Resistance signatures manifested in early drug response across cancer types and species. - Related Documents




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960301.0000Resistance 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.202541019980
960510.9998Gene 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.201527623410
892320.9998The Genome-Wide Interaction Network of Nutrient Stress Genes in Escherichia coli. Conventional efforts to describe essential genes in bacteria have typically emphasized nutrient-rich growth conditions. Of note, however, are the set of genes that become essential when bacteria are grown under nutrient stress. For example, more than 100 genes become indispensable when the model bacterium Escherichia coli is grown on nutrient-limited media, and many of these nutrient stress genes have also been shown to be important for the growth of various bacterial pathogens in vivo To better understand the genetic network that underpins nutrient stress in E. coli, we performed a genome-scale cross of strains harboring deletions in some 82 nutrient stress genes with the entire E. coli gene deletion collection (Keio) to create 315,400 double deletion mutants. An analysis of the growth of the resulting strains on rich microbiological media revealed an average of 23 synthetic sick or lethal genetic interactions for each nutrient stress gene, suggesting that the network defining nutrient stress is surprisingly complex. A vast majority of these interactions involved genes of unknown function or genes of unrelated pathways. The most profound synthetic lethal interactions were between nutrient acquisition and biosynthesis. Further, the interaction map reveals remarkable metabolic robustness in E. coli through pathway redundancies. In all, the genetic interaction network provides a powerful tool to mine and identify missing links in nutrient synthesis and to further characterize genes of unknown function in E. coli Moreover, understanding of bacterial growth under nutrient stress could aid in the development of novel antibiotic discovery platforms. IMPORTANCE: With the rise of antibiotic drug resistance, there is an urgent need for new antibacterial drugs. Here, we studied a group of genes that are essential for the growth of Escherichia coli under nutrient limitation, culture conditions that arguably better represent nutrient availability during an infection than rich microbiological media. Indeed, many such nutrient stress genes are essential for infection in a variety of pathogens. Thus, the respective proteins represent a pool of potential new targets for antibacterial drugs that have been largely unexplored. We have created all possible double deletion mutants through a genetic cross of nutrient stress genes and the E. coli deletion collection. An analysis of the growth of the resulting clones on rich media revealed a robust, dense, and complex network for nutrient acquisition and biosynthesis. Importantly, our data reveal new genetic connections to guide innovative approaches for the development of new antibacterial compounds targeting bacteria under nutrient stress.201627879333
938130.9998Cross-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.201830281587
961240.9998Using 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.201121509046
960750.9997Transcriptome-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.201728217741
427160.9997Multi-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.202134001313
960470.9997Extreme 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.202031964772
634180.9997Monitoring lineages of growing and dividing bacteria reveals an inducible memory of mar operon expression. In Gram negative bacteria, the multiple antibiotic resistance or mar operon, is known to control the expression of multi-drug efflux genes that protect bacteria from a wide range of drugs. As many different chemical compounds can induce this operon, identifying the parameters that govern the dynamics of its induction is crucial to better characterize the processes of tolerance and resistance. Most experiments have assumed that the properties of the mar transcriptional network can be inferred from population measurements. However, measurements from an asynchronous population of cells can mask underlying phenotypic variations of single cells. We monitored the activity of the mar promoter in single Escherichia coli cells in linear micro-colonies and established that the response to a steady level of inducer was most heterogeneous within individual colonies for an intermediate value of inducer. Specifically, sub-lineages defined by contiguous daughter-cells exhibited similar promoter activity, whereas activity was greatly variable between different sub-lineages. Specific sub-trees of uniform promoter activity persisted over several generations. Statistical analyses of the lineages suggest that the presence of these sub-trees is the signature of an inducible memory of the promoter state that is transmitted from mother to daughter cells. This single-cell study reveals that the degree of epigenetic inheritance changes as a function of inducer concentration, suggesting that phenotypic inheritance may be an inducible phenotype.202337485524
