Metabolic fitness landscapes predict the evolution of antibiotic resistance

Proline mitigates antibiotic resistance evolution induced by ciprofloxacin at environmental concentrations

Antibiotics-induced resistance development in the environment has emerged as a critical issue under the 'one health' framework. Although there have been approaches to control antibiotic resistance evolution in clinical settings, they are rarely applicable in environmental contexts. Amino acids can affect the metabolic states of bacteria and potentially influence their resistance evolution. In this study, we screened 18 amino acids and identified proline as an efficient agent capable of mitigating ciprofloxacin-induced resistance of a soil-isolated Escherichia coli by over 50 % during a 24-day evolutionary experiment. Using transcriptomics and 13C metabolic flux analysis, we revealed the evolution mitigation mechanism of proline, which mainly involves down-regulated gene expressions and reduced metabolic flux of the TCA cycle, thereby decreasing NADH production, proton motive force, and uptake of ciprofloxacin. Based on single-cell RNA-seq, proline also reduced the size of resistant subgroups in the evolved E. coli population. Based on soil microcosm experiments, proline not only reduced the overall antibiotic resistance but also increased community diversity and robustness (optimal dosage: 5 mg/kg). Moreover, proline's evolution mitigation potentials likely extend to other antibiotics (e.g., streptomycin) and populations (e.g., Pseudomonas and Serratia spp.). Overall, proline addition holds promising potentials for mitigating antibiotic resistance in diverse antibiotics-polluted environments.

Mutualism breakdown underpins evolutionary rescue in an obligate cross-feeding bacterial consortium

Populations facing lethal environmental change can escape extinction through rapid genetic adaptation, a process known as evolutionary rescue. Despite extensive study, evolutionary rescue is largely unexplored in mutualistic communities, where it is likely constrained by the less adaptable partner. Here, we explored empirically the likelihood, population dynamics, and genetic mechanisms underpinning evolutionary rescue in an obligate mutualism involving cross-feeding of amino acids between auxotrophic Escherichia coli strains. We found that over 80% of populations overcame a severe decline when exposed to two distinct types of abrupt, lethal stress. Of note, in all cases only one of the strains survived by metabolically bypassing the auxotrophy. Crucially, the mutualistic consortium exhibited greater sensitivity to both stressors than a prototrophic control strain, such that reversion to autonomy was sufficient to alleviate stress below lethal levels. This sensitivity was common across other stresses, suggesting it may be a general feature of amino acid-dependent obligate mutualisms. Our results reveal that evolutionary rescue may depend critically on the specific genetic and physiological details of the interacting partners, adding rich layers of complexity to the endeavor of predicting the fate of microbial communities facing intense environmental deterioration.

Rational design of peptides to overcome drug resistance by metabolic regulation

Chemotherapy is widely used clinically, however, its efficacy is often compromised by the development of drug resistance, which arises from prolonged administration of drugs or other stimuli. One of the driven causes of drug resistance in tumors or bacterial infections is metabolic reprogramming, which alters mitochondrial metabolism, disrupts metabolic pathways and causes ion imbalance. Bioactive peptide materials, due to their biocompatibility, diverse bioactivities, customizable sequences, and ease of modification, have shown promise in overcoming drug resistance. This review provides an in-depth analysis of metabolic reprogramming and associated microenvironmental changes that contribute to drug resistance in common tumors and bacterial infections, suggesting potential therapeutic targets. Additionally, we explore peptide-based materials for regulating metabolism and their potential synergic effect with other therapies, highlighting the mechanisms by which these peptides reverse drug resistance. Finally, we discuss future perspectives and the clinical challenges in peptide-based treatments, aiming to offer insights for overcoming drug-resistant diseases.

Progression of ampC amplification during de novo amoxicillin resistance development in E. coli

Beta-lactam antibiotics are the most applied antimicrobials in human and veterinarian health care. Hence, beta-lactam resistance is a major health problem. Gene amplification of AmpC beta-lactamase is a main contributor to de novo β-lactam resistance in Escherichia coli. However, the time course of amplification and the accompanying DNA mutations are unclear. Here, we study the progression of ampC amplification and ampC promoter mutations during the evolution of resistance induced by stepwise increasing amoxicillin concentrations. AmpC promoter mutations occurred by day 2, while the approximately eight-fold amplification occurred after more than 6 days of amoxicillin exposure. The combination of the amplification and the promoter mutations increased the ampC mRNA level by an average factor of 200 after 22 days. An IS1 insertion is identified in the amplification junction after resistance induction in the wild type (WT) and the ampC gene complementation strain (CompA), but not in ∆ampC, suggesting that the amplification depends on mobile genetic element transposition. In order to elucidate the correlation between gene mutations and ampC amplification, the DNA mutations acquired during resistance evolution by the WT, ∆ampC, and CompA were analyzed. Compared to evolved ∆ampC, several resistance-causing mutations are absent in evolved WT, while more mutations accumulated in stress response. The amoxicillin-resistant ∆ampC did not show amplification of the fragment around the original ampC position but exhibited a large duplication or triplication at another position, suggesting the essential role of the duplicated genes in resistance development.IMPORTANCEAmoxicillin is the most used antimicrobial against bacterial infections. DNA fragments containing ampC are amplified upon prolonged and stepwise increasing exposure to amoxicillin, causing resistance. These ampC-containing fragments have been identified in extended-spectrum beta-lactamase plasmids, which are considered the main cause of beta-lactam resistance. In this study, we document the time course of two important factors for ampC transcription enhancement, ampC amplification and ampC promoter mutations, during de novo amoxicillin resistance evolution. We propose that the transposon IS1 contributes to the amplification ampC region, that the sigma factor 70 regulates ampC overexpression, and that these combined form the backbone of a putative mechanism for ampC amplification.

