Applying ecological resistance and resilience to dissect bacterial antibiotic responses

Private benefit of β-lactamase dictates selection dynamics of combination antibiotic treatment

β-lactam antibiotics have been prescribed for most bacterial infections since their discovery. However, resistance to β-lactams, mediated by β-lactamase (Bla) enzymes such as extended spectrum β-lactamases (ESBLs), has become widespread. Bla inhibitors can restore the efficacy of β-lactams against resistant bacteria, an approach which preserves existing antibiotics despite declining industry investment. However, the effects of combination treatment on selection for β-lactam resistance are not well understood. Bla production confers both private benefits for resistant cells and public benefits which faster-growing sensitive cells can also exploit. These benefits may be differentially impacted by Bla inhibitors, leading to non-intuitive selection dynamics. In this study, we demonstrate strain-to-strain variation in effective combination doses, with complex growth dynamics in mixed populations. Using modeling, we derive a criterion for the selection outcome of combination treatment, dependent on the burden and effective private benefit of Bla production. We then use engineered strains and natural isolates to show that strong private benefits of Bla are associated with increased selection for resistance. Finally, we demonstrate that this parameter can be coarsely estimated using high-throughput phenotyping of clonal populations. Our analysis shows that quantifying the phenotypic responses of bacteria to combination treatment can facilitate resistance-minimizing optimization of treatment.

A 'rich-get-richer' mechanism drives patchy dynamics and resistance evolution in antibiotic-treated bacteria

Bacteria in nature often form surface-attached communities that initially comprise distinct subpopulations, or patches. For pathogens, these patches can form at infection sites, persist during antibiotic treatment, and develop into mature biofilms. Evidence suggests that patches can emerge due to heterogeneity in the growth environment and bacterial seeding, as well as cell-cell signaling. However, it is unclear how these factors contribute to patch formation and how patch formation might affect bacterial survival and evolution. Here, we demonstrate that a 'rich-get-richer' mechanism drives patch formation in bacteria exhibiting collective survival (CS) during antibiotic treatment. Modeling predicts that the seeding heterogeneity of these bacteria is amplified by local CS and global resource competition, leading to patch formation. Increasing the dose of a non-eradicating antibiotic treatment increases the degree of patchiness. Experimentally, we first demonstrated the mechanism using engineered Escherichia coli and then demonstrated its applicability to a pathogen, Pseudomonas aeruginosa. We further showed that the formation of P. aeruginosa patches promoted the evolution of antibiotic resistance. Our work provides new insights into population dynamics and resistance evolution during surface-attached bacterial growth.

Measuring single-cell susceptibility to antibiotics within monoclonal bacterial populations

The emergence of new resistant bacterial strains is a worldwide challenge. A resistant bacterial population can emerge from a single cell that acquires resistance or persistence. Hence, new ways of tackling the mechanism of antibiotic response, such as single cell studies are required. It is necessary to see what happens at the single cell level, in order to understand what happens at the population level. To date, linking the heterogeneity of single-cell susceptibility to the population-scale response to antibiotics remains challenging due to the trade-offs between the resolution and the field of view. Here we present a platform that measures the ability of individual E. coli cells to form small colonies at different ciprofloxacin concentrations, by using anchored microfluidic drops and an image and data analysis pipelines. The microfluidic results are benchmarked against classical microbiology measurements of antibiotic susceptibility, showing an agreement between the pooled microfluidic chip and replated bulk measurements. Further, the experimental likelihood of a single cell to form a colony is used to provide a probabilistic antibiotic susceptibility curve. In addition to the probabilistic viewpoint, the microfluidic format enables the characterization of morphological features over time for a large number of individual cells. This pipeline can be used to compare the response of different bacterial strains to antibiotics with different action mechanisms.

