When the most potent combination of antibiotics selects for the greatest bacterial load: the smile-frown transition

Repurposing tavaborole to combat resistant bacterial infections through competitive inhibition of KPC-2 and metabolic disruption

The rise of carbapenem-resistant Enterobacteriaceae (CRE) strains has emerged as an increasing threat to global public health. The development of antibiotic adjuvants presents an economical and promising approach to address this crisis. Through a high-throughput screen of the FDA-approved compound library, we identified tavaborole (AN2690) as a broad-spectrum β-lactamase inhibitor. The mechanistic study revealed that tavaborole formed a reversible binding with the active serine of KPC-2, showing effective competitive inhibition. Its electron-deficient boron atom formed a borate ester bond with hydroxyl group of the serine residue at the active site of KPC-2, transitioning to an sp3-hybridized state that mimicked the tetrahedral intermediate during KPC-2 catalytic. Moreover, transcriptomic analysis and bacterial metabolism assays further unveiled tavaborole addition can inhibit tricarboxylic acid (TCA) cycle, coupled with downregulation of intracellular ATP levels, indicating that tavaborole compromised the bacterial metabolic homeostasis and exerted synergistic antibacterial activity. Notably, the combination treatment further suppressed the development of meropenem resistance. In mouse intraperitoneal infection models, tavaborole effectively restored the efficacy of meropenem against CRE bacteria. These findings elucidate the synergistic mechanisms of tavaborole, expand its potential applications in anti-infection therapeutics, and provide a promising strategy for addressing CRE infections.

Interactions within higher-order antibiotic combinations do not influence the rate of adaptation in bacteria

The prevalence and strength of antibiotic resistance has led to an ongoing battle between the development of new treatments and the evolution of resistance. Combining multiple drugs simultaneously is a potential solution for combating antibiotic resistance. However, this approach introduces new factors that must be considered, including the influence of drug interactions on the rate of resistance evolution. When antibiotics are used in combination, their effects can be additive, synergistic, or antagonistic. In this study, we investigated the effect of higher-order interactions involving 3 drugs on resistance evolution in Staphylococcus epidermidis. Previous studies have shown that synergistic interactions can increase the adaptation rate. However, the effects of higher-order interactions on rates of adaptation are unclear. We investigated the adaptation of Staphylococcus epidermidis to single-, 2-, and 3-drug environments to assess how interactions within drug combinations influence the rate of adaptation. We analyzed both the overall interaction and emergent interaction, the latter being a unique interaction that occurs in 3-drug combinations due to the presence of all three drugs, rather than simply strong pairwise interactions. Our results show that neither the overall interactions nor the emergent interactions affect adaptation rates.

Revitalizing Antibiotics with Macromolecular Engineering: Tackling Gram-Negative Superbugs and Mixed Species Bacterial Biofilm Infections In Vivo

The escalating prevalence of multidrug-resistant Gram-negative pathogens, coupled with dwindling antibiotic development, has created a critical void in the clinical pipeline. This alarming issue is exacerbated by the formation of biofilms by these superbugs and their frequent coexistence in mixed-species biofilms, conferring extreme antibiotic tolerance. Herein, we present an amphiphilic cationic macromolecule, ACM-AHex, as an innovative antibiotic adjuvant to rejuvenate and repurpose resistant antibiotics, for instance, rifampicin, fusidic acid, erythromycin, and chloramphenicol. ACM-AHex mildly perturbs the bacterial membrane, enhancing antibiotic permeability, hampers efflux machinery, and produces reactive oxygen species, resulting in a remarkable 64-1024-fold potentiation in antibacterial activity. The macromolecule reduces bacterial virulence and macromolecule-drug cocktail significantly eradicate both mono- and multispecies bacterial biofilms, achieving >99.9% bacterial reduction in the murine biofilm infection model. Demonstrating potent biocompatibility across multiple administration routes, ACM-AHex offers a promising strategy to restore obsolete antibiotics and combat recalcitrant Gram-negative biofilm-associated infections, advocating for further clinical evaluation as a next-generation macromolecular antibiotic adjuvant.

Meeting Report on the Symposium "Evolutionary Applications" at the 3rd Joint Congress on Evolutionary Biology

The symposium "Evolutionary Applications" took place on June 28, 2024 in the virtual part of the 3rd Joint Congress on Evolutionary Biology. It was contributed to the conference by the European Society for Evolutionary Biology (ESEB). The symposium highlighted research on evolutionary biology applied to address questions and contemporary problems in medicine and public health, conservation biology, and food production and agriculture. Each of the six talks covered a different application and a different organism: domestication of cheese-making fungi, restoration of long-lived bird populations, evolution of herbicide resistance, coral reef conservation, gene drive systems targeting Malaria vectors, and antibiotic resistance evolution in bacteria. By including speakers who are active in a consortium or work in an NGO, the symposium also showed how to make the step from scientific findings to practical application. The symposium furthermore featured a range of scientific methods, ranging from genomic analyses and mathematical modeling to laboratory evolution and field experiments. Speakers from across 15 time zones highlighted the potential of virtual symposia to foster global collaboration in evolutionary biology.

Drug combinations targeting antibiotic resistance

While the rise of antibiotic resistance poses a global health challenge, the development of new antibiotics has slowed down over the past decades. This turned the attention of researchers towards the rational design of drug combination therapies to combat antibiotic resistance. In this review we discuss how drug combinations can exploit the deleterious pleiotropic effects of antibiotic resistance and conclude that each drug interaction has its prospective therapeutic application.

The many dimensions of combination therapy: How to combine antibiotics to limit resistance evolution

In combination therapy, bacteria are challenged with two or more antibiotics simultaneously. Ideally, separate mutations are required to adapt to each of them, which is a priori expected to hinder the evolution of full resistance. Yet, the success of this strategy ultimately depends on how well the combination controls the growth of bacteria with and without resistance mutations. To design a combination treatment, we need to choose drugs and their doses and decide how many drugs get mixed. Which combinations are good? To answer this question, we set up a stochastic pharmacodynamic model and determine the probability to successfully eradicate a bacterial population. We consider bacteriostatic and two types of bactericidal drugs-those that kill independent of replication and those that kill during replication. To establish results for a null model, we consider non-interacting drugs and implement the two most common models for drug independence-Loewe additivity and Bliss independence. Our results show that combination therapy is almost always better in limiting the evolution of resistance than administering just one drug, even though we keep the total drug dose constant for a 'fair' comparison. Yet, exceptions exist for drugs with steep dose-response curves. Combining a bacteriostatic and a bactericidal drug which can kill non-replicating cells is particularly beneficial. Our results suggest that a 50:50 drug ratio-even if not always optimal-is usually a good and safe choice. Applying three or four drugs is beneficial for treatment of strains with large mutation rates but adding more drugs otherwise only provides a marginal benefit or even a disadvantage. By systematically addressing key elements of treatment design, our study provides a basis for future models which take further factors into account. It also highlights conceptual challenges with translating the traditional concepts of drug independence to the single-cell level.

Variation in the response to antibiotics and life-history across the major Pseudomonas aeruginosa clone type (mPact) panel

Pseudomonas aeruginosa is a ubiquitous, opportunistic human pathogen. Since it often expresses multidrug resistance, new treatment options are urgently required. Such new treatments are usually assessed with one of the canonical laboratory strains, PAO1 or PA14. However, these two strains are unlikely representative of the strains infecting patients, because they have adapted to laboratory conditions and do not capture the enormous genomic diversity of the species. Here, we characterized the major P. aeruginosa clone type (mPact) panel. This panel consists of 20 strains, which reflect the species' genomic diversity, cover all major clone types, and have both patient and environmental origins. We found significant strain variation in distinct responses toward antibiotics and general growth characteristics. Only few of the measured traits are related, suggesting independent trait optimization across strains. High resistance levels were only identified for clinical mPact isolates and could be linked to known antimicrobial resistance (AMR) genes. One strain, H01, produced highly unstable AMR combined with reduced growth under drug-free conditions, indicating an evolutionary cost to resistance. The expression of microcolonies was common among strains, especially for strain H15, which also showed reduced growth, possibly indicating another type of evolutionary trade-off. By linking isolation source, growth, and virulence to life history traits, we further identified specific adaptive strategies for individual mPact strains toward either host processes or degradation pathways. Overall, the mPact panel provides a reasonably sized set of distinct strains, enabling in-depth analysis of new treatment designs or evolutionary dynamics in consideration of the species' genomic diversity.

