Evolutionary paths to antibiotic resistance under dynamically sustained drug selection

Population and comparative genetics of thermotolerance divergence between yeast species

Many familiar traits in the natural world-from lions' manes to the longevity of bristlecone pine trees-arose in the distant past, and have long since fixed in their respective species. A key challenge in evolutionary genetics is to figure out how and why species-defining traits have come to be. We used the thermotolerance growth advantage of the yeast Saccharomyces cerevisiae over its sister species Saccharomyces paradoxus as a model for addressing these questions. Analyzing loci at which the S. cerevisiae allele promotes thermotolerance, we detected robust evidence for positive selection, including amino acid divergence between the species and conservation within S. cerevisiae populations. Because such signatures were particularly strong at the chromosome segregation gene ESP1, we used this locus as a case study for focused mechanistic follow-up. Experiments revealed that, in culture at high temperature, the S. paradoxus ESP1 allele conferred a qualitative defect in biomass accumulation and cell division relative to the S. cerevisiae allele. Only genetic divergence in the ESP1 coding region mattered phenotypically, with no functional impact detectable from the promoter. Our data support a model in which an ancient ancestor of S. cerevisiae, under selection to boost viability at high temperature, acquired amino acid variants at ESP1 and many other loci, which have been constrained since then. Complex adaptations of this type hold promise as a paradigm for interspecies genetics, especially in deeply diverged traits that may have taken millions of years to evolve.

Shared and Unique Evolutionary Trajectories to Ciprofloxacin Resistance in Gram-Negative Bacterial Pathogens

Resistance to the broad-spectrum antibiotic ciprofloxacin is detected at high rates for a wide range of bacterial pathogens. To investigate the dynamics of ciprofloxacin resistance development, we applied a comparative resistomics workflow for three clinically relevant species of Gram-negative bacteria: Escherichia coli, Acinetobacter baumannii, and Pseudomonas aeruginosa. We combined experimental evolution in a morbidostat with deep sequencing of evolving bacterial populations in time series to reveal both shared and unique aspects of evolutionary trajectories. Representative clone characterization by sequencing and MIC measurements enabled direct assessment of the impact of mutations on the extent of acquired drug resistance. In all three species, we observed a two-stage evolution: (i) early ciprofloxacin resistance reaching 4- to 16-fold the MIC for the wild type, commonly as a result of single mutations in DNA gyrase target genes (gyrA or gyrB), and (ii) additional genetic alterations affecting the transcriptional control of the drug efflux machinery or secondary target genes (DNA topoisomerase parC or parE).

Relevance of Higher-Order Epistasis in Drug Resistance

We studied five chemically distinct but related 1,3,5-triazine antifolates with regard to their effects on growth of a set of mutants in dihydrofolate reductase. The mutants comprise a combinatorially complete data set of all 16 possible combinations of four amino acid replacements associated with resistance to pyrimethamine in the malaria parasite Plasmodium falciparum. Pyrimethamine was a mainstay medication for malaria for many years, and it is still in use in intermittent treatment during pregnancy or as a partner drug in artemisinin combination therapy. Our goal was to investigate the extent to which the alleles yield similar adaptive topographies and patterns of epistasis across chemically related drugs. We find that the adaptive topographies are indeed similar with the same or closely related alleles being fixed in computer simulations of stepwise evolution. For all but one of the drugs the topography features at least one suboptimal fitness peak. Our data are consistent with earlier results indicating that third order and higher epistatic interactions appear to contribute only modestly to the overall adaptive topography, and they are largely conserved. In regard to drug development, our data suggest that higher-order interactions are likely to be of little value as an advisory tool in the choice of lead compounds.

Genomic evolution of antibiotic resistance is contingent on genetic background following a long-term experiment with Escherichia coli

Antibiotic resistance is a growing health concern. Efforts to control resistance would benefit from an improved ability to forecast when and how it will evolve. Epistatic interactions between mutations can promote divergent evolutionary trajectories, which complicates our ability to predict evolution. We recently showed that differences between genetic backgrounds can lead to idiosyncratic responses in the evolvability of phenotypic resistance, even among closely related Escherichia coli strains. In this study, we examined whether a strain's genetic background also influences the genotypic evolution of resistance. Do lineages founded by different genotypes take parallel or divergent mutational paths to achieve their evolved resistance states? We addressed this question by sequencing the complete genomes of antibiotic-resistant clones that evolved from several different genetic starting points during our earlier experiments. We first validated our statistical approach by quantifying the specificity of genomic evolution with respect to antibiotic treatment. As expected, mutations in particular genes were strongly associated with each drug. Then, we determined that replicate lines evolved from the same founding genotypes had more parallel mutations at the gene level than lines evolved from different founding genotypes, although these effects were more subtle than those showing antibiotic specificity. Taken together with our previous work, we conclude that historical contingency can alter both genotypic and phenotypic pathways to antibiotic resistance.

From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.

A trimethoprim derivative impedes antibiotic resistance evolution

The antibiotic trimethoprim (TMP) is used to treat a variety of Escherichia coli infections, but its efficacy is limited by the rapid emergence of TMP-resistant bacteria. Previous laboratory evolution experiments have identified resistance-conferring mutations in the gene encoding the TMP target, bacterial dihydrofolate reductase (DHFR), in particular mutation L28R. Here, we show that 4'-desmethyltrimethoprim (4'-DTMP) inhibits both DHFR and its L28R variant, and selects against the emergence of TMP-resistant bacteria that carry the L28R mutation in laboratory experiments. Furthermore, antibiotic-sensitive E. coli populations acquire antibiotic resistance at a substantially slower rate when grown in the presence of 4'-DTMP than in the presence of TMP. We find that 4'-DTMP impedes evolution of resistance by selecting against resistant genotypes with the L28R mutation and diverting genetic trajectories to other resistance-conferring DHFR mutations with catalytic deficiencies. Our results demonstrate how a detailed characterization of resistance-conferring mutations in a target enzyme can help identify potential drugs against antibiotic-resistant bacteria, which may ultimately increase long-term efficacy of antimicrobial therapies by modulating evolutionary trajectories that lead to resistance.

Experimental evolution in morbidostat reveals converging genomic trajectories on the path to triclosan resistance

Understanding the dynamics and mechanisms of acquired drug resistance across major classes of antibiotics and bacterial pathogens is of critical importance for the optimization of current anti-infective therapies and the development of novel ones. To systematically address this challenge, we developed a workflow combining experimental evolution in a morbidostat continuous culturing device with deep genomic sequencing of population samples collected in time series. This approach was applied to the experimental evolution of six populations of Escherichia coli BW25113 towards acquiring resistance to triclosan (TCS), an antibacterial agent in various consumer products. This study revealed the rapid emergence and expansion (up to 100% in each culture within 4 days) of missense mutations in the fabI gene, encoding enoyl-acyl carrier protein reductase, the known TCS molecular target. A follow-up analysis of isolated clones showed that distinct amino acid substitutions increased the drug IC₉₀ in a 3-16-fold range, reflecting their proximity to the TCS-binding site. In contrast to other antibiotics, efflux-upregulating mutations occurred only rarely and with low abundance. Mutations in several other genes were detected at an earlier stage of evolution. Most notably, three distinct amino acid substitutions were mapped in the C-terminal periplasmic domain of CadC protein, an acid stress-responsive transcriptional regulator. While these mutations do not confer robust TCS resistance, they appear to play a certain, yet unknown, role in adaptation to relatively low drug pressure. Overall, the observed evolutionary trajectories suggest that the FabI enzyme is the sole target of TCS (at least up to the ~50 µm level), and amino acid substitutions in the TCS-binding site represent the main mechanism of robust TCS resistance in E. coli. This model study illustrates the potential utility of the established morbidostat-based approach for uncovering resistance mechanisms and target identification for novel drug candidates with yet unknown mechanisms of action.

