Synergism of dual AAV gene therapy and rapamycin rescues GSDIII phenotype in muscle and liver.

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.

Deciphering the Molecular Mechanism of Incurable Muscle Disease by a Novel Method for the Interpretation of miRNA Dysregulation.

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.

Inference of an Integrative, Executable 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 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.

Identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference.

Rheumatoid arthritis (RA) is a systemic autoimmune disease that affects the synovial joints of the body. Rheumatoid arthritis fibroblast-like synoviocytes (RA FLS) are central players in the disease pathogenesis, as they are involved in the secretion of cytokines and proteolytic enzymes, exhibit invasive traits, high rate of self-proliferation and an apoptosis-resistant phenotype. We aim at characterizing transcription factors (TFs) that are master regulators in RA FLS and could potentially explain phenotypic traits. We make use of differentially expressed genes in synovial tissue from patients suffering from RA and osteoarthritis (OA) to infer a TF co-regulatory network, using dedicated software. The co-regulatory network serves as a reference to analyze microarray and single-cell RNA-seq data from isolated RA FLS. We identified five master regulators specific to RA FLS, namely BATF, POU2AF1, STAT1, LEF1 and IRF4. TF activity of the identified master regulators was also estimated with the use of two additional, independent software. The identified TFs contribute to the regulation of inflammation, proliferation and apoptosis, as indicated by the comparison of their differentially expressed target genes with hallmark molecular signatures derived from the Molecular Signatures Database (MSigDB). Our results show that TFs influence could be used to identify putative master regulators of phenotypic traits and suggest novel, druggable targets for experimental validation.