IMPORTANCE

New treatment strategies are urgently needed for high-risk pathogens such as the opportunistic and often multidrug-resistant pathogen Pseudomonas aeruginosa. Here, we characterize the major P. aeruginosa clone type (mPact) panel. It consists of 20 strains with different origins that cover the major clone types of the species as well as its genomic diversity. This mPact panel shows significant variation in (i) resistance against distinct antibiotics, including several last resort antibiotics; (ii) related traits associated with the response to antibiotics; and (iii) general growth characteristics. We further developed a novel approach that integrates information on resistance, growth, virulence, and life-history characteristics, allowing us to demonstrate the presence of distinct adaptive strategies of the strains that focus either on host interaction or resource processing. In conclusion, the mPact panel provides a manageable number of representative strains for this important pathogen for further in-depth analyses of treatment options and evolutionary dynamics.

The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is severely constrained by random peptide mixtures

The prevalence of antibiotic-resistant pathogens has become a major threat to public health, requiring swift initiatives for discovering new strategies to control bacterial infections. Hence, antibiotic stewardship and rapid diagnostics, but also the development, and prudent use, of novel effective antimicrobial agents are paramount. Ideally, these agents should be less likely to select for resistance in pathogens than currently available conventional antimicrobials. The usage of antimicrobial peptides (AMPs), key components of the innate immune response, and combination therapies, have been proposed as strategies to diminish the emergence of resistance. Herein, we investigated whether newly developed random antimicrobial peptide mixtures (RPMs) can significantly reduce the risk of resistance evolution in vitro to that of single sequence AMPs, using the ESKAPE pathogen Pseudomonas aeruginosa (P. aeruginosa) as a model gram-negative bacterium. Infections of this pathogen are difficult to treat due the inherent resistance to many drug classes, enhanced by the capacity to form biofilms. P. aeruginosa was experimentally evolved in the presence of AMPs or RPMs, subsequentially assessing the extent of resistance evolution and cross-resistance/collateral sensitivity between treatments. Furthermore, the fitness costs of resistance on bacterial growth were studied and whole-genome sequencing used to investigate which mutations could be candidates for causing resistant phenotypes. Lastly, changes in the pharmacodynamics of the evolved bacterial strains were examined. Our findings suggest that using RPMs bears a much lower risk of resistance evolution compared to AMPs and mostly prevents cross-resistance development to other treatments, while maintaining (or even improving) drug sensitivity. This strengthens the case for using random cocktails of AMPs in favour of single AMPs, against which resistance evolved in vitro, providing an alternative to classic antibiotics worth pursuing.

Preserving the efficacy of antibiotics to tackle antibiotic resistance

Different international agencies recognize that antibiotic resistance is one of the most severe human health problems that humankind is facing. Traditionally, the introduction of new antibiotics solved this problem but various scientific and economic reasons have led to a shortage of novel antibiotics at the pipeline. This situation makes mandatory the implementation of approaches to preserve the efficacy of current antibiotics. The concept is not novel, but the only action taken for such preservation had been the 'prudent' use of antibiotics, trying to reduce the selection pressure by reducing the amount of antibiotics. However, even if antibiotics are used only when needed, this will be insufficient because resistance is the inescapable outcome of antibiotics' use. A deeper understanding of the alterations in the bacterial physiology upon acquisition of resistance and during infection will help to design improved strategies to treat bacterial infections. In this article, we discuss the interconnection between antibiotic resistance (and antibiotic activity) and bacterial metabolism, particularly in vivo, when bacteria are causing infection. We discuss as well how understanding evolutionary trade-offs, as collateral sensitivity, associated with the acquisition of resistance may help to define evolution-based therapeutic strategies to fight antibiotic resistance and to preserve currently used antibiotics.

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria based on a drug-concentration rescaling approach. We show how the resistance to drugs in space, and the consequent adaptation of growth rate, is governed by a Price equation with diffusion, integrating features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Although in many evolution models, per capita growth rate is a natural surrogate for fitness, in spatially-extended, potentially heterogeneous habitats, fitness is an emergent property that potentially reflects additional complexities, from boundary conditions to the specific spatial variation of growth rates. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical metric for characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem, λ1. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits to the relative advantage of each mutant across the environment. Our approach allows one to predict the precise outcomes of selection among mutants over space, ultimately from comparing their λ1 values, which encode a critical interplay between growth functions, movement traits, habitat size and boundary conditions. Such mathematical understanding opens new avenues for multi-drug therapeutic optimization.

Occurrence, distribution, and risk assessment of antibiotics in a typical aquaculture area around the Dongzhai Harbor mangrove forest on Hainan Island

The trees of the Dongzhai Harbor mangrove forest suffer from antibiotic contamination from surrounding aquaculture areas. Despite this being one of the largest mangrove forests in China, few studies have focused on the antibiotic pollution status in these aquaculture areas. In the present study, the occurrence, distribution, and risk assessment of 37 antibiotics in surface water and sediment samples from aquaculture areas around Dongzhai Harbor mangrove forests were analyzed. The concentration of total antibiotics (∑antibiotics) ranged from 78.4 ng/L to 225.6 ng/L in surface water (except S14-A2) and from 19.5 ng/g dry weight (dw) to 229 ng/g dw in sediment. In the sediment, the concentrations of ∑antibiotics were relatively low (19.5-52.3 ng/g dw) at 75 % of the sampling sites, while they were high (95.7-229.0 ng/g dw) at a few sampling sites (S13-A1, S13D, S8D). The correlation analysis results showed that the Kd values of the 9 antibiotics were significantly positively correlated with molecular weight (MW), Kow, and LogKow. Risk assessment revealed that sulfamethoxazole (SMX) in surface water and SMX, enoxacin (ENX), ciprofloxacin (CFX), enrofloxacin (EFX), ofloxacin (OFX), and norfloxacin (NFX) in sediment had medium/high risk quotients (RQs) at 62.5 % and 25-100 %, respectively, of the sampling sites. The antibiotic mixture in surface water (0.06-3.36) and sediment (0.43-309) posed a high risk at 37.5 % and 66.7 %, respectively, of the sampling sites. SMX was selected as an indicator of antibiotic pollution in surface water to assist regulatory authorities in monitoring and managing antibiotic pollution in the aquaculture zone of Dongzhai Harbor. Overall, the results of the present study provide a comprehensive and detailed analysis of the characteristics of antibiotics in the aquaculture environment around the Dongzhai Harbor mangrove system and provide a theoretical basis for the source control of antibiotics in mangrove systems.

Plasmid-mediated phenotypic noise leads to transient antibiotic resistance in bacteria

The rise of antibiotic resistance is a critical public health concern, requiring an understanding of mechanisms that enable bacteria to tolerate antimicrobial agents. Bacteria use diverse strategies, including the amplification of drug-resistance genes. In this paper, we showed that multicopy plasmids, often carrying antibiotic resistance genes in clinical bacteria, can rapidly amplify genes, leading to plasmid-mediated phenotypic noise and transient antibiotic resistance. By combining stochastic simulations of a computational model with high-throughput single-cell measurements of blaTEM-1 expression in Escherichia coli MG1655, we showed that plasmid copy number variability stably maintains populations composed of cells with both low and high plasmid copy numbers. This diversity in plasmid copy number enhances the probability of bacterial survival in the presence of antibiotics, while also rapidly reducing the burden of carrying multiple plasmids in drug-free environments. Our results further support the tenet that multicopy plasmids not only act as vehicles for the horizontal transfer of genetic information between cells but also as drivers of bacterial adaptation, enabling rapid modulation of gene copy numbers. Understanding the role of multicopy plasmids in antibiotic resistance is critical, and our study provides insights into how bacteria can transiently survive lethal concentrations of antibiotics.