Nosocomial Infections: Pathogenicity, Resistance and Novel Antimicrobials

Background. The fight against the spread of infectious diseases creates the problem of resistance to pathogens and the most resistant of them – the propagators of nosocomial infections – are formed in hospitals because of a number of reasons. The solution of the problem lies in different areas, but the search of new effective means for the treatment of such diseases remains relevant right today. The shortest way to do this is to find the "pain points" of the pathogens themselves, i.e. the factors of their pathogenicity and resistance to which the action of novel antiseptics should be directed. Objective. We aimed to analyse and evaluate the main factors of pathogenicity and resistance of pathogens of nosocomial infections to determine modern approaches to the development of novel antimicrobials. Methods. Search and systematization of new scientific data and results concerning pathogenic factors of microbial pathogens that can be used as targets for the action of drugs. Results. Over the last 10–20 years, due to the development of new research methods in biology, it has become possible to clarify the features and additional conditions for the detection of pathogenic factors of nosocomial infections. Additional mechanisms of manifestation of resistance, adhesiveness, invasiveness, transmission of signs, secretion of toxins by pathogens are shownthat determines the general increase of their resistance to the action of currently used means. The general idea of ​​creating antiseptics that will not increase the resistance of pathogens can now be implemented by using substances with multidirectional or indirect mechanisms of action that minimally affect the metabolism of the cell and significantly reduce its resistance and pathogenicity. Conclusions. Factors of pathogenicity of propagators of nosocomial infections and mechanisms of their implementation can be considered as the main targets for the action of novel antiseptics that will inhibit the spread of pathogens without increasing their resistance. The promising substances for such drugs, among other things, are bacteriophages and their modifications, enzybiotics, immunobiotics, autoinducer inhibitors, quorum sensing-system inhibitors, b-lactamase inhibitors and others. Some of these substances in combination with the new generation of antibiotics significantly enhance their effectiveness and together they are able to overcome the resistance of even multidrug-resistant pathogens.

An Automated Tabletop Continuous Culturing System with Multicolor Fluorescence Monitoring for Microbial Gene Expression and Long-Term Population Dynamics

Real-time monitoring of gene expression dynamics and population levels in a multispecies microbial community could enable the study of the role of changing gene expression patterns on eco-evolutionary outcomes. Here we report the design and validation of a unique experimental platform with an in situ fluorescence measurement system that has high dynamic range and temporal resolution and is capable of monitoring multiple fluorophores for long-term gene expression and population dynamics experiments. We demonstrate the capability of our system to capture gene expression dynamics in response to external perturbations in two synthetic genetic systems: a simple inducible genetic circuit and a bistable toggle switch. Finally, in exploring the population dynamics of a two species microbial community, we show that our system can capture the switch between competitive exclusion and long-term coexistence in response to different nutrient conditions.

Lateral Gene Transfer Acts As an Evolutionary Shortcut to Efficient C4 Biochemistry

The adaptation of proteins for novel functions often requires changes in their kinetics via amino acid replacement. This process can require multiple mutations, and therefore extended periods of selection. The transfer of genes among distinct species might speed up the process, by providing proteins already adapted for the novel function. However, this hypothesis remains untested in multicellular eukaryotes. The grass Alloteropsis is an ideal system to test this hypothesis due to its diversity of genes encoding phosphoenolpyruvate carboxylase, an enzyme that catalyzes one of the key reactions in the C₄ pathway. Different accessions of Alloteropsis either use native isoforms relatively recently co-opted from other functions or isoforms that were laterally acquired from distantly related species that evolved the C₄ trait much earlier. By comparing the enzyme kinetics, we show that native isoforms with few amino acid replacements have substrate K_M values similar to the non-C₄ ancestral form, but exhibit marked increases in catalytic efficiency. The co-option of native isoforms was therefore followed by rapid catalytic improvements, which appear to rely on standing genetic variation observed within one species. Native C₄ isoforms with more amino acid replacements exhibit additional changes in affinities, suggesting that the initial catalytic improvements are followed by gradual modifications. Finally, laterally acquired genes show both strong increases in catalytic efficiency and important changes in substrate handling. We conclude that the transfer of genes among distant species sharing the same physiological novelty creates an evolutionary shortcut toward more efficient enzymes, effectively accelerating evolution.

Stepwise accumulation of mutations in CesA3 in Phytophthora sojae results in increasing resistance to CAA fungicides

Flumorph is a carboxylic acid amide (CAA) fungicide with high activity against oomycetes. However, evolution to CAAs from low resistance to high resistance has never been reported. This study investigated the basis of resistance evolution of flumorph in Phytophthora sojae. Total of 120 P. sojae isolates were collected and their sensitivity to flumorph was evaluated. Although no spontaneous resistance was found among the field isolates, adaptation on flumorph-amended media resulted in the selection of five stable mutant types exhibiting varying degrees of resistance to CAAs. Type I, which exhibited the lowest resistance level, was obtained when the wild-type isolate was exposed to a low concentration of flumorph, but no resistant mutants were obtained by direct exposure to higher concentrations. However, the more resistant types (Type II, III, IV and V) were obtained when Type I were exposed to higher concentrations of flumorph. Similar results were obtained when the entire screening process was repeated, which implied that evolution of resistance to flumorph in P. sojae could be a two-step process, where high resistance phenotypes could develop gradually from low resistance ones. Further investigation into molecular mechanism strongly confirmed that evolution of isolates highly resistant to flumorph occurs in a stepwise process with Type I as intermediary, through accumulation of mutations in their target protein of CAAs (CesA3). Together, our findings indicate that application of low rates of flumorph in field could result in selection of low resistance Type I isolates, but that raising dosage to maintain comparable levels of control could elicit rapid evolution of more resistant Type II, III, IV and V isolates with stepwise accumulation of mutations in CesA3, which would render flumorph ineffective as a control method. Precautionary resistance management strategy should be implemented. The phenomenon described in the study could have broader biological significance.

Antibiotic Toxicity Profiles of Escherichia coli Strains Lacking DNA Methyltransferases

Antibiotic-resistant bacteria are causing more antibiotic treatment failures. Developing new antibiotics and identifying bacterial targets will help to mitigate the emergence and reduce the spread of antibiotic resistance in the environment. We investigated whether DNA methyltransferase (MTase) can be an adjunct target for improving antibiotic toxicity. We used Escherichia coli as an example. The genes encoding DNA adenine MTase and cytosine MTase, dam and dcm, respectively, were separately knocked out using the λRed system in E. coli MG1655. MG1655 and the two knockout strains were separately exposed in 96-well plates to 20 antibiotics from five classes. The EC₅₀ values of almost all of the tested antibiotics were lower in the dam and dcm knockout lines than that of the control. Our statistical analysis showed that the variations observed in EC₅₀ values were independent of the mechanism underlying each antibiotic's mechanistic action.

Clinically relevant mutations in core metabolic genes confer antibiotic resistance

Although metabolism plays an active role in antibiotic lethality, antibiotic resistance is generally associated with drug target modification, enzymatic inactivation, and/or transport rather than metabolic processes. Evolution experiments of Escherichia coli rely on growth-dependent selection, which may provide a limited view of the antibiotic resistance landscape. We sequenced and analyzed E. coli adapted to representative antibiotics at increasingly heightened metabolic states. This revealed various underappreciated noncanonical genes, such as those related to central carbon and energy metabolism, which are implicated in antibiotic resistance. These metabolic alterations lead to lower basal respiration, which prevents antibiotic-mediated induction of tricarboxylic acid cycle activity, thus avoiding metabolic toxicity and minimizing drug lethality. Several of the identified metabolism-specific mutations are overrepresented in the genomes of >3500 clinical E. coli pathogens, indicating clinical relevance.

