New Genomic Atlas Brings Order to the Mycobacterium avium Complex

The Mycobacterium avium complex, a group of closely related nontuberculous mycobacteria that infect humans and animals across the globe, has long been a taxonomic headache for microbiologists. Species boundaries within the group have shifted repeatedly over the past decades, with names added, merged, and redefined as new sequencing technologies revealed that many isolates labeled as distinct species were, genomically speaking, barely distinguishable. That instability has had real consequences: clinical laboratories cannot reliably compare results across studies, epidemiologists struggle to track outbreaks, and treatment decisions can be complicated by uncertainty over which organism a patient actually carries. A new study published in Genome Medicine by Corentin Poignon of Sorbonne Université and colleagues, working with teams at the Institut Pasteur and the French National Reference Centre for Mycobacteria, now offers a comprehensive, statistically grounded reorganization of the entire complex, packaged in an open-access web tool called MAC-Explorer that any laboratory can use to identify isolates automatically.

The scale of the underlying analysis is considerable. The researchers assembled and examined 2,737 MAC genomes, drawn largely from publicly available data repositories such as the NCBI and the European Nucleotide Archive, and subjected them to exhaustive pairwise comparison using average nucleotide identity, or ANI. ANI is a standard metric in modern bacterial taxonomy: it measures the fraction of the two genomes that can be aligned with high sequence similarity, averaged across all shared genes. Closely related strains typically share more than 95 percent ANI, while organisms conventionally considered the same species usually exceed 98 percent. Rather than simply adopting these thresholds by convention, the team statistically modeled the distribution of pairwise ANI values across the entire complex, determining where natural gaps in genomic similarity occurred and using those gaps to define species and subspecies boundaries at the 95 and 98 percent levels respectively.

From this analysis, fifteen species-level clusters emerged, and the agreement with independent classification schemes was striking. When the researchers compared their clusters against assignments produced by GTDB-Tk, the Genome Taxonomy Database toolkit that has become a de facto standard for genome-based prokaryotic taxonomy, they found 99.9 percent concordance. This near-perfect alignment between a bottom-up, data-driven clustering approach and an established reference framework suggests that the MAC’s genomic structure is far more orderly than its tangled nomenclature would imply. The problem, in other words, is not that the bacteria defy classification; it is that the names attached to them have not kept pace with what the genomes actually show.

Some of the study’s most consequential findings concern the internal structure of the two best-known members of the complex. Within Mycobacterium avium, the subspecies avium, hominissuis, and silvaticum did not form three separate genomic groups but instead constituted a single genomic continuum, blurring the boundaries that clinical and veterinary microbiology have traditionally drawn between these lineages. Notably, the IS900-positive lineage responsible for paratuberculosis, a chronic intestinal disease of ruminants with debated links to human illness, remained genomically distinct from the rest of the continuum, yet its divergence did not reach the level that would justify subspecies status under the framework’s statistical criteria. This finding will resonate in veterinary medicine, where Mycobacterium avium subsp. paratuberculosis, commonly known as Map, is a pathogen of major economic importance, and it raises the question of whether the current subspecies designations within M. avium reflect biology or historical convention.

The picture within Mycobacterium intracellulare proved equally revealing. The subspecies chimaera, implicated in device-associated infections and notorious for the 2015 heater-cooler unit outbreaks that affected cardiac surgery patients worldwide, displayed deep internal structure, suggesting that it is not a monolithic entity. At the same time, the data supported formal recognition of M. intracellulare subsp. yongonense as a distinct lineage, while genomes assigned to the nominal species M. paraintracellulare clustered squarely within M. intracellulare subsp. intracellulare, effectively dissolving that species into its neighbor. The researchers also found that several recently described species names, including M. bouchedurhonense and M. timonense, lacked genomic coherence altogether, meaning that isolates carrying these labels do not form recognizable genetic groups and the names should be treated with skepticism in future work.