Mathematical modelling of antibiotic interaction on evolution of antibiotic resistance: an analytical approach

Background

The emergence and spread of antibiotic-resistant pathogens have led to the exploration of antibiotic combinations to enhance clinical effectiveness and counter resistance development. Synergistic and antagonistic interactions between antibiotics can intensify or diminish the combined therapy's impact. Moreover, these interactions can evolve as bacteria transition from wildtype to mutant (resistant) strains. Experimental studies have shown that the antagonistically interacting antibiotics against wildtype bacteria slow down the evolution of resistance. Interestingly, other studies have shown that antibiotics that interact antagonistically against mutants accelerate resistance. However, it is unclear if the beneficial effect of antagonism in the wildtype bacteria is more critical than the detrimental effect of antagonism in the mutants. This study aims to illuminate the importance of antibiotic interactions against wildtype bacteria and mutants on the deacceleration of antimicrobial resistance.

Methods

To address this, we developed and analyzed a mathematical model that explores the population dynamics of wildtype and mutant bacteria under the influence of interacting antibiotics. The model investigates the relationship between synergistic and antagonistic antibiotic interactions with respect to the growth rate of mutant bacteria acquiring resistance. Stability analysis was conducted for equilibrium points representing bacteria-free conditions, all-mutant scenarios, and coexistence of both types. Numerical simulations corroborated the analytical findings, illustrating the temporal dynamics of wildtype and mutant bacteria under different combination therapies.

Results

Our analysis provides analytical clarification and numerical validation that antibiotic interactions against wildtype bacteria exert a more significant effect on reducing the rate of resistance development than interactions against mutants. Specifically, our findings highlight the crucial role of antagonistic antibiotic interactions against wildtype bacteria in slowing the growth rate of resistant mutants. In contrast, antagonistic interactions against mutants only marginally affect resistance evolution and may even accelerate it.

Conclusion

Our results emphasize the importance of considering the nature of antibiotic interactions against wildtype bacteria rather than mutants when aiming to slow down the acquisition of antibiotic resistance.

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria, based on a rescaling approach (Gjini and Wood, 2021). We show how the resistance to drugs in space, and the consequent adaptation of growth rate is governed by a Price equation with diffusion. The covariance terms in this equation integrate features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits, to the relative advantage of each mutant across the environment. Such a mathematical understanding allows to predict the precise outcomes of selection over space, ultimately from the fundamental balance between growth and movement traits, and their diversity in a population.

Evolutionary rescue on genotypic fitness landscapes

Populations facing adverse environments, novel pathogens or invasive competitors may be destined to extinction if they are unable to adapt rapidly. Quantitative predictions of the probability of survival through adaptation, evolutionary rescue, have been previously developed for one of the most natural and well-studied mappings from an organism's traits to its fitness, Fisher's geometric model (FGM). While FGM assumes that all possible trait values are accessible via mutation, in many applications only a finite set of rescue mutations will be available, such as mutations conferring resistance to a parasite, predator or toxin. We predict the probability of evolutionary rescue, via de novo mutation, when this underlying genetic structure is included. We find that rescue probability is always reduced when its genetic basis is taken into account. Unlike other known features of the genotypic FGM, however, the probability of rescue increases monotonically with the number of available mutations and approaches the behaviour of the classical FGM as the number of available mutations approaches infinity.

Synergy of Antibiotics and Antibiofilm Agents against Methicillin-Resistant Staphylococcus aureus Biofilms

Methicillin-resistant Staphylococcus aureus (MRSA) infections are some of the most common antibiotic-resistant infections, often exacerbated by the formation of biofilms. Here, we evaluated six compounds, three common antibiotics used against MRSA and three antibiofilm compounds, in nine combinations to investigate the mechanisms of synergistic eradication of MRSA biofilms. Using metabolic assessment, colony enumeration, confocal fluorescence microscopy, and scanning electron microscopy, we identified two promising combinations of antibiotics with antibiofilm agents against preformed MRSA biofilms. The broad-spectrum protease, proteinase K, and membrane-targeting antibiotic, daptomycin, worked in synergy against MRSA biofilms by manipulating the protein content, increasing access to the cell membrane of biofilm bacteria. We also found that the combination of cationic peptide, IDR-1018, with the cell wall cross-linking inhibitor, vancomycin, exhibited synergy against MRSA biofilms by causing bacterial damage and preventing repair. Our findings identify synergistic combinations of antibiotics and antibiofilm agents, providing insight into mechanisms that may be explored further for the development of effective treatments against MRSA biofilm.

The uncertain role of substandard and falsified medicines in the emergence and spread of antimicrobial resistance

Approximately 10% of antimicrobials used by humans in low- and middle-income countries are estimated to be substandard or falsified. In addition to their negative impact on morbidity and mortality, they may also be important drivers of antimicrobial resistance. Despite such concerns, our understanding of this relationship remains rudimentary. Substandard and falsified medicines have the potential to either increase or decrease levels of resistance, and here we discuss a range of mechanisms that could drive these changes. Understanding these effects and their relative importance will require an improved understanding of how different drug exposures affect the emergence and spread of resistance and of how the percentage of active pharmaceutical ingredients in substandard and falsified medicines is temporally and spatially distributed.

Mutators can drive the evolution of multi-resistance to antibiotics

Antibiotic combination therapies are an approach used to counter the evolution of resistance; their purported benefit is they can stop the successive emergence of independent resistance mutations in the same genome. Here, we show that bacterial populations with 'mutators', organisms with defects in DNA repair, readily evolve resistance to combination antibiotic treatment when there is a delay in reaching inhibitory concentrations of antibiotic-under conditions where purely wild-type populations cannot. In populations of Escherichia coli subjected to combination treatment, we detected a diverse array of acquired mutations, including multiple alleles in the canonical targets of resistance for the two drugs, as well as mutations in multi-drug efflux pumps and genes involved in DNA replication and repair. Unexpectedly, mutators not only allowed multi-resistance to evolve under combination treatment where it was favoured, but also under single-drug treatments. Using simulations, we show that the increase in mutation rate of the two canonical resistance targets is sufficient to permit multi-resistance evolution in both single-drug and combination treatments. Under both conditions, the mutator allele swept to fixation through hitch-hiking with single-drug resistance, enabling subsequent resistance mutations to emerge. Ultimately, our results suggest that mutators may hinder the utility of combination therapy when mutators are present. Additionally, by raising the rates of genetic mutation, selection for multi-resistance may have the unwanted side-effect of increasing the potential to evolve resistance to future antibiotic treatments.