Assessing the ecological risk of croplands in loess drylands by combining environmental disturbance with ecosystem vulnerability

Agroecosystems suffer various ecological risks due to the intensive production of crops. However, comprehensive assessments of cropland ecological risks remain limited. This study developed an assessment method for cropland ecological risks by combining environmental disturbance with ecosystem vulnerability. Environmental disturbance reflects stresses caused by risk sources in an environment, while ecosystem vulnerability is the susceptibility of an ecosystem to adverse disturbances and its capacity to cope and adapt. The proposed method is conducive to understanding the complex exposure-response relationship between croplands and environmental stresses. Cropland ecological risk was evaluated by conducting a case study on a loess dryland region in Shaanxi. The hot spots and driving factors of risk were explored using spatial autocorrelation and quantile regression methods, respectively. Results show that overall cropland ecological risk is at medium low level. Risk hot spots are concentrated in the north of the loess dryland. Ecosystem vulnerability exerts greater effect on the distribution of hot spots than environmental disturbance in the study area. Road density (RDD), river density, and soil organic matter exert the most important effects on cropland ecological risk. Moreover, the same driving factor exhibits various effects on cropland ecological risk in different risk level areas. RDD, slope, precipitation, elevation, fertilizer application rate, gross domestic product, and distance to town center have greater effects on risk in regions with high cropland ecological risk than in regions with low cropland ecological risk. The findings of this study must be considered in formulating targeted policies for controlling cropland ecological risk in loess drylands to realize sustainable crop production.

Understanding and applying biological resilience, from genes to ecosystems

The natural world is under unprecedented and accelerating pressure. Much work on understanding resilience to local and global environmental change has, so far, focussed on ecosystems. However, understanding a system's behaviour requires knowledge of its component parts and their interactions. Here we call for increased efforts to understand 'biological resilience', or the processes that enable components across biological levels, from genes to communities, to resist or recover from perturbations. Although ecologists and evolutionary biologists have the tool-boxes to examine form and function, efforts to integrate this knowledge across biological levels and take advantage of big data (e.g. ecological and genomic) are only just beginning. We argue that combining eco-evolutionary knowledge with ecosystem-level concepts of resilience will provide the mechanistic basis necessary to improve management of human, natural and agricultural ecosystems, and outline some of the challenges in achieving an understanding of biological resilience.

Ocean acidification increases the impact of typhoons on algal communities

Long-term environmental change, sudden pulses of extreme perturbation, or a combination of both can trigger regime shifts by changing the processes and feedbacks which determine community assembly, structure, and function, altering the state of ecosystems. Our understanding of the mechanisms that stabilise against regime shifts or lock communities into altered states is limited, yet also critical to anticipating future states, preventing regime shifts, and reversing unwanted state change. Ocean acidification contributes to the restructuring and simplification of algal systems, however the mechanisms through which this occurs and whether additional drivers are involved requires further study. Using monthly surveys over three years at a shallow-water volcanic seep we examined how the composition of algal communities change seasonally and following periods of significant physical disturbance by typhoons at three levels of ocean acidification (equivalent to means of contemporary ∼350 and future ∼500 and 900 μatm pCO₂). Sites exposed to acidification were increasingly monopolised by structurally simple, fast-growing turf algae, and were clearly different to structurally complex macrophyte-dominated reference sites. The distinct contemporary and acidified community states were stabilised and maintained at their respective sites by different mechanisms following seasonal typhoon disturbance. Macroalgal-dominated sites were resistant to typhoon damage. In contrast, significant losses of algal biomass represented a near total ecosystem reset by typhoons for the turf-dominated communities at the elevated pCO₂ sites (i.e. negligible resistance). A combination of disturbance and subsequent turf recovery maintained the same simplified state between years (elevated CO₂ levels promote turf growth following algal removal, inhibiting macroalgal recruitment). Thus, ocean acidification may promote shifts in algal systems towards degraded ecosystem states, and short-term disturbances which reset successional trajectories may 'lock-in' these alternative states of low structural and functional diversity.

