ArticlePloS one2023
Multiomic characterization of disease progression in mice lacking dystrophin.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Characterization of a humanized mouse model of Duchenne muscular dystrophy to support the development of genetic medicines.Disease models & mechanisms · 2025Article
- Article
- D- and L-Amino Acid Blood Concentrations Are Affected in Children With Duchenne Muscular Dystrophy.Journal of cellular and molecular medicine · 2025Article
- How Can Proteomics Help to Elucidate the Pathophysiological Crosstalk in Muscular Dystrophy and Associated Multi-System Dysfunction?Proteomes · 2024Article
- Serum metabolomic signatures of patients with rare neurogenetic diseases: an insight into potential biomarkers and treatment targets.Frontiers in molecular neuroscience · 2024Article
- The metabolomic plasma profile of patients with Duchenne muscular dystrophy: providing new evidence for its pathogenesis.Orphanet journal of rare diseases · 2023Article
- Extracellular Matrix Proteomics: TheBiomolecules · 2023Review
- Considering the Promise of Vamorolone for Treating Duchenne Muscular Dystrophy.Journal of neuromuscular diseases · 2023Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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