Model organism databases (MODs) have been collecting and integrating biomedical research data for 30 years and were designed to meet specific needs of each model organism research community. The contributions of model organism research to understanding biological systems would be hard to overstate. Modern molecular biology methods and cost reductions in nucleotide sequencing have opened avenues for direct application of model organism research to elucidating mechanisms of human diseases. Thus, the mandate for model organism research and databases has now grown to include facilitating use of these data in translational applications. Challenges in meeting this opportunity include the distribution of research data across many databases and websites, a lack of data format standards for some data types, and sustainability of scale and cost for genomic database resources like MODs. The issues of widely distributed data and application of data standards are some of the challenges addressed by FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles. The Alliance of Genome Resources is now moving to address these challenges by bringing together expertly curated research data from fly, mouse, rat, worm, yeast, zebrafish, and the Gene Ontology consortium. Centralized multi-species data access, integration, and format standardization will lower the data utilization barrier in comparative genomics and translational applications and will provide a framework in which sustainable scale and cost can be addressed. This article presents a brief historical perspective on how the Alliance model organisms are complementary and how they have already contributed to understanding the etiology of human diseases. In addition, we discuss four challenges for using data from MODs in translational applications and how the Alliance is working to address them, in part by applying FAIR data principles. Ultimately, combined data from these animal models are more powerful than the sum of the parts.
Subscribe to Journal
Get full journal access for 1 year
We are sorry, but there is no personal subscription option available for your country.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Ericsson, A. C., Crim, M. J. & Franklin, C. L. A brief history of animal modeling. Mo. Med. 110, 201–205 (2013).
Duronio, R. J., O’Farrell, P. H., Sluder, G. & Su, T. T. Sophisticated lessons from simple organisms: appreciating the value of curiosity-driven research. Dis. Model. Mech. 10, 1381–1389 (2017).
Krogh, A. The progress of physiology. Science 70, 200–204 (1929).
Rigden, D. J. & Fernández, X. M. The 2018 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res. 46(D1), D1–D7 (2018).
Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049–D1056 (2015).
Donlin, M. J. Using the Generic Genome Browser (GBrowse). Curr. Protoc. Bioinforma. Chapter 9, Unit 9.9 (2009).
Chao, H.-T., Liu, L. & Bellen, H. J. Building dialogues between clinical and biomedical research through cross-species collaborations. Semin. Cell Dev. Biol. 70, 49–57 (2017).
Wangler, M. F. et al. Model organisms facilitate rare disease diagnosis and therapeutic research. Genetics 207, 9–27 (2017).
Manolio, T. A. et al. Bedside back to bench: building bridges between basic and clinical genomic research. Cell 169, 6–12 (2017).
Oliver, S. G., Lock, A., Harris, M. A., Nurse, P. & Wood, V. Model organism databases: essential resources that need the support of both funders and users. BMC Biol. 14, 49 (2016).
Poux, S. et al. On expert curation and scalability: UniProtKB/Swiss-Prot as a case study. Bioinformatics 33, 3454–3460 (2017).
Gramates, L. S. et al. FlyBase at 25: looking to the future. Nucleic Acids Res. 45(D1), D663–D671 (2017).
Wangler, M. F., Yamamoto, S. & Bellen, H. J. Fruit flies in biomedical research. Genetics 199, 639–653 (2015).
Rubin, G. M. & Spradling, A. C. Genetic transformation of Drosophila with transposable element vectors. Science 218, 348–353 (1982).
Spradling, A. C. et al. The Berkeley Drosophila Genome Project gene disruption project: single P-element insertions mutating 25% of vital Drosophila genes. Genetics 153, 135–177 (1999).
Bellen, H. J. et al. The Drosophila gene disruption project: progress using transposons with distinctive site specificities. Genetics 188, 731–743 (2011).
Perrimon, N., Bonini, N. M. & Dhillon, P. Fruit flies on the front line: the translational impact of Drosophila. Dis. Model. Mech. 9, 229–231 (2016).
Bilder, D. & Irvine, K. D. Taking stock of the Drosophila research ecosystem. Genetics 206, 1227–1236 (2017).
Kaufman, T. C. A short history and description of Drosophila melanogaster classical genetics: chromosome aberrations, forward genetic screens, and the nature of mutations. Genetics 206, 665–689 (2017).
Kanca, O. & Bellen, H. J. & Schnorrer, F. Gene tagging strategies to assess protein expression, localization, and function in Drosophila. Genetics 207, 389–412 (2017).
