Accurate detection of mitochondrial DNA (mtDNA) alterations is essential for the diagnosis of mitochondrial diseases. The development of high-throughput sequencing technologies has enhanced the detection sensitivity of mtDNA pathogenic variants, but the detection of mtDNA rearrangements, especially multiple deletions, is still poorly processed. Here, we present eKLIPse, a sensitive and specific tool allowing the detection and quantification of large mtDNA rearrangements from single and paired-end sequencing data.
The methodology was first validated using a set of simulated data to assess the detection sensitivity and specificity, and second with a series of sequencing data from mitochondrial disease patients carrying either single or multiple deletions, related to pathogenic variants in nuclear genes involved in mtDNA maintenance.
eKLIPse provides the precise breakpoint positions and the cumulated percentage of mtDNA rearrangements at a given gene location with a detection sensitivity lower than 0.5% mutant. eKLIPse software is available either as a script to be integrated in a bioinformatics pipeline, or as user-friendly graphical interface to visualize the results through a Circos representation (https://github.com/dooguypapua/eKLIPse).
Thus, eKLIPse represents a useful resource to study the causes and consequences of mtDNA rearrangements, for further genotype/phenotype correlations in mitochondrial disorders.
Subscribe to Journal
Get full journal access for 1 year
only $94.83 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Wallace DC, Lott MT, Procaccio V. Mitochondrial medicine: the mitochondrial biology and genetics of metabolic and degenerative diseases, cancer, and aging. In: Rimoin D, Pyeritz R, Korf B, eds. Emery and Rimoin’s principles and practice of medical genetics. Oxford: Academic Press; 2013. p. 1–153.
Damas J, Samuels DC, Carneiro J, Amorim A, Pereira F. Mitochondrial DNA rearrangements in health and disease—a comprehensive study. Hum Mutat. 2014;35:1–14.
Kirby DM, McFarland R, Ohtake A, et al. Mutations of the mitochondrial ND1 gene as a cause of MELAS. J Med Genet. 2004;41:784–789.
Bannwarth S, Procaccio V, Lebre AS, et al. Prevalence of rare mitochondrial DNA mutations in mitochondrial disorders. J Med Genet. 2013;50:704–714.
Ballinger SW, Shoffner JM, Gebhart S, Koontz DA, Wallace DC. Mitochondrial diabetes revisited. Nat Genet. 1994;7:458–459.
El-Hattab AW, Craigen WJ, Scaglia F. Mitochondrial DNA maintenance defects. Biochim Biophys Acta. 2017;1863:1539–1555.
Sadikovic B, Wang J, El-Hattab A, et al. Sequence homology at the breakpoint and clinical phenotype of mitochondrial DNA deletion syndromes. PLoS One. 2010;5:e15687.
Holt IJ, Harding AE, Morgan-Hughes JA. Deletions of muscle mitochondrial DNA in mitochondrial myopathies: sequence analysis and possible mechanisms. Nucleic Acids Res. 1989;17:4465–4469.
Schon EA, Rizzuto R, Moraes CT, Nakase H, Zeviani M, DiMauro S. A direct repeat is a hotspot for large-scale deletion of human mitochondrial DNA. Science. 1989;244:346–349.
Keogh MJ, Chinnery PF. Mitochondrial DNA mutations in neurodegeneration. Biochim Biophys Acta. 2015;1847:1401–1411.
Seneca S, Vancampenhout K, Van Coster R, et al. Analysis of the whole mitochondrial genome: translation of the Ion Torrent Personal Genome Machine system to the diagnostic bench? Eur J Hum Genet. 2015;23:41–48.
Vancampenhout K, Caljon B, Spits C, et al. A bumpy ride on the diagnostic bench of massive parallel sequencing, the case of the mitochondrial genome. PLoS One. 2014;9:e112950.
Wong LJ, Boles RG. Mitochondrial DNA analysis in clinical laboratory diagnostics. Clin Chim Acta. 2005;354:1–20.
Pirooznia M, Goes FS, Zandi PP. Whole-genome CNV analysis: advances in computational approaches. Front Genet. 2015;6:138.
Suzuki S, Yasuda T, Shiraishi Y, Miyano S, Nagasaki M. ClipCrop: a tool for detecting structural variations with single-base resolution using soft-clipping information. BMC Bioinformatics. 2011;12 Suppl 14:S7.
