Integrated Analysis Of Omics Data To Identify Pathomechanisms And Potential Treatments For Congenital Myopathies
Reference : PhD Jocelyn Laporte
Offer publication : Jan. 24, 2018
Congenital myopathies are severe genetic diseases that are characterized by muscle weakness at birth and the prevalence of specific histological abnormalities on muscle biopsy. These rare diseases are models to study the changes that occur with age, such as sarcopenia and the accumulation of skeletal muscle dysfunction. There is no specific therapy for these myopathies. Main bottlenecks for the development of these therapies are 1) the partial understanding of the pathological mechanisms and how these disorders develop, 2) the lack of therapeutic targets.
The PhD project aims to identify the pathological mechanisms of some congenital myopathies and novel therapeutic targets through a bioinformatics analysis of omics data (big data). We are generating transcriptome (RNAseq), proteome (mass spectrometry), interactome (BioID, immunoprecipitation) data from wild-type mouse muscles at different time-points in the muscle maturation to better understand the normal processes leading to the correct organization of myofibers. In parallel, we generate similar data from mice models of congenital myopathies (mutated in different genes), at different time-points during the development of the disease. Compared to wildtype data, this will help to define the pathological mechanisms and disease progression. Moreover, we also generate data from the same mouse models treated with different therapeutic approaches that we previously validated, to identify the pathways that need to be rescued for a therapy, and thus identify novel therapeutic targets that may be amenable for modulation to better treat the disease. Lastly, we aim to compare these different models to identify common defects leading to congenital myopathies, independent of the mutated genes, and common therapeutic targets independent of the rescuing approaches used.
Bioinformatic and statistical analyses will be performed. We will compare with public database and create an integrated knowledge database that will allow an overview and in-depth view of the muscle maturation under normal and pathological conditions.
Application Deadline : Nov. 1, 2018