Metagenomics reveals the resistance patterns of electrochemically treated erythromycin fermentation residue

Erythromycin fermentation residue (EFR) represents a typical hazardous waste produced by the microbial pharmaceutical industry. Although electrolysis is promising for EFR disposal, its microbial threats remain unclear. Herein, metagenomics was coupled with the random forest technique to decipher the antibiotic resistance patterns of electrochemically treated EFR. Results showed that 95.75% of erythromycin could be removed in 2 hr. Electrolysis temporarily influenced EFR microbiota, where the relative abundances of Proteobacteria and Actinobacteria increased, while those of Fusobacteria, Firmicutes, and Bacteroidetes decreased. A total of 505 antibiotic resistance gene (ARG) subtypes encoding resistance to 21 antibiotic types and 150 mobile genetic elements (MGEs), mainly including plasmid (72) and transposase (52) were assembled in EFR. Significant linear regression models were identified among microbial richness, ARG subtypes, and MGE numbers (r2=0.50-0.81, p< 0.001). Physicochemical factors of EFR (Total nitrogen, total organic carbon, protein, and humus) regulated ARG and MGE assembly (%IncMSE value = 5.14-14.85). The core ARG, MGE, and microbe sets (93.08%-99.85%) successfully explained 89.71%-92.92% of total ARG and MGE abundances. Specifically, gene aph(3')-I, transposase tnpA, and Mycolicibacterium were the primary drivers of the resistance dissemination system. This study also proposes efficient resistance mitigation measures, and provides recommendations for future management of antibiotic fermentation residue.

Use of adjuvants to improve antibiotic efficacy and reduce the burden of antimicrobial resistance

INTRODUCTION

The increase in antibiotic resistance, together with the absence of novel antibiotics, makes mandatory the introduction of novel strategies to optimize the use of existing antibiotics. Among these strategies, the use of molecules that increase their activity looks promising.

AREAS COVERED

Different categories of adjuvants have been reviewed. Anti-resistance adjuvants increase the activity of antibiotics by inhibiting antibiotic resistance determinants. Anti-virulence approaches focus on the infection process itself; reducing virulence in combination with an antibiotic can improve therapeutic efficacy. Combination of phages with antibiotics can also be useful, since they present different mechanisms of action and targets. Finally, combining antibiotics with adjuvants in the same molecule may serve to improve antibiotics' efficacy and to overcome potential problems of differential pharmacokinetics/pharmacodynamics.

EXPERT OPINION

The successful combination of inhibitors of β-lactamases with β-lactams has shown that adjuvants can improve the efficacy of current antibiotics. In this sense, novel anti-resistance adjuvants able to inhibit efflux pumps are still needed, as well as anti-virulence compounds that improve the efficacy of antibiotics by interfering with the infection process. Although adjuvants may present different pharmacodynamics/pharmacokinetics than antibiotics, conjugates containing both compounds can solve this problem. Finally, already approved drugs can be a promising source of antibiotic adjuvants.

Predicting the evolution of antibiotic resistance

Predicting the evolution of antibiotic resistance is critical for realizing precision antibiotic therapies. How exactly to achieve such predictions is a theoretical challenge. Insights from mathematical models that reflect future behavior of microbes under antibiotic stress can inform intervention protocols. However, this requires going beyond heuristic approaches by modeling ecological and evolutionary responses linked to metabolic pathways and cellular functions. Developing such models is now becoming possible due to increasing data availability from systematic experiments with microbial systems. Here, I review recent theoretical advances promising building blocks to piece together a predictive theory of antibiotic resistance evolution. I focus on the conceptual framework of eco-evolutionary response models grounded on quantitative laws of bacterial physiology. These forward-looking models can predict previously unknown behavior of bacteria upon antibiotic exposure. With current developments covering mostly the case of ribosome-targeting antibiotics, I write this Opinion piece as an invitation to generalize the principles discussed here to a broader range of drugs and context dependencies.

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

Nutrient competition predicts gut microbiome restructuring under drug perturbations

Human gut commensal bacteria are routinely exposed to various stresses, including therapeutic drugs, and collateral effects are difficult to predict. To systematically interrogate community-level effects of drug perturbations, we screened stool-derived in vitro communities with 707 clinically relevant small molecules. Across ∼5,000 community-drug interaction conditions, compositional and metabolomic responses were predictably impacted by nutrient competition, with certain species exhibiting improved growth due to adverse impacts on competitors. Changes to community composition were generally reversed by reseeding with the original community, although occasionally species promotion was long-lasting, due to higher-order interactions, even when the competitor was reseeded. Despite strong selection pressures, emergence of resistance within communities was infrequent. Finally, while qualitative species responses to drug perturbations were conserved across community contexts, nutrient competition quantitatively affected their abundances, consistent with predictions of consumer-resource models. Our study reveals that quantitative understanding of the interaction landscape, particularly nutrient competition, can be used to anticipate and potentially mitigate side effects of drug treatment on the gut microbiota.

Preserving the efficacy of antibiotics to tackle antibiotic resistance

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

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into 6 classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

Shaping of microbial phenotypes by trade-offs

Growth rate maximization is an important fitness strategy for microbes. However, the wide distribution of slow-growing oligotrophic microbes in ecosystems suggests that rapid growth is often not favored across ecological environments. In many circumstances, there exist trade-offs between growth and other important traits (e.g., adaptability and survival) due to physiological and proteome constraints. Investments on alternative traits could compromise growth rate and microbes need to adopt bet-hedging strategies to improve fitness in fluctuating environments. Here we review the mechanistic role of trade-offs in controlling bacterial growth and further highlight its ecological implications in driving the emergences of many important ecological phenomena such as co-existence, population heterogeneity and oligotrophic/copiotrophic lifestyles.

