# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 9604 | 0 | 1.0000 | Extreme Antibiotic Persistence via Heterogeneity-Generating Mutations Targeting Translation. Antibiotic persistence, the noninherited tolerance of a subpopulation of bacteria to high levels of antibiotics, is a bet-hedging phenomenon with broad clinical implications. Indeed, the isolation of bacteria with substantially increased persistence rates from chronic infections suggests that evolution of hyperpersistence is a significant factor in clinical therapy resistance. However, the pathways that lead to hyperpersistence and the underlying cellular states have yet to be systematically studied. Here, we show that laboratory evolution can lead to increase in persistence rates by orders of magnitude for multiple independently evolved populations of Escherichia coli and that the driving mutations are highly enriched in translation-related genes. Furthermore, two distinct adaptive mutations converge on concordant transcriptional changes, including increased population heterogeneity in the expression of several genes. Cells with extreme expression of these genes showed dramatic differences in persistence rates, enabling isolation of subpopulations in which a substantial fraction of cells are persisters. Expression analysis reveals coherent regulation of specific pathways that may be critical to establishing the hyperpersistence state. Hyperpersister mutants can thus enable the systematic molecular characterization of this unique physiological state, a critical prerequisite for developing antipersistence strategies.IMPORTANCE Bacterial persistence is a fascinating phenomenon in which a small subpopulation of bacteria becomes phenotypically tolerant to lethal antibiotic exposure. There is growing evidence that populations of bacteria in chronic clinical infections develop a hyperpersistent phenotype, enabling a substantially larger subpopulation to survive repeated antibiotic treatment. The mechanisms of persistence and modes of increasing persistence rates remain largely unknown. Here, we utilized experimental evolution to select for Escherichia coli mutants that have more than a thousandfold increase in persistence rates. We discovered that a variety of individual mutations to translation-related processes are causally involved. Furthermore, we found that these mutations lead to population heterogeneity in the expression of specific genes. We show that this can be used to isolate populations in which the majority of bacteria are persisters, thereby enabling systems-level characterization of this fascinating and clinically significant microbial phenomenon. | 2020 | 31964772 |
| 9607 | 1 | 0.9999 | Transcriptome-Level Signatures in Gene Expression and Gene Expression Variability during Bacterial Adaptive Evolution. Antibiotic-resistant bacteria are an increasingly serious public health concern, as strains emerge that demonstrate resistance to almost all available treatments. One factor that contributes to the crisis is the adaptive ability of bacteria, which exhibit remarkable phenotypic and gene expression heterogeneity in order to gain a survival advantage in damaging environments. This high degree of variability in gene expression across biological populations makes it a challenging task to identify key regulators of bacterial adaptation. Here, we research the regulation of adaptive resistance by investigating transcriptome profiles of Escherichia coli upon adaptation to disparate toxins, including antibiotics and biofuels. We locate potential target genes via conventional gene expression analysis as well as using a new analysis technique examining differential gene expression variability. By investigating trends across the diverse adaptation conditions, we identify a focused set of genes with conserved behavior, including those involved in cell motility, metabolism, membrane structure, and transport, and several genes of unknown function. To validate the biological relevance of the observed changes, we synthetically perturb gene expression using clustered regularly interspaced short palindromic repeat (CRISPR)-dCas9. Manipulation of select genes in combination with antibiotic treatment promotes adaptive resistance as demonstrated by an increased degree of antibiotic tolerance and heterogeneity in MICs. We study the mechanisms by which identified genes influence adaptation and find that select differentially variable genes have the potential to impact metabolic rates, mutation rates, and motility. Overall, this work provides evidence for a complex nongenetic response, encompassing shifts in gene expression and gene expression variability, which underlies adaptive resistance. IMPORTANCE Even initially sensitive bacteria can rapidly thwart antibiotic treatment through stress response processes known as adaptive resistance. Adaptive resistance fosters transient tolerance increases and the emergence of mutations conferring heritable drug resistance. In order to extend the applicable lifetime of new antibiotics, we must seek to hinder the occurrence of bacterial adaptive resistance; however, the regulation of adaptation is difficult to identify due to immense heterogeneity emerging during evolution. This study specifically seeks to generate heterogeneity by adapting bacteria to different stresses and then examines gene expression trends across the disparate populations in order to pinpoint key genes and pathways associated with adaptive resistance. The targets identified here may eventually inform strategies for impeding adaptive resistance and prolonging the effectiveness of antibiotic treatment. | 2017 | 28217741 |