Metabolic fitness landscapes predict the evolution of antibiotic resistance

Bacteria evolve resistance to antibiotics by a multitude of mechanisms. A central, yet unsolved question is how resistance evolution affects cell growth at different drug levels. Here, we develop a fitness model that predicts growth rates of common resistance mutants from their effects on cell metabolism. The model maps metabolic effects of resistance mutations in drug-free environments and under drug challenge; the resulting fitness trade-off defines a Pareto surface of resistance evolution. We predict evolutionary trajectories of growth rates and resistance levels, which characterize Pareto resistance mutations emerging at different drug dosages. We also predict the prevalent resistance mechanism depending on drug and nutrient levels: low-dosage drug defence is mounted by regulation, evolution of distinct metabolic sectors sets in at successive threshold dosages. Evolutionary resistance mechanisms include membrane permeability changes and drug target mutations. These predictions are confirmed by empirical growth inhibition curves and genomic data of Escherichia coli populations. Our results show that resistance evolution, by coupling major metabolic pathways, is strongly intertwined with systems biology and ecology of microbial populations.

Bacterial defenses against a natural antibiotic promote collateral resilience to clinical antibiotics

Bacterial opportunistic human pathogens frequently exhibit intrinsic antibiotic tolerance and resistance, resulting in infections that can be nearly impossible to eradicate. We asked whether this recalcitrance could be driven by these organisms' evolutionary history as environmental microbes that engage in chemical warfare. Using Pseudomonas aeruginosa as a model, we demonstrate that the self-produced antibiotic pyocyanin (PYO) activates defenses that confer collateral tolerance specifically to structurally similar synthetic clinical antibiotics. Non-PYO-producing opportunistic pathogens, such as members of the Burkholderia cepacia complex, likewise display elevated antibiotic tolerance when cocultured with PYO-producing strains. Furthermore, by widening the population bottleneck that occurs during antibiotic selection and promoting the establishment of a more diverse range of mutant lineages, PYO increases apparent rates of mutation to antibiotic resistance to a degree that can rival clinically relevant hypermutator strains. Together, these results reveal an overlooked mechanism by which opportunistic pathogens that produce natural toxins can dramatically modulate the efficacy of clinical antibiotics and the evolution of antibiotic resistance, both for themselves and other members of clinically relevant polymicrobial communities.

Reconstruction of Microbial Haplotypes by Integration of Statistical and Physical Linkage in Scaffolding

DNA sequencing technologies provide unprecedented opportunities to analyze within-host evolution of microorganism populations. Often, within-host populations are analyzed via pooled sequencing of the population, which contains multiple individuals or "haplotypes." However, current next-generation sequencing instruments, in conjunction with single-molecule barcoded linked-reads, cannot distinguish long haplotypes directly. Computational reconstruction of haplotypes from pooled sequencing has been attempted in virology, bacterial genomics, metagenomics, and human genetics, using algorithms based on either cross-host genetic sharing or within-host genomic reads. Here, we describe PoolHapX, a flexible computational approach that integrates information from both genetic sharing and genomic sequencing. We demonstrated that PoolHapX outperforms state-of-the-art tools tailored to specific organismal systems, and is robust to within-host evolution. Importantly, together with barcoded linked-reads, PoolHapX can infer whole-chromosome-scale haplotypes from 50 pools each containing 12 different haplotypes. By analyzing real data, we uncovered dynamic variations in the evolutionary processes of within-patient HIV populations previously unobserved in single position-based analysis.

Isopropoxy Benzene Guanidine Kills Staphylococcus aureus Without Detectable Resistance

Serious infections caused by multidrug-resistant Staphylococcus aureus clearly urge the development of new antimicrobial agents. Drug repositioning has emerged as an alternative approach that enables us to rapidly identify effective drugs. We first reported a guanidine compound, isopropoxy benzene guanidine, had potent antibacterial activity against S. aureus. Unlike conventional antibiotics, repeated use of isopropoxy benzene guanidine had a lower probability of resistance section. We found that isopropoxy benzene guanidine triggered membrane damage by disrupting the cell membrane potential and cytoplasmic membrane integrity. Furthermore, we demonstrated that isopropoxy benzene guanidine is capable of treating invasive MRSA infections in vivo studies. These findings provided strong evidence that isopropoxy benzene guanidine represents a new chemical lead for novel antibacterial agent against multidrug-resistant S. aureus infections.

Adaptation is influenced by the complexity of environmental change during evolution in a dynamic environment

The environmental conditions of microorganisms' habitats may fluctuate in unpredictable ways, such as changes in temperature, carbon source, pH, and salinity to name a few. Environmental heterogeneity presents a challenge to microorganisms, as they have to adapt not only to be fit under a specific condition, but they must also be robust across many conditions and be able to deal with the switch between conditions itself. While experimental evolution has been used to gain insight into the adaptive process, this has largely been in either unvarying or consistently varying conditions. In cases where changing environments have been investigated, relatively little is known about how such environments influence the dynamics of the adaptive process itself, as well as the genetic and phenotypic outcomes. We designed a systematic series of evolution experiments where we used two growth conditions that have differing timescales of adaptation and varied the rate of switching between them. We used lineage tracking to follow adaptation, and whole genome sequenced adaptive clones from each of the experiments. We find that both the switch rate and the order of the conditions influences adaptation. We also find different adaptive outcomes, at both the genetic and phenotypic levels, even when populations spent the same amount of total time in the two different conditions, but the order and/or switch rate differed. Thus, in a variable environment adaptation depends not only on the nature of the conditions and phenotypes under selection, but also on the complexity of the manner in which those conditions are combined to result in a given dynamic environment.

Transcriptomic Basis of Serum Resistance and Virulence Related Traits in XDR P. aeruginosa Evolved Under Antibiotic Pressure in a Morbidostat Device

Colistin is a last resort antibiotic against the critical status pathogen Pseudomonas aeruginosa. Virulence and related traits such as biofilm formation and serum resistance after exposure to sub-inhibitory levels of colistin have been underexplored. We cultivated P. aeruginosa in a semi-automated morbidostat device with colistin, metronidazole and a combination of the two antibiotics for 21 days, and completed RNA-Seq to uncover the transcriptional changes over time. Strains became resistant to colistin within this time period. Colistin-resistant strains show significantly increased biofilm formation: the cell density in biofilm increases under exposure to colistin, while the addition of metronidazole can remove this effect. After 7 days of colistin exposure, strains develop an ability to grow in serum, suggesting that colistin drives bacterial modifications conferring a protective effect from serum complement factors. Of note, strains exposed to colistin showed a decrease in virulence, when measured using the Galleria mellonella infection model. These phenotypic changes were characterized by a series of differential gene expression changes, particularly those related to LPS modifications, spermidine synthesis (via speH and speE) and the major stress response regulator rpoS. Our results suggest a clinically important bacterial evolution under sub-lethal antibiotic concentration leading to potential for significant changes in the clinical course of infection.

