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Alghamdi SM, Schofield PN, Hoehndorf R. How much do model organism phenotypes contribute to the computational identification of human disease genes? Dis Model Mech 2022; 15:275986. [PMID: 35758016 PMCID: PMC9366895 DOI: 10.1242/dmm.049441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 06/13/2022] [Indexed: 12/04/2022] Open
Abstract
Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype–phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past decade, cross-species phenotype comparisons have proven valuble, and several ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of disease-associated genes is not fully explored. We used phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in model organisms to disease-associated phenotypes in humans. Semantic machine learning methods were used to measure the contribution of different model organisms to the identification of known human gene–disease associations. We found that mouse genotype–phenotype data provided the most important dataset in the identification of human disease genes by semantic similarity and machine learning over phenotype ontologies. Other model organisms' data did not improve identification over that obtained using the mouse alone, and therefore did not contribute significantly to this task. Our work impacts on the development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation. This article has an associated First Person interview with the first author of the paper. Editor's choice: We investigated the use of model organism phenotypes in the computational identification of disease genes, identifying several data biases and concluding that mouse model phenotypes contribute most to computational disease gene identification.
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Affiliation(s)
- Sarah M Alghamdi
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, 23955 Thuwal, Saudi Arabia
| | - Paul N Schofield
- Department of Physiology, Development & Neuroscience, University of Cambridge, Downing Street, CB2 3EG, Cambridge, UK
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, 23955 Thuwal, Saudi Arabia
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Tracey LJ, An Y, Justice MJ. CyTOF: An Emerging Technology for Single-Cell Proteomics in the Mouse. Curr Protoc 2021; 1:e118. [PMID: 33887117 DOI: 10.1002/cpz1.118] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The ability to analyze the proteome of single cells is critical for the advancement of studies of steady-state and pathological processes. Mass cytometry, or CyTOF, combines principles of mass spectrometry and flow cytometry to enable single-cell analysis of protein expression. CyTOF can simultaneously assess DNA content and proteins and has the capacity to measure 40 to 100 parameters in each cell. Applying this technology to tissues or cells on slides, termed imaging mass cytometry (IMC), allows for visualization of normal and diseased tissues in situ. The high-dimensional proteomic analysis that can be undertaken with CyTOF and IMC has the potential to enhance our understanding of complex and heterogeneous developmental and disease pathways. This article will describe the CyTOF experimental workflow, including reagent selection, sample preparation, and data analysis. CyTOF is compared to flow cytometry, focusing on the strengths and weaknesses of these powerful techniques. Importantly, we review key studies in mouse models of human disease that highlight the strength of CyTOF and IMC to drive discovery research and therapeutic advancement. © 2021 Wiley Periodicals LLC.
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Affiliation(s)
- Lauren J Tracey
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Yeji An
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Program in Cell Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Monica J Justice
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
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Sundberg JP, Boyd K, Hogenesch H, Nikitin AY, Treuting PM, Ward JM. Training mouse pathologists: 16 th annual workshop on the pathology of mouse models of human disease. Lab Anim (NY) 2018; 47:38-40. [PMID: 29384517 DOI: 10.1038/laban.1399] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
| | - Kelli Boyd
- Departments of Comparative Medicine and Pathology, Vanderbilt University, Nashville, TN
| | - Harm Hogenesch
- Purdue University College of Veterinary Medicine, West Lafayette, IN
| | | | - Piper M Treuting
- Departments of Comparative Medicine and Pathology, University of Washington, Seattle, WA
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Krupke DM, Begley DA, Sundberg JP, Richardson JE, Neuhauser SB, Bult CJ. The Mouse Tumor Biology Database: A Comprehensive Resource for Mouse Models of Human Cancer. Cancer Res 2017; 77:e67-e70. [PMID: 29092943 DOI: 10.1158/0008-5472.can-17-0584] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Revised: 06/16/2017] [Accepted: 08/07/2017] [Indexed: 01/28/2023]
Abstract
Research using laboratory mice has led to fundamental insights into the molecular genetic processes that govern cancer initiation, progression, and treatment response. Although thousands of scientific articles have been published about mouse models of human cancer, collating information and data for a specific model is hampered by the fact that many authors do not adhere to existing annotation standards when describing models. The interpretation of experimental results in mouse models can also be confounded when researchers do not factor in the effect of genetic background on tumor biology. The Mouse Tumor Biology (MTB) database is an expertly curated, comprehensive compendium of mouse models of human cancer. Through the enforcement of nomenclature and related annotation standards, MTB supports aggregation of data about a cancer model from diverse sources and assessment of how genetic background of a mouse strain influences the biological properties of a specific tumor type and model utility. Cancer Res; 77(21); e67-70. ©2017 AACR.
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Sundberg JP, Boyd K, Hogenesch H, Nikitin AY, Treuting PM, Ward JM. Training mouse pathologists: 15 years of workshops on the pathology of mouse models of human disease. Lab Anim (NY) 2017; 46:204-206. [PMID: 28422111 DOI: 10.1038/laban.1256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
| | | | - Harm Hogenesch
- Purdue University College of Veterinary Medicine, West Lafayette, IN
| | | | - Piper M Treuting
- Departments of Comparative Medicine and Pathology, University of Washington, Seattle, WA
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Ludwig R. Immune mechanism-targeted treatment of experimental epidermolysis bullosa acquisita. Expert Rev Clin Immunol 2015; 11:1365-78. [PMID: 26471717 DOI: 10.1586/1744666x.2015.1085801] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Epidermolysis bullosa acquisita (EBA) is an autoimmune bullous dermatosis characterized by chronic mucocutaneous blistering caused by autoantibodies directed against type VII collagen. EBA causes a high morbidity and is difficult to treat. Model systems have significantly broadened our understanding of EBA pathogenesis, leading to the identification of numerous therapeutic targets. Of these, so far, a few have been evaluated for their therapeutic potential in preclinical models. In mice, EBA can be induced by transfer of anti-type VII collagen antibodies or by immunization with the protein. The latter model, immunization-induced EBA, is ideal to test drugs for their therapeutic efficacy. Here, mice with already established disease can be treated for prolonged periods. Albeit time consuming, results from immunization-induced EBA will pave the way for clinical application in patients. As the key pathogenic principle, that is, autoantibody-induced, leukocyte-mediated tissue injury and inflammation, is shared by other diseases, these findings may have translational applications beyond EBA.
