TB-Profiler was developed to profile M. tuberculosis directly from next-generation sequencing data. Rather than waiting days or weeks for laboratory cultures to grow, the tool scans raw sequencing reads for known resistance-conferring mutations and assigns the isolate to one of the major phylogenetic lineages of the tuberculosis bacillus. This approach, known as genotypic antimicrobial susceptibility prediction, has become increasingly important as sequencing costs fall and national tuberculosis programmes adopt whole-genome sequencing for routine diagnostics. The original version of the tool covered 16 anti-TB drugs; the updated release adds delamanid and pretomanid, two newer agents that are central to modern regimens for multidrug-resistant and extensively drug-resistant disease.

The technical heart of the upgrade lies in its expanded and refined mutation libraries. TB-Profiler does not attempt to predict resistance from first principles; instead, it relies on carefully curated catalogues of mutations that have been experimentally or epidemiologically linked to reduced drug susceptibility. The updated version incorporates interpretation rules endorsed by the World Health Organization, ensuring that the tool’s output aligns with internationally recognised standards for reporting resistance to drugs such as bedaquiline and clofazimine, cycloserine and terizidone, and para-aminosalicylic acid. The developers have also added curated loss-of-function mutations, which are changes that disable a gene entirely and are increasingly recognised as important drivers of resistance to newer drug classes.

Why does this matter clinically? Regimens for drug-resistant tuberculosis have changed dramatically in recent years, shifting towards shorter, all-oral combinations built around bedaquiline, pretomanid and linezolid. Choosing the right regimen requires knowing exactly which drugs the infecting strain can tolerate, and phenotypic antimicrobial susceptibility testing, the traditional gold standard, can take weeks because M. tuberculosis grows so slowly. Genotypic prediction from sequencing data can deliver a resistance profile in a fraction of that time, and tools like TB-Profiler make that analysis accessible to laboratories that lack deep bioinformatics expertise, through both a command-line version and a web interface.

The second major pillar of the update is population context. The team has integrated a continuously expanding global genomic database of more than 170,000 isolates, representing all major lineages of the M. tuberculosis complex and spanning 136 countries. This resource allows the tool to place any newly sequenced genome within its global population framework, comparing allele frequencies and identifying how unusual or common a given mutation is across the worldwide diversity of the pathogen. That context is technically important because not every mutation observed in a resistant isolate actually causes resistance; some are simply neutral variants that happen to be common in certain lineages. A global reference panel helps distinguish the two.

Built on top of this database is a new analytical capability for calculating and visualising genomic relatedness between isolates. In tuberculosis surveillance, genomic relatedness is the key to identifying transmission. When two isolates from different patients are nearly identical across their genomes, it strongly suggests that one infected the other, or that both were infected by a common source. By computing distances between genomes and presenting the results in accessible visualisations, the updated TB-Profiler allows public health teams to spot potential transmission chains and outbreaks directly within the same workflow they already use for resistance prediction, rather than juggling separate tools.

To demonstrate the new functionality, the researchers applied it to isolates from Uganda, where they were able to identify potential transmission events among the sampled genomes. The demonstration illustrates the practical value of combining resistance profiling and relatedness analysis in a single platform. In high-burden settings, distinguishing between reactivation of a latent infection picked up years ago and recent active transmission changes the public health response entirely: recent transmission calls for intensified contact tracing and community screening, whereas reactivation points towards the management of latent infection in vulnerable populations.

Accessibility has also been broadened in a way that could prove decisive for global uptake. The updated tool introduces multilingual reporting capabilities, meaning that the clinical reports generated from sequencing data can be delivered in the languages spoken by the health workers who use them. This may sound like a modest feature, but in tuberculosis control it addresses a real barrier: the countries carrying the greatest burden of drug-resistant disease are often those where English is not the primary working language of laboratory and clinical staff. By lowering the language barrier, the developers hope to widen implementation of genomic surveillance in precisely the regions where it is needed most.

The work is a product of an international collaboration spanning the London School of Hygiene and Tropical Medicine, Thailand’s Ministry of Public Health Medical Genetics Center, the South African National Bioinformatics Institute at the University of the Western Cape, Nottingham Trent University and the University of Cambridge. The informatics development was supported by a UKRI EPSRC award in artificial intelligence innovation to accelerate health research, with additional support from the UKRI Medical Research Council. The tool and its associated databases are available in both stand-alone and web-based versions at the team’s hosted platform, and the underlying article is published open access under a Creative Commons licence.

The developers are explicit that this release is a stepping stone rather than a destination. Future versions will leverage the ever-expanding sequencing database to deploy artificial intelligence approaches aimed at refining lineage assignment, improving drug-resistance prediction and sharpening transmission classification, including the identification of resistance-associated mutations that have not yet been characterised. As the global database grows with every sequenced isolate, the statistical power available to such machine-learning methods grows with it, promising a feedback loop in which each new genome makes the tool smarter for the next patient. For a disease that kills well over a million people each year, turning sequencing data into faster treatment decisions and earlier outbreak detection is a goal worth the engineering effort, and the updated TB-Profiler brings that goal measurably closer.