Lonicera japonica Thunb. as a promising antibacterial agent for Bacillus cereus ATCC14579 based on network pharmacology, metabolomics, and in vitro experiments

Lonicera japonica Thunb. has attracted much attention for its treatment of bacterial and viral infectious diseases, while its active ingredients and potential mechanisms of action have not been fully elucidated. Here, we combined metabolomics, and network pharmacology to explore the molecular mechanism of Bacillus cereus ATCC14579 inhibition by Lonicera japonica Thunb. In vitro inhibition experiments showed that the Lonicera japonica Thunb.'s water extracts, ethanolic extract, luteolin, quercetin, and kaempferol strongly inhibited Bacillus cereus ATCC14579. In contrast, chlorogenic acid and macranthoidin B had no inhibitory effect on Bacillus cereus ATCC14579. Meanwhile, the minimum inhibitory concentrations of luteolin, quercetin, and kaempferol against Bacillus cereus ATCC14579 were 15.625 μg mL-1, 31.25 μg mL-1, and 15.625 μg mL-1. Based on the previous experimental basis, the metabolomic analysis showed the presence of 16 active ingredients in Lonicera japonica Thunb.'s water extracts and ethanol extracts, with differences in the luteolin, quercetin, and kaempferol contents between the water extracts and ethanol extracts. Network pharmacology studies indicated that fabZ, tig, glmU, secA, deoD, nagB, pgi, rpmB, recA, and upp were potential key targets. Active ingredients of Lonicera japonica Thunb. may exert their inhibitory effects by inhibiting ribosome assembly, the peptidoglycan biosynthesis process, and the phospholipid biosynthesis process of Bacillus cereus ATCC14579. An alkaline phosphatase activity assay, peptidoglycan concentration assay, and protein concentration assay showed that luteolin, quercetin, and kaempferol disrupted the Bacillus cereus ATCC14579 cell wall and cell membrane integrity. Transmission electron microscopy results showed significant changes in the morphology and ultrastructure of the cell wall and cell membrane of Bacillus cereus ATCC14579, further confirming the disruption of the cell wall and cell membrane integrity of Bacillus cereus ATCC14579 by luteolin, quercetin, and kaempferol. In conclusion, Lonicera japonica Thunb. can be used as a potential antibacterial agent for Bacillus cereus ATCC14579, which may exert its antibacterial activity by destroying the integrity of the cell wall and membrane.

Examining the efficacy of localised gemcitabine therapy for the treatment of pancreatic cancer using a hybrid agent-based model

The prognosis for pancreatic ductal adenocarcinoma (PDAC) patients has not significantly improved in the past 3 decades, highlighting the need for more effective treatment approaches. Poor patient outcomes and lack of response to therapy can be attributed, in part, to a lack of uptake of perfusion of systemically administered chemotherapeutic drugs into the tumour. Wet-spun alginate fibres loaded with the chemotherapeutic agent gemcitabine have been developed as a potential tool for overcoming the barriers in delivery of systemically administrated drugs to the PDAC tumour microenvironment by delivering high concentrations of drug to the tumour directly over an extended period. While exciting, the practicality, safety, and effectiveness of these devices in a clinical setting requires further investigation. Furthermore, an in-depth assessment of the drug-release rate from these devices needs to be undertaken to determine whether an optimal release profile exists. Using a hybrid computational model (agent-based model and partial differential equation system), we developed a simulation of pancreatic tumour growth and response to treatment with gemcitabine loaded alginate fibres. The model was calibrated using in vitro and in vivo data and simulated using a finite volume method discretisation. We then used the model to compare different intratumoural implantation protocols and gemcitabine-release rates. In our model, the primary driver of pancreatic tumour growth was the rate of tumour cell division. We were able to demonstrate that intratumoural placement of gemcitabine loaded fibres was more effective than peritumoural placement. Additionally, we quantified the efficacy of different release profiles from the implanted fibres that have not yet been tested experimentally. Altogether, the model developed here is a tool that can be used to investigate other drug delivery devices to improve the arsenal of treatments available for PDAC and other difficult-to-treat cancers in the future.

Sequential antibiotic therapy in the laboratory and in the patient

Laboratory experiments suggest that rapid cycling of antibiotics during the course of treatment could successfully counter resistance evolution. Drugs involving collateral sensitivity could be particularly suitable for such therapies. However, the environmental conditions in vivo differ from those in vitro. One key difference is that drugs can be switched abruptly in the laboratory, while in the patient, pharmacokinetic processes lead to changing antibiotic concentrations including periods of dose overlaps from consecutive administrations. During such overlap phases, drug-drug interactions may affect the evolutionary dynamics. To address the gap between the laboratory and potential clinical applications, we set up two models for comparison-a 'laboratory model' and a pharmacokinetic-pharmacodynamic 'patient model'. The analysis shows that in the laboratory, the most rapid cycling suppresses the bacterial population always at least as well as other regimens. For patient treatment, however, a little slower cycling can sometimes be preferable if the pharmacodynamic curve is steep or if drugs interact antagonistically. When resistance is absent prior to treatment, collateral sensitivity brings no substantial benefit unless the cell division rate is low and drug cycling slow. By contrast, drug-drug interactions strongly influence the treatment efficiency of rapid regimens, demonstrating their importance for the optimal choice of drug pairs.

Evaluation of a Sequential Antibiotic Treatment Regimen of Ampicillin, Ciprofloxacin and Fosfomycin against Escherichia coli CFT073 in the Hollow Fiber Infection Model Compared with Simultaneous Combination Treatment

OBJECTIVE

Employ the hollow fiber infection model (HFIM) to study sequential antibiotic administration (ampicillin, ciprofloxacin and fosfomycin) using human pharmacokinetic profiles to measure changes in the rate of antibiotic resistance development and compare this to simultaneous combination therapy with the same antibiotic combinations.

METHODS

Escherichia coli CFT073, a clinical uropathogenic strain, was exposed individually to clinically relevant pharmacokinetic concentrations of ampicillin on day 1, ciprofloxacin on day 2 and fosfomycin on day 3. This sequence was continued for 10 days in the HFIM. Bacterial samples were collected at different time points to enumerate total and resistant bacterial populations. The results were compared with the simultaneous combination therapy previously studied.

RESULTS

Sequential antibiotic treatment (ampicillin-ciprofloxacin-fosfomycin sequence) resulted in the early emergence of single and multi-antibiotic-resistant subpopulations, while the simultaneous treatment regimen significantly delayed or prevented the emergence of resistant subpopulations.

CONCLUSION

Sequential administration of these antibiotic monotherapies did not significantly delay the emergence of resistant subpopulations compared to simultaneous treatment with combinations of the same antibiotics. Further studies are warranted to evaluate different sequences of the same antibiotics in delaying emergent resistance.

In vitro antibacterial effect of probiotics against Carbapenamase-producing multidrug-resistant Klebsiella pneumoniae clinical isolates, Cairo, Egypt

Background

Searching for a non-antibiotic therapeutic option such as probiotics is gaining momentum nowadays. We aimed to evaluate the in vitro antibacterial ability of cell-free supernatant (CFS) of selected Lactobacillus strains (with probiotic properties) against clinical isolates of OXA-48-producing multidrug-resistant (MDR) Klebsiella pneumoniae separately and in combination with cefoperazone antibiotic.

Methods

Over a period of 8 months, a cross-sectional experimental study involving 590 Klebsiella pneumoniae isolates was done. Our study took place at The Specialized Pediatric Teaching Hospital of Cairo University. Of the 590 Klebsiella pneumoniae isolates collected from blood cultures, pus, endotracheal aspirates, and pleural fluid, only 50 unrepeated clinical isolates of MDR Klebsiella pneumoniae-producing OXA-48-like detected by CHROMID® OXA-48 (bioMérieux, France) were selected for our study. After determining the minimal inhibitory concentration of CFS of ten Lactobacillus strains and cefoperazone each, the synergistic effect of both was tested.

Results

Among ten tested Lactobacillus spp., a significant increase in the mean value of inhibition zone diameter with CFS of L. helveticus (14.32 mm) and L. rhamnosus (13.3 mm) was detected separately. On the contrary, an antagonistic activity against all tested isolates was detected upon combination of Lactobacilli with cefoperazone (512 μg/ml). The mean value of inhibition zone diameter of L. helveticus CFS+ cefoperazone was (11.0 mm) and for L. rhamnosus CFS+ cefoperazone was (10.88 mm) (p value <0.001).

Conclusion

The antimicrobial efficiency of using CFS of Lactobacillus species separately indicates that these therapies may be a substitute treatment strategy against MDR Klebsiella pneumoniae.

