Toxin release by conditional remodelling of ParDE1 from Mycobacterium tuberculosis leads to gyrase inhibition

Mycobacterium tuberculosis, the causative agent of tuberculosis, is a growing threat to global health, with recent efforts towards its eradication being reversed in the wake of the COVID-19 pandemic. Increasing resistance to gyrase-targeting second-line fluoroquinolone antibiotics indicates the necessity to develop both novel therapeutics and our understanding of M. tuberculosis growth during infection. ParDE toxin-antitoxin systems also target gyrase and are regulated in response to both host-associated and drug-induced stress during infection. Here, we present microbiological, biochemical, structural, and biophysical analyses exploring the ParDE1 and ParDE2 systems of M. tuberculosis H37Rv. The structures reveal conserved modes of toxin-antitoxin recognition, with complex-specific interactions. ParDE1 forms a novel heterohexameric ParDE complex, supported by antitoxin chains taking on two distinct folds. Curiously, ParDE1 exists in solution as a dynamic equilibrium between heterotetrameric and heterohexameric complexes. Conditional remodelling into higher order complexes can be thermally driven in vitro. Remodelling induces toxin release, tracked through concomitant inhibition and poisoning of gyrase activity. Our work aids our understanding of gyrase inhibition, allowing wider exploration of toxin-antitoxin systems as inspiration for potential therapeutic agents. [Abstract copyright: © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.

MenT nucleotidyltransferase toxins extend tRNA acceptor stems and can be inhibited by asymmetrical antitoxin binding

Mycobacterium tuberculosis, the bacterium responsible for human tuberculosis, has a genome encoding a remarkably high number of toxin-antitoxin systems of largely unknown function. We have recently shown that the M. tuberculosis genome encodes four of a widespread, MenAT family of nucleotidyltransferase toxin-antitoxin systems. In this study we characterize MenAT1, using tRNA sequencing to demonstrate MenT1 tRNA modification activity. MenT1 activity is blocked by MenA1, a short protein antitoxin unrelated to the MenA3 kinase. X-ray crystallographic analysis shows blockage of the conserved MenT fold by asymmetric binding of MenA1 across two MenT1 protomers, forming a heterotrimeric toxin-antitoxin complex. Finally, we also demonstrate tRNA modification by toxin MenT4, indicating conserved activity across the MenT family. Our study highlights variation in tRNA target preferences by MenT toxins, selective use of nucleotide substrates, and diverse modes of MenA antitoxin activity

Inducible auto-phosphorylation regulates a widespread family of nucleotidyltransferase toxins

Nucleotidyltransferases (NTases) control diverse physiological processes, including RNA modification, DNA replication and repair, and antibiotic resistance. The Mycobacterium tuberculosis NTase toxin family, MenT, modifies tRNAs to block translation. MenT toxin activity can be stringently regulated by diverse MenA antitoxins. There has been no unifying mechanism linking antitoxicity across MenT homologues. Here we demonstrate through structural, biochemical, biophysical and computational studies that despite lacking kinase motifs, antitoxin MenA1 induces auto-phosphorylation of MenT1 by repositioning the MenT1 phosphoacceptor T39 active site residue towards bound nucleotide. Finally, we expand this predictive model to explain how unrelated antitoxin MenA3 is similarly able to induce auto-phosphorylation of cognate toxin MenT3. Our study reveals a conserved mechanism for the control of tuberculosis toxins, and demonstrates how active site auto-phosphorylation can regulate the activity of widespread NTases

Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3) —The Time-Independent Model

The 2014 Working Group on California Earthquake Probabilities (WGCEP14) present the time-independent component of the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3), which provides authoritative estimates of the magnitude, location, and time-averaged frequency of potentially damaging earthquakes in California. The primary achievements have been to relax fault segmentation and include multifault ruptures, both limitations of UCERF2. The rates of all earthquakes are solved for simultaneously and from a broader range of data, using a system-level inversion that is both conceptually simple and extensible. The inverse problem is large and underdetermined, so a range of models is sampled using an efficient simulated annealing algorithm. The approach is more derivative than prescriptive (e.g., magnitude–frequency distributions are no longer assumed), so new analysis tools were developed for exploring solutions. Epistemic uncertainties were also accounted for using 1440 alternative logic-tree branches, necessitating access to supercomputers. The most influential uncertainties include alternative deformation models (fault slip rates), a new smoothed seismicity algorithm, alternative values for the total rate of M[subscript w] ≥ 5 events, and different scaling relationships, virtually all of which are new. As a notable first, three deformation models are based on kinematically consistent inversions of geodetic and geologic data, also providing slip-rate constraints on faults previously excluded due to lack of geologic data. The grand inversion constitutes a system-level framework for testing hypotheses and balancing the influence of different experts. For example, we demonstrate serious challenges with the Gutenberg–Richter hypothesis for individual faults. UCERF3 is still an approximation of the system, however, and the range of models is limited (e.g., constrained to stay close to UCERF2). Nevertheless, UCERF3 removes the apparent UCERF2 overprediction of M 6.5–7 earthquake rates and also includes types of multifault ruptures seen in nature. Although UCERF3 fits the data better than UCERF2 overall, there may be areas that warrant further site-specific investigation. Supporting products may be of general interest, and we list key assumptions and avenues for future model improvements