Bier, E., Harrison, M. M., O’Connor-Giles, K. M. & Wildonger, J. Advances in engineering the fly genome with the CRISPR–Cas system. Genetics 208, 1–18 (2018).
Germani, F., Bergantinos, C. & Johnston, L. A. Mosaic analysis inDrosophila. Genetics 208, 473–490 (2018).
Bandura, J. L. et al. humpty dumpty is required for developmental DNA amplification and cell proliferation in Drosophila. Curr. Biol. 15, 755–759 (2005).
Evrony, G. D. et al. Integrated genome and transcriptome sequencing identifies a noncoding mutation in the genome replication factor DONSON as the cause of microcephaly–micromelia syndrome. Genome Res. 27, 1323–1335 (2017).
Lesly, S., Bandura, J. L. & Calvi, B. R. Rapid DNA synthesis during early Drosophila embryogenesis is sensitive to maternal Humpty Dumpty protein function. Genetics 207, 935–947 (2017).
Reynolds, J. J. et al. Mutations in DONSON disrupt replication fork stability and cause microcephalic dwarfism. Nat. Genet. 49, 537–549 (2017).
Vidal, M., Wells, S., Ryan, A. & Cagan, R. ZD6474 suppresses oncogenic RET isoforms in a Drosophila model for type 2 multiple endocrine neoplasia syndromes and papillary thyroid carcinoma. Cancer Res. 65, 3538–3541 (2005).
Dar, A. C., Das, T. K., Shokat, K. M. & Cagan, R. L. Chemical genetic discovery of targets and anti-targets for cancer polypharmacology. Nature 486, 80–84 (2012).
Millburn, G. H., Crosby, M. A., Gramates, L. S. & Tweedie, S. FlyBase portals to human disease research using Drosophila models. Dis. Model. Mech. 9, 245–252 (2016).
Hu, Y., Comjean, A., Mohr, S.E. & Perrimon, N. Gene2Function: an integrated online resource for gene function discovery. G3 7, 2855–2858 (2017).
Hu, Y. et al. Molecular Interaction Search Tool (MIST): an integrated resource for mining gene and protein interaction data. Nucleic Acids Res. 46(D1), D567–D574 (2018).
Wang, J. et al. MARRVEL: integration of human and model organism genetic resources to facilitate functional annotation of the human genome. Am. J. Hum. Genet. 100, 843–853 (2017).
Gelbart, W. M. et al. FlyBase: a Drosophila database. The FlyBase consortium. Nucleic Acids Res. 25, 63–66 (1997).
Keane, T. M. et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294 (2011).
International Mouse Knockout Consortium. A mouse for all reasons. Cell 128, 9–13 (2007).
Bradley, A. et al. The mammalian gene function resource: the International Knockout Mouse Consortium. Mamm. Genome 23, 580–586 (2012).
Brown, S. D. M. & Moore, M. W. The International Mouse Phenotyping Consortium: past and future perspectives on mouse phenotyping. Mamm. Genome 23, 632–640 (2012).
Collaborative Cross Consortium. The genome architecture of the Collaborative Cross mouse genetic reference population. Genetics 190, 389–401 (2012).
Threadgill, D.W. & Churchill, G.A. Ten years of the collaborative cross. G3 2, 153–156 (2012).
Churchill, G. A., Gatti, D. M., Munger, S. C. & Svenson, K. L. The Diversity Outbred mouse population. Mamm. Genome 23, 713–718 (2012).
Svenson, K. L. et al. High-resolution genetic mapping using the Mouse Diversity outbred population. Genetics 190, 437–447 (2012).
Philip, V. M. et al. Genetic analysis in the Collaborative Cross breeding population. Genome Res. 21, 1223–1238 (2011).
Logan, R. W. et al. High-precision genetic mapping of behavioral traits in the diversity outbred mouse population. Genes Brain Behav. 12, 424–437 (2013).
Chesler, E. J. Out of the bottleneck: the Diversity Outcross and Collaborative Cross mouse populations in behavioral genetics research. Mamm. Genome 25, 3–11 (2014).
Wilke, M. et al. Mouse models of cystic fibrosis: phenotypic analysis and research applications. J. Cyst. Fibros. 10(Suppl. 2), S152–S171 (2011).
Tsuji, T. & Kunieda, T. A loss-of-function mutation in natriuretic peptide receptor 2 (Npr2) gene is responsible for disproportionate dwarfism in cn/cn mouse. J. Biol. Chem. 280, 14288–14292 (2005).
