Use of Viscous medium to study anthelmintic drug action in Caenorhabditis elegans.

Caenorhabditis elegans is an appealing tool for experimental evolution and for working with antiparasitic drugs, from understanding the molecular mechanisms of drug action and resistance to uncover new drug targets. We present a new methodology for studying the impact of antiparasitic drugs in C. elegans. Viscous medium was initially designed for C. elegans maintenance during long-term evolution experiments. Viscous medium provides a less structured environment than the standard nematode growth media agar, yet the bacteria food source remains suspended. Further, the Viscous medium offers the worm population enough support to move freely, mate, and reproduce at a rate comparable to standard agar cultures. Here, the Viscous medium was adapted for use in antiparasitic research. We observed a similar sensitivity of C. elegans to anthelmintic drugs as in standard liquid media and statistical difference to the standard agar media through a larval development assay. Using Viscous medium in C. elegans studies will considerably improve antiparasitic resistance research, and this medium could be used in studies aimed at understanding long-term multigenerational drug activity.

Gut-associated functions are favored during microbiome assembly across a major part of C. elegans life.

The microbiome expresses a variety of functions that influence host biology. The range of functions depends on the microbiome's composition, which can change during the host's lifetime due to neutral assembly processes, host-mediated selection, and environmental conditions. To date, the exact dynamics of microbiome assembly, the underlying determinants, and the effects on host-associated functions remain poorly understood. Here, we used the nematode Caenorhabditis elegans and a defined community of fully sequenced, naturally associated bacteria to study microbiome dynamics and functions across a major part of the worm's lifetime of hosts under controlled experimental conditions. Bacterial community composition initially shows strongly declining levels of stochasticity, which increases during later time points, suggesting selective effects in younger animals as opposed to more random processes in older animals. The adult microbiome is enriched in genera Ochrobactrum and Enterobacter compared to the direct substrate and a host-free control environment. Using pathway analysis, metabolic, and ecological modeling, we further find that the lifetime assembly dynamics increase competitive strategies and gut-associated functions in the host-associated microbiome, indicating that the colonizing bacteria benefit the worm. Overall, our study introduces a framework for studying microbiome assembly dynamics based on stochastic, ecological, and metabolic models, yielding new insights into the processes that determine host-associated microbiome composition and function. IMPORTANCE: The microbiome plays a crucial role in host biology. Its functions depend on the microbiome composition that can change during a host's lifetime. To date, the dynamics of microbiome assembly and the resulting functions still need to be better understood. This study introduces a new approach to characterize the functional consequences of microbiome assembly by modeling both the relevance of stochastic processes and metabolic characteristics of microbial community changes. The approach was applied to experimental time-series data obtained for the microbiome of the nematode Caenorhabditis elegans across the major part of its lifetime. Stochastic processes played a minor role, whereas beneficial bacteria as well as gut-associated functions enriched in hosts. This indicates that the host might actively shape the composition of its microbiome. Overall, this study provides a framework for studying microbiome assembly dynamics and yields new insights into C. elegans microbiome functions.

The archaeome in metaorganism research, with a focus on marine models and their bacteria-archaea interactions.

Metaorganism research contributes substantially to our understanding of the interaction between microbes and their hosts, as well as their co-evolution. Most research is currently focused on the bacterial community, while archaea often remain at the sidelines of metaorganism-related research. Here, we describe the archaeome of a total of eleven classical and emerging multicellular model organisms across the phylogenetic tree of life. To determine the microbial community composition of each host, we utilized a combination of archaea and bacteria-specific 16S rRNA gene amplicons. Members of the two prokaryotic domains were described regarding their community composition, diversity, and richness in each multicellular host. Moreover, association with specific hosts and possible interaction partners between the bacterial and archaeal communities were determined for the marine models. Our data show that the archaeome in marine hosts predominantly consists of Nitrosopumilaceae and Nanoarchaeota, which represent keystone taxa among the porifera. The presence of an archaeome in the terrestrial hosts varies substantially. With respect to abundant archaeal taxa, they harbor a higher proportion of methanoarchaea over the aquatic environment. We find that the archaeal community is much less diverse than its bacterial counterpart. Archaeal amplicon sequence variants are usually host-specific, suggesting adaptation through co-evolution with the host. While bacterial richness was higher in the aquatic than the terrestrial hosts, a significant difference in diversity and richness between these groups could not be observed in the archaeal dataset. Our data show a large proportion of unclassifiable archaeal taxa, highlighting the need for improved cultivation efforts and expanded databases.

