Biology subjects

Ioerger, T.

Publications and source records attributed to Ioerger, T..

5 recordsLinked to original sources

bioRxiv2026

Non-canonical peptidoglycan cross-linking is essential for Mycobacterium tuberculosis acid resistance

Mycobacterium tuberculosis withstands acidic conditions to survive and replicate within macrophages. To define the genetic determinants of this adaptation, we performed a transposon screen in a lipid-rich, acidic medium that mimics the host environment and supports robust M. tuberculosis growth. This screen identified ldtB, encoding an L,D-transpeptidase, as essential for growth and survival under acid stress. Loss of LdtB decreased 3-3 peptidoglycan cross-linking, disrupted cell wall architecture, and impaired intrabacterial pH homeostasis, resulting in increased susceptibility to cell wall-active antibiotics. Notably, M. tuberculosis lacking LdtB displayed heightened sensitivity to meropenem within macrophages, suggesting that targeting this enzyme could potentiate {beta}-lactam efficacy during infection. These findings establish LdtB as a key mediator linking peptidoglycan homeostasis to acid stress resistance and underscores the importance of in vitro culture models that recapitulate the host microenvironment for uncovering new in vivo active therapeutic targets. TeaserDisabling a key peptidoglycan cross-linking enzyme compromises M. tuberculosis survival under acidic stress and antibiotic exposure.

microbiology↗

bioRxiv2023

A dose-response based model for statistical analysis of chemical genetic interactions in CRISPRi libraries

An important application of CRISPR interference (CRISPRi) technology is for identifying chemical-genetic interactions (CGIs). Discovery of genes that interact with exposure to antibiotics can yield insights to drug targets and mechanisms of action or resistance. The objective is to identify CRISPRi mutants whose relative abundance is suppressed (or enriched) in the presence of a drug when the target protein is depleted, reflecting synergistic behavior. Different sgRNAs for a given target can induce a wide range of protein depletion and differential effects on growth rate. The effect of sgRNA strength can be partially predicted based on sequence features. However, the actual growth phenotype depends on the sensitivity of cells to depletion of the target protein. For essential genes, sgRNA efficiency can be empirically measured by quantifying effects on growth rate. We observe that the most efficient sgRNAs are not always optimal for detecting synergies with drugs. sgRNA efficiency interacts in a non-linear way with drug sensitivity, producing an effect where the concentration-dependence is maximized for sgRNAs of intermediate strength (and less so for sgRNAs that induce too much or too little target depletion). To capture this interaction, we propose a novel statistical method called CRISPRi-DR (for Dose-Response model) that incorporates both sgRNA efficiencies and drug concentrations in a modified dose-response equation. We use CRISPRi-DR to re-analyze data from a recent CGI experiment in Mycobacterium tuberculosis to identify genes that interact with antibiotics. This approach can be generalized to non-CGI datasets, which we show via an CRISPRi dataset for E. coli growth on different carbon sources. The performance is competitive with the best of several related analytical methods. However, for noisier datasets, some of these methods generate far more significant interactions, likely including many false positives, whereas CRISPRi-DR maintains higher precision, which we observed in both empirical and simulated data. Author SummaryCRISPRi technology is revolutionizing research in various areas of the life sciences, including microbiology, affording the ability to partially deplete the expression of target proteins in a specific and controlled way. Among the applications of CRISPRi, it can be used to construct large (even genome-wide) libraries of knock-down mutants for profiling antibacterial inhibitors and identifying chemical-genetic interactions (CGIs), which can yield insights on drug targets and mechanisms of action and resistance. The data generated by these experiments (i.e., sgRNA counts from high throughput sequencing) is voluminous and subject to various sources of noise. The goal of statistical analysis of such data is to identify significant CGIs, which are genes whose depletion sensitizes cells to an inhibitor. In this paper, we show how to incorporate both sgRNA efficiency and drug concentration simultaneously in a model (CRISPRi-DR) based on an extension of the classic dose-response (Hill) equation in enzymology. This model has advantages over other analytical methods for CRISPRi, which we show using empirical and simulated data.

bioinformatics↗

bioRxiv2018

Mutations in Rv2983 as a novel determinant of resistance to nitroimidazole drugs in Mycobacterium tuberculosis