Morelli, K. H. et al. Severity of demyelinating and axonal neuropathy mouse models is modified by genes affecting structure and function of peripheral nodes. Cell Rep. 18, 3178–3191 (2017).
Wu, W.-H. et al. CRISPR repair reveals causative mutation in a preclinical model of retinitis pigmentosa. Mol. Ther. 24, 1388–1394 (2016).
Metzger, M. W. et al. Heterozygosity for the mood disorder-associated variant Gln460Arg alters P2X7 receptor function and sleep quality. J. Neurosci. 37, 11688–11700 (2017).
Bjursell, M. et al. Therapeutic genome editing with CRISPR/Cas9 in a humanized mouse model ameliorates α1-antitrypsin deficiency phenotype. EBioMedicine 29, 104–111 (2018).
Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).
Wang, H. et al. One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 153, 910–918 (2013).
Hara, S. & Takada, S. Genome editing for the reproduction and remedy of human diseases in mice. J. Hum. Genet. 63, 107–113 (2018).
Molenhuis, R. T., Bruining, H. & Kas, M. J. Modelling autistic features in mice using quantitative genetic approaches. Adv. Anat. Embryol. Cell Biol. 224, 65–84 (2017).
St Clair, D. & Johnstone, M. Using mouse transgenic and human stem cell technologies to model genetic mutations associated with schizophrenia and autism. Phil. Trans. R. Soc. Lond. B 373, 20170037 (2018).
Attie, A. D., Churchill, G. A. & Nadeau, J. H. How mice are indispensable for understanding obesity and diabetes genetics. Curr. Opin. Endocrinol. Diabetes Obes. 24, 83–91 (2017).
Skelton, J. K., Ortega-Prieto, A. M. & Dorner, M. A Hitchhiker’s guide to humanized mice: new pathways to studying viral infections. Immunology 154, 50–61 (2018).
Gunawan, M. et al. A novel human systemic lupus erythematosus model in humanised mice. Sci. Rep. 7, 16642 (2017).
Kitada, M., Ogura, Y. & Koya, D. Rodent models of diabetic nephropathy: their utility and limitations. Int. J. Nephrol. Renovasc. Dis. 9, 279–290 (2016).
Leite, J. P., Garcia-Cairasco, N. & Cavalheiro, E. A. New insights from the use of pilocarpine and kainate models. Epilepsy Res. 50, 93–103 (2002).
Cenci, M. A. & Crossman, A. R. Animal models of l-dopa-induced dyskinesia in Parkinson’s disease. Mov. Disord. https://doi.org/10.1002/mds.27337 (2018).
Kless, C., Rink, N., Rozman, J. & Klingenspor, M. Proximate causes for diet-induced obesity in laboratory mice: a case study. Eur. J. Clin. Nutr. 71, 306–317 (2017).
Combe, R. et al. How does circadian rhythm impact salt sensitivity of blood pressure in mice? A study in two close C57Bl/6 substrains. PLoS One 11, e0153472 (2016).
Dickson, P. E. et al. Association of novelty-related behaviors and intravenous cocaine self-administration in Diversity Outbred mice. Psychopharmacology 232, 1011–1024 (2015).
Cervantes, M. C., Laughlin, R. E. & Jentsch, J. D. Cocaine self-administration behavior in inbred mouse lines segregating different capacities for inhibitory control. Psychopharmacology 229, 515–525 (2013).
Sittig, L. J. et al. Genetic background limits generalizability of genotype–phenotype relationships. Neuron 91, 1253–1259 (2016).
Chesler, E. J. et al. Quantitative trait loci for sensitivity to ethanol intoxication in a C57BL/6J × 129S1/SvImJ inbred mouse cross. Mamm. Genome 23, 305–321 (2012).
Thompson, M. B. The Min mouse: a genetic model for intestinal carcinogenesis. Toxicol. Pathol. 25, 329–332 (1997).
Dietrich, W. F. et al. Genetic identification of Mom-1, a major modifier locus affecting Min-induced intestinal neoplasia in the mouse. Cell 75, 631–639 (1993).
MacPhee, M. et al. The secretory phospholipase A2 gene is a candidate for the Mom1 locus, a major modifier of Apc Min-induced intestinal neoplasia. Cell 81, 957–966 (1995).
