Mixed infection with multiple species of nontuberculous mycobacteria (NTM) is difficult to identify and to treat. Current conventional molecular-based methods for identifying mixed infections are limited due to low specificity. Here, we evaluated the utility of whole-genome sequencing (WGS) analysis to detect and identify mixed NTM infections. Analytical tools used included PubMLST, MetaPhlAn3, Kraken2, Mykrobe-Predictor and analysis of heterozygous SNP frequencies. The ability of each to identify mixed infections of NTM species was compared. Sensitivity was tested using 101 samples (sequence sets) including 100 in-silico simulated mixed samples with various proportions of known NTM species and one sample of known mixed NTM species from a public database. Single-species NTM control samples (155 WGS samples from public databases and 15 samples from simulated reads) were tested for specificity. Kraken2 exhibited 100% sensitivity and 98.23% specificity for detection and identification of mixed NTM species with accurate estimation of relative abundance of each species in the mixture. PubMLST (99% and 96.47%) and MetaPhlAn3 (95.04% and 83.52%) had slightly lower sensitivity and specificity. Mykrobe-Predictor had the lowest sensitivity (57.42%). Analysis of read frequencies supporting single nucleotide polymorphisms (SNPs) could not detect mixed NTM samples. Clinical NTM samples (n = 16), suspected on the basis of a 16S–23S rRNA gene sequence-based line-probe assay (LPA) to contain more than one NTM species, were investigated using WGS-analysis tools. This identified only a small proportion (37.5%, 6/16 samples) of the samples as mixed infections and exhibited only partial agreement with LPA results. LPAs seem to be inadequate for detecting mixed NTM species infection. This study demonstrated that WGS-analysis tools can be used for diagnosis of mixed infections with different species of NTM.

Mycobacterium abscessus is an important pathogen that can cause serious human diseases and is difficult to treat due to antibiotic resistance. In this study, we analyzed, using whole-genome sequence (WGS) data, M. abscessus strains serially isolated from patients at various time intervals. We undertook genetic diversity analysis between subspecies, mutation-rate estimation and identification of drug-resistant mutations with minimum inhibitory concentration (MIC) analysis. Clonal isolates of M. abscessus:—subsp. abscessus (MAB) and subsp. massiliense (MMAS)—causing persistent infection through time, differed by 0–7 and 0–14 SNPs, respectively, despite being isolated 1 to 659 days apart. Two cases caused by MMAS differed by ≥102 SNPs at 350 days apart and were regarded as examples of reinfection. Isolates collected ≤7 days apart exhibited a high mutation rate (133.83 ± 0.00 SNPs/genome (5 Mb)/year for MMAS and 127.75 SNPs/genome (5 Mb)/year for MAB). Mutation rates declined in a time-dependent manner in both subspecies. Based on isolates collected > 180 days apart, MMAS had a significantly higher average mutation rate than MAB (2.89 ± 1.02 versus 0.82 ± 0.83 SNPs/genome (5 Mb)/year, (p = 0.01), respectively). All well-known drug-resistance mutations were found to be strongly associated with high MIC levels for clarithromycin and ciprofloxacin. No known mutations were identified for strains resistant to linezolid and amikacin. MAB strains in the study were susceptible to amikacin, while most MMAS strains were susceptible to clarithromycin, amikacin and linezolid. No hetero-resistance was found in the strains analyzed. Our study reports the genetic diversity and mutation rate of M. abscessus between the two major subspecies and confirms the drug resistance-associated mutations. Information about drug-resistance and associated mutations can be applied in diagnosis and patient management.