An optogenetic toolkit for light-inducible antibiotic resistance

Antibiotics are a key control mechanism for synthetic biology and microbiology. Resistance genes are used to select desired cells and regulate bacterial populations, however their use to-date has been largely static. Precise spatiotemporal control of antibiotic resistance could enable a wide variety of applications that require dynamic control of susceptibility and survival. Here, we use light-inducible Cre recombinase to activate expression of drug resistance genes in Escherichia coli. We demonstrate light-activated resistance to four antibiotics: carbenicillin, kanamycin, chloramphenicol, and tetracycline. Cells exposed to blue light survive in the presence of lethal antibiotic concentrations, while those kept in the dark do not. To optimize resistance induction, we vary promoter, ribosome binding site, and enzyme variant strength using chromosome and plasmid-based constructs. We then link inducible resistance to expression of a heterologous fatty acid enzyme to increase production of octanoic acid. These optogenetic resistance tools pave the way for spatiotemporal control of cell survival.

Achieving Ultrasensitive Chromogenic Probes for Rapid, Direct Detection of Carbapenemase-Producing Bacteria in Sputum

Carbapenemase-producing bacteria (CPB) stand as the most dangerous "superbugs" in the clinic. Rapid point-of-care (POC) detection of CPB in clinical samples is key to timely and effective infection management. We herein report the first ultrasensitive chromogenic probe that allows direct POC detection of CPB in clinical sputum samples at a sample-to-result time of less than 15 min. This chromogenic probe is modularly designed by conjugating the carbapenem core with a benzene derivative bearing an electronegativity-tunable substituent. Unexpectedly high sensitivity was achieved simply by choosing strong electron-withdrawing substituents, such as -N⁺(CH₃)₃, without resorting to complex molecular design. Through integrating the probes with a portable paper chip, 24 out of 80 clinical sputum samples from sepsis patients with lung infections were quickly diagnosed as CPB-positive, exhibiting 100% clinical sensitivity and specificity. This low-cost paper chip assay can be readily performed on-site, breaking through the dilemma of rapid CPB detection, especially in resource-limited settings.

Ecological life strategies of microbes in response to antibiotics as a driving factor in soils

Antibiotics as a selection pressure driving the evolution of soil microbial communities is not well understood. Since microbial functions govern ecosystem services, an ecological framework is required to understand and predict antibiotic-induced functional and structural changes in microbial communities. Therefore, metagenomic studies explaining the impacts of antibiotics on soil microbial communities were mined, and alterations in microbial taxa were analyzed through an ecological lens using Grimes's Competitor-Stress tolerator-Ruderal (CSR) model. We propose considering antibiotics as the primary abiotic factor mentioned in the CSR model and classifying non-susceptible microbial taxa as degraders, resistant, and resilient groups analogous to competitors, stress tolerators, and ruderal strategists, respectively. Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria were among the phyla harboring most members with antibiotic-resistant groups. However, some antibiotic-resistant microbes in these phyla could not only tolerate but also subsist solely on antibiotics, while others degraded antibiotics as a part of secondary metabolism. Irrespective of their taxonomic affiliation, microbes with each life strategy displayed similar phenotypic characteristics. Therefore, it is recommended to consider microbial functional traits associated with each life strategy while analyzing the ecological impacts of antibiotics. Also, potential ecological crises posed by antibiotics through changes in microbial community and ecosystem functions were visualized. Applying ecological theory to understand and predict antibiotics-induced changes in microbial communities will also provide better insight into microbial behavior in the background of emerging contaminants and help develop a robust ecological classification system of microbes.

Breaching Bacterial Biofilm Barriers: Efficient Combinatorial Theranostics for Multidrug-Resistant Bacterial Biofilms with a Novel Penetration-Enhanced AIEgen Probe

The formation of microbial biofilms is acknowledged as a major virulence factor in a range of persistent local infections. Failures to remove biofilms with antibiotics foster the emergence of antibiotic-resistant bacteria and result in chronic infections. As a result, the construction of effective biofilm-inhibiting and biofilm-eradicating chemicals is urgently required. Herein, we designed a water-soluble probe APDIS for membrane-active fluorescence and broad-spectrum antimicrobial actions, particularly against methicillin-resistant Staphylococcus aureus (MRSA), which shows multidrug resistance. In vitro and in vivo experiments demonstrate its high antibacterial effects comparable to vancomycin. Furthermore, it inhibits biofilm formation by effectively killing planktonic bacteria at low inhibitory concentrations, without toxicity to mammalian cells. More importantly, this probe can efficiently penetrate through biofilm barriers and exterminate bacteria that are enclosed within biofilms and startle existing biofilms. In the mouse model of implant-related biofilm infections, this probe exhibits strong antibiofilm activity against MRSA biofilms, thus providing a novel theranostic strategy to disrupt biofilms in vivo effectively. Our results indicate that this probe has the potential to be used for the development of a combinatorial theranostic platform with synergistic enhanced effects for the treatment of multidrug-resistant bacterial infections and antibiofilm medications.