Evolutionary history and strength of selection determine the rate of antibiotic resistance adaptation

Bacterial adaptation to stressful environments often produces evolutionary constraints whereby increases in resistance are associated with reduced fitness in a different environment. The exploitation of this resistance-cost trade-off has been proposed as the basis of rational antimicrobial treatment strategies designed to limit the evolution of drug resistance in bacterial pathogens. Recent theoretical, laboratory, and clinical studies have shown that fluctuating selection can maintain drug efficacy and even restore drug susceptibility, but can also increase the rate of adaptation and promote cross-resistance to other antibiotics. In this paper, we combine mathematical modeling, experimental evolution, and whole-genome sequencing to follow evolutionary trajectories towards β-lactam resistance under fluctuating selective conditions. Our experimental model system consists of eight populations of Escherichia coli K12 evolving in parallel to a serial dilution protocol designed to dynamically control the strength of selection for resistance. We implemented adaptive ramps with mild and strong selection, resulting in evolved populations with similar levels of resistance, but with different evolutionary dynamics and diverging genotypic profiles. We found that mutations that emerged under strong selection are unstable in the absence of selection, in contrast to resistance mutations previously selected in the mild selection regime that were stably maintained in drug-free environments and positively selected for when antibiotics were reintroduced. Altogether, our population dynamics model and the phenotypic and genomic analysis of the evolved populations show that the rate of resistance adaptation is contingent upon the strength of selection, but also on evolutionary constraints imposed by prior drug exposures.

Mutational Switch-Backs Can Accelerate Evolution of Francisella to a Combination of Ciprofloxacin and Doxycycline

Combination antimicrobial therapy has been considered a promising strategy to combat the evolution of antimicrobial resistance. Francisella tularensis is the causative agent of tularemia and in addition to being found in the nature, is recognized as a threat agent that requires vigilance. We investigated the evolutionary outcome of adapting the Live Vaccine Strain (LVS) of F. tularensis subsp. holarctica to two non-interacting drugs, ciprofloxacin and doxycycline, individually, sequentially, and in combination. Despite their individual efficacies and independence of mechanisms, evolution to the combination arose on a shorter time scale than evolution to the two drugs sequentially. We conducted a longitudinal mutational analysis of the populations evolving to the drug combination, genetically reconstructed the identified evolutionary pathway, and carried out biochemical validation. We discovered that, after the appearance of an initial weak generalist mutation (FupA/B), each successive mutation alternated between adaptation to one drug or the other. In combination, these mutations allowed the population to more efficiently ascend the fitness peak through a series of evolutionary switch-backs. Clonal interference, weak pleiotropy, and positive epistasis also contributed to combinatorial evolution. This finding suggests that the use of this non-interacting drug pair against F. tularensis may render both drugs ineffective because of mutational switch-backs that accelerate evolution of dual resistance.

Gentamicin at sub-inhibitory concentrations selects for antibiotic resistance in the environment

Antibiotics released into the environment at low (sub-inhibitory) concentrations could select for antibiotic resistance that might disseminate to the human microbiome. In this case, low-level anthropogenic sources of antibiotics would have a significant impact on human health risk. In order to provide data necessary for the evaluation of this risk, we implemented river water microcosms at both sub-inhibitory and inhibitory concentrations of gentamicin as determined previously based on bacterial growth in enriched media. Using metagenomic sequencing and qPCR/RT-qPCR, we assessed the effects of gentamicin on water bacterial communities and their resistome. A change in the composition of total and active communities, as well as a gentamicin resistance gene selection identified via mobile genetic elements, was observed during a two-day exposure. We demonstrated the effects of sub-inhibitory concentrations of gentamicin on bacterial communities and their associated resistome in microcosms (simulating in situ conditions). In addition, we established relationships between antibiotic dose and the magnitude of the community response in the environment. The scope of resistance selection under sub-inhibitory concentrations of antibiotics and the mechanisms underlying this process might provide the basis for understanding resistance dispersion and associated risks in relatively low impacted ecosystems.

Population genetics, biofilm recalcitrance, and antibiotic resistance evolution

Biofilms are communities of bacteria forming high-density sessile colonies. Such a lifestyle comes associated with costs and benefits: while the growth rate of biofilms is often lower than that of their free-living counterparts, this cost is readily repaid once the colony is subjected to antibiotics. Biofilms can grow in antibiotic concentrations a thousand times higher than planktonic bacteria. While numerous mechanisms have been proposed to explain biofilm recalcitrance towards antibiotics, little is yet known about their effect on the evolution of resistance. We synthesize the current understanding of biofilm recalcitrance from a pharmacodynamic and a population genetics perspective. Using the pharmacodynamic framework, we discuss the effects of various mechanisms and show that biofilms can either promote or impede resistance evolution.

Exploring synergy and its role in antimicrobial peptide biology

Antimicrobial peptides will be an essential component in combating the escalating issue of antibiotic resistance. Identifying synergistic combinations of two or more substances will increase the value of these peptides further. Several potential pitfalls in conducting synergy testing with peptides are discussed in detail. As case studies, we describe observations of AMP synergy with peptides, antibiotics, and metal ions as well as some of the mechanistic details that have been uncovered. The Bliss and Loewe models for synergy are presented prior to recommending protocols for conducting checkerboard, minimal inhibitory concentration, and time-kill assays. Establishing mechanisms of action and exploring the potential for resistance will be crucial to translate these studies into the clinic.

Resistance response to Arenicin derivatives in Escherichia coli

The rising prevalence of antibiotic resistance poses the greatest health threats. Antimicrobial peptides (AMPs) are regarded as the potentially effective therapy. To avoid current crisis of antibiotic resistance, a comprehensive understanding of AMP resistance is necessary before clinical application. In this study, the development of resistance to the anti-Gram-negative bacteria peptide N6NH₂ (21 residues, β-sheet) was characterized in E. coli ATCC25922. Three N6NH₂-resistant E. coli mutants with 32-fold increase in MIC were isolated by serially passaging bacterial lineages in progressively increasing concentrations of N6NH₂ and we mainly focus on the phenotype of N6NH₂-resistant bacteria different from sensitive bacteria. The results showed that the resistance mechanism was attributed to synergy effect of multiple mechanisms: (i) increase biofilm formation capacity (3 ~ 4-fold); (ii) weaken the affinity of lipopolysaccharide (LPS) with N6NH₂ (3 ~ 8-fold); and (iii) change the cell membrane permeability and potential. Interestingly, a chimeric peptide-G6, also a N6NH₂ analog, which keep the same antibacterial activity to both wild-type and resistant clones (MIC value: 16 μg/mL), could curb N6NH₂-resistant mutants by stronger inhibition of biofilm formation, stronger affinity with LPS, and stronger membrane permeability and depolarization than that of N6NH₂.

Antibiotic resistance in microbes: History, mechanisms, therapeutic strategies and future prospects

Antibiotics have been used to cure bacterial infections for more than 70 years, and these low-molecular-weight bioactive agents have also been used for a variety of other medicinal applications. In the battle against microbes, antibiotics have certainly been a blessing to human civilization by saving millions of lives. Globally, infections caused by multidrug-resistant (MDR) bacteria are on the rise. Antibiotics are being used to combat diversified bacterial infections. Synthetic biology techniques, in combination with molecular, functional genomic, and metagenomic studies of bacteria, plants, and even marine invertebrates are aimed at unlocking the world's natural products faster than previous methods of antibiotic discovery. There are currently only few viable remedies, potential preventive techniques, and a limited number of antibiotics, thereby necessitating the discovery of innovative medicinal approaches and antimicrobial therapies. MDR is also facilitated by biofilms, which makes infection control more complex. In this review, we have spotlighted comprehensively various aspects of antibiotics viz. overview of antibiotics era, mode of actions of antibiotics, development and mechanisms of antibiotic resistance in bacteria, and future strategies to fight the emerging antimicrobial resistant threat.

