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Cardiac ion channel expression in the equine model ‐ In‐silico prediction utilising RNA sequencing data from mixed tissue samples
Understanding cardiomyocyte ion channel expression is crucial to understanding normal cardiac electrophysiology and underlying mechanisms of cardiac pathologies particularly arrhythmias. Hitherto, equine cardiac ion channel expression has rarely been investigated. Therefore, we aim to predict equine...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316921/ https://www.ncbi.nlm.nih.gov/pubmed/35880716 http://dx.doi.org/10.14814/phy2.15273 |
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author | Premont, Antoine Saadeh, Khalil Edling, Charlotte Lewis, Rebecca Marr, Celia M. Jeevaratnam, Kamalan |
author_facet | Premont, Antoine Saadeh, Khalil Edling, Charlotte Lewis, Rebecca Marr, Celia M. Jeevaratnam, Kamalan |
author_sort | Premont, Antoine |
collection | PubMed |
description | Understanding cardiomyocyte ion channel expression is crucial to understanding normal cardiac electrophysiology and underlying mechanisms of cardiac pathologies particularly arrhythmias. Hitherto, equine cardiac ion channel expression has rarely been investigated. Therefore, we aim to predict equine cardiac ion channel gene expression. Raw RNAseq data from normal horses from 9 datasets was retrieved from ArrayExpress and European Nucleotide Archive and reanalysed. The normalised (FPKM) read counts for a gene in a mix of tissue were hypothesised to be the average of the expected expression in each tissue weighted by the proportion of the tissue in the mix. The cardiac‐specific expression was predicted by estimating the mean expression in each other tissues. To evaluate the performance of the model, predicted gene expression values were compared to the human cardiac gene expression. Cardiac‐specific expression could be predicted for 91 ion channels including most expressed Na(+) channels, K(+) channels and Ca(2+)‐handling proteins. These revealed interesting differences from what would be expected based on human studies. These differences included predominance of Na(V)1.4 rather than Na(V)1.5 channel, and RYR1, SERCA1 and CASQ1 rather than RYR2, SERCA2, CASQ2 Ca(2+)‐handling proteins. Differences in channel expression not only implicate potentially different regulatory mechanisms but also pathological mechanisms of arrhythmogenesis. |
format | Online Article Text |
id | pubmed-9316921 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93169212022-07-27 Cardiac ion channel expression in the equine model ‐ In‐silico prediction utilising RNA sequencing data from mixed tissue samples Premont, Antoine Saadeh, Khalil Edling, Charlotte Lewis, Rebecca Marr, Celia M. Jeevaratnam, Kamalan Physiol Rep Original Articles Understanding cardiomyocyte ion channel expression is crucial to understanding normal cardiac electrophysiology and underlying mechanisms of cardiac pathologies particularly arrhythmias. Hitherto, equine cardiac ion channel expression has rarely been investigated. Therefore, we aim to predict equine cardiac ion channel gene expression. Raw RNAseq data from normal horses from 9 datasets was retrieved from ArrayExpress and European Nucleotide Archive and reanalysed. The normalised (FPKM) read counts for a gene in a mix of tissue were hypothesised to be the average of the expected expression in each tissue weighted by the proportion of the tissue in the mix. The cardiac‐specific expression was predicted by estimating the mean expression in each other tissues. To evaluate the performance of the model, predicted gene expression values were compared to the human cardiac gene expression. Cardiac‐specific expression could be predicted for 91 ion channels including most expressed Na(+) channels, K(+) channels and Ca(2+)‐handling proteins. These revealed interesting differences from what would be expected based on human studies. These differences included predominance of Na(V)1.4 rather than Na(V)1.5 channel, and RYR1, SERCA1 and CASQ1 rather than RYR2, SERCA2, CASQ2 Ca(2+)‐handling proteins. Differences in channel expression not only implicate potentially different regulatory mechanisms but also pathological mechanisms of arrhythmogenesis. John Wiley and Sons Inc. 2022-07-26 /pmc/articles/PMC9316921/ /pubmed/35880716 http://dx.doi.org/10.14814/phy2.15273 Text en © 2022 The Authors. Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Articles Premont, Antoine Saadeh, Khalil Edling, Charlotte Lewis, Rebecca Marr, Celia M. Jeevaratnam, Kamalan Cardiac ion channel expression in the equine model ‐ In‐silico prediction utilising RNA sequencing data from mixed tissue samples |
title | Cardiac ion channel expression in the equine model ‐ In‐silico prediction utilising RNA sequencing data from mixed tissue samples |
title_full | Cardiac ion channel expression in the equine model ‐ In‐silico prediction utilising RNA sequencing data from mixed tissue samples |
title_fullStr | Cardiac ion channel expression in the equine model ‐ In‐silico prediction utilising RNA sequencing data from mixed tissue samples |
title_full_unstemmed | Cardiac ion channel expression in the equine model ‐ In‐silico prediction utilising RNA sequencing data from mixed tissue samples |
title_short | Cardiac ion channel expression in the equine model ‐ In‐silico prediction utilising RNA sequencing data from mixed tissue samples |
title_sort | cardiac ion channel expression in the equine model ‐ in‐silico prediction utilising rna sequencing data from mixed tissue samples |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316921/ https://www.ncbi.nlm.nih.gov/pubmed/35880716 http://dx.doi.org/10.14814/phy2.15273 |
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