Integrated phenotypic and transcriptomic characterization of desmin-related cardiomyopathy in hiPSC-derived cardiomyocytes and machine learning-based classification of disease features.

Journal: European journal of cell biology
Published Date:

Abstract

Desmin-related diseases are characterized by skeletal muscle weakness, cardiomyopathy, and respiratory dysfunction due to mutations in the desmin gene (DES), which encodes a protein essential for muscle cell integrity. This study investigates the effects of a pathogenic desmin mutation (DES) in human cardiomyocytes derived from human induced pluripotent stem cells (hiPSCs) obtained from a patient carrying the DES mutation, compared to cardiomyocytes derived from hiPSCs of three healthy donors. To further validate our findings a genome edited cell line has been obtained following the insertion of the mutation in a control hiPSC line. Using advanced technologies, including transcriptomics and phenotypic machine learning algorithms, we analyzed how this mutation disrupts cellular function and contributes to disease phenotypes. Our findings reveal that cardiomyocytes carrying DES exhibit cytoplasmic protein aggregation, mitochondrial and sarcomere defects, and contractile dysfunctions, highlighting key phenotypic defects in desmin-related cardiomyopathy. Finaly, we developed a machine learning prediction model to classify cellular phenotypes, which can be used for translational research, including drug candidate screening. This research opens new avenues for understanding the molecular mechanisms of desmin-related cardiomyopathies and fosters the development of novel therapeutic strategies.

Authors

  • Vivien Batoumeni
    Ksilink, Strasbourg F-67000, France; Sorbonne Université, Institut de Biologie Paris-Seine (IBPS), UMR CNRS 8263, INSERM U1345, Development, Adaptation and Ageing, Paris F-75005, France.
  • Yeranuhi Hovhannisyan
    Sorbonne Université, Institut de Biologie Paris-Seine (IBPS), UMR CNRS 8263, INSERM U1345, Development, Adaptation and Ageing, Paris F-75005, France.
  • Bénédicte Gobert
    Ksilink, Strasbourg F-67000, France.
  • Keyhan Alvandipour
    Ksilink, Strasbourg F-67000, France.
  • Jennifer Arthur Ataam
    Ksilink, Strasbourg F-67000, France.
  • Ombline Conrad
    Ksilink, Strasbourg F-67000, France.
  • David Hoffmann
    Ksilink, Strasbourg F-67000, France.
  • Nicolas Wiest-Daesslé
    Ksilink, Strasbourg F-67000, France.
  • Jochen Dobner
    Leibniz Research Institute for Environmental Medicine, Auf'm Hennekamp 50, Düsseldorf D-40225, Germany.
  • José Américo Nabuco Leva Ferreira Freitas
    Sorbonne Université, Institut de Biologie Paris-Seine (IBPS), UMR CNRS 8263, INSERM U1345, Development, Adaptation and Ageing, Paris F-75005, France.
  • Hakim Hocini
    Université Paris-Est Créteil, INSERM U955, Créteil F-94010, France.
  • Zhenlin Li
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, P.R.China.
  • Laurent Brino
    Ksilink, Strasbourg F-67000, France.
  • Andrea Rossi
    ASST Spedali Civili di Brescia, Ospedale dei Bambini, Brescia, Italy.
  • Onnik Agbulut
    Sorbonne Université, Institut de Biologie Paris-Seine (IBPS), UMR CNRS 8263, INSERM U1345, Development, Adaptation and Ageing, Paris F-75005, France. Electronic address: onnik.agbulut@sorbonne-universite.fr.
  • Peter Sommer
    Ksilink, Strasbourg F-67000, France.
  • Pierre Joanne
    Sorbonne Université, Institut de Biologie Paris-Seine (IBPS), UMR CNRS 8263, INSERM U1345, Development, Adaptation and Ageing, Paris F-75005, France.
  • Konstantinos Gkatzis
    Ksilink, Strasbourg F-67000, France. Electronic address: konstantinosgkatzis@gmail.com.