Protein Engineering with Lightweight Graph Denoising Neural Networks

Protein engineering faces challenges in finding optimal mutants from a massive pool of candidate mutants. In this study, we introduce a deep-learning-based data-efficient fitness prediction tool to steer protein engineering. Our methodology establishes a lightweight graph neural network scheme for protein structures, which efficiently analyzes the microenvironment of amino acids in wild-type proteins and reconstructs the distribution of the amino acid sequences that are more likely to pass natural selection. This distribution serves as a general guidance for scoring proteins toward arbitrary properties on any order of mutations. Our proposed solution undergoes extensive wet-lab experimental validation spanning diverse physicochemical properties of various proteins, including fluorescence intensity, antigen-antibody affinity, thermostability, and DNA cleavage activity. More than 40% of ProtLGN-designed single-site mutants outperform their wild-type counterparts across all studied proteins and targeted properties. More importantly, our model can bypass the negative epistatic effect to combine single mutation sites and form deep mutants with up to seven mutation sites in a single round, whose physicochemical properties are significantly improved. This observation provides compelling evidence of the structure-based model's potential to guide deep mutations in protein engineering. Overall, our approach emerges as a versatile tool for protein engineering, benefiting both the computational and bioengineering communities.

The Effect of the Stringent Response and Oxidative Stress Response on Fitness Costs of De Novo Acquisition of Antibiotic Resistance

Resistance evolution during exposure to non-lethal levels of antibiotics is influenced by various stress responses of bacteria which are known to affect growth rate. Here, we aim to disentangle how the interplay between resistance development and associated fitness costs is affected by stress responses. We performed de novo resistance evolution of wild-type strains and single-gene knockout strains in stress response pathways using four different antibiotics. Throughout resistance development, the increase in minimum inhibitory concentration (MIC) is accompanied by a gradual decrease in growth rate, most pronounced in amoxicillin or kanamycin. By measuring biomass yield on glucose and whole-genome sequences at intermediate and final time points, we identified two patterns of how the stress responses affect the correlation between MIC and growth rate. First, single-gene knockout E. coli strains associated with reactive oxygen species (ROS) acquire resistance faster, and mutations related to antibiotic permeability and pumping out occur earlier. This increases the metabolic burden of resistant bacteria. Second, the ΔrelA knockout strain, which has reduced (p)ppGpp synthesis, is restricted in its stringent response, leading to diminished growth rates. The ROS-related mutagenesis and the stringent response increase metabolic burdens during resistance development, causing lower growth rates and higher fitness costs.

Interspecific competition prevents the proliferation of social cheaters in an unstructured environment

Bacterial communities are intricate ecosystems in which various members interact, compete for resources, and influence each other's growth. Antibiotics intensify this complexity, posing challenges in maintaining biodiversity. In this study, we delved into the behavior of kin bacterial communities when subjected to antibiotic perturbations, with a particular focus on how interspecific interactions shape these responses. We hypothesized that social cheating-where resistant strains shield both themselves and neighboring cheaters-obstructed coexistence, especially when kin bacteria exhibited varied growth rates and antibiotic sensitivities. To explore potential pathways to coexistence, we incorporated a third bacterial member, anticipating a shift in the dynamics of community coexistence. Simulations and experimental bacterial communities confirmed our predictions, emphasizing the pivotal role of interspecific competition in promoting coexistence under antibiotic interference. These insights are crucial for understanding bacterial ecosystem stability, interpreting drug-microbiome interactions, and predicting bacterial community adaptations to environmental changes.

Steering and controlling evolution - from bioengineering to fighting pathogens

Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost-benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.

Evolutionary Epidemiology Consequences of Trait-Dependent Control of Heterogeneous Parasites

AbstractDisease control can induce both demographic and evolutionary responses in host-parasite systems. Foreseeing the outcome of control therefore requires knowledge of the eco-evolutionary feedback between control and system. Previous work has assumed that control strategies have a homogeneous effect on the parasite population. However, this is not true when control targets those traits that confer to the parasite heterogeneous levels of resistance, which can additionally be related to other key parasite traits through evolutionary trade-offs. In this work, we develop a minimal model coupling epidemiological and evolutionary dynamics to explore possible trait-dependent effects of control strategies. In particular, we consider a parasite expressing continuous levels of a trait-determining resource exploitation and a control treatment that can be either positively or negatively correlated with that trait. We demonstrate the potential of trait-dependent control by considering that the decision maker may want to minimize both the damage caused by the disease and the use of treatment, due to possible environmental or economic costs. We identify efficient strategies showing that the optimal type of treatment depends on the amount applied. Our results pave the way for the study of control strategies based on evolutionary constraints, such as collateral sensitivity and resistance costs, which are receiving increasing attention for both public health and agricultural purposes.

Translating eco-evolutionary biology into therapy to tackle antibiotic resistance

Antibiotic resistance is currently one of the most important public health problems. The golden age of antibiotic discovery ended decades ago, and new approaches are urgently needed. Therefore, preserving the efficacy of the antibiotics currently in use and developing compounds and strategies that specifically target antibiotic-resistant pathogens is critical. The identification of robust trends of antibiotic resistance evolution and of its associated trade-offs, such as collateral sensitivity or fitness costs, is invaluable for the design of rational evolution-based, ecology-based treatment approaches. In this Review, we discuss these evolutionary trade-offs and how such knowledge can aid in informing combination or alternating antibiotic therapies against bacterial infections. In addition, we discuss how targeting bacterial metabolism can enhance drug activity and impair antibiotic resistance evolution. Finally, we explore how an improved understanding of the original physiological function of antibiotic resistance determinants, which have evolved to reach clinical resistance after a process of historical contingency, may help to tackle antibiotic resistance.

