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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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Moraleva A, Magoulas C, Polzikov M, Hacot S, Mertani HC, Diaz JJ, Zatsepina O. Involvement of the specific nucleolar protein SURF6 in regulation of proliferation and ribosome biogenesis in mouse NIH/3T3 fibroblasts. Cell Cycle 2017; 16:1979-1991. [PMID: 28873013 DOI: 10.1080/15384101.2017.1371880] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The nucleolar proteins which link cell proliferation to ribosome biogenesis are regarded to be potentially oncogenic. Here, in order to examine the involvement of an evolutionary conserved nucleolar protein SURF6/Rrp14 in proliferation and ribosome biogenesis in mammalian cells, we established stably transfected mouse NIH/3T3 fibroblasts capable of conditional overexpression of the protein. Cell proliferation was monitored in real-time, and various cell cycle parameters were quantified based on flow cytometry, Br-dU-labeling and conventional microscopy data. We show that overexpression of SURF6 accelerates cell proliferation and promotes transition through all cell cycle phases. The most prominent SURF6 pro-proliferative effects include a significant reduction of the population doubling time, from 19.8 ± 0.7 to 16.2 ± 0.5 hours (t-test, p < 0.001), and of the length of cell division cycle, from 17.6 ± 0.6 to 14.0 ± 0.4 hours (t-test, p < 0.001). The later was due to the shortening of all cell cycle phases but the length of G1 period was reduced most, from 5.7 ± 0.4 to 3.8 ± 0.3 hours, or by ∼30%, (t-test, p < 0.05). By Northern blots and qRT-PCR, we further showed that the acceleration of cell proliferation was concomitant with an accumulation of rRNA species along both ribosomal subunit maturation pathways. It is evident, therefore, that like the yeast homologue Rrp14, mammalian SURF6 is involved in various steps of rRNA processing during ribosome biogenesis. We concluded that SURF6 is a novel positive regulator of proliferation and G1/S transition in mammals, implicating that SURF6 is a potential oncogenic protein, which can be further studied as a putative target in anti-cancer therapy.
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Affiliation(s)
- Anastasiia Moraleva
- a Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences , Moscow , Russian Federation
| | - Charalambos Magoulas
- b Centre for Investigative and Diagnostic Oncology, Department of Natural Sciences, School of Science and Technology , Middlesex University , London , United Kingdom
| | - Mikhail Polzikov
- a Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences , Moscow , Russian Federation
| | - Sabine Hacot
- c Centre de Recherche en Cancérologie de Lyon, UMR INSERM 1052-CNRS 5286, Centre Léon Bérard , Université Claude Bernard Lyon I, Université de Lyon , Lyon , France
| | - Hichem C Mertani
- c Centre de Recherche en Cancérologie de Lyon, UMR INSERM 1052-CNRS 5286, Centre Léon Bérard , Université Claude Bernard Lyon I, Université de Lyon , Lyon , France
| | - Jean-Jacques Diaz
- c Centre de Recherche en Cancérologie de Lyon, UMR INSERM 1052-CNRS 5286, Centre Léon Bérard , Université Claude Bernard Lyon I, Université de Lyon , Lyon , France
| | - Olga Zatsepina
- a Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences , Moscow , Russian Federation.,c Centre de Recherche en Cancérologie de Lyon, UMR INSERM 1052-CNRS 5286, Centre Léon Bérard , Université Claude Bernard Lyon I, Université de Lyon , Lyon , France
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Smith CM, Finger JH, Hayamizu TF, McCright IJ, Xu J, Berghout J, Campbell J, Corbani LE, Forthofer KL, Frost PJ, Miers D, Shaw DR, Stone KR, Eppig JT, Kadin JA, Richardson JE, Ringwald M. The mouse Gene Expression Database (GXD): 2014 update. Nucleic Acids Res 2013; 42:D818-24. [PMID: 24163257 PMCID: PMC3965015 DOI: 10.1093/nar/gkt954] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The Gene Expression Database (GXD; http://www.informatics.jax.org/expression.shtml) is an extensive and well-curated community resource of mouse developmental expression information. GXD collects different types of expression data from studies of wild-type and mutant mice, covering all developmental stages and including data from RNA in situ hybridization, immunohistochemistry, RT-PCR, northern blot and western blot experiments. The data are acquired from the scientific literature and from researchers, including groups doing large-scale expression studies. Integration with the other data in Mouse Genome Informatics (MGI) and interconnections with other databases places GXD's gene expression information in the larger biological and biomedical context. Since the last report, the utility of GXD has been greatly enhanced by the addition of new data and by the implementation of more powerful and versatile search and display features. Web interface enhancements include the capability to search for expression data for genes associated with specific phenotypes and/or human diseases; new, more interactive data summaries; easy downloading of data; direct searches of expression images via associated metadata; and new displays that combine image data and their associated annotations. At present, GXD includes >1.4 million expression results and 250,000 images that are accessible to our search tools.
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Reconstruction and analysis of human kidney-specific metabolic network based on omics data. BIOMED RESEARCH INTERNATIONAL 2013; 2013:187509. [PMID: 24222897 PMCID: PMC3814056 DOI: 10.1155/2013/187509] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2013] [Revised: 08/23/2013] [Accepted: 08/26/2013] [Indexed: 01/15/2023]
Abstract
With the advent of the high-throughput data production, recent studies of tissue-specific metabolic networks have largely advanced our understanding of the metabolic basis of various physiological and pathological processes. However, for kidney, which plays an essential role in the body, the available kidney-specific model remains incomplete. This paper reports the reconstruction and characterization of the human kidney metabolic network based on transcriptome and proteome data. In silico simulations revealed that house-keeping genes were more essential than kidney-specific genes in maintaining kidney metabolism. Importantly, a total of 267 potential metabolic biomarkers for kidney-related diseases were successfully explored using this model. Furthermore, we found that the discrepancies in metabolic processes of different tissues are directly corresponding to tissue's functions. Finally, the phenotypes of the differentially expressed genes in diabetic kidney disease were characterized, suggesting that these genes may affect disease development through altering kidney metabolism. Thus, the human kidney-specific model constructed in this study may provide valuable information for the metabolism of kidney and offer excellent insights into complex kidney diseases.
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Kouskoumvekaki I, Shublaq N, Brunak S. Facilitating the use of large-scale biological data and tools in the era of translational bioinformatics. Brief Bioinform 2013; 15:942-52. [PMID: 23908249 DOI: 10.1093/bib/bbt055] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
As both the amount of generated biological data and the processing compute power increase, computational experimentation is no longer the exclusivity of bioinformaticians, but it is moving across all biomedical domains. For bioinformatics to realize its translational potential, domain experts need access to user-friendly solutions to navigate, integrate and extract information out of biological databases, as well as to combine tools and data resources in bioinformatics workflows. In this review, we present services that assist biomedical scientists in incorporating bioinformatics tools into their research. We review recent applications of Cytoscape, BioGPS and DAVID for data visualization, integration and functional enrichment. Moreover, we illustrate the use of Taverna, Kepler, GenePattern, and Galaxy as open-access workbenches for bioinformatics workflows. Finally, we mention services that facilitate the integration of biomedical ontologies and bioinformatics tools in computational workflows.
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