961090.9997The evolutionary rate of antibacterial drug targets. BACKGROUND: One of the major issues in the fight against infectious diseases is the notable increase in multiple drug resistance in pathogenic species. For that reason, newly acquired high-throughput data on virulent microbial agents attract the attention of many researchers seeking potential new drug targets. Many approaches have been used to evaluate proteins from infectious pathogens, including, but not limited to, similarity analysis, reverse docking, statistical 3D structure analysis, machine learning, topological properties of interaction networks or a combination of the aforementioned methods. From a biological perspective, most essential proteins (knockout lethal for bacteria) or highly conserved proteins (broad spectrum activity) are potential drug targets. Ribosomal proteins comprise such an example. Many of them are well-known drug targets in bacteria. It is intuitive that we should learn from nature how to design good drugs. Firstly, known antibiotics are mainly originating from natural products of microorganisms targeting other microorganisms. Secondly, paleontological data suggests that antibiotics have been used by microorganisms for million years. Thus, we have hypothesized that good drug targets are evolutionary constrained and are subject of evolutionary selection. This means that mutations in such proteins are deleterious and removed by selection, which makes them less susceptible to random development of resistance. Analysis of the speed of evolution seems to be good approach to test this hypothesis. RESULTS: In this study we show that pN/pS ratio of genes coding for known drug targets is significantly lower than the genome average and also lower than that for essential genes identified by experimental methods. Similar results are observed in the case of dN/dS analysis. Both analyzes suggest that drug targets tend to evolve slowly and that the rate of evolution is a better predictor of drugability than essentiality. CONCLUSIONS: Evolutionary rate can be used to score and find potential drug targets. The results presented here may become a useful addition to a repertoire of drug target prediction methods. As a proof of concept, we analyzed GO enrichment among the slowest evolving genes. These may become the starting point in the search for antibiotics with a novel mechanism.201323374913
9615100.9997Persistence 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.201626999656
8922110.9997Transitioning from Soil to Host: Comparative Transcriptome Analysis Reveals the Burkholderia pseudomallei Response to Different Niches. Burkholderia pseudomallei, a soil and water saprophyte, is responsible for the tropical human disease melioidosis. A hundred years since its discovery, there is still much to learn about B. pseudomallei proteins that are essential for the bacterium's survival in and interaction with the infected host, as well as their roles within the bacterium's natural soil habitat. To address this gap, bacteria grown under conditions mimicking the soil environment were subjected to transcriptome sequencing (RNA-seq) analysis. A dual RNA-seq approach was used on total RNA from spleens isolated from a B. pseudomallei mouse infection model at 5 days postinfection. Under these conditions, a total of 1,434 bacterial genes were induced, with 959 induced in the soil environment and 475 induced in bacteria residing within the host. Genes encoding metabolism and transporter proteins were induced when the bacteria were present in soil, while virulence factors, metabolism, and bacterial defense mechanisms were upregulated during active infection of mice. On the other hand, capsular polysaccharide and quorum-sensing pathways were inhibited during infection. In addition to virulence factors, reactive oxygen species, heat shock proteins, siderophores, and secondary metabolites were also induced to assist bacterial adaptation and survival in the host. Overall, this study provides crucial insights into the transcriptome-level adaptations which facilitate infection by soil-dwelling B. pseudomallei. Targeting novel therapeutics toward B. pseudomallei proteins required for adaptation provides an alternative treatment strategy given its intrinsic antimicrobial resistance and the absence of a vaccine. IMPORTANCE Burkholderia pseudomallei, a soil-dwelling bacterium, is the causative agent of melioidosis, a fatal infectious disease of humans and animals. The bacterium has a large genome consisting of two chromosomes carrying genes that encode proteins with important roles for survival in diverse environments as well as in the infected host. While a general mechanism of pathogenesis has been proposed, it is not clear which proteins have major roles when the bacteria are in the soil and whether the same proteins are key to successful infection and spread. To address this question, we grew the bacteria in soil medium and then in infected mice. At 5 days postinfection, bacteria were recovered from infected mouse organs and their gene expression was compared against that of bacteria grown in soil medium. The analysis revealed a list of genes expressed under soil growth conditions and a different set of genes encoding proteins which may be important for survival, replication, and dissemination in an infected host. These proteins are a potential resource for understanding the full adaptation mechanism of this pathogen. In the absence of a vaccine for melioidosis and with treatment being reliant on combinatorial antibiotic therapy, these proteins may be ideal targets for designing antimicrobials to treat melioidosis.202336856434
