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

Nontuberculous mycobacteria, often abbreviated as NTM, are a sprawling group of environmental organisms found in soil and water. Unlike their notorious relatives Mycobacterium tuberculosis and Mycobacterium leprae, most NTM species do not spread from person to person, but a subset of them can cause serious lung disease, particularly in people with underlying respiratory conditions or weakened immune systems. Clinicians have watched with growing concern as the prevalence of NTM pulmonary disease rises worldwide, imposing substantial morbidity on aging populations. At the same time, advances in multilocus sequence analysis and whole-genome sequencing have revealed a hidden layer of diversity within this group: novel species that look unremarkable in routine cultures yet are genetically distinct from anything in the reference databases. The Japanese case is a textbook example of this emerging frontier.

The patient at the center of the report was a 73-year-old woman with a history of acute myeloid leukemia. She had received an umbilical cord blood transplantation at the age of 67, a procedure in which stem cells harvested from umbilical cord blood are used to rebuild a patient’s blood and immune system after intensive chemotherapy. Although her leukemia was in remission, she carried a heavy burden of comorbidities, including a prior myocardial infarction, diabetes mellitus, dyslipidemia, and hypertension. Roughly a year before she was referred for specialist evaluation, she developed a cough and hemoptysis — the coughing up of blood — a symptom combination that immediately signals something is seriously wrong in the airways.

What followed was a microbiological puzzle that stumped the laboratory for months. Sputum cultures tested positive for mycobacteria from the very onset of her symptoms, and acid-fast smears — the classic stain used to detect mycobacterial cells — were repeatedly positive as well. Yet the organism could not be identified. Chest computed tomography painted a characteristic picture of chronic infection: peripheral granular opacities scattered through both lower lobes, accompanied by bronchiectasis, a permanent widening and scarring of the airways, along with thickened bronchial walls and mucus plugging. Two separate mycobacterial isolates, designated KO_240205 and KO_240413 from sputum specimens collected on different dates, resisted identification by MALDI-TOF mass spectrometry, the workhorse technique most clinical laboratories rely on for bacterial identification.

The team then turned to the molecular level, sequencing three classic mycobacterial marker genes — 16S rRNA, hsp65, and rpoB — but even here the results were inconclusive, with no 100 percent match to any known species in BLAST database searches. Taken together, however, the clinical picture, the radiological findings, and the repeated microbiological evidence fulfilled the accepted diagnostic criteria for nontuberculous mycobacterial pulmonary disease. An additional complication emerged: Pseudomonas aeruginosa, another opportunistic pathogen notorious in chronic lung infections, was first detected approximately eleven months after the respiratory symptoms began, while mycobacterial cultures had remained positive throughout. Because the mycobacterial evidence preceded the Pseudomonas isolation by nearly a year, the researchers judged the mycobacterium to be a genuine contributor to the lung disease, although they could not entirely exclude a role for the later-arriving bacterium.

The decisive step came from whole-genome sequencing. The researchers assembled the genomes of both clinical isolates and compared them against a reference panel of 227 mycobacterial genomes in a phylogenomic analysis. The result was striking: the two clinical isolates clustered together with a previously unnamed organism known as Mycobacterium sp. strain MFM001. The two isolates from the patient shared 100.00 percent average nucleotide identity with each other, confirming they were the same organism recovered on two occasions. Each isolate showed 99.98 percent average nucleotide identity and 99.8 percent digital DNA–DNA hybridization with strain MFM001 — values well above the thresholds microbiologists use to assign organisms to the same species. By contrast, comparisons with the closest named relatives, M. branderi, M. celatum, and M. kyorinense, yielded average nucleotide identities of only 90.08 to 90.64 percent and hybridization values of 37.9 to 39.7 percent, leaving no doubt that this was a species apart.

The history of strain MFM001 adds an intriguing zoological twist. The strain was previously reported by other researchers in a case of disseminated infection with granulomatous gastroenterocolitis in a cat receiving immunosuppressive treatment. That same team also noted partial 16S rRNA gene sequence identity between MFM001 and a strain isolated from a human pulmonary infection in Germany, hinting at a connection to human disease — but without whole-genome comparison, species identity could not be confirmed. The new genomic analysis now firmly links the Japanese clinical isolates to the feline strain, and on that basis the authors propose the name Mycobacterium meguroense for the previously unnamed species. Together, the feline report and the present human case suggest this organism can cause disease in different mammalian hosts, though its environmental reservoir and the routes by which people acquire it remain unknown.

Treatment posed its own questions. Published clinical information on this species is so limited that no established regimen exists, so the researchers looked to reports of disease caused by its close relatives for context. In those accounts, pulmonary infections with M. branderi, M. celatum, and M. kyorinense were all managed with multidrug regimens, though the combinations and durations varied considerably, and some cases required surgical resection of damaged lung tissue. For the present patient, broth microdilution testing yielded low clarithromycin minimum inhibitory concentrations of 0.125 and 0.25 micrograms per milliliter, providing microbiological support for including a macrolide in the regimen. Five months after referral, she began a three-drug combination of clarithromycin, ethambutol, and levofloxacin, and her condition improved. The last positive sputum cultures for both the mycobacterium and Pseudomonas aeruginosa were obtained in the month treatment began, and neither organism was recovered afterward. She has now continued treatment for a full year without relapse.