Metabolic model predictions enable targeted microbiome manipulation through precision prebiotics.

While numerous health-beneficial interactions between host and microbiota have been identified, there is still a lack of targeted approaches for modulating these interactions. Thus, we here identify precision prebiotics that specifically modulate the abundance of a microbiome member species of interest. In the first step, we show that defining precision prebiotics by compounds that are only taken up by the target species but no other species in a community is usually not possible due to overlapping metabolic niches. Subsequently, we use metabolic modeling to identify precision prebiotics for a two-member Caenorhabditis elegans microbiome community comprising the immune-protective target species Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71. We experimentally confirm four of the predicted precision prebiotics, L-serine, L-threonine, D-mannitol, and γ-aminobutyric acid, to specifically increase the abundance of MYb11. L-serine was further assessed in vivo, leading to an increase in MYb11 abundance also in the worm host. Overall, our findings demonstrate that metabolic modeling is an effective tool for the design of precision prebiotics as an important cornerstone for future microbiome-targeted therapies.IMPORTANCEWhile various mechanisms through which the microbiome influences disease processes in the host have been identified, there are still only few approaches that allow for targeted manipulation of microbiome composition as a first step toward microbiome-based therapies. Here, we propose the concept of precision prebiotics that allow to boost the abundance of already resident health-beneficial microbial species in a microbiome. We present a constraint-based modeling pipeline to predict precision prebiotics for a minimal microbial community in the worm Caenorhabditis elegans comprising the host-beneficial Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71 with the aim to boost the growth of MYb11. Experimentally testing four of the predicted precision prebiotics, we confirm that they are specifically able to increase the abundance of MYb11 in vitro and in vivo. These results demonstrate that constraint-based modeling could be an important tool for the development of targeted microbiome-based therapies against human diseases.

The intricate triangular interaction between protective microbe, pathogen and host determines fitness of the metaorganism.

The microbiota shapes host biology in numerous ways. One example is protection against pathogens, which is likely critical for host fitness in consideration of the ubiquity of pathogens. The host itself can affect abundance of microbiota or pathogens, which has usually been characterized in separate studies. To date, however, it is unclear how the host influences the interaction with both simultaneously and how this triangular interaction determines fitness of the host-microbe assemblage, the so-called metaorganism. To address this current knowledge gap, we focused on a triangular model interaction, consisting of the nematode Caenorhabditis elegans, its protective symbiont Pseudomonas lurida MYb11 and its pathogen Bacillus thuringiensis Bt679. We combined the two microbes with C. elegans mutants with altered immunity and/or microbial colonization, and found that (i) under pathogen stress, immunocompetence has a larger influence on metaorganism fitness than colonization with the protective microbe; (ii) in almost all cases, MYb11 still improves fitness; and (iii) disruption of p38 MAPK signalling, which contributes centrally to immunity against Bt679, completely reverses the protective effect of MYb11, which further reduces nematode survival and fitness upon infection with Bt679. Our study highlights the complex interplay between host, protective microbe and pathogen in shaping metaorganism biology.

Ribosome-binding antibiotics increase bacterial longevity and growth efficiency.