Quantitative systems-based prediction of antimicrobial resistance evolution

Predicting evolution is a fundamental problem in biology with practical implications for treating antimicrobial resistance, which is a complex system-level phenomenon. In this perspective article, we explore the limits of predicting antimicrobial resistance evolution, quantitatively define the predictability and repeatability of microevolutionary processes, and speculate on how these quantities vary across temporal, biological, and complexity scales. The opportunities and challenges for predicting antimicrobial resistance in the context of systems biology are also discussed. Based on recent research, we conclude that the evolution of antimicrobial resistance can be predicted using a systems biology approach integrating quantitative models with multiscale data from microbial evolution experiments.

Risk assessment and dissemination mechanism of antibiotic resistance genes in compost

In recent years, the excessive of antibiotics in livestock and poultry husbandry, stemming from extensive industry experience, has resulted in the accumulation of residual antibiotics and antibiotic resistance genes (ARGs) in livestock manure. Composting, as a crucial approach for the utilization of manure resources, has the potential to reduce the levels of antibiotics and ARGs in manure, although complete elimination is challenging. Previous studies have primarily focused on the diversity and abundance of ARGs in compost or have solely examined the correlation between ARGs and their carriers, potentially leading to a misjudgment of the actual risk associated with ARGs in compost. To address this gap, this study investigated the transfer potential of ARGs in compost and their co-occurrence with opportunistic pathogenic bacteria by extensively analyzing metagenomic sequencing data of compost worldwide. The results demonstrated that the potential risk of ARGs in compost was significantly lower than in manure, suggesting that composting effectively reduces the risk of ARGs. Further analysis showed that the microbes shifted their life history strategy in manure and compost due to antibiotic pressure and formed metabolic interactions dominated by antibiotic-resistant microbes, increasing ARG dissemination frequency. Therefore, husbandry practice without antibiotic addition was recommended to control ARG evolution, dissemination, and abatement both at the source and throughout processing.

Epistasis decreases with increasing antibiotic pressure but not temperature

Predicting mutational effects is essential for the control of antibiotic resistance (ABR). Predictions are difficult when there are strong genotype-by-environment (G × E), gene-by-gene (G × G or epistatic) or gene-by-gene-by-environment (G × G × E) interactions. We quantified G × G × E effects in Escherichia coli across environmental gradients. We created intergenic fitness landscapes using gene knock-outs and single-nucleotide ABR mutations previously identified to vary in the extent of G × E effects in our environments of interest. Then, we measured competitive fitness across a complete combinatorial set of temperature and antibiotic dosage gradients. In this way, we assessed the predictability of 15 fitness landscapes across 12 different but related environments. We found G × G interactions and rugged fitness landscapes in the absence of antibiotic, but as antibiotic concentration increased, the fitness effects of ABR genotypes quickly overshadowed those of gene knock-outs, and the landscapes became smoother. Our work reiterates that some single mutants, like those conferring resistance or susceptibility to antibiotics, have consistent effects across genetic backgrounds in stressful environments. Thus, although epistasis may reduce the predictability of evolution in benign environments, evolution may be more predictable in adverse environments. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Shaping bacterial gene expression by physiological and proteome allocation constraints

Networks of molecular regulators are often the primary objects of focus in the study of gene regulation, with the machinery of protein synthesis tacitly relegated to the background. Shifting focus to the constraints imposed by the allocation of protein synthesis flux reveals surprising ways in which the actions of molecular regulators are shaped by physiological demands. Using carbon catabolite repression as a case study, we describe how physiological constraints are sensed through metabolic fluxes and how flux-controlled regulation gives rise to simple empirical relations between protein levels and the rate of cell growth.

Collateral Sensitivity to Fosfomycin of Tobramycin-Resistant Mutants of Pseudomonas aeruginosa Is Contingent on Bacterial Genomic Background

Understanding the consequences in bacterial physiology of the acquisition of drug resistance is needed to identify and exploit the weaknesses derived from it. One of them is collateral sensitivity, a potentially exploitable phenotype that, unfortunately, is not always conserved among different isolates. The identification of robust, conserved collateral sensitivity patterns is then relevant for the translation of this knowledge into clinical practice. We have previously identified a robust fosfomycin collateral sensitivity pattern of Pseudomonas aeruginosa that emerged in different tobramycin-resistant clones. To go one step further, here, we studied if the acquisition of resistance to tobramycin is associated with robust collateral sensitivity to fosfomycin among P. aeruginosa isolates. To that aim, we analyzed, using adaptive laboratory evolution approaches, 23 different clinical isolates of P. aeruginosa presenting diverse mutational resistomes. Nine of them showed collateral sensitivity to fosfomycin, indicating that this phenotype is contingent on the genetic background. Interestingly, collateral sensitivity to fosfomycin was linked to a larger increase in tobramycin minimal inhibitory concentration. Further, we unveiled that fosA low expression, rendering a higher intracellular accumulation of fosfomycin, and a reduction in the expression of the P. aeruginosa alternative peptidoglycan-recycling pathway enzymes, might be on the basis of the collateral sensitivity phenotype.

