Evolutionary paths to antibiotic resistance under dynamically sustained drug selection

Bacteria under antibiotic attack: Different strategies for evolutionary adaptation

Bacteria are well known for their extremely high adaptability in stressful environments. The clinical relevance of this property is clearly illustrated by the ever-decreasing efficacy of antibiotic therapies. Frequent exposures to antibiotics favor bacterial strains that have acquired mechanisms to overcome drug inhibition and lethality. Many strains, including life-threatening pathogens, exhibit increased antibiotic resistance or tolerance, which considerably complicates clinical practice. Alarmingly, recent studies show that in addition to resistance, tolerance levels of bacterial populations are extremely flexible in an evolutionary context. Here, we summarize laboratory studies providing insight in the evolution of resistance and tolerance and shed light on how the treatment conditions could affect the direction of bacterial evolution under antibiotic stress.

Phenotypic delay in the evolution of bacterial antibiotic resistance: Mechanistic models and their implications

Phenotypic delay-the time delay between genetic mutation and expression of the corresponding phenotype-is generally neglected in evolutionary models, yet recent work suggests that it may be more common than previously assumed. Here, we use computer simulations and theory to investigate the significance of phenotypic delay for the evolution of bacterial resistance to antibiotics. We consider three mechanisms which could potentially cause phenotypic delay: effective polyploidy, dilution of antibiotic-sensitive molecules and accumulation of resistance-enhancing molecules. We find that the accumulation of resistant molecules is relevant only within a narrow parameter range, but both the dilution of sensitive molecules and effective polyploidy can cause phenotypic delay over a wide range of parameters. We further investigate whether these mechanisms could affect population survival under drug treatment and thereby explain observed discrepancies in mutation rates estimated by Luria-Delbrück fluctuation tests. While the effective polyploidy mechanism does not affect population survival, the dilution of sensitive molecules leads both to decreased probability of survival under drug treatment and underestimation of mutation rates in fluctuation tests. The dilution mechanism also changes the shape of the Luria-Delbrück distribution of mutant numbers, and we show that this modified distribution provides an improved fit to previously published experimental data.

Wireless bioreactor for anaerobic cultivation of bacteria

Anaerobic cultivation methods of bacteria are indispensable in microbiology. One methodology is to cultivate the microbes in anaerobic enclosure with oxygen-adosrbing chemicals. Here, we report an electronic extension of such strategy for facultative anaerobic bacteria. The technique is based a bioreactor with entire operation including turbidity measurement, fluidic mixing, and gas delivery in an anaerobic enclosure. Wireless data transmission is employed and the anaerobic condition is achieved with gas pack. Although the technique is not meant to completely replace the anaerobic chamber for strict anaerobic bacteria, it provides a convenient way to bypass the cumbersome operation in anaerobic chamber for facultative anaerobic bacteria. Such a cultivation strategy is demonstrated with Escherichia coli with different carbon sources and hydrogen as energy source.

Simultaneous Control of Endogenous and User-Defined Genetic Pathways Using Unique ecDHFR Pharmacological Chaperones

Destabilizing domains (DDs), such as a mutated form of Escherichia coli dihydrofolate reductase (ecDHFR), confer instability and promote protein degradation. However, when combined with small-molecule stabilizers (e.g., the antibiotic trimethoprim), DDs allow positive regulation of fusion protein abundance. Using a combinatorial screening approach, we identified and validated 17 unique 2,4-diaminopyrimidine/triazine-based ecDHFR DD stabilizers, at least 15 of which were ineffective antibiotics against E. coli and S. aureus. Identified stabilizers functioned in vivo to control an ecDHFR DD-firefly luciferase in the mouse eye and/or the liver. Next, stabilizers were leveraged to perform synergistic dual functions in vitro (HeLa cell death sensitization) and in vivo (repression of ocular inflammation) by stabilizing a user-defined ecDHFR DD while also controlling endogenous signaling pathways. Thus, these newly identified pharmacological chaperones allow for simultaneous control of compound-specific endogenous and user-defined genetic pathways, the combination of which may provide synergistic effects in complex biological scenarios.

Integrating CRISPR-Enabled Trackable Genome Engineering and Transcriptomic Analysis of Global Regulators for Antibiotic Resistance Selection and Identification in Escherichia coli

The growing threat of antimicrobial resistance poses a serious threat to public health care and motivates efforts to understand the means by which resistance acquisition occurs and how this can be combatted. To address these challenges, we expedited the identification of novel mutations that enable complex phenotypic changes that result in improved tolerance to antibiotics by integrating CREATE and transcriptomic analysis of global regulators. The results give us a better understanding of the mechanisms of resistance to tetracycline antibiotics and aminoglycoside antibiotics and also indicate that the method may be used for quickly identifying resistance-related mutations.

It is important to expedite our understanding of antibiotic resistance to address the increasing numbers of fatalities and environmental pollution due to the emergence of antibiotic resistance and multidrug-resistant strains. Here, we combined the CRISPR-enabled trackable genome engineering (CREATE) technology and transcriptomic analysis to investigate antibiotic tolerance in Escherichia coli. We developed rationally designed site saturation mutagenesis libraries targeting 23 global regulators to identify fitness-conferring mutations in response to diverse antibiotic stresses. We identified seven novel mutations that confer resistance to the ribosome-targeting antibiotics doxycycline, thiamphenicol, and gentamicin in E. coli. To the best of our knowledge, these mutations that we identified have not been reported previously during treatment with the indicated antibiotics. Transcriptome sequencing-based transcriptome analysis was further employed to evaluate the genome-wide changes in gene expression in E. coli for SoxR G121P and cAMP receptor protein (CRP) V140W reconstructions, and improved fitness in response to doxycycline and gentamicin was seen. In the case of doxycycline, we speculated that SoxR G121P significantly increased the expression of genes involved in carbohydrate metabolism and energy metabolism to promote cell growth for improved adaptation. In the CRP V140W mutant with improved gentamicin tolerance, the expression of several amino acid biosynthesis genes and fatty acid degradation genes was significantly changed, and these changes probably altered the cellular energy state to improve adaptation. These findings have important significance for understanding such nonspecific mechanisms of antibiotic resistance and developing new antibacterial drugs.

IMPORTANCE The growing threat of antimicrobial resistance poses a serious threat to public health care and motivates efforts to understand the means by which resistance acquisition occurs and how this can be combatted. To address these challenges, we expedited the identification of novel mutations that enable complex phenotypic changes that result in improved tolerance to antibiotics by integrating CREATE and transcriptomic analysis of global regulators. The results give us a better understanding of the mechanisms of resistance to tetracycline antibiotics and aminoglycoside antibiotics and also indicate that the method may be used for quickly identifying resistance-related mutations.

Deep reinforcement learning for the control of microbial co-cultures in bioreactors

Multi-species microbial communities are widespread in natural ecosystems. When employed for biomanufacturing, engineered synthetic communities have shown increased productivity in comparison with monocultures and allow for the reduction of metabolic load by compartmentalising bioprocesses between multiple sub-populations. Despite these benefits, co-cultures are rarely used in practice because control over the constituent species of an assembled community has proven challenging. Here we demonstrate, in silico, the efficacy of an approach from artificial intelligence-reinforcement learning-for the control of co-cultures within continuous bioreactors. We confirm that feedback via a trained reinforcement learning agent can be used to maintain populations at target levels, and that model-free performance with bang-bang control can outperform a traditional proportional integral controller with continuous control, when faced with infrequent sampling. Further, we demonstrate that a satisfactory control policy can be learned in one twenty-four hour experiment by running five bioreactors in parallel. Finally, we show that reinforcement learning can directly optimise the output of a co-culture bioprocess. Overall, reinforcement learning is a promising technique for the control of microbial communities.