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Affiliation(s)
- Ralf Ludwig
- a University of Luebeck, Luebeck Institute of Experimental Dermatology, Ratzeburger Allee 160, Luebeck, Germany
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Everitt JI. The Future of Preclinical Animal Models in Pharmaceutical Discovery and Development. Toxicol Pathol 2014; 43:70-7. [DOI: 10.1177/0192623314555162] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Animal models have provided an important tool to help make the decision to take potential therapies from preclinical studies to humans. In the past several years, the strong reliance of the pharmaceutical discovery and development process on the use of animal models has come under increasing scrutiny for ethical and scientific reasons. Several prominent and widely publicized articles have reported limited concordance of animal experiments with subsequent human clinical trials. Recent assessments of the quality of animal studies have suggested that this translational failure may be due in part to shortcomings in the planning, conduct, and reporting of in vivo studies. This article will emphasize methods to assure best practice rigor in animal study methods and reporting. It will introduce the so-called scientific 3Rs of relevance, robustness, and reproducibility to the in vivo study approach and will review important new trends in the animal research and pharmaceutical discovery and development communities.
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Bult CJ, Krupke DM, Begley DA, Richardson JE, Neuhauser SB, Sundberg JP, Eppig JT. Mouse Tumor Biology (MTB): a database of mouse models for human cancer. Nucleic Acids Res 2014; 43:D818-24. [PMID: 25332399 PMCID: PMC4384039 DOI: 10.1093/nar/gku987] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
The Mouse Tumor Biology (MTB; http://tumor.informatics.jax.org) database is a unique online compendium of mouse models for human cancer. MTB provides online access to expertly curated information on diverse mouse models for human cancer and interfaces for searching and visualizing data associated with these models. The information in MTB is designed to facilitate the selection of strains for cancer research and is a platform for mining data on tumor development and patterns of metastases. MTB curators acquire data through manual curation of peer-reviewed scientific literature and from direct submissions by researchers. Data in MTB are also obtained from other bioinformatics resources including PathBase, the Gene Expression Omnibus and ArrayExpress. Recent enhancements to MTB improve the association between mouse models and human genes commonly mutated in a variety of cancers as identified in large-scale cancer genomics studies, provide new interfaces for exploring regions of the mouse genome associated with cancer phenotypes and incorporate data and information related to Patient-Derived Xenograft models of human cancers.
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Affiliation(s)
- Carol J Bult
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, USA
| | - Debra M Krupke
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, USA
| | - Dale A Begley
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, USA
| | | | | | - John P Sundberg
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, USA
| | - Janan T Eppig
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, USA
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Oellrich A, Koehler S, Washington N, Mungall C, Lewis S, Haendel M, Robinson PN, Smedley D. The influence of disease categories on gene candidate predictions from model organism phenotypes. J Biomed Semantics 2014; 5:S4. [PMID: 25093073 PMCID: PMC4108905 DOI: 10.1186/2041-1480-5-s1-s4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND The molecular etiology is still to be identified for about half of the currently described Mendelian diseases in humans, thereby hindering efforts to find treatments or preventive measures. Advances, such as new sequencing technologies, have led to increasing amounts of data becoming available with which to address the problem of identifying disease genes. Therefore, automated methods are needed that reliably predict disease gene candidates based on available data. We have recently developed Exomiser as a tool for identifying causative variants from exome analysis results by filtering and prioritising using a number of criteria including the phenotype similarity between the disease and mouse mutants involving the gene candidates. Initial investigations revealed a variation in performance for different medical categories of disease, due in part to a varying contribution of the phenotype scoring component. RESULTS In this study, we further analyse the performance of our cross-species phenotype matching algorithm, and examine in more detail the reasons why disease gene filtering based on phenotype data works better for certain disease categories than others. We found that in addition to misleading phenotype alignments between species, some disease categories are still more amenable to automated predictions than others, and that this often ties in with community perceptions on how well the organism works as model. CONCLUSIONS In conclusion, our automated disease gene candidate predictions are highly dependent on the organism used for the predictions and the disease category being studied. Future work on computational disease gene prediction using phenotype data would benefit from methods that take into account the disease category and the source of model organism data.
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Affiliation(s)
- Anika Oellrich
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB10 1SA Hinxton, UK
| | - Sebastian Koehler
- Institute for Medical Genetics and Human Genetics, Universitaetsklinikum Charite, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Nicole Washington
- Berkeley Bioinformatics Open-Source Projects, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, CA 94720 Berkeley, USA
| | - Sanger Mouse Genetic Project
- Berkeley Bioinformatics Open-Source Projects, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, CA 94720 Berkeley, USA
| | - Chris Mungall
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB10 1SA Hinxton, UK
| | - Suzanna Lewis
- Berkeley Bioinformatics Open-Source Projects, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, CA 94720 Berkeley, USA
| | - Melissa Haendel
- Ontology Development Group, OHSU Library, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Rd, OR 97239 Portland, USA
| | - Peter N Robinson
- Institute for Medical Genetics and Human Genetics, Universitaetsklinikum Charite, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Damian Smedley
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB10 1SA Hinxton, UK
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