Delamanid represents one of two novel antimicrobial classes approved to treat tuberculosis in over 40 years. Pretomanid is another promising nitroimidazole pro-drug in clinical development. Characterization of the full spectrum of mutations conferring resistance to nitroimidazoles and their related phenotypes in Mycobacterium tuberculosis will inform development of suitable genotypic and phenotypic drug susceptibility tests. Here, we used a range of pretomanid doses to select pretomanid-resistant mutants in two pathologically distinct murine TB models. The frequency of spontaneous pretomanid resistance mutations was approximately 10-5 CFU. Pretomanid demonstrated dose-dependent bactericidal activity and selective amplification of resistant mutants. Whole genome sequencing of 161 resistant isolates from 47 mice revealed 99 unique mutations, 90% of which were found in 1 of 5 genes previously associated with nitroimidazole activation and resistance. The remaining 10% harbored isolated mutations in Rv2983. Complementing an Rv2983 mutant with a wild-type copy of Rv2983 restored wild-type susceptibility to pretomanid and delamanid, confirming that loss of Rv2983 function causes nitroimidazole resistance. By quantifying F420 and its precursor Fo in Mycobacterium smegmatis overexpressing Rv2983 and an M. tuberculosis Rv2983 mutant, we provide evidence that Rv2983 is necessary for F420 biosynthesis and nitroimidazole activation, perhaps as the guanylyltransferase CofC. F420H2-deficient mutants displayed hypersusceptibility to malachite green (MG), a selective decontaminant present in solid media used to isolate and propagate mycobacteria from clinical samples. The wide diversity of mutations causing high-level pretomanid resistance and MG hypersusceptibility of most mutants poses significant challenges to clinical detection of nitroimidazole resistance using either genotypic or phenotypic methods.\n\nSignificanceNitroimidazole pro-drugs represent a promising new class of anti-tuberculosis drugs. Reliable methods to assure nitroimidazole susceptibility are critical to assure their optimal use. Yet, the spectrum of nitroimidazole resistance mutations remains incompletely characterized. Using 161 pretomanid-resistant Mycobacterium tuberculosis isolates selected in pretomanid-treated mice, we discovered a novel resistance determinant, Rv2983, required for cofactor F420 biosynthesis and characterized the remarkable diversity of mutations in this and 5 other genes involved in nitroimidazole activation. We show that F420H2-deficient nitroimidazole-resistant mutants are hypersusceptible to the selective decontaminant malachite green used in solid media to isolate mycobacteria and may evade detection on such media. These results have important implications for development and clinical use of genotypic and phenotypic methods for nitroimidazole susceptibility testing.

microbiology↗

bioRxiv2018

Genome wide association with quantitative resistance phenotypes in Mycobacterium tuberculosis reveals novel resistance genes and regulatory regions

Drug resistance is threatening attempts at tuberculosis epidemic control. Molecular diagnostics for drug resistance that rely on the detection of resistance-related mutations could expedite patient care and accelerate progress in TB eradication. We performed minimum inhibitory concentration testing for 12 anti-TB drugs together with Illumina whole genome sequencing on 1452 clinical Mycobacterium tuberculosis (MTB) isolates. We then used a linear mixed model to evaluate genome wide associations between mutations in MTB genes or noncoding regions and drug resistance, followed by validation of our findings in an independent dataset of 792 patient isolates. Novel associations at 13 genomic loci were confirmed in the validation set, with 2 involving noncoding regions. We found promoter mutations to have smaller average effects on resistance levels than gene body mutations in genes where both can contribute to resistance. Enabled by a quantitative measure of resistance, we estimated the heritability of the resistance phenotype to 11 anti-TB drugs and identify a lower than expected contribution from known resistance genes. We also report the proportion of variation in resistance levels explained by the novel loci identified here. This study highlights the complexity of the genomic mechanisms associated with the MTB resistance phenotype, including the relatively large number of potentially causative or compensatory loci, and emphasizes the contribution of the noncoding portion of the genome.

evolutionary biology↗

bioRxiv2018

Genetic variation/evolution and differential host responses resulting from in-patient adaptation of Mycobacterium avium

Mycobacterium avium (Mav) complex (MAC) are characterized as non-tuberculosis mycobacteria and are pathogenic mainly in immunocompromised individuals. MAC strains show a wide genetic variability, and there is growing evidence suggesting that genetic differences may contribute to a varied immune response that may impact on the infection outcome. The current study aimed to characterize the genomic changes within Mav isolates collected from single patients over time and test the host immune responses to these clinical isolates. Pulsed field gel electrophoresis and whole genome sequencing was performed on 40 MAC isolates isolated from 15 patients at the Department of Medical Microbiology at St. Olavs Hospital in Trondheim, Norway. Patients (4, 9 and 13) who contributed more than two isolates were selected for further analysis. These isolates exhibited extensive sequence variation in the form of single nucleotide polymorphisms (SNPs), suggesting that Mav accumulates mutations at high rates during persistent infections. Infection of murine macrophages and mice with sequential isolates from patients showed a tendency towards increased persistence and down-regulation of inflammatory cytokines by host-adapted Mav strains. The study revealed rapid genetic evolution of Mav in chronically infected patients accompanied with change in virulence properties of the sequential mycobacterial isolates.\n\nIMPORTANCEMAC are a group of opportunistic pathogens, consisting of Mav and M. intracellulare species. Mav is found ubiquitously in the environment. In Mav infected individuals, Mav has been known to persist for long periods of time, and anti-mycobacterial drugs are unable to effectively clear the infection. The continued presence of the bacteria, could be attributed to either a single persistent strain or reinfection with the same or different strain. We examined sequential isolates collected over time from Mav infected individuals and observed that most patients carried the same strain overtime and were not re infected. We observed high rates of mutation within the serial isolates, accompanied with changes in virulence properties. In the light of increase in incidence of MAC related infections, this study highlights the possibility that host adapted Mav undergo genetic modifications to cope with the host environment and thereby persisting longer.

immunology↗