Antibiotic Resistance.

Bacterial mechanisms of drug resistance operate at sequential lines of defense tackling drug at entry, accumulation, target binding, or downstream toxicity. These mechanisms are encoded by genomic changes ranging in scale from point mutations, through assembly of preexisting genetic elements, to horizontal import of genes from the environment. A many-to-many relationship prevails between resistance mechanisms and the spectrum of genetic changes encoding them.

Antibiotic combinations reduce Staphylococcus aureus clearance.

The spread of antibiotic resistance is attracting increased attention to combination-based treatments. Although drug combinations have been studied extensively for their effects on bacterial growth1-11, much less is known about their effects on bacterial long-term clearance, especially at cidal, clinically relevant concentrations12-14. Here, using en masse microplating and automated image analysis, we systematically quantify Staphylococcus aureus survival during prolonged exposure to pairwise and higher-order cidal drug combinations. By quantifying growth inhibition, early killing and longer-term population clearance by all pairs of 14 antibiotics, we find that clearance interactions are qualitatively different, often showing reciprocal suppression whereby the efficacy of the drug mixture is weaker than any of the individual drugs alone. Furthermore, in contrast to growth inhibition6-10 and early killing, clearance efficacy decreases rather than increases as more drugs are added. However, specific drugs targeting non-growing persisters15-17 circumvent these suppressive effects. Competition experiments show that reciprocal suppressive drug combinations select against resistance to any of the individual drugs, even counteracting methicillin-resistant Staphylococcus aureus both in vitro and in a Galleria mellonella larva model. As a consequence, adding a ß-lactamase inhibitor that is commonly used to potentiate treatment against ß-lactam-resistant strains can reduce rather than increase treatment efficacy. Together, these results underscore the importance of systematic mapping the long-term clearance efficacy of drug combinations for designing more-effective, resistance-proof multidrug regimes.

Systematic identification of gene-altering programmed inversions across the bacterial domain.

Programmed chromosomal inversions allow bacteria to generate intra-population genotypic and functional heterogeneity, a bet-hedging strategy important in changing environments. Some programmed inversions modify coding sequences, producing different alleles in several gene families, most notably in specificity-determining genes such as Type I restriction-modification systems, where systematic searches revealed cross phylum abundance. Yet, a broad, gene-independent, systematic search for gene-altering programmed inversions has been absent, and little is known about their genomic sequence attributes and prevalence across gene families. Here, identifying intra-species variation in genomes of over 35 000 species, we develop a predictive model of gene-altering inversions, revealing key attributes of their genomic sequence attributes, including gene-pseudogene size asymmetry and orientation bias. The model predicted over 11,000 gene-altering loci covering known targeted gene families, as well as novel targeted families including Type II restriction-modification systems, a protein of unknown function, and a fusion-protein containing conjugative-pilus and phage tail domains. Publicly available long-read sequencing datasets validated representatives of these newly predicted inversion-targeted gene families, confirming intra-population genetic heterogeneity. Together, these results reveal gene-altering programmed inversions as a key strategy adopted across the bacterial domain, and highlight programmed inversions that modify Type II restriction-modification systems as a possible new mechanism for maintaining intra-population heterogeneity.

Quiet gene circuit more fragile than its noisy peer.

Why is a particular architecture for a pathway chosen over seemingly equivalent alternatives? Cagatay et al. (2009) use a synthetic biology approach to show that fluctuations--or noise--in protein levels may play a key role in determining which network design is selected during evolution.

Nonoptimal microbial response to antibiotics underlies suppressive drug interactions.

Suppressive drug interactions, in which one antibiotic can actually help bacterial cells to grow faster in the presence of another, occur between protein and DNA synthesis inhibitors. Here, we show that this suppression results from nonoptimal regulation of ribosomal genes in the presence of DNA stress. Using GFP-tagged transcription reporters in Escherichia coli, we find that ribosomal genes are not directly regulated by DNA stress, leading to an imbalance between cellular DNA and protein content. To test whether ribosomal gene expression under DNA stress is nonoptimal for growth rate, we sequentially deleted up to six of the seven ribosomal RNA operons. These synthetic manipulations of ribosomal gene expression correct the protein-DNA imbalance, lead to improved survival and growth, and completely remove the suppressive drug interaction. A simple mathematical model explains the nonoptimal regulation in different nutrient environments. These results reveal the genetic mechanism underlying an important class of suppressive drug interactions.