Genetic Determinants Enabling Medium-Dependent Adaptation to Nafcillin in Methicillin-Resistant Staphylococcus aureus

Antimicrobial susceptibility testing standards driving clinical decision-making have centered around the use of cation-adjusted Mueller-Hinton broth (CA-MHB) as the medium with the notion of supporting bacterial growth, without consideration of recapitulating the in vivo environment. However, it is increasingly recognized that various medium conditions have tremendous influence on antimicrobial activity, which in turn may have major implications on the ability of in vitro susceptibility assays to predict antibiotic activity in vivo. To elucidate differential growth optimization and antibiotic resistance mechanisms, adaptive laboratory evolution was performed in the presence or absence of the antibiotic nafcillin with methicillin-resistant Staphylococcus aureus (MRSA) TCH1516 in either (i) CA-MHB, a traditional bacteriological nutritionally rich medium, or (ii) Roswell Park Memorial Institute (RPMI), a medium more reflective of the in vivo host environment. Medium adaptation analysis showed an increase in growth rate in RPMI, but not CA-MHB, with mutations in apt, adenine phosphoribosyltransferase, and the manganese transporter subunit, mntA, occurring reproducibly in parallel replicate evolutions. The medium-adapted strains showed no virulence attenuation. Continuous exposure of medium-adapted strains to increasing concentrations of nafcillin led to medium-specific evolutionary strategies. Key reproducibly occurring mutations were specific for nafcillin adaptation in each medium type and did not confer resistance in the other medium environment. Only the vraRST operon, a regulator of membrane- and cell wall-related genes, showed mutations in both CA-MHB- and RPMI-evolved strains. Collectively, these results demonstrate the medium-specific genetic adaptive responses of MRSA and establish adaptive laboratory evolution as a platform to study clinically relevant resistance mechanisms.IMPORTANCE The ability of pathogens such as Staphylococcus aureus to evolve resistance to antibiotics used in the treatment of infections has been an important concern in the last decades. Resistant acquisition usually translates into treatment failure and puts patients at risk of unfavorable outcomes. Furthermore, the laboratory testing of antibiotic resistance does not account for the different environment the bacteria experiences within the human body, leading to results that do not translate into the clinic. In this study, we forced methicillin-resistant S. aureus to develop nafcillin resistance in two different environments, a laboratory environment and a physiologically more relevant environment. This allowed us to identify genetic changes that led to nafcillin resistance under both conditions. We concluded that not only does the environment dictate the evolutionary strategy of S. aureus to nafcillin but also that the evolutionary strategy is specific to that given environment.

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

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

A Strategic Target Rescues Trimethoprim Sensitivity in Escherichia coli

Trimethoprim, a preferred treatment for urinary tract infections, is becoming obsolete owing to the rapid dissemination of resistant E. coli. Although direct resistance mechanisms such as overexpression of a mutant FolA and dfr enzymes are well characterized, associated alterations that drive or sustain resistance are unknown. We identify the repertoire of resistance-associated perturbations by constructing and interrogating a transcriptome-integrated functional interactome. From the cross talk between perturbations in stress-response and metabolic pathways, we identify the critical dependence on serine hydroxymethyltransferase (GlyA) as an emergent vulnerability. Through its deletion, we demonstrate that GlyA is necessary to sustain high levels of resistance in both laboratory-evolved resistant E. coli and a multidrug-resistant clinical isolate. Through comparative evolution, we show that the absence of GlyA activity decelerates the acquisition of resistance in E. coli. Put together, our results identify GlyA as a promising target, providing a basis for the rational design of drug combinations.

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TMP-resistant E. coli show cross talk between stress response and metabolic pathways

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Dependence on glyA is an emergent vulnerability associated with TMP resistance

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Knockout of glyA partially rescues sensitivity to TMP in E. coli

Loss of the Periplasmic Chaperone Skp and Mutations in the Efflux Pump AcrAB-TolC Play a Role in Acquired Resistance to Antimicrobial Peptides in Salmonella typhimurium

Bacterial resistance to antibiotics is a major concern worldwide, leading to an extensive search for alternative drugs. Promising candidates are antimicrobial peptides (AMPs), innate immunity molecules, shown to be highly efficient against multidrug resistant bacteria. Therefore, it is essential to study bacterial resistance mechanisms against them. For that purpose, we used experimental evolution, and isolated a Salmonella enterica serovar typhimurium-resistant line to the AMP 4DK5L7. This AMP displayed promising features including widespread activity against Gram-negative bacteria and protection from proteolytic degradation. However, the resistance that evolved in the isolated strain was particularly high. Whole genome sequencing revealed that five spontaneous mutations had evolved. Of these, three are novel in the context of acquired AMP resistance. Two mutations are related to the AcrAB-TolC multidrug efflux pump. One occurred in AcrB, the substrate-binding domain of the system, and the second in RamR, a transcriptional regulator of the system. Together, the mutations increased the minimal inhibitory concentration (MIC) by twofold toward this AMP. Moreover, the mutation in AcrB induced hypersusceptibility toward ampicillin and colistin. The last mutation occurred in Skp, a periplasmic chaperone that participates in the biogenesis of outer membrane proteins (OMPs). This mutation increased the MIC by twofold to 4DK5L7 and by fourfold to another AMP, seg5D. Proteomic analysis revealed that the mutation abolished Skp expression, reduced OMP abundance, and increased DegP levels. DegP, a protease that was reported to have an additional chaperone activity, escorts OMPs through the periplasm along with Skp, but is also associated with AMP resistance. In conclusion, our data demonstrate that both loss of Skp and manipulation of the AcrAB-TolC system are alternative strategies of AMP acquired resistance in Salmonella typhimurium and might represent a common mechanism in other Gram-negative bacteria.

Gut resistomes, microbiota and antibiotic residues in Chinese patients undergoing antibiotic administration and healthy individuals

Human gut microbiota is an important reservoir of antibiotic resistance genes (ARGs). Although dysbacteriosis after the antibiotic course has been previously observed in the patient guts, a comprehensive comparison of gut resistomes, microbiota and antibiotic residues in healthy individuals and patients undergoing antibiotic administration is little. Using high-throughput qPCR, 16S rRNA gene amplicon sequencing and UPLC-MS/MS, we systematically examined the antibiotic resistome, gut microbiota, and antibiotic residues in fecal samples from both Chinese healthy individuals and patients receiving antibiotic therapy. Compared with healthy individuals, patients' guts harbored lower diverse gut resistome and microbiota, but higher concentrations of antibiotics and ARGs. Antibiotic concentration in human guts was positively correlated with ARG total abundance, but was negatively related to the diversity of both ARGs and bacterial communities, which demonstrated that antibiotic administration could shape the antibiotic resistomes and bacterial communities in the patient guts. Gene cfxA was evaluated as a potential biomarker to distinguish the patients receiving antibiotic therapy from the healthy individuals in China since its wide detection and significant enrichment in the guts of the patients. The detection of some veterinary antibiotics in human guts illustrated the potential transmission of antibiotic from the external environment to human via the food chain. The obtained results could help to better understand the influence of antibiotic therapy in shaping antibiotic reistomes and bacterial communities in Chinese individuals.

Chromosomal barcoding of E. coli populations reveals lineage diversity dynamics at high resolution

Evolutionary dynamics in large asexual populations is strongly influenced by multiple competing beneficial lineages, most of which segregate at very low frequencies. However, technical barriers to tracking a large number of these rare lineages in bacterial populations have so far prevented a detailed elucidation of evolutionary dynamics. Here, we overcome this hurdle by developing a chromosomal-barcoding technique that allows simultaneous tracking of approximately 450,000 distinct lineages in Escherichia coli, which we use to test the effect of sub-inhibitory concentrations of common antibiotics on the evolutionary dynamics of low-frequency lineages. We find that populations lose lineage diversity at distinct rates that correspond to their antibiotic regimen. We also determine that some lineages have similar fates across independent experiments. By analysing the trajectory dynamics, we attribute the reproducible fates of these lineages to the presence of pre-existing beneficial mutations, and we demonstrate how the relative contribution of pre-existing and de novo mutations varies across drug regimens. Finally, we reproduce the observed lineage dynamics by simulations. Altogether, our results provide a valuable methodology for studying bacterial evolution as well as insights into evolution under sub-inhibitory antibiotic levels.

