Genome-scale analysis of Mycobacterium avium complex isolates from Portugal reveals extensive genetic diversity

Abstract

Opportunist infections caused by nontuberculous mycobacteria (NTM) have emerged as a significant public health problem. Among these, species of the Mycobacterium avium complex (MAC) are the main responsible for the increase in the number of human disease cases. In order to address the current needs in the detection and surveillance of MAC disease cases, we evaluated different species classification methodologies (BLASTn-based marker-gene approach, Kraken v2, rMLST and MLST databases) and their congruence with a core-SNP phylogenetic approach, based on whole genome sequencing (WGS) data. For this purpose, we used a collection of 142 MAC isolates from Portuguese patients diagnosed between 2014 and 2022. The marker-gene approach (based on the rpoB, hsp65 and groEL genes), showed the best results, allowing the identification of the 142 MAC isolates to the species/subspecies level (M. avium subsp. hominissuis, M. intracellulare, M. intracellulare subsp. chimaera, M. intracellulare subsp. yongonense, M. marseillence and M. colombiense). Additionally, we performed drug susceptibility testing that confirmed clarithromycin efficacy as a first-line treatment for MAC disease, as 93 % of the Portuguese isolates were susceptible. Using a core-SNP approach we also performed an in-depth phylogenetic analysis within each identified species group, and despite the high genetic diversity within the MAC species, we were able to clearly distinguish all the species/subspecies and identify genetic clusters with epidemiological potential. We highlight not only the need for the standardization of an appropriate genotyping approach for species identification and management of MAC disease, but also a more robust large-scale WGS data analysis, in a One Health perspective, in order to identify potential routes of transmission.

Keywords: Genomic diversity; Mycobacterium avium complex; Nontuberculous mycobacteria; Phylogenetic core-SNP analysis; Whole-genome sequencing.