Kennedy, B. P. et al. A natural disruption of the secretory group II phospholipase A2 gene in inbred mouse strains. J. Biol. Chem. 270, 22378–22385 (1995).
Quach, N. D., Arnold, R. D. & Cummings, B. S. Secretory phospholipase A2 enzymes as pharmacological targets for treatment of disease. Biochem. Pharmacol. 90, 338–348 (2014).
Yarla, N. S. et al. Phospholipase A2 isoforms as novel targets for prevention and treatment of inflammatory and oncologic diseases. Curr. Drug Targets 17, 1940–1962 (2016).
Shultz, L. D. et al. Human cancer growth and therapy in immunodeficient mouse models. Cold Spring Harb. Protoc. 2014, pdb.top073585 (2014).
Wang, M. et al. Humanized mice in studying efficacy and mechanisms of PD-1-targeted cancer immunotherapy. FASEB J. 32, 1537–1549 (2018).
Pauli, C. et al. Personalized in vitro and in vivo cancer models to guide precision medicine. Cancer Discov. 7, 462–477 (2017).
Dobrolecki, L. E. et al. Patient-derived xenograft (PDX) models in basic and translational breast cancer research. Cancer Metastasis Rev. 35, 547–573 (2016).
Williams, J. A. Using PDX for preclinical cancer drug discovery: the evolving field. J. Clin. Med. 7, 41 (2018).
Pan, C. X. et al. Development and characterization of bladder cancer patient-derived xenografts for molecularly guided targeted therapy. PLoS One 10, e0134346 (2015).
Garralda, E. et al. Integrated next-generation sequencing and avatar mouse models for personalized cancer treatment. Clin. Cancer Res. 20, 2476–2484 (2014).
Hidalgo, M. et al. A pilot clinical study of treatment guided by personalized tumorgrafts in patients with advanced cancer. Mol. Cancer Ther. 10, 1311–1316 (2011).
Smith, C. L., Blake, J. A., Kadin, J. A., Richardson, J. E. & Bult, C. J. Mouse Genome Database (MGD)-2018: knowledgebase for the laboratory mouse. Nucleic Acids Res. 46(D1), D836–D842 (2018).
Finger, J. H. et al. The mouse Gene Expression Database (GXD): 2017 update. Nucleic Acids Res. 45(D1), D730–D736 (2017).
Krupke, D. M. et al. The Mouse Tumor Biology Database: a comprehensive resource for mouse models of human cancer. Cancer Res. 77, e67–e70 (2017).
Drabkin, H. J. & Blake, J. A. Manual Gene Ontology annotation workflow at the Mouse Genome Informatics Database. Database 2012, bas045 (2012).
Aitman, T., Dhillon, P. & Geurts, A. M. A RATional choice for translational research? Dis. Model. Mech. 9, 1069–1072 (2016).
Jacob, H. J. et al. Genetic dissection of autoimmune type I diabetes in the BB rat. Nat. Genet. 2, 56–60 (1992).
Rapp, J. P. Genetic analysis of inherited hypertension in the rat. Physiol. Rev. 80, 135–172 (2000).
Remmers, E. F. et al. A genome scan localizes five non-MHC loci controlling collagen-induced arthritis in rats. Nat. Genet. 14, 82–85 (1996).
Shepel, L. A. et al. Genetic identification of multiple loci that control breast cancer susceptibility in the rat. Genetics 149, 289–299 (1998).
Jacob, H. J., Lazar, J., Dwinell, M. R., Moreno, C. & Geurts, A. M. Gene targeting in the rat: advances and opportunities. Trends Genet. 26, 510–518 (2010).
Jacob, H. From rat pathophysiology to genomic medicine: an interview with Howard Jacob. Dis. Model. Mech. 9, 1073–1077 (2016).
Robbins, T. W. Cross-species studies of cognition relevant to drug discovery: a translational approach. Br. J. Pharmacol. 174, 3191–3199 (2017).
Jordan, V. C. Proven value of translational research with appropriate animal models to advance breast cancer treatment and save lives: the tamoxifen tale. Br. J. Clin. Pharmacol. 79, 254–267 (2015).
Chou, M. Y. & Mani, A. A successful story of translational orthodontic research: micro-osteoperforation—from experiments to clinical practice. APOS Trends Orthod 7, 6–11 (2017).
Teixeira, C. C. et al. Cytokine expression and accelerated tooth movement. J. Dent. Res. 89, 1135–1141 (2010).