The Genomic Basis of Rapid Adaptation to Antibiotic Combination Therapy in Pseudomonas aeruginosa

Combination therapy is a common antibiotic treatment strategy that aims at minimizing the risk of resistance evolution in several infectious diseases. Nonetheless, evidence supporting its efficacy against the nosocomial opportunistic pathogen Pseudomonas aeruginosa remains elusive. Identification of the possible evolutionary paths to resistance in multidrug environments can help to explain treatment outcome. For this purpose, we here performed whole-genome sequencing of 127 previously evolved populations of P. aeruginosa adapted to sublethal doses of distinct antibiotic combinations and corresponding single-drug treatments, and experimentally characterized several of the identified variants. We found that alterations in the regulation of efflux pumps are the most favored mechanism of resistance, regardless of the environment. Unexpectedly, we repeatedly identified intergenic variants in the adapted populations, often with no additional mutations and usually associated with genes involved in efflux pump expression, possibly indicating a regulatory function of the intergenic regions. The experimental analysis of these variants demonstrated that the intergenic changes caused similar increases in resistance against single and multidrug treatments as those seen for efflux regulatory gene mutants. Surprisingly, we could find no substantial fitness costs for a majority of these variants, most likely enhancing their competitiveness toward sensitive cells, even in antibiotic-free environments. We conclude that the regulation of efflux is a central target of antibiotic-mediated selection in P. aeruginosa and that, importantly, changes in intergenic regions may represent a usually neglected alternative process underlying bacterial resistance evolution, which clearly deserves further attention in the future.

A new analysis method for evaluating bacterial growth with microplate readers

Growth curve measurements are commonly used in microbiology, while the use of microplate readers for such measurements provides better temporal resolution and higher throughput. However, evaluating bacterial growth with microplate readers has been hurdled by barriers such as multiple scattering. Here, we report our development of a method based on the time derivatives of the optical density (OD) and/or fluorescence (FL) of bacterial cultures to overcome these barriers. First, we illustrated our method using quantitative models and numerical simulations, which predicted the number of bacteria and the number of fluorescent proteins in time as well as their time derivatives. Then, we systematically investigated how the time derivatives depend on the parameters in the models/simulations, providing a framework for understanding the FL growth curves. In addition, as a demonstration, we applied our method to study the lag time elongation of bacteria subjected to treatment with silver (Ag⁺) ions and found that the results from our method corroborated well with that from growth curve fitting by the Gompertz model that has been commonly used in the literature. Furthermore, this method was applied to the growth of bacteria in the presence of silver nanoparticles (AgNPs) at various concentrations, where the OD curve measurements failed. We showed that our method allowed us to successfully extract the growth behavior of the bacteria from the FL measurements and understand how the growth was affected by the AgNPs.

A simplified strategy for titrating gene expression reveals new relationships between genotype, environment, and bacterial growth

A lack of high-throughput techniques for making titrated, gene-specific changes in expression limits our understanding of the relationship between gene expression and cell phenotype. Here, we present a generalizable approach for quantifying growth rate as a function of titrated changes in gene expression level. The approach works by performing CRISPRi with a series of mutated single guide RNAs (sgRNAs) that modulate gene expression. To evaluate sgRNA mutation strategies, we constructed a library of 5927 sgRNAs targeting 88 genes in Escherichia coli MG1655 and measured the effects on growth rate. We found that a compounding mutational strategy, through which mutations are incrementally added to the sgRNA, presented a straightforward way to generate a monotonic and gradated relationship between mutation number and growth rate effect. We also implemented molecular barcoding to detect and correct for mutations that 'escape' the CRISPRi targeting machinery; this strategy unmasked deleterious growth rate effects obscured by the standard approach of ignoring escapers. Finally, we performed controlled environmental variations and observed that many gene-by-environment interactions go completely undetected at the limit of maximum knockdown, but instead manifest at intermediate expression perturbation strengths. Overall, our work provides an experimental platform for quantifying the phenotypic response to gene expression variation.

Exploring the Prevalence and Distribution Patterns of Antibiotic Resistance Genes in Bovine Gut Microbiota Using a Metagenomic Approach

Antibiotic resistance genes (ARGs) have become recognized contaminants and pose a high public health risk. The animal gut microbiota is a reservoir of ARGs, but the knowledge of the origin and dissemination of ARGs remains unclear. In this study, we provide a comprehensive profile of ARGs and mobile genetic elements in the gut microbiota from 30 bovines to study the impact of modern antibiotics on resistance. A total of 42 ARG types were detected by annotating the metagenomic sequencing data from Comprehensive Antibiotic Resistance Database (CARD). We found that the diversity and abundance of ARGs in individual yaks were significantly lower than those in dairy and beef cattle (p < 0.0001). The results of heat map and single nucleotide polymorphism clustering suggest that ARGs from dairy and beef cattle are more similar, whereas those from yaks cluster separately. The long-term use of antibiotics may contribute to this difference, suggesting that antibiotic consumption is the main cause of ARG prevalence. Furthermore, abundant insertions were also found in this study, signifying a strong potential for horizontal transfer of ARGs among microbes, especially pathogens.

Mapping the Role of AcrAB-TolC Efflux Pumps in the Evolution of Antibiotic Resistance Reveals Near-MIC Treatments Facilitate Resistance Acquisition

Antibiotic resistance has become a major public health concern as bacteria evolve to evade drugs, leading to recurring infections and a decrease in antibiotic efficacy. Systematic efforts have revealed mechanisms involved in resistance. Yet, in many cases, how these specific mechanisms accelerate or slow the evolution of resistance remains unclear. Here, we conducted a systematic study of the impact of the AcrAB-TolC efflux pump on the evolution of antibiotic resistance. We mapped how population growth rate and resistance change over time as a function of both the antibiotic concentration and the parent strain's genetic background. We compared the wild-type strain to a strain overexpressing AcrAB-TolC pumps and a strain lacking functional pumps. In all cases, resistance emerged when cultures were treated with chloramphenicol concentrations near the MIC of their respective parent strain. The genetic background of the parent strain also influenced resistance acquisition. The wild-type strain evolved resistance within 24 h through mutations in the acrAB operon and its associated regulators. Meanwhile, the strain overexpressing AcrAB-TolC evolved resistance more slowly than the wild-type strain; this strain achieved resistance in part through point mutations in acrB and the acrAB promoter. Surprisingly, the strain without functional AcrAB-TolC efflux pumps still gained resistance, which it achieved through upregulation of redundant efflux pumps. Overall, our results suggest that treatment conditions just above the MIC pose the largest risk for the evolution of resistance and that AcrAB-TolC efflux pumps impact the pathway by which chloramphenicol resistance is achieved.IMPORTANCE Combatting the rise of antibiotic resistance is a significant challenge. Efflux pumps are an important contributor to drug resistance; they exist across many cell types and can export numerous classes of antibiotics. Cells can regulate pump expression to maintain low intracellular drug concentrations. Here, we explored how resistance emerged depending on the antibiotic concentration, as well as the presence of efflux pumps and their regulators. We found that treatments near antibiotic concentrations that inhibit the parent strain's growth were most likely to promote resistance. While wild-type, pump overexpression, and pump knockout strains were all able to evolve resistance, they differed in the absolute level of resistance evolved, the speed at which they achieved resistance, and the genetic pathways involved. These results indicate that specific treatment regimens may be especially problematic for the evolution of resistance and that the strain background can influence how resistance is achieved.