Evolutionary Responses to Acquiring a Multidrug Resistance Plasmid Are Dominated by Metabolic Functions across Diverse Escherichia coli Lineages

Multidrug resistance (MDR) plasmids drive the spread of antibiotic resistance between bacterial lineages. The immediate impact of MDR plasmid acquisition on fitness and cellular processes varies among bacterial lineages, but how the evolutionary processes enabling the genomic integration of MDR plasmids vary is less well understood, particularly in clinical pathogens. Using diverse Escherichia coli lineages experimentally evolved for ~700 generations, we show that the evolutionary response to gaining the MDR plasmid pLL35 was dominated by chromosomal mutations affecting metabolic and regulatory functions, with both strain-specific and shared mutational targets. The expression of several of these functions, such as anaerobic metabolism, is known to be altered upon acquisition of pLL35. Interactions with resident mobile genetic elements, notably several IS-elements, potentiated parallel mutations, including insertions upstream of hns that were associated with its upregulation and the downregulation of the plasmid-encoded extended-spectrum beta-lactamase gene. Plasmid parallel mutations targeted conjugation-related genes, whose expression was also commonly downregulated in evolved clones. Beyond their role in horizontal gene transfer, plasmids can be an important selective force shaping the evolution of bacterial chromosomes and core cellular functions. IMPORTANCE Plasmids drive the spread of antimicrobial resistance genes between bacterial genomes. However, the evolutionary processes allowing plasmids to be assimilated by diverse bacterial genomes are poorly understood, especially in clinical pathogens. Using experimental evolution with diverse E. coli lineages and a clinical multidrug resistance plasmid, we show that although plasmids drove unique evolutionary paths per lineage, there was a surprising degree of convergence in the functions targeted by mutations across lineages, dominated by metabolic functions. Remarkably, these same metabolic functions show higher evolutionary rates in MDR-lineages in nature and in some cases, like anaerobic metabolism, their expression is directly manipulated by the plasmid. Interactions with other mobile elements resident in the genomes accelerated adaptation by disrupting genes and regulatory sequences that they inserted into. Beyond their role in horizontal gene transfer, plasmids are an important selective force driving the evolution of bacterial genomes and core cellular functions.

Molecular Origins of Force-Dependent Protein Complex Stabilization during Bacterial Infections

The unbinding pathway of a protein complex can vary significantly depending on biochemical and mechanical factors. Under mechanical stress, a complex may dissociate through a mechanism different from that used in simple thermal dissociation, leading to different dissociation rates under shear force and thermal dissociation. This is a well-known phenomenon studied in biomechanics whose molecular and atomic details are still elusive. A particularly interesting case is the complex formed by bacterial adhesins with their human peptide target. These protein interactions have a force resilience equivalent to those of covalent bonds, an order of magnitude stronger than the widely used streptavidin:biotin complex, while having an ordinary affinity, much lower than that of streptavidin:biotin. Here, in an in silico single-molecule force spectroscopy approach, we use molecular dynamics simulations to investigate the dissociation mechanism of adhesin/peptide complexes. We show how the Staphylococcus epidermidis adhesin SdrG uses a catch-bond mechanism to increase complex stability with increasing mechanical stress. While allowing for thermal dissociation in a low-force regime, an entirely different mechanical dissociation path emerges in a high-force regime, revealing an intricate mechanism that does not depend on the peptide's amino acid sequence. Using a dynamic network analysis approach, we identified key amino acid contacts that describe the mechanics of this complex, revealing differences in dynamics that hinder thermal dissociation and establish the mechanical dissociation path. We then validate the information content of the selected amino acid contacts using their dynamics to successfully predict the rupture forces for this complex through a machine learning model.

Towards evolutionary predictions: Current promises and challenges

Evolution has traditionally been a historical and descriptive science, and predicting future evolutionary processes has long been considered impossible. However, evolutionary predictions are increasingly being developed and used in medicine, agriculture, biotechnology and conservation biology. Evolutionary predictions may be used for different purposes, such as to prepare for the future, to try and change the course of evolution or to determine how well we understand evolutionary processes. Similarly, the exact aspect of the evolved population that we want to predict may also differ. For example, we could try to predict which genotype will dominate, the fitness of the population or the extinction probability of a population. In addition, there are many uses of evolutionary predictions that may not always be recognized as such. The main goal of this review is to increase awareness of methods and data in different research fields by showing the breadth of situations in which evolutionary predictions are made. We describe how diverse evolutionary predictions share a common structure described by the predictive scope, time scale and precision. Then, by using examples ranging from SARS-CoV2 and influenza to CRISPR-based gene drives and sustainable product formation in biotechnology, we discuss the methods for predicting evolution, the factors that affect predictability and how predictions can be used to prevent evolution in undesirable directions or to promote beneficial evolution (i.e. evolutionary control). We hope that this review will stimulate collaboration between fields by establishing a common language for evolutionary predictions.

CanB is a metabolic mediator of antibiotic resistance in Neisseria gonorrhoeae

The evolution of the obligate human pathogen Neisseria gonorrhoeae has been shaped by selective pressures from diverse host niche environments and antibiotics. The varying prevalence of antibiotic resistance across N. gonorrhoeae lineages suggests that underlying metabolic differences may influence the likelihood of acquisition of specific resistance mutations. We hypothesized that the requirement for supplemental CO₂, present in approximately half of isolates, reflects one such example of metabolic variation. Here, using a genome-wide association study and experimental investigations, we show that CO₂ dependence is attributable to a single substitution in a β-carbonic anhydrase, CanB. CanB^19E is necessary and sufficient for growth in the absence of CO₂, and the hypomorphic CanB^19G variant confers CO₂ dependence. Furthermore, ciprofloxacin resistance is correlated with CanB^19G in clinical isolates, and the presence of CanB^19G increases the likelihood of acquisition of ciprofloxacin resistance. Together, our results suggest that metabolic variation has affected the acquisition of fluoroquinolone resistance.

