Ion torrent high throughput mitochondrial genome sequencing (HTMGS)


Autoři: N. R. Harvey aff001;  C. L. Albury aff001;  S. Stuart aff001;  M. C. Benton aff001;  D. A. Eccles aff001;  J. R. Connell aff001;  H. G. Sutherland aff001;  R. J. N. Allcock aff003;  R. A. Lea aff001;  L. M. Haupt aff001;  L. R. Griffiths aff001
Působiště autorů: Genomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia aff001;  Health Sciences and Medicine faculty, Bond University, Robina, Queensland, Australia aff002;  School of Biomedical Sciences, University of Western Australia (M504), Crawley, Western Australia, Australia aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224847

Souhrn

The implementation and popularity of next generation sequencing (NGS) has led to the development of various rapid whole mitochondrial genome sequencing techniques. We summarise an efficient and cost-effective NGS approach for mitochondrial genomic DNA in humans using the Ion Torrent platform, and further discuss our bioinformatics pipeline for streamlined variant calling. Ion 316 chips were utilised with the Ion Torrent semi-conductor platform Personal Genome Machine (PGM) to perform tandem sequencing of mitochondrial genomes from the core pedigree (n = 315) of the Norfolk Island Health Study. Key improvements from commercial methods focus on the initial PCR step, which currently requires extensive optimisation to ensure the accurate and reproducible elongation of each section of the complete mitochondrial genome. Dual-platform barcodes were incorporated into our protocol thereby extending its potential application onto Illumina-based systems. Our bioinformatics pipeline consists of a modified version of GATK best practices tailored for mitochondrial genomic data. When compared with current commercial methods, our method, termed high throughput mitochondrial genome sequencing (HTMGS), allows high multiplexing of samples and the use of alternate library preparation reagents at a lower cost per sample (~1.7 times) when compared to current commercial methodologies. Our HTMGS methodology also provides robust mitochondrial sequencing data (>450X average coverage) that can be applied and modified to suit various study designs. On average, we were able to identify ~30 variants per sample with 572 variants observed across 315 samples. We have developed a high throughput sequencing and analysis method targeting complete mitochondrial genomes; with the potential to be platform agnostic with analysis options that adhere to current best practices.

Klíčová slova:

DNA libraries – Genome sequencing – Genomic libraries – Mitochondria – Mitochondrial DNA – Next-generation sequencing – Polymerase chain reaction – High throughput sequencing


Zdroje

1. Wang J, Schmitt ES, Landsverk ML, Zhang VW, Li FY, Graham BH, et al. An integrated approach for classifying mitochondrial DNA variants: one clinical diagnostic laboratory’s experience. Genet Med. 2012;14(6):620–6. doi: 10.1038/gim.2012.4 22402757

2. Barshad G, Marom S, Cohen T, Mishmar D. Mitochondrial DNA Transcription and Its Regulation: An Evolutionary Perspective. Trends Genet. 2018.

3. Holland MM, Makova KD, McElhoe JA. Deep-Coverage MPS Analysis of Heteroplasmic Variants within the mtGenome Allows for Frequent Differentiation of Maternal Relatives. Genes (Basel). 2018;9(3).

4. Stenton SL, Prokisch H. Advancing genomic approaches to the molecular diagnosis of mitochondrial disease. Essays Biochem. 2018.

5. Zole E, Zadinane K, Pliss L, Ranka R. Linkage between mitochondrial genome alterations, telomere length and aging population. Mitochondrial DNA A DNA Mapp Seq Anal. 2018;29(3):431–8. doi: 10.1080/24701394.2017.1303490 28340313

6. Dos Santos Rocha A, de Amorim ISS, Simao TA, da Fonseca AS, Garrido RG, Mencalha AL. High-Resolution Melting (HRM) of Hypervariable Mitochondrial DNA Regions for Forensic Science. J Forensic Sci. 2018;63(2):536–40. doi: 10.1111/1556-4029.13552 28834547

7. Ballard D. Analysis of Mitochondrial Control Region Using Sanger Sequencing. Methods Mol Biol. 2016;1420:143–55. doi: 10.1007/978-1-4939-3597-0_12 27259738

8. Diroma MA, Calabrese C, Simone D, Santorsola M, Calabrese FM, Gasparre G, et al. Extraction and annotation of human mitochondrial genomes from 1000 Genomes Whole Exome Sequencing data. BMC Genomics. 2014;15 Suppl 3:S2.

9. Theunissen TEJ, Nguyen M, Kamps R, Hendrickx AT, Sallevelt S, Gottschalk RWH, et al. Whole Exome Sequencing Is the Preferred Strategy to Identify the Genetic Defect in Patients With a Probable or Possible Mitochondrial Cause. Front Genet. 2018;9:400. doi: 10.3389/fgene.2018.00400 30369941

10. Zascavage RR, Thorson K, Planz JV. Nanopore sequencing: An enrichment-free alternative to mitochondrial DNA sequencing. Electrophoresis. 2019;40(2):272–80. doi: 10.1002/elps.201800083 30511783

11. Parson W, Strobl C, Huber G, Zimmermann B, Gomes SM, Souto L, et al. Reprint of: Evaluation of next generation mtGenome sequencing using the Ion Torrent Personal Genome Machine (PGM). Forensic Sci Int Genet. 2013;7(6):632–9. doi: 10.1016/j.fsigen.2013.09.007 24119954

12. Seo SB, Zeng X, King JL, Larue BL, Assidi M, Al-Qahtani MH, et al. Underlying Data for Sequencing the Mitochondrial Genome with the Massively Parallel Sequencing Platform Ion Torrent PGM. BMC Genomics. 2015;16 Suppl 1:S4.