Winge, Ø. On haplophase and diplophase of some Saccharomycetes. C. R. Trav. Lab. Carlsberg. Ser. Physiol. 21, 77–111 (1935).
Lindegren, C.C. The Yeast Cell: Its Genetics and Cytology (Education Publishers, Saint Louis, MO, USA, 1949).
Lindegren., C. C., Lindegren, G., Shult, E. E. & Desborough, S. Chromosome maps of Saccharomyces. Nature 183, 800–802 (1959).
Lindegren, C. C. & Lindegren, G. Linkage relationships in Saccharomyces of genes controlling the fermentation of carbohydrates and the synthesis of vitamins, amino acids and nucleic acid components. Indian Phytopathol. 4, 11–20 (1951).
Goffeau, A. et al. Life with 6000 genes. Science 274, 546–567 (1996).
Hieter, P. et al. Functional selection and analysis of yeast centromeric DNA. Cell 42, 913–921 (1985).
Deshpande, A. M. & Newlon, C. S. The ARS consensus sequence is required for chromosomal origin function in Saccharomyces cerevisiae. Mol. Cell. Biol. 12, 4305–4313 (1992).
Louis, E. J., Naumova, E. S., Lee, A., Naumov, G. & Haber, J. E. The chromosome end in yeast: its mosaic nature and influence on recombinational dynamics. Genetics 136, 789–802 (1994).
Lowe, T. M. & Eddy, S. R. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955–964 (1997).
Lowe, T. M. & Eddy, S. R. A computational screen for methylation guide snoRNAs in yeast. Science 283, 1168–1171 (1999).
Planta, R. J. & Mager, W. H. The list of cytoplasmic ribosomal proteins of Saccharomyces cerevisiae. Yeast 14, 471–477 (1998).
Kim, J. M., Vanguri, S., Boeke, J. D., Gabriel, A. & Voytas, D. F. Transposable elements and genome organization: a comprehensive survey of retrotransposons revealed by the complete Saccharomyces cerevisiae genome sequence. Genome Res. 8, 464–478 (1998).
Winzeler, E. A. et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901–906 (1999).
Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391 (2002).
Ghaemmaghami, S. et al. Global analysis of protein expression in yeast. Nature 425, 737–741 (2003).
Huh, W.-K. et al. Global analysis of protein localization in budding yeast. Nature 425, 686–691 (2003).
Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016).
Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).
Botstein, D. & Fink, G. R. Yeast: an experimental organism for 21st century biology. Genetics 189, 695–704 (2011).
Lasserre, J.-P. et al. Yeast as a system for modeling mitochondrial disease mechanisms and discovering therapies. Dis. Model. Mech. 8, 509–526 (2015).
Dolinski, K. & Botstein, D. Orthology and functional conservation in eukaryotes. Annu. Rev. Genet. 41, 465–507 (2007).
Engel, S. R. & Cherry, J. M. The new modern era of yeast genomics: community sequencing and the resulting annotation of multiple Saccharomyces cerevisiae strains at the Saccharomyces Genome. Database. Database 2013, bat012 (2013).
Novo, M. et al. Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast Saccharomyces cerevisiae EC1118. Proc. Natl Acad. Sci. USA 106, 16333–16338 (2009).
Wenger, J. W., Schwartz, K. & Sherlock, G. Bulk segregant analysis by high-throughput sequencing reveals a novel xylose utilization gene from Saccharomyces cerevisiae. PLoS Genet. 6, e1000942 (2010).
Libkind, D. et al. Microbe domestication and the identification of the wild genetic stock of lager-brewing yeast. Proc. Natl Acad. Sci. USA 108, 14539–14544 (2011).
Kachroo, A. H. et al. Systematic humanization of yeast genes reveals conserved functions and genetic modularity. Science 348, 921–925 (2015).
Kachroo, A. H. et al. Systematic bacterialization of yeast genes identifies a near-universally swappable pathway. eLife 6, e25093 (2017).
Gabaldón, T. & Koonin, E. V. Functional and evolutionary implications of gene orthology. Nat. Rev. Genet. 14, 360–366 (2013).
Skrzypek, M. S. et al. Saccharomyces genome database informs human biology. Nucleic Acids Res. 46(D1), D736–D742 (2018).
Apfeld, J. & Alper, S. What can we learn about human disease from the nematode C. elegans?. Methods Mol. Biol. 1706, 53–75 (2018).
Riessland, M. et al. Neurocalcin delta suppression protects against spinal muscular atrophy in humans and across species by restoring impaired endocytosis. Am. J. Hum. Genet. 100, 297–315 (2017).
