Nonoptimal microbial response to antibiotics underlies suppressive drug interactions

Structural basis of Cfr-mediated antimicrobial resistance and mechanisms to evade it

The bacterial ribosome is an essential drug target as many clinically important antibiotics bind and inhibit its functional centers. The catalytic peptidyl transferase center (PTC) is targeted by the broadest array of inhibitors belonging to several chemical classes. One of the most abundant and clinically prevalent resistance mechanisms to PTC-acting drugs in Gram-positive bacteria is C8-methylation of the universally conserved A2503 nucleobase by Cfr methylase in 23S ribosomal RNA. Despite its clinical importance, a sufficient understanding of the molecular mechanisms underlying Cfr-mediated resistance is currently lacking. Here, we report a set of high-resolution structures of the Cfr-modified 70S ribosome containing aminoacyl- and peptidyl-transfer RNAs. These structures reveal an allosteric rearrangement of nucleotide A2062 upon Cfr-mediated methylation of A2503 that likely contributes to the reduced potency of some PTC inhibitors. Additionally, we provide the structural bases behind two distinct mechanisms of engaging the Cfr-methylated ribosome by the antibiotics iboxamycin and tylosin.

Cultivating efficiency: high-throughput growth analysis of anaerobic bacteria in compact microplate readers

Anaerobic microbes play crucial roles in environmental processes, industry, and human health. Traditional methods for monitoring the growth of anaerobes, including plate counts or subsampling broth cultures for optical density measurements, are time and resource-intensive. The advent of microplate readers revolutionized bacterial growth studies by enabling high-throughput and real-time monitoring of microbial growth kinetics. Yet, their use in anaerobic microbiology has remained limited. Here, we present a workflow for using small-footprint microplate readers and the Growthcurver R package to analyze the kinetic growth metrics of anaerobic bacteria. We benchmarked the small-footprint Cerillo Stratus microplate reader against a BioTek Synergy HTX microplate reader in aerobic conditions using Escherichia coli DSM 28618 cultures. The growth rates and carrying capacities obtained from the two readers were statistically indistinguishable. However, the area under the logistic curve was significantly higher in cultures monitored by the Stratus reader. We used the Stratus to quantify the growth responses of anaerobically grown E. coli and Clostridium bolteae DSM 29485 to different doses of the toxin sodium arsenite. The growth of E. coli and C. bolteae was sensitive to arsenite doses of 1.3 µM and 0.4 µM, respectively. Complete inhibition of growth was achieved at 38 µM arsenite for C. bolteae and 338 µM in E. coli. These results show that the Stratus performs similarly to a leading brand of microplate reader and can be reliably used in anaerobic conditions. We discuss the advantages of the small format microplate readers and our experiences with the Stratus.

IMPORTANCE

We present a workflow that facilitates the production and analysis of growth curves for anaerobic microbes using small-footprint microplate readers and an R script. This workflow is a cost and space-effective solution to most high-throughput solutions for collecting growth data from anaerobic microbes. This technology can be used for applications where high throughput would advance discovery, including microbial isolation, bioprospecting, co-culturing, host-microbe interactions, and drug/toxin-microbial interactions.

A low-footprint, fluorescence-based bacterial time-kill assay for estimating dose-dependent cell death dynamics

Dose-response curves that describe the relationship between antibiotic dose and growth rate in bacteria are commonly measured with optical density (OD) based assays. While being simple and high-throughput, any dose-dependent cell death dynamics are obscured, as OD assays in batch culture can only quantify a positive net change in cells. Time-kill experiments can be used to quantify cell death rates, but current techniques are extremely resource-intensive and may be biased by residual drug carried over into the quantification assay. Here, we report a novel, fluorescence-based time-kill assay leveraging resazurin as a viable cell count indicator. Our method improves upon previous techniques by greatly reducing the material cost and being robust to residual drug carry-over. We demonstrate our technique by quantifying a dose-response curve in Escherichia coli subject to cefotaxime, revealing dose-dependent death rates. We also show that our method is robust to extracellular debris and cell aggregation. Dose-response curves quantified with our method may provide a more accurate description of pathogen response to therapy, paving the way for more accurate integrated pharmacodynamic-pharmacokinetic studies.

Within-species variability of antibiotic interactions in Gram-negative bacteria

Treatments with antibiotic combinations are becoming increasingly important even though the supposed clinical benefits of combinations are, in many cases, unclear. Here, we systematically examined how several clinically used antibiotics interact and affect the antimicrobial efficacy against five especially problematic Gram-negative pathogens. A total of 232 bacterial isolates were tested against different pairwise antibiotic combinations spanning five classes, and the ability of all combinations in inhibiting growth was quantified. Descriptive statistics, principal component analysis (PCA), and Spearman's rank correlation matrix were used to determine the correlations between the different combinations on interaction outcome. Several important conclusions can be drawn from the 696 examined interactions. Firstly, within a species, the interactions are in general conserved but can be isolate-specific for a given antibiotic combination and can range from antagonistic to synergistic. Secondly, additive and antagonistic interactions are the most common observed across species and antibiotics, with 87.1% of isolate-antibiotic combinations being additive, 11.6% antagonistic, and only 0.3% showing synergy. These findings suggest that to achieve the highest precision and efficacy of combination therapy, not only isolate-specific interaction profiling ought to be routinely performed, in particular to avoid using drug combinations that show antagonistic interaction and an expected associated reduction in efficacy, but also discovering rare and potentially valuable synergistic interactions.IMPORTANCEAntibiotic combinations are often used to treat bacterial infections, which aim to increase treatment efficacy and reduce resistance evolution. Typically, it is assumed that one specific antibiotic combination has the same effect on different isolates of the same species, i.e., the interaction is conserved. Here, we tested this idea by examining how several clinically used antibiotics interact and affect the antimicrobial efficacy against several bacterial pathogens. Our results show that, even though within a species the interactions are often conserved, there are also isolate-specific differences for a given antibiotic combination that can range from antagonistic to synergistic. These findings suggest that isolate-specific interaction profiling ought to be performed in clinical microbiology routine to avoid using antagonistic drug combinations that might reduce treatment efficacy.

