The development and spread of antibiotic resistance in bacteria is a universal threat to both humans and animals that is generally not preventable, but can nevertheless be controlled and must be tackled in the most effective ways possible. To explore how the problem of antibiotic resistance might best be addressed, a group of thirty scientists from academia and industry gathered at the Banbury Conference Centre in Cold Spring Harbor, New York, May 16-18, 2011. From these discussions emerged a priority list of steps that need to be taken to resolve this global crisis.

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.

Large-scale, systems biology approaches now allow us to systematically map synergistic and antagonistic interactions between drugs. Consequently, drug antagonism is emerging as a powerful tool to study biological function and relatedness between cellular components as well as to uncover mechanisms of drug action. Furthermore, theoretical models and new experiments suggest that antagonistic interactions between antibiotics can counteract the evolution of drug resistance.Since the early days of Mendelian genetics, in the early 1900s, investigators have realized that interactions between alleles (or epistasis) often indicate that alleles are functionally related 1 . Epistatic interaction can be classified as synergistic, additive or antagonistic, depending on whether the combined effect of two perturbations is greater than, equal to or less than predicted on the basis of the individual effects 2-9 (BOX 1). With the advent of molecular genomics, which makes it possible to systematically knock out or impair genes alone and in combination, and to conduct high-throughput phenotypic screens, epistasis can now be used to establish functional connections between genes and genetic modules in microbial systems. Defining drug interactions: bliss independence and loewe additivityInteractions between drugs are, in principle, analogous to genetic interactions, except for the additional complexity of dosage variability. There has been debate about the appropriate way to define drug interactions (reviewed in REF. 4 ). Antagonistic and synergistic classifications usually rely on deviations from additivity. Properly defining additivity is therefore crucial for classification of drug interactions. There are two main methods for defining additivity. Bliss independenceBliss independence 3 assumes that the relative effect of a drug at a particular concentration is independent of the presence of the other drug. For example, if drugs A and B individually cause growth inhibition of 50% each, then Bliss independence predicts that, in combination, drugs A and B decrease growth by 1-0.5*0.5, or 75%. Positive or negative deviations from this prediction describe synergistic and antagonistic interactions, respectively (see the figure, part a; φ represents no drug). A special class of antagonism, called suppression (or hyper-antagonism), occurs when the combined effect of the two drugs is weaker not only compared with their expected additive effect, but also compared with one (directional suppression) or both (reciprocal suppression) of their individual effects.The Bliss definition is simple, easy to measure and provides an exact analogy to the definition of epistasis that is conventionally used for genetic perturbations 7,9 . However, it does not account for nonlinearity in the dose response curve of each of the individual drugs and therefore conflates deviation from additivity due to the interactions between the drugs with deviations due to the increase in total drug dosage. Loewe additivityLoewe additivity 2 defines a drug as non-int...

Revealing the genetic changes responsible for antibiotic resistance can be critical for developing novel antibiotic therapies. However, systematic studies correlating genotype to phenotype in the context of antibiotic resistance have been missing. In order to fill in this gap, we evolved 88 isogenic Escherichia coli populations against 22 antibiotics for 3 weeks. For every drug, two populations were evolved under strong selection and two populations were evolved under mild selection. By quantifying evolved populations’ resistances against all 22 drugs, we constructed two separate cross-resistance networks for strongly and mildly selected populations. Subsequently, we sequenced representative colonies isolated from evolved populations for revealing the genetic basis for novel phenotypes. Bacterial populations that evolved resistance against antibiotics under strong selection acquired high levels of cross-resistance against several antibiotics, whereas other bacterial populations evolved under milder selection acquired relatively weaker cross-resistance. In addition, we found that strongly selected strains against aminoglycosides became more susceptible to five other drug classes compared with their wild-type ancestor as a result of a point mutation on TrkH, an ion transporter protein. Our findings suggest that selection strength is an important parameter contributing to the complexity of antibiotic resistance problem and use of high doses of antibiotics to clear infections has the potential to promote increase of cross-resistance in clinics.