| 9605 | 2 | 0.9999 | Gene Expression Variability Underlies Adaptive Resistance in Phenotypically Heterogeneous Bacterial Populations. The root cause of the antibiotic resistance crisis is the ability of bacteria to evolve resistance to a multitude of antibiotics and other environmental toxins. The regulation of adaptation is difficult to pinpoint due to extensive phenotypic heterogeneity arising during evolution. Here, we investigate the mechanisms underlying general bacterial adaptation by evolving wild-type Escherichia coli populations to dissimilar chemical toxins. We demonstrate the presence of extensive inter- and intrapopulation phenotypic heterogeneity across adapted populations in multiple traits, including minimum inhibitory concentration, growth rate, and lag time. To search for a common response across the heterogeneous adapted populations, we measured gene expression in three stress-response networks: the mar regulon, the general stress response, and the SOS response. While few genes were differentially expressed, clustering revealed that interpopulation gene expression variability in adapted populations was distinct from that of unadapted populations. Notably, we observed both increases and decreases in gene expression variability upon adaptation. Sequencing select genes revealed that the observed gene expression trends are not necessarily attributable to genetic changes. To further explore the connection between gene expression variability and adaptation, we propagated single-gene knockout and CRISPR (clustered regularly interspaced short palindromic repeats) interference strains and quantified impact on adaptation to antibiotics. We identified significant correlations that suggest genes with low expression variability have greater impact on adaptation. This study provides evidence that gene expression variability can be used as an indicator of bacterial adaptive resistance, even in the face of the pervasive phenotypic heterogeneity underlying adaptation. | 2015 | 27623410 |
| 8992 | 3 | 0.9998 | Epigenetic-Based Regulation of Transcriptome in Escherichia coli Adaptive Antibiotic Resistance. Adaptive antibiotic resistance is a transient metabolic adaptation of bacteria limiting their sensitivity to low, progressively increased, concentrations of antibiotics. Unlike innate and acquired resistance, adaptive resistance is dependent on the presence of antibiotics, and it disappears when the triggering factor is removed. Low concentrations of antibiotics are largely diffused in natural environments, in the food industry or in certain body compartments of humans when used therapeutically, or in animals when used for growth promotion. However, molecular mechanisms underlying this phenomenon are still poorly characterized. Here, we present experiments suggesting that epigenetic modifications, triggered by low concentrations of ampicillin, gentamicin, and ciprofloxacin, may modulate the sensitivity of bacteria to antibiotics. The epigenetic modifications we observed were paralleled by modifications of the expression pattern of many genes, including some of those that have been found mutated in strains with permanent antibiotic resistance. As the use of low concentrations of antibiotics is spreading in different contexts, our findings may suggest new targets and strategies to avoid adaptive antibiotic resistance. This might be very important as, in the long run, this transient adaptation may increase the chance, allowing the survival and the flourishing of bacteria populations, of the onset of mutations leading to stable resistance. IMPORTANCE In this study, we characterized the modifications of epigenetic marks and of the whole transcriptome in the adaptive response of Escherichia coli cells to low concentrations of ampicillin, gentamicin, and ciprofloxacin. As the transient adaptation does increase the chance of permanent resistance, possibly allowing the survival and flourishing of bacteria populations where casual mutations providing resistance may give an immediate advantage, the importance of this study is not only in the identification of possible molecular mechanisms underlying adaptive resistance to antibiotics, but also in suggesting new strategies to avoid adaptation. | 2023 | 37184386 |
| 9612 | 4 | 0.9998 | Using experimental evolution to explore natural patterns between bacterial motility and resistance to bacteriophages. Resistance of bacteria to phages may be gained by alteration of surface proteins to which phages bind, a mechanism that is likely to be costly as these molecules typically have critical functions such as movement or nutrient uptake. To address this potential trade-off, we combine a systematic study of natural bacteria and phage populations with an experimental evolution approach. We compare motility, growth rate and susceptibility to local phages for 80 bacteria isolated from horse chestnut leaves and, contrary to expectation, find no negative association between resistance to phages and bacterial motility or growth rate. However, because correlational patterns (and their absence) are open to numerous interpretations, we test for any causal association between resistance to phages and bacterial motility using experimental evolution of a subset of bacteria in both the presence and absence of naturally associated phages. Again, we find no clear link between the acquisition of resistance and bacterial motility, suggesting that for these natural bacterial populations, phage-mediated selection is unlikely to shape bacterial motility, a key fitness trait for many bacteria in the phyllosphere. The agreement between the observed natural pattern and the experimental evolution results presented here demonstrates the power of this combined approach for testing evolutionary trade-offs. | 2011 | 21509046 |