Gene expression tradeoffs determine bacterial survival and adaptation to antibiotic stress

To optimize their fitness, cells face the crucial task of efficiently responding to various stresses. This necessitates striking a balance between conserving resources for survival and allocating resources for growth and division. The fundamental principles governing these tradeoffs is an outstanding challenge in the physics of living systems. In this study, we introduce a coarse-grained theoretical framework for bacterial physiology that establishes a connection between the physiological state of cells and their survival outcomes in dynamic environments, particularly in the context of antibiotic exposure. Predicting bacterial survival responses to varying antibiotic doses proves challenging due to the profound influence of the physiological state on critical parameters, such as the Minimum Inhibitory Concentration (MIC) and killing rates, even within an isogenic cell population. Our proposed theoretical model bridges the gap by linking extracellular antibiotic concentration and nutrient quality to intracellular damage accumulation and gene expression. This framework allows us to predict and explain the control of cellular growth rate, death rate, MIC and survival fraction in a wide range of time-varying environments. Surprisingly, our model reveals that cell death is rarely due to antibiotic levels being above the maximum physiological limit, but instead survival is limited by the inability to alter gene expression sufficiently quickly to transition to a less susceptible physiological state. Moreover, bacteria tend to overexpress stress response genes at the expense of reduced growth, conferring greater protection against further antibiotic exposure. This strategy is in contrast to those employed in different nutrient environments, in which bacteria allocate resources to maximize growth rate. This highlights an important tradeoff between the cellular capacity for growth and the ability to survive antibiotic exposure.

Tradeoffs in bacterial physiology determine the efficiency of antibiotic killing

Antibiotic effectiveness depends on a variety of factors. While many mechanistic details of antibiotic action are known, the connection between death rate and bacterial physiology is poorly understood. A common observation is that death rate in antibiotics rises linearly with growth rate; however, it remains unclear how other factors, such as environmental conditions and whole-cell physiological properties, affect bactericidal activity. To address this, we developed a high-throughput assay to precisely measure antibiotic-mediated death. We found that death rate is linear in growth rate, but the slope depends on environmental conditions. Growth under stress lowers death rate compared to nonstressed environments with similar growth rate. To understand stress's role, we developed a mathematical model of bacterial death based on resource allocation that includes a stress-response sector; we identify this sector using RNA-seq. Our model accurately predicts the minimal inhibitory concentration (MIC) with zero free parameters across a wide range of growth conditions. The model also quantitatively predicts death and MIC when sectors are experimentally modulated using cyclic adenosine monophosphate (cAMP), including protection from death at very low cAMP levels. The present study shows that different conditions with equal growth rate can have different death rates and establishes a quantitative relation between growth, death, and MIC that suggests approaches to improve antibiotic efficacy.

The evolution of resistance to synergistic multi-drug combinations is more complex than evolving resistance to each individual drug component

Multidrug antibiotic resistance is an urgent public health concern. Multiple strategies have been suggested to alleviate this problem, including the use of antibiotic combinations and cyclic therapies. We examine how adaptation to (1) combinations of drugs affects resistance to individual drugs, and to (2) individual drugs alters responses to drug combinations. To evaluate this, we evolved multiple strains of drug resistant Staphylococcus epidermidis in the lab. We show that evolving resistance to four highly synergistic combinations does not result in cross-resistance to all of their components. Likewise, prior resistance to one antibiotic in a combination does not guarantee survival when exposed to the combination. We also identify four 3-step and four 2-step treatments that inhibit bacterial growth and confer collateral sensitivity with each step, impeding the development of multidrug resistance. This study highlights the importance of considering higher-order drug combinations in sequential therapies and how antibiotic interactions can influence the evolutionary trajectory of bacterial populations.

Systematic analysis of drug combinations against Gram-positive bacteria

Drug combinations can expand options for antibacterial therapies but have not been systematically tested in Gram-positive species. We profiled ~8,000 combinations of 65 antibacterial drugs against the model species Bacillus subtilis and two prominent pathogens, Staphylococcus aureus and Streptococcus pneumoniae. Thereby, we recapitulated previously known drug interactions, but also identified ten times more novel interactions in the pathogen S. aureus, including 150 synergies. We showed that two synergies were equally effective against multidrug-resistant S. aureus clinical isolates in vitro and in vivo. Interactions were largely species-specific and synergies were distinct from those of Gram-negative species, owing to cell surface and drug uptake differences. We also tested 2,728 combinations of 44 commonly prescribed non-antibiotic drugs with 62 drugs with antibacterial activity against S. aureus and identified numerous antagonisms that might compromise the efficacy of antimicrobial therapies. We identified even more synergies and showed that the anti-aggregant ticagrelor synergized with cationic antibiotics by modifying the surface charge of S. aureus. All data can be browsed in an interactive interface ( https://apps.embl.de/combact/ ).

Cultivating efficiency: High-throughput growth analysis of anaerobic bacteria in compact microplate readers

Anaerobic microbes play crucial roles in environmental processes, industry, and human health. Traditional methods for monitoring the growth of anaerobes, including plate counts or subsampling broth cultures for optical density measurements, are time and resource intensive. The advent of microplate readers revolutionized bacterial growth studies by enabling high-throughput and real-time monitoring of microbial growth kinetics but their use in anaerobic microbiology has remained limited. Here, we present a workflow for using small-footprint microplate readers and the Growthcurver R package to analyze the kinetic growth metrics of anaerobic bacteria. We benchmarked the small-footprint Cerillo Stratus microplate reader against a BioTek Synergy HTX microplate reader in aerobic conditions using Escherichia coli DSM 28618 cultures. The growth rates and carrying capacities obtained from the two readers were statistically indistinguishable. However, the area under the logistic curve was significantly higher in cultures monitored by the Stratus reader. We used the Stratus to quantify the growth responses of anaerobically grown E. coli and Clostridium bolteae DSM 29485 to different doses of the toxin sodium arsenite. The growth of E. coli and C. bolteae was sensitive to arsenite doses of 1.3 μM and 0.4 μM, respectively. Complete inhibition of growth was achieved at 38 μM arsenite for C. bolteae, and 338 μM in E. coli. These results show that the Stratus performs similarly to a leading brand of microplate reader and can be reliably used in anaerobic conditions. We discuss the advantages of the small format microplate readers and our experiences with the Stratus.