Transcriptomic Analysis of the Activity and Mechanism of Action of a Ruthenium(II)-Based Antimicrobial That Induces Minimal Evolution of Pathogen Resistance

Increasing concern over rising levels of antibiotic resistance among pathogenic bacteria has prompted significant research into developing efficacious alternatives to antibiotic treatment. Previously, we have reported on the therapeutic activity of a dinuclear ruthenium(II) complex against pathogenic, multi-drug-resistant bacterial pathogens. Herein, we report that the solubility properties of this lead are comparable to those exhibited by orally available therapeutics that in comparison to clinically relevant antibiotics it induces very slow evolution of resistance in the uropathogenic, therapeutically resistant, E. coli strain EC958, and this resistance was lost when exposure to the compound was temporarily removed. With the aim of further investigating the mechanism of action of this compound, the regulation of nine target genes relating to the membrane, DNA damage, and other stress responses provoked by exposure to the compound was also studied. This analysis confirmed that the compound causes a significant transcriptional downregulation of genes involved in membrane transport and the tricarboxylic acid cycle. By contrast, expression of the chaperone protein-coding gene, spy, was significantly increased suggesting a requirement for repair of damaged proteins in the region of the outer membrane. The complex was also found to display activity comparable to that in E. coli in a range of other therapeutically relevant Gram-negative pathogens.

High-throughput characterization of the expressed antibiotic resistance genes in sewage sludge with transcriptional analysis

Antibiotic resistance genes (ARGs) are emerging micro-pollutants that pose potential threats to environments and humans. Sewage sludge from wastewater is an important source for ARGs and current studies mainly focus on their existence in microbial genomes. However, little is known about which ARGs are expressed even though ARGs expression remains a better proxy for functional activity. In this study, the expressed ARGs in sewage sludge were characterized by high-throughput quantitative PCR (296 primer sets) combined with transcriptional analysis. A total of 202 ARG transcripts were detected and their abundances ranged from 3.1 × 10⁹ to 1.2 × 10¹⁰ copies/g dry weight. The sum abundance of five most abundant ARG transcripts (qacEdelta1-02, sul2, qacEdelta1-01, aadA2-03, tetX) exhibited a linear correlation with the total abundance of ARG transcripts (R² = 0.88, p < 10^-4), suggesting that these genes could be regarded as indicators to quantitatively predict the total abundance of expressed ARGs. Dynamics of expressed ARGs were observed with lower abundances in summer and winter than those in other seasons (p < 0.05, Kruskal-Wallis test). Variation partitioning analysis indicated that the shift in bacterial community structures induced by changes in environmental attributes might be the main driver for the dynamics of expressed ARGs. Results of this study provided new insights into the ARGs in sewage sludge.

Acetate and glycerol are not uniquely suited for the evolution of cross-feeding in E. coli

The evolution of cross-feeding among individuals of the same species can help generate genetic and phenotypic diversity even in completely homogeneous environments. Cross-feeding Escherichia coli strains, where one strain feeds on a carbon source excreted by another strain, rapidly emerge during experimental evolution in a chemically minimal environment containing glucose as the sole carbon source. Genome-scale metabolic modeling predicts that cross-feeding of 58 carbon sources can emerge in the same environment, but only cross-feeding of acetate and glycerol has been experimentally observed. Here we use metabolic modeling to ask whether acetate and glycerol cross-feeding are especially likely to evolve, perhaps because they require less metabolic change, and thus perhaps also less genetic change than other cross-feeding interactions. However, this is not the case. The minimally required metabolic changes required for acetate and glycerol cross feeding affect dozens of chemical reactions, multiple biochemical pathways, as well as multiple operons or regulons. The complexity of these changes is consistent with experimental observations, where cross-feeding strains harbor multiple mutations. The required metabolic changes are also no less complex than those observed for multiple other of the 56 cross feeding interactions we study. We discuss possible reasons why only two cross-feeding interactions have been discovered during experimental evolution and argue that multiple new cross-feeding interactions may await discovery.

The evolution of cross-feeding interactions, where one organism thrives by consuming the excretions of others, can create diversity even in simple and homogeneous environments. In past work, we had predicted that 58 cross-feeding interactions could evolve in populations of E. coli grown in glucose minimal media, yet only two have been experimentally observed, those involving acetate and glycerol. We hypothesized that multiple mutations might be required for the evolution of computationally predicted but not experimentally observed cross-feeding interactions. To answer this question, we developed a method that searches for the minimal number of metabolic changes required for individuals to change their metabolic state (from an ancestral glucose-consuming state to an evolved state that produces or consumes other metabolite). We observed that the metabolic changes required for the evolution of acetate and glycerol cross-feeding are no less complex than those required for the evolution of the other predicted cross-feeding interactions, which suggests that multiple cross-feeding interactions may still await discovery.

High-throughput laboratory evolution reveals evolutionary constraints in Escherichia coli

Understanding the constraints that shape the evolution of antibiotic resistance is critical for predicting and controlling drug resistance. Despite its importance, however, a systematic investigation of evolutionary constraints is lacking. Here, we perform a high-throughput laboratory evolution of Escherichia coli under the addition of 95 antibacterial chemicals and quantified the transcriptome, resistance, and genomic profiles for the evolved strains. Utilizing machine learning techniques, we analyze the phenotype–genotype data and identified low dimensional phenotypic states among the evolved strains. Further analysis reveals the underlying biological processes responsible for these distinct states, leading to the identification of trade-off relationships associated with drug resistance. We also report a decelerated evolution of β-lactam resistance, a phenomenon experienced by certain strains under various stresses resulting in higher acquired resistance to β-lactams compared to strains directly selected by β-lactams. These findings bridge the genotypic, gene expression, and drug resistance gap, while contributing to a better understanding of evolutionary constraints for antibiotic resistance.

Understanding evolutionary constraints in antibiotic resistance is crucial for prediction and control. Here, the authors use high-throughput laboratory evolution of Escherichia coli alongside machine learning to identify trade-off relationships associated with drug resistance.

Exploration into the origins and mobilization of di-hydrofolate reductase genes and the emergence of clinical resistance to trimethoprim

Trimethoprim is a synthetic antibacterial agent that targets folate biosynthesis by competitively binding to the di-hydrofolate reductase enzyme (DHFR). Trimethoprim is often administered synergistically with sulfonamide, another chemotherapeutic agent targeting the di-hydropteroate synthase (DHPS) enzyme in the same pathway. Clinical resistance to both drugs is widespread and mediated by enzyme variants capable of performing their biological function without binding to these drugs. These mutant enzymes were assumed to have arisen after the discovery of these synthetic drugs, but recent work has shown that genes conferring resistance to sulfonamide were present in the bacterial pangenome millions of years ago. Here, we apply phylogenetics and comparative genomics methods to study the largest family of mobile trimethoprim-resistance genes (dfrA). We show that most of the dfrA genes identified to date map to two large clades that likely arose from independent mobilization events. In contrast to sulfonamide resistance (sul) genes, we find evidence of recurrent mobilization in dfrA genes. Phylogenetic evidence allows us to identify novel dfrA genes in the emerging pathogen Acinetobacter baumannii, and we confirm their resistance phenotype in vitro. We also identify a cluster of dfrA homologues in cryptic plasmid and phage genomes, but we show that these enzymes do not confer resistance to trimethoprim. Our methods also allow us to pinpoint the chromosomal origin of previously reported dfrA genes, and we show that many of these ancient chromosomal genes also confer resistance to trimethoprim. Our work reveals that trimethoprim resistance predated the clinical use of this chemotherapeutic agent, but that novel mutations have likely also arisen and become mobilized following its widespread use within and outside the clinic. Hence, this work confirms that resistance to novel drugs may already be present in the bacterial pangenome, and stresses the importance of rapid mobilization as a fundamental element in the emergence and global spread of resistance determinants.