Subject of Research: Genomic prediction of antimicrobial resistance and transmission in Mycobacterium tuberculosis using the updated TB-Profiler tool

Article Title: Updated TB-Profiler: enhanced genotypic antimicrobial resistance prediction and relatedness analysis powered by a database of over 171,000 Mycobacterium tuberculosis genomes

Article References: Phelan, J. E., Sawaengdee, W., Thorpe, J., Thawong, N., Piboonsiri, P., Billows, N., Wang, L., van Heusden, P., Meehan, C., Köser, C. U., Mahasirimongkol, S., Campino, S., & Clark, T. G. (2026). Updated TB-Profiler: enhanced genotypic antimicrobial resistance prediction and relatedness analysis powered by a database of over 171,000 Mycobacterium tuberculosis genomes. Genome Medicine. https://doi.org/10.1186/s13073-026-01767-y

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01767-y

Keywords: tuberculosis, TB-Profiler, drug resistance, whole-genome sequencing, Mycobacterium tuberculosis, antimicrobial susceptibility prediction, genomic surveillance, transmission analysis, WHO mutation catalogue, bedaquiline, pretomanid, public health genomics

The DprE1–DprE2 complex performs a deceptively simple but absolutely essential chemical transformation. It converts decaprenylphosphoribose into decaprenylphosphoarabinose, the activated sugar donor required to build arabinan, a critical polysaccharide component of the mycobacterial cell wall. The reaction is a two-step epimerization: DprE1, a flavin-dependent oxidase, oxidizes the ribose moiety of the lipid-linked substrate, and DprE2, an NADH-dependent reductase belonging to the short-chain dehydrogenase/reductase superfamily, then reduces the intermediate back to yield the arabinose epimer. Because arabinan is indispensable for the bacterium’s survival and integrity, blocking either half of this two-enzyme relay starves the cell wall of its building blocks and kills the pathogen. This vulnerability has made the epimerase one of the most intensively pursued targets in modern anti-tubercular drug discovery, yet the physical architecture of the complex and the mechanism of its inhibition by clinical compounds had remained poorly defined.

The structural work now published reveals that the functional unit of the epimerase is far more elaborate than a simple pair of enzymes. Cryo-EM analysis shows that DprE1 and DprE2 assemble into a membrane-associated tetramer in which two DprE2 molecules form a central homodimer, flanked on each side by a single DprE1 subunit. Each DprE2 monomer cradles a molecule of NADH in its catalytic core, while the neighboring DprE1 subunits carry their own FAD cofactors and bind the lipid-like decaprenylphosphoribose substrate. The interfaces holding this four-subunit assembly together were mapped in detail: the DprE2 dimer is stabilized through contacts involving its alpha-5 and alpha-7 helices and its C-terminal region, while the heterodimeric contacts between DprE1 and DprE2 involve the N-terminal loop of DprE2, residues 2 through 10, reaching into the FAD-binding and substrate-binding domains of DprE1. This arrangement positions the two active sites in close proximity, providing a structural rationale for how the oxidized intermediate can be handed efficiently from DprE1 to DprE2 without diffusing away into the membrane.

Perhaps the most consequential finding concerns pretomanid and delamanid, two nitroimidazole prodrugs that have become cornerstones of therapy for multidrug-resistant tuberculosis. Both compounds require activation by the bacterial deazaflavin F420-dependent nitroreductase Ddn, a process that generates reactive des-nitro metabolites. Earlier work had established that the activated derivatives of these drugs ultimately target DprE2, but the structural basis of that interaction was unknown. The new structures show that both activated pretomanid and activated delamanid bind to DprE2 in the form of covalent NADH adducts. Mass spectrometric analysis confirmed the formation of these adducts, with the drugs attached to the nicotinamide ribose of NADH, producing an inhibitory species that occupies an extended, conserved binding pocket in DprE2. Strikingly, this pocket stretches all the way from the NADH-binding site at the catalytic center to the substrate-binding site, meaning the drug adduct simultaneously blocks cofactor chemistry and substrate access in one motion.

This mechanism is a striking echo of the way isoniazid, the oldest frontline tuberculosis drug, kills mycobacteria. Isoniazid is also a prodrug, activated by the catalase-peroxidase KatG, and its activated form forms a covalent NAD adduct that potently inhibits the enoyl reductase InhA, halting mycolic acid synthesis. Pretomanid and delamanid, it now appears, deploy essentially the same chemical strategy against a different cell wall pathway: they hijack the cell’s own NADH to forge a tight-binding inhibitor inside the DprE2 active site. The structural overlays show that the drug portion of each adduct sits in a hydrophobic channel adapted to accept the long lipophilic tail of the decaprenyl substrate, with hydrogen-bonding interactions anchoring the drug in place. The catalytic triad of DprE2, comprising serine 147, tyrosine 160 and lysine 164, is highly conserved across the SDR superfamily, and the structures explain how the adduct wedges into the space adjacent to this catalytic machinery.