Ribosome-binding antibiotics increase bacterial longevity and growth efficiency

Antibiotics, by definition, reduce bacterial growth rates in optimal culture conditions; however, the real-world environments bacteria inhabit see rapid growth punctuated by periods of low nutrient availability. How antibiotics mediate population decline during these periods is poorly understood. Bacteria cannot optimize for all environmental conditions because a growth-longevity tradeoff predicts faster growth results in faster population decline, and since bacteriostatic antibiotics slow growth, they should also mediate longevity. We quantify how antibiotics, their targets, and resistance mechanisms influence longevity using populations of Escherichia coli and, as the tradeoff predicts, populations are maintained for longer if they encounter ribosome-binding antibiotics doxycycline and erythromycin, a finding that is not observed using antibiotics with alternative cellular targets. This tradeoff also predicts resistance mechanisms that increase growth rates during antibiotic treatment could be detrimental during nutrient stresses, and indeed, we find resistance by ribosomal protection removes benefits to longevity provided by doxycycline. We therefore liken ribosomal protection to a "Trojan horse" because it provides protection from an antibiotic but, during nutrient stresses, it promotes the demise of the bacteria. Seeking mechanisms to support these observations, we show doxycycline promotes efficient metabolism and reduces the concentration of reactive oxygen species. Seeking generality, we sought another mechanism that affects longevity and we found the number of doxycycline targets, namely, the ribosomal RNA operons, mediates growth and longevity even without antibiotics. We conclude that slow growth, as observed during antibiotic treatment, can help bacteria overcome later periods of nutrient stress.

Trade-offs between the instantaneous growth rate and long-term fitness: Consequences for microbial physiology and predictive computational models

Microbial systems biology has made enormous advances in relating microbial physiology to the underlying biochemistry and molecular biology. By meticulously studying model microorganisms, in particular Escherichia coli and Saccharomyces cerevisiae, increasingly comprehensive computational models predict metabolic fluxes, protein expression, and growth. The modeling rationale is that cells are constrained by a limited pool of resources that they allocate optimally to maximize fitness. As a consequence, the expression of particular proteins is at the expense of others, causing trade-offs between cellular objectives such as instantaneous growth, stress tolerance, and capacity to adapt to new environments. While current computational models are remarkably predictive for E. coli and S. cerevisiae when grown in laboratory environments, this may not hold for other growth conditions and other microorganisms. In this contribution, we therefore discuss the relationship between the instantaneous growth rate, limited resources, and long-term fitness. We discuss uses and limitations of current computational models, in particular for rapidly changing and adverse environments, and propose to classify microbial growth strategies based on Grimes's CSR framework.

Coping with the ESKAPE pathogens: Evolving strategies, challenges and future prospects

Globally, the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) are the major cause of nosocomial infections. These pathogens are multidrug resistant, and their negative impacts have brought serious health challenges and economic burden on many countries worldwide. Thus, this narrative review exploits different emerging alternative therapeutic strategies including combination antibiotics, antimicrobial peptides ((AMPs), bacteriophage and photodynamic therapies used in the treatment of the ESKAPE pathogens, their merits, limitations, and future prospects. Our findings indicate that ESKAPE pathogens exhibit resistance to drug using different mechanisms including drug inactivation by irreversible enzyme cleavage, drug-binding site alteration, diminution in permeability of drug or drug efflux increment to reduce accumulation of drug as well as biofilms production. However, the scientific community has shown significant interest in using these novel strategies with numerous benefits although they have some limitations including but not limited to instability and toxicity of the therapeutic agents, or the host developing immune response against the therapeutic agents. Thus, comprehension of resistance mechanisms of these pathogens is necessary to further develop or modify these approaches in order to overcome these health challenges including the barriers of bacterial resistance.

Community interactions drive the evolution of antibiotic tolerance in bacteria

The emergence of antibiotic tolerance (prolonged survival against exposure) in natural bacterial populations is a major concern. Since it has been studied primarily in isogenic populations, we do not yet understand how ecological interactions in a diverse community impact the evolution of tolerance. To address this, we studied the evolutionary dynamics of a synthetic bacterial community composed of two interacting strains. In this community, an antibiotic-resistant strain protected the other, susceptible strain by degrading the antibiotic ampicillin in the medium. Surprisingly, we found that in the presence of antibiotics, the susceptible strain evolved tolerance. Tolerance was typified by an increase in survival as well as an accompanying decrease in the growth rate, highlighting a trade-off between the two. A simple mathematical model explained that the observed decrease in the death rate, even when coupled with a decreased growth rate, is beneficial in a community with weak protective interactions. In the presence of strong interactions, the model predicted that the trade-off would instead be detrimental, and tolerance would not emerge, which we experimentally verified. By whole genome sequencing the evolved tolerant isolates, we identified two genetic hot spots which accumulated mutations in parallel lines, suggesting their association with tolerance. Our work highlights that ecological interactions can promote antibiotic tolerance in bacterial communities, which has remained understudied.

Estimating microbial population data from optical density

The spectrophotometer has been used for decades to measure the density of bacterial populations as the turbidity expressed as optical density-OD. However, the OD alone is an unreliable metric and is only proportionately accurate to cell titers to about an OD of 0.1. The relationship between OD and cell titer depends on the configuration of the spectrophotometer, the length of the light path through the culture, the size of the bacterial cells, and the cell culture density. We demonstrate the importance of plate reader calibration to identify the exact relationship between OD and cells/mL. We use four bacterial genera and two sizes of micro-titer plates (96-well and 384-well) to show that the cell/ml per unit OD depends heavily on the bacterial cell size and plate size. We applied our calibration curve to real growth curve data and conclude the cells/mL-rather than OD-is a metric that can be used to directly compare results across experiments, labs, instruments, and species.

Antibiotic combinations reduce Staphylococcus aureus clearance

The spread of antibiotic resistance is attracting increased attention to combination-based treatments. Although drug combinations have been studied extensively for their effects on bacterial growth^1–11, much less is known about their effects on bacterial long-term clearance, especially at cidal, clinically relevant concentrations^12–14. Here, using en masse microplating and automated image analysis, we systematically quantify Staphylococcus aureus survival during prolonged exposure to pairwise and higher-order cidal drug combinations. By quantifying growth inhibition, early killing and longer-term population clearance by all pairs of 14 antibiotics, we find that clearance interactions are qualitatively different, often showing reciprocal suppression whereby the efficacy of the drug mixture is weaker than any of the individual drugs alone. Furthermore, in contrast to growth inhibition^6–10 and early killing, clearance efficacy decreases rather than increases as more drugs are added. However, specific drugs targeting non-growing persisters^15–17 circumvent these suppressive effects. Competition experiments show that reciprocal suppressive drug combinations select against resistance to any of the individual drugs, even counteracting methicillin-resistant Staphylococcus aureus both in vitro and in a Galleria mellonella larva model. As a consequence, adding a β-lactamase inhibitor that is commonly used to potentiate treatment against β-lactam-resistant strains can reduce rather than increase treatment efficacy. Together, these results underscore the importance of systematic mapping the long-term clearance efficacy of drug combinations for designing more-effective, resistance-proof multidrug regimes.

