Mutations in rpoB That Confer Rifampicin Resistance Can Alter Levels of Peptidoglycan Precursors and Affect β-Lactam Susceptibility

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

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.

The recent rapid expansion of multidrug resistant Ural lineage Mycobacterium tuberculosis in Moldova

The projected trajectory of multidrug resistant tuberculosis (MDR-TB) epidemics depends on the reproductive fitness of circulating strains of MDR M. tuberculosis (Mtb). Previous efforts to characterize the fitness of MDR Mtb have found that Mtb strains of the Beijing sublineage (Lineage 2.2.1) may be more prone to develop resistance and retain fitness in the presence of resistance-conferring mutations than other lineages. Using Mtb genome sequences from all culture-positive cases collected over two years in Moldova, we estimate the fitness of Ural (Lineage 4.2) and Beijing strains, the two lineages in which MDR is concentrated in the country. We estimate that the fitness of MDR Ural strains substantially exceeds that of other susceptible and MDR strains, and we identify several mutations specific to these MDR Ural strains. Our findings suggest that MDR Ural Mtb has been transmitting efficiently in Moldova and poses a substantial risk of spreading further in the region.

8-OxoG-Dependent Regulation of Global Protein Responses Leads to Mutagenesis and Stress Survival in Bacillus subtilis

The guanine oxidized (GO) system of Bacillus subtilis, composed of the YtkD (MutT), MutM and MutY proteins, counteracts the cytotoxic and genotoxic effects of the oxidized nucleobase 8-OxoG. Here, we report that in growing B. subtilis cells, the genetic inactivation of GO system potentiated mutagenesis (HPM), and subsequent hyperresistance, contributes to the damaging effects of hydrogen peroxide (H2O2) (HPHR). The mechanism(s) that connect the accumulation of the mutagenic lesion 8-OxoG with the ability of B. subtilis to evolve and survive the noxious effects of oxidative stress were dissected. Genetic and biochemical evidence indicated that the synthesis of KatA was exacerbated, in a PerR-independent manner, and the transcriptional coupling repair factor, Mfd, contributed to HPHR and HPM of the ΔGO strain. Moreover, these phenotypes are associated with wider pleiotropic effects, as revealed by a global proteome analysis. The inactivation of the GO system results in the upregulated production of KatA, and it reprograms the synthesis of the proteins involved in distinct types of cellular stress; this has a direct impact on (i) cysteine catabolism, (ii) the synthesis of iron-sulfur clusters, (iii) the reorganization of cell wall architecture, (iv) the activation of AhpC/AhpF-independent organic peroxide resistance, and (v) increased resistance to transcription-acting antibiotics. Therefore, to contend with the cytotoxic and genotoxic effects derived from the accumulation of 8-OxoG, B. subtilis activates the synthesis of proteins belonging to transcriptional regulons that respond to a wide, diverse range of cell stressors.

(p)ppGpp modifies RNAP function to confer β-lactam resistance in a peptidoglycan-independent manner

(p)ppGpp is a nucleotide alarmone that controls bacterial response to nutrient deprivation. Since elevated (p)ppGpp levels confer mecillinam resistance and are essential for broad-spectrum β-lactam resistance as mediated by the β-lactam-insensitive transpeptidase YcbB (LdtD), we hypothesized that (p)ppGpp might affect cell wall peptidoglycan metabolism. Here we report that (p)ppGpp-dependent β-lactam resistance does not rely on any modification of peptidoglycan metabolism, as established by analysis of Escherichia coli peptidoglycan structure using high-resolution mass spectrometry. Amino acid substitutions in the β or β' RNA polymerase (RNAP) subunits, alone or in combination with the CRISPR interference-mediated downregulation of three of seven ribosomal RNA operons, were sufficient for resistance, although β-lactams have no known impact on the RNAP or ribosomes. This implies that modifications of RNAP and ribosome functions are critical to prevent downstream effects of the inactivation of peptidoglycan transpeptidases by β-lactams.

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

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.

Antibiotic action and resistance: updated review of mechanisms, spread, influencing factors, and alternative approaches for combating resistance

Antibiotics represent a frequently employed therapeutic modality for the management of bacterial infections across diverse domains, including human health, agriculture, livestock breeding, and fish farming. The efficacy of antibiotics relies on four distinct mechanisms of action, which are discussed in detail in this review, along with accompanying diagrammatic illustrations. Despite their effectiveness, antibiotic resistance has emerged as a significant challenge to treating bacterial infections. Bacteria have developed defense mechanisms against antibiotics, rendering them ineffective. This review delves into the specific mechanisms that bacteria have developed to resist antibiotics, with the help of diagrammatic illustrations. Antibiotic resistance can spread among bacteria through various routes, resulting in previously susceptible bacteria becoming antibiotic-resistant. Multiple factors contribute to the worsening crisis of antibiotic resistance, including human misuse of antibiotics. This review also emphasizes alternative solutions proposed to mitigate the exacerbation of antibiotic resistance.

Metagenome meta-analysis reveals an increase in the abundance of some multidrug efflux pumps and mobile genetic elements in chemically polluted environments

Many human activities contaminate terrestrial and aquatic environments with numerous chemical pollutants that not only directly alter the environment but also affect microbial communities in ways that are potentially concerning to human health, such as selecting for the spread of antibiotic-resistance genes (ARGs) through horizontal gene transfer. In the present study, metagenomes available in the public domain from polluted (with antibiotics, with petroleum, with metal mining, or with coal-mining effluents) and unpolluted terrestrial and aquatic environments were compared to examine whether pollution has influenced the abundance and composition of ARGs and mobile elements, with specific focus on IS26 and class 1 integrons (intI1). When aggregated together, polluted environments had a greater relative abundance of ARGs than unpolluted environments and a greater relative abundance of IS26 and intI1. In general, chemical pollution, notably with petroleum, was associated with an increase in the prevalence of ARGs linked to multidrug efflux pumps. Included in the suite of efflux pumps were mexK, mexB, mexF, and mexW that are polyspecific and whose substrate ranges include multiple classes of critically important antibiotics. Also, in some instances, β-lactam resistance (TEM181 and OXA-541) genes increased, and genes associated with rifampicin resistance (RNA polymerases subunits rpoB and rpoB2) decreased in relative abundance. This meta-analysis suggests that different types of chemical pollution can enrich populations that carry efflux pump systems associated with resistance to multiple classes of medically critical antibiotics.IMPORTANCEThe United Nations has identified chemical pollution as being one of the three greatest threats to environmental health, through which the evolution of antimicrobial resistance, a seminally important public health challenge, may be favored. While this is a very plausible outcome of continued chemical pollution, there is little evidence or research evaluating this risk. The objective of the present study was to examine existing metagenomes from chemically polluted environments and evaluate whether there is evidence that pollution increases the relative abundance of genes and mobile genetic elements that are associated with antibiotic resistance. The key finding is that for some types of pollution, particularly in environments exposed to petroleum, efflux pumps are enriched, and these efflux pumps can confer resistance to multiple classes of medically important antibiotics that are typically associated with Pseudomonas spp. or other Gram-negative bacteria. This finding makes clear the need for more investigation on the impact of chemical pollution on the environmental reservoir of ARGs and their association with mobile genetic elements that can contribute to horizontal gene transfer events.

Rifampicin drug resistance and host immunity in tuberculosis: more than meets the eye

Tuberculosis (TB) is the leading cause of death due to an infectious agent, with more than 1.5 million deaths attributed to TB annually worldwide. The global dissemination of drug resistance across Mycobacterium tuberculosis (Mtb) strains, causative of TB, resulted in an estimated 450 000 cases of drug-resistant (DR) TB in 2021. Dysregulated immune responses have been observed in patients with multidrug resistant (MDR) TB, but the effects of drug resistance acquisition and impact on host immunity remain obscure. In this review, we compile studies that span aspects of altered host-pathogen interactions and highlight research that explores how drug resistance and immunity might intersect. Understanding the immune processes differentially induced during DR TB would aid the development of rational therapeutics and vaccines for patients with MDR TB.

Bacterial metabolism and susceptibility to cell wall-active antibiotics

Bacterial infections are increasingly resistant to antimicrobial therapy. Intense research focus has thus been placed on identifying the mechanisms that bacteria use to resist killing or growth inhibition by antibiotics and the ways in which bacteria share these traits with one another. This work has led to the advancement of new drugs, combination therapy regimens, and a deeper appreciation for the adaptability seen in microorganisms. However, while the primary mechanisms of action of most antibiotics are well understood, the more subtle contributions of bacterial metabolic state to repairing or preventing damage caused by antimicrobials (thereby promoting survival) are still understudied. Here, we review a modern viewpoint on a classical system: examining bacterial metabolism's connection to antibiotic susceptibility. We dive into the relationship between metabolism and antibiotic efficacy through the lens of growth rate, energy state, resource allocation, and the infection environment, focusing on cell wall-active antibiotics.

Recent Advances in Peptidoglycan Synthesis and Regulation in Bacteria

Bacteria must synthesize their cell wall and membrane during their cell cycle, with peptidoglycan being the primary component of the cell wall in most bacteria. Peptidoglycan is a three-dimensional polymer that enables bacteria to resist cytoplasmic osmotic pressure, maintain their cell shape and protect themselves from environmental threats. Numerous antibiotics that are currently used target enzymes involved in the synthesis of the cell wall, particularly peptidoglycan synthases. In this review, we highlight recent progress in our understanding of peptidoglycan synthesis, remodeling, repair, and regulation in two model bacteria: the Gram-negative Escherichia coli and the Gram-positive Bacillus subtilis. By summarizing the latest findings in this field, we hope to provide a comprehensive overview of peptidoglycan biology, which is critical for our understanding of bacterial adaptation and antibiotic resistance.

Comparison of freeze-thaw and sonication cycle-based methods for extracting AMR-associated metabolites from Staphylococcus aureus

Emerging antimicrobial resistance (AMR) among Gram-positive pathogens, specifically in Staphylococcus aureus (S. aureus), is becoming a leading public health concern demanding effective therapeutics. Metabolite modulation can improve the efficacy of existing antibiotics and facilitate the development of effective therapeutics. However, it remained unexplored for drug-resistant S. aureus (gentamicin and methicillin-resistant), primarily due to the dearth of optimal metabolite extraction protocols including a protocol for AMR-associated metabolites. Therefore, in this investigation, we have compared the performance of the two most widely used methods, i.e., freeze-thaw cycle (FTC) and sonication cycle (SC), alone and in combination (FTC + SC), and identified the optimal method for this purpose. A total of 116, 119, and 99 metabolites were identified using the FTC, SC, and FTC + SC methods, respectively, leading to the identification of 163 metabolites cumulatively. Out of 163, 69 metabolites were found to be associated with AMR in published literature consisting of the highest number of metabolites identified by FTC (57) followed by SC (54) and FTC + SC (40). Thus, the performances of FTC and SC methods were comparable with no additional benefits of combining both. Moreover, each method showed biasness toward specific metabolite(s) or class of metabolites, suggesting that the choice of metabolite extraction method shall be decided based on the metabolites of interest in the investigation.