| 9377 | 5 | 0.9998 | Experimental Evolution of the TolC-Receptor Phage U136B Functionally Identifies a Tail Fiber Protein Involved in Adsorption through Strong Parallel Adaptation. Bacteriophages have received recent attention for their therapeutic potential to treat antibiotic-resistant bacterial infections. One particular idea in phage therapy is to use phages that not only directly kill their bacterial hosts but also rely on particular bacterial receptors, such as proteins involved in virulence or antibiotic resistance. In such cases, the evolution of phage resistance would correspond to the loss of those receptors, an approach termed evolutionary steering. We previously found that during experimental evolution, phage U136B can exert selection pressure on Escherichia coli to lose or modify its receptor, the antibiotic efflux protein TolC, often resulting in reduced antibiotic resistance. However, for TolC-reliant phages like U136B to be used therapeutically, we also need to study their own evolutionary potential. Understanding phage evolution is critical for the development of improved phage therapies as well as the tracking of phage populations during infection. Here, we characterized phage U136B evolution in 10 replicate experimental populations. We quantified phage dynamics that resulted in five surviving phage populations at the end of the 10-day experiment. We found that phages from all five surviving populations had evolved higher rates of adsorption on either ancestral or coevolved E. coli hosts. Using whole-genome and whole-population sequencing, we established that these higher rates of adsorption were associated with parallel molecular evolution in phage tail protein genes. These findings will be useful in future studies to predict how key phage genotypes and phenotypes influence phage efficacy and survival despite the evolution of host resistance. IMPORTANCE Antibiotic resistance is a persistent problem in health care and a factor that may help maintain bacterial diversity in natural environments. Bacteriophages ("phages") are viruses that specifically infect bacteria. We previously discovered and characterized a phage called U136B, which infects bacteria through TolC. TolC is an antibiotic resistance protein that helps bacteria pump antibiotics out of the cell. Over short timescales, phage U136B can be used to evolutionarily "steer" bacterial populations to lose or modify the TolC protein, sometimes reducing antibiotic resistance. In this study, we investigate whether U136B itself evolves to better infect bacterial cells. We discovered that the phage can readily evolve specific mutations that increase its infection rate. This work will be useful for understanding how phages can be used to treat bacterial infections. | 2023 | 37191555 |
| 9376 | 6 | 0.9998 | Historical Contingency Drives Compensatory Evolution and Rare Reversal of Phage Resistance. Bacteria and lytic viruses (phages) engage in highly dynamic coevolutionary interactions over time, yet we have little idea of how transient selection by phages might shape the future evolutionary trajectories of their host populations. To explore this question, we generated genetically diverse phage-resistant mutants of the bacterium Pseudomonas syringae. We subjected the panel of mutants to prolonged experimental evolution in the absence of phages. Some populations re-evolved phage sensitivity, whereas others acquired compensatory mutations that reduced the costs of resistance without altering resistance levels. To ask whether these outcomes were driven by the initial genetic mechanisms of resistance, we next evolved independent replicates of each individual mutant in the absence of phages. We found a strong signature of historical contingency: some mutations were highly reversible across replicate populations, whereas others were highly entrenched. Through whole-genome sequencing of bacteria over time, we also found that populations with the same resistance gene acquired more parallel sets of mutations than populations with different resistance genes, suggesting that compensatory adaptation is also contingent on how resistance initially evolved. Our study identifies an evolutionary ratchet in bacteria-phage coevolution and may explain previous observations that resistance persists over time in some bacterial populations but is lost in others. We add to a growing body of work describing the key role of phages in the ecological and evolutionary dynamics of their host communities. Beyond this specific trait, our study provides a new insight into the genetic architecture of historical contingency, a crucial component of interpreting and predicting evolution. | 2022 | 35994371 |