Different pairs of antibiotics show qualitatively different bacterial clearance interactions—some pairs show reciprocal suppression whereby the drug mixture efficacy is weaker than the individual drugs alone, and the clearance efficacy decreases as more drugs are added.

Growth-mediated negative feedback shapes quantitative antibiotic response

Dose–response relationships are a general concept for quantitatively describing biological systems across multiple scales, from the molecular to the whole‐cell level. A clinically relevant example is the bacterial growth response to antibiotics, which is routinely characterized by dose–response curves. The shape of the dose–response curve varies drastically between antibiotics and plays a key role in treatment, drug interactions, and resistance evolution. However, the mechanisms shaping the dose–response curve remain largely unclear. Here, we show in Escherichia coli that the distinctively shallow dose–response curve of the antibiotic trimethoprim is caused by a negative growth‐mediated feedback loop: Trimethoprim slows growth, which in turn weakens the effect of this antibiotic. At the molecular level, this feedback is caused by the upregulation of the drug target dihydrofolate reductase (FolA/DHFR). We show that this upregulation is not a specific response to trimethoprim but follows a universal trend line that depends primarily on the growth rate, irrespective of its cause. Rewiring the feedback loop alters the dose–response curve in a predictable manner, which we corroborate using a mathematical model of cellular resource allocation and growth. Our results indicate that growth‐mediated feedback loops may shape drug responses more generally and could be exploited to design evolutionary traps that enable selection against drug resistance.

Bacterial Growth Curve Measurements with a Multimode Microplate Reader

Bacterial studies based on growth curves are common in microbiology and related fields. Compared to the standard photometer and cuvette based protocols, bacterial growth curve measurements with microplate readers provide better temporal resolution, higher efficiency, and are less laborious, while analysis and interpretation of the microplate-based measurements are less straightforward. Recently, we developed a new analysis method for evaluating bacterial growth with microplate readers based on time derivatives. Here, we describe a detailed protocol for this development and provide the homemade program for the new analysis method.

Mathematical models to study the biology of pathogens and the infectious diseases they cause

Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.

The context-dependent, combinatorial logic of BMP signaling

Cell-cell communication systems typically comprise families of ligand and receptor variants that function together in combinations. Pathway activation depends on the complex way in which ligands are presented extracellularly and receptors are expressed by the signal-receiving cell. To understand the combinatorial logic of such a system, we systematically measured pairwise bone morphogenetic protein (BMP) ligand interactions in cells with varying receptor expression. Ligands could be classified into equivalence groups based on their profile of positive and negative synergies with other ligands. These groups varied with receptor expression, explaining how ligands can functionally replace each other in one context but not another. Context-dependent combinatorial interactions could be explained by a biochemical model based on the competitive formation of alternative signaling complexes with distinct activities. Together, these results provide insights into the roles of BMP combinations in developmental and therapeutic contexts and establish a framework for analyzing other combinatorial, context-dependent signaling systems.

Applications of Coarse-Grained Models in Metabolic Engineering

Mathematical modeling is a promising tool for better understanding of cellular processes. In recent years, the development of coarse-grained models has gained attraction since these simple models are able to capture and describe a broad range of growth conditions. Coarse-grained models often comprise only two cellular components, a low molecular component as representative for central metabolism and energy generation and a macromolecular component, representing the entire proteome. A framework is presented that presents a strict mass conservative model for bacterial growth during a biotechnological production process. After providing interesting properties for the steady-state solution, applications are presented 1) for a production process of an amino acid and 2) production of a metabolite from central metabolism.

The physiology and genetics of bacterial responses to antibiotic combinations

Several promising strategies based on combining or cycling different antibiotics have been proposed to increase efficacy and counteract resistance evolution, but we still lack a deep understanding of the physiological responses and genetic mechanisms that underlie antibiotic interactions and the clinical applicability of these strategies. In antibiotic-exposed bacteria, the combined effects of physiological stress responses and emerging resistance mutations (occurring at different time scales) generate complex and often unpredictable dynamics. In this Review, we present our current understanding of bacterial cell physiology and genetics of responses to antibiotics. We emphasize recently discovered mechanisms of synergistic and antagonistic drug interactions, hysteresis in temporal interactions between antibiotics that arise from microbial physiology and interactions between antibiotics and resistance mutations that can cause collateral sensitivity or cross-resistance. We discuss possible connections between the different phenomena and indicate relevant research directions. A better and more unified understanding of drug and genetic interactions is likely to advance antibiotic therapy.

Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli

Understanding interactions between antibiotics used in combination is an important theme in microbiology. Using the interactions between the antifolate drug trimethoprim and the ribosome-targeting antibiotic erythromycin in Escherichia coli as a model, we applied a transcriptomic approach for dissecting interactions between two antibiotics with different modes of action. When trimethoprim and erythromycin were combined, the transcriptional response of genes from the sulfate reduction pathway deviated from the dominant effect of trimethoprim on the transcriptome. We successfully altered the drug interaction from additivity to suppression by increasing the sulfate level in the growth environment and identified sulfate reduction as an important metabolic determinant that shapes the interaction between the two drugs. Our work highlights the potential of using prioritization of gene expression patterns as a tool for identifying key metabolic determinants that shape drug-drug interactions. We further demonstrated that the sigma factor-binding protein gene crl shapes the interactions between the two antibiotics, which provides a rare example of how naturally occurring variations between strains of the same bacterial species can sometimes generate very different drug interactions.

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.

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.

Technologies for High-Throughput Identification of Antibiotic Mechanism of Action

There are two main strategies for antibiotic discovery: target-based and phenotypic screening. The latter has been much more successful in delivering first-in-class antibiotics, despite the major bottleneck of delayed Mechanism-of-Action (MOA) identification. Although finding new antimicrobial compounds is a very challenging task, identifying their MOA has proven equally challenging. MOA identification is important because it is a great facilitator of lead optimization and improves the chances of commercialization. Moreover, the ability to rapidly detect MOA could enable a shift from an activity-based discovery paradigm towards a mechanism-based approach. This would allow to probe the grey chemical matter, an underexplored source of structural novelty. In this study we review techniques with throughput suitable to screen large libraries and sufficient sensitivity to distinguish MOA. In particular, the techniques used in chemical genetics (e.g., based on overexpression and knockout/knockdown collections), promoter-reporter libraries, transcriptomics (e.g., using microarrays and RNA sequencing), proteomics (e.g., either gel-based or gel-free techniques), metabolomics (e.g., resourcing to nuclear magnetic resonance or mass spectrometry techniques), bacterial cytological profiling, and vibrational spectroscopy (e.g., Fourier-transform infrared or Raman scattering spectroscopy) were discussed. Ultimately, new and reinvigorated phenotypic assays bring renewed hope in the discovery of a new generation of antibiotics.

