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Bosnyak I, Farkas N, Molitor D, Meresz B, Patko E, Atlasz T, Vaczy A, Reglodi D. Optimization of an Ischemic Retinopathy Mouse Model and the Consequences of Hypoxia in a Time-Dependent Manner. Int J Mol Sci 2024; 25:8008. [PMID: 39125579 PMCID: PMC11311598 DOI: 10.3390/ijms25158008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 07/18/2024] [Accepted: 07/19/2024] [Indexed: 08/12/2024] Open
Abstract
The retina is one of the highest metabolically active tissues with a high oxygen consumption, so insufficient blood supply leads to visual impairment. The incidence of related conditions is increasing; however, no effective treatment without side effects is available. Furthermore, the pathomechanism of these diseases is not fully understood. Our aim was to develop an optimal ischemic retinopathy mouse model to investigate the retinal damage in a time-dependent manner. Retinal ischemia was induced by bilateral common carotid artery occlusion (BCCAO) for 10, 13, 15 or 20 min, or by right permanent unilateral common carotid artery occlusion (UCCAO). Optical coherence tomography was used to follow the changes in retinal thickness 3, 7, 14, 21 and 28 days after surgery. The number of ganglion cells was evaluated in the central and peripheral regions on whole-mount retina preparations. Expression of glial fibrillary acidic protein (GFAP) was analyzed with immunohistochemistry and Western blot. Retinal degeneration and ganglion cell loss was observed in multiple groups. Our results suggest that the 20 min BCCAO is a good model to investigate the consequences of ischemia and reperfusion in the retina in a time-dependent manner, while the UCCAO causes more severe damage in a short time, so it can be used for testing new drugs.
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Affiliation(s)
- Inez Bosnyak
- Department of Anatomy, HUN-REN-PTE PACAP Research Team, Medical School, University of Pecs, 7624 Pecs, Hungary; (I.B.); (D.M.); (B.M.); (E.P.); (T.A.)
| | - Nelli Farkas
- Institute of Bioanalysis, Medical School, University of Pecs, 7624 Pecs, Hungary;
| | - Dorottya Molitor
- Department of Anatomy, HUN-REN-PTE PACAP Research Team, Medical School, University of Pecs, 7624 Pecs, Hungary; (I.B.); (D.M.); (B.M.); (E.P.); (T.A.)
| | - Balazs Meresz
- Department of Anatomy, HUN-REN-PTE PACAP Research Team, Medical School, University of Pecs, 7624 Pecs, Hungary; (I.B.); (D.M.); (B.M.); (E.P.); (T.A.)
| | - Evelin Patko
- Department of Anatomy, HUN-REN-PTE PACAP Research Team, Medical School, University of Pecs, 7624 Pecs, Hungary; (I.B.); (D.M.); (B.M.); (E.P.); (T.A.)
| | - Tamas Atlasz
- Department of Anatomy, HUN-REN-PTE PACAP Research Team, Medical School, University of Pecs, 7624 Pecs, Hungary; (I.B.); (D.M.); (B.M.); (E.P.); (T.A.)
- Department of Sportbiology, Faculty of Sciences, University of Pecs, 7624 Pecs, Hungary
| | - Alexandra Vaczy
- Department of Anatomy, HUN-REN-PTE PACAP Research Team, Medical School, University of Pecs, 7624 Pecs, Hungary; (I.B.); (D.M.); (B.M.); (E.P.); (T.A.)
| | - Dora Reglodi
- Department of Anatomy, HUN-REN-PTE PACAP Research Team, Medical School, University of Pecs, 7624 Pecs, Hungary; (I.B.); (D.M.); (B.M.); (E.P.); (T.A.)
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2
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Hill DP, Drabkin HJ, Smith CL, Van Auken KM, D’Eustachio P. Biochemical pathways represented by Gene Ontology-Causal Activity Models identify distinct phenotypes resulting from mutations in pathways. Genetics 2023; 225:iyad152. [PMID: 37579192 PMCID: PMC10550311 DOI: 10.1093/genetics/iyad152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 07/13/2023] [Accepted: 08/02/2023] [Indexed: 08/16/2023] Open
Abstract
Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a functional network. Computable representations of biological pathways include detailed process descriptions in the Reactome Knowledgebase and causal activity flows between molecular functions in Gene Ontology-Causal Activity Models (GO-CAMs). A computational process has been developed to convert Reactome pathways to GO-CAMs. Laboratory mice are widely used models of normal and pathological human processes. We have converted human Reactome GO-CAMs to orthologous mouse GO-CAMs, as a resource to transfer pathway knowledge between humans and model organisms. These mouse GO-CAMs allowed us to define sets of genes that function in a causally connected way. To demonstrate that individual variant genes from connected pathways result in similar but distinguishable phenotypes, we used the genes in our pathway models to cross-query mouse phenotype annotations in the Mouse Genome Database (MGD). Using GO-CAM representations of 2 related but distinct pathways, gluconeogenesis and glycolysis, we show that individual causal paths in gene networks give rise to discrete phenotypic outcomes resulting from perturbations of glycolytic and gluconeogenic genes. The accurate and detailed descriptions of gene interactions recovered in this analysis of well-studied processes suggest that this strategy can be applied to less well-understood processes in less well-studied model systems to predict phenotypic outcomes of novel gene variants and to identify potential gene targets in altered processes.
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Affiliation(s)
- David P Hill
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | | | | | - Kimberly M Van Auken
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Peter D’Eustachio
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, NY 10016, USA
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Stefancsik R, Balhoff JP, Balk MA, Ball RL, Bello SM, Caron AR, Chesler EJ, de Souza V, Gehrke S, Haendel M, Harris LW, Harris NL, Ibrahim A, Koehler S, Matentzoglu N, McMurry JA, Mungall CJ, Munoz-Torres MC, Putman T, Robinson P, Smedley D, Sollis E, Thessen AE, Vasilevsky N, Walton DO, Osumi-Sutherland D. The Ontology of Biological Attributes (OBA)-computational traits for the life sciences. Mamm Genome 2023; 34:364-378. [PMID: 37076585 PMCID: PMC10382347 DOI: 10.1007/s00335-023-09992-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/06/2023] [Indexed: 04/21/2023]
Abstract
Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.
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Affiliation(s)
- Ray Stefancsik
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK.
| | - James P Balhoff
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, 27517, USA
| | - Meghan A Balk
- Natural History Museum, University of Oslo, Oslo, Norway
| | - Robyn L Ball
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA
| | | | - Anita R Caron
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | - Vinicius de Souza
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Sarah Gehrke
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Melissa Haendel
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Laura W Harris
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Nomi L Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Arwa Ibrahim
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | | | - Julie A McMurry
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Tim Putman
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | | | - Damian Smedley
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Elliot Sollis
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Anne E Thessen
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Nicole Vasilevsky
- Data Collaboration Center, Critical Path Institute, Tucson, AZ, 85718, USA
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4
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Stefancsik R, Balhoff JP, Balk MA, Ball R, Bello SM, Caron AR, Chessler E, de Souza V, Gehrke S, Haendel M, Harris LW, Harris NL, Ibrahim A, Koehler S, Matentzoglu N, McMurry JA, Mungall CJ, Munoz-Torres MC, Putman T, Robinson P, Smedley D, Sollis E, Thessen AE, Vasilevsky N, Walton DO, Osumi-Sutherland D. The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.26.525742. [PMID: 36747660 PMCID: PMC9900877 DOI: 10.1101/2023.01.26.525742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.
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Affiliation(s)
- Ray Stefancsik
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - James P. Balhoff
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27517, USA
| | - Meghan A. Balk
- National Ecological Observatory Network, Battelle, Boulder, CO 80301, USA
| | - Robyn Ball
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | | | - Anita R. Caron
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | - Vinicius de Souza
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Sarah Gehrke
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Melissa Haendel
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Laura W. Harris
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Nomi L. Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Arwa Ibrahim
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | | | - Julie A. McMurry
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Christopher J. Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Tim Putman
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | | | - Damian Smedley
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Elliot Sollis
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Anne E Thessen
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Nicole Vasilevsky
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
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Abstract
For many years, the laboratory mouse has been the favored model organism to study mammalian development, biology and disease. Among its advantages for these studies are its close concordance with human biology, the syntenic relationship between the mouse and other mammalian genomes, the existence of many inbred strains, its short gestation period, its relatively low cost for housing and husbandry, and the wide array of tools for genome modification, mutagenesis, and for cryopreserving embryos, sperm and eggs. The advent of CRISPR genome modification techniques has considerably broadened the landscape of model organisms available for study, including other mammalian species. However, the mouse remains the most popular and utilized system to model human development, biology, and disease processes. In this review, we will briefly summarize the long history of mice as a preferred mammalian genetic and model system, and review current large-scale mutagenesis efforts using genome modification to produce improved models for mammalian development and disease.
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Affiliation(s)
- Thomas Gridley
- Center for Clinical and Translational Research, Maine Medical Center Research Institute, Scarborough, ME, United States.
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Turning fifty. Lab Anim (NY) 2021; 50:257. [PMID: 34561681 DOI: 10.1038/s41684-021-00867-z] [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]
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