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Machine intelligence identifies soluble TNFa as a therapeutic target for spinal cord injury
Traumatic spinal cord injury (SCI) produces a complex syndrome that is expressed across multiple endpoints ranging from molecular and cellular changes to functional behavioral deficits. Effective therapeutic strategies for CNS injury are therefore likely to manifest multi-factorial effects across a...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873211/ https://www.ncbi.nlm.nih.gov/pubmed/33564058 http://dx.doi.org/10.1038/s41598-021-82951-5 |
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author | Huie, J. R. Ferguson, A. R. Kyritsis, N. Pan, J. Z. Irvine, K.-A. Nielson, J. L. Schupp, P. G. Oldham, M. C. Gensel, J. C. Lin, A. Segal, M. R. Ratan, R. R. Bresnahan, J. C. Beattie, M. S. |
author_facet | Huie, J. R. Ferguson, A. R. Kyritsis, N. Pan, J. Z. Irvine, K.-A. Nielson, J. L. Schupp, P. G. Oldham, M. C. Gensel, J. C. Lin, A. Segal, M. R. Ratan, R. R. Bresnahan, J. C. Beattie, M. S. |
author_sort | Huie, J. R. |
collection | PubMed |
description | Traumatic spinal cord injury (SCI) produces a complex syndrome that is expressed across multiple endpoints ranging from molecular and cellular changes to functional behavioral deficits. Effective therapeutic strategies for CNS injury are therefore likely to manifest multi-factorial effects across a broad range of biological and functional outcome measures. Thus, multivariate analytic approaches are needed to capture the linkage between biological and neurobehavioral outcomes. Injury-induced neuroinflammation (NI) presents a particularly challenging therapeutic target, since NI is involved in both degeneration and repair. Here, we used big-data integration and large-scale analytics to examine a large dataset of preclinical efficacy tests combining five different blinded, fully counter-balanced treatment trials for different acute anti-inflammatory treatments for cervical spinal cord injury in rats. Multi-dimensional discovery, using topological data analysis (TDA) and principal components analysis (PCA) revealed that only one showed consistent multidimensional syndromic benefit: intrathecal application of recombinant soluble TNFα receptor 1 (sTNFR1), which showed an inverse-U dose response efficacy. Using the optimal acute dose, we showed that clinically-relevant 90 min delayed treatment profoundly affected multiple biological indices of NI in the first 48 h after injury, including reduction in pro-inflammatory cytokines and gene expression of a coherent complex of acute inflammatory mediators and receptors. Further, a 90 min delayed bolus dose of sTNFR1 reduced the expression of NI markers in the chronic perilesional spinal cord, and consistently improved neurological function over 6 weeks post SCI. These results provide validation of a novel strategy for precision preclinical drug discovery that is likely to improve translation in the difficult landscape of CNS trauma, and confirm the importance of TNFα signaling as a therapeutic target. |
format | Online Article Text |
id | pubmed-7873211 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78732112021-02-11 Machine intelligence identifies soluble TNFa as a therapeutic target for spinal cord injury Huie, J. R. Ferguson, A. R. Kyritsis, N. Pan, J. Z. Irvine, K.-A. Nielson, J. L. Schupp, P. G. Oldham, M. C. Gensel, J. C. Lin, A. Segal, M. R. Ratan, R. R. Bresnahan, J. C. Beattie, M. S. Sci Rep Article Traumatic spinal cord injury (SCI) produces a complex syndrome that is expressed across multiple endpoints ranging from molecular and cellular changes to functional behavioral deficits. Effective therapeutic strategies for CNS injury are therefore likely to manifest multi-factorial effects across a broad range of biological and functional outcome measures. Thus, multivariate analytic approaches are needed to capture the linkage between biological and neurobehavioral outcomes. Injury-induced neuroinflammation (NI) presents a particularly challenging therapeutic target, since NI is involved in both degeneration and repair. Here, we used big-data integration and large-scale analytics to examine a large dataset of preclinical efficacy tests combining five different blinded, fully counter-balanced treatment trials for different acute anti-inflammatory treatments for cervical spinal cord injury in rats. Multi-dimensional discovery, using topological data analysis (TDA) and principal components analysis (PCA) revealed that only one showed consistent multidimensional syndromic benefit: intrathecal application of recombinant soluble TNFα receptor 1 (sTNFR1), which showed an inverse-U dose response efficacy. Using the optimal acute dose, we showed that clinically-relevant 90 min delayed treatment profoundly affected multiple biological indices of NI in the first 48 h after injury, including reduction in pro-inflammatory cytokines and gene expression of a coherent complex of acute inflammatory mediators and receptors. Further, a 90 min delayed bolus dose of sTNFR1 reduced the expression of NI markers in the chronic perilesional spinal cord, and consistently improved neurological function over 6 weeks post SCI. These results provide validation of a novel strategy for precision preclinical drug discovery that is likely to improve translation in the difficult landscape of CNS trauma, and confirm the importance of TNFα signaling as a therapeutic target. Nature Publishing Group UK 2021-02-09 /pmc/articles/PMC7873211/ /pubmed/33564058 http://dx.doi.org/10.1038/s41598-021-82951-5 Text en © The Author(s) 2021 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Huie, J. R. Ferguson, A. R. Kyritsis, N. Pan, J. Z. Irvine, K.-A. Nielson, J. L. Schupp, P. G. Oldham, M. C. Gensel, J. C. Lin, A. Segal, M. R. Ratan, R. R. Bresnahan, J. C. Beattie, M. S. Machine intelligence identifies soluble TNFa as a therapeutic target for spinal cord injury |
title | Machine intelligence identifies soluble TNFa as a therapeutic target for spinal cord injury |
title_full | Machine intelligence identifies soluble TNFa as a therapeutic target for spinal cord injury |
title_fullStr | Machine intelligence identifies soluble TNFa as a therapeutic target for spinal cord injury |
title_full_unstemmed | Machine intelligence identifies soluble TNFa as a therapeutic target for spinal cord injury |
title_short | Machine intelligence identifies soluble TNFa as a therapeutic target for spinal cord injury |
title_sort | machine intelligence identifies soluble tnfa as a therapeutic target for spinal cord injury |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873211/ https://www.ncbi.nlm.nih.gov/pubmed/33564058 http://dx.doi.org/10.1038/s41598-021-82951-5 |
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