Nutritional and Volatile Characterisation of Milk Inoculated with Thermo-Tolerant Lactobacillus bulgaricus through Adaptive Laboratory Evolution

In this study, thermo-tolerant strain of Lactobacillus bulgaricus (L. bulgaricus) was developed using gradual increase in temperature to induce Adaptive Laboratory Evolution (ALE). Viable colony count of 1.87 ± 0.98 log cfu/mL was achieved at 52 °C, using MRS agar supplemented with 2% lactose. Changes in bacteria morphology were discovered, from rod (control) to filament (52 °C) to cocci after frozen storage (−80 °C). When milk was inoculated with thermo-tolerant L. bulgaricus, lactic acid production was absent, leaving pH at 6.84 ± 0.13. This has caused weakening of the protein network, resulting in high whey separation and lower water-holding capacity (37.1 ± 0.35%) compared to the control (98.10 ± 0.60%). Significantly higher proteolytic activity was observed through free amino acids analysis by LC-MS. Arginine and methionine (237.24 ± 5.94 and 98.83 ± 1.78 µg/100 g, respectively) were found to be 115- and 275-fold higher than the control, contributing to changing the aroma similar to cheese. Further volatile analysis through SPME-GC-MS has confirmed significant increase in cheese-aroma volatiles compared to the control, with increase in diacetyl formation. Further work on DNA profiling, metabolomics and peptidomics will help to answer mechanisms behind the observed changes made in the study.

Are Efficient-Dose Mixtures a Solution to Reduce Fungicide Load and Delay Evolution of Resistance? An Experimental Evolutionary Approach

Pesticide resistance poses a critical threat to agriculture, human health and biodiversity. Mixtures of fungicides are recommended and widely used in resistance management strategies. However, the components of the efficiency of such mixtures remain unclear. We performed an experimental evolutionary study on the fungal pathogen Z. tritici to determine how mixtures managed resistance. We compared the effect of the continuous use of single active ingredients to that of mixtures, at the minimal dose providing full control of the disease, which we refer to as the "efficient" dose. We found that the performance of efficient-dose mixtures against an initially susceptible population depended strongly on the components of the mixture. Such mixtures were either as durable as the best mixture component used alone, or worse than all components used alone. Moreover, efficient dose mixture regimes probably select for generalist resistance profiles as a result of the combination of selection pressures exerted by the various components and their lower doses. Our results indicate that mixtures should not be considered a universal strategy. Experimental evaluations of specificities for the pathogens targeted, their interactions with fungicides and the interactions between fungicides are crucial for the design of sustainable resistance management strategies.

Quantitative biology of survival under antibiotic treatments

The mathematical formulation for the dynamics of growth reduction and/or killing under antibiotic treatments has a long history. Even before the extensive use of antibiotics, attempts to model the killing dynamics of biocides were made [1]. Here, we review relatively simple quantitative formulations of the two main modes of survival under antibiotics, resistance and tolerance, as well as their heterogeneity in bacterial populations. We focus on the two main types of heterogeneity that have been described: heteroresistance and antibiotic persistence, each linked to the variation in a different parameter of the antibiotic response dynamics. Finally, we review the effects on survival of combining resistance and tolerance mutations as well as on the mode and tempo of evolution under antibiotic treatments.

High potency of sequential therapy with only β-lactam antibiotics

Evolutionary adaptation is a major source of antibiotic resistance in bacterial pathogens. Evolution-informed therapy aims to constrain resistance by accounting for bacterial evolvability. Sequential treatments with antibiotics that target different bacterial processes were previously shown to limit adaptation through genetic resistance trade-offs and negative hysteresis. Treatment with homogeneous sets of antibiotics is generally viewed to be disadvantageous as it should rapidly lead to cross-resistance. We here challenged this assumption by determining the evolutionary response of Pseudomonas aeruginosa to experimental sequential treatments involving both heterogenous and homogeneous antibiotic sets. To our surprise, we found that fast switching between only β-lactam antibiotics resulted in increased extinction of bacterial populations. We demonstrate that extinction is favored by low rates of spontaneous resistance emergence and low levels of spontaneous cross-resistance among the antibiotics in sequence. The uncovered principles may help to guide the optimized use of available antibiotics in highly potent, evolution-informed treatment designs.

Overuse of antibiotic drugs is leading to the appearance of antibiotic-resistant bacteria; this is, bacteria with mutations that allow them to survive treatment with specific antibiotics. This has made some bacterial infections difficult or impossible to treat. Learning more about how bacteria evolve resistance to antibiotics could help scientists find ways to prevent it and develop more effective treatments.

Changing antibiotics frequently may be one way to prevent bacteria from evolving resistance. That way if a bacterium acquires mutations that allow it to escape one antibiotic, another antibiotic will kill it, stopping it from dividing and preventing the appearance of descendants with resistance to several antibiotics. In order to use this approach, testing is needed to find the best sequences of antibiotics to apply and the optimal timings of treatment.

To find out more, Batra, Roemhild et al. grew bacteria in the laboratory and exposed them to different sequences of antibiotics, switching antibiotics at different time intervals. This showed that sequential treatments with different antibiotics can limit bacterial evolution, especially when antibiotics are switched quickly. Unexpectedly, one of the most effective sequences used very similar antibiotics. This was surprising because using similar antibiotics should lead to the evolution of cross-resistance, which is when a drug causes changes that make the bacterium less sensitive to other treatments. However, in the tested case, cross-resistance did not evolve when antibiotics were switched quickly, thereby ensuring efficiency of treatment.

Batra et al. show that alternating sequences of antibiotics may be an effective strategy to prevent drug resistance. Because the experiments were done in a laboratory setting it will be important to verify the results in studies in animals and humans before the approach can be used in medical or veterinary settings. If the results are confirmed, it could reduce the need to develop new antibiotics, which is expensive and time consuming.

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction [DI]) and how resistance to one drug impacts resistance to the other (collateral effects [CE]). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug-sensitive (ancestral) and drug-resistant (mutant) populations. We show that evolved resistance to the component drugs - and in turn, the adaptation of growth rate - is governed by a Price equation whose covariance terms encode geometric features of both the two-drug-response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. We also demonstrate how constraints on available mutational pathways can be incorporated into the framework, adding a third key driver of evolution. Our results clarify the complex relationship between drug interactions and collateral effects in multidrug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally explicit selective outcomes.

Compatibility of Evolutionary Responses to Constituent Antibiotics Drive Resistance Evolution to Drug Pairs

Antibiotic combinations are considered a relevant strategy to tackle the global antibiotic resistance crisis since they are believed to increase treatment efficacy and reduce resistance evolution (WHO treatment guidelines for drug-resistant tuberculosis: 2016 update.). However, studies of the evolution of bacterial resistance to combination therapy have focused on a limited number of drugs and have provided contradictory results (Lipsitch, Levin BR. 1997; Hegreness et al. 2008; Munck et al. 2014). To address this gap in our understanding, we performed a large-scale laboratory evolution experiment, adapting eight replicate lineages of Escherichia coli to a diverse set of 22 different antibiotics and 33 antibiotic pairs. We found that combination therapy significantly limits the evolution of de novode novo resistance in E. coli, yet different drug combinations vary substantially in their propensity to select for resistance. In contrast to current theories, the phenotypic features of drug pairs are weak predictors of resistance evolution. Instead, the resistance evolution is driven by the relationship between the evolutionary trajectories that lead to resistance to a drug combination and those that lead to resistance to the component drugs. Drug combinations requiring a novel genetic response from target bacteria compared with the individual component drugs significantly reduce resistance evolution. These data support combination therapy as a treatment option to decelerate resistance evolution and provide a novel framework for selecting optimized drug combinations based on bacterial evolutionary responses.

