD) is the model organism database component of the mouse genome informatics system at The Jackson Laboratory. MGD is the international data resource for the laboratory mouse and facilitates the use of mice in the study of human health and disease. Since its beginnings, MGD has included comparative genomics data with a particular focus on human-mouse orthology, an essential component of the use of mouse as a model organism. Over the past 25 years, novel algorithms and addition of orthologs from other model organisms have enriched comparative genomics in MGD data, extending the use of orthology data to support the laboratory mouse as a model of human biology. Here, we describe current comparative data in MGD and review the history and refinement of orthology representation in this resource.
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Affiliation(s)
- Mary E Dolan
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA,
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Abstract
Mouse anatomy ontologies provide standard nomenclature for describing normal and mutant mouse anatomy, and are essential for the description and integration of data directly related to anatomy such as gene expression patterns. Building on our previous work on anatomical ontologies for the embryonic and adult mouse, we have recently developed a new and substantially revised anatomical ontology covering all life stages of the mouse. Anatomical terms are organized in complex hierarchies enabling multiple relationships between terms. Tissue classification as well as partonomic, developmental, and other types of relationships can be represented. Hierarchies for specific developmental stages can also be derived. The ontology forms the core of the eMouse Atlas Project (EMAP) and is used extensively for annotating and integrating gene expression patterns and other data by the Gene Expression Database (GXD), the eMouse Atlas of Gene Expression (EMAGE) and other database resources. Here we illustrate the evolution of the developmental and adult mouse anatomical ontologies toward one combined system. We report on recent ontology enhancements, describe the current status, and discuss future plans for mouse anatomy ontology development and application in integrating data resources.
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The pattern of congenital heart defects arising from reduced Tbx5 expression is altered in a Down syndrome mouse model. BMC DEVELOPMENTAL BIOLOGY 2015. [PMID: 26208718 PMCID: PMC4514943 DOI: 10.1186/s12861-015-0080-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND Nearly half of all individuals with Down Syndrome (DS) have some type of congenital heart defect (CHD), suggesting that DS sensitizes to CHD but does not cause it. We used a common mouse model of DS, the Ts65Dn mouse, to study the contribution of Tbx5, a known modifier of CHD, to heart defects on a trisomic backgroun. Mice that were heterozygous for a Tbx5 null allele were crossed with Ts65Dn mice. Thoraxes of progeny were fixed in 10% formalin, embedded in paraffin, and sectioned for analysis of CHD. Gene expression in embryonic hearts was examined by quantitative PCR and in situ hybridization. A TBX5 DNA binding site was verified by luciferase assays. METHODS Mice that were heterozygous for a Tbx5 null allele were crossed with Ts65Dn mice. Thoraxes of progeny were fixed in 10% formalin, embedded in paraffin, and sectioned for analysis of CHD. Gene expression in embryonic hearts was examined by quantitative PCR and in situ hybridization. A TBX5 DNA binding site was verified by luciferase assays. RESULTS We crossed mice that were heterozygous for a Tbx5 null allele with Ts65Dn mice. Mice that were trisomic and carried the Tbx5 mutation (Ts65Dn;Tbx5 (+/-) ) had a significantly increased incidence of overriding aorta compared to their euploid littermates. Ts65Dn;Tbx5 (+/-) mice also showed reduced expression of Pitx2, a molecular marker for the left atrium. Transcript levels of the trisomic Adamts1 gene were decreased in Tbx5 (+/-) mice compared to their euploid littermates. Evidence of a valid binding site for TBX5 upstream of the trisomic Adamts1 locus was also shown. CONCLUSION Haploinsufficiency of Tbx5 and trisomy affects alignment of the aorta and this effect may stem from deviations from normal left-right patterning in the heart. We have unveiled a previously unknown interaction between the Tbx5 gene and trisomy, suggesting a connection between Tbx5 and trisomic genes important during heart development.
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