13. Shendure J, Mitra RD, Varma C, Church GM. Advanced sequencing technologies: methods and goals. Nat Rev Genet. 2004;5(5):335–44. doi: 10.1038/nrg1325 15143316

14. Bellis C, Hughes RM, Begley KN, Quinlan S, Lea RA, Heath SC, et al. Phenotypical characterisation of the isolated norfolk island population focusing on epidemiological indicators of cardiovascular disease. Hum Hered. 2005;60(4):211–9. doi: 10.1159/000090545 16391489

15. Benton MC, Lea RA, Macartney-Coxson D, Carless MA, Goring HH, Bellis C, et al. Mapping eQTLs in the Norfolk Island genetic isolate identifies candidate genes for CVD risk traits. Am J Hum Genet. 2013;93(6):1087–99. doi: 10.1016/j.ajhg.2013.11.004 24314549

16. Benton MC, Stuart S, Bellis C, Macartney-Coxson D, Eccles D, Curran JE, et al. ‘Mutiny on the Bounty’: the genetic history of Norfolk Island reveals extreme gender-biased admixture. Investig Genet. 2015;6:11. doi: 10.1186/s13323-015-0028-9 26339467

17. Benton MC, Stuart S, Bellis C, Macartney-Coxson D, Eccles D, Curran JE, et al. Erratum to: ‘Mutiny on the Bounty’: genetic history of Norfolk Island reveals extreme gender-biased admixture. Investig Genet. 2015;6:12. doi: 10.1186/s13323-015-0029-8 26451237

18. Cox HC, Bellis C, Lea RA, Quinlan S, Hughes R, Dyer T, et al. Principal component and linkage analysis of cardiovascular risk traits in the Norfolk isolate. Hum Hered. 2009;68(1):55–64. doi: 10.1159/000210449 19339786

19. Head SR, Komori HK, LaMere SA, Whisenant T, Van Nieuwerburgh F, Salomon DR, et al. Library construction for next-generation sequencing: overviews and challenges. Biotechniques. 2014;56(2):61–4, 6, 8, passim. doi: 10.2144/000114133 24502796

20. Deiner K, Renshaw MA, Li YY, Olds BP, Lodge DM, Pfrender ME. Long-range PCR allows sequencing of mitochondrial genomes from environmental DNA. Methods Ecol Evol. 2017;8(12):1888–98.

21. De Fanti S, Vianello D, Giuliani C, Quagliariello A, Cherubini A, Sevini F, et al. Massive parallel sequencing of human whole mitochondrial genomes with Ion Torrent technology: an optimized workflow for Anthropological and Population Genetics studies. Mitochondrial DNA A DNA Mapp Seq Anal. 2016:1–8.

22. FastQC: a quality control tool for High throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 30 Jan 2019.

23. Ewels P, Magnusson M, Lundin S, Kaller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047–8. doi: 10.1093/bioinformatics/btw354 27312411

24. Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011;27(21):2987–93. doi: 10.1093/bioinformatics/btr509 21903627

25. Li H. Improving SNP discovery by base alignment quality. Bioinformatics. 2011;27(8):1157–8. doi: 10.1093/bioinformatics/btr076 21320865

26. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078–9. doi: 10.1093/bioinformatics/btp352 19505943

27. Andrews RM, Kubacka I, Chinnery PF, Lightowlers RN, Turnbull DM, Howell N. Reanalysis and revision of the Cambridge reference sequence for human mitochondrial DNA. Nat Genet. 1999;23(2):147. doi: 10.1038/13779 10508508

28. Mitomaster online database. https://www.mitomap.org/foswiki/bin/view/MITOMASTER/WebHome. Accessed 30 Jan 2019.

29. HaploGrep2 https://haplogrep.uibk.ac.at/. Accessed 30 Jan 2019.

30. Mitochondrial Solarplot. https://github.com/stephenturner/solarplot Accessed 30 Jan 2019.

31. Quail MA, Smith M, Coupland P, Otto TD, Harris SR, Connor TR, et al. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics. 2012;13:341. doi: 10.1186/1471-2164-13-341 22827831

32. Abicht A, Scharf F, Kleinle S, Schon U, Holinski-Feder E, Horvath R, et al. Mitochondrial and nuclear disease panel (Mito-aND-Panel): Combined sequencing of mitochondrial and nuclear DNA by a cost-effective and sensitive NGS-based method. Mol Genet Genomic Med. 2018;6(6):1188–98. doi: 10.1002/mgg3.500 30406974

33. Jennings LJ, Arcila ME, Corless C, Kamel-Reid S, Lubin IM, Pfeifer J, et al. Guidelines for Validation of Next-Generation Sequencing-Based Oncology Panels: A Joint Consensus Recommendation of the Association for Molecular Pathology and College of American Pathologists. J Mol Diagn. 2017;19(3):341–65. doi: 10.1016/j.jmoldx.2017.01.011 28341590

34. Matthijs G, Souche E, Alders M, Corveleyn A, Eck S, Feenstra I, et al. Guidelines for diagnostic next-generation sequencing. Eur J Hum Genet. 2016;24(1):2–5. doi: 10.1038/ejhg.2015.226 26508566


Článek vyšel v časopise

PLOS One


2019 Číslo 11