Bosworth CM, Grandhi S, Gould MP, LaFramboise T Detection and quantification of mitochondrial DNA deletions from next-generation sequence data. BMC Bioinformatics 2017;18:407.
Wu Y, Tian L, Pirastu M, Stambolian D, Li H. MATCHCLIP: locate precise breakpoints for copy number variation using CIGAR string by matching soft clipped reads. Front Genet. 2013;4:157.
Camacho C, Coulouris G, Avagyan V, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421.
Li H, Handsaker B, Wysoker A, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–2079.
Krzywinski M, Schein J, Birol I, et al. Circos: an information aesthetic for comparative genomics. Genome Res. 2009;19:1639–1645.
Huang W, Li L, Myers JR, Marth GT. ART: a next-generation sequencing read simulator. Bioinformatics. 2012;28:593–594.
Caboche S, Audebert C, Lemoine Y, Hot D. Comparison of mapping algorithms used in high-throughput sequencing: application to Ion Torrent data. BMC Genomics. 2014;15:264.
Damas J, Carneiro J, Amorim A, Pereira F. MitoBreak: the mitochondrial DNA breakpoints database. Nucleic Acids Res. 2014;42 Database issue:D1261–1268.
Boucret L, Bris C, Seegers V, et al. Deep sequencing shows that oocytes are not prone to accumulate mtDNA heteroplasmic mutations during ovarian ageing. Hum Reprod. 2017;32:2101–2109.
Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17:405–424.
Wong LJ. Challenges of bringing next generation sequencing technologies to clinical molecular diagnostic laboratories. Neurotherapeutics. 2013;10:262–272.
He L, Chinnery PF, Durham SE, et al. Detection and quantification of mitochondrial DNA deletions in individual cells by real‐time PCR. Nucleic Acids Res. 2002;30:e68–e68.
Tonska K, Piekutowska-Abramczuk D, Kaliszewska M, et al. Molecular investigations of mitochondrial deletions: evaluating the usefulness of different genetic tests. Gene. 2012;506:161–165.
Belmonte FR, Martin JL, Frescura K, et al. Digital PCR methods improve detection sensitivity and measurement precision of low abundance mtDNA deletions. Sci Rep. 2016;6:25186.
Pitceathly RD, Rahman S, Hanna MG. Single deletions in mitochondrial DNA--molecular mechanisms and disease phenotypes in clinical practice. Neuromuscul Disord. 2012;22:577–586.
Moraes CT, Atencio DP, Oca-Cossio J, Diaz F. Techniques and pitfalls in the detection of pathogenic mitochondrial DNA mutations. J Mol Diagn. 2003;5:197–208.
Grady JP, Murphy JL, Blakely EL, et al. Accurate measurement of mitochondrial DNA deletion level and copy number differences in human skeletal muscle. PLoS One. 2014;9:e114462.
Guo Y, Li J, Li CI, Shyr Y, Samuels DC. MitoSeek: extracting mitochondria information and performing high-throughput mitochondria sequencing analysis. Bioinformatics. 2013;29:1210–1211.
Moraes CT, Sciacco M, Ricci E, et al. Phenotype-genotype correlations in skeletal muscle of patients with mtDNA deletions. Muscle Nerve Suppl. 1995;3:S150–153.
Spelbrink JN, Van Oost BA, Van den Bogert C. The relationship between mitochondrial genotype and mitochondrial phenotype in lymphoblasts with a heteroplasmic mtDNA deletion. Hum Mol Genet. 1994;3:1989–1997.
Yamashita S, Nishino I, Nonaka I, Goto Y. Genotype and phenotype analyses in 136 patients with single large-scale mitochondrial DNA deletions. J Hum Genet. 2008;53:598–606.
Mancuso M, Filosto M, Oh SJ, DiMauro S. A novel polymerase γ mutation in a family with ophthalmoplegia, neuropathy, and parkinsonism. Arch Neurol. 2004;61:1777–1779.
This work was supported by grants from Association contre les Maladies Mitochondriales, and the University and Hospital of Angers.
The authors declare no conflicts of interest.
Joint First Authors: David Goudenège and Celine Bris.