Culetto, E. & Sattelle, D. B. A role for Caenorhabditis elegans in understanding the function and interactions of human disease genes. Hum. Mol. Genet. 9, 869–877 (2000).
Ganner, A. & Neumann-Haefelin, E. Genetic kidney diseases: Caenorhabditis elegans as model system. Cell Tissue Res. 369, 105–118 (2017).
Bank, E. M. & Gruenbaum, Y. Caenorhabditis elegans as a model system for studying the nuclear lamina and laminopathic diseases. Nucleus 2, 350–357 (2011).
Lin, J. & Hackam, D. J. Worms, flies and four-legged friends: the applicability of biological models to the understanding of intestinal inflammatory diseases. Dis. Model. Mech. 4, 447–456 (2011).
Williams, M. J., Almén, M. S., Fredriksson, R. & Schiöth, H. B. What model organisms and interactomics can reveal about the genetics of human obesity. Cell. Mol. Life Sci. 69, 3819–3834 (2012).
Howe, K. L. et al. WormBase 2016: expanding to enable helminth genomic research. Nucleic Acids Res. 44(D1), D774–D780 (2016).
Kim, D.-K., Kim, T. H. & Lee, S.-J. Mechanisms of aging-related proteinopathies in Caenorhabditis elegans. Exp. Mol. Med. 48, e263 (2016).
Alexander, A. G., Marfil, V. & Li, C. Use of Caenorhabditis elegans as a model to study Alzheimer’s disease and other neurodegenerative diseases. Front. Genet. 5, 279 (2014).
Ma, L. et al. Caenorhabditis elegans as a model system for target identification and drug screening against neurodegenerative diseases. Eur. J. Pharmacol. 819, 169–180 (2018).
Griffin, E. F., Caldwell, K. A. & Caldwell, G. A. Genetic and pharmacological discovery for Alzheimer’s disease using Caenorhabditis elegans. ACS Chem. Neurosci. 8, 2596–2606 (2017).
Hindle, S., Hebbar, S. & Sweeney, S. T. Invertebrate models of lysosomal storage disease: what have we learned so far? Invert. Neurosci. 11, 59–71 (2011).
de Voer, G., Peters, D. & Taschner, P. E. M. Caenorhabditis elegans as a model for lysosomal storage disorders. Biochim. Biophys. Acta 1782, 433–446 (2008).
Laale, H. The biology and use of zebrafish Brachydanio rerio in fisheries research: a literature review. J. Fish Biol. 10, 121–173 (1977).
Fishman, M. C. Zebrafish—the canonical vertebrate. Science 294, 1290–1291 (2001).
Phillips, J. B. & Westerfield, M. Zebrafish models in translational research: tipping the scales toward advancements in human health. Dis. Model. Mech. 7, 739–743 (2014).
Lieschke, G. J. & Currie, P. D. Animal models of human disease: zebrafish swim into view. Nat. Rev. Genet. 8, 353–367 (2007).
Howe, D. G. D. G. et al. ZFIN, the Zebrafish Model Organism Database: increased support for mutants and transgenics. Nucleic Acids Res. 41, D854–D860 (2013).
Bradford, Y. M. et al. Zebrafish models of human disease: gaining insight into human disease at ZFIN. ILAR J. 58, 4–16 (2017).
Berger, J. & Currie, P. D. Zebrafish models flex their muscles to shed light on muscular dystrophies. Dis. Model. Mech. 5, 726–732 (2012).
Taylor, A. M. & Zon, L. I. Modeling Diamond Blackfan anemia in the zebrafish. Semin. Hematol. 48, 81–88 (2011).
Pena, I. A. et al. Pyridoxine-dependent epilepsy in zebrafish caused by Aldh7a1 deficiency. Genetics 207, 1501–1518 (2017).
Cortelazzo, A. et al. Proteomic analysis of the Rett syndrome experimental model mecp2 Q63X mutant zebrafish. J. Proteomics 154, 128–133 (2017).
Pietri, T. et al. The first mecp2-null zebrafish model shows altered motor behaviors. Front. Neural Circuits 7, 118 (2013).
Gao, H. et al. Mecp2 regulates neural cell differentiation by suppressing the Id1 to Her2 axis in zebrafish. J. Cell Sci. 128, 2340–2350 (2015).
