Understanding and eventually curing rare genetic disorders require systemic and generic approaches. Inherited mitochondrial disorders comprise an example par excellence for a large collection of individually rare diseases, in this case caused by a dysfunction of the mitochondrial energy supply. Currently, more than 250 genetically defined disease entities are known. For most mitochondrial disorders there is no effective treatment. Present clinical management is mainly focused on treating complications.
To address this situation, national (mitoNET) and international (GENOMIT and TIRCON) networks have been formed to study natural history of disease, to perform genome sequencing, to collect biomaterials and search for biomarker of disease progression, and to establish patient derived cell lines for functional studies. The TUM-MED mitochondrial biomaterial bank comprises DNA, whole blood RNA and plasma samples collected at about 2000 patient visits including standardized phenotyping [Buechner, Med. Gen. 2012]. We have established more than 400 fibroblast cell lines, and most of them have been analysed by genotyping (exome sequencing, Illumina) and bioenergetic profiling (Seahorse), and in part by transcription profiling (RNA-seq). Moreover, we have obtained exome sequences of 500 index cases, from which we could derive a molecular diagnosis for only half of the patients; the found mutations cover a surprisingly large set of 100 different genes. In 250 plasma samples, we have quantified up to 700 metabolites (www.metabolon.com), half from solved and half from unsolved cases. This dataset is currently being extended by plasma metabolomics and whole blood RNA-seq using samples collected in a time series over 2 years with up to 8 time points. In WP7 we will apply, benchmark and validate novel statistical methods to analyse these existing multi-omic patient datasets, as well as coming data, with the aim to increase our success rate in identifying the pathogenic variants, to improve our understanding of the underlying biology, to identify biomarkers for disease progression and to generate hypotheses for therapeutic intervention.
- Technische Universität München, Klinikum Rechts der Isar (Lead Partner)
- Roswell Park Cancer Institute, Buffalo
- Technische Universität München
- BeDataDriven, The Hague