Evolthon: A community endeavor to evolve lab evolution.

In experimental evolution, scientists evolve organisms in the lab, typically by challenging them to new environmental conditions. How best to evolve a desired trait? Should the challenge be applied abruptly, gradually, periodically, sporadically? Should one apply chemical mutagenesis, and do strains with high innate mutation rate evolve faster? What are ideal population sizes of evolving populations? There are endless strategies, beyond those that can be exposed by individual labs. We therefore arranged a community challenge, Evolthon, in which students and scientists from different labs were asked to evolve Escherichia coli or Saccharomyces cerevisiae for an abiotic stress-low temperature. About 30 participants from around the world explored diverse environmental and genetic regimes of evolution. After a period of evolution in each lab, all strains of each species were competed with one another. In yeast, the most successful strategies were those that used mating, underscoring the importance of sex in evolution. In bacteria, the fittest strain used a strategy based on exploration of different mutation rates. Different strategies displayed variable levels of performance and stability across additional challenges and conditions. This study therefore uncovers principles of effective experimental evolutionary regimens and might prove useful also for biotechnological developments of new strains and for understanding natural strategies in evolutionary arms races between species. Evolthon constitutes a model for community-based scientific exploration that encourages creativity and cooperation.

Counteraction of antibiotic production and degradation stabilizes microbial communities.

A major challenge in theoretical ecology is understanding how natural microbial communities support species diversity, and in particular how antibiotic-producing, -sensitive and -resistant species coexist. While cyclic 'rock­paper­scissors' interactions can stabilize communities in spatial environments, coexistence in unstructured environments remains unexplained. Here, using simulations and analytical models, we show that the opposing actions of antibiotic production and degradation enable coexistence even in well-mixed environments. Coexistence depends on three-way interactions in which an antibiotic-degrading species attenuates the inhibitory interactions between two other species. These interactions enable coexistence that is robust to substantial differences in inherent species growth rates and to invasion by 'cheating' species that cease to produce or degrade antibiotics. At least two antibiotics are required for stability, with greater numbers of antibiotics enabling more complex communities and diverse dynamic behaviours ranging from stable fixed points to limit cycles and chaos. Together, these results show how multi-species antibiotic interactions can generate ecological stability in both spatially structured and mixed microbial communities, suggesting strategies for engineering synthetic ecosystems and highlighting the importance of toxin production and degradation for microbial biodiversity.

Polyploidy can drive rapid adaptation in yeast.

Polyploidy is observed across the tree of life, yet its influence on evolution remains incompletely understood. Polyploidy, usually whole-genome duplication, is proposed to alter the rate of evolutionary adaptation. This could occur through complex effects on the frequency or fitness of beneficial mutations. For example, in diverse cell types and organisms, immediately after a whole-genome duplication, newly formed polyploids missegregate chromosomes and undergo genetic instability. The instability following whole-genome duplications is thought to provide adaptive mutations in microorganisms and can promote tumorigenesis in mammalian cells. Polyploidy may also affect adaptation independently of beneficial mutations through ploidy-specific changes in cell physiology. Here we perform in vitro evolution experiments to test directly whether polyploidy can accelerate evolutionary adaptation. Compared with haploids and diploids, tetraploids undergo significantly faster adaptation. Mathematical modelling suggests that rapid adaptation of tetraploids is driven by higher rates of beneficial mutations with stronger fitness effects, which is supported by whole-genome sequencing and phenotypic analyses of evolved clones. Chromosome aneuploidy, concerted chromosome loss, and point mutations all provide large fitness gains. We identify several mutations whose beneficial effects are manifest specifically in the tetraploid strains. Together, these results provide direct quantitative evidence that in some environments polyploidy can accelerate evolutionary adaptation.

Evaluation of COVID-19 RT-qPCR Test in Multi sample Pools.