Noël, E. S. et al. A zebrafish loss-of-function model for human CFAP53 mutations reveals its specific role in laterality organ function. Hum. Mutat. 37, 194–200 (2016).
Cast, A. E., Gao, C., Amack, J. D. & Ware, S. M. An essential and highly conserved role for Zic3 in left–right patterning, gastrulation and convergent extension morphogenesis. Dev. Biol. 364, 22–31 (2012).
Wu, S.-Y. et al. Expression of cataract-linked gamma-crystallin variants in zebrafish reveals a proteostasis network that senses protein stability. J. Biol. Chem. 291, 25387–25397 (2016).
Hunyadi, B., Siekierska, A., Sourbron, J., Copmans, D. & de Witte, P. A. M. Automated analysis of brain activity for seizure detection in zebrafish models of epilepsy. J. Neurosci. Methods 287, 13–24 (2017).
Feng, C.-W. et al. Effects of 6-hydroxydopamine exposure on motor activity and biochemical expression in zebrafish (Danio rerio) larvae. Zebrafish 11, 227–239 (2014).
Díaz-Casado, M. E. et al. Melatonin rescues zebrafish embryos from the parkinsonian phenotype restoring the parkin/PINK1/DJ-1/MUL1 network. J. Pineal Res. 61, 96–107 (2016).
Seth, A., Stemple, D. L. & Barroso, I. The emerging use of zebrafish to model metabolic disease. Dis. Model. Mech. 6, 1080–1088 (2013).
Chu, C.-Y. et al. Overexpression of Akt1 enhances adipogenesis and leads to lipoma formation in zebrafish. PLoS One 7, e36474 (2012).
Song, Y. & Cone, R. D. Creation of a genetic model of obesity in a teleost. FASEB J. 21, 2042–2049 (2007).
Oka, T. et al. Diet-induced obesity in zebrafish shares common pathophysiological pathways with mammalian obesity. BMC Physiol. 10, 21 (2010).
Montalbano, G. et al. Morphological differences in adipose tissue and changes in BDNF/Trkb expression in brain and gut of a diet induced obese zebrafish model. Ann. Anat. 204, 36–44 (2016).
Chakraborty, C., Hsu, C. H., Wen, Z. H., Lin, C. S. & Agoramoorthy, G. Zebrafish: a complete animal model for in vivo drug discovery and development. Curr. Drug Metab. 10, 116–124 (2009).
Parng, C., Seng, W. L., Semino, C. & McGrath, P. Zebrafish: a preclinical model for drug screening. Assay Drug Dev. Technol. 1, 41–48 (2002).
Williams, C. H. & Hong, C. C. Zebrafish small molecule screens: taking the phenotypic plunge. Comput. Struct. Biotechnol. J. 14, 350–356 (2016).
Deveau, A. P., Bentley, V. L. & Berman, J. N. Using zebrafish models of leukemia to streamline drug screening and discovery. Exp. Hematol. 45, 1–9 (2017).
White, R. M. et al. DHODH modulates transcriptional elongation in the neural crest and melanoma. Nature 471, 518–522 (2011).
Ordas, A. et al. Testing tuberculosis drug efficacy in a zebrafish high-throughput translational medicine screen. Antimicrob. Agents Chemother. 59, 753–762 (2015).
Baxendale, S., van Eeden, F. & Wilkinson, R. The power of zebrafish in personalised medicine. Adv. Exp. Med. Biol. 1007, 179–197 (2017).
Wu, J.-Q. et al. Patient-derived xenograft in zebrafish embryos: a new platform for translational research in gastric cancer. J. Exp. Clin. Cancer Res. 36, 160 (2017).
Gaudenzi, G. et al. Patient-derived xenograft in zebrafish embryos: a new platform for translational research in neuroendocrine tumors. Endocrine 57, 214–219 (2017).
Duck, G., Nenadic, G., Brass, A., Robertson, D. L. & Stevens, R. bioNerDS: exploring bioinformatics’ database and software use through literature mining. BMC Bioinformatics 14, 194 (2013).
Beagrie, N. & Houghton, J. The value and impact of the European Bioinformatics Institute. https://beagrie.com/static/resource/EBI-impact-report.pdf (2016).
Hirsch, T. et al. Regeneration of the entire human epidermis using transgenic stem cells. Nature 551, 327–332 (2017).
Smedley, D. et al. Next-generation diagnostics and disease-gene discovery with the Exomiser. Nat. Protoc. 10, 2004–2015 (2015).