Analysis of the evolution of resistance to multiple antibiotics enables prediction of the Escherichia coli phenotype-based fitness landscape

The fitness landscape represents the complex relationship between genotype or phenotype and fitness under a given environment, the structure of which allows the explanation and prediction of evolutionary trajectories. Although previous studies have constructed fitness landscapes by comprehensively studying the mutations in specific genes, the high dimensionality of genotypic changes prevents us from developing a fitness landscape capable of predicting evolution for the whole cell. Herein, we address this problem by inferring the phenotype-based fitness landscape for antibiotic resistance evolution by quantifying the multidimensional phenotypic changes, i.e., time-series data of resistance for eight different drugs. We show that different peaks of the landscape correspond to different drug resistance mechanisms, thus supporting the validity of the inferred phenotype-fitness landscape. We further discuss how inferred phenotype-fitness landscapes could contribute to the prediction and control of evolution. This approach bridges the gap between phenotypic/genotypic changes and fitness while contributing to a better understanding of drug resistance evolution.

On the incongruence of genotype-phenotype and fitness landscapes

The mapping from genotype to phenotype to fitness typically involves multiple nonlinearities that can transform the effects of mutations. For example, mutations may contribute additively to a phenotype, but their effects on fitness may combine non-additively because selection favors a low or intermediate value of that phenotype. This can cause incongruence between the topographical properties of a fitness landscape and its underlying genotype-phenotype landscape. Yet, genotype-phenotype landscapes are often used as a proxy for fitness landscapes to study the dynamics and predictability of evolution. Here, we use theoretical models and empirical data on transcription factor-DNA interactions to systematically study the incongruence of genotype-phenotype and fitness landscapes when selection favors a low or intermediate phenotypic value. Using the theoretical models, we prove a number of fundamental results. For example, selection for low or intermediate phenotypic values does not change simple sign epistasis into reciprocal sign epistasis, implying that genotype-phenotype landscapes with only simple sign epistasis motifs will always give rise to single-peaked fitness landscapes under such selection. More broadly, we show that such selection tends to create fitness landscapes that are more rugged than the underlying genotype-phenotype landscape, but this increased ruggedness typically does not frustrate adaptive evolution because the local adaptive peaks in the fitness landscape tend to be nearly as tall as the global peak. Many of these results carry forward to the empirical genotype-phenotype landscapes, which may help to explain why low- and intermediate-affinity transcription factor-DNA interactions are so prevalent in eukaryotic gene regulation.

Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations

Antibiotic-resistant pathogens are a major public health threat. A deeper understanding of how an antibiotic's mechanism of action influences the emergence of resistance would aid in the design of new drugs and help to preserve the effectiveness of existing ones. To this end, we developed a model that links bacterial population dynamics with antibiotic-target binding kinetics. Our approach allows us to derive mechanistic insights on drug activity from population-scale experimental data and to quantify the interplay between drug mechanism and resistance selection. We find that both bacteriostatic and bactericidal agents can be equally effective at suppressing the selection of resistant mutants, but that key determinants of resistance selection are the relationships between the number of drug-inactivated targets within a cell and the rates of cellular growth and death. We also show that heterogeneous drug-target binding within a population enables resistant bacteria to evolve fitness-improving secondary mutations even when drug doses remain above the resistant strain's minimum inhibitory concentration. Our work suggests that antibiotic doses beyond this "secondary mutation selection window" could safeguard against the emergence of high-fitness resistant strains during treatment.

Non-antibiotic pharmaceuticals promote conjugative plasmid transfer at a community-wide level

BACKGROUND

Horizontal gene transfer (HGT) plays a critical role in the spread of antibiotic resistance and the evolutionary shaping of bacterial communities. Conjugation is the most well characterized pathway for the spread of antibiotic resistance, compared to transformation and transduction. While antibiotics have been found to induce HGT, it remains unknown whether non-antibiotic pharmaceuticals can facilitate conjugation at a microbial community-wide level.

RESULTS

In this study, we demonstrate that several commonly consumed non-antibiotic pharmaceuticals (including carbamazepine, ibuprofen, naproxen and propranolol), at environmentally relevant concentrations (0.5 mg/L), can promote the conjugative transfer of IncP1-α plasmid-borne antibiotic resistance across entire microbial communities. The over-generation of reactive oxygen species in response to these non-antibiotic pharmaceuticals may contribute to the enhanced conjugation ratios. Cell sorting and 16S rRNA gene amplicon sequencing analyses indicated that non-antibiotic pharmaceuticals modulate transconjugant microbial communities at both phylum and genus levels. Moreover, microbial uptake ability of the IncP1-α plasmid was also upregulated under non-antibiotic pharmaceutical exposure. Several opportunistic pathogens, such as Acinetobacter and Legionella, were more likely to acquire the plasmid conferring multidrug resistance.

CONCLUSIONS

Considering the high possibility of co-occurrence of pathogenic bacteria, conjugative IncP1-α plasmids and non-antibiotic pharmaceuticals in various environments (e.g., activated sludge systems), our findings illustrate the potential risk associated with increased dissemination of antibiotic resistance promoted by non-antibiotic pharmaceuticals in complex environmental settings. Video abstract.

