Common pathological mechanisms in mouse models for muscular dystrophies

R Turk1, E Sterrenburg, C G C van der Wees

  • 1Leiden University Medical Center, Center for Human and Clinical Genetics, Leiden, The Netherlands.

Insights

Gene expression profiling reveals distinct molecular signatures for Duchenne/Becker and limb-girdle muscular dystrophies, identifying biomarkers for disease severity and progression in mouse models.

Area of Science:

  • Molecular biology
  • Genomics
  • Biomarker discovery

Background:

  • Duchenne/Becker muscular dystrophy (DBMD) and limb-girdle muscular dystrophies (LGMDs) present with muscle weakness and wasting.
  • These conditions differ in clinical presentation and disease severity, suggesting distinct underlying molecular mechanisms.

Purpose of the Study:

  • To compare gene expression profiles in mouse models of various muscular dystrophies.
  • To identify molecular events that differentiate disease severity and progression.

Main Methods:

  • Comparative gene expression profiling of hindlimb muscles from multiple mouse models: dystrophin-deficient (mdx), sarcoglycan-deficient (Sgca, Sgcb, Sgcg, Sgcd null), dysferlin-deficient (Dysf null), sarcospan-deficient (Sspn null), and wild-type controls.
  • Analysis of expression profiles to identify shared and distinct molecular processes.

Main Results:

  • Gene expression profiles clearly discriminated between severely affected (dystrophinopathies, sarcoglycanopathies) and mildly affected (dysferlinopathies, sarcospan-deficiency) models.
  • Dystrophin-deficient and sarcoglycan-deficient models shared inflammatory and structural remodeling processes, which were also present at lower levels in dysferlin-deficient models.
  • Upregulation of inflammatory proteins Spp1 and S100a9 was observed in all models, suggesting a subtle phenotype in sarcospan-deficient mice.

Conclusions:

  • Identified biomarker genes whose expression correlates with muscular dystrophy severity, potentially aiding disease monitoring.
  • This comparative study provides a foundation for developing expression profiling-based diagnostic approaches for human muscular dystrophies.