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Comprehensive multi-cohort transcriptional meta-analysis of muscle diseases identifies a signature of disease severity

Muscle diseases share common pathological features suggesting common underlying mechanisms. We hypothesized there is a common set of genes dysregulated across muscle diseases compared to healthy muscle and that these genes correlate with severity of muscle disease. We performed meta-analysis of tran...

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Autores principales: Walsh, C. J., Batt, J., Herridge, M. S., Mathur, S., Bader, G. D., Hu, P., Khatri, P., dos Santos, C. C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9253003/
https://www.ncbi.nlm.nih.gov/pubmed/35789175
http://dx.doi.org/10.1038/s41598-022-15003-1
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author Walsh, C. J.
Batt, J.
Herridge, M. S.
Mathur, S.
Bader, G. D.
Hu, P.
Khatri, P.
dos Santos, C. C.
author_facet Walsh, C. J.
Batt, J.
Herridge, M. S.
Mathur, S.
Bader, G. D.
Hu, P.
Khatri, P.
dos Santos, C. C.
author_sort Walsh, C. J.
collection PubMed
description Muscle diseases share common pathological features suggesting common underlying mechanisms. We hypothesized there is a common set of genes dysregulated across muscle diseases compared to healthy muscle and that these genes correlate with severity of muscle disease. We performed meta-analysis of transcriptional profiles of muscle biopsies from human muscle diseases and healthy controls. Studies obtained from public microarray repositories fulfilling quality criteria were divided into six categories: (i) immobility, (ii) inflammatory myopathies, (iii) intensive care unit (ICU) acquired weakness (ICUAW), (iv) congenital muscle diseases, (v) chronic systemic diseases, (vi) motor neuron disease. Patient cohorts were separated in discovery and validation cohorts retaining roughly equal proportions of samples for the disease categories. To remove bias towards a specific muscle disease category we repeated the meta-analysis five times by removing data sets corresponding to one muscle disease class at a time in a “leave-one-disease-out” analysis. We used 636 muscle tissue samples from 30 independent cohorts to identify a 52 gene signature (36 up-regulated and 16 down-regulated genes). We validated the discriminatory power of this signature in 657 muscle biopsies from 12 additional patient cohorts encompassing five categories of muscle diseases with an area under the receiver operating characteristic curve of 0.91, 83% sensitivity, and 85.3% specificity. The expression score of the gene signature inversely correlated with quadriceps muscle mass (r = −0.50, p-value = 0.011) in ICUAW and shoulder abduction strength (r = −0.77, p-value = 0.014) in amyotrophic lateral sclerosis (ALS). The signature also positively correlated with histologic assessment of muscle atrophy in ALS (r = 0.88, p-value = 1.62 × 10(–3)) and fibrosis in muscular dystrophy (Jonckheere trend test p-value = 4.45 × 10(–9)). Our results identify a conserved transcriptional signature associated with clinical and histologic muscle disease severity. Several genes in this conserved signature have not been previously associated with muscle disease severity.
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spelling pubmed-92530032022-07-06 Comprehensive multi-cohort transcriptional meta-analysis of muscle diseases identifies a signature of disease severity Walsh, C. J. Batt, J. Herridge, M. S. Mathur, S. Bader, G. D. Hu, P. Khatri, P. dos Santos, C. C. Sci Rep Article Muscle diseases share common pathological features suggesting common underlying mechanisms. We hypothesized there is a common set of genes dysregulated across muscle diseases compared to healthy muscle and that these genes correlate with severity of muscle disease. We performed meta-analysis of transcriptional profiles of muscle biopsies from human muscle diseases and healthy controls. Studies obtained from public microarray repositories fulfilling quality criteria were divided into six categories: (i) immobility, (ii) inflammatory myopathies, (iii) intensive care unit (ICU) acquired weakness (ICUAW), (iv) congenital muscle diseases, (v) chronic systemic diseases, (vi) motor neuron disease. Patient cohorts were separated in discovery and validation cohorts retaining roughly equal proportions of samples for the disease categories. To remove bias towards a specific muscle disease category we repeated the meta-analysis five times by removing data sets corresponding to one muscle disease class at a time in a “leave-one-disease-out” analysis. We used 636 muscle tissue samples from 30 independent cohorts to identify a 52 gene signature (36 up-regulated and 16 down-regulated genes). We validated the discriminatory power of this signature in 657 muscle biopsies from 12 additional patient cohorts encompassing five categories of muscle diseases with an area under the receiver operating characteristic curve of 0.91, 83% sensitivity, and 85.3% specificity. The expression score of the gene signature inversely correlated with quadriceps muscle mass (r = −0.50, p-value = 0.011) in ICUAW and shoulder abduction strength (r = −0.77, p-value = 0.014) in amyotrophic lateral sclerosis (ALS). The signature also positively correlated with histologic assessment of muscle atrophy in ALS (r = 0.88, p-value = 1.62 × 10(–3)) and fibrosis in muscular dystrophy (Jonckheere trend test p-value = 4.45 × 10(–9)). Our results identify a conserved transcriptional signature associated with clinical and histologic muscle disease severity. Several genes in this conserved signature have not been previously associated with muscle disease severity. Nature Publishing Group UK 2022-07-04 /pmc/articles/PMC9253003/ /pubmed/35789175 http://dx.doi.org/10.1038/s41598-022-15003-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Walsh, C. J.
Batt, J.
Herridge, M. S.
Mathur, S.
Bader, G. D.
Hu, P.
Khatri, P.
dos Santos, C. C.
Comprehensive multi-cohort transcriptional meta-analysis of muscle diseases identifies a signature of disease severity
title Comprehensive multi-cohort transcriptional meta-analysis of muscle diseases identifies a signature of disease severity
title_full Comprehensive multi-cohort transcriptional meta-analysis of muscle diseases identifies a signature of disease severity
title_fullStr Comprehensive multi-cohort transcriptional meta-analysis of muscle diseases identifies a signature of disease severity
title_full_unstemmed Comprehensive multi-cohort transcriptional meta-analysis of muscle diseases identifies a signature of disease severity
title_short Comprehensive multi-cohort transcriptional meta-analysis of muscle diseases identifies a signature of disease severity
title_sort comprehensive multi-cohort transcriptional meta-analysis of muscle diseases identifies a signature of disease severity
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9253003/
https://www.ncbi.nlm.nih.gov/pubmed/35789175
http://dx.doi.org/10.1038/s41598-022-15003-1
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