Fundamental immune-oncogenicity trade-offs define driver mutation fitness

Missense driver mutations in cancer are concentrated in a few hotspots1. Various mechanisms have been proposed to explain this skew, including biased mutational processes2, phenotypic differences3-6 and immunoediting of neoantigens7,8; however, to our knowledge, no existing model weighs the relative contribution of these features to tumour evolution. We propose a unified theoretical 'free fitness' framework that parsimoniously integrates multimodal genomic, epigenetic, transcriptomic and proteomic data into a biophysical model of the rate-limiting processes underlying the fitness advantage conferred on cancer cells by driver gene mutations. Focusing on TP53, the most mutated gene in cancer1, we present an inference of mutant p53 concentration and demonstrate that TP53 hotspot mutations optimally solve an evolutionary trade-off between oncogenic potential and neoantigen immunogenicity. Our model anticipates patient survival in The Cancer Genome Atlas and patients with lung cancer treated with immunotherapy as well as the age of tumour onset in germline carriers of TP53 variants. The predicted differential immunogenicity between hotspot mutations was validated experimentally in patients with cancer and in a unique large dataset of healthy individuals. Our data indicate that immune selective pressure on TP53 mutations has a smaller role in non-cancerous lesions than in tumours, suggesting that targeted immunotherapy may offer an early prophylactic opportunity for the former. Determining the relative contribution of immunogenicity and oncogenic function to the selective advantage of hotspot mutations thus has important implications for both precision immunotherapies and our understanding of tumour evolution.

Growth-dependent heterogeneity in the DNA damage response in Escherichia coli

In natural environments, bacteria are frequently exposed to sub-lethal levels of DNA damage, which leads to the induction of a stress response (the SOS response in Escherichia coli). Natural environments also vary in nutrient availability, resulting in distinct physiological changes in bacteria, which may have direct implications on their capacity to repair their chromosomes. Here, we evaluated the impact of varying the nutrient availability on the expression of the SOS response induced by chronic sub-lethal DNA damage in E. coli. We found heterogeneous expression of the SOS regulon at the single-cell level in all growth conditions. Surprisingly, we observed a larger fraction of high SOS-induced cells in slow growth as compared with fast growth, despite a higher rate of SOS induction in fast growth. The result can be explained by the dynamic balance between the rate of SOS induction and the division rates of cells exposed to DNA damage. Taken together, our data illustrate how cell division and physiology come together to produce growth-dependent heterogeneity in the DNA damage response.

Population size mediates the contribution of high-rate and large-benefit mutations to parallel evolution

Mutations with large fitness benefits and mutations occurring at high rates may both cause parallel evolution, but their contribution is predicted to depend on population size. Moreover, high-rate and large-benefit mutations may have different long-term adaptive consequences. We show that small and 100-fold larger bacterial populations evolve resistance to a β-lactam antibiotic by using similar numbers, but different types of mutations. Small populations frequently substitute similar high-rate structural variants and loss-of-function point mutations, including the deletion of a low-activity β-lactamase, and evolve modest resistance levels. Large populations more often use low-rate, large-benefit point mutations affecting the same targets, including mutations activating the β-lactamase and other gain-of-function mutations, leading to much higher resistance levels. Our results demonstrate the separation by clonal interference of mutation classes with divergent adaptive consequences, causing a shift from high-rate to large-benefit mutations with increases in population size.

Unraveling antimicrobial resistance using metabolomics

The emergence of antimicrobial resistance (AMR) in bacterial pathogens represents a global health threat. The metabolic state of bacteria is associated with a range of genetic and phenotypic resistance mechanisms. This review provides an overview of the roles of metabolic processes that are associated with AMR mechanisms, including energy production, cell wall synthesis, cell-cell communication, and bacterial growth. These metabolic processes can be targeted with the aim of re-sensitizing resistant pathogens to antibiotic treatments. We discuss how state-of-the-art metabolomics approaches can be used for comprehensive analysis of microbial AMR-related metabolism, which may facilitate the discovery of novel drug targets and treatment strategies. TEASER: Novel treatment strategies are needed to address the emerging threat of antimicrobial resistance (AMR) in bacterial pathogens. Metabolomics approaches may help to unravel the biochemical underpinnings of AMR, thereby facilitating the discovery of metabolism-associated drug targets and treatment strategies.

Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance

Microbial communities are composed of cells of varying metabolic capacity, and regularly include auxotrophs that lack essential metabolic pathways. Through analysis of auxotrophs for amino acid biosynthesis pathways in microbiome data derived from >12,000 natural microbial communities obtained as part of the Earth Microbiome Project (EMP), and study of auxotrophic–prototrophic interactions in self-establishing metabolically cooperating yeast communities (SeMeCos), we reveal a metabolically imprinted mechanism that links the presence of auxotrophs to an increase in metabolic interactions and gains in antimicrobial drug tolerance. As a consequence of the metabolic adaptations necessary to uptake specific metabolites, auxotrophs obtain altered metabolic flux distributions, export more metabolites and, in this way, enrich community environments in metabolites. Moreover, increased efflux activities reduce intracellular drug concentrations, allowing cells to grow in the presence of drug levels above minimal inhibitory concentrations. For example, we show that the antifungal action of azoles is greatly diminished in yeast cells that uptake metabolites from a metabolically enriched environment. Our results hence provide a mechanism that explains why cells are more robust to drug exposure when they interact metabolically.

Using microbiome data analysis and a self-establishing metabolically cooperating yeast community model, the authors show that the presence of auxotrophs in a microbial community increases metabolic interactions between cells and fosters antimicrobial drug tolerance.

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.

