![]() PLoS ONE 18(3):Įditor: Shinsuke Yuasa, Keio University - Shinanomachi Campus: Keio Gijuku Daigaku - Shinanomachi Campus, JAPAN Our results show that omic observations in blood can be directly related to skeletal muscle pathology in dystrophic muscle.Ĭitation: Signorelli M, Tsonaka R, Aartsma-Rus A, Spitali P (2023) Multiomic characterization of disease progression in mice lacking dystrophin. ![]() 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. Latent factors could discriminate dystrophic and healthy mice, as well as different time-points. 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. 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. ![]() 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. Duchenne muscular dystrophy (DMD) is caused by genetic mutations leading to lack of dystrophin in skeletal muscle. ![]()
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