MGIT Enriched Shotgun Metagenomics for Routine Identification of Nontuberculous Mycobacteria: a Route to Personalized Health Care

Currently, nontuberculous mycobacteria (NTM) are identified using small genomic regions, and species-level identification is often not possible. We introduce a next-generation sequencing (NGS) workflow that identifies mycobacteria to (sub)species level on the basis of the whole genome extracted from enriched shotgun metagenomic data. This technique is used to study the association between genotypes and clinical manifestations to pave the way to more personalized health care. Two sets of clinical isolates (explorative set [n = 212] and validation set [n = 235]) were included. All data were analyzed using a custom pipeline called MyCodentifier. Sequences were matched against a custom hsp65 database (NGS-hsp65) and whole-genome database (NGS-WG) created based on the phylogeny presented by Tortoli et al. (E. Tortoli, T. Fedrizzi, C. J. Meehan, A. Trovato, et al., Infect Genet Evol 56:19-25, 2017, ). Lastly, phylogenetic analysis was performed and correlated with clinical manifestation. In the explorative set, we observed 98.6% agreement between the line probe assay and the NGS-hsp65 database. In the validation set, 99.1% agreement between the NGS-WG and NGS-hsp65 databases was seen on the complex level. We identified a cluster of Mycobacterium marinum isolates not represented by the Tortoli et al. phylogeny. Phylogenetic analysis of M. avium complex isolates confirmed misclassification of M. timonense and M. bouchedurhonense and identified subclusters within M. avium although no correlation with clinical manifestation was observed. We performed routine NGS to identify NTM from MGIT enriched shotgun metagenomic data. Phylogenetic analyses identified subtypes of M. avium, but in our set of isolates no correlation with clinical manifestation was found. However, this NGS workflow paves a way for more personalized health care in the future.

Automated summary

We introduce a new sequencing technique that can identify nontuberculous mycobacteria (NTM) to (sub)species level based on whole-genome extracted from enriched shotgun metagenomic data. This technique can be used to study the association between genotypes and clinical manifestations and pave the way for personalized healthcare.