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 by integrating 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 use 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. Then, the integrative network is used as a template to analyse patients’ data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, 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.

Glycogen storage disease type III (GSDIII) is a rare metabolic disorder due to glycogen debranching enzyme (GDE) deficiency. Reduced GDE activity leads to pathological glycogen accumulation responsible for impaired hepatic metabolism and muscle weakness. To date, there is no curative treatment for GSDIII. We previously reported that 2 distinct dual AAV vectors encoding for GDE were needed to correct liver and muscle in a GSDIII mouse model. Here, we evaluated the efficacy of rapamycin in combination with AAV gene therapy. Simultaneous treatment with rapamycin and a potentially novel dual AAV vector expressing GDE in the liver and muscle resulted in a synergic effect demonstrated at biochemical and functional levels. Transcriptomic analysis confirmed synergy and suggested a putative mechanism based on the correction of lysosomal impairment. In GSDIII mice livers, dual AAV gene therapy combined with rapamycin reduced the effect of the immune response to AAV observed in this disease model. These data provide proof of concept of an approach exploiting the combination of gene therapy and rapamycin to improve efficacy and safety and to support clinical translation.

It is now well-established that microRNA dysregulation is a hallmark of human diseases, and that aberrant expression of miRNA is not randomly associated with human pathologies but plays a causal role in the pathological process. Investigations of the molecular mechanism that links miRNA dysregulation to pathophysiology can therefore further the understanding of human diseases. The biological effect of miRNA is thought to be mediated principally by miRNA target genes. Consequently, the target genes of dysregulated miRNA serve as a proxy for the biological interpretation of miRNA dysregulation, which is performed by target gene pathway enrichment analysis. However, this method unfortunately often fails to provide testable hypotheses concerning disease mechanisms. In this paper, we describe a method for the interpretation of miRNA dysregulation, which is based on miRNA host genes rather than target genes. Using this approach, we have recently identified the perturbations of lipid metabolism, and cholesterol in particular, in Duchenne muscular dystrophy (DMD). The host gene-based interpretation of miRNA dysregulation therefore represents an attractive alternative method for the biological interpretation of miRNA dysregulation.

Motivation Rheumatoid arthritis (RA) is a multifactorial autoimmune disease that causes chronic inflammation of the joints. RA is considered a complex disease as it involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating 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 approach, we integrate three different biological layers (gene expression, signalling cascades, mutations), obtained by bottom-up and top-down methods to build an integrative, disease-specific network. The goal behind this endeavour is to see if we can unravel mechanisms governing the regulation of key genes identified as mutation carriers in RA and derive patient-specific models to gain more insights into the disease heterogeneity. Results In this work, we combine biological data relevant to Rheumatoid Arthritis, in the form of a global, integrative network. 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 prior knowledge encoded in a state-of-the-art, RA-specific molecular map. Lastly, a list of RA specific variants highlights key genes associated with known disease mutations. Availability Datasets used for the analysis are publicly available. All scripts used to generate results and the Shiny app will be freely accessible after peer-reviewed publication.

Glycogen storage disease type II (GSDII), or Pompe disease, is a rare autosomal recessive metabolic disorder characterized by the deficiency of the lysosomal enzyme acid alpha-glucosidase (GAA). GAA deficiency results in the progressive accumulation of glycogen in cardiac and skeletal muscle tissues, leading to cellular dysfunction and clinical manifestations, including muscle weakness, respiratory difficulties, and cardiomyopathy. In this study, we report the derivation of three induced pluripotent stem cell (iPSC) lines from peripheral blood mononuclear cells of GSDII patients. These patient-derived iPSC lines can be used to study pathological features of the disease and evaluate therapeutic strategies for Pompe disease.

Limb-girdle muscular dystrophy R2 (LGMD R2) is an autosomal recessive disorder caused by dysferlin deficiency, leading to progressive muscle weakness and wasting. The lack of reliable clinical biomarkers has limited disease monitoring and therapeutic evaluation. Here, we identified Disabled-2 (DAB2) as a molecular and clinical indicator of disease state in LGMD R2. Transcriptomic profiling revealed a significant upregulation of DAB2 in induced pluripotent stem cell–derived (iPSC-derived) myotubes from patients, a finding validated in muscle biopsies from 14 dysferlin-deficient individuals and in dysferlin-deficient Bla/J mice, where DAB2 levels increased with disease progression. Importantly, AAV-mediated expression of full-length dysferlin restored DAB2 levels, supporting its value as a dynamic readout of disease activity for both disease monitoring and therapeutic response. Given the established role of DAB2 in clathrin-mediated endocytosis, particularly in LDL receptor internalization and cholesterol homeostasis, and the pathological lipid accumulation reported in LGMD R2, we investigated its contribution to lipid dysregulation. High DAB2 expression paralleled lipid deposition in patient muscles, iPSC-derived myotubes, and mouse tissue, whereas siRNA-mediated DAB2 knockdown reduced lipid accumulation in LGMD R2 myotubes. Collectively, these findings suggest that DAB2 functions as a mechanistic link between dysferlin deficiency, altered lipid handling, and disease severity, and they highlight its potential as a prognostic marker and therapeutic response measure for LGMD R2.