In the absence of antibiotic-mediated selection, sensitive bacteria are expected to displace their resistant counterparts if resistance genes are costly. However, many resistance genes persist for ...long periods in the absence of antibiotics. Horizontal gene transfer (primarily conjugation) could explain this persistence, but it has been suggested that very high conjugation rates would be required. Here, we show that common conjugal plasmids, even when costly, are indeed transferred at sufficiently high rates to be maintained in the absence of antibiotics in Escherichia coli. The notion is applicable to nine plasmids from six major incompatibility groups and mixed populations carrying multiple plasmids. These results suggest that reducing antibiotic use alone is likely insufficient for reversing resistance. Therefore, combining conjugation inhibition and promoting plasmid loss would be an effective strategy to limit conjugation-assisted persistence of antibiotic resistance.
Bacterial Metabolism and Antibiotic Efficacy Stokes, Jonathan M.; Lopatkin, Allison J.; Lobritz, Michael A. ...
Cell metabolism,
08/2019, Letnik:
30, Številka:
2
Journal Article
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Antibiotics target energy-consuming processes. As such, perturbations to bacterial metabolic homeostasis are significant consequences of treatment. Here, we describe three postulates that ...collectively define antibiotic efficacy in the context of bacterial metabolism: (1) antibiotics alter the metabolic state of bacteria, which contributes to the resulting death or stasis; (2) the metabolic state of bacteria influences their susceptibility to antibiotics; and (3) antibiotic efficacy can be enhanced by altering the metabolic state of bacteria. Altogether, we aim to emphasize the close relationship between bacterial metabolism and antibiotic efficacy as well as propose areas of exploration to develop novel antibiotics that optimally exploit bacterial metabolic networks.
The metabolic state of bacteria significantly contributes to the efficacy of antibiotics. In this Perspective, Stokes et al. highlight the close relationship between bacterial cell metabolism and antibiotic efficacy, leveraging prior observations to describe areas for further exploration, with the goal of developing next-generation antibiotics that can optimally exploit the complex metabolic networks of bacteria.
Predictive biology is the next great chapter in synthetic and systems biology, particularly for microorganisms. Tasks that once seemed infeasible are increasingly being realized such as designing and ...implementing intricate synthetic gene circuits that perform complex sensing and actuation functions, and assembling multi-species bacterial communities with specific, predefined compositions. These achievements have been made possible by the integration of diverse expertise across biology, physics and engineering, resulting in an emerging, quantitative understanding of biological design. As ever-expanding multi-omic data sets become available, their potential utility in transforming theory into practice remains firmly rooted in the underlying quantitative principles that govern biological systems. In this Review, we discuss key areas of predictive biology that are of growing interest to microbiology, the challenges associated with the innate complexity of microorganisms and the value of quantitative methods in making microbiology more predictable.
Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop ...an integrated “white-box” biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.
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•A white-box machine learning approach is developed for antibiotics research•Network modeling is coupled to a biochemical screen to identify pathway mechanisms•Antibiotic-induced adenine limitation increases purine biosynthesis and ATP demand•Increased ATP demand drives central carbon metabolism and oxygen consumption
Causal metabolic pathways underlying antibiotic lethality in bacteria are illuminated by a network model-driven machine learning approach, overcoming limitations of existing “black-box” approaches that cannot reveal causal relationships from large biological datasets.
Although metabolism plays an active role in antibiotic lethality, antibiotic resistance is generally associated with drug target modification, enzymatic inactivation, and/or transport rather than ...metabolic processes. Evolution experiments of
rely on growth-dependent selection, which may provide a limited view of the antibiotic resistance landscape. We sequenced and analyzed
adapted to representative antibiotics at increasingly heightened metabolic states. This revealed various underappreciated noncanonical genes, such as those related to central carbon and energy metabolism, which are implicated in antibiotic resistance. These metabolic alterations lead to lower basal respiration, which prevents antibiotic-mediated induction of tricarboxylic acid cycle activity, thus avoiding metabolic toxicity and minimizing drug lethality. Several of the identified metabolism-specific mutations are overrepresented in the genomes of >3500 clinical
pathogens, indicating clinical relevance.
Plasmid conjugation is a major mechanism responsible for the spread of antibiotic resistance. Plasmid fitness costs are known to impact long‐term growth dynamics of microbial populations by providing ...plasmid‐carrying cells a relative (dis)advantage compared to plasmid‐free counterparts. Separately, plasmid acquisition introduces an immediate, but transient, metabolic perturbation. However, the impact of these short‐term effects on subsequent growth dynamics has not previously been established. Here, we observed that de novo transconjugants grew significantly slower and/or with overall prolonged lag times, compared to lineages that had been replicating for several generations, indicating the presence of a plasmid acquisition cost. These effects were general to diverse incompatibility groups, well‐characterized and clinically captured plasmids, Gram‐negative recipient strains and species, and experimental conditions. Modeling revealed that both fitness and acquisition costs modulate overall conjugation dynamics, validated with previously published data. These results suggest that the hours immediately following conjugation may play a critical role in both short‐ and long‐term plasmid prevalence. This time frame is particularly relevant to microbiomes with high plasmid/strain diversity considered to be hot spots for conjugation.
