skip to content

ESR 6 Project: Detection of tRNA processing faults as a screen for deleterious mttRNA mutations.

Partner: James Stewart

Institution: MPI-AGE, Cologne, Germany

Duration: 36 months

Objectives: Mammalian mitochondria are transcribed in two large, polycistronic RNAs that proceed around most of the mitochondrial DNA. These large transcripts are then processed into the resulting tRNA, mRNA and rRNAs via tRNA excisions by a mitochondrial-specific RNase P and an RNase Z. We have recently demonstrated that mutations in tRNA genes have a duel effect – the anticipated impairment of translation of mitochondrial proteins via the manipulation of the tRNA pool, and effects on the processing and maturation of nearby genes in some tissues. While most mutations show a tight correlation between relative levels in both mtDNA and mtRNA pools, tRNA mutations lead to changes in the pools of unprocessed RNA species, which are enriched in the mutant tRNA sequence. This impairment of processing may be due to the inability of the tRNA to form a proper structure, and may therefore predict deleterious mutations in the tRNAs that are unable to fold into a functional tertiary structure. The mtDNA mutator mice generate large numbers of mutations in the mDNA due to their proof-reading deficient mitochondrial polymerase. These mtDNA mutations can be transmitted through the female germline to wildtype progeny. These progeny mice will be assayed using Next Generation RNA Sequencing technology. Mutations revealing poor correlation between the proportion of mutation in the RNA and DNA pools will be identified, and a database of mutations and their consequences to the tRNA processing efficiency will be identified.

Expected results: With female derived lineages obtained from mtDNA mutator mice, we expect to near-saturate the coding areas of the tRNAs with mutations. In a similar manner to our recent publication in human tumours, comparisons of RNA and DANN levels out of the same tissue sample will reveal the subset of the tRNA mutations that are leading to structural deficiencies, on a genome-wide scale. This data will provide a database of putative disease-causing alleles in the mouse, and aid in our attempts to identify mouse mtDNA mutations that may be models of human mtDNA diseases. This dataset will also provide a resource for clinicians who are attempting to estimate the potential pathogenicity of novel mt-tRNA mutations observed in human patients.