Low Cassette Variability in Class 2 and Class 1 Integrons of Aeromonas spp. Isolated from Environmental Samples

Integrons are prokaryotic genetic elements known to carry and exchange antibiotic resistance gene cassettes through a site-specific recombinase called integrase. In this work, 107 Aeromonas isolates from environmental origin, including fish, water, and sediments, were investigated for the presence of integrons. Using specific primers for Class 1, 2 and 3 integrases, only Class 1 and Class 2 integrons were detected. Detection of Class 2 integrases and their associated variable regions required two rounds of polymerase chain reaction (PCR). Sequencing of the intI2 amplicons confirmed them as integrase-derived products. Class 1 integrons were detected in 26 out of 107 isolates. PCR amplification of the variable regions associated to these integrons revealed an outstanding homogeneity, 25 of them having variable regions with an identical dfrA12-orfF-aadA2 cassette array and one integron carrying only the dfrA16 cassette. To assess clone diversity, chromosomal DNA from isolates was subjected to enterobacterial repetitive intergenic consensus-PCR (ERIC-PCR), which discarded clonality in all instances. Class 2 integrons were surprisingly more prevalent than Class1 integrons, being detected in 60 out of 107 isolates. Forty-six of them showed a unique ERIC profile, while the remaining 14 strains displayed profiles that could be grouped in five different patterns. Cassette arrangements of all Class 2 variable regions were those described as the most prevalent (dfrA1-sat2-aadA1). A rather startling result of this work is the sensitivity to trimethoprim, streptomycin, and streptothricin of most strains, despite the presence of the cognate resistance genes. To know the integron distribution in environmental Aeromonas species, a phylogenetic reconstruction was done using rpoD/gyrB or rpoD/gyrA gene sequences. Isolates bearing these elements corresponded to Aeromonas hydrophila, Aeromonas veronii, Aeromonas salmonicida, Aeromonas dhakensis, Aeromonas sanarellii, Aeromonas taiwanensis, Aeromonas media, Aeromonas caviae, Aeromonas jandaei, and Aeromonas sp. This work revealed an unusual high incidence of Class 2 integrons and a low variability of cassette arrangements in environmental Aeromonas species.

Molecular basis of antimicrobial resistance in Mycoplasma genitalium

Mycoplasma genitalium is a sexually transmitted urogenital pathogen, and infection can result in serious symptoms. As M. genitalium is rather difficult to culture, infections are usually detected by molecular methods. Unfortunately, there has recently been a significant increase in resistance to azithromycin and moxifloxacin used for the treatment of M. genitalium infections. The increased resistance to (often empirically prescribed) M. genitalium treatments has resulted in frequent therapy failures and stresses the need for routine detection of antimicrobial resistance. In M. genitalium, antimicrobial resistance is almost always the result of DNA mutations and thus can easily be detected by molecular techniques. Regrettably, many microbiology laboratories do not use molecular techniques for the detection of bacterial antimicrobial resistance. As molecular tests are becoming available for M. genitalium, both for the establishment of infection and the detection of antimicrobial resistance, it is now more important to ensure that knowledge on the resistance mechanisms is transferred from the laboratory to the clinician. This review will provide a brief summary of the current status of antimicrobial resistance, its molecular mechanisms and the impact on the current status of M. genitalium treatment.

Highly Contingent Phenotypes of Lon Protease Deficiency in Escherichia coli upon Antibiotic Challenge

Evolutionary trajectories and mutational landscapes of drug-resistant bacteria are influenced by cell-intrinsic and extrinsic factors. In this study, I demonstrated that loss of the Lon protease altered susceptibility of Escherichia coli to trimethoprim and that these effects were strongly contingent on the drug concentration and genetic background. Lon, an AAA⁺ ATPase, is a bacterial master regulator protease involved in cytokinesis, suppression of transposition events, and clearance of misfolded proteins. I show that Lon deficiency enhances intrinsic drug tolerance at sub-MIC levels of trimethoprim. As a result, loss of Lon, though disadvantageous under drug-free conditions, has a selective advantage at low concentrations of trimethoprim. At high drug concentrations, however, Lon deficiency is detrimental for E. coli I show that the former is explained by suppression of drug efflux by Lon, while the latter can be attributed to SulA-dependent hyperfilamentation. On the other hand, deletion of lon in a trimethoprim-resistant mutant E. coli strain (harboring the Trp30Gly dihydrofolate reductase [DHFR] allele) directly potentiates resistance by enhancing the in vivo stability of mutant DHFR. Using extensive mutational analysis at 3 hot spots of resistance, I show that many resistance-conferring mutations render DHFR prone to proteolysis. This trade-off between gaining resistance and losing in vivo stability limits the number of mutations in DHFR that can confer trimethoprim resistance. Loss of Lon expands the mutational capacity for acquisition of trimethoprim resistance. This paper identifies the multipronged action of Lon in trimethoprim resistance in E. coli and provides mechanistic insight into how genetic backgrounds and drug concentrations may alter the potential for antimicrobial resistance evolution.IMPORTANCE Understanding the evolutionary dynamics of antimicrobial resistance is vital to curb its emergence and spread. Being fundamentally similar to natural selection, the fitness of resistant mutants is a key parameter to consider in the evolutionary dynamics of antimicrobial resistance (AMR). Various intrinsic and extrinsic factors modulate the fitness of resistant bacteria. This study demonstrated that Lon, a bacterial master regulator protease, influences drug tolerance and resistance. Lon is a key regulator of several fundamental processes in bacteria, including cytokinesis. I demonstrated that Lon deficiency produces highly contingent phenotypes in E. coli challenged with trimethoprim and can expand the mutational repertoire available to E. coli to evolve resistance. This multipronged influence of Lon on drug resistance provides an illustrative instance of how master regulators shape the response of bacteria to antibiotics.

A Bioinformatic Analysis of Integrative Mobile Genetic Elements Highlights Their Role in Bacterial Adaptation

Mobile genetic elements (MGEs) contribute to bacterial adaptation and evolution; however, high-throughput, unbiased MGE detection remains challenging. We describe MGEfinder, a bioinformatic toolbox that identifies integrative MGEs and their insertion sites by using short-read sequencing data. MGEfinder identifies the genomic site of each MGE insertion and infers the identity of the inserted sequence. We apply MGEfinder to 12,374 sequenced isolates of 9 prevalent bacterial pathogens, including Mycobacterium tuberculosis, Staphylococcus aureus, and Escherichia coli, and identify thousands of MGEs, including candidate insertion sequences, conjugative transposons, and prophage elements. The MGE repertoire and insertion rates vary across species, and integration sites often cluster near genes related to antibiotic resistance, virulence, and pathogenicity. MGE insertions likely contribute to antibiotic resistance in laboratory experiments and clinical isolates. Additionally, we identified thousands of mobility genes, a subset of which have unknown function opening avenues for exploration. Future application of MGEfinder to commensal bacteria will further illuminate bacterial adaptation and evolution.