The Use of Translational Modelling and Simulation to Develop Immunomodulatory Therapy as an Adjunct to Antibiotic Treatment in the Context of Pneumonia

The treatment of respiratory tract infections is threatened by the emergence of bacterial resistance. Immunomodulatory drugs, which enhance airway innate immune defenses, may improve therapeutic outcome. In this concept paper, we aim to highlight the utility of pharmacometrics and Bayesian inference in the development of immunomodulatory therapeutic agents as an adjunct to antibiotics in the context of pneumonia. For this, two case studies of translational modelling and simulation frameworks are introduced for these types of drugs up to clinical use. First, we evaluate the pharmacokinetic/pharmacodynamic relationship of an experimental combination of amoxicillin and a TLR4 agonist, monophosphoryl lipid A, by developing a pharmacometric model accounting for interaction and potential translation to humans. Capitalizing on this knowledge and associating clinical trial extrapolation and statistical modelling approaches, we then investigate the TLR5 agonist flagellin. The resulting workflow combines expert and prior knowledge on the compound with the in vitro and in vivo data generated during exploratory studies in order to construct high-dimensional models considering the pharmacokinetics and pharmacodynamics of the compound. This workflow can be used to refine preclinical experiments, estimate the best doses for human studies, and create an adaptive knowledge-based design for the next phases of clinical development.

Point prevalence survey of antibiotic consumption across three hospitals in Ghana

BACKGROUND

Actionable data on antimicrobial use is important when planning strategic interventions such as antimicrobial stewardship to address the challenge of drug resistance, particularly in resource-constrained settings.

OBJECTIVES

To assess the prevalence of antibiotic use, the pattern of commonly used antibiotics and patient factors that may be associated with the increased use of antibiotics in the study hospitals.

METHODS

This was a cross-sectional study conducted using the WHO Methodology for Point Prevalence Surveys in hospitals. Chi-squared analysis, Fisher's exact test and logistic regression were employed to analyse statistically the data obtained.

RESULTS

The overall prevalence of antibiotic use in the hospitals was 60.5%. The commonest indications for antibiotic recommendations were community-acquired infections (36.5%), surgical prophylaxis (26.1%) and hospital-acquired infections (15.7%), among others. Very few (2.7%) of the patients had their samples taken for culture and susceptibility testing to guide therapy. Penicillins (48.7%), cephalosporins (23.5%) and fluoroquinolones (17.4%) were the most commonly prescribed antibiotics. Concurrent malaria infection [adjusted OR (AOR) 0.33, 95% CI 0.11-0.94, P = 0.04] and increasing age (AOR 0.98, 95% CI 0.96-1.00, P = 0.02) were associated with lower risk of antibiotic use.

CONCLUSIONS

The prevalence of antibiotic consumption in the hospitals was lower than that reported in similar studies in Ghana, but high relative to some reports from high-income countries. Most antibiotic therapy was empirical and not guided by culture and susceptibility testing. There is the need for application of the WHO AWaRe classification for the selection of antibiotics and increased use of culture and susceptibility data to guide infectious disease therapy.

The Genomic Basis of Rapid Adaptation to Antibiotic Combination Therapy in Pseudomonas aeruginosa

Combination therapy is a common antibiotic treatment strategy that aims at minimizing the risk of resistance evolution in several infectious diseases. Nonetheless, evidence supporting its efficacy against the nosocomial opportunistic pathogen Pseudomonas aeruginosa remains elusive. Identification of the possible evolutionary paths to resistance in multidrug environments can help to explain treatment outcome. For this purpose, we here performed whole-genome sequencing of 127 previously evolved populations of P. aeruginosa adapted to sublethal doses of distinct antibiotic combinations and corresponding single-drug treatments, and experimentally characterized several of the identified variants. We found that alterations in the regulation of efflux pumps are the most favored mechanism of resistance, regardless of the environment. Unexpectedly, we repeatedly identified intergenic variants in the adapted populations, often with no additional mutations and usually associated with genes involved in efflux pump expression, possibly indicating a regulatory function of the intergenic regions. The experimental analysis of these variants demonstrated that the intergenic changes caused similar increases in resistance against single and multidrug treatments as those seen for efflux regulatory gene mutants. Surprisingly, we could find no substantial fitness costs for a majority of these variants, most likely enhancing their competitiveness toward sensitive cells, even in antibiotic-free environments. We conclude that the regulation of efflux is a central target of antibiotic-mediated selection in P. aeruginosa and that, importantly, changes in intergenic regions may represent a usually neglected alternative process underlying bacterial resistance evolution, which clearly deserves further attention in the future.

Quantifying relative virulence: when μmax fails and AUC alone just is not enough

A challenge in virology is quantifying relative virulence (VR) between two (or more) viruses that exhibit different replication dynamics in a given susceptible host. Host growth curve analysis is often used to mathematically characterize virus-host interactions and to quantify the magnitude of detriment to host due to viral infection. Quantifying VR using canonical parameters, like maximum specific growth rate (μmax), can fail to provide reliable information regarding virulence. Although area-under-the-curve (AUC) calculations are more robust, they are sensitive to limit selection. Using empirical data from Sulfolobus Spindle-shaped Virus (SSV) infections, we introduce a novel, simple metric that has proven to be more robust than existing methods for assessing VR. This metric (ISC) accurately aligns biological phenomena with quantified metrics to determine VR. It also addresses a gap in virology by permitting comparisons between different non-lytic virus infections or non-lytic versus lytic virus infections on a given host in single-virus/single-host infections.

Systematic Investigation of Resistance Evolution to Common Antibiotics Reveals Conserved Collateral Responses across Common Human Pathogens

As drug resistance continues to grow, treatment strategies that turn resistance into a disadvantage for the organism will be increasingly relied upon to treat infections and to lower the rate of multidrug resistance. The majority of work in this area has investigated how resistance evolution toward a single antibiotic effects a specific organism’s collateral response to a wide variety of antibiotics. The results of these studies have been used to identify networks of drugs which can be used to drive resistance in a particular direction.

As drug resistance continues to grow, treatment strategies that turn resistance into a disadvantage for the organism will be increasingly relied upon to treat infections and to lower the rate of multidrug resistance. The majority of work in this area has investigated how resistance evolution toward a single antibiotic effects a specific organism’s collateral response to a wide variety of antibiotics. The results of these studies have been used to identify networks of drugs which can be used to drive resistance in a particular direction. However, little is known about the extent of evolutionary conservation of these responses across species. We sought to address this knowledge gap by performing a systematic resistance evolution study of the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter cloacae) under uniform growth conditions using five clinically relevant antibiotics with diverse modes of action. Evolved lineages were analyzed for collateral effects and the molecular mechanisms behind the observed phenotypes. Fourteen universal cross-resistance and two global collateral sensitivity relationships were found among the lineages. Genomic analyses revealed drug-dependent divergent and conserved evolutionary trajectories among the pathogens. Our findings suggest that collateral responses may be preserved across species. These findings may help extend the contribution of previous collateral network studies in the development of treatment strategies to address the problem of antibiotic resistance.

Extreme Antagonism Arising from Gene-Environment Interactions

Antagonistic interactions in biological systems, which occur when one perturbation blunts the effect of another, are typically interpreted as evidence that the two perturbations impact the same cellular pathway or function. Yet, this interpretation ignores extreme antagonistic interactions wherein an otherwise deleterious perturbation compensates for the function lost because of a prior perturbation. Here, we report on gene-environment interactions involving genetic mutations that are deleterious in a permissive environment but beneficial in a specific environment that restricts growth. These extreme antagonistic interactions constitute gene-environment analogs of synthetic rescues previously observed for gene-gene interactions. Our approach uses two independent adaptive evolution steps to address the lack of experimental methods to systematically identify such extreme interactions. We apply the approach to Escherichia coli by successively adapting it to defined glucose media without and with the antibiotic rifampicin. The approach identified multiple mutations that are beneficial in the presence of rifampicin and deleterious in its absence. The analysis of transcription shows that the antagonistic adaptive mutations repress a stringent response-like transcriptional program, whereas nonantagonistic mutations have an opposite transcriptional profile. Our approach represents a step toward the systematic characterization of extreme antagonistic gene-drug interactions, which can be used to identify targets to select against antibiotic resistance.