Optimal dose of lactoferrin reduces the resilience of in vitro Staphylococcus aureus colonies

The rise in antibiotic resistance has stimulated research into adjuvants that can improve the efficacy of broad-spectrum antibiotics. Lactoferrin is a candidate adjuvant; it is a multifunctional iron-binding protein with antimicrobial properties. It is known to show dose-dependent antimicrobial activity against Staphylococcus aureus through iron sequestration and repression of β-lactamase expression. However, S. aureus can extract iron from lactoferrin through siderophores for their growth, which confounds the resolution of lactoferrin's method of action. We measured the minimum inhibitory concentration (MIC) for a range of lactoferrin/ β-lactam antibiotic dose combinations and observed that at low doses (< 0.39 μM), lactoferrin contributes to increased S. aureus growth, but at higher doses (> 6.25 μM), iron-depleted native lactoferrin reduced bacterial growth and reduced the MIC of the β-lactam-antibiotic cefazolin. This differential behaviour points to a bacterial population response to the lactoferrin/ β-lactam dose combination. Here, with the aid of a mathematical model, we show that lactoferrin stratifies the bacterial population, and the resulting population heterogeneity is at the basis of the dose dependent response seen. Further, lactoferrin disables a sub-population from β-lactam-induced production of β-lactamase, which when sufficiently large reduces the population's ability to recover after being treated by an antibiotic. Our analysis shows that an optimal dose of lactoferrin acts as a suitable adjuvant to eliminate S. aureus colonies using β-lactams, but sub-inhibitory doses of lactoferrin reduces the efficacy of β-lactams.

Enhancing bioreactor arrays for automated measurements and reactive control with ReacSight

Small-scale, low-cost bioreactors provide exquisite control of environmental parameters of microbial cultures over long durations. Their use is gaining popularity in quantitative systems and synthetic biology. However, existing setups are limited in their measurement capabilities. Here, we present ReacSight, a strategy to enhance bioreactor arrays for automated measurements and reactive experiment control. ReacSight leverages low-cost pipetting robots for sample collection, handling and loading, and provides a flexible instrument control architecture. We showcase ReacSight capabilities on three applications in yeast. First, we demonstrate real-time optogenetic control of gene expression. Second, we explore the impact of nutrient scarcity on fitness and cellular stress using competition assays. Third, we perform dynamic control of the composition of a two-strain consortium. We combine custom or chi.bio reactors with automated cytometry. To further illustrate ReacSight's genericity, we use it to enhance plate-readers with pipetting capabilities and perform repeated antibiotic treatments on a bacterial clinical isolate.

Fast growth can counteract antibiotic susceptibility in shaping microbial community resilience to antibiotics

SignificanceAntibiotic exposure stands among the most used interventions to drive microbial communities away from undesired states. How the ecology of microbial communities shapes their recovery-e.g., posttreatment shifts toward Clostridioides difficile infections in the gut-after antibiotic exposure is poorly understood. We study community response to antibiotics using a model community that can reach two alternative states. Guided by theory, our experiments show that microbial growth following antibiotic exposure can counteract antibiotic susceptibility in driving transitions between alternative community states. This makes it possible to reverse the outcome of antibiotic exposure through modifying growth dynamics, including cooperative growth, of community members. Our research highlights the relevance of simple ecological models to better understand the long-term effects of antibiotic treatment.