9606120.9997Rapid identification of key antibiotic resistance genes in E. coli using high-resolution genome-scale CRISPRi screening. Bacteria possess a vast repertoire of genes to adapt to environmental challenges. Understanding the gene fitness landscape under antibiotic stress is crucial for elucidating bacterial resistance mechanisms and antibiotic action. To explore this, we conducted a genome-scale CRISPRi screen using a high-density sgRNA library in Escherichia coli exposed to various antibiotics. This screen identified essential genes under antibiotic-induced stress and offered insights into the molecular mechanisms underlying bacterial responses. We uncovered previously unrecognized genes involved in antibiotic resistance, including essential membrane proteins. The screen also underscored the importance of transcriptional modulation of essential genes in antibiotic tolerance. Our findings emphasize the utility of genome-wide CRISPRi screening in mapping the genetic landscape of antibiotic resistance. This study provides a valuable resource for identifying potential targets for antibiotics or antimicrobial strategies. Moreover, it offers a framework for exploring transcriptional regulatory networks and resistance mechanisms in E. coli and other bacterial pathogens.202540352728
8921130.9997Multivariate approach to comparing whole-cell proteomes of Bacillus cereus indicates a biofilm-specific proteome. Biofilm bacteria are widely held to exhibit a unique phenotype, typified by their increased resistance to antimicrobial agents. Numerous studies have been devoted to the identification of biofilm-specific genes, but surprisingly few have been reported to date. We compared the whole cell proteomes of 24 h old Bacillus cereus biofilms and the associated suspended population to exponential, transient and stationary phase planktonic cultures using the unbiased approach of principal component analysis, comparing the quantity variations of the 823 detected spots. The analyses support the hypothesis that biofilms of Gram positive bacteria have a unique pattern of gene expression. The data provides proteomic evidence for a new biofilm and surface influenced planktonic population which is distinct to both planktonic and biofilm cells.200616889414
8414140.9997Patterns of Piscirickettsia salmonis load in susceptible and resistant families of Salmo salar. The pathogen Piscirickettsia salmonis produces a systemic aggressive infection that involves several organs and tissues in salmonids. In spite of the great economic losses caused by this pathogen in the Atlantic salmon (Salmo salar) industry, very little is known about the resistance mechanisms of the host to this pathogen. In this paper, for the first time, we aimed to identify the bacterial load in head kidney and muscle of Atlantic salmon exhibiting differential familiar mortality. Furthermore, in order to assess the patterns of gene expression of immune related genes in susceptible and resistant families, a set of candidate genes was evaluated using deep sequencing of the transcriptome. The results showed that the bacterial load was significantly lower in resistant fish, when compared with the susceptible individuals. Based on the candidate genes analysis, we infer that the resistant hosts triggered up-regulation of specific genes (such as for example the LysC), which may explain a decrease in the bacterial load in head kidney, while the susceptible fish presented an exacerbated innate response, which is unable to exert an effective response against the bacteria. Interestingly, we found a higher bacterial load in muscle when compared with head kidney. We argue that this is possible due to the availability of an additional source of iron in muscle. Besides, the results show that the resistant fish could not be a likely reservoir of the bacteria.201525862974
9613150.9997Using 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.202031851309