The authors are careful to frame this success appropriately. Low clarithromycin minimum inhibitory concentrations supported the choice of a macrolide, but phylogenetic relatedness alone does not guarantee that related species share antimicrobial susceptibility patterns or respond similarly to therapy. The improvement seen in this patient remains a single clinical observation rather than evidence of a preferred regimen for M. meguroense, and much more work is needed to define the species’ full clinical spectrum, the relationship between laboratory susceptibility values and treatment outcomes, and the optimal drug combination and duration of therapy. What the case does demonstrate, emphatically, is the essential role of accurate molecular identification and careful assessment of competing causes of lung disease when clinicians encounter rare or unclassified mycobacteria. As whole-genome sequencing becomes more accessible, organisms like M. meguroense — invisible to conventional methods and hiding in plain sight in laboratories around the world — are likely to keep emerging from the shadows, one painstaking case at a time.

Subject of Research: A newly identified slow-growing mycobacterium, Mycobacterium meguroense, causing pulmonary disease after umbilical cord blood transplantation

Article Title: Pulmonary disease caused by Mycobacterium meguroense , a newly identified slow-growing mycobacterium, after umbilical cord blood transplantation: A case report from Japan and a mini-review of the literature

Article References: Iizuka, S., Yoshida, M., Shinozaki, T., Uwamino, Y., Inose, R., Oyamada, Y., Fukunaga, K., Asakura, T., & Namkoong, H. (2026). Pulmonary disease caused by Mycobacterium meguroense, a newly identified slow-growing mycobacterium, after umbilical cord blood transplantation: A case report from Japan and a mini-review of the literature. New Microbes and New Infections, Article 101865. https://doi.org/10.1016/j.nmni.2026.101865

Image Credits: AI Generated

DOI: 10.1016/j.nmni.2026.101865

Keywords: Mycobacterium meguroense, nontuberculous mycobacteria, whole-genome sequencing, umbilical cord blood transplantation, pulmonary disease, NTM-PD, MALDI-TOF, clarithromycin, average nucleotide identity, emerging pathogens, clinical microbiology, case report

In research published in BMC Genomics, Tomasz Grzyb, Małgorzata Wlaźlak and Justyna Szulc took six bacterial strains isolated from soils in post-maize cultivation fields and subjected them to a battery of genome-based species assignment methods. Their targets belonged to the order Caryophanales, a group that includes the enormously consequential genera Bacillus, Priestia and Paenibacillus, organisms used in agriculture, industry and biotechnology, and close relatives of dangerous pathogens. What they found was a taxonomic landscape riddled with contradiction: species boundaries that shift depending on which metric you trust, genomes that intermix across supposedly distinct species names, and a widely used identity threshold that may be significantly miscalibrated.

The team compared several complementary approaches. Average Nucleotide Identity, or ANI, was computed with two different tools, FastANI and skani, which estimate the overall similarity between two genomes by aligning shared DNA sequence. Digital DNA-DNA hybridization, dDDH, a computational descendant of the wet-laboratory hybridization experiments that once defined bacterial species, was calculated using formula 2. The researchers also employed tetranucleotide Z-score distance, TZMD, which measures differences in short-word DNA composition, and single-copy gene phylogenomics, reconstructing evolutionary trees from sets of genes present exactly once in each genome. Crucially, rather than comparing each strain against only its nearest named neighbors, the authors performed comprehensive pairwise comparisons among all available RefSeq genomes at both complete and chromosome assembly levels for each taxonomic neighborhood, an unusually thorough sweep designed to reveal the true structure of variation around each strain.

For two of the six strains, the answer came back clean. Both could be assigned without ambiguity, one to Bacillus subtilis and one to Bacillus licheniformis, with every ANI comparison against their respective species exceeding the conventional 95 percent threshold and with clear phylogenomic separation from neighboring taxa. These cases show that the classical framework still works when species boundaries are genuinely well separated. The complications began with the remaining four strains, each of which landed in a different taxonomic minefield.

Strain Bac2 fell within the Operational Group Bacillus amyloliquefaciens, and here the analysis documented what the authors call fundamental boundary inconsistency. ANI values measured between recognized species within this group exceeded the ANI values measured among genomes assigned to Bacillus amyloliquefaciens itself. In other words, genomes bearing different species names were more similar to each other than genomes carrying the same name, a direct inversion of what a coherent species concept requires. Single-copy gene phylogenomics confirmed the chaos, revealing extensive intermixing of named species across the tree. A name assigned under these conditions conveys little about evolutionary relatedness.