Mechanistic insights into synergy between nalidixic acid and tetracycline against clinical isolates of Acinetobacter baumannii and Escherichia coli

The increasing prevalence of antimicrobial resistance has become a global health problem. Acinetobacter baumannii is an important nosocomial pathogen due to its capacity to persist in the hospital environment. It has a high mortality rate and few treatment options. Antibiotic combinations can help to fight multi-drug resistant (MDR) bacterial infections, but they are rarely used in the clinics and mostly unexplored. The interaction between bacteriostatic and bactericidal antibiotics are mostly reported as antagonism based on the results obtained in the susceptible model laboratory strain Escherichia coli. However, in the present study, we report a synergistic interaction between nalidixic acid and tetracycline against clinical multi-drug resistant A. baumannii and E. coli. Here we provide mechanistic insight into this dichotomy. The synergistic combination was studied by checkerboard assay and time-kill curve analysis. We also elucidate the mechanism behind this synergy using several techniques such as fluorescence spectroscopy, flow cytometry, fluorescence microscopy, morphometric analysis, and real-time polymerase chain reaction. Nalidixic acid and tetracycline combination displayed synergy against most of the MDR clinical isolates of A. baumannii and E. coli but not against susceptible isolates. Finally, we demonstrate that this combination is also effective in vivo in an A. baumannii/Caenorhabditis elegans infection model (p < 0.001).

Hidden suppressive interactions are common in higher-order drug combinations

The rapid increase of multi-drug resistant bacteria has led to a greater emphasis on multi-drug combination treatments. However, some combinations can be suppressive—that is, bacteria grow faster in some drug combinations than when treated with a single drug. Typically, when studying interactions, the overall effect of the combination is only compared with the single-drug effects. However, doing so could miss “hidden” cases of suppression, which occur when the highest order is suppressive compared with a lower-order combination but not to a single drug. We examined an extensive dataset of 5-drug combinations and all lower-order—single, 2-, 3-, and 4-drug—combinations. We found that a majority of all combinations—54%—contain hidden suppression. Examining hidden interactions is critical to understanding the architecture of higher-order interactions and can substantially affect our understanding and predictions of the evolution of antibiotic resistance under multi-drug treatments.

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Most instances of suppressive interactions are missed by standard methods

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A majority (54%) of all antibiotic combinations tested contain hidden suppression

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Identifying hidden suppression can affect what combinations should be used

Searching for principles of microbial physiology

Why do evolutionarily distinct microorganisms display similar physiological behaviours? Why are transitions from high-ATP yield to low(er)-ATP yield metabolisms so widespread across species? Why is fast growth generally accompanied with low stress tolerance? Do these regularities occur because most microbial species are subject to the same selective pressures and physicochemical constraints? If so, a broadly-applicable theory might be developed that predicts common microbiological behaviours. Microbial systems biologists have been working out the contours of this theory for the last two decades, guided by experimental data. At its foundations lie basic principles from evolutionary biology, enzyme biochemistry, metabolism, cell composition and steady-state growth. The theory makes predictions about fitness costs and benefits of protein expression, physicochemical constraints on cell growth and characteristics of optimal metabolisms that maximise growth rate. Comparisons of the theory with experimental data indicates that microorganisms often aim for maximisation of growth rate, also in the presence of stresses; they often express optimal metabolisms and metabolic proteins at optimal concentrations. This review explains the current status of the theory for microbiologists; its roots, predictions, experimental evidence and future directions.

Global Transcriptional Response of Methylorubrum extorquens to Formaldehyde Stress Expands the Role of EfgA and Is Distinct from Antibiotic Translational Inhibition

The potency and indiscriminate nature of formaldehyde reactivity upon biological molecules make it a universal stressor. However, some organisms such as Methylorubrum extorquens possess means to rapidly and effectively mitigate formaldehyde-induced damage. EfgA is a recently identified formaldehyde sensor predicted to halt translation in response to elevated formaldehyde as a means to protect cells. Herein, we investigate growth and changes in gene expression to understand how M. extorquens responds to formaldehyde with and without the EfgA-formaldehyde-mediated translational response, and how this mechanism compares to antibiotic-mediated translation inhibition. These distinct mechanisms of translation inhibition have notable differences: they each involve different specific players and in addition, formaldehyde also acts as a general, multi-target stressor and a potential carbon source. We present findings demonstrating that in addition to its characterized impact on translation, functional EfgA allows for a rapid and robust transcriptional response to formaldehyde and that removal of EfgA leads to heightened proteotoxic and genotoxic stress in the presence of increased formaldehyde levels. We also found that many downstream consequences of translation inhibition were shared by EfgA-formaldehyde- and kanamycin-mediated translation inhibition. Our work uncovered additional layers of regulatory control enacted by functional EfgA upon experiencing formaldehyde stress, and further demonstrated the importance this protein plays at both transcriptional and translational levels in this model methylotroph.

A new analysis method for evaluating bacterial growth with microplate readers

Growth curve measurements are commonly used in microbiology, while the use of microplate readers for such measurements provides better temporal resolution and higher throughput. However, evaluating bacterial growth with microplate readers has been hurdled by barriers such as multiple scattering. Here, we report our development of a method based on the time derivatives of the optical density (OD) and/or fluorescence (FL) of bacterial cultures to overcome these barriers. First, we illustrated our method using quantitative models and numerical simulations, which predicted the number of bacteria and the number of fluorescent proteins in time as well as their time derivatives. Then, we systematically investigated how the time derivatives depend on the parameters in the models/simulations, providing a framework for understanding the FL growth curves. In addition, as a demonstration, we applied our method to study the lag time elongation of bacteria subjected to treatment with silver (Ag⁺) ions and found that the results from our method corroborated well with that from growth curve fitting by the Gompertz model that has been commonly used in the literature. Furthermore, this method was applied to the growth of bacteria in the presence of silver nanoparticles (AgNPs) at various concentrations, where the OD curve measurements failed. We showed that our method allowed us to successfully extract the growth behavior of the bacteria from the FL measurements and understand how the growth was affected by the AgNPs.