Competition delays multi-drug resistance evolution during combination therapy

Combination therapies have shown remarkable success in preventing the evolution of resistance to multiple drugs, including HIV, tuberculosis, and cancer. Nevertheless, the rise in drug resistance still remains an important challenge. The capability to accurately predict the emergence of resistance, either to one or multiple drugs, may help to improve treatment options. Existing theoretical approaches often focus on exponential growth laws, which may not be realistic when scarce resources and competition limit growth. In this work, we study the emergence of single and double drug resistance in a model of combination therapy of two drugs. The model describes a sensitive strain, two types of single-resistant strains, and a double-resistant strain. We compare the probability that resistance emerges for three growth laws: exponential growth, logistic growth without competition between strains, and logistic growth with competition between strains. Using mathematical estimates and numerical simulations, we show that between-strain competition only affects the emergence of single resistance when resources are scarce. In contrast, the probability of double resistance is affected by between-strain competition over a wider space of resource availability. This indicates that competition between different resistant strains may be pertinent to identifying strategies for suppressing drug resistance, and that exponential models may overestimate the emergence of resistance to multiple drugs. A by-product of our work is an efficient strategy to evaluate probabilities of single and double resistance in models with multiple sequential mutations. This may be useful for a range of other problems in which the probability of resistance is of interest.

CombiANT: Antibiotic interaction testing made easy

Antibiotic combination therapies are important for the efficient treatment of many types of infections, including those caused by antibiotic-resistant pathogens. Combination treatment strategies are typically used under the assumption that synergies are conserved across species and strains, even though recent results show that the combined treatment effect is determined by specific drug–strain interactions that can vary extensively and unpredictably, both between and within bacterial species. To address this problem, we present a new method in which antibiotic synergy is rapidly quantified on a case-by-case basis, allowing for improved combination therapy. The novel CombiANT methodology consists of a 3D-printed agar plate insert that produces defined diffusion landscapes of 3 antibiotics, permitting synergy quantification between all 3 antibiotic pairs with a single test. Automated image analysis yields fractional inhibitory concentration indices (FICis) with high accuracy and precision. A technical validation with 3 major pathogens, Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, showed equivalent performance to checkerboard methodology, with the advantage of strongly reduced assay complexity and costs for CombiANT. A synergy screening of 10 antibiotic combinations for 12 E. coli urinary tract infection (UTI) clinical isolates illustrates the need for refined combination treatment strategies. For example, combinations of trimethoprim (TMP) + nitrofurantoin (NIT) and TMP + mecillinam (MEC) showed synergy, but only for certain individual isolates, whereas MEC + NIT combinations showed antagonistic interactions across all tested strains. These data suggest that the CombiANT methodology could allow personalized clinical synergy testing and large-scale screening. We anticipate that CombiANT will greatly facilitate clinical and basic research of antibiotic synergy.

Existing methods for identifying efficient combinations of antibiotics are time-consuming and costly, restricting their use in clinics and research. This study describes the novel CombiANT methodology, which uses defined diffusion landscapes of three antibiotics to permit rapid and low-cost synergy quantification between all antibiotic pairs.

Evolutionary Approaches to Combat Antibiotic Resistance: Opportunities and Challenges for Precision Medicine

The rise of antimicrobial resistance (AMR) in bacterial pathogens is acknowledged by the WHO as a major global health crisis. It is estimated that in 2050 annually up to 10 million people will die from infections with drug resistant pathogens if no efficient countermeasures are implemented. Evolution of pathogens lies at the core of this crisis, which enables rapid adaptation to the selective pressures imposed by antimicrobial usage in both medical treatment and agriculture, consequently promoting the spread of resistance genes or alleles in bacterial populations. Approaches developed in the field of Evolutionary Medicine attempt to exploit evolutionary insight into these adaptive processes, with the aim to improve diagnostics and the sustainability of antimicrobial therapy. Here, we review the concept of evolutionary trade-offs in the development of AMR as well as new therapeutic approaches and their impact on host-microbiome-pathogen interactions. We further discuss the possible translation of evolution-informed treatments into clinical practice, considering both the rapid cure of the individual patients and the prevention of AMR.

How antibiotics work together: molecular mechanisms behind combination therapy

Antibiotics used in combination are an effective strategy for combatting numerous infectious diseases in clinical and veterinary settings, particularly as a last-line therapy for difficult-to-treat cases. Combination therapy can either increase or slow the rate of killing, broaden the antibiotic spectrum, reduce dosage and unwanted side-effects, and even control the emergence of resistance. The administration of antibiotics in combination has been used effectively against bacterial infections for >70 years, first used to treat tuberculosis. However, effective antibiotic combinations and their dosage regimes have been largely determined empirically in the clinic, and the molecular mechanisms underpinning how these combinations work remains surprisingly elusive. This review focuses on studies that have outlined the genetics and molecular mechanisms of action underlying antibiotic combinations, as well as those that examine how resistance develops. We highlight the need for further experimentation and genetic validation to fully realise the potential of combination therapy.

Multitarget Approaches against Multiresistant Superbugs

Despite efforts to develop new antibiotics, antibacterial resistance still develops too fast for drug discovery to keep pace. Often, resistance against a new drug develops even before it reaches the market. This continued resistance crisis has demonstrated that resistance to antibiotics with single protein targets develops too rapidly to be sustainable. Most successful long-established antibiotics target more than one molecule or possess targets, which are encoded by multiple genes. This realization has motivated a change in antibiotic development toward drug candidates with multiple targets. Some mechanisms of action presuppose multiple targets or at least multiple effects, such as targeting the cytoplasmic membrane or the carrier molecule bactoprenol phosphate and are therefore particularly promising. Moreover, combination therapy approaches are being developed to break antibiotic resistance or to sensitize bacteria to antibiotic action. In this Review, we provide an overview of antibacterial multitarget approaches and the mechanisms behind them.

Effect of drug dose and timing of treatment on the emergence of drug resistance in vivo in a malaria model

BACKGROUND AND OBJECTIVES

There is a significant interest in identifying clinically effective drug treatment regimens that minimize the de novo evolution of antimicrobial resistance in pathogen populations. However, in vivo studies that vary treatment regimens and directly measure drug resistance evolution are rare. Here, we experimentally investigate the role of drug dose and treatment timing on resistance evolution in an animal model.

METHODOLOGY

In a series of experiments, we measured the emergence of atovaquone-resistant mutants of Plasmodium chabaudi in laboratory mice, as a function of dose or timing of treatment (day post-infection) with the antimalarial drug atovaquone.

RESULTS

The likelihood of high-level resistance emergence increased with atovaquone dose. When varying the timing of treatment, treating either very early or late in infection reduced the risk of resistance. When we varied starting inoculum, resistance was more likely at intermediate inoculum sizes, which correlated with the largest population sizes at time of treatment.

CONCLUSIONS AND IMPLICATIONS

(i) Higher doses do not always minimize resistance emergence and can promote the emergence of high-level resistance. (ii) Altering treatment timing affects the risk of resistance emergence, likely due to the size of the population at the time of treatment, although we did not test the effect of immunity whose influence may have been important in the case of late treatment. (iii) Finding the 'right' dose and 'right' time to maximize clinical gains and limit resistance emergence can vary depending on biological context and was non-trivial even in our simplified experiments.

LAY SUMMARY

In a mouse model of malaria, higher drug doses led to increases in drug resistance. The timing of drug treatment also impacted resistance emergence, likely due to the size of the population at the time of treatment.

Phages for Biofilm Removal

Biofilms are clusters of bacteria that live in association with surfaces. Their main characteristic is that the bacteria inside the biofilms are attached to other bacterial cells and to the surface by an extracellular polymeric matrix. Biofilms are capable of adhering to a wide variety of surfaces, both biotic and abiotic, including human tissues, medical devices, and other materials. On these surfaces, biofilms represent a major threat causing infectious diseases and economic losses. In addition, current antibiotics and common disinfectants have shown limited ability to remove biofilms adequately, and phage-based treatments are proposed as promising alternatives for biofilm eradication. This review analyzes the main advantages and challenges that phages can offer for the elimination of biofilms, as well as the most important factors to be taken into account in order to design effective phage-based treatments.