Genome-Wide Screening and Characterization of Genes Involved in Response to High Dose of Ciprofloxacin in Escherichia coli

The global emergence of antibiotic resistance, especially in Gram-negative bacteria, is an urgent threat to public health. Inevitably, considering its extensive use and misuse, resistance toward ciprofloxacin has increased in almost all clinically relevant bacteria. This study aimed to investigate the transcriptome changes at a high concentration of ciprofloxacin in Escherichia coli. In brief, 1,418 differentially expressed genes (DEGs) were identified, from which 773 genes were upregulated by ciprofloxacin, whereas 651 genes were downregulated. Enriched biological pathways reflected the upregulation of biological processes such as DNA damage and repair system, toxin/antitoxin systems, formaldehyde detoxification system. With kyoto encyclopedia of genes and genomes pathway analysis, higher expressed DEGs were associated with "LPS biosynthesis," "streptomycin biosynthesis," and "polyketide sugar unit biosynthesis." Lower expressed DEGs were associated with "biosynthesis of amino acids" and "flagellar assembly" pathways. After treatment of ciprofloxacin, lipopolysaccharide (LPS) release was increased by two times, and the gene expression level of LPS synthesis was elevated (p < 0.05) in both reference and clinical strains. Our results demonstrated that transient exposure to high-dose ciprofloxacin is a double-edged sword. Cautions should be taken when administering high-dose antibiotic treatment for infectious diseases.

The population genetics of collateral resistance and sensitivity

Resistance mutations against one drug can elicit collateral sensitivity against other drugs. Multi-drug treatments exploiting such trade-offs can help slow down the evolution of resistance. However, if mutations with diverse collateral effects are available, a treated population may evolve either collateral sensitivity or collateral resistance. How to design treatments robust to such uncertainty is unclear. We show that many resistance mutations in Escherichia coli against various antibiotics indeed have diverse collateral effects. We propose to characterize such diversity with a joint distribution of fitness effects (JDFE) and develop a theory for describing and predicting collateral evolution based on simple statistics of the JDFE. We show how to robustly rank drug pairs to minimize the risk of collateral resistance and how to estimate JDFEs. In addition to practical applications, these results have implications for our understanding of evolution in variable environments.

Drugs known as antibiotics are the main treatment for most serious infections caused by bacteria. However, many bacteria are acquiring genetic mutations that make them resistant to the effects of one or more types of antibiotics, making them harder to eliminate.

One way to tackle drug-resistant bacteria is to develop new types of antibiotics; however, in recent years, the rate at which new antibiotics have become available has been dwindling. Using two or more existing drugs, one after another, can also be an effective way to eliminate resistant bacteria. The success of any such ‘multi-drug’ treatment lies in being able to predict whether mutations that make the bacteria resistant to one drug simultaneously make it sensitive to another, a phenomenon known as collateral sensitivity.

Different resistance mutations may have different collateral effects: some may increase the bacteria’s sensitivity to the second drug, while others might make the bacteria more resistant. However, it is currently unclear how to design robust multi-drug treatments that take this diversity of collateral effects into account.

Here, Ardell and Kryazhimskiy used a concept called JDFE (short for the joint distribution of fitness effects) to describe the diversity of collateral effects in a population of bacteria exposed to a single drug. This information was then used to mathematically model how collateral effects evolved in the population over time.

Ardell and Kryazhimskiy showed that this approach can predict how likely a population is to become collaterally sensitive or collaterally resistant to a second antibiotic. Drug pairs can then be ranked according to the risk of collateral resistance emerging, so long as information on the variety of resistance mutations available to the bacteria are included in the model.

Each year, more than 700,000 people die from infections caused by bacteria that are resistant to one or more antibiotics. The findings of Ardell and Kryazhimskiy may eventually help clinicians design multi-drug treatments that effectively eliminate bacterial infections and help to prevent more bacteria from evolving resistance to antibiotics. However, to achieve this goal, more research is needed to fully understand the range collateral effects caused by resistance mutations.

Forecasting of phenotypic and genetic outcomes of experimental evolution in Pseudomonas protegens

Experimental evolution with microbes is often highly repeatable under identical conditions, suggesting the possibility to predict short-term evolution. However, it is not clear to what degree evolutionary forecasts can be extended to related species in non-identical environments, which would allow testing of general predictive models and fundamental biological assumptions. To develop an extended model system for evolutionary forecasting, we used previous data and models of the genotype-to-phenotype map from the wrinkly spreader system in Pseudomonas fluorescens SBW25 to make predictions of evolutionary outcomes on different biological levels for Pseudomonas protegens Pf-5. In addition to sequence divergence (78% amino acid and 81% nucleotide identity) for the genes targeted by mutations, these species also differ in the inability of Pf-5 to make cellulose, which is the main structural basis for the adaptive phenotype in SBW25. The experimental conditions were changed compared to the SBW25 system to test if forecasts were extendable to a non-identical environment. Forty-three mutants with increased ability to colonize the air-liquid interface were isolated, and the majority had reduced motility and was partly dependent on the Pel exopolysaccharide as a structural component. Most (38/43) mutations are expected to disrupt negative regulation of the same three diguanylate cyclases as in SBW25, with a smaller number of mutations in promoter regions, including an uncharacterized polysaccharide synthase operon. A mathematical model developed for SBW25 predicted the order of the three main pathways and the genes targeted by mutations, but differences in fitness between mutants and mutational biases also appear to influence outcomes. Mutated regions in proteins could be predicted in most cases (16/22), but parallelism at the nucleotide level was low and mutational hot spot sites were not conserved. This study demonstrates the potential of short-term evolutionary forecasting in experimental populations and provides testable predictions for evolutionary outcomes in other Pseudomonas species.