Rapid pulsed whole genome sequencing for comprehensive acute diagnostics of inborn errors of metabolism

BMC Genomics

Anna Wedell¹  Ulrika von Döbeln³  Valtteri Wirta²  Rolf H Zetterström¹  Rolf Wibom³  Måns Magnusson⁴  Tesfail Emahazion¹  Helene Bruhn³  Michela Barbaro¹  Chris Freyer³  Anna Wredenberg³  Robin Andeer⁴  Mats Dahlberg⁵  Pontus Larsson⁵  Nicole Lesko³  Karin Naess³  Martin Engvall¹  Henrik Stranneheim¹ 

[1] Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden;School of Biotechnology, Science for Life Laboratory, KTH Royal Institute of Technology, Box 1031, 171 21 Solna, Sweden;Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden;Department of Molecular Medicine and Surgery, Science for Life Laboratory, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden;Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Box 1031, 171 21 Solna, Sweden

关键词: WGS;    MPS;    Mendelian disease;    Inborn Errors of Metabolism;    Clinical diagnosis;    Bioinformatics;   

Others  :  1127434
DOI  :  10.1186/1471-2164-15-1090

 received in 2014-04-14, accepted in 2014-12-03,  发布年份 2014

【 摘 要 】

Background

Massively parallel DNA sequencing (MPS) has the potential to revolutionize diagnostics, in particular for monogenic disorders. Inborn errors of metabolism (IEM) constitute a large group of monogenic disorders with highly variable clinical presentation, often with acute, nonspecific initial symptoms. In many cases irreversible damage can be reduced by initiation of specific treatment, provided that a correct molecular diagnosis can be rapidly obtained. MPS thus has the potential to significantly improve both diagnostics and outcome for affected patients in this highly specialized area of medicine.

Results

We have developed a conceptually novel approach for acute MPS, by analysing pulsed whole genome sequence data in real time, using automated analysis combined with data reduction and parallelization. We applied this novel methodology to an in-house developed customized work flow enabling clinical-grade analysis of all IEM with a known genetic basis, represented by a database containing 474 disease genes which is continuously updated. As proof-of-concept, two patients were retrospectively analysed in whom diagnostics had previously been performed by conventional methods. The correct disease-causing mutations were identified and presented to the clinical team after 15 and 18 hours from start of sequencing, respectively. With this information available, correct treatment would have been possible significantly sooner, likely improving outcome.

Conclusions

We have adapted MPS to fit into the dynamic, multidisciplinary work-flow of acute metabolic medicine. As the extent of irreversible damage in patients with IEM often correlates with timing and accuracy of management in early, critical disease stages, our novel methodology is predicted to improve patient outcome. All procedures have been designed such that they can be implemented in any technical setting and to any genetic disease area. The strategy conforms to international guidelines for clinical MPS, as only validated disease genes are investigated and as clinical specialists take responsibility for translation of results. As follow-up in patients without any known IEM, filters can be lifted and the full genome investigated, after genetic counselling and informed consent.