Synopsis
Quantification of plasmid conjugation dynamics shows the presence of a plasmid acquisition cost and indicates that the hours immediately following conjugation may be critical in both short and long‐term plasmid prevalence.
A novel experimental framework quantifies plasmid acquisition costs independently of fitness effects.
The magnitude of the acquisition costs is potentially dictated by the initial energetic burden imposed by the newly acquired plasmid, as well as the host cells’ ability to accommodate that burden in a given environment.
Incorporating acquisition effects into a mathematical model of conjugation improves the temporal predictions of long‐term conjugation dynamics.
The time window immediately following plasmid acquisition may represent a critical time interval for quantifying conjugation dynamics.
Quantification of plasmid conjugation dynamics shows the presence of a plasmid acquisition cost and indicates that the hours immediately following conjugation may be critical in both short and long‐term plasmid prevalence.
•Microbial HGT is a complex process influenced by a variety of modulating factors, including cellular genetics and environmental conditions.•HGT dynamics are governed by the underlying biochemical ...kinetic rate of gene transfer.•Quantification of HGT rates can be done at various complementary levels of experimental complexity, which necessarily seek to strike a balance between precision and fidelity to natural systems.•In vitro, in vivo, and in situ rate measurements are often complementary; these studies engender a deeper understanding of transfer dynamics, and will lead to future insights and applications.
Horizontal gene transfer (HGT) plays a significant role in rapidly propagating diverse traits throughout bacterial populations, thereby accelerating natural evolution and leading to complex community structures. Critical gene transfer rates underlying these occurrences dictate the efficiency and speed of gene spread; these rates are often highly specific to HGT mechanism and environmental context, and have historically been challenging to reliably quantify. In this review, we examine recent works that leverage rigorous quantitative methods to precisely measure these rates in a variety of settings beginning with in vitro studies and advancing to in situ measurements; we emphasize contexts where quantification across multiple scales of complexity has led to fundamental biological insights. Finally, we highlight the applications of these measurements and suggest potential methodological advances to improve our understanding.
Abstract Activated sludge is the centerpiece of biological wastewater treatment, as it facilitates removal of sewage-associated pollutants, fecal bacteria, and pathogens from wastewater through ...semi-controlled microbial ecology. It has been hypothesized that horizontal gene transfer facilitates the spread of antibiotic resistance genes within the wastewater treatment plant, in part because of the presence of residual antibiotics in sewage. However, there has been surprisingly little evidence to suggest that sewage-associated antibiotics select for resistance at wastewater treatment plants via horizontal gene transfer or otherwise. We addressed the role of sewage-associated antibiotics in promoting antibiotic resistance using lab-scale sequencing batch reactors fed field-collected wastewater, metagenomic sequencing, and our recently developed bioinformatic tool Kairos. Here, we found confirmatory evidence that fluctuating levels of antibiotics in sewage are associated with horizontal gene transfer of antibiotic resistance genes, microbial ecology, and microdiversity-level differences in resistance gene fate in activated sludge.
The annual risks of colonization, skin infection, bloodstream infection (BSI), and disease burden from exposures to antibiotic-resistant and susceptible Staphylococcus aureus (S. aureus) were ...estimated using quantitative microbial risk assessment (QMRA). We estimated the probability of nasal colonization after immersion in wastewater (WW) or greywater (GW) treated across a range of treatment alternatives and subsequent infection. Horizontal gene transfer was incorporated into the treatment model but had little effect on the predicted risk. The cumulative annual probability of infection (resulting from self-inoculation) was most sensitive to the treatment log10 reduction value (LRV), S. aureus concentration, and the newly calculated morbidity ratios and was below the health benchmark of 10–4 infections per person per year (ppy) given a treatment LRV of roughly 3.0. The predicted annual disability-adjusted life years (DALYs), which were dominated by BSI, were below the health benchmark of 10–6 DALYs ppy for resistant and susceptible S. aureus, given LRVs of 4.5 and 3.5, respectively. Thus, the estimated infection risks and disease burdens resulting from nasal colonization are below the relevant health benchmarks for risk-based, nonpotable, or potable reuse systems but possibly above for immersion in minimally treated GW or WW. Strain-specific data to characterize dose–response and concentration in WW are needed to substantiate the QMRA.
Bacteria have developed resistance against every antibiotic at a rate that is alarming considering the timescale at which new antibiotics are developed. Thus, there is a critical need to use ...antibiotics more effectively, extend the shelf life of existing antibiotics and minimize their side effects. This requires understanding the mechanisms underlying bacterial drug responses. Past studies have focused on survival in the presence of antibiotics by individual cells, as genetic mutants or persisters. Also important, however, is the fact that a population of bacterial cells can collectively survive antibiotic treatments lethal to individual cells. This tolerance can arise by diverse mechanisms, including resistance-conferring enzyme production, titration-mediated bistable growth inhibition, swarming and interpopulation interactions. These strategies can enable rapid population recovery after antibiotic treatment and provide a time window during which otherwise susceptible bacteria can acquire inheritable genetic resistance. Here, we emphasize the potential for targeting collective antibiotic tolerance behaviors as an antibacterial treatment strategy.
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