Dataset Information

Background sequence characteristics influence the occurrence and severity of disease-causing mtDNA mutations.

ABSTRACT: Inherited mitochondrial DNA (mtDNA) mutations have emerged as a common cause of human disease, with mutations occurring multiple times in the world population. The clinical presentation of three pathogenic mtDNA mutations is strongly associated with a background mtDNA haplogroup, but it is not clear whether this is limited to a handful of examples or is a more general phenomenon. To address this, we determined the characteristics of 30,506 mtDNA sequences sampled globally. After performing several quality control steps, we ascribed an established pathogenicity score to the major alleles for each sequence. The mean pathogenicity score for known disease-causing mutations was significantly different between mtDNA macro-haplogroups. Several mutations were observed across all haplogroup backgrounds, whereas others were only observed on specific clades. In some instances this reflected a founder effect, but in others, the mutation recurred but only within the same phylogenetic cluster. Sequence diversity estimates showed that disease-causing mutations were more frequent on young sequences, and genomes with two or more disease-causing mutations were more common than expected by chance. These findings implicate the mtDNA background more generally in recurrent mutation events that have been purified through natural selection in older populations. This provides an explanation for the low frequency of mtDNA disease reported in specific ethnic groups.

SUBMITTER: Wei W

PROVIDER: S-EPMC5757940 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundCystinuria is an inherited disease that results in the formation of cystine stones in the kidney, which can have serious health complications. Two genes (SLC7A9 and SLC3A1) that form an amino acid transporter are known to be responsible for the disease. Variants that cause the disease disrupt amino acid transport across the cell membrane, leading to the build-up of relatively insoluble cystine, resulting in formation of stones. Assessing the effects of each mutation is critical in order to provide tailored treatment options for patients. We used various computational methods to assess the effects of cystinuria associated mutations, utilising information on protein function, evolutionary conservation and natural population variation of the two genes. We also analysed the ability of some methods to predict the phenotypes of individuals with cystinuria, based on their genotypes, and compared this to clinical data.ResultsUsing a literature search, we collated a set of 94 SLC3A1 and 58 SLC7A9 point mutations known to be associated with cystinuria. There are differences in sequence location, evolutionary conservation, allele frequency, and predicted effect on protein function between these mutations and other genetic variants of the same genes that occur in a large population. Structural analysis considered how these mutations might lead to cystinuria. For SLC7A9, many mutations swap hydrophobic amino acids for charged amino acids or vice versa, while others affect known functional sites. For SLC3A1, functional information is currently insufficient to make confident predictions but mutations often result in the loss of hydrogen bonds and largely appear to affect protein stability. Finally, we showed that computational predictions of mutation severity were significantly correlated with the disease phenotypes of patients from a clinical study, despite different methods disagreeing for some of their predictions.ConclusionsThe results of this study are promising and highlight the areas of research which must now be pursued to better understand how mutations in SLC3A1 and SLC7A9 cause cystinuria. The application of our approach to a larger data set is essential, but we have shown that computational methods could play an important role in designing more effective personalised treatment options for patients with cystinuria.

Dataset Information

Background sequence characteristics influence the occurrence and severity of disease-causing mtDNA mutations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets