Mechanisms of drug interactions between translation-inhibiting antibiotics.
Ontology highlight
ABSTRACT:
SUBMITTER: Kavcic B
PROVIDER: S-EPMC7421507 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Antibiotic-producing streptomycetes are rich sources of resistance mechanisms against endogenous and exogenous antibiotics. An ECF sigma factor ?(R) (SigR) is known to govern the thiol-oxidative stress response in Streptomyces coelicolor. Amplification of this response is achieved by producing an unstable isoform of ?(R) called ?(R'). In this work, we present evidence that antibiotics induce the SigR regulon via a redox-independent pathway, leading to antibiotic resistance. The translation-inhibiting antibiotics enhanced the synthesis of stable ?(R), eliciting a prolonged response. WblC/WhiB7, a WhiB-like DNA-binding protein, is responsible for inducing sigRp1 transcripts encoding the stable ?(R). The amount of WblC protein and its binding to the sigRp1 promoter in vivo increased upon antibiotic treatment. A similar phenomenon appears to exist in Mycobacterium tuberculosis as well. These findings reveal a novel antibiotic-induced resistance mechanism conserved among actinomycetes, and also give an explicit example of overlap in cellular damage and defense mechanisms between thiol-oxidative and anti- translational stresses.
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Project description:Understanding the phenotypic heterogeneity of antibiotic-resistant bacteria following treatment and the transitions between different phenotypes is crucial for developing effective infection control strategies. The study expands upon previous work by explicating chloramphenicol-induced phenotypic heterogeneities in growth rate, gene expression, and morphology of resistant Escherichia coli using time-lapse microscopy. Correlating the bacterial growth rate and cspC expression, four interchangeable phenotypic subpopulations across varying antibiotic concentrations are identified, surpassing the previously described growth rate bistability. Notably, bacterial cells exhibiting either fast or slow growth rates can concurrently harbor subpopulations characterized by high and low gene expression levels, respectively. To elucidate the mechanisms behind this enhanced heterogeneity, a concise gene expression network model is proposed and the biological significance of the four phenotypes is further explored. Additionally, by employing Hidden Markov Model fitting and integrating the non-equilibrium landscape and flux theory, the real-time data encompassing diverse bacterial traits are analyzed. This approach reveals dynamic changes and switching kinetics in different cell fates, facilitating the quantification of observable behaviors and the non-equilibrium dynamics and thermodynamics at play. The results highlight the multi-dimensional heterogeneous behaviors of antibiotic-resistant bacteria under antibiotic stress, providing new insights into the compromised antibiotic efficacy, microbial response, and associated evolution processes.
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Project description:Suppressive drug interactions, in which one antibiotic can actually help bacterial cells to grow faster in the presence of another, occur between protein and DNA synthesis inhibitors. Here, we show that this suppression results from nonoptimal regulation of ribosomal genes in the presence of DNA stress. Using GFP-tagged transcription reporters in Escherichia coli, we find that ribosomal genes are not directly regulated by DNA stress, leading to an imbalance between cellular DNA and protein content. To test whether ribosomal gene expression under DNA stress is nonoptimal for growth rate, we sequentially deleted up to six of the seven ribosomal RNA operons. These synthetic manipulations of ribosomal gene expression correct the protein-DNA imbalance, lead to improved survival and growth, and completely remove the suppressive drug interaction. A simple mathematical model explains the nonoptimal regulation in different nutrient environments. These results reveal the genetic mechanism underlying an important class of suppressive drug interactions.
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Project description:Antibiotics produced by bacteria play important roles in microbial interactions and competition Antibiosis can induce resistance mechanisms in target organisms, and at sublethal doses, antibiotics have been shown to globally alter gene expression patterns. Here, we show that hygromycin A from Streptomyces sp. strain 2AW. induces Chromobacterium violaceum ATCC 31532 to produce the purple antibiotic violacein. Sublethal doses of other antibiotics that similarly target the polypeptide elongation step of translation likewise induced violacein production, unlike antibiotics with different targets. C. violaceum biofilm formation and virulence against Drosophila melanogaster were also induced by translation-inhibiting antibiotics, and we identified an antibiotic-induced response (air) two-component regulatory system that is required for these responses. Genetic analyses indicated a connection between the Air system, quorum-dependent signaling, and the negative regulator VioS, leading us to propose a model for induction of violacein production. This work suggests a novel mechanism of interspecies interaction in which a bacterium produces an antibiotic in response to inhibition by another bacterium and supports the role of antibiotics as signal molecules.IMPORTANCE Secondary metabolites play important roles in microbial communities, but their natural functions are often unknown and may be more complex than appreciated. While compounds with antibiotic activity are often assumed to underlie microbial competition, they may alternatively act as signal molecules. In either scenario, microorganisms might evolve responses to sublethal concentrations of these metabolites, either to protect themselves from inhibition or to change certain behaviors in response to the local abundance of another species. Here, we report that violacein production by C. violaceum ATCC 31532 is induced in response to hygromycin A from Streptomyces sp. 2AW, and we show that this response is dependent on inhibition of translational polypeptide elongation and a previously uncharacterized two-component regulatory system. The breadth of the transcriptional response beyond violacein induction suggests a surprisingly complex metabolite-mediated microbe-microbe interaction and supports the hypothesis that antibiotics evolved as signal molecules. These novel insights will inform predictive models of soil community dynamics and the unintended effects of clinical antibiotic administration.
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Project description:Antibiotics can cause dormancy (bacteriostasis) or induce death (cidality) of the targeted bacteria. The bactericidal capacity is one of the most important properties of antibacterial agents. However, the understanding of the fundamental differences in the mode of action of bacteriostatic or bactericidal antibiotics, especially those belonging to the same chemical class, is very rudimentary. Here, by examining the activity and binding properties of chemically distinct macrolide inhibitors of translation, we have identified a key difference in their interaction with the ribosome, which correlates with their ability to cause cell death. While bacteriostatic and bactericidal macrolides bind in the nascent peptide exit tunnel of the large ribosomal subunit with comparable affinities, the bactericidal antibiotics dissociate from the ribosome with significantly slower rates. The sluggish dissociation of bactericidal macrolides correlates with the presence in their structure of an extended alkyl-aryl side chain, which establishes idiosyncratic interactions with the ribosomal RNA. Mutations or chemical alterations of the rRNA nucleotides in the drug binding site can protect cells from macrolide-induced killing, even with inhibitor concentrations that significantly exceed those required for cell growth arrest. We propose that the increased translation downtime due to slow dissociation of the antibiotic may damage cells beyond the point where growth can be reinitiated upon the removal of the drug due to depletion of critical components of the gene-expression pathway.
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Project description:The inhibition of protein-protein interactions and their ensuing signaling processes play an increasingly important role in modern medicine. Small molecular-weight inhibitors that can be administered orally are the preferred approach but efficient strategies for developing them are not yet generally available. Due to the large size difference between the protein-protein interface and the small molecule, inhibitor interactions are expected to extend to only a small region of the interface. If this is the case, classical competitive inhibition may be hard to achieve. In addition, competitive inhibition wastes binding energy that can be effectively used to inhibit signaling. The best and most energy-efficient approach would be the development of small molecules that bind at the protein-protein interface and inhibit the signaling process without displacing the protein ligand. This approach seems feasible knowing that the binding energy is not evenly distributed within the binding interface but concentrated in discrete hotspots, and that the initiation of signaling may not overlap with those hotspots. We outline a general protein-protein inhibition model that extends from competitive to noncompetitive scenarios and apply it to the development of HIV-1 gp120-CD4 inhibitors. This rigorous model can be easily applied to the analysis of protein-protein inhibition data and used as a tool in the optimization of inhibitor molecules.
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