The third drug interrogated in the study, quabodepistat, acts on the other half of the complex. Unlike the nitroimidazoles, quabodepistat is a noncovalent inhibitor of DprE1, the oxidase component, and it is currently being evaluated in phase 2b/c clinical trials in combination with delamanid and bedaquiline. The cryo-EM structure of the quabodepistat-bound complex reveals a distinctive binding mode. The inhibitor occupies the substrate-binding site of DprE1, with its head, trunk and tail regions engaging separate polar and nonpolar pockets, and it induces a conformational change in two surface loops of DprE1 that adopt an open, ordered conformation not seen in structures with other noncovalent DprE1 inhibitors. Differential scanning fluorimetry experiments showed that wild-type DprE1 binds quabodepistat completely, whereas a catalytically relevant C387S mutant shows only partial binding, underscoring the specificity of the interaction and distinguishing quabodepistat’s binding mode from that of covalent inhibitors such as the benzothiazinones, which attack the same cysteine residue.

The technical achievement underlying these structures should not be understated. DprE1 and DprE2 are membrane-associated proteins, and capturing the intact tetramer in a homogeneous, grid-ready state required careful biochemical optimization, including crosslinking of the complex. The researchers collected cryo-EM datasets for four distinct states: the substrate-bound complex, the complex bound to the NADH-pretomanid adduct, the complex bound to the NADH-delamanid adduct, and the quabodepistat-bound complex. Atomic coordinates were deposited in the Protein Data Bank under accession codes 21GR, 21GQ, 43FX and 21GT, with corresponding maps in the Electron Microscopy Data Bank. The resulting models reveal not only ligand-binding details but also the electrostatic character of DprE2’s surface, including a loop region that likely helps position the enzyme at the membrane interface where its long lipid substrate resides.

The therapeutic implications are significant. By showing exactly which residues line the DprE2 drug pocket and how the NADH-adduct form of pretomanid and delamanid nestles within it, the structures give medicinal chemists a template for designing next-generation nitroimidazoles with improved potency and a reduced likelihood of resistance. Resistance to nitroimidazoles in the clinic is already known to arise through mutations in the F420 biosynthetic pathway or in the nitroreductase Ddn, which prevent drug activation; the new structures now make it possible to anticipate and monitor resistance mutations directly in DprE2 as well. Similarly, the unique binding mode of quabodepistat in DprE1, with its ordered surface loops and its three-part engagement of the substrate pocket, offers a scaffold for optimizing noncovalent DprE1 inhibitors, a class that includes the advanced clinical candidate OPC-167832 and has been the focus of intense structure-based drug design for over a decade.

More broadly, the study closes a long-standing gap in mycobacterial cell wall biology. Since the discovery in 2005 that decaprenylphosphoarabinose is formed by a two-step epimerization, the field has studied DprE1 and DprE2 largely as separate enzymes, crystallizing DprE1 with numerous inhibitors and probing DprE2 biochemically. The tetrameric complex captured here, a DprE2 dimer flanked by two DprE1 subunits, provides the first complete architectural picture of the functional epimerase and suggests how its two active sites are coordinated in a single membrane-associated assembly. As tuberculosis continues to claim more than a million lives each year and drug resistance spreads, structures of this quality do more than satisfy curiosity: they convert a validated but opaque drug target into an engineerable one, and they explain at atomic resolution why some of the newest weapons against the world’s deadliest bacterial infection work the way they do. The path from these electron density maps to better tuberculosis regimens now runs directly through rational design, guided by the pocket architecture the researchers have laid bare.

Subject of Research: Structural and mechanistic analysis of the Mycobacterium tuberculosis DprE1–DprE2 epimerase complex and its inhibition by anti-tubercular drugs

Article Title: Mechanism of the M. tuberculosis DprE1–DprE2 epimerase complex and inhibition by anti-tubercular drugs

Article References: Gao, S., Wu, F., Zhang, Y., Batt, S. M., Wei, C., Wang, Z., Yang, X., Abrahams, K. A., Fütterer, K., Liu, X., Wang, P., Besra, G. S., Rao, Z., & Zhang, L. (2026). Mechanism of the M. tuberculosis DprE1–DprE2 epimerase complex and inhibition by anti-tubercular drugs. Nature Chemical Biology. https://doi.org/10.1038/s41589-026-02317-8

Image Credits: AI Generated

DOI: 10.1038/s41589-026-02317-8

Keywords: tuberculosis, Mycobacterium tuberculosis, DprE1, DprE2, cryo-EM, pretomanid, delamanid, quabodepistat, cell wall synthesis, drug resistance, structural biology, enzyme mechanism