Bone, W. P. et al. Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency. Genet. Med. 18, 608–617 (2016).
Köhler, S. et al. The Human Phenotype Ontology in 2017. Nucleic Acids Res. 45(D1), D865–D876 (2017).
Chong, J. X. et al. The genetic basis of Mendelian phenotypes: discoveries, challenges, and opportunities. Am. J. Hum. Genet. 97, 199–215 (2015).
Picher-Martel, V., Valdmanis, P. N., Gould, P. V., Julien, J. P. & Dupré, N. From animal models to human disease: a genetic approach for personalized medicine in ALS. Acta Neuropathol. Commun. 4, 70 (2016).
Renna, M., Jimenez-Sanchez, M., Sarkar, S. & Rubinsztein, D. C. Chemical inducers of autophagy that enhance the clearance of mutant proteins in neurodegenerative diseases. J. Biol. Chem. 285, 11061–11067 (2010).
Bond, M., Holthaus, S.-M. K., Tammen, I., Tear, G. & Russell, C. Use of model organisms for the study of neuronal ceroid lipofuscinosis. Biochim. Biophys. Acta 1832, 1842–1865 (2013).
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
Hu, Y. et al. An integrative approach to ortholog prediction for disease-focused and other functional studies. BMC Bioinformatics 12, 357 (2011).
GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
Sansone, S.-A. et al. DATS, the data tag suite to enable discoverability of datasets. Sci. Data 4, 170059 (2017).
Chard, K. et al. I’ll take that to go: big data bags and minimal identifiers for exchange of large, complex datasets. in IEEE International Conference on Big Data (Big Data) 319–328 (2016).
Carbon, S. et al. AmiGO: online access to ontology and annotation data. Bioinformatics 25, 288–289 (2009).
Gkoutos, G.V., Schofield, P.N. & Hoehndorf, R. The anatomy of phenotype ontologies: principles, properties and applications. Brief. Bioinform. https://doi.org/10.1093/bib/bbx035 (2017).
Sprague, J. et al. The Zebrafish Information Network: the zebrafish model organism database provides expanded support for genotypes and phenotypes. Nucleic Acids Res. 36, D768–D772 (2008).
Köhler, S. et al. Clinical interpretation of CNVs with cross-species phenotype data. J. Med. Genet. 51, 766–772 (2014).
Köhler, S. et al. Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research. F1000Res. 2, 30 (2013).
Rodríguez-García, M. Á., Gkoutos, G. V., Schofield, P. N. & Hoehndorf, R. Integrating phenotype ontologies with PhenomeNET. J. Biomed. Semantics 8, 58 (2017)..
Oliveira, D. & Pesquita, C. Improving the interoperability of biomedical ontologies with compound alignments. J. Biomed. Semantics 9, 1 (2018).
Haendel, M. Phenopackets: making phenotype profiles FAIR++ for disease diagnosis and discovery. FigShare https://doi.org/10.6084/m9.figshare.3180898.v1 (2016).
Rine, J. A future of the model organism model. Mol. Biol. Cell 25, 549–553 (2014).
The authors would like to thank all the current and past members of FlyBase, WormBase, ZFIN, MGI, SGD, RGD, and the Gene Ontology Consortium for their dedication over the past 30 years. The resulting resources are truly amazing and have a real impact on human health. The following funding sources are acknowledged for each. ZFIN: National Human Genome Research Institute at the US National Institutes of Health (U41 HG002659). MGD: National Human Genome Research Institute at the US National Institutes of Health (U41 HG000330, R25 HG007053). WB: National Human Genome Research Institute at the US National Institutes of Health (U41 HG002223), the UK Medical Research Council and the UK Biotechnology and Biological Sciences Research Council. FB: National Human Genome Research Institute at the US National Institutes of Health (U41 HG000739); British Medical Research Council. SGD: National Human Genome Research Institute at the US National Institutes of Health (U41 HG001315). RGD: National Heart, Lung, and Blood Institute at the US National Institutes of Health (RO1 HL64541). GO: National Human Genome Research Institute at the US National Institutes of Health (U41 HG002273). Alliance of Genome Resources: National Human Genome Research Institute at the US National Institutes of Health (U41 H002223).
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Howe, D.G., Blake, J.A., Bradford, Y.M. et al. Model organism data evolving in support of translational medicine. Lab Anim 47, 277–289 (2018). https://doi.org/10.1038/s41684-018-0150-4
Applied Microbiology and Biotechnology (2021)