BACKGROUND: The recent emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to a current pandemic of unprecedented scale. Although diagnostic tests are fundamental to the ability to detect and respond, overwhelmed healthcare systems are already experiencing shortages of reagents associated with this test, calling for a lean immediately applicable protocol. METHODS: RNA extracts of positive samples were tested for the presence of SARS-CoV-2 using reverse transcription quantitative polymerase chain reaction, alone or in pools of different sizes (2-, 4-, 8-, 16-, 32-, and 64-sample pools) with negative samples. Transport media of additional 3 positive samples were also tested when mixed with transport media of negative samples in pools of 8. RESULTS: A single positive sample can be detected in pools of up to 32 samples, using the standard kits and protocols, with an estimated false negative rate of 10%. Detection of positive samples diluted in even up to 64 samples may also be attainable, although this may require additional amplification cycles. Single positive samples can be detected when pooling either after or prior to RNA extraction. CONCLUSIONS: As it uses the standard protocols, reagents, and equipment, this pooling method can be applied immediately in current clinical testing laboratories. We hope that such implementation of a pool test for coronavirus disease 2019 would allow expanding current screening capacities, thereby enabling the expansion of detection in the community, as well as in close organic groups, such as hospital departments, army units, or factory shifts.

Distinct single-cell morphological dynamics under beta-lactam antibiotics.

The bacterial cell wall is conserved in prokaryotes, stabilizing cells against osmotic stress. Beta-lactams inhibit cell-wall synthesis and induce lysis through a bulge-mediated mechanism; however, little is known about the formation dynamics and stability of these bulges. To capture processes of different timescales, we developed an imaging platform combining automated image analysis with live-cell microscopy at high time resolution. Beta-lactam killing of Escherichia coli cells proceeded through four stages: elongation, bulge formation, bulge stagnation, and lysis. Both the cell wall and outer membrane (OM) affect the observed dynamics; damaging the cell wall with different beta-lactams and compromising OM integrity cause different modes and rates of lysis. Our results show that the bulge-formation dynamics are determined by how the cell wall is perturbed. The OM plays an independent role in stabilizing the bulge once it is formed. The stabilized bulge delays lysis and allows recovery upon drug removal.

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.

Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance.

The evolution of antibiotic resistance can now be rapidly tracked with high-throughput technologies for bacterial genotyping and phenotyping. Combined with new approaches to evolve resistance in the laboratory and to characterize clinically evolved resistant pathogens, these methods are revealing the molecular basis and rate of evolution of antibiotic resistance under treatment regimens of single drugs or drug combinations. In this Progress article, we review these new tools for studying the evolution of antibiotic resistance and discuss how the genomic and evolutionary insights they provide could transform the diagnosis, treatment and predictability of antibiotic resistance in bacterial infections.

Resolution of gene regulatory conflicts caused by combinations of antibiotics.

Regulatory conflicts occur when two signals that individually trigger opposite cellular responses are present simultaneously. Here, we investigate regulatory conflicts in the bacterial response to antibiotic combinations. We use an Escherichia coli promoter-GFP library to study the transcriptional response of many promoters to either additive or antagonistic drug pairs at fine two-dimensional (2D) resolution of drug concentration. Surprisingly, we find that this data set can be characterized as a linear sum of only two principal components. Component one, accounting for over 70% of the response, represents the response to growth inhibition by the drugs. Component two describes how regulatory conflicts are resolved. For the additive drug pair, conflicts are resolved by linearly interpolating the single drug responses, while for the antagonistic drug pair, the growth-limiting drug dominates the response. Importantly, for a given drug pair, the same conflict resolution strategy applies to almost all genes. These results provide a recipe for predicting gene expression responses to antibiotic combinations.

Compounds that select against the tetracycline-resistance efflux pump.

We developed a competition-based screening strategy to identify compounds that invert the selective advantage of antibiotic resistance. Using our assay, we screened over 19,000 compounds for the ability to select against the TetA tetracycline-resistance efflux pump in Escherichia coli and identified two hits, ß-thujaplicin and disulfiram. Treating a tetracycline-resistant population with ß-thujaplicin selects for loss of the resistance gene, enabling an effective second-phase treatment with doxycycline.

A Hybrid Drug Limits Resistance by Evading the Action of the Multiple Antibiotic Resistance Pathway.

