Discovery of Arr-X: A Novel, Highly Efficient Rifamycin-Inactivating Enzyme Family Reveals New Antibiotic Resistance Mechanisms Across Mycobacteria
Key Insights
A macroevolution-inspired screen of 44 mycobacterial species revealed dramatic, 100- to 1000-fold differences in antibiotic sensitivity across the genus, challenging the assumption that nontuberculous mycobacteria are intrinsically more drug-resistant than Mycobacterium tuberculosis (search).
Researchers discovered a previously unknown family of rifamycin ADP-ribosyltransferase enzymes, designated Arr-X (search), which are up to 30-fold more efficient at inactivating rifamycins than the known Arr-1 (search) enzymes.
CRISPR interference experiments confirmed that Arr-X (search) is an active "rifabutinase" conferring rifamycin resistance in Mycobacterium conceptionense, with Arr-X enzymes broadly distributed across multiple bacterial phyla.
A comprehensive, genus-wide investigation into antibiotic resistance across mycobacteria has uncovered a previously unknown family of enzymes that potently inactivate rifamycin antibiotics, shedding new light on why certain mycobacterial infections remain exceptionally difficult to treat. The study, published in eLife, assembled a collection of 44 tractable mycobacterial species spanning most of the phylogenetic tree and employed a macroevolution-inspired approach to reveal novel resistance mechanisms that have eluded conventional research paradigms.
The research team determined minimal inhibitory concentrations (MIC99) for 15 antibiotics spanning most classes employed to treat mycobacterial infections, including tuberculosis. The results revealed striking biological diversity: several species displayed MIC99 values considerably different from the mean, with differences of the order of 100- to 1000-fold when compared to M. tuberculosis in some instances.
Antibiotic sensitivity varies dramatically across the genus
Contrary to common assumptions, the data demonstrated that nontuberculous mycobacteria (NTM) are not intrinsically more drug-resistant than M. tuberculosis. When ordered by antibiotic sensitivity, M. tuberculosis positioned at the middle of the heatmap, with the number of NTM more resistant to antibiotics nearly equal to the number more sensitive. Notably, M. mageritense, Mycobacterium salmoniphilum, and Mycobacterium houstonense were highly resistant to most antibiotics tested, while the notoriously multidrug-resistant M. abscessus was not the most resistant species studied.
The study also challenged the widespread use of M. smegmatis as a model organism for M. tuberculosis in antibiotic discovery. M. smegmatis displayed a completely different sensitivity profile, being highly resistant to para-aminosalicylic acid (PAS), ethionamide (ETH), D-cycloserine (DCS), and rifampicin (RIF). Instead, M. marinum emerged as a better proxy for M. tuberculosis, with both species sharing a similar sensitivity profile except to ofloxacin.
From the antibiotics that inhibit protein synthesis, linezolid (LZD) displayed the lowest overall MIC99 and was effective against most species. No correlation was apparent between growth rate and antibiotic sensitivity in mycobacteria, with the researchers noting that doubling times varied dramatically—from 1.3 hours for M. szulgai to 17.1 hours for M. tuberculosis.
Discovery of the Arr-X (search) enzyme family
To explore the molecular causes of dramatic changes in antibiotic potency, the team focused on bedaquiline (BDQ), linezolid (LZD), and rifampicin (RIF). Mining for arr sequences in reference genomes revealed that Arr proteins are widespread in both fast- and slow-growing mycobacteria, forming two distinct orthologous groups.
The first group, designated Arr-1 (search), corresponds to previously known sequences closely related to Arr-ms with a median sequence identity of 80%. The second group, discovered in this study and named Arr-X (search), is taxonomically more broadly distributed, including members from Actinomycetota, Bacillota, Pseudomonadota, and Bacteroidota. Within mycobacteria, more species harbor an arr-1 gene than arr-X, though a few species—including M. conceptionense and M. flavescens—possess both.
The three residues shown by Baysarowich and collaborators to be necessary for enzymatic activity in Arr-ms (Asp84, His19, and Tyr49) are conserved in all mycobacterial Arr-1 (search) and Arr-X (search) proteins, suggesting they are all active ADP-ribosyltransferases. However, the hydrophobic nature of the RIF binding cleft of Arr-ms is not completely preserved in the Arr-X group, hinting at probable differences in substrate binding preference.
Arr-X (search) enzymes are significantly more efficient rifamycin inactivators
Enzymatic assays with six rifamycins demonstrated that while all Arr-1 (search) enzymes had similar activity and substrate preference, Arr-X (search) enzymes were markedly superior at inactivating rifamycins. M. flavescens Arr-X was four-fold faster with rifapentine than Arr-ms. Most strikingly, M. conceptionense Arr-X was 30-fold faster at inactivating rifabutin compared to Arr-ms.
CRISPR interference experiments confirmed the functional relevance of Arr-X (search) in bacteria. Silencing arr-X in M. conceptionense rendered the bacterium more sensitive to rifabutin, while silencing arr-1 (search) alone did not affect rifabutin resistance. This established Arr-X as an active "rifabutinase" that confers rifamycin resistance in M. conceptionense.
Implications for precision therapeutics
The findings arrive at a critical juncture in the fight against antimicrobial resistance in mycobacterial infections. As noted in a related Frontiers Research Topic, treatment failure cannot be explained solely by the acquisition of genetic resistance—mycobacteria employ diverse adaptive strategies including drug tolerance, persistence, dormancy, metabolic remodeling, and biofilm formation that collectively reduce antimicrobial efficacy and contribute to relapse.
The discovery of Arr-X (search) highlights the value of genus-wide comparative approaches that integrate whole-genome sequencing, functional genomics, and biochemical characterization. Such multidisciplinary strategies are increasingly being complemented by artificial intelligence and machine learning to predict resistance phenotypes, identify novel therapeutic targets, and optimize drug combinations—accelerating the transition from descriptive studies of resistance toward precision therapeutics for both tuberculosis and nontuberculous mycobacterial diseases.