Ubiquitous selection for mecA in community-associated MRSA across diverse chemical environments

Community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) is threatening public health as it spreads worldwide across diverse environments. Its genetic hallmark, the mecA gene, confers resistance to many β-lactam antibiotics. Here, we show that, in addition, mecA provides a broad selective advantage across diverse chemical environments. Competing fluorescently labelled wild-type and mecA-deleted CA-MRSA USA400 strains across ~57,000 compounds supplemented with subinhibitory levels of the β-lactam drug cefoxitin, we find that mecA provides a widespread advantage across β-lactam and non β-lactam antibiotics, non-antibiotic drugs and even diverse natural and synthetic compounds. This advantage depends on the presence of cefoxitin and is strongly associated with the compounds’ physicochemical properties, suggesting that it may be mediated by differential compounds permeability into the cell. Indeed, mecA protects the bacteria against increased cell-envelope permeability under subinhibitory cefoxitin treatment. Our findings suggest that CA-MRSA success might be driven by a cell-envelope mediated selective advantage across diverse chemical compounds.

The mecA gene confers resistance to many β-lactam antibiotics in community-associated MRSA bacteria. Here, Snitser et al. show that mecA also provides broad selective advantage across diverse chemical environments in the presence of subinhibitory β-lactam concentrations, by protecting the bacteria against increased cell-envelope permeability.

Rewiring of growth-dependent transcription regulation by a point mutation in region 1.1 of the housekeeping σ factor

In bacteria, rapid adaptation to changing environmental conditions depends on the interplay between housekeeping and alternative σ factors, responsible for transcription of specific regulons by RNA polymerase (RNAP). In comparison with alternative σ factors, primary σs contain poorly conserved region 1.1, whose functions in transcription are only partially understood. We found that a single mutation in region 1.1 in Escherichia coli σ⁷⁰ rewires transcription regulation during cell growth resulting in profound phenotypic changes. Despite its destabilizing effect on promoter complexes, this mutation increases the activity of rRNA promoters and also decreases RNAP sensitivity to the major regulator of stringent response DksA. Using total RNA sequencing combined with single-cell analysis of gene expression we showed that changes in region 1.1 disrupt the balance between the "greed" and "fear" strategies thus making the cells more susceptible to environmental threats and antibiotics. Our results reveal an unexpected role of σ region 1.1 in growth-dependent transcription regulation and suggest that changes in this region may facilitate rapid switching of RNAP properties in evolving bacterial populations.

3D microfluidic gradient generator for combination antimicrobial susceptibility testing

Microfluidic concentration gradient generators (µ-CGGs) have been utilized to identify optimal drug compositions through antimicrobial susceptibility testing (AST) for the treatment of antimicrobial-resistant (AMR) infections. Conventional µ-CGGs fabricated via photolithography-based micromachining processes, however, are fundamentally limited to two-dimensional fluidic routing, such that only two distinct antimicrobial drugs can be tested at once. This work addresses this limitation by employing Multijet-3D-printed microchannel networks capable of fluidic routing in three dimensions to generate symmetric multidrug concentration gradients. The three-fluid gradient generation characteristics of the fabricated 3D µ-CGG prototype were quantified through both theoretical simulations and experimental validations. Furthermore, the antimicrobial effects of three highly clinically relevant antibiotic drugs, tetracycline, ciprofloxacin, and amikacin, were evaluated via experimental single-antibiotic minimum inhibitory concentration (MIC) and pairwise and three-way antibiotic combination drug screening (CDS) studies against model antibiotic-resistant Escherichia coli bacteria. As such, this 3D µ-CGG platform has great potential to enable expedited combination AST screening for various biomedical and diagnostic applications.

Cytosolic Crowding Drives the Dynamics of Both Genome and Cytosol in Escherichia coli Challenged with Sub-lethal Antibiotic Treatments

In contrast to their molecular mode of action, the system-level effect of antibiotics on cells is only beginning to be quantified. Molecular crowding is expected to be a relevant global regulator, which we explore here through the dynamic response phenotypes in Escherichia coli, at single-cell resolution, under sub-lethal regimes of different classes of clinically relevant antibiotics, acting at very different levels in the cell. We measure chromosomal mobility through tracking of fast (<15 s timescale) fluctuations of fluorescently tagged chromosomal loci, and we probe the fluidity of the cytoplasm by tracking cytosolic aggregates. Measuring cellular density, we show how the overall levels of macromolecular crowding affect both quantities, regardless of antibiotic-specific effects. The dominant trend is a strong correlation between the effects in different parts of the chromosome and between the chromosome and cytosol, supporting the concept of an overall global role of molecular crowding in cellular physiology.

Weakest-Link Dynamics Predict Apparent Antibiotic Interactions in a Model Cross-Feeding Community

With the growing global threat of antimicrobial resistance, novel strategies are required for combatting resistant pathogens. Combination therapy, in which multiple drugs are used to treat an infection, has proven highly successful in the treatment of cancer and HIV. However, this practice has proven challenging for the treatment of bacterial infections due to difficulties in selecting the correct combinations and dosages. An additional challenge in infection treatment is the polymicrobial nature of many infections, which may respond to antibiotics differently than a monoculture pathogen.

With the growing global threat of antimicrobial resistance, novel strategies are required for combatting resistant pathogens. Combination therapy, in which multiple drugs are used to treat an infection, has proven highly successful in the treatment of cancer and HIV. However, this practice has proven challenging for the treatment of bacterial infections due to difficulties in selecting the correct combinations and dosages. An additional challenge in infection treatment is the polymicrobial nature of many infections, which may respond to antibiotics differently than a monoculture pathogen. This study tests whether patterns of antibiotic interactions (synergy, antagonism, or independence/additivity) in monoculture can be used to predict antibiotic interactions in an obligate cross-feeding coculture. Using our previously described weakest-link hypothesis, we hypothesized antibiotic interactions in coculture based on the interactions we observed in monoculture. We then compared our predictions to observed antibiotic interactions in coculture. We tested the interactions between 10 previously identified antibiotic combinations using checkerboard assays. Although our antibiotic combinations interacted differently than predicted in our monocultures, our monoculture results were generally sufficient to predict coculture patterns based solely on the weakest-link hypothesis. These results suggest that combination therapy for cross-feeding multispecies infections may be successfully designed based on antibiotic interaction patterns for their component species.