Antimicrobial treatments increasingly rely on multidrug combinations, in part because of the emergence and spread of antibiotic resistance. The continued effectiveness of combination treatments depends crucially on the frequency with which multidrug resistance arises. Yet, it is unknown how this propensity for resistance depends on cross-resistance and on epistatic interactions-ranging from synergy to antagonism-between the drugs. Here, we analyzed how interactions between pairs of drugs affect the spontaneous emergence of resistance in the medically important pathogen Staphylococcus aureus. Resistance is selected for within a window of drug concentrations high enough to inhibit wild-type growth but low enough for some resistant mutants to grow. Introducing an experimental method for high-throughput colony imaging, we counted resistant colonies arising across a twodimensional matrix of drug concentrations for each of three drug pairs. Our data show that these different drug combinations have significantly different impacts on the size of the window of drug concentrations where resistance is selected for. We framed these results in a mathematical model in which the frequencies of resistance to single drugs, cross-resistance, and epistasis combine to determine the propensity for multidrug resistance. The theory suggests that drug pairs which interact synergistically, preferred for their immediate efficacy, may in fact favor the future evolution of resistance. This framework reveals the central role of drug epistasis in the evolution of resistance and points to new strategies for combating the emergence of drug-resistant bacteria.antibiotic resistance ͉ drug combinations ͉ epistasis ͉ Staphylococcus aureus ͉ mutant selection window T he widespread use of antibiotics pits clinical need against the reality of evolution (1-3). The clinical goal is to kill as many pathogenic bacteria as possible, or inhibit their growth to allow the immune system to gain the upper hand; but a drug that kills or inhibits the growth of susceptible pathogens confers a dramatic selective advantage to resistant lineages, eventually making the drug ineffective. Although major advances have been made in describing the impact of single drugs on bacterial resistance (3), it is still unclear how drugs in combination affect the evolution of resistance. Combinations of drugs may inhibit bacterial growth in complex ways, deviating from the neutral situation expected when the drugs do not interact (4-6). Compared with this null situation, drug combinations that interact to increase each other's effects are termed ''synergistic''; drugs whose combined effect is smaller than expected are termed ''antagonistic'' (4-7, 39) (Fig. 1D). We have previously shown that these epistatic drug interactions profoundly affect the selective advantage of a single horizontally transferred resistance allele (8). Here, we focus on the more complex scenario of the evolution of multidrug resistance by spontaneously occurring mutations.In many infectious and noninfectious ...

Behavior and other forms of phenotypic plasticity potentially enable individuals to deal with novel situations. This implies that establishment of a population in a new environment is aided by plastic responses, as first suggested by Baldwin (1896). In the early 1980s, a small population of dark-eyed juncos from a temperate, montane environment became established in a Mediterranean climate in coastal San Diego. The breeding season of coastal juncos is more than twice as long as that of the ancestral population, and they fledge approximately twice as many young. We investigated the adaptive significance of the longer breeding season and its consequences for population persistence. Within the coastal population, individuals with longer breeding seasons have higher offspring production and recruitment, with no measured detrimental effects such as higher mortality or lower reproductive success the following year. Population size has remained approximately constant during the 6 years of study (1998-2003). The increase in reproductive effort in the coastal population contributes substantially to the persistence of this population because there is no evidence of density-dependent recruitment, which would otherwise negate the effects of increased fledgling production. These results provide the first quantitative support of Baldwin's proposition that plasticity can be crucial for population persistence during the early stages of colonization.

Colonization of novel environments creates new selection pressures. Sexually selected traits are affected by the physical and social environment and should be especially susceptible to change, but this has rarely been studied. In southern California, dark-eyed juncos, (Junco hyemalis) naturally breed in mixed-coniferous temperate forests, typically from 1500 m to 3000 m in elevation. In the early 1980s, a small population became established in a coastal habitat, the University of California, San Diego campus, which has a mild, Mediterranean climate. I show that a sexually and socially selected signaling trait--the amount of white in the tail--has declined by approximately 22% as compared to mountain juncos. I address three main factors that could explain the difference between mountain and coastal juncos: phenotypic plasticity, genetic drift, and selection. Results indicate that the first two can be ruled out as the sole cause of the plumage change, which implies that selection contributed to the genetic differentiation from the mountain population. The estimated rate of evolution is about 0.2 haldanes, comparable with rates of change in systems where individuals have been artificially introduced into new environments (e.g., guppies and Drosophila). This is the first study to demonstrate evolution of a sexually selected trait after only several generations resulting from a natural invasion into a novel environment.

An isolated population of dark-eyed juncos, Junco hyemalis, became established on the campus of the University of California at San Diego (UCSD), probably in the early 1980s. It now numbers about 70 breeding pairs. Populations across the entire natural range of the subspecies J. h. thurberi are weakly differentiated from each other at five microsatellite loci (FST = 0.01). The UCSD population is significantly different from these populations, the closest of which is 70 km away. It has 88% of the genetic heterozygosity and 63% of the allelic richness of populations in the montane range of the subspecies, consistent with a harmonic mean effective population size of 32 (but with 95% confidence limits from four to > 70) over the eight generations since founding. Results suggest a moderate bottleneck in the early establishment phase but with more than seven effective founders. Individuals in the UCSD population have shorter wings and tails than those in the nearby mountains and a common garden experiment indicates that the morphological differences are genetically based. The moderate effective population size is not sufficient for the observed morphological differences to have evolved as a consequence of genetic drift, indicating a major role for selection subsequent to the founding of the UCSD population.