| 9613 | 7 | 0.9998 | Using Selection by Nonantibiotic Stressors to Sensitize Bacteria to Antibiotics. Evolutionary adaptation of bacteria to nonantibiotic selective forces, such as osmotic stress, has been previously associated with increased antibiotic resistance, but much less is known about potentially sensitizing effects of nonantibiotic stressors. In this study, we use laboratory evolution to investigate adaptation of Enterococcus faecalis, an opportunistic bacterial pathogen, to a broad collection of environmental agents, ranging from antibiotics and biocides to extreme pH and osmotic stress. We find that nonantibiotic selection frequently leads to increased sensitivity to other conditions, including multiple antibiotics. Using population sequencing and whole-genome sequencing of single isolates from the evolved populations, we identify multiple mutations in genes previously linked with resistance to the selecting conditions, including genes corresponding to known drug targets or multidrug efflux systems previously tied to collateral sensitivity. Finally, we hypothesized based on the measured sensitivity profiles that sequential rounds of antibiotic and nonantibiotic selection may lead to hypersensitive populations by harnessing the orthogonal collateral effects of particular pairs of selective forces. To test this hypothesis, we show experimentally that populations evolved to a sequence of linezolid (an oxazolidinone antibiotic) and sodium benzoate (a common preservative) exhibit increased sensitivity to more stressors than adaptation to either condition alone. The results demonstrate how sequential adaptation to drug and nondrug environments can be used to sensitize bacteria to antibiotics and highlight new potential strategies for exploiting shared constraints governing adaptation to diverse environmental challenges. | 2020 | 31851309 |
| 8339 | 8 | 0.9998 | Dynamical model of antibiotic responses linking expression of resistance genes to metabolism explains emergence of heterogeneity during drug exposures. Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug's action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistancetetoperon inE. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis. | 2024 | 38412523 |
| 9615 | 9 | 0.9998 | Persistence and resistance as complementary bacterial adaptations to antibiotics. Bacterial persistence represents a simple of phenotypic heterogeneity, whereby a proportion of cells in an isogenic bacterial population can survive exposure to lethal stresses such as antibiotics. In contrast, genetically based antibiotic resistance allows for continued growth in the presence of antibiotics. It is unclear, however, whether resistance and persistence are complementary or alternative evolutionary adaptations to antibiotics. Here, we investigate the co-evolution of resistance and persistence across the genus Pseudomonas using comparative methods that correct for phylogenetic nonindependence. We find that strains of Pseudomonas vary extensively in both their intrinsic resistance to antibiotics (ciprofloxacin and rifampicin) and persistence following exposure to these antibiotics. Crucially, we find that persistence correlates positively to antibiotic resistance across strains. However, we find that different genes control resistance and persistence implying that they are independent traits. Specifically, we find that the number of type II toxin-antitoxin systems (TAs) in the genome of a strain is correlated to persistence, but not resistance. Our study shows that persistence and antibiotic resistance are complementary, but independent, evolutionary adaptations to stress and it highlights the key role played by TAs in the evolution of persistence. | 2016 | 26999656 |
| 4271 | 10 | 0.9998 | Multi-step vs. single-step resistance evolution under different drugs, pharmacokinetics, and treatment regimens. The success of antimicrobial treatment is threatened by the evolution of drug resistance. Population genetic models are an important tool in mitigating that threat. However, most such models consider resistance emergence via a single mutational step. Here, we assembled experimental evidence that drug resistance evolution follows two patterns: (i) a single mutation, which provides a large resistance benefit, or (ii) multiple mutations, each conferring a small benefit, which combine to yield high-level resistance. Using stochastic modeling, we then investigated the consequences of these two patterns for treatment failure and population diversity under various treatments. We find that resistance evolution is substantially limited if more than two mutations are required and that the extent of this limitation depends on the combination of drug type and pharmacokinetic profile. Further, if multiple mutations are necessary, adaptive treatment, which only suppresses the bacterial population, delays treatment failure due to resistance for a longer time than aggressive treatment, which aims at eradication. | 2021 | 34001313 |