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.

The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies

Our ability to discover effective drug combinations is limited, in part by insufficient understanding of how the transcriptional response of two monotherapies results in that of their combination. We analyzed matched time course RNAseq profiling of cells treated with single drugs and their combinations and found that the transcriptional signature of the synergistic combination was unique relative to that of either constituent monotherapy. The sequential activation of transcription factors in time in the gene regulatory network was implicated. The nature of this transcriptional cascade suggests that drug synergy may ensue when the transcriptional responses elicited by two unrelated individual drugs are correlated. We used these results as the basis of a simple prediction algorithm attaining an AUROC of 0.77 in the prediction of synergistic drug combinations in an independent dataset.

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.

A Roadblock-and-Kill Mechanism of Action Model for the DNA-Targeting Antibiotic Ciprofloxacin

Fluoroquinolones, antibiotics that cause DNA damage by inhibiting DNA topoisomerases, are clinically important, but their mechanism of action is not yet fully understood. In particular, the dynamical response of bacterial cells to fluoroquinolone exposure has hardly been investigated, although the SOS response, triggered by DNA damage, is often thought to play a key role. Here, we investigated the growth inhibition of the bacterium Escherichia coli by the fluoroquinolone ciprofloxacin at low concentrations. We measured the long-term and short-term dynamical response of the growth rate and DNA production rate to ciprofloxacin at both the population and single-cell levels. We show that, despite the molecular complexity of DNA metabolism, a simple roadblock-and-kill model focusing on replication fork blockage and DNA damage by ciprofloxacin-poisoned DNA topoisomerase II (gyrase) quantitatively reproduces long-term growth rates in the presence of ciprofloxacin. The model also predicts dynamical changes in the DNA production rate in wild-type E. coli and in a recombination-deficient mutant following a step-up of ciprofloxacin. Our work highlights that bacterial cells show a delayed growth rate response following fluoroquinolone exposure. Most importantly, our model explains why the response is delayed: it takes many doubling times to fragment the DNA sufficiently to inhibit gene expression. We also show that the dynamical response is controlled by the timescale of DNA replication and gyrase binding/unbinding to the DNA rather than by the SOS response, challenging the accepted view. Our work highlights the importance of including detailed biophysical processes in biochemical-systems models to quantitatively predict the bacterial response to antibiotics.

Stability of β-lactam antibiotics in bacterial growth media

Laboratory assays such as MIC tests assume that antibiotic molecules are stable in the chosen growth medium-but rapid degradation has been observed for antibiotics including β-lactams under some conditions in aqueous solution. Degradation rates in bacterial growth medium are less well known. Here, we develop a 'delay time bioassay' that provides a simple way to estimate antibiotic stability in bacterial growth media, using only a plate reader and without the need to measure the antibiotic concentration directly. We use the bioassay to measure degradation half-lives of the β-lactam antibiotics mecillinam, aztreonam and cefotaxime in widely-used bacterial growth media based on MOPS and Luria-Bertani (LB) broth. We find that mecillinam degradation can occur rapidly, with a half-life as short as 2 hours in MOPS medium at 37°C and pH 7.4, and 4-5 hours in LB, but that adjusting the pH and temperature can increase its stability to a half-life around 6 hours without excessively perturbing growth. Aztreonam and cefotaxime were found to have half-lives longer than 6 hours in MOPS medium at 37°C and pH 7.4, but still shorter than the timescale of a typical minimum inhibitory concentration (MIC) assay. Taken together, our results suggest that care is needed in interpreting MIC tests and other laboratory growth assays for β-lactam antibiotics, since there may be significant degradation of the antibiotic during the assay.

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.

Synergistic and antagonistic drug interactions in the treatment of systemic fungal infections

Invasive fungal infections cause 1.6 million deaths annually, primarily in immunocompromised individuals. Mortality rates are as high as 90% due to limited treatments. The azole class antifungal, fluconazole, is widely available and has multi-species activity but only inhibits growth instead of killing fungal cells, necessitating long treatments. To improve treatment, we used our novel high-throughput method, the overlap² method (O2M) to identify drugs that interact with fluconazole, either increasing or decreasing efficacy. We identified 40 molecules that act synergistically (amplify activity) and 19 molecules that act antagonistically (decrease efficacy) when combined with fluconazole. We found that critical frontline beta-lactam antibiotics antagonize fluconazole activity. A promising fluconazole-synergizing anticholinergic drug, dicyclomine, increases fungal cell permeability and inhibits nutrient intake when combined with fluconazole. In vivo, this combination doubled the time-to-endpoint of mice with Cryptococcus neoformans meningitis. Thus, our ability to rapidly identify synergistic and antagonistic drug interactions can potentially alter the patient outcomes.

Individuals with weakened immune systems – such as recipients of organ transplants – can fall prey to illnesses caused by fungi that are harmless to most people. These infections are difficult to manage because few treatments exist to fight fungi, and many have severe side effects. Antifungal drugs usually slow the growth of fungi cells rather than kill them, which means that patients must remain under treatment for a long time, or even for life.

One way to boost efficiency and combat resistant infections is to combine antifungal treatments with drugs that work in complementary ways: the drugs strengthen each other’s actions, and together they can potentially kill the fungus rather than slow its progression. However, not all drug combinations are helpful. In fact, certain drugs may interact in ways that make treatment less effective. This is particularly concerning because people with weakened immune systems often take many types of medications.

Here, Wambaugh et al. harnessed a new high-throughput system to screen how 2,000 drugs (many of which already approved to treat other conditions) affected the efficiency of a common antifungal called fluconazole. This highlighted 19 drugs that made fluconazole less effective, some being antibiotics routinely used to treat patients with weakened immune systems.

On the other hand, 40 drugs boosted the efficiency of fluconazole, including dicyclomine, a compound currently used to treat inflammatory bowel syndrome. In fact, pairing dicyclomine and fluconazole more than doubled the survival rate of mice with severe fungal infections. The combined treatment could target many species of harmful fungi, even those that had become resistant to fluconazole alone.

The results by Wambaugh et al. point towards better treatments for individuals with serious fungal infections. Drugs already in circulation for other conditions could be used to boost the efficiency of fluconazole, while antibiotics that do not decrease the efficiency of this medication should be selected to treat at-risk patients.