Antibiotic Resistance Evolution Is Contingent on the Quorum-Sensing Response in Pseudomonas aeruginosa

Different works have explored independently the evolution toward antibiotic resistance and the role of eco-adaptive mutations in the adaptation to a new habitat (as the infected host) of bacterial pathogens. However, knowledge about the connection between both processes is still limited. We address this issue by comparing the evolutionary trajectories toward antibiotic resistance of a Pseudomonas aeruginosa lasR defective mutant and its parental wild-type strain, when growing in presence of two ribosome-targeting antibiotics. Quorum-sensing lasR defective mutants are selected in P. aeruginosa populations causing chronic infections. Further, we observed they are also selected in vitro as a first adaptation for growing in culture medium. By using experimental evolution and whole-genome sequencing, we found that the evolutionary trajectories of P. aeruginosa in presence of these antibiotics are different in lasR defective and in wild-type backgrounds, both at the phenotypic and the genotypic levels. Recreation of a set of mutants in both genomic backgrounds (either wild type or lasR defective) allowed us to determine the existence of negative epistatic interactions between lasR and antibiotic resistance determinants. These epistatic interactions could lead to mutual contingency in the evolution of antibiotic resistance when P. aeruginosa colonizes a new habitat in presence of antibiotics. If lasR mutants are selected first, this would constraint antibiotic resistance evolution. Conversely, when resistance mutations (at least those studied in the present work) are selected, lasR mutants may not be selected in presence of antibiotics. These results underlie the importance of contingency and epistatic interactions in modulating antibiotic resistance evolution.

Stochastic Gene Expression Influences the Selection of Antibiotic Resistance Mutations

Bacteria can resist antibiotics by expressing enzymes that remove or deactivate drug molecules. Here, we study the effects of gene expression stochasticity on efflux and enzymatic resistance. We construct an agent-based model that stochastically simulates multiple biochemical processes in the cell and we observe the growth and survival dynamics of the cell population. Resistance-enhancing mutations are introduced by varying parameters that control the enzyme expression or efficacy. We find that stochastic gene expression can cause complex dynamics in terms of survival and extinction for these mutants. Regulatory mutations, which augment the frequency and duration of resistance gene transcription, can provide limited resistance by increasing mean expression. Structural mutations, which modify the enzyme or efflux efficacy, provide most resistance by improving the binding affinity of the resistance protein to the antibiotic; increasing the enzyme's catalytic rate alone may contribute to resistance if drug binding is not rate limiting. Overall, we identify conditions where regulatory mutations are selected over structural mutations, and vice versa. Our findings show that stochastic gene expression is a key factor underlying efflux and enzymatic resistances and should be taken into consideration in future antibiotic research.

Evolving Antibiotics against Resistance: a Potential Platform for Natural Product Development?

To avoid an antibiotic resistance crisis, we need to develop antibiotics at a pace that matches the rate of evolution of resistance. However, the complex functions performed by antibiotics-combining, e.g., penetration of membranes, counteraction of resistance mechanisms, and interaction with molecular targets-have proven hard to achieve with current methods for drug development, including target-based screening and rational design. Here, we argue that we can meet the evolution of resistance in the clinic with evolution of antibiotics in the laboratory. On the basis of the results of experimental evolution studies of microbes in general and antibiotic production in Actinobacteria in particular, we propose methodology for evolving antibiotics to circumvent mechanisms of resistance. This exploits the ability of evolution to find solutions to complex problems without a need for design. We review evolutionary theory critical to this approach and argue that it is feasible and has important advantages over current methods for antibiotic discovery.

Experimental Challenges for Reduced Genomes: The Cell Model Escherichia coli

Genome reduction, as a top-down approach to obtain the minimal genetic information essential for a living organism, has been conducted with bacterial cells for decades. The most popular and well-studied cell models for genome reduction are Escherichia coli strains. As the previous literature intensively introduced the genetic construction and application of the genome-reduced Escherichia coli strains, the present review focuses the design principles and compares the reduced genome collections from the specific viewpoint of growth, which represents a fundamental property of living cells and is an important feature for their biotechnological application. For the extended simplification of the genomic sequences, the approach of experimental evolution and concern for medium optimization are newly proposed. The combination of the current techniques of genomic construction and the newly proposed methodologies could allow us to acquire growing Escherichia coli cells carrying the extensively reduced genome and to address the question of what the minimal genome essential for life is.

Ligands and Receptors with Broad Binding Capabilities Have Common Structural Characteristics: An Antibiotic Design Perspective

The spread of antibiotic resistance is one of the most serious global public-health problems. Here we show that a particular class of homomers with binding sites spanning multiple protein chains is particularly suitable for targeting by broad-spectrum antibacterial agents because due to the slow evolutionary change of such binding pockets, ligands of such homomers are much more likely to bind their homologs than ligands of monomers, or homomers with a single-chain binding site. Additionally, using de novo ligand design and deep learning, we show that the chemical compounds that can bind several different receptors have common structural characteristics and that halogens and fragments similar to the building blocks existing antimicrobials are overrepresented in them. Finally, we show that binding multiple receptors selects for flexible compounds, which are less likely to accumulate in Gram-negative bacteria; thus there is trade-off between reducing the emergence of resistance by multitargeting and broad-spectrum antibacterial activity.

The effect of spatiotemporal antibiotic inhomogeneities on the evolution of resistance

Combating the evolution of widespread antibiotic resistance is one of the most pressing challenges facing modern medicine. Recent research has demonstrated that the evolution of pathogens with high levels of resistance can be accelerated by spatial and temporal inhomogeneities in antibiotic concentration, which frequently arise in patients and the environment. Strategies to predict and counteract the effects of such inhomogeneities will be critical in the fight against resistance. In this paper we develop a mechanistic framework for modelling the adaptive evolution of resistance in the presence of spatiotemporal antibiotic concentrations, which treats the adaptive process as an interaction between two mutually orthogonal forces; the first returns cells to their wild-type state in the absence of antibiotic selection, and the second selects for increased coping ability in the presence of an antibiotic. We apply our model to investigate laboratory adaptation experiments, and then extend it to consider the case in which multiple strategies for resistance undergo competitive evolution.

The mutational landscape of quinolone resistance in Escherichia coli

The evolution of antibiotic resistance is influenced by a variety of factors, including the availability of resistance mutations, and the pleiotropic effects of such mutations. Here, we isolate and characterize chromosomal quinolone resistance mutations in E. coli, in order to gain a systematic understanding of the rate and consequences of resistance to this important class of drugs. We isolated over fifty spontaneous resistance mutants on nalidixic acid, ciprofloxacin, and levofloxacin. This set of mutants includes known resistance mutations in gyrA, gyrB, and marR, as well as two novel gyrB mutations. We find that, for most mutations, resistance tends to be higher to nalidixic acid than relative to the other two drugs. Resistance mutations had deleterious impacts on one or more growth parameters, suggesting that quinolone resistance mutations are generally costly. Our findings suggest that the prevalence of specific gyrA alleles amongst clinical isolates are driven by high levels of resistance, at no more cost than other resistance alleles.

A multihost bacterial pathogen overcomes continuous population bottlenecks to adapt to new host species

While many bacterial pathogens are restricted to single host species, some have the capacity to undergo host switches, leading to the emergence of new clones that are a threat to human and animal health. However, the bacterial traits that underpin a multihost ecology are not well understood. Following transmission to a new host, bacterial populations are influenced by powerful forces such as genetic drift that reduce the fixation rate of beneficial mutations, limiting the capacity for host adaptation. Here, we implement a novel experimental model of bacterial host switching to investigate the ability of the multihost pathogen Staphylococcus aureus to adapt to new species under continuous population bottlenecks. We demonstrate that beneficial mutations accumulated during infection can overcome genetic drift and sweep through the population, leading to host adaptation. Our findings highlight the remarkable capacity of some bacteria to adapt to distinct host niches in the face of powerful antagonistic population forces.