Beyond taxonomy, the study delivered a panoramic view of the complex’s collective gene pool. The MAC pangenome, the total set of gene clusters found across all 2,737 genomes, came to 191,904 gene clusters, a number that continues to grow as new genomes are added. Only 0.8 percent of these clusters constitute the universal core, the genes shared by every member of the complex. This open pangenome architecture is characteristic of bacterial groups with extensive horizontal gene transfer and diverse ecological niches, and it helps explain why members of the MAC can colonize everything from natural water sources and soil to bathroom drains, medical devices, and the lungs of patients with underlying respiratory disease. A tiny shared core combined with an enormous accessory genome means that individual isolates can differ dramatically in gene content even when their core sequences are nearly identical.

Defining the taxonomy was only half of the project. The team recognized that the biggest barrier to bringing whole-genome sequencing into routine clinical and epidemiological practice for the MAC is not the absence of data but the absence of standardized, accessible tools that translate raw genomic similarity into harmonized taxonomic assignments. Laboratories that sequence a MAC isolate today may run different pipelines, apply different thresholds, and arrive at different names for the same organism. To close that gap, the researchers developed a consensus genome-based framework that combines FastANI, a fast alignment-based ANI calculator, with dRep, a widely used genome clustering and dereplication tool, and then went a step further by training machine learning classifiers to reproduce the framework’s delineations automatically.

The machine learning approach relies on k-mer frequency profiles, a technique that represents each genome by counting the occurrences of all possible nucleotide words of a fixed length. This representation captures the overall compositional signature of a genome without requiring computationally expensive whole-genome alignments, making it well suited for rapid classification of large numbers of isolates. Trained on the ANI-based delineations established in the study, the k-mer classifiers achieved near-perfect performance, with a Cohen’s kappa statistic of 0.994, a measure of agreement that corrects for chance and approaches the theoretical maximum of 1.0. In practical terms, the classifiers can reproduce the full genome-based taxonomic assignment of a MAC isolate from its sequence composition alone, with essentially no loss of accuracy relative to the slower reference pipeline.

All of this capability has been consolidated into MAC-Explorer, an open-access web tool available at mycobacteriaexplorer.fr. The tool integrates the k-mer-based classification with insertion sequence screening, allowing users to upload a MAC genome and receive a reproducible, harmonized assignment at both the species and lineage levels. The insertion sequence component is particularly relevant for surveillance, since elements such as IS900 and IS1245 have historically been used as markers for specific pathogenic lineages, and their systematic detection within a unified framework brings molecular epidemiology and taxonomy onto the same footing. Because the tool is web-based and free to use, it lowers the technical barrier for diagnostic laboratories and public health agencies that lack dedicated bioinformatics teams, potentially enabling genome-based identification and lineage tracking of MAC isolates as a routine rather than exceptional practice.

The study, which was supported by Santé publique France and by a Poste d’Accueil from the Assistance Publique-Hôpitaux de Paris in partnership with the Institut Pasteur, arrives at a moment when nontuberculous mycobacterial infections are increasingly recognized as a growing clinical challenge, particularly among patients with cystic fibrosis, bronchiectasis, and other structural lung diseases. By replacing an unstable, convention-driven nomenclature with a statistically modeled, genome-based framework, and by making that framework immediately usable through an open web tool, Poignon and colleagues have provided the kind of shared reference system that diagnostics, antimicrobial management, and outbreak investigation all require. The fifteen species clusters, the clarified status of the M. avium subspecies continuum, the dissolution of several questionable species names, and the near-perfect machine learning classifiers together form a foundation on which future MAC surveillance and research can be built, one genome at a time.

Subject of Research: Genome-based taxonomy and identification of the Mycobacterium avium complex

Article Title: MAC-explorer: bridging genome-based taxonomy and an identification tool for the Mycobacterium avium complex

Article References: Poignon, C., Matondo, M., Gianetto, Q. G., Douché, T., Saffarian, A., Veziris, N., Aubry, A., & Godmer, A. (2026). MAC-explorer: bridging genome-based taxonomy and an identification tool for the Mycobacterium avium complex. Genome Medicine. https://doi.org/10.1186/s13073-026-01783-y

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01783-y

Keywords: Mycobacterium avium complex, nontuberculous mycobacteria, average nucleotide identity, genome-based taxonomy, pangenome, machine learning, k-mer classification, whole-genome sequencing, MAC-Explorer, subspecies delineation, bacterial taxonomy, genomic surveillance