Antisense antibacterial compounds

Extensive antibiotic use combined with poor historical drug stewardship practices have created a medical crisis in which once treatable bacterial infections are now increasingly unmanageable. To combat this, new antibiotics will need to be developed and safeguarded. An emerging class of antibiotics based upon nuclease-stable antisense technologies has proven valuable in preclinical testing against a variety of bacterial pathogens. This review describes the current state of development of antisense-based antibiotics, the mechanisms thus far employed by these compounds, and possible future avenues of research.

Transcriptional Rewiring, Adaptation, and the Role of Gene Duplication in the Metabolism of Ethanol of Saccharomyces cerevisiae

Ethanol is the main by-product of yeast sugar fermentation that affects microbial growth parameters, being considered a dual molecule, a nutrient and a stressor. Previous works demonstrated that the budding yeast arose after an ancient hybridization process resulted in a tier of duplicated genes within its genome, many of them with implications in this ethanol "produce-accumulate-consume" strategy. The evolutionary link between ethanol production, consumption, and tolerance versus ploidy and stability of the hybrids is an ongoing debatable issue. The implication of ancestral duplicates in this metabolic rewiring, and how these duplicates differ transcriptionally, remains unsolved. Here, we study the transcriptomic adaptive signatures to ethanol as a nonfermentative carbon source to sustain clonal yeast growth by experimental evolution, emphasizing the role of duplicated genes in the adaptive process. As expected, ethanol was able to sustain growth but at a lower rate than glucose. Our results demonstrate that in asexual populations a complete transcriptomic rewiring was produced, strikingly by downregulation of duplicated genes, mainly whole-genome duplicates, whereas small-scale duplicates exhibited significant transcriptional divergence between copies. Overall, this study contributes to the understanding of evolution after gene duplication, linking transcriptional divergence with duplicates' fate in a multigene trait as ethanol tolerance.IMPORTANCE Gene duplication events have been related with increasing biological complexity through the tree of life, but also with illnesses, including cancer. Early evolutionary theories indicated that duplicated genes could explore alternative functions due to relaxation of selective constraints in one of the copies, as the other remains as ancestral-function backup. In unicellular eukaryotes like yeasts, it has been demonstrated that the fate and persistence of duplicates depend on duplication mechanism (whole-genome or small-scale events), shaping their actual genomes. Although it has been shown that small-scale duplicates tend to innovate and whole-genome duplicates specialize in ancestral functions, the implication of duplicates' transcriptional plasticity and transcriptional divergence on environmental and metabolic responses remains largely obscure. Here, by experimental adaptive evolution, we show that Saccharomyces cerevisiae is able to respond to metabolic stress (ethanol as nonfermentative carbon source) due to the persistence of duplicated genes. These duplicates respond by transcriptional rewiring, depending on their transcriptional background. Our results shed light on the mechanisms that determine the role of duplicates, and on their evolvability.

Efflux pump activity potentiates the evolution of antibiotic resistance across S. aureus isolates

The rise of antibiotic resistance in many bacterial pathogens has been driven by the spread of a few successful strains, suggesting that some bacteria are genetically pre-disposed to evolving resistance. Here, we test this hypothesis by challenging a diverse set of 222 isolates of Staphylococcus aureus with the antibiotic ciprofloxacin in a large-scale evolution experiment. We find that a single efflux pump, norA, causes widespread variation in evolvability across isolates. Elevated norA expression potentiates evolution by increasing the fitness benefit provided by DNA topoisomerase mutations under ciprofloxacin treatment. Amplification of norA provides a further mechanism of rapid evolution in isolates from the CC398 lineage. Crucially, chemical inhibition of NorA effectively prevents the evolution of resistance in all isolates. Our study shows that pre-existing genetic diversity plays a key role in shaping resistance evolution, and it may be possible to predict which strains are likely to evolve resistance and to optimize inhibitor use to prevent this outcome.

Escherichia coli with a Tunable Point Mutation Rate for Evolution Experiments

The mutation rate and mutations' effects on fitness are crucial to evolution. Mutation rates are under selection due to linkage between mutation rate modifiers and mutations' effects on fitness. The linkage between a higher mutation rate and more beneficial mutations selects for higher mutation rates, while the linkage between a higher mutation rate and more deleterious mutations selects for lower mutation rates. The net direction of selection on mutations rates depends on the fitness landscape, and a great deal of work has elucidated the fitness landscapes of mutations. However, tests of the effect of varying a mutation rate on evolution in a single organism in a single environment have been difficult. This has been studied using strains of antimutators and mutators, but these strains may differ in additional ways and typically do not allow for continuous variation of the mutation rate. To help investigate the effects of the mutation rate on evolution, we have genetically engineered a strain of Escherichia coli with a point mutation rate that can be smoothly varied over two orders of magnitude. We did this by engineering a strain with inducible control of the mismatch repair proteins MutH and MutL. We used this strain in an approximately 350 generation evolution experiment with controlled variation of the mutation rate. We confirmed the construct and the mutation rate were stable over this time. Sequencing evolved strains revealed a higher number of single nucleotide polymorphisms at higher mutations rates, likely due to either the beneficial effects of these mutations or their linkage to beneficial mutations.

Evolutionary stalling and a limit on the power of natural selection to improve a cellular module

Cells consist of molecular modules which perform vital biological functions. Cellular modules are key units of adaptive evolution because organismal fitness depends on their performance. Theory shows that in rapidly evolving populations, such as those of many microbes, adaptation is driven primarily by common beneficial mutations with large effects, while other mutations behave as if they are effectively neutral. As a consequence, if a module can be improved only by rare and/or weak beneficial mutations, its adaptive evolution would stall. However, such evolutionary stalling has not been empirically demonstrated, and it is unclear to what extent stalling may limit the power of natural selection to improve modules. Here we empirically characterize how natural selection improves the translation machinery (TM), an essential cellular module. We experimentally evolved populations of Escherichia coli with genetically perturbed TMs for 1,000 generations. Populations with severe TM defects initially adapted via mutations in the TM, but TM adaptation stalled within about 300 generations. We estimate that the genetic load in our populations incurred by residual TM defects ranges from 0.5 to 19%. Finally, we found evidence that both epistasis and the depletion of the pool of beneficial mutations contributed to evolutionary stalling. Our results suggest that cellular modules may not be fully optimized by natural selection despite the availability of adaptive mutations.

Escherichia coli Has a Unique Transcriptional Program in Long-Term Stationary Phase Allowing Identification of Genes Important for Survival

Microbes live in complex and constantly changing environments, but it is difficult to replicate this in the laboratory. Escherichia coli has been used as a model organism in experimental evolution studies for years; specifically, we and others have used it to study evolution in complex environments by incubating the cells into long-term stationary phase (LTSP) in rich media. In LTSP, cells experience a variety of stresses and changing conditions. While we have hypothesized that this experimental system is more similar to natural environments than some other lab conditions, we do not yet know how cells respond to this environment biochemically or physiologically. In this study, we began to unravel the cells' responses to this environment by characterizing the transcriptome of cells during LTSP. We found that cells in LTSP have a unique transcriptional program and that several genes are uniquely upregulated or downregulated in this phase. Further, we identified two genes, cspB and cspI, which are most highly expressed in LTSP, even though these genes are primarily known to respond to cold shock. By competing cells lacking these genes with wild-type cells, we show that these genes are also important for survival during LTSP. These data can help identify gene products that may play a role in survival in this complex environment and lead to identification of novel functions of proteins.IMPORTANCE Experimental evolution studies have elucidated evolutionary processes, but usually in chemically well-defined and/or constant environments. Using complex environments is important to begin to understand how evolution may occur in natural environments, such as soils or within a host. However, characterizing the stresses that cells experience in these complex environments can be challenging. One way to approach this is by determining how cells biochemically acclimate to heterogenous environments. In this study, we began to characterize physiological changes by analyzing the transcriptome of cells in a dynamic complex environment. By characterizing the transcriptional profile of cells in long-term stationary phase, a heterogenous and stressful environment, we can begin to understand how cells physiologically and biochemically react to the laboratory environment, and how this compares to more-natural conditions.