Changes in the Carbon Metabolism of Escherichia coli During the Evolution of Doxycycline Resistance

Despite our continuous improvement in understanding the evolution of antibiotic resistance, the changes in the carbon metabolism during the evolution of antibiotic resistance remains unclear. To investigate the evolution of antibiotic resistance and the changes in carbon metabolism under antibiotic pressure, Escherichia coli K-12 was evolved for 38 passages under a concentration gradient of doxycycline (DOX). The 0th-passage sensitive strain W0, the 20th-passage moderately resistant strain M20, and the 38th-passage highly resistant strain E38 were selected for the determination of biofilm formation, colony area, and carbon metabolism levels, as well as genome and transcriptome sequencing. The MIC of DOX with E. coli significantly increased from 4 to 96 μg/ml, and the IC₅₀ increased from 2.18 ± 0.08 to 64.79 ± 0.75 μg/ml after 38 passages of domestication. Compared with the sensitive strain W0, the biofilm formation amount of the resistant strains M20 and E38 was significantly increased (p < 0.05). Single-nucleotide polymorphisms (SNPs) were distributed in antibiotic resistance-related genes such as ribosome targets, cell membranes, and multiple efflux pumps. In addition, there were no mutated genes related to carbon metabolism. However, the genes involved in the biosynthesis of secondary metabolites and carbon metabolism pathway were downregulated, showing a significant decrease in the metabolic intensity of 23 carbon sources (p < 0.05). The results presented here show that there may be a correlation between the evolution of E. coli DOX resistance and the decrease of carbon metabolism, and the mechanism was worthy of further research, providing a theoretical basis for the prevention and control of microbial resistance.

AcrB: a mean, keen, drug efflux machine

Gram-negative bacteria are intrinsically resistant against cytotoxic substances by means of their outer membrane and a network of multidrug efflux systems, acting in synergy. Efflux pumps from various superfamilies with broad substrate preferences sequester and pump drugs across the inner membrane to supply the highly polyspecific and powerful tripartite resistance-nodulation-cell division (RND) efflux pumps with compounds to be extruded across the outer membrane barrier. In Escherichia coli, the tripartite efflux system AcrAB-TolC is the archetype RND multiple drug efflux pump complex. The homotrimeric inner membrane component acriflavine resistance B (AcrB) is the drug specificity and energy transduction center for the drug/proton antiport process. Drugs are bound and expelled via a cycle of mainly three consecutive states in every protomer, constituting a flexible alternating access channel system. This review recapitulates the molecular basis of drug and inhibitor binding, including mechanistic insights into drug efflux by AcrB. It also summarizes 17 years of mutational analysis of the gene acrB, reporting the effect of every substitution on the ability of E. coli to confer resistance toward antibiotics (http://goethe.link/AcrBsubstitutions). We emphasize the functional robustness of AcrB toward single-site substitutions and highlight regions that are more sensitive to perturbation.

Genome editing retraces the evolution of toxin resistance in the monarch butterfly

Identifying the genetic mechanisms of adaptation requires the elucidation of links between the evolution of DNA sequence, phenotype, and fitness¹. Convergent evolution can be used as a guide to identify candidate mutations that underlie adaptive traits^2-4, and new genome editing technology is facilitating functional validation of these mutations in whole organisms^1,5. We combined these approaches to study a classic case of convergence in insects from six orders, including the monarch butterfly (Danaus plexippus), that have independently evolved to colonize plants that produce cardiac glycoside toxins^6-11. Many of these insects evolved parallel amino acid substitutions in the α-subunit (ATPα) of the sodium pump (Na⁺/K⁺-ATPase)^7-11, the physiological target of cardiac glycosides¹². Here we describe mutational paths involving three repeatedly changing amino acid sites (111, 119 and 122) in ATPα that are associated with cardiac glycoside specialization^13,14. We then performed CRISPR-Cas9 base editing on the native Atpα gene in Drosophila melanogaster flies and retraced the mutational path taken across the monarch lineage^11,15. We show in vivo, in vitro and in silico that the path conferred resistance and target-site insensitivity to cardiac glycosides¹⁶, culminating in triple mutant 'monarch flies' that were as insensitive to cardiac glycosides as monarch butterflies. 'Monarch flies' retained small amounts of cardiac glycosides through metamorphosis, a trait that has been optimized in monarch butterflies to deter predators^17-19. The order in which the substitutions evolved was explained by amelioration of antagonistic pleiotropy through epistasis^13,14,20-22. Our study illuminates how the monarch butterfly evolved resistance to a class of plant toxins, eventually becoming unpalatable, and changing the nature of species interactions within ecological communities^{2,6-11,15,17-19}.

Historical contingency in the evolution of antibiotic resistance after decades of relaxed selection

Populations often encounter changed environments that remove selection for the maintenance of particular phenotypic traits. The resulting genetic decay of those traits under relaxed selection reduces an organism's fitness in its prior environment. However, whether and how such decay alters the subsequent evolvability of a population upon restoration of selection for a previously diminished trait is not well understood. We addressed this question using Escherichia coli strains from the long-term evolution experiment (LTEE) that independently evolved for multiple decades in the absence of antibiotics. We first confirmed that these derived strains are typically more sensitive to various antibiotics than their common ancestor. We then subjected the ancestral and derived strains to various concentrations of these drugs to examine their potential to evolve increased resistance. We found that evolvability was idiosyncratic with respect to initial genotype; that is, the derived strains did not generally compensate for their greater susceptibility by "catching up" to the resistance level of the ancestor. Instead, the capacity to evolve increased resistance was constrained in some backgrounds, implying that evolvability depended upon prior mutations in a historically contingent fashion. We further subjected a time series of clones from one LTEE population to tetracycline and determined that an evolutionary constraint arose early in that population, corroborating the role of contingency. In summary, relaxed selection not only can drive populations to increased antibiotic susceptibility, but it can also affect the subsequent evolvability of antibiotic resistance in an unpredictable manner. This conclusion has potential implications for public health, and it underscores the need to consider the genetic context of pathogens when designing drug-treatment strategies.

Evolving generalists in switching rugged landscapes

Evolving systems, be it an antibody repertoire in the face of mutating pathogens or a microbial population exposed to varied antibiotics, constantly search for adaptive solutions in time-varying fitness landscapes. Generalists refer to genotypes that remain fit across diverse selective pressures; while multi-drug resistant microbes are undesired yet prevalent, broadly-neutralizing antibodies are much wanted but rare. However, little is known about under what conditions such generalists with a high capacity to adapt can be efficiently discovered by evolution. In addition, can epistasis-the source of landscape ruggedness and path constraints-play a different role, if the environment varies in a non-random way? We present a generative model to estimate the propensity of evolving generalists in rugged landscapes that are tunably related and alternating relatively slowly. We find that environmental cycling can substantially facilitate the search for fit generalists by dynamically enlarging their effective basins of attraction. Importantly, these high performers are most likely to emerge at intermediate levels of ruggedness and environmental relatedness. Our approach allows one to estimate correlations across environments from the topography of experimental fitness landscapes. Our work provides a conceptual framework to study evolution in time-correlated complex environments, and offers statistical understanding that suggests general strategies for eliciting broadly neutralizing antibodies or preventing microbes from evolving multi-drug resistance.

Adaptation to mutational inactivation of an essential gene converges to an accessible suboptimal fitness peak

The mechanisms of adaptation to inactivation of essential genes remain unknown. Here we inactivate E. coli dihydrofolate reductase (DHFR) by introducing D27G,N,F chromosomal mutations in a key catalytic residue with subsequent adaptation by an automated serial transfer protocol. The partial reversal G27- > C occurred in three evolutionary trajectories. Conversely, in one trajectory for D27G and in all trajectories for D27F,N strains adapted to grow at very low metabolic supplement (folAmix) concentrations but did not escape entirely from supplement auxotrophy. Major global shifts in metabolome and proteome occurred upon DHFR inactivation, which were partially reversed in adapted strains. Loss-of-function mutations in two genes, thyA and deoB, ensured adaptation to low folAmix by rerouting the 2-Deoxy-D-ribose-phosphate metabolism from glycolysis towards synthesis of dTMP. Multiple evolutionary pathways of adaptation converged to a suboptimal solution due to the high accessibility to loss-of-function mutations that block the path to the highest, yet least accessible, fitness peak.

