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

Hemoplasmas, short for hemotropic mycoplasmas, are wall-less bacteria belonging to the class Mollicutes. Unlike most bacteria, they cannot be grown easily in the laboratory, which has long hampered efforts to study them. Instead, scientists rely on molecular techniques, chiefly the polymerase chain reaction, or PCR, to detect their genetic material in blood samples. Two species are known to infect cattle worldwide: Mycoplasma wenyonii, often abbreviated Mw, and a bacterium currently designated “Candidatus Mycoplasma haematobovis,” formerly known as “Candidatus Mycoplasma haemobos.” The Candidatus designation is applied by microbiologists to organisms that can be characterized by molecular methods but have not yet been cultivated or fully formally described.

Infections with these organisms are often subtle. Some animals show no obvious signs of disease, while others may develop anemia, fever, weight loss, reduced milk yield, or swelling of the limbs and teats. Because the clinical picture can be vague and the bacteria cannot be cultured routinely, hemoplasma infections in cattle are frequently overlooked, and their economic impact on dairy production is difficult to quantify. In tropical countries such as Thailand, where dairy farming is an important agricultural sector and arthropod vectors such as flies and ticks are abundant year-round, the question of what these bacteria are doing to herd health takes on added urgency.

To address this, a team led by Kritsada Thongmeesee of Chulalongkorn University in Bangkok, working with colleagues at Prince of Songkla University, Vongchavalitkul University, and the University of Melbourne in Australia, collected blood samples from 522 dairy cattle across Thailand. The animals were screened using a PCR assay that targets the 16S ribosomal RNA gene, a workhorse of bacterial identification. This gene, which encodes part of the small subunit of the bacterial ribosome, contains regions that are conserved across all bacteria as well as variable stretches that allow researchers to distinguish between closely related organisms. The team also examined the 23S ribosomal RNA gene, which encodes part of the large ribosomal subunit and provides an independent set of variable positions for comparison.

The screening produced a striking result: 161 of the 522 cattle, or 30.8 percent, tested positive for hemoplasma DNA. The 95 percent confidence interval for this estimate ranged from 26.9 to 35.0 percent, meaning that roughly one in three animals in the sampled population was carrying at least one of these blood-dwelling bacteria. That prevalence figure alone underscores how common hemoplasmas are in Thai dairy herds, even if most infections remain clinically silent or undiagnosed.

From the pool of positive samples, the researchers selected 52 for sequencing of both the 16S and 23S rRNA genes. The selection was not random; the team prioritized samples that had produced strong, discrete PCR bands, which are more likely to yield clean sequence data. While this approach maximizes the quality of the genetic information obtained, it also means the 52 sequenced samples represent a curated subset rather than a statistical sample of all infections, a caveat the authors acknowledge in their interpretation.

When the sequences were subjected to phylogenetic analysis, a method that reconstructs evolutionary relationships by comparing genetic differences, the results revealed a picture more complicated than the standard two-species model. As expected, some sequences clustered with Mycoplasma wenyonii and others with “Candidatus Mycoplasma haematobovis.” But in addition to these two recognized taxa, the analyses identified two further lineages that were clearly divergent, sitting on separate branches within what the researchers describe as the M. wenyonii complex. These lineages are distinct enough in their ribosomal RNA sequences to suggest they may represent previously unrecognized organisms, yet the team has deliberately refrained from naming them as new species.

That restraint reflects a growing consensus in bacterial taxonomy. Ribosomal RNA genes are powerful tools for detection and preliminary classification, but they capture only a small fraction of a bacterium’s genome, and organisms with similar rRNA profiles can nonetheless differ substantially in their metabolism, virulence, and ecology. The authors therefore regard the two divergent lineages as candidate lineages, a provisional status that will stand until whole-genome sequencing, additional independent genetic markers, and biological characterization, including studies of pathogenicity and transmission, can establish whether they truly deserve species-level recognition. This cautious approach avoids inflating the taxonomy of a group that is already difficult to study while flagging the lineages for future investigation.

The discovery has practical implications for veterinary medicine and livestock management in the region. If multiple genetically distinct hemoplasmas circulate in Thai dairy cattle, then diagnostic tests designed to detect one species may miss others, and studies that lump all hemoplasma infections together may obscure differences in disease potential between the lineages. Understanding which lineages cause illness, how they are transmitted, whether arthropod vectors play a role, and how they respond to antimicrobial treatment will require the kind of genomic and biological work the authors call for. It also raises questions about whether the divergent lineages are confined to Thailand or circulate more widely in cattle and buffalo populations across Asia and beyond, having simply gone undetected because most surveys rely on short diagnostic fragments rather than comparative sequencing of multiple genes.

For now, the study stands as a reminder that even well-known pathogens can harbor hidden diversity when examined closely. A routine prevalence survey of dairy cattle, conducted with standard molecular tools and a careful two-gene approach, was enough to double the number of recognized lineages within the bovine M. wenyonii complex. As sequencing becomes cheaper and whole-genome approaches reach veterinary microbiology, researchers expect more such surprises, not only in cattle but across the many mammalian species that host these elusive red blood cell parasites. The Thai findings provide a foundation for that work and a caution against assuming that the hemoplasmas infecting a herd are always the ones in the textbooks.

Subject of Research: Genetic diversity of hemotropic Mycoplasma lineages in dairy cattle in Thailand

Article Title: Two divergent hemoplasma lineages within the Mycoplasma wenyonii complex in dairy cattle from Thailand revealed by comparative analyses of partial 16S and 23S rRNA gene sequences

Article References: Thongmeesee, K., Tiyamanee, W., Narapakdeesakul, D., Kamkong, P., Wechtaisong, W., Colella, V., & Tiawsirisup, S. (2026). Two divergent hemoplasma lineages within the Mycoplasma wenyonii complex in dairy cattle from Thailand revealed by comparative analyses of partial 16S and 23S rRNA gene sequences. Parasites & Vectors. https://doi.org/10.1186/s13071-026-07678-6

Image Credits: AI Generated

DOI: 10.1186/s13071-026-07678-6

Keywords: hemoplasmas, Mycoplasma wenyonii, Candidatus Mycoplasma haematobovis, dairy cattle, Thailand, 16S rRNA gene, 23S rRNA gene, molecular diagnostics, phylogenetics, bacterial taxonomy, livestock health, Parasites & Vectors

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