Whole-Genome Sequencing Analysis to Identify Infection with Multiple Species of Nontuberculous Mycobacteria

Abstract

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.

Whole Genome Sequencing in the Management of Non-Tuberculous Mycobacterial Infections

“…The results of this study are also important for the clinical diagnosis of the disease, as they confirmed a higher diversity of M. abscessus in samples of lungs, chest wounds, and pleural fluid compared to sputum [52]. Sequencing data provided a more detailed subspecies classification in mixed cultures compared to conventionally used methods [53,54]. WGS also contributed to the finding of a high interspecies and intraspecies M. avium complex diversity in a patient with an ongoing pulmonary form of the disease.…”

Isolation and Antimicrobial Susceptibility of Nontuberculous Mycobacteria in a Tertiary Hospital in Korea, 2016 to 2020

“…Unfortunately, the correlation between mixed NTM infection and patient outcomes could not be investigated in our study due to the lack of data pertaining to clinical treatment. Misidentification of mixed NTM infection is not an uncommon finding, which can cause a critical problem in patient management 37 . Therefore, the future studies are needed to elucidate the clinical outcomes of mixed NTM infection with accurate diagnosis of single or mixed NTM infection.…”

“…Although the clinical significance of mixed NTM species infection from the same patient is not fully understood yet, it is a frequent finding of NTM lung infection [ 35 ]. Disease status and clinical outcomes of patients with mixed NTM infection might be different from those with a single NTM infection [ 37 ]. Unfortunately, the correlation between mixed NTM infection and patient outcomes could not be investigated in our study due to the lack of data pertaining to clinical treatment.…”

Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics

“…For example, currently available software tools such as PubMLST, MetaPhlAn3, and Mykrobe-predictor showed variable performances in correctly identifying non-tuberculous mycobacterial species. Sensitivities ranged from 57–100% and specificities from 83–98%, which could be attributed to the different databases and algorithms used [ 50 ].…”