Drug-dependent growth curve reshaping reveals mechanisms of antifungal resistance in Saccharomyces cerevisiae

Microbial drug resistance is an emerging global challenge. Current drug resistance assays tend to be simplistic, ignoring complexities of resistance manifestations and mechanisms, such as multicellularity. Here, we characterize multicellular and molecular sources of drug resistance upon deleting the AMN1 gene responsible for clumping multicellularity in a budding yeast strain, causing it to become unicellular. Computational analysis of growth curve changes upon drug treatment indicates that the unicellular strain is more sensitive to four common antifungals. Quantitative models uncover entwined multicellular and molecular processes underlying these differences in sensitivity and suggest AMN1 as an antifungal target in clumping pathogenic yeasts. Similar experimental and mathematical modeling pipelines could reveal multicellular and molecular drug resistance mechanisms, leading to more effective treatments against various microbial infections and possibly even cancers.

Combined growth curve experiments and quantitative modeling reveal antifungal responses of planktonic yeast, providing a future framework to examine antimicrobial drug resistance.

Differential Multi-cellularity Is Required for the Adaptation for Bacillus licheniformis to Withstand Heavy Metals Toxicity

Bacillus licheniformis is a multi-metal tolerant bacteria, isolated from the paddy rhizospheric soil sample. Upon the multiple metal toxicity, B. licheniformis altered their phenotypic/morphogenesis. Here we examined the effects of cadmium (Cd2+), chromium (Cr2+), and mercury (Hg2+) on the morphogenesis of B. licheniformis in comparison to control. We found that the ability of bacteria to grow effectively in presence of cadmium and chromium comes at a cost of acquiring cell density-driven mobility and reformation of filamentous to donut shape respectively. In particular, when bacteria grown on mercury it showed the bacteriostatic strategy to resist mercury. Furthermore, the findings suggest a large variation in the production of exo-polysaccharides (EPS) and suggest the possible role of EPS in gaining resistance to cadmium and chromium. Together this study identifies previously unknown characteristics of B. licheniformis to participate in bioremediation and provides the first evidence on positive effects of bacterial morphogenesis and the involvement of EPS in bacteria to resisting metal toxicity.

Spatial segregation and cooperation in radially expanding microbial colonies under antibiotic stress

Antibiotic resistance in microbial communities reflects a combination of processes operating at different scales. In this work, we investigate the spatiotemporal dynamics of bacterial colonies comprised of drug-resistant and drug-sensitive cells undergoing range expansion under antibiotic stress. Using the opportunistic pathogen Enterococcus faecalis with plasmid-encoded β-lactamase, we track colony expansion dynamics and visualize spatial patterns in fluorescently labeled populations exposed to antibiotics. We find that the radial expansion rate of mixed communities is approximately constant over a wide range of drug concentrations and initial population compositions. Imaging of the final populations shows that resistance to ampicillin is cooperative, with sensitive cells surviving in the presence of resistant cells at otherwise lethal concentrations. The populations exhibit a diverse range of spatial segregation patterns that depend on drug concentration and initial conditions. Mathematical models indicate that the observed dynamics are consistent with global cooperation, despite the fact that β-lactamase remains cell-associated. Experiments confirm that resistant colonies provide a protective effect to sensitive cells on length scales multiple times the size of a single colony, and populations seeded with (on average) no more than a single resistant cell can produce mixed communities in the presence of the drug. While biophysical models of drug degradation suggest that individual resistant cells offer only short-range protection to neighboring cells, we show that long-range protection may arise from synergistic effects of multiple resistant cells, providing surprisingly large protection zones even at small population fractions.

