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Jarrett, L.

Publications and source records attributed to Jarrett, L..

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Identifying mixed Mycobacterium tuberculosis infection and laboratory cross-contamination during Mycobacterial sequencing programs

IntroductionDetecting laboratory cross-contamination and mixed tuberculosis infection are important goals of clinical Mycobacteriology laboratories.\n\nObjectivesTo develop a method detecting mixtures of different M. tuberculosis lineages in laboratories performing Mycobacterial next generation sequencing (NGS).\n\nSettingPublic Health England National Mycobacteriology Laboratory Birmingham, which performs Illumina sequencing on DNA extracted from positive Mycobacterial Growth Indicator tubes.\n\nMethodsWe analysed 4,156 samples yielding M. tuberculosis from 663 MiSeq runs, obtained during development and production use of a diagnostic process using NGS. Counts of the most common (major) variant, and all other variants (non-major variants) were determined from reads mapping to positions defining M. tuberculosis lineages. Expected variation was estimated during process development.\n\nResultsFor each sample we determined the non-major variant proportions at 55 sets of lineage defining positions. The non-major variant proportion in the two most mixed lineage defining sets (F2 metric) was compared with that in the 47 least mixed lineage defining sets (F47 metric). Three patterns were observed: (i) not mixed by either metric, (ii) high F47 metric suggesting mixtures of multiple lineages, and (iii) samples compatible with mixtures of two lineages, detected by differential F2 metric elevation relative to F47. Pattern (ii) was observed in batches, with similar patterns in the H37Rv control present in each run, and is likely to reflect cross-contamination. During production, the proportions of samples in each pattern were 97%, 2.8%, and 0.001%, respectively.\n\nConclusionThe F2 and F47 metrics described could be used for laboratory process control in laboratories sequencing M. tuberculosis.

microbiology

Automated detection of Mycobacterial growth on 96-well plates for rapid and accurate Tuberculosis drug susceptibility testing

M. tuberculosis grows slowly and is challenging to work with experimentally compared with many other bacteria. Although microtitre plates have the potential to enable high-throughput phenotypic testing of M. tuberculosis, they can be difficult to read and interpret. Here we present a software package, the Automated Mycobacterial Growth Detection Algorithm (AMyGDA), that measures how much M. tuberculosis is growing in each well of a 96-well microtitre plate. The plate used here has serial dilutions of 14 anti-tuberculosis drugs, thereby permitting the minimum inhibitory concentrations (MICs) to be elucidated. The two participating laboratories each inoculated ten 96-well plates with the standard H37Rv reference strain and, after two weeks incubation, measured the MICs for all 14 drugs on each plate and took a photograph. By analysing the images, we demonstrate that AMyGDA is reproducible, and that the MICs measured are comparable to those measured by a laboratory scientist. AMyGDA software will be used by the Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) to measure the drug susceptibility profile of a large number (> 30,000) of samples of M. tuberculosis from patients over the next few years.

microbiology