【 授权许可】

   
2014 Stranneheim et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]Yang Y, Muzny DM, Reid JG, Bainbridge MN, Willis A, Ward PA, Braxton A, Beuten J, Xia F, Niu Z, Hardison M, Person R, Bekheirnia MR, Leduc MS, Kirby A, Pham P, Scull J, Wang M, Ding Y, Plon SE, Lupski JR, Beaudet AL, Gibbs RA, Eng CM: Clinical whole-exome sequencing for the diagnosis of mendelian disorders. N Engl J Med 2013, 369(16):1502-1511.
• [2]American Society of Human Genetics Board of Directors: Points to consider: ethical, legal, and psychosocial implications of genetic testing in children and adolescents. Am J Hum Genet 1995, 57(5):1233-1241.
• [3]Rehm HL, Bale SJ, Bayrak-Toydemir P, Berg JS, Brown KK, Deignan JL, Friez MJ, Funke BH, Hegde MR, Lyon E: ACMG clinical laboratory standards for next-generation sequencing. Genet Med 2013, 15(9):733-747.
• [4]van El CG, Cornel MC, Borry P, Hastings RJ, Fellmann F, Hodgson SV, Howard HC, Cambon-Thomsen A, Knoppers BM, Meijers-Heijboer H, Scheffer H, Tranebjaerg L, Dondorp W, de Wert GM: Whole-genome sequencing in health care: recommendations of the European Society of Human Genetics. Eur J Hum Genet 2013, 21(6):580-584.
• [5]Saunders CJ, Miller NA, Soden SE, Dinwiddie DL, Noll A, Alnadi NA, Andraws N, Patterson ML, Krivohlavek LA, Fellis J, Humphray S, Saffrey P, Kingsbury Z, Weir JC, Betley J, Grocock RJ, Margulies EH, Farrow EG, Artman M, Safina NP, Petrikin JE, Hall KP, Kingsmore SF: Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units. Sci Transl Med 2012, 4(154):154ra135.
• [6]Mutation Identification Pipeline [https://github.com/henrikstranneheim/MIP webcite]
• [7]Kraus JP, Spector E, Venezia S, Estes P, Chiang PW, Creadon-Swindell G, Mullerleile S, de Silva L, Barth M, Walter M, Walter K, Meissner T, Lindner M, Ensenauer R, Santer R, Bodamer OA, Baumgartner MR, Brunner-Krainz M, Karall D, Haase C, Knerr I, Marquardt T, Hennermann JB, Steinfeld R, Beblo S, Koch HG, Konstantopoulou V, Scholl-Burgi S, van Teeffelen-Heithoff A, Suormala T, et al.: Mutation analysis in 54 propionic acidemia patients. J Inherit Metab Dis 2012, 35(1):51-63.
• [8]Dahl HH, Hansen LL, Brown RM, Danks DM, Rogers JG, Brown GK: X-linked pyruvate dehydrogenase E1 alpha subunit deficiency in heterozygous females: variable manifestation of the same mutation. J Inherit Metab Dis 1992, 15(6):835-847.
• [9]Pruitt KD, Harrow J, Harte RA, Wallin C, Diekhans M, Maglott DR, Searle S, Farrell CM, Loveland JE, Ruef BJ, Hart E, Suner MM, Landrum MJ, Aken B, Ayling S, Baertsch R, Fernandez-Banet J, Cherry JL, Curwen V, Dicuccio M, Kellis M, Lee J, Lin MF, Schuster M, Shkeda A, Amid C, Brown G, Dukhanina O, Frankish A, Hart J, et al.: The consensus coding sequence (CCDS) project: Identifying a common protein-coding gene set for the human and mouse genomes. Genome Res 2009, 19(7):1316-1323.
• [10]Ghosh S, Bee G, Reddy M, Pickle L, Dudas M, Del Mistro G, Sedova M, Reed B, Bennett R, and the Ion Torrent R&D Team: Semiconductor Sequencing of Human Exomes on the Ion Proton System. J Biomol Tech 2013, 24(Suppl):S44.
• [11]Chanjo [https://github.com/robinandeer/chanjo webcite]
• [12]Stenson PD, Ball EV, Howells K, Phillips AD, Mort M, Cooper DN: The Human Gene Mutation Database: providing a comprehensive central mutation database for molecular diagnostics and personalized genomics. Hum Genomics 2009, 4(2):69-72. BioMed Central Full Text
• [13]pulse_conversion.py [https://github.com/SciLifeLab/scilifelab/blob/master/scripts/pulse_conversion.py webcite]
• [14]Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009, 25(14):1754-1760.
• [15]Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Genome Project Data Processing S: The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009, 25(16):2078-2079.
• [16]Quinlan AR, Hall IM: BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 2010, 26(6):841-842.
• [17]PicardTools [http://broadinstitute.github.io/picard/ webcite]
• [18]DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY, Cibulskis K, Gabriel SB, Altshuler D, Daly MJ: A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 2011, 43(5):491-498.
• [19]McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA: The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010, 20(9):1297-1303.
• [20]Wang K, Li M, Hakonarson H: ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010, 38(16):e164.