Airborne and indigenous microbiomes co-drive the rebound of antibiotic resistome during compost storage

Composting is widely used to reduce the abundance of antibiotic resistance genes (ARGs) in solid waste. While ARG dynamics have been extensively investigated during composting, the fate and abundance of residual ARGs during the storage remain unexplored. Here, we tested experimentally how ARG and mobile genetic element (MGE) abundances change during compost storage using metagenomics, quantitative PCR and direct culturing. We found that 43.8% of ARGs and 39.9% of MGEs quickly recovered already during the first week of storage. This rebound effect was mainly driven by the regrowth of indigenous, antibiotic-resistant bacteria that survived the composting. Bacterial transmission from the surrounding air had a much smaller effect, being most evident as MGE rebound during the later stages of storage. While hyperthermophilic composting was more efficient at reducing the relative abundance of ARGs and MGEs, relatively greater ARG rebound was observed during the storage of hyperthermophilic compost, exceeding the initial levels of untreated sewage sludge. Our study reveals that residual ARGs and MGEs left in the treated compost can quickly rebound during the storage via airborne introduction and regrowth of surviving bacteria, highlighting the need to develop better storage strategies to prevent the rebound of ARGs and MGEs after composting.

Digital Insights Into Nucleotide Metabolism and Antibiotic Treatment Failure

Nucleotide metabolism plays a central role in bacterial physiology, producing the nucleic acids necessary for DNA replication and RNA transcription. Recent studies demonstrate that nucleotide metabolism also proactively contributes to antibiotic-induced lethality in bacterial pathogens and that disruptions to nucleotide metabolism contributes to antibiotic treatment failure in the clinic. As antimicrobial resistance continues to grow unchecked, new approaches are needed to study the molecular mechanisms responsible for antibiotic efficacy. Here we review emerging technologies poised to transform understanding into why antibiotics may fail in the clinic. We discuss how these technologies led to the discovery that nucleotide metabolism regulates antibiotic drug responses and why these are relevant to human infections. We highlight opportunities for how studies into nucleotide metabolism may enhance understanding of antibiotic failure mechanisms.

The ecology of plasmid-coded antibiotic resistance: a basic framework for experimental research and modeling

Many antibiotic resistance genes are associated with plasmids. The ecological success of these mobile genetic elements within microbial communities depends on varying mechanisms to secure their own propagation, not only on environmental selection. Among the most important are the cost of plasmids and their ability to be transferred to new hosts through mechanisms such as conjugation. These are regulated by dynamic control systems of the conjugation machinery and genetic adaptations that plasmid-host pairs can acquire in coevolution. However, in complex communities, these processes and mechanisms are subject to a variety of interactions with other bacterial species and other plasmid types. This article summarizes basic plasmid properties and ecological principles particularly important for understanding the persistence of plasmid-coded antibiotic resistance in aquatic environments. Through selected examples, it further introduces to the features of different types of simulation models such as systems of ordinary differential equations and individual-based models, which are considered to be important tools to understand these complex systems. This ecological perspective aims to improve the way we study and understand the dynamics, diversity and persistence of plasmids and associated antibiotic resistance genes.

Quantitative modeling predicts mechanistic links between pre-treatment microbiome composition and metronidazole efficacy in bacterial vaginosis

Bacterial vaginosis is a condition associated with adverse reproductive outcomes and characterized by a shift from a Lactobacillus-dominant vaginal microbiota to a polymicrobial microbiota, consistently colonized by strains of Gardnerella vaginalis. Metronidazole is the first-line treatment; however, treatment failure and recurrence rates remain high. To understand complex interactions between Gardnerella vaginalis and Lactobacillus involved in efficacy, here we develop an ordinary differential equation model that predicts bacterial growth as a function of metronidazole uptake, sensitivity, and metabolism. The model shows that a critical factor in efficacy is Lactobacillus sequestration of metronidazole, and efficacy decreases when the relative abundance of Lactobacillus is higher pre-treatment. We validate results in Gardnerella and Lactobacillus co-cultures, and in two clinical cohorts, finding women with recurrence have significantly higher pre-treatment levels of Lactobacillus relative to bacterial vaginosis-associated bacteria. Overall results provide mechanistic insight into how personalized differences in microbial communities influence vaginal antibiotic efficacy.