Predicting how viruses and bacteria evolve remains a challenge. The ability to anticipate when and how bacteria might develop drug resistance would make treating life-threatening diseases easier and could potentially help prevent drug resistance altogether. Studying bacterial evolution under different conditions and cataloguing all possible DNA mutations that allow these bacteria to survive are crucial steps in predicting the appearance of drug resistance.

Studies have revealed that bacteria can adapt to sources of stress, such as antibiotics, in different ways – each involving mutations in distinct genes. However, not all the mutations provide the same benefits to the organism, and the factors that influence how bacteria will adapt are unclear.

Now, Rodrigues and Shakhnovich have used a new approach to push the adaptation ability of the bacterium Escherichia coli to the limit. First, they genetically engineered different E. coli strains by introducing distinct mutations to an enzyme the bacterium needs to make DNA. These mutations make the resulting strains dependent on external molecules to synthesize new DNA. Next, the cells were grown in conditions where the supply of these DNA precursors was progressively decreased, forcing them to adapt. The obvious way for bacteria to adapt to these conditions would be to ‘revert’ the mutations that Rodrigues and Shakhnovich introduced in the first place. By using this approach, Rodrigues and Shakhnovich were able to test how often the obvious evolutionary solution happens compared with presumably less-preferred alternative routes.

In rare cases, a specific mutation did restore the activity of the enzyme that enabled DNA synthesis. However, in most cases the bacteria found a different evolutionary solution whereby they all adapt to the decrease in external DNA precursors in the same way, but not by reverting the original mutation. Instead, further mutations disrupt the activity of two metabolic genes, allowing the cells to use the external DNA precursors more efficiently, and keep building DNA. These cells are therefore able to survive even when the levels of the external DNA components are very low, but they will die in the complete absence of these precursor molecules.

This evolutionary solution leads to a non-optimal effect: mutations that restore the activity of the original enzyme, which are the best solution when the two metabolic genes are intact, are no longer as effective. This finding represents a clear example of interactions between genes determining evolutionary outcomes. Rodrigues and Shakhnovich showed that, since it is more likely for a random mutation to disrupt a gene than to revert a previous mutation, adaptations that are less-than-optimal but still work might predominate simply because they happen faster.

Understanding why certain evolutionary adaptations prevail is an important step in predicting evolution and may lead to breakthroughs in many areas. For example, if scientists can identify mutations likely to make bacteria resistant to drugs, it may be possible to act proactively against the bacterial strains that carry those mutations. Eventually, if the factors that lead to specific adaptations are known, it may be possible to exploit this knowledge to create weaknesses in the bacteria’s own defences.

Modified Antibiotic Adjuvant Ratios Can Slow and Steer the Evolution of Resistance: Co-amoxiclav as a Case Study

As antibiotic resistance spreads, developing sustainable methods to restore the efficacy of existing antibiotics is increasingly important. One widespread method is to combine antibiotics with synergistically acting adjuvants that inhibit resistance mechanisms, allowing drug killing. Here we use co-amoxiclav (a clinically important combination of the β-lactam antibiotic amoxicillin and the β-lactamase inhibitor clavulanate) to ask whether treatment efficacy and resistance evolution can be decoupled via component dosing modifications. A simple mathematical model predicts that different ratios of these two drug components can produce distinct evolutionary responses irrespective of the initial efficacy. We test this hypothesis by selecting Escherichia coli with a plasmid-encoded β-lactamase (CTX-M-14), against different concentrations of amoxicillin and clavulanate. Consistent with our theory, we found that while resistance evolved under all conditions, the component ratio influenced both the rate and mechanism of resistance evolution. Specifically, we found that the current clinical practice of high amoxicillin-to-clavulanate ratios resulted in the most rapid adaptation to antibiotics via gene dosing responses. Increased plasmid copy number allowed E. coli to increase β-lactamase dosing and effectively titrate out low quantities of clavulanate, restoring amoxicillin resistance. In contrast, high clavulanate ratios were more robust-plasmid copy number did not increase, although porin or efflux resistance mechanisms were found, as for all drug ratios. Our results indicate that by changing the ratio of adjuvant to antibiotic we can slow and steer the path of resistance evolution. We therefore suggest using increased adjuvant dosing regimens to slow the rate of resistance evolution.IMPORTANCE As antibiotic resistance spreads, a promising approach is to restore the effectiveness of existing drugs via coadministration with adjuvants that inhibit resistance. However, as for monotherapy, antibiotic-adjuvant therapies can select for a variety of resistance mechanisms, so it is imperative that adjuvants be used in a sustainable manner. We test whether the rate of resistance evolution can be decoupled from treatment efficacy using co-amoxiclav, a clinically important combination of the β-lactam amoxicillin and β-lactamase inhibitor clavulanate. Using experimental evolution and a simple theoretical model, we show that the current co-amoxiclav formulation with a high proportion of amoxicillin rapidly selects for resistance via increased β-lactamase production. On the other hand, formulations with more clavulanate and less amoxicillin have similar efficacies yet prevent the selective benefit of increased β-lactamase. We suggest that by blocking common paths to resistance, treatment combinations with the adjuvant in excess can slow the evolution of resistance.

AcrAB-TolC efflux pump system plays a role in carbapenem non-susceptibility in Escherichia coli

Background

Efflux pump mediated antibiotic resistance is an unnoticed and undetected mechanism in clinical microbiology laboratory. RND efflux systems are known for aminoglycoside and tetracycline resistance whereas their role in carbapenem non-susceptibility is not established. The study was undertaken to investigate the role of efflux pump in providing resistance against carbapenems and their response against concentration gradient carbapenem stress on the transcriptional level of the AcrAB gene in the clinical isolates of Escherichia coli from a tertiary referral hospital of Northeast India.

Results

Out of 298 non-susceptible Escherichia coli isolates 98 isolates were found to have efflux pump mediated carbapenem non-susceptibility. Among them thirty-five were non carbapenemase producers and their expressional levels were verified using qRT-PCR under concentration gradient carbapenem stress. In this study, a strong correlation between ertapenem resistance and AcrA overexpression was observed which has not been reported previously. Further, it was observed that imipenem stress increased AcrB expression in Escherichia coli which holds the novelty of this study. Additionally, the transcription of AcrR was insistently increased which is much higher than the transcriptional level of AcrA under concentration gradient carbapenem stress condition.

Conclusion

The study established that AcrAB pump is a relevant antibiotic resistance determinant in bacterial pathogen, has an important role in developing resistance against carbapenem group of antibiotics.

Electronic supplementary material

The online version of this article (10.1186/s12866-019-1589-1) contains supplementary material, which is available to authorized users.

Dissecting Trait Variation across Species Barriers

Dissecting the basis of naturally occurring trait variation is one of the central goals of modern genetics. For eukaryotes, classic methods for this purpose rely on screens of recombinants from matings between distinct parents. These tools cannot be used in studies of species that cannot mate to form recombinant progeny in the first place. However, new approaches are coming online to shuffle the genomes of otherwise incompatible species. With them, geneticists can elucidate how evolution built a new trait, even if it happened millions of years ago, in a lineage that is now reproductively cutoff from its closest relatives.

Silver ions cause oscillation of bacterial length of Escherichia coli

Silver (Ag) in various forms have recently gained broad interest and been revisited due to their promising antimicrobial effects. Here we report our study on the morphological dynamics of live bacteria when subjected to Ag⁺ ions. Using time-lapse microscopy, we observed oscillations of cell-length for a large fraction of bacteria exposed to 60 μM of Ag⁺ ions. In addition, we found that the responses of bacteria to Ag⁺ ions were heterogeneous. We quantified the oscillations of cell-length with power spectral density, which appeared different from that of bacteria growing in the absence of Ag⁺ ions. Furthermore, a model similar to the predator-prey argument was developed to understand the observed oscillations of cell-length upon exposure to Ag⁺ ions. This model not only successfully produced the oscillations but also explained the observed heterogeneity in the bacterial responses to Ag⁺ ions.

