Miagoux, Q. | Search bioRxiv

bioRxiv2025

DAB2 as a biomarker and mechanistic link between lipid dysregulation and disease progression in LGMD R2

Limb-girdle muscular dystrophy R2 (LGMD R2) is an autosomal recessive disorder caused by dysferlin deficiency, leading to progressive muscle weakness and wasting. Despite advances in understanding the mechanisms linking dysferlin loss to membrane fragility and muscle degeneration, the lack of robust clinical biomarkers has limited disease monitoring and therapeutic evaluation. Here, we identify Disabled-2 (DAB2) as a molecular and clinical biomarker for LGMD R2. Transcriptomic profiling revealed a significant upregulation of DAB2 in induced pluripotent stem cell (iPSC)-derived myotubes from patients with LGMD R2. Its expression correlated with disease severity in muscle biopsies from a cohort of 14 dysferlin-deficient individuals and in dysferlin knockout Bla/J mice, where levels increased with disease progression. Crucially, we demonstrate that DAB2 upregulation in muscle is normalized following treatment with AAV gene therapy expressing full-length dysferlin, positioning DAB2 as a dynamic biomarker for both disease monitoring and therapeutic response. Based on the role of DAB2 in lipid trafficking and the reported pathological lipid accumulation in LGMD R2, we then investigated its contribution to disease-associated lipid dysregulation. Consistent with this hypothesis, we show that high DAB2 levels paralleled lipid deposition in affected patients, iPSC-derived myotubes and mouse muscles, while siRNA- mediated DAB2 knockdown reduced lipid accumulation in LGMD R2 myotubes. Together, our findings establish DAB2 as a mechanistic link between disease severity and lipid dysregulation, and highlight its potential as a key prognostic marker, opening new avenues for precision medicine approaches in LGMD R2 and other related muscular dystrophies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/675589v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@152c1f1org.highwire.dtl.DTLVardef@3af57eorg.highwire.dtl.DTLVardef@cb4630org.highwire.dtl.DTLVardef@16d384b_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2021

Inference of an integrative network for Rheumatoid Arthritis combining data-driven machine learning approaches and a state-of-the-art mechanistic disease map

Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental and epigenetic factors. Systems biology approaches provide the means to study complex diseases through the integration of different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first make use of publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. The integrative network is then used as a template to analyze patients data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. We use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.

bioinformatics↗