| 8993 | 11 | 0.9998 | Adaptation Through Lifestyle Switching Sculpts the Fitness Landscape of Evolving Populations: Implications for the Selection of Drug-Resistant Bacteria at Low Drug Pressures. Novel genotypes evolve under selection through mutations in pre-existing genes. However, mutations have pleiotropic phenotypic effects that influence the fitness of emerging genotypes in complex ways. The evolution of antimicrobial resistance is mediated by selection of mutations in genes coding for antibiotic-target proteins. Drug-resistance is commonly associated with a fitness cost due to the impact of resistance-conferring mutations on protein function and/or stability. These costs are expected to prohibit the selection of drug-resistant mutations at low drug pressures. Using laboratory evolution of rifampicin resistance in Escherichia coli, we show that when exposed intermittently to low concentration (0.1 × minimal inhibitory concentration) of rifampicin, the evolution of canonical drug resistance was indeed unfavorable. Instead, these bacterial populations adapted by evolving into small-colony variants that displayed enhanced pellicle-forming ability. This shift in lifestyle from planktonic to pellicle-like was necessary for enhanced fitness at low drug pressures, and was mediated by the genetic activation of the fim operon promoter, which allowed expression of type I fimbriae. Upon continued low drug exposure, these bacteria evolved exclusively into high-level drug-resistant strains through mutations at a limited set of loci within the rifampicin-resistance determining region of the rpoB gene. We show that our results are explained by mutation-specific epistasis, resulting in differential impact of lifestyle switching on the competitive fitness of different rpoB mutations. Thus, lifestyle-alterations that are selected at low selection pressures have the potential to modify the fitness effects of mutations, change the genetic structure, and affect the ultimate fate of evolving populations. | 2019 | 30670539 |
| 9611 | 12 | 0.9997 | Parallel evolution of Pseudomonas aeruginosa phage resistance and virulence loss in response to phage treatment in vivo and in vitro. With rising antibiotic resistance, there has been increasing interest in treating pathogenic bacteria with bacteriophages (phage therapy). One limitation of phage therapy is the ease at which bacteria can evolve resistance. Negative effects of resistance may be mitigated when resistance results in reduced bacterial growth and virulence, or when phage coevolves to overcome resistance. Resistance evolution and its consequences are contingent on the bacteria-phage combination and their environmental context, making therapeutic outcomes hard to predict. One solution might be to conduct 'in vitro evolutionary simulations' using bacteria-phage combinations from the therapeutic context. Overall, our aim was to investigate parallels between in vitro experiments and in vivo dynamics in a human participant. Evolutionary dynamics were similar, with high levels of resistance evolving quickly with limited evidence of phage evolution. Resistant bacteria-evolved in vitro and in vivo-had lower virulence. In vivo, this was linked to lower growth rates of resistant isolates, whereas in vitro phage resistant isolates evolved greater biofilm production. Population sequencing suggests resistance resulted from selection on de novo mutations rather than sorting of existing variants. These results highlight the speed at which phage resistance can evolve in vivo, and how in vitro experiments may give useful insights for clinical evolutionary outcomes. | 2022 | 35188102 |
| 8991 | 13 | 0.9997 | Salicylate Increases Fitness Cost Associated with MarA-Mediated Antibiotic Resistance. Antibiotic resistance is generally associated with a fitness deficit resulting from the burden of producing and maintaining resistance machinery. This additional cost suggests that resistant bacteria will be outcompeted by susceptible bacteria in conditions without antibiotics. However, in practice, this process is slow in part because of regulation that minimizes expression of these genes in the absence of antibiotics. This suggests that if it were possible to turn on their expression, the cost would increase, thereby accelerating removal of resistant strains. Experimental and theoretical studies have shown that environmental chemicals can change the fitness cost associated with resistance and therefore have a significant impact on population dynamics. The multiple antibiotic resistance activator (MarA) is a clinically important regulator in Escherichia coli that activates downstream genes to increase resistance against multiple classes of antibiotics. Salicylate is an inducer of MarA that can be found in the environment and derepresses marA's expression. In this study, we sought to unravel the interplay between salicylate and the fitness cost of MarA-mediated antibiotic resistance. Using salicylate as an inducer of MarA, we found that a wide spectrum of concentrations can increase burden in resistant strains compared to susceptible strains. Induction resulted in rapid exclusion of resistant bacteria from mixed populations of antibiotic-resistant and susceptible cells. A mathematical model captures the process and predicts its effect in various environmental conditions. Our work provides a quantitative understanding of salicylate exposure on the fitness of different MarA variants and suggests that salicylate can lead to selection against MarA-mediated resistant strains. More generally, our findings show that natural inducers may serve to bias population membership and could impact antibiotic resistance and other important phenotypes. | 2019 | 31349991 |