Strain zielonkawy was assigned to the Bacillus cereus sensu stricto genomospecies, but its placement highlighted a familiar and stubborn problem: the deep intermixing of Bacillus cereus sensu stricto and Bacillus thuringiensis. These two names describe bacteria with dramatically different ecological roles, one an opportunistic pathogen and the other an insecticidal biocontrol agent, yet their genomes remain so entangled that no genomic metric reliably separates them. The new data add one more well-documented instance to a debate that has persisted since whole genomes first became available.

The two remaining strains, assigned to Priestia megaterium and Paenibacillus amylolyticus, produced perhaps the most novel findings. Both ANI multi-comparison analysis and single-copy gene phylogenomics suggested possible species intermixing or mislabelling within the public reference databases themselves. To the authors’ knowledge, this is the first study to quantitatively document species delimitation problems between Priestia megaterium and Priestia aryabhattai, and between Paenibacillus amylolyticus and Paenibacillus xylanexedens. The Paenibacillus analysis carried the caveat of a small available sample size, but the Priestia result points to a quietly widespread issue: reference databases, which thousands of labs treat as ground truth, may contain genomes whose species labels do not survive close genomic scrutiny.

Beyond the individual assignments, the study delivers a quantitative contribution to the methodology of bacterial taxonomy itself. Concordance analysis between FastANI and dDDH formula 2 revealed a non-linear relationship, well described by a quadratic fit with an R-squared of 0.991. From this relationship, the researchers derived a striking number: the traditional 70 percent dDDH species threshold, inherited from the pre-genomic era of DNA reassociation experiments, corresponds not to the conventionally assumed 95 percent ANI but to approximately 96.16 percent ANI. In practical terms, dDDH formula 2 is the more conservative of the two metrics, meaning that genomes judged to be the same species by the 95 percent ANI rule could still fail the dDDH test. Laboratories relying on ANI alone may be lumping together organisms that a stricter standard would split.

The comparison between alignment-free and tree-based approaches added further nuance. Correlations between skani genomic distances and single-copy gene phylogenomic patristic distances, the branch-length distances separating genomes on the reconstructed trees, were high across all datasets, with R-squared values ranging from 0.900 to 0.997. Overall genomic structure, in other words, is consistent between methods. But Spearman rank correlations, while still strong, were consistently lower, at 0.780 to 0.916, indicating that the precise identity of a genome’s closest neighbors can shift depending on whether you measure raw sequence similarity or reconstructed evolutionary distance. For taxonomists deciding whether a strain belongs to one species or its nearest rival, that rank-order disagreement is exactly where decisions get made.

As a constructive response, the authors propose a complementary diagnostic tool: within- and between-species ANI multi-comparison analysis paired with PERMANOVA, a non-parametric statistical test for differences among groups, followed by post-hoc pairwise testing. The idea is to treat species boundaries not as a single threshold but as a statistical question: do the ANI distributions within a named species differ significantly from the distributions between it and its relatives? Applied alongside single-copy gene phylogenomics, this framework could flag boundary inconsistencies that single-threshold assignment silently passes over, giving taxonomists an explicit, reproducible way to test whether a species name still carves nature at its joints.

The broader significance of the work extends well beyond six soil isolates from Polish maize fields. The Caryophanales taxa examined here anchor industries from probiotics to pest control and include the closest relatives of the anthrax bacillus. If species boundaries in these groups are inconsistent, then everything built on top of those names, from safety assessments of biocontrol strains to regulatory definitions of pathogenic species, inherits the uncertainty. The study also lands amid an ongoing, sometimes contentious community effort to redefine prokaryotic species entirely, with competing proposals for genome-based circumscriptions. By showing that even gold-standard tools disagree at the margins, and that the canonical thresholds themselves encode hidden conservatism, the Lodz team’s results argue for pluralism: no single number can settle species questions in difficult groups, but a disciplined combination of metrics, statistics and phylogenetics can at least make the disagreements visible, quantifiable and, ultimately, resolvable.

Subject of Research: Genome-based species delimitation and taxonomic boundary evaluation in Caryophanales bacteria using ANI, dDDH and phylogenomics

Article Title: Challenges in Caryophanales species delimitation: comparative evaluation of genomic similarity metrics and phylogenomics based on six environmental strains

Article References: Grzyb, T., Wlaźlak, M., & Szulc, J. (2026). Challenges in Caryophanales species delimitation: comparative evaluation of genomic similarity metrics and phylogenomics based on six environmental strains. BMC Genomics. https://doi.org/10.1186/s12864-026-13320-7

Image Credits: AI Generated

DOI: 10.1186/s12864-026-13320-7

Keywords: Caryophanales, species delimitation, phylogenomics, Average Nucleotide Identity, digital DNA-DNA hybridization, Bacillus subtilis group, Bacillus cereus group, Priestia, Paenibacillus, bacterial taxonomy, BMC Genomics, environmental strains