Resist or perish: Fate of a microbial population subjected to a periodic presence of antimicrobial

The evolution of antimicrobial resistance can be strongly affected by variations of antimicrobial concentration. Here, we study the impact of periodic alternations of absence and presence of antimicrobial on resistance evolution in a microbial population, using a stochastic model that includes variations of both population composition and size, and fully incorporates stochastic population extinctions. We show that fast alternations of presence and absence of antimicrobial are inefficient to eradicate the microbial population and strongly favor the establishment of resistance, unless the antimicrobial increases enough the death rate. We further demonstrate that if the period of alternations is longer than a threshold value, the microbial population goes extinct upon the first addition of antimicrobial, if it is not rescued by resistance. We express the probability that the population is eradicated upon the first addition of antimicrobial, assuming rare mutations. Rescue by resistance can happen either if resistant mutants preexist, or if they appear after antimicrobial is added to the environment. Importantly, the latter case is fully prevented by perfect biostatic antimicrobials that completely stop division of sensitive microorganisms. By contrast, we show that the parameter regime where treatment is efficient is larger for biocidal drugs than for biostatic drugs. This sheds light on the respective merits of different antimicrobial modes of action.

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

Antibiotic combinations are increasingly used to combat bacterial infections. Multidrug therapies are a particularly important treatment option for E. faecalis, an opportunistic pathogen that contributes to high-inoculum infections such as infective endocarditis. While numerous synergistic drug combinations for E. faecalis have been identified, much less is known about how different combinations impact the rate of resistance evolution. In this work, we use high-throughput laboratory evolution experiments to quantify adaptation in growth rate and drug resistance of E. faecalis exposed to drug combinations exhibiting different classes of interactions, ranging from synergistic to suppressive. We identify a wide range of evolutionary behavior, including both increased and decreased rates of growth adaptation, depending on the specific interplay between drug interaction and drug resistance profiles. For example, selection in a dual β-lactam combination leads to accelerated growth adaptation compared to selection with the individual drugs, even though the resulting resistance profiles are nearly identical. On the other hand, populations evolved in an aminoglycoside and β-lactam combination exhibit decreased growth adaptation and resistant profiles that depend on the specific drug concentrations. We show that the main qualitative features of these evolutionary trajectories can be explained by simple rescaling arguments that correspond to geometric transformations of the two-drug growth response surfaces measured in ancestral cells. The analysis also reveals multiple examples where resistance profiles selected by drug combinations are nearly growth-optimized along a contour connecting profiles selected by the component drugs. Our results highlight trade-offs between drug interactions and resistance profiles during the evolution of multi-drug resistance and emphasize evolutionary benefits and disadvantages of particular drug pairs targeting enterococci.

A Compendium of Genetic Modifiers of Mitochondrial Dysfunction Reveals Intra-organelle Buffering

Mitochondrial dysfunction is associated with a spectrum of human conditions, ranging from rare, inborn errors of metabolism to the aging process. To identify pathways that modify mitochondrial dysfunction, we performed genome-wide CRISPR screens in the presence of small-molecule mitochondrial inhibitors. We report a compendium of chemical-genetic interactions involving 191 distinct genetic modifiers, including 38 that are synthetic sick/lethal and 63 that are suppressors. Genes involved in glycolysis (PFKP), pentose phosphate pathway (G6PD), and defense against lipid peroxidation (GPX4) scored high as synthetic sick/lethal. A surprisingly large fraction of suppressors are pathway intrinsic and encode mitochondrial proteins. A striking example of such "intra-organelle" buffering is the alleviation of a chemical defect in complex V by simultaneous inhibition of complex I, which benefits cells by rebalancing redox cofactors, increasing reductive carboxylation, and promoting glycolysis. Perhaps paradoxically, certain forms of mitochondrial dysfunction may best be buffered with "second site" inhibitors to the organelle.

Unexpected synergistic and antagonistic antibiotic activity against Staphylococcus biofilms

Objectives

To evaluate putative anti-staphylococcal biofilm antibiotic combinations used in the management of periprosthetic joint infections (PJIs).

Methods

Using the dissolvable bead biofilm assay, the minimum biofilm eradication concentration (MBEC) was determined for the most commonly used antimicrobial agents and combination regimens against staphylococcal PJIs. The established fractional inhibitory concentration (FIC) index was modified to create the fractional biofilm eradication concentration (FBEC) index to evaluate synergism or antagonism between antibiotics.

Results

Only gentamicin (MBEC 64 mg/L) and daptomycin (MBEC 64 mg/L) were observed to be effective antistaphylococcal agents at clinically achievable concentrations. Supplementation of gentamicin with daptomycin, vancomycin or ciprofloxacin resulted in a similar or lower MBEC than gentamicin alone (FBEC index 0.25-2). Conversely, when rifampicin, clindamycin or linezolid was added to gentamicin, there was an increase in the MBEC of gentamicin relative to its use as a monotherapy (FBEC index 8-32).

Conclusions

This study found that gentamicin and daptomycin were the only effective single-agent antibiotics against established Staphylococcus biofilms. Interestingly the addition of a bacteriostatic antibiotic was found to antagonize the ability of gentamicin to eradicate Staphylococcus biofilms.

Ribosome Provisioning Activates a Bistable Switch Coupled to Fast Exit from Stationary Phase

Observations of bacteria at the single-cell level have revealed many instances of phenotypic heterogeneity within otherwise clonal populations, but the selective causes, molecular bases, and broader ecological relevance remain poorly understood. In an earlier experiment in which the bacterium Pseudomonas fluorescens SBW25 was propagated under a selective regime that mimicked the host immune response, a genotype evolved that stochastically switched between capsulation states. The genetic cause was a mutation in carB that decreased the pyrimidine pool (and growth rate), lowering the activation threshold of a preexisting but hitherto unrecognized phenotypic switch. Genetic components surrounding bifurcation of UTP flux toward DNA/RNA or UDP-glucose (a precursor of colanic acid forming the capsules) were implicated as key components. Extending these molecular analyses-and based on a combination of genetics, transcriptomics, biochemistry, and mathematical modeling-we show that pyrimidine limitation triggers an increase in ribosome biosynthesis and that switching is caused by competition between ribosomes and CsrA/RsmA proteins for the mRNA transcript of a positively autoregulated activator of colanic acid biosynthesis. We additionally show that in the ancestral bacterium the switch is part of a program that determines stochastic entry into a semiquiescent capsulated state, ensures that such cells are provisioned with excess ribosomes, and enables provisioned cells to exit rapidly from stationary phase under permissive conditions.