8932160.9997Alternative Evolutionary Paths to Bacterial Antibiotic Resistance Cause Distinct Collateral Effects. When bacteria evolve resistance against a particular antibiotic, they may simultaneously gain increased sensitivity against a second one. Such collateral sensitivity may be exploited to develop novel, sustainable antibiotic treatment strategies aimed at containing the current, dramatic spread of drug resistance. To date, the presence and molecular basis of collateral sensitivity has only been studied in few bacterial species and is unknown for opportunistic human pathogens such as Pseudomonas aeruginosa. In the present study, we assessed patterns of collateral effects by experimentally evolving 160 independent populations of P. aeruginosa to high levels of resistance against eight commonly used antibiotics. The bacteria evolved resistance rapidly and expressed both collateral sensitivity and cross-resistance. The pattern of such collateral effects differed to those previously reported for other bacterial species, suggesting interspecific differences in the underlying evolutionary trade-offs. Intriguingly, we also identified contrasting patterns of collateral sensitivity and cross-resistance among the replicate populations adapted to the same drug. Whole-genome sequencing of 81 independently evolved populations revealed distinct evolutionary paths of resistance to the selective drug, which determined whether bacteria became cross-resistant or collaterally sensitive towards others. Based on genomic and functional genetic analysis, we demonstrate that collateral sensitivity can result from resistance mutations in regulatory genes such as nalC or mexZ, which mediate aminoglycoside sensitivity in β-lactam-adapted populations, or the two-component regulatory system gene pmrB, which enhances penicillin sensitivity in gentamicin-resistant populations. Our findings highlight substantial variation in the evolved collateral effects among replicates, which in turn determine their potential in antibiotic therapy.201728541480
8917170.9997Evolutionary responses of Escherichia coli to phage pressure: insights into mucoidy and colanic acid overexpression. BACKGROUND: Antibiotic resistance is a major issue affecting all spheres of human activity, including agriculture. One significant example is the Avian Pathogenic Escherichia coli (APEC), a bacterium that infects poultry and leads to substantial economic losses in the farming industry. As antibiotics lose efficacity, bacteriophages (phages) -viruses that specifically target bacteria-are emerging as a promising alternative to antibiotics for treating and preventing bacterial infections. However, bacteria can develop resistance to phages through various mechanisms. Studying the coevolution between a phage and its host bacterium is important to gain insight into the phage's potential as a therapeutic agent. This study investigates the evolutionary responses of an APEC strain and a laboratory E. coli strain to a commercial phage originally isolated from APEC. RESULTS: In most cases, phage resistance resulted in a significant increase in mucoidy. Genomic analysis revealed that this resistance consistently correlated with amino acid changes, particularly in proteins involved in colanic acid production, such as YrfF. Further investigation of a mutation found in the YrfF protein demonstrated that this mutation altered the protein's structure and its interaction with the membrane. Transcriptomic analysis confirmed that the genes involved in colanic acid production were significantly overexpressed. Although the strains possessed a CRISPR-Cas system, it did not contribute to phage resistance. CONCLUSIONS: This study suggests that specific amino acid changes in key proteins may be a mechanism employed by E. coli, including APEC, to defend against phage infections.202540329173
9611180.9997Parallel 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.202235188102
8920190.9997A systems biology approach to drug targets in Pseudomonas aeruginosa biofilm. Antibiotic resistance is an increasing problem in the health care system and we are in a constant race with evolving bacteria. Biofilm-associated growth is thought to play a key role in bacterial adaptability and antibiotic resistance. We employed a systems biology approach to identify candidate drug targets for biofilm-associated bacteria by imitating specific microenvironments found in microbial communities associated with biofilm formation. A previously reconstructed metabolic model of Pseudomonas aeruginosa (PA) was used to study the effect of gene deletion on bacterial growth in planktonic and biofilm-like environmental conditions. A set of 26 genes essential in both conditions was identified. Moreover, these genes have no homology with any human gene. While none of these genes were essential in only one of the conditions, we found condition-dependent genes, which could be used to slow growth specifically in biofilm-associated PA. Furthermore, we performed a double gene deletion study and obtained 17 combinations consisting of 21 different genes, which were conditionally essential. While most of the difference in double essential gene sets could be explained by different medium composition found in biofilm-like and planktonic conditions, we observed a clear effect of changes in oxygen availability on the growth performance. Eight gene pairs were found to be synthetic lethal in oxygen-limited conditions. These gene sets may serve as novel metabolic drug targets to combat particularly biofilm-associated PA. Taken together, this study demonstrates that metabolic modeling of human pathogens can be used to identify oxygen-sensitive drug targets and thus, that this systems biology approach represents a powerful tool to identify novel candidate antibiotic targets.201222523548