Unchartered waters: the unintended impacts of residual chlorine on water quality and biofilms

Disinfection residuals in drinking water protect water quality and public heath by limiting planktonic microbial regrowth during distribution. However, we do not consider the consequences and selective pressures of such residuals on the ubiquitous biofilms that persist on the vast internal surface area of drinking water distribution systems. Using a full scale experimental facility, integrated analyses were applied to determine the physical, chemical and biological impacts of different free chlorine regimes on biofilm characteristics (composition, structure and microbiome) and water quality. Unexpectedly, higher free chlorine concentrations resulted in greater water quality degredation, observable as elevated inorganic loading and greater discolouration (a major cause of water quality complaints and a mask for other failures). High-chlorine concentrations also reduced biofilm cell concentrations but selected for a distinct biofilm bacterial community and inorganic composition, presenting unique risks. The results challenge the assumption that a measurable free chlorine residual necessarily assures drinking water safety.

Delayed antibiotic exposure induces population collapse in enterococcal communities with drug-resistant subpopulations

The molecular underpinnings of antibiotic resistance are increasingly understood, but less is known about how these molecular events influence microbial dynamics on the population scale. Here, we show that the dynamics of E. faecalis communities exposed to antibiotics can be surprisingly rich, revealing scenarios where increasing population size or delaying drug exposure can promote population collapse. Specifically, we demonstrate how density-dependent feedback loops couple population growth and antibiotic efficacy when communities include drug-resistant subpopulations, leading to a wide range of behavior, including population survival, collapse, or one of two qualitatively distinct bistable behaviors where survival is favored in either small or large populations. These dynamics reflect competing density-dependent effects of different subpopulations, with growth of drug-sensitive cells increasing but growth of drug-resistant cells decreasing effective drug inhibition. Finally, we demonstrate how populations receiving immediate drug influx may sometimes thrive, while identical populations exposed to delayed drug influx collapse.

Antibiotic resilience: a necessary concept to complement antibiotic resistance?

Resilience is the capacity of systems to recover their initial state or functions after a disturbance. The concepts of resilience and resistance are complementary in ecology and both represent different aspects of the stability of ecosystems. However, antibiotic resilience is not used in clinical bacteriology whereas antibiotic resistance is a recognized major problem. To join the fields of ecology and clinical bacteriology, we first review the resilience concept from ecology, socio-ecological systems and microbiology where it is widely developed. We then review resilience-related concepts in microbiology, including bacterial tolerance and persistence, phenotypic heterogeneity and collective tolerance and resistance. We discuss how antibiotic resilience could be defined and argue that the use of this concept largely relies on its experimental measure and its clinical relevance. We review indicators in microbiology which could be used to reflect antibiotic resilience and used as valuable indicators to anticipate the capacity of bacteria to recover from antibiotic treatments.

Antibiotic resistance in Pseudomonas aeruginosa - Mechanisms, epidemiology and evolution

Antibiotics are powerful drugs used in the treatment of bacterial infections. The inappropriate use of these medicines has driven the dissemination of antibiotic resistance (AR) in most bacteria. Pseudomonas aeruginosa is an opportunistic pathogen commonly involved in environmental- and difficult-to-treat hospital-acquired infections. This species is frequently resistant to several antibiotics, being in the "critical" category of the WHO's priority pathogens list for research and development of new antibiotics. In addition to a remarkable intrinsic resistance to several antibiotics, P. aeruginosa can acquire resistance through chromosomal mutations and acquisition of AR genes. P. aeruginosa has one of the largest bacterial genomes and possesses a significant assortment of genes acquired by horizontal gene transfer (HGT), which are frequently localized within integrons and mobile genetic elements (MGEs), such as transposons, insertion sequences, genomic islands, phages, plasmids and integrative and conjugative elements (ICEs). This genomic diversity results in a non-clonal population structure, punctuated by specific clones that are associated with significant morbidity and mortality worldwide, the so-called high-risk clones. Acquisition of MGEs produces a fitness cost in the host, that can be eased over time by compensatory mutations during MGE-host coevolution. Even though plasmids and ICEs are important drivers of AR, the underlying evolutionary traits that promote this dissemination are poorly understood. In this review, we provide a comprehensive description of the main strategies involved in AR in P. aeruginosa and the leading drivers of HGT in this species. The most recently developed genomic tools that allowed a better understanding of the features contributing for the success of P. aeruginosa are discussed.