Amoxicillin Administration Regimen and Resistance Mechanisms of Staphylococcus aureus Established in Tissue Cage Infection Model

Staphylococcus aureus is a zoonotic pathogen that causes various life-threatening diseases. The mechanisms of action of amoxicillin against S. aureus are unclear. Here, we established a rabbit tissue cage infection model to evaluate the relationship between the pharmacokinetic/pharmacodynamic (PK/PD) parameters of amoxicillin and selective enrichment of resistant strains of S. aureus and to elucidate the evolution of its resistance to amoxicillin. S. aureus was injected into the tissue cages at 10¹⁰ colony forming units (CFU)/mL. We injected different intramuscular concentrations of amoxicillin at doses of 5, 10, 20, and 30 mg/kg body weight once a day for 5 days and 5, 10, 20, and 30 mg/kg body weight twice a day for 2.5 days. Differences in gene expression between two differentially resistant strains and a sensitive strain were evaluated using Illumina sequencing followed by COG and KEGG analysis. RT-qPCR was carried out to validate the difference in protein translation levels. Our results demonstrated that the emergence of resistant bacteria was dose dependent within a given time interval. In the same dosage group, the appearance of resistant bacteria increased with time. The resistant bacteria showed cumulative growth, and the level of resistance increased over time. The resistant bacteria were completely inhibited when the cumulative percentage of time over a 24-h period that the drug concentration exceeded the mutant prevention concentration (MPC) (%T > MPC) was ≥52%. We also found that mecA and femX in S. aureus played a leading role in the development of resistance to amoxicillin. In conclusion, it provide references for optimizing amoxicillin regimens to treat infections caused by S. aureus.

Every which way? On predicting tumor evolution using cancer progression models

Successful prediction of the likely paths of tumor progression is valuable for diagnostic, prognostic, and treatment purposes. Cancer progression models (CPMs) use cross-sectional samples to identify restrictions in the order of accumulation of driver mutations and thus CPMs encode the paths of tumor progression. Here we analyze the performance of four CPMs to examine whether they can be used to predict the true distribution of paths of tumor progression and to estimate evolutionary unpredictability. Employing simulations we show that if fitness landscapes are single peaked (have a single fitness maximum) there is good agreement between true and predicted distributions of paths of tumor progression when sample sizes are large, but performance is poor with the currently common much smaller sample sizes. Under multi-peaked fitness landscapes (i.e., those with multiple fitness maxima), performance is poor and improves only slightly with sample size. In all cases, detection regime (when tumors are sampled) is a key determinant of performance. Estimates of evolutionary unpredictability from the best performing CPM, among the four examined, tend to overestimate the true unpredictability and the bias is affected by detection regime; CPMs could be useful for estimating upper bounds to the true evolutionary unpredictability. Analysis of twenty-two cancer data sets shows low evolutionary unpredictability for several of the data sets. But most of the predictions of paths of tumor progression are very unreliable, and unreliability increases with the number of features analyzed. Our results indicate that CPMs could be valuable tools for predicting cancer progression but that, currently, obtaining useful predictions of paths of tumor progression from CPMs is dubious, and emphasize the need for methodological work that can account for the probably multi-peaked fitness landscapes in cancer.

Cholic Acid-Derived Amphiphile which Combats Gram-Positive Bacteria-Mediated Infections via Disintegration of Lipid Clusters

Inappropriate and uncontrolled use of antibiotics results in the emergence of antibiotic resistance, thereby threatening the present clinical regimens to treat infectious diseases. Therefore, new antimicrobial agents that can prevent bacteria from developing drug resistance are urgently needed. Selective disruption of bacterial membranes is the most effective strategy for combating microbial infections as accumulation of genetic mutations will not allow for the emergence of drug resistance against these antimicrobials. In this work, we tested cholic acid (CA) derived amphiphiles tethered with different alkyl chains for their ability to combat Gram-positive bacterial infections. In-depth biophysical and biomolecular simulation studies suggested that the amphiphile with a hexyl chain (6) executes more effective interactions with Gram-positive bacterial membranes as compared to other hydrophobic counterparts. Amphiphile 6 is effective against multidrug resistant Gram-positive bacterial strains as well and does not allow the adherence of S. aureus on amphiphile 6 coated catheters implanted in mice. Further, treatment of wound infections with amphiphile 6 clears the bacterial infections. Therefore, the current study presents strategic guidelines in design and development of CA-derived membrane-targeting antimicrobials for Gram-positive bacterial infections.

Adaptive Evolution of Escherichia coli to Ciprofloxacin in Controlled Stress Environments: Contrasting Patterns of Resistance in Spatially Varying versus Uniformly Mixed Concentration Conditions

A microfluidic gradient chamber (MGC) and a homogeneous batch culturing system were used to evaluate whether spatial concentration gradients of the antibiotic ciprofloxacin allow development of greater antibiotic resistance in Escherichia coli strain 307 (E. coli 307) compared to exclusively temporal concentration gradients, as indicated in an earlier study. A linear spatial gradient of ciprofloxacin and Luria-Bertani broth (LB) medium was established and maintained by diffusion over 5 days across a well array in the MGC, with relative concentrations along the gradient of 1.7-7.7× the original minimum inhibitory concentration (MIC_original). The E. coli biomass increased in wells with lower ciprofloxacin concentrations, and only a low level of resistance to ciprofloxacin was detected in the recovered cells (∼2× MIC_original). Homogeneous batch culture experiments were performed with the same temporal exposure history to ciprofloxacin concentration, the same and higher initial cell densities, and the same and higher nutrient (i.e., LB) concentrations as in the MGC. In all batch experiments, E. coli 307 developed higher ciprofloxacin resistance after exposure, ranging from 4 to 24× MIC_original in all replicates. Hence, these results suggest that the presence of spatial gradients appears to reduce the driving force for E. coli 307 adaptation to ciprofloxacin, which suggests that results from batch experiments may over predict the development of antibiotic resistance in natural environments.

Epistasis between antibiotic tolerance, persistence, and resistance mutations

Understanding the evolution of microorganisms under antibiotic treatments is a burning issue. Typically, several resistance mutations can accumulate under antibiotic treatment, and the way in which resistance mutations interact, i.e., epistasis, has been extensively studied. We recently showed that the evolution of antibiotic resistance in Escherichia coli is facilitated by the early appearance of tolerance mutations. In contrast to resistance, which reduces the effectiveness of the drug concentration, tolerance increases resilience to antibiotic treatment duration in a nonspecific way, for example when bacteria transiently arrest their growth. Both result in increased survival under antibiotics, but the interaction between resistance and tolerance mutations has not been studied. Here, we extend our analysis to include the evolution of a different type of tolerance and a different antibiotic class and measure experimentally the epistasis between tolerance and resistance mutations. We derive the expected model for the effect of tolerance and resistance mutations on the dynamics of survival under antibiotic treatment. We find that the interaction between resistance and tolerance mutations is synergistic in strains evolved under intermittent antibiotic treatment. We extend our analysis to mutations that result in antibiotic persistence, i.e., to tolerance that is conferred only on a subpopulation of cells. We show that even when this population heterogeneity is included in our analysis, a synergistic interaction between antibiotic persistence and resistance mutations remains. We expect our general framework for the epistasis in killing conditions to be relevant for other systems as well, such as bacteria exposed to phages or cancer cells under treatment.

Estimating the predictability of cancer evolution

MOTIVATION

How predictable is the evolution of cancer? This fundamental question is of immense relevance for the diagnosis, prognosis and treatment of cancer. Evolutionary biologists have approached the question of predictability based on the underlying fitness landscape. However, empirical fitness landscapes of tumor cells are impossible to determine in vivo. Thus, in order to quantify the predictability of cancer evolution, alternative approaches are required that circumvent the need for fitness landscapes.