A Two-Enzyme Adaptive Unit within Bacterial Folate Metabolism

Enzyme function and evolution are influenced by the larger context of a metabolic pathway. Deleterious mutations or perturbations in one enzyme can often be compensated by mutations to others. We used comparative genomics and experiments to examine evolutionary interactions with the essential metabolic enzyme dihydrofolate reductase (DHFR). Analyses of synteny and co-occurrence across bacterial species indicate that DHFR is coupled to thymidylate synthase (TYMS) but relatively independent from the rest of folate metabolism. Using quantitative growth rate measurements and forward evolution in Escherichia coli, we demonstrate that the two enzymes adapt as a relatively independent unit in response to antibiotic stress. Metabolomic profiling revealed that TYMS activity must not exceed DHFR activity to prevent the depletion of reduced folates and the accumulation of the intermediate dihydrofolate. Comparative genomics analyses identified >200 gene pairs with similar statistical signatures of modular co-evolution, suggesting that cellular pathways may be decomposable into small adaptive units.

The incidence of antibiotic resistance within and beyond the agricultural ecosystem: A concern for public health

The agricultural ecosystem creates a platform for the development and dissemination of antimicrobial resistance, which is promoted by the indiscriminate use of antibiotics in the veterinary, agricultural, and medical sectors. This results in the selective pressure for the intrinsic and extrinsic development of the antimicrobial resistance phenomenon, especially within the aquaculture‐animal‐manure‐soil‐water‐plant nexus. The existence of antimicrobial resistance in the environment has been well documented in the literature. However, the possible transmission routes of antimicrobial agents, their resistance genes, and naturally selected antibiotic‐resistant bacteria within and between the various niches of the agricultural environment and humans remain poorly understood. This study, therefore, outlines an overview of the discovery and development of commonly used antibiotics; the timeline of resistance development; transmission routes of antimicrobial resistance in the agro‐ecosystem; detection methods of environmental antimicrobial resistance determinants; factors involved in the evolution and transmission of antibiotic resistance in the environment and the agro‐ecosystem; and possible ways to curtail the menace of antimicrobial resistance.

From Microbial Communities to Distributed Computing Systems

A distributed biological system can be defined as a system whose components are located in different subpopulations, which communicate and coordinate their actions through interpopulation messages and interactions. We see that distributed systems are pervasive in nature, performing computation across all scales, from microbial communities to a flock of birds. We often observe that information processing within communities exhibits a complexity far greater than any single organism. Synthetic biology is an area of research which aims to design and build synthetic biological machines from biological parts to perform a defined function, in a manner similar to the engineering disciplines. However, the field has reached a bottleneck in the complexity of the genetic networks that we can implement using monocultures, facing constraints from metabolic burden and genetic interference. This makes building distributed biological systems an attractive prospect for synthetic biology that would alleviate these constraints and allow us to expand the applications of our systems into areas including complex biosensing and diagnostic tools, bioprocess control and the monitoring of industrial processes. In this review we will discuss the fundamental limitations we face when engineering functionality with a monoculture, and the key areas where distributed systems can provide an advantage. We cite evidence from natural systems that support arguments in favor of distributed systems to overcome the limitations of monocultures. Following this we conduct a comprehensive overview of the synthetic communities that have been built to date, and the components that have been used. The potential computational capabilities of communities are discussed, along with some of the applications that these will be useful for. We discuss some of the challenges with building co-cultures, including the problem of competitive exclusion and maintenance of desired community composition. Finally, we assess computational frameworks currently available to aide in the design of microbial communities and identify areas where we lack the necessary tools.

Stability of β-lactam antibiotics in bacterial growth media

Laboratory assays such as MIC tests assume that antibiotic molecules are stable in the chosen growth medium-but rapid degradation has been observed for antibiotics including β-lactams under some conditions in aqueous solution. Degradation rates in bacterial growth medium are less well known. Here, we develop a 'delay time bioassay' that provides a simple way to estimate antibiotic stability in bacterial growth media, using only a plate reader and without the need to measure the antibiotic concentration directly. We use the bioassay to measure degradation half-lives of the β-lactam antibiotics mecillinam, aztreonam and cefotaxime in widely-used bacterial growth media based on MOPS and Luria-Bertani (LB) broth. We find that mecillinam degradation can occur rapidly, with a half-life as short as 2 hours in MOPS medium at 37°C and pH 7.4, and 4-5 hours in LB, but that adjusting the pH and temperature can increase its stability to a half-life around 6 hours without excessively perturbing growth. Aztreonam and cefotaxime were found to have half-lives longer than 6 hours in MOPS medium at 37°C and pH 7.4, but still shorter than the timescale of a typical minimum inhibitory concentration (MIC) assay. Taken together, our results suggest that care is needed in interpreting MIC tests and other laboratory growth assays for β-lactam antibiotics, since there may be significant degradation of the antibiotic during the assay.

In Vitro and In Vivo Comparison of Changes in Antibiotics Susceptibility of E. coli and Chicken's Intestinal Flora after Exposure to Amoxicillin or Thymol

This study aims at verifying, in vitro, the extent to which the use of amoxicillin or thymol induces the selection of resistant bacteria and at evaluating in vivo their effects on the development of antimicrobial resistance in the intestinal flora of poultry. E. coli strain was subcultured on agar plates containing increasing concentrations of either amoxicillin or thymol. Thereafter, minimal inhibitory concentrations (MICs) of thymol, amoxicillin, and two other antibiotics, tylosin and colistin, were determined using the microdilution method. Groups of chicks were subjected to a 2-week regime of either amoxicillin or thymol added to their drinking water. During the treatment with either thymol or amoxicillin, the total aerobic mesophilic flora (TAMF) was counted on thymol-gradient plates or amoxicillin-gradient plates and the MICs of antibiotics and thymol for E. coli isolates were determined. The in vitro test showed that for E. coli, which had been serially subcultured on increasing concentrations of amoxicillin, a 32-fold increase in MIC values for amoxicillin and a 4-fold increase for colistin and tylosin were noted. However, the MIC of thymol for this strain remained constant. For the E. coli, which had been serially subcultured on increasing concentrations of thymol, no change in the MIC values for antibiotics and thymol was observed. The in vivo test confirmed the in vitro one. It demonstrated that exposure to amoxicillin induced a selection of antimicrobial resistance in TAMF and intestinal E. coli, whereas exposure to thymol did not. The results showed that the group receiving thymol had a lower consumption index compared to the other groups. This study demonstrates the feasibility of this natural product as an alternative solution to the current use of antibiotics in poultry farming.

