Multiomic characterization of disease progression in mice lacking dystrophin

Duchenne muscular dystrophy (DMD) is caused by genetic mutations leading to lack of dystrophin in skeletal muscle. A better understanding of how objective biomarkers for DMD vary across subjects and over time is needed to model disease progression and response to therapy more effectively, both in pr...

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Bibliographic Details
Published in:PloS one 2023-03, Vol.18 (3), p.e0283869-e0283869
Main Authors: Signorelli, Mirko, Tsonaka, Roula, Aartsma-Rus, Annemieke, Spitali, Pietro
Format: Article
Language:English
Subjects:
Analysis
Animal models
Animals
Biology and Life Sciences
Biomarkers
Blood
Clinical trials
Datasets
Development and progression
Disease Models, Animal
Disease Progression
Duchenne muscular dystrophy
Duchenne's muscular dystrophy
Dystrophin
Dystrophin - genetics
Dystrophin - metabolism
Dystrophy
Energy metabolism
Experiments
Factor analysis
Gene expression
Genes
Genetic aspects
Health aspects
Lipids
Mass spectrometry
Mass spectroscopy
Medicine and Health Sciences
Metabolism
Metabolites
Metabolomics
Mice
Mice, Inbred mdx
Multiomics
Muscle, Skeletal - metabolism
Muscles
Muscular dystrophy
Muscular Dystrophy, Duchenne - pathology
Plasma
Research and Analysis Methods
RNA
Skeletal muscle
Statistical analysis
Steroids
Trajectory analysis
Trends
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Summary:Duchenne muscular dystrophy (DMD) is caused by genetic mutations leading to lack of dystrophin in skeletal muscle. A better understanding of how objective biomarkers for DMD vary across subjects and over time is needed to model disease progression and response to therapy more effectively, both in pre-clinical and clinical research. We present an in-depth characterization of disease progression in 3 murine models of DMD by multiomic analysis of longitudinal trajectories between 6 and 30 weeks of age. Integration of RNA-seq, mass spectrometry-based metabolomic and lipidomic data obtained in muscle and blood samples by Multi-Omics Factor Analysis (MOFA) led to the identification of 8 latent factors that explained 78.8% of the variance in the multiomic dataset. Latent factors could discriminate dystrophic and healthy mice, as well as different time-points. MOFA enabled to connect the gene expression signature in dystrophic muscles, characterized by pro-fibrotic and energy metabolism alterations, to inflammation and lipid signatures in blood. Our results show that omic observations in blood can be directly related to skeletal muscle pathology in dystrophic muscle.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0283869