| 9608 | 14 | 0.9997 | A genome-wide atlas of antibiotic susceptibility targets and pathways to tolerance. Detailed knowledge on how bacteria evade antibiotics and eventually develop resistance could open avenues for novel therapeutics and diagnostics. It is thereby key to develop a comprehensive genome-wide understanding of how bacteria process antibiotic stress, and how modulation of the involved processes affects their ability to overcome said stress. Here we undertake a comprehensive genetic analysis of how the human pathogen Streptococcus pneumoniae responds to 20 antibiotics. We build a genome-wide atlas of drug susceptibility determinants and generated a genetic interaction network that connects cellular processes and genes of unknown function, which we show can be used as therapeutic targets. Pathway analysis reveals a genome-wide atlas of cellular processes that can make a bacterium less susceptible, and often tolerant, in an antibiotic specific manner. Importantly, modulation of these processes confers fitness benefits during active infections under antibiotic selection. Moreover, screening of sequenced clinical isolates demonstrates that mutations in genes that decrease antibiotic sensitivity and increase tolerance readily evolve and are frequently associated with resistant strains, indicating such mutations could be harbingers for the emergence of antibiotic resistance. | 2022 | 35672367 |
| 9614 | 15 | 0.9997 | Antibiotic-Independent Adaptive Effects of Antibiotic Resistance Mutations. Antibiotic usage selects for the accumulation and spread of antibiotic-resistant bacteria. However, resistance can also accumulate in the absence of antibiotic exposure. Antibiotics are often designed to target widely distributed regulatory housekeeping genes. The targeting of such genes enables these antibiotics to be useful against a wider variety of pathogens. This review highlights work suggesting that regulatory housekeeping genes of the type targeted by many antibiotics function as hubs of adaptation to conditions unrelated to antibiotic exposure. As a result of this, some mutations to the regulatory housekeeping gene targets of antibiotics confer both antibiotic resistance and an adaptive effect unrelated to antibiotic exposure. Such antibiotic-independent adaptive effects of resistance mutations may substantially affect the dynamics of antibiotic resistance accumulation and spread. | 2017 | 28629950 |
| 9603 | 16 | 0.9997 | Resistance signatures manifested in early drug response across cancer types and species. Aim: Growing evidence points to non-genetic mechanisms underlying long-term resistance to cancer therapies. These mechanisms involve pre-existing or therapy-induced transcriptional cell states that confer resistance. However, the relationship between early transcriptional responses to treatment and the eventual emergence of resistant states remains poorly understood. Furthermore, it is unclear whether such early resistance-associated transcriptional responses are evolutionarily conserved. In this study, we examine the similarity between early transcriptional responses and long-term resistant states, assess their clinical relevance, and explore their evolutionary conservation across species. Methods: We integrated datasets on early drug responses and long-term resistance from multiple cancer cell lines, bacteria, and yeast to identify early transcriptional changes predictive of long-term resistance and assess their evolutionary conservation. Using genome-wide CRISPR-Cas9 knockout screens, we evaluated the impact of genes associated with resistant transcriptional states on drug sensitivity. Clinical datasets were analyzed to explore the prognostic value of the identified resistance-associated gene signatures. Results: We found that transcriptional states observed in drug-naive cells and shortly after treatment overlapped with those seen in fully resistant populations. Some of these shared features appear to be evolutionarily conserved. Knockout of genes marking resistant states sensitized ovarian cancer cells to Prexasertib. Moreover, early resistance gene signatures effectively distinguished therapy responders from non-responders in multiple clinical cancer trials and differentiated premalignant breast lesions that progressed to malignancy from those that remained benign. Conclusion: Early cellular transcriptional responses to therapy exhibit key similarities to fully resistant states across different drugs, cancer types, and species. Gene signatures defining these early resistance states have prognostic value in clinical settings. | 2025 | 41019980 |
| 9381 | 17 | 0.9997 | Cross-resistance is modular in bacteria-phage interactions. Phages shape the structure of natural bacterial communities and can be effective therapeutic agents. Bacterial resistance to phage infection, however, limits the usefulness of phage therapies and could destabilise community structures, especially if individual resistance mutations provide cross-resistance against multiple phages. We currently understand very little about the evolution of cross-resistance in bacteria-phage interactions. Here we show that the network structure of cross-resistance among spontaneous resistance mutants of Pseudomonas aeruginosa evolved against each of 27 phages is highly modular. The cross-resistance network contained both symmetric (reciprocal) and asymmetric (nonreciprocal) cross-resistance, forming two cross-resistance modules defined by high within- but low between-module cross-resistance. Mutations conferring cross-resistance within modules targeted either lipopolysaccharide or type