Multidrug Resistance (MDR) and Collateral Sensitivity in Bacteria, with Special Attention to Genetic and Evolutionary Aspects and to the Perspectives of Antimicrobial Peptides-A Review

Antibiotic poly-resistance (multidrug-, extreme-, and pan-drug resistance) is controlled by adaptive evolution. Darwinian and Lamarckian interpretations of resistance evolution are discussed. Arguments for, and against, pessimistic forecasts on a fatal “post-antibiotic era” are evaluated. In commensal niches, the appearance of a new antibiotic resistance often reduces fitness, but compensatory mutations may counteract this tendency. The appearance of new antibiotic resistance is frequently accompanied by a collateral sensitivity to other resistances. Organisms with an expanding open pan-genome, such as Acinetobacter baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae, can withstand an increased number of resistances by exploiting their evolutionary plasticity and disseminating clonally or poly-clonally. Multidrug-resistant pathogen clones can become predominant under antibiotic stress conditions but, under the influence of negative frequency-dependent selection, are prevented from rising to dominance in a population in a commensal niche. Antimicrobial peptides have a great potential to combat multidrug resistance, since antibiotic-resistant bacteria have shown a high frequency of collateral sensitivity to antimicrobial peptides. In addition, the mobility patterns of antibiotic resistance, and antimicrobial peptide resistance, genes are completely different. The integron trade in commensal niches is fortunately limited by the species-specificity of resistance genes. Hence, we theorize that the suggested post-antibiotic era has not yet come, and indeed might never come.

Current Status and Applications of Adaptive Laboratory Evolution in Industrial Microorganisms

Adaptive laboratory evolution (ALE) is an evolutionary engineering approach in artificial conditions that improves organisms through the imitation of natural evolution. Due to the development of multi-level omics technologies in recent decades, ALE can be performed for various purposes at the laboratory level. This review delineates the basics of the experimental design of ALE based on several ALE studies of industrial microbial strains and updates current strategies combined with progressed metabolic engineering, in silico modeling and automation to maximize the evolution efficiency. Moreover, the review sheds light on the applicability of ALE as a strain development approach that complies with non-recombinant preferences in various food industries. Overall, recent progress in the utilization of ALE for strain development leading to successful industrialization is discussed.

Genetic dominance governs the evolution and spread of mobile genetic elements in bacteria

Mobile genetic elements (MGEs), such as plasmids, promote bacterial evolution through horizontal gene transfer (HGT). However, the rules governing the repertoire of traits encoded on MGEs remain unclear. In this study, we uncovered the central role of genetic dominance shaping genetic cargo in MGEs, using antibiotic resistance as a model system. MGEs are typically present in more than one copy per host bacterium, and as a consequence, genetic dominance favors the fixation of dominant mutations over recessive ones. In addition, genetic dominance also determines the phenotypic effects of horizontally acquired MGE-encoded genes, silencing recessive alleles if the recipient bacterium already carries a wild-type copy of the gene. The combination of these two effects governs the catalog of genes encoded on MGEs. Our results help to understand how MGEs evolve and spread, uncovering the neglected influence of genetic dominance on bacterial evolution. Moreover, our findings offer a framework to forecast the spread and evolvability of MGE-encoded genes, which encode traits of key human interest, such as virulence or antibiotic resistance.

Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance

Genetic perturbations that affect bacterial resistance to antibiotics have been characterized genome-wide, but how do such perturbations interact with subsequent evolutionary adaptation to the drug? Here, we show that strong epistasis between resistance mutations and systematically identified genes can be exploited to control spontaneous resistance evolution. We evolved hundreds of Escherichia coli K-12 mutant populations in parallel, using a robotic platform that tightly controls population size and selection pressure. We find a global diminishing-returns epistasis pattern: strains that are initially more sensitive generally undergo larger resistance gains. However, some gene deletion strains deviate from this general trend and curtail the evolvability of resistance, including deletions of genes for membrane transport, LPS biosynthesis, and chaperones. Deletions of efflux pump genes force evolution on inferior mutational paths, not explored in the wild type, and some of these essentially block resistance evolution. This effect is due to strong negative epistasis with resistance mutations. The identified genes and cellular functions provide potential targets for development of adjuvants that may block spontaneous resistance evolution when combined with antibiotics.

The antibiotic resistance crisis calls for new ways of restricting the ability of bacteria to evolve resistance. Here, Lukačišinová et al. perform highly controlled evolution experiments in E. coli strains to identify genetic perturbations that strongly limit the evolution of antibiotic resistance through epistasis.

Automated Continuous Evolution of Proteins in Vivo

We present automated continuous evolution (ACE), a platform for the hands-free directed evolution of biomolecules. ACE pairs OrthoRep, a genetic system for continuous targeted mutagenesis of user-selected genes in vivo, with eVOLVER, a scalable and automated continuous culture device for precise, multiparameter regulation of growth conditions. By implementing real-time feedback-controlled tuning of selection stringency with eVOLVER, genes of interest encoded on OrthoRep autonomously traversed multimutation adaptive pathways to reach desired functions, including drug resistance and improved enzyme activity. The durability, scalability, and speed of biomolecular evolution with ACE should be broadly applicable to protein engineering as well as prospective studies on how selection parameters and schedules shape adaptation.

Tuning environmental timescales to evolve and maintain generalists

Natural environments can present diverse challenges, but some genotypes remain fit across many environments. Such "generalists" can be hard to evolve, outcompeted by specialists fitter in any particular environment. Here, inspired by the search for broadly neutralizing antibodies during B cell affinity maturation, we demonstrate that environmental changes on an intermediate timescale can reliably evolve generalists, even when faster or slower environmental changes are unable to do so. We find that changing environments on timescales comparable with evolutionary transients in a population enhance the rate of evolving generalists from specialists, without enhancing the reverse process. The yield of generalists is further increased in more complex dynamic environments, such as a "chirp" of increasing frequency. Our work offers design principles for how nonequilibrium fitness "seascapes" can dynamically funnel populations to genotypes unobtainable in static environments.

A Microfluidic Device for Imaging Samples from Microbial Suspension Cultures

Traditional methods to assess microbial cells during suspension culture require laborious and frequent manual sampling. Approaches to automate sampling and assessment utilize dedicated, sophisticated equipment and suffer from a lack of temporal resolution and sampling efficiency. In this study we describe a simple microfluidic device that allows microbial cells to be sampled from suspension culture and rapidly slowed and concentrated for single-cell imaging on a standard laboratory microscope. We demonstrate a device that: •slows and concentrates microbial cells, specifically budding yeast, sampled from suspension culture and improves imaging of individual cells by concentrating them in a single focal plane•provides imaging quality and temporal resolution that is capable of monitoring dynamic spatiotemporal processes, such as nuclear localization of a protein•is inexpensive and simple enough to be fabricated and used in laboratories equipped for standard molecular and cellular biology.

Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations

Standard infectious disease practice calls for aggressive drug treatment that rapidly eliminates the pathogen population before resistance can emerge. When resistance is absent, this elimination strategy can lead to complete cure. However, when resistance is already present, removing drug-sensitive cells as quickly as possible removes competitive barriers that may slow the growth of resistant cells. In contrast to the elimination strategy, a containment strategy aims to maintain the maximum tolerable number of pathogens, exploiting competitive suppression to achieve chronic control. Here, we combine in vitro experiments in computer-controlled bioreactors with mathematical modeling to investigate whether containment strategies can delay failure of antibiotic treatment regimens. To do so, we measured the "escape time" required for drug-resistant Escherichia coli populations to eclipse a threshold density maintained by adaptive antibiotic dosing. Populations containing only resistant cells rapidly escape the threshold density, but we found that matched resistant populations that also contain the maximum possible number of sensitive cells could be contained for significantly longer. The increase in escape time occurs only when the threshold density-the acceptable bacterial burden-is sufficiently high, an effect that mathematical models attribute to increased competition. The findings provide decisive experimental confirmation that maintaining the maximum number of sensitive cells can be used to contain resistance when the size of the population is sufficiently large.

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.