Abstract
The detection of laboratory cross-contamination and mixed tuberculosis infections is an important goal of clinical mycobacteriology laboratories. The objective of this study was to develop a method to detect mixtures of different Mycobacterium tuberculosis lineages in laboratories performing mycobacterial next-generation sequencing (NGS). The setting was the Public Health England National Mycobacteriology Laboratory Birmingham, which performs Illumina sequencing on DNA extracted from positive mycobacterial growth indicator tubes. We analyzed 4,156 samples yielding M. tuberculosis from 663 MiSeq runs, which were obtained during development and production use of a diagnostic process using NGS. The counts of the most common (major) variant and all other variants (nonmajor variants) were determined from reads mapping to positions defining M. tuberculosis lineages. Expected variation was estimated during process development. For each sample, we determined the nonmajor variant proportions at 55 sets of lineage-defining positions. The nonmajor variant proportion in the two most mixed lineage-defining sets (F2 metric) was compared with that of the 47 least-mixed lineage-defining sets (F47 metric). The following 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 elevations relative to F47. Pattern ii was observed in batches, with similar patterns in the M. tuberculosis H37Rv control present in each run, and is likely to reflect cross-contamination. During production, the proportions of samples in the patterns were 97%, 2.8%, and 0.001%, respectively. The F2 and F47 metrics described could be used for laboratory process control in laboratories sequencing M. tuberculosis genomes.
Highlights
The detection of laboratory cross-contamination and mixed tuberculosis infections is an important goal of clinical mycobacteriology laboratories
Distinct lineages, corresponding to evolution occurring during these early migrations, are readily identified by nextgeneration sequencing (NGS), with each lineage characterized by ancient singlenucleotide variants (SNVs) which define deep branches in the M. tuberculosis phylogeny [1, 2]
As part of the quality control and accreditation of the routine process operating in these laboratories, we describe an approach to identifying mixed samples using Illumina next-generation sequencing data, illustrating its use by studying over 4,000 consecutive positive cultures from a single reference laboratory
Summary
The detection of laboratory cross-contamination and mixed tuberculosis infections is an important goal of clinical mycobacteriology laboratories. Mixed infection complicates the interpretation of drug resistance tests, whether phenotypic or genotypic, as one or other coinfecting strains may dominate the results from these tests It complicates the understanding of relatedness when techniques, such as SNV distance computation, are applied, as these generally assume that a single sequence is present when basecalling [5, 12,13,14], marking mixed sites as uncertain. As part of the quality control and accreditation of the routine process operating in these laboratories, we describe an approach to identifying mixed samples using Illumina next-generation sequencing data, illustrating its use by studying over 4,000 consecutive positive cultures from a single reference laboratory
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