IV pilus biosynthesis, suggesting that the modularity of cross-resistance was structured by distinct phage receptors. In contrast, between-module cross-resistance was provided by mutations affecting the alternative sigma factor, RpoN, which controls many lifestyle-associated functions, including motility, biofilm formation, and quorum sensing. Broader cross-resistance range was not associated with higher fitness costs or weaker resistance against the focal phage used to select resistance. However, mutations in rpoN, providing between-module cross-resistance, were associated with higher fitness costs than mutations associated with within-module cross-resistance, i.e., in genes encoding either lipopolysaccharide or type IV pilus biosynthesis. The observed structure of cross-resistance predicted both the frequency of resistance mutations and the ability of phage combinations to suppress bacterial growth. These findings suggest that the evolution of cross-resistance is common, is likely to play an important role in the dynamic structure of bacteria-phage communities, and could inform the design principles for phage therapy treatments. | 2018 | 30281587 |
| 9622 | 18 | 0.9997 | Stable Neutralization of a Virulence Factor in Bacteria Using Temperate Phage in the Mammalian Gut. Elimination or alteration of select members of the gut microbiota is key to therapeutic efficacy. However, the complexity of these microbial inhabitants makes it challenging to precisely target bacteria. Here, we deliver exogenous genes to specific bacteria by genomic integration of temperate phage for long-lasting modification. As a real-world therapeutic test, we engineered λ phage to transcriptionally repress Shiga toxin by using genetic hybrids between λ and other lambdoid phages to overcome resistance encoded by the virulence-expressing prophage. We show that a single dose of engineered phage propagates throughout the bacterial community and reduces Shiga toxin production in an enteric mouse model of infection without markedly affecting bacterial concentrations. Our work reveals a new framework for transferring functions to bacteria within their native environment.IMPORTANCE With the increasing frequency of antibiotic resistance, it is critical to explore new therapeutic strategies for treating bacterial infections. Here, we use a temperate phage, i.e., one that integrates itself into the bacterial genome, to neutralize the expression of a virulence factor by modifying bacterial function at the genetic level. We show that Shiga toxin production can be significantly reduced in vitro and in the mammalian gut. Alternative to traditional applications of phage therapy that rely on killing bacteria, our genetics-based antivirulence approach introduces a new framework for treating bacterial infections. | 2020 | 31992629 |
| 9374 | 19 | 0.9997 | Mathematical modelling of antibiotic interaction on evolution of antibiotic resistance: an analytical approach. BACKGROUND: The emergence and spread of antibiotic-resistant pathogens have led to the exploration of antibiotic combinations to enhance clinical effectiveness and counter resistance development. Synergistic and antagonistic interactions between antibiotics can intensify or diminish the combined therapy's impact. Moreover, these interactions can evolve as bacteria transition from wildtype to mutant (resistant) strains. Experimental studies have shown that the antagonistically interacting antibiotics against wildtype bacteria slow down the evolution of resistance. Interestingly, other studies have shown that antibiotics that interact antagonistically against mutants accelerate resistance. However, it is unclear if the beneficial effect of antagonism in the wildtype bacteria is more critical than the detrimental effect of antagonism in the mutants. This study aims to illuminate the importance of antibiotic interactions against wildtype bacteria and mutants on the deacceleration of antimicrobial resistance. METHODS: To address this, we developed and analyzed a mathematical model that explores the population dynamics of wildtype and mutant bacteria under the influence of interacting antibiotics. The model investigates the relationship between synergistic and antagonistic antibiotic interactions with respect to the growth rate of mutant bacteria acquiring resistance. Stability analysis was conducted for equilibrium points representing bacteria-free conditions, all-mutant scenarios, and coexistence of both types. Numerical simulations corroborated the analytical findings, illustrating the temporal dynamics of wildtype and mutant bacteria under different combination therapies. RESULTS: Our analysis provides analytical clarification and numerical validation that antibiotic interactions against wildtype bacteria exert a more significant effect on reducing the rate of resistance development than interactions against mutants. Specifically, our findings highlight the crucial role of antagonistic antibiotic interactions against wildtype bacteria in slowing the growth rate of resistant mutants. In contrast, antagonistic interactions against mutants only marginally affect resistance evolution and may even accelerate it. CONCLUSION: Our results emphasize the importance of considering the nature of antibiotic interactions against wildtype bacteria rather than mutants when aiming to slow down the acquisition of antibiotic resistance. | 2024 | 38426146 |