4.7 Article

Mycobacterial DNA Extraction for Whole-Genome Sequencing from Early Positive Liquid (MGIT) Cultures

Journal

JOURNAL OF CLINICAL MICROBIOLOGY
Volume 53, Issue 4, Pages 1137-1143

Publisher

AMER SOC MICROBIOLOGY
DOI: 10.1128/JCM.03073-14

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Funding

  1. Health Innovation Challenge Fund grants [G0800778, 087646/Z/08/Z, HICF-T5-358]
  2. National Institute for Health Research (NIHR) Oxford Biomedical Research Centre
  3. Wellcome Trust
  4. Medical Research Council, Biotechnology and Biological Sciences Research Council on behalf of the Department of Health
  5. MRC [MR/J011398/1, G0800778] Funding Source: UKRI
  6. Medical Research Council [G0800778, MR/J011398/1] Funding Source: researchfish
  7. National Institute for Health Research [NF-SI-0512-10047, NF-SI-0513-10110, NF-SI-0508-10279] Funding Source: researchfish

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We developed a low-cost and reliable method of DNA extraction from as little as 1 ml of early positive mycobacterial growth indicator tube (MGIT) cultures that is suitable for whole-genome sequencing to identify mycobacterial species and predict antibiotic resistance in clinical samples. The DNA extraction method is based on ethanol precipitation supplemented by pretreatment steps with a MolYsis kit or saline wash for the removal of human DNA and a final DNA cleanup step with solid-phase reversible immobilization beads. The protocol yielded >= 0.2 ng/mu l of DNA for 90% (MolYsis kit) and 83% (saline wash) of positive MGIT cultures. A total of 144 (94%) of the 154 samples sequenced on the MiSeq platform (Illumina) achieved the target of 1 million reads, with <5% of reads derived from human or nasopharyngeal flora for 88% and 91% of samples, respectively. A total of 59 (98%) of 60 samples that were identified by the national mycobacterial reference laboratory (NMRL) as Mycobacterium tuberculosis were successfully mapped to the H37Rv reference, with >90% coverage achieved. The DNA extraction protocol, therefore, will facilitate fast and accurate identification of mycobacterial species and resistance using a range of bioinformatics tools.

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