RESULTS

We developed a computational method based on conjunctive Bayesian networks (CBNs) to quantify the predictability of cancer evolution directly from mutational data, without the need for measuring or estimating fitness. Using simulated data derived from >200 different fitness landscapes, we show that our CBN-based notion of evolutionary predictability strongly correlates with the classical notion of predictability based on fitness landscapes under the strong selection weak mutation assumption. The statistical framework enables robust and scalable quantification of evolutionary predictability. We applied our approach to driver mutation data from the TCGA and the MSK-IMPACT clinical cohorts to systematically compare the predictability of 15 different cancer types. We found that cancer evolution is remarkably predictable as only a small fraction of evolutionary trajectories are feasible during cancer progression.

AVAILABILITY AND IMPLEMENTATION

https://github.com/cbg-ethz/predictability\_of\_cancer\_evolution.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

VEGAS as a Platform for Facile Directed Evolution in Mammalian Cells

Directed evolution, artificial selection toward designed objectives, is routinely used to develop new molecular tools and therapeutics. Successful directed molecular evolution campaigns repeatedly test diverse sequences with a designed selective pressure. Unicellular organisms and their viral pathogens are exceptional for this purpose and have been used for decades. However, many desirable targets of directed evolution perform poorly or unnaturally in unicellular backgrounds. Here, we present a system for facile directed evolution in mammalian cells. Using the RNA alphavirus Sindbis as a vector for heredity and diversity, we achieved 24-h selection cycles surpassing 10^-3 mutations per base. Selection is achieved through genetically actuated sequences internal to the host cell, thus the system's name: viral evolution of genetically actuating sequences, or "VEGAS." Using VEGAS, we evolve transcription factors, GPCRs, and allosteric nanobodies toward functional signaling endpoints each in less than 1 weeks' time.

High-Order Epistasis in Catalytic Power of Dihydrofolate Reductase Gives Rise to a Rugged Fitness Landscape in the Presence of Trimethoprim Selection

Evolutionary fitness landscapes of several antibiotic target proteins have been comprehensively mapped showing strong high-order epistasis between mutations, but understanding these effects at the biochemical and structural levels remained open. Here, we carried out an extensive experimental and computational study to quantitatively understand the evolutionary dynamics of Escherichia coli dihydrofolate reductase (DHFR) enzyme in the presence of trimethoprim-induced selection. To facilitate this, we developed a new in vitro assay for rapidly characterizing DHFR steady-state kinetics. Biochemical and structural characterization of resistance-conferring mutations targeting a total of ten residues spanning the substrate binding pocket of DHFR revealed distinct changes in the catalytic efficiencies of mutated DHFR enzymes. Next, we measured biochemical parameters (Km, Ki, and kcat) for a mutant library carrying all possible combinations of six resistance-conferring DHFR mutations and quantified epistatic interactions between them. We found that the high-order epistasis in catalytic power of DHFR (kcat and Km) creates a rugged fitness landscape under trimethoprim selection. Taken together, our data provide a concrete illustration of how epistatic coupling at the level of biochemical parameters can give rise to complex fitness landscapes, and suggest new strategies for developing mutant specific inhibitors.

The Essential Role of Hypermutation in Rapid Adaptation to Antibiotic Stress

A common outcome of antibiotic exposure in patients and in vitro is the evolution of a hypermutator phenotype that enables rapid adaptation by pathogens. While hypermutation is a robust mechanism for rapid adaptation, it requires trade-offs between the adaptive mutations and the more common "hitchhiker" mutations that accumulate from the increased mutation rate. Using quantitative experimental evolution, we examined the role of hypermutation in driving the adaptation of Pseudomonas aeruginosa to colistin. Metagenomic deep sequencing revealed 2,657 mutations at ≥5% frequency in 1,197 genes and 761 mutations in 29 endpoint isolates. By combining genomic information, phylogenetic analyses, and statistical tests, we showed that evolutionary trajectories leading to resistance could be reliably discerned. In addition to known alleles such as pmrB, hypermutation allowed identification of additional adaptive alleles with epistatic relationships. Although hypermutation provided a short-term fitness benefit, it was detrimental to overall fitness. Alarmingly, a small fraction of the colistin-adapted population remained colistin susceptible and escaped hypermutation. In a clinical population, such cells could play a role in reestablishing infection upon withdrawal of colistin. We present here a framework for evaluating the complex evolutionary trajectories of hypermutators that applies to both current and emerging pathogen populations.

Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance

Bacteria regulate genes to survive antibiotic stress, but regulation can be far from perfect. When regulation is not optimal, mutations that change gene expression can contribute to antibiotic resistance. It is not systematically understood to what extent natural gene regulation is or is not optimal for distinct antibiotics, and how changes in expression of specific genes quantitatively affect antibiotic resistance. Here we discover a simple quantitative relation between fitness, gene expression, and antibiotic potency, which rationalizes our observation that a multitude of genes and even innate antibiotic defense mechanisms have expression that is critically nonoptimal under antibiotic treatment. First, we developed a pooled-strain drug-diffusion assay and screened Escherichia coli overexpression and knockout libraries, finding that resistance to a range of 31 antibiotics could result from changing expression of a large and functionally diverse set of genes, in a primarily but not exclusively drug-specific manner. Second, by synthetically controlling the expression of single-drug and multidrug resistance genes, we observed that their fitness–expression functions changed dramatically under antibiotic treatment in accordance with a log-sensitivity relation. Thus, because many genes are nonoptimally expressed under antibiotic treatment, many regulatory mutations can contribute to resistance by altering expression and by activating latent defenses.

Evolutionary Dynamics in the RNA Bacteriophage Qβ Depends on the Pattern of Change in Selective Pressures

The rate of change in selective pressures is one of the main factors that determines the likelihood that populations can adapt to stress conditions. Generally, the reduction in the population size that accompanies abrupt environmental changes makes it difficult to generate and select adaptive mutations. However, in systems with high genetic diversity, as happens in RNA viruses, mutations with beneficial effects under new conditions can already be present in the population, facilitating adaptation. In this work, we have propagated an RNA bacteriophage (Qβ) at temperatures higher than the optimum, following different patterns of change. We have determined the fitness values and the consensus sequences of all lineages throughout the evolutionary process in order to establish correspondences between fitness variations and adaptive pathways. Our results show that populations subjected to a sudden temperature change gain fitness and fix mutations faster than those subjected to gradual changes, differing also in the particular selected mutations. The life-history of populations prior to the environmental change has great importance in the dynamics of adaptation. The conclusion is that in the bacteriophage Qβ, the standing genetic diversity together with the rate of temperature change determine both the rapidity of adaptation and the followed evolutionary pathways.

A one health perspective on dairy production and dairy food safety

As the global population approaches 9.7 billion inhabitants by the year 2050, humanity faces enormous challenges to feed, house, and provide basic living requirements for the growing population while preserving the health of wildlife and the ecosystem. Dairy source foods play an important part in providing nutrient and energy dense sources of calories and establishing Bifidobacterium as a keystone species in the gut for positive health outcomes in infants and children. In developed countries, dairy products have a high food safety record when pasteurized and properly processed. However, when milk is consumed unpasteurized, as often occurs in developing countries where regulation and oversight of the dairy industry is lacking, dairy can serve as a vector for zoonotic transmission of disease and can contain adulterants such as antibiotic residues. Here we provide an overview for the importance of dairy source foods for nutrition and with a One Health perspective and discuss the historical events that have resulted in a high standard of dairy food safety in the United States. This review article covers the Origins of One Health, the role of milk in transmission of disease, management practices and regulations to ensure safe dairy products reach consumers, current challenges facing the dairy industry and impacts on public health, and how these standards can be employed in low and middle income countries to improve public health, nutrition and economic benefits to farmers.