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Scoring disease in an animal model of multiple sclerosis using a novel infrared-based automated activity-monitoring system

Multiple sclerosis (MS) is a chronic demyelinating disorder of the central nervous system (CNS). Its corresponding animal model, experimental autoimmune encephalomyelitis (EAE), is widely used to understand disease pathogenesis and test novel therapeutic agents. However, existing methods to score EA...

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Autores principales: Shahi, Shailesh K., Freedman, Samantha N., Dahl, Rachel A., Karandikar, Nitin J., Mangalam, Ashutosh K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6915774/
https://www.ncbi.nlm.nih.gov/pubmed/31844134
http://dx.doi.org/10.1038/s41598-019-55713-7
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author Shahi, Shailesh K.
Freedman, Samantha N.
Dahl, Rachel A.
Karandikar, Nitin J.
Mangalam, Ashutosh K.
author_facet Shahi, Shailesh K.
Freedman, Samantha N.
Dahl, Rachel A.
Karandikar, Nitin J.
Mangalam, Ashutosh K.
author_sort Shahi, Shailesh K.
collection PubMed
description Multiple sclerosis (MS) is a chronic demyelinating disorder of the central nervous system (CNS). Its corresponding animal model, experimental autoimmune encephalomyelitis (EAE), is widely used to understand disease pathogenesis and test novel therapeutic agents. However, existing methods to score EAE disease severity are subjective and often vary between individual researchers, making it difficult to translate findings across different studies. An enhanced automated method of disease scoring would eliminate subjectivity and reduce operator-dependent errors. Here, we used an Infra-Red Activity Monitoring System (IRAMS) to measure murine locomotor activity as a surrogate measure of disease severity and compared it to standard EAE scoring methods. In mice immunized with CNS-specific myelin antigens, we observed an inverse correlation between disease severity and mouse activity, with the IRAMS showing enhanced disease scoring compared to standard EAE scoring methods. Relative to standard EAE scoring methods, IRAMS showed comparable measurement of disease relapses and remissions in the SJL/J-relapsing-remitting model of EAE, and could comparably assess the therapeutic efficiency of the MS drug, Copaxone (Glatiramer acetate-GA). Thus, the IRAMS is a method to measure disease severity in EAE without subjective bias and is a tool to consistently assess the efficacy of novel therapeutic agents for MS.
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spelling pubmed-69157742019-12-18 Scoring disease in an animal model of multiple sclerosis using a novel infrared-based automated activity-monitoring system Shahi, Shailesh K. Freedman, Samantha N. Dahl, Rachel A. Karandikar, Nitin J. Mangalam, Ashutosh K. Sci Rep Article Multiple sclerosis (MS) is a chronic demyelinating disorder of the central nervous system (CNS). Its corresponding animal model, experimental autoimmune encephalomyelitis (EAE), is widely used to understand disease pathogenesis and test novel therapeutic agents. However, existing methods to score EAE disease severity are subjective and often vary between individual researchers, making it difficult to translate findings across different studies. An enhanced automated method of disease scoring would eliminate subjectivity and reduce operator-dependent errors. Here, we used an Infra-Red Activity Monitoring System (IRAMS) to measure murine locomotor activity as a surrogate measure of disease severity and compared it to standard EAE scoring methods. In mice immunized with CNS-specific myelin antigens, we observed an inverse correlation between disease severity and mouse activity, with the IRAMS showing enhanced disease scoring compared to standard EAE scoring methods. Relative to standard EAE scoring methods, IRAMS showed comparable measurement of disease relapses and remissions in the SJL/J-relapsing-remitting model of EAE, and could comparably assess the therapeutic efficiency of the MS drug, Copaxone (Glatiramer acetate-GA). Thus, the IRAMS is a method to measure disease severity in EAE without subjective bias and is a tool to consistently assess the efficacy of novel therapeutic agents for MS. Nature Publishing Group UK 2019-12-16 /pmc/articles/PMC6915774/ /pubmed/31844134 http://dx.doi.org/10.1038/s41598-019-55713-7 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Shahi, Shailesh K.
Freedman, Samantha N.
Dahl, Rachel A.
Karandikar, Nitin J.
Mangalam, Ashutosh K.
Scoring disease in an animal model of multiple sclerosis using a novel infrared-based automated activity-monitoring system
title Scoring disease in an animal model of multiple sclerosis using a novel infrared-based automated activity-monitoring system
title_full Scoring disease in an animal model of multiple sclerosis using a novel infrared-based automated activity-monitoring system
title_fullStr Scoring disease in an animal model of multiple sclerosis using a novel infrared-based automated activity-monitoring system
title_full_unstemmed Scoring disease in an animal model of multiple sclerosis using a novel infrared-based automated activity-monitoring system
title_short Scoring disease in an animal model of multiple sclerosis using a novel infrared-based automated activity-monitoring system
title_sort scoring disease in an animal model of multiple sclerosis using a novel infrared-based automated activity-monitoring system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6915774/
https://www.ncbi.nlm.nih.gov/pubmed/31844134
http://dx.doi.org/10.1038/s41598-019-55713-7
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