Hybrid drugs are a promising strategy to address the growing problem of drug resistance, but the mechanism by which they modulate the evolution of resistance is poorly understood. Integrating high-throughput resistance measurements and genomic sequencing, we compared Escherichia coli populations evolved in a hybrid antibiotic that links ciprofloxacin and neomycin B with populations evolved in combinations of the component drugs. We find that populations evolved in the hybrid gain less resistance than those evolved in an equimolar mixture of the hybrid's components, in part because the hybrid evades resistance mediated by the multiple antibiotic resistance (mar) operon. Furthermore, we find that the ciprofloxacin moiety of the hybrid inhibits bacterial growth whereas the neomycin B moiety diminishes the effectiveness of mar activation. More generally, comparing the phenotypic and genotypic paths to resistance across different drug treatments can pinpoint unique properties of new compounds that limit the emergence of resistance.

Hydroxyurea triggers cellular responses that actively cause bacterial cell death.

In this issue of Molecular Cell, Davies et al. (2009) work out a sequence of active cellular events that lead to the death of Escherichia coli in the presence of the drug hydroxyurea.

Alternating antibiotic treatments constrain evolutionary paths to multidrug resistance.

Alternating antibiotic therapy, in which pairs of drugs are cycled during treatment, has been suggested as a means to inhibit the evolution of de novo resistance while avoiding the toxicity associated with more traditional combination therapy. However, it remains unclear under which conditions and by what means such alternating treatments impede the evolution of resistance. Here, we tracked multistep evolution of resistance in replicate populations of Staphylococcus aureus during 22 d of continuously increasing single-, mixed-, and alternating-drug treatment. In all three tested drug pairs, the alternating treatment reduced the overall rate of resistance by slowing the acquisition of resistance to one of the two component drugs, sometimes as effectively as mixed treatment. This slower rate of evolution is reflected in the genome-wide mutational profiles; under alternating treatments, bacteria acquire mutations in different genes than under corresponding single-drug treatments. To test whether this observed constraint on adaptive paths reflects trade-offs in which resistance to one drug is accompanied by sensitivity to a second drug, we profiled many single-step mutants for cross-resistance. Indeed, the average cross-resistance of single-step mutants can help predict whether or not evolution was slower in alternating drugs. Together, these results show that despite the complex evolutionary landscape of multidrug resistance, alternating-drug therapy can slow evolution by constraining the mutational paths toward resistance.

Regulation of cell size in response to nutrient availability by fatty acid biosynthesis in Escherichia coli.

Cell size varies greatly among different types of cells, but the range in size that a specific cell type can reach is limited. A long-standing question in biology is how cells control their size. Escherichia coli adjusts size and growth rate according to the availability of nutrients so that it grows larger and faster in nutrient-rich media than in nutrient-poor media. Here, we describe how, using classical genetics, we have isolated a remarkably small E. coli mutant that has undergone a 70% reduction in cell volume with respect to wild type. This mutant lacks FabH, an enzyme involved in fatty acid biosynthesis that previously was thought to be essential for the viability of E. coli. We demonstrate that although FabH is not essential in wild-type E. coli, it is essential in cells that are defective in the production of the small-molecule and global regulator ppGpp. Furthermore, we have found that the loss of FabH causes a reduction in the rate of envelope growth and renders cells unable to regulate cell size properly in response to nutrient excess. Therefore we propose a model in which fatty acid biosynthesis plays a central role in regulating the size of E. coli cells in response to nutrient availability.

Functional classification of drugs by properties of their pairwise interactions.

Multidrug treatments are increasingly important in medicine and for probing biological systems. Although many studies have focused on interactions between specific drugs, little is known about the system properties of a full drug interaction network. Like their genetic counterparts, two drugs may have no interaction, or they may interact synergistically or antagonistically to increase or suppress their individual effects. Here we use a sensitive bioluminescence technique to provide quantitative measurements of pairwise interactions among 21 antibiotics that affect growth rate in Escherichia coli. We find that the drug interaction network possesses a special property: it can be separated into classes of drugs such that any two classes interact either purely synergistically or purely antagonistically. These classes correspond directly to the cellular functions affected by the drugs. This network approach provides a new conceptual framework for understanding the functional mechanisms of drugs and their cellular targets and can be applied in systems intractable to mutant screening, biochemistry or microscopy.