Antibiotics, by definition, reduce bacterial growth rates in optimal culture conditions; however, the real-world environments bacteria inhabit see rapid growth punctuated by periods of low nutrient availability. How antibiotics mediate population decline during these periods is poorly understood. Bacteria cannot optimize for all environmental conditions because a growth-longevity tradeoff predicts faster growth results in faster population decline, and since bacteriostatic antibiotics slow growth, they should also mediate longevity. We quantify how antibiotics, their targets, and resistance mechanisms influence longevity using populations of Escherichia coli and, as the tradeoff predicts, populations are maintained for longer if they encounter ribosome-binding antibiotics doxycycline and erythromycin, a finding that is not observed using antibiotics with alternative cellular targets. This tradeoff also predicts resistance mechanisms that increase growth rates during antibiotic treatment could be detrimental during nutrient stresses, and indeed, we find resistance by ribosomal protection removes benefits to longevity provided by doxycycline. We therefore liken ribosomal protection to a "Trojan horse" because it provides protection from an antibiotic but, during nutrient stresses, it promotes the demise of the bacteria. Seeking mechanisms to support these observations, we show doxycycline promotes efficient metabolism and reduces the concentration of reactive oxygen species. Seeking generality, we sought another mechanism that affects longevity and we found the number of doxycycline targets, namely, the ribosomal RNA operons, mediates growth and longevity even without antibiotics. We conclude that slow growth, as observed during antibiotic treatment, can help bacteria overcome later periods of nutrient stress.

Host and microbiome jointly contribute to environmental adaptation.

Most animals and plants have associated microorganisms, collectively referred to as their microbiomes, which can provide essential functions. Given their importance, host-associated microbiomes have the potential to contribute substantially to adaptation of the host-microbiome assemblage (the "metaorganism"). Microbiomes may be especially important for rapid adaptation to novel environments because microbiomes can change more rapidly than host genomes. However, it is not well understood how hosts and microbiomes jointly contribute to metaorganism adaptation. We developed a model system with which to disentangle the contributions of hosts and microbiomes to metaorganism adaptation. We established replicate mesocosms containing the nematode Caenorhabditis elegans co-cultured with microorganisms in a novel complex environment (laboratory compost). After approximately 30 nematode generations (100 days), we harvested worm populations and associated microbiomes, and subjected them to a common garden experiment designed to unravel the impacts of microbiome composition and host genetics on metaorganism adaptation. We observed that adaptation took different trajectories in different mesocosm lines, with some increasing in fitness and others decreasing, and that interactions between host and microbiome played an important role in these contrasting evolutionary paths. We chose two exemplary mesocosms (one with a fitness increase and one with a decrease) for detailed study. For each example, we identified specific changes in both microbiome composition (for both bacteria and fungi) and nematode gene expression associated with each change in fitness. Our study provides experimental evidence that adaptation to a novel environment can be jointly influenced by host and microbiome.

Bacterial c-di-GMP has a key role in establishing host-microbe symbiosis.

Most microbes evolve faster than their hosts and should therefore drive evolution of host-microbe interactions. However, relatively little is known about the characteristics that define the adaptive path of microbes to host association. Here we identified microbial traits that mediate adaptation to hosts by experimentally evolving the free-living bacterium Pseudomonas lurida with the nematode Caenorhabditis elegans as its host. After ten passages, we repeatedly observed the evolution of beneficial host-specialist bacteria, with improved persistence in the nematode being associated with increased biofilm formation. Whole-genome sequencing revealed mutations that uniformly upregulate the bacterial second messenger, cyclic diguanylate (c-di-GMP). We subsequently generated mutants with upregulated c-di-GMP in different Pseudomonas strains and species, which consistently increased host association. Comparison of pseudomonad genomes from various environments revealed that c-di-GMP underlies adaptation to a variety of hosts, from plants to humans. This study indicates that c-di-GMP is fundamental for establishing host association.

Meet the Metaorganism: A web-based learning app for undergraduate and graduate biology students.

Meet the Metaorganism is a web-based learning app that combines three fundamental biological concepts (coevolution, community dynamics, and immune system) with latest scientific findings using the metaorganism as a central case study. In a transdisciplinary team of scientists, information designers, programmers, science communicators, and educators, we conceptualized and developed the app according to the latest didactic and scientific findings and aimed at setting new standards in visual design, digital knowledge transfer, and online education. A content management system allows continuous integration of new findings, which enables us to expand the app with the dynamics of the research field. Students can thus gain a close insight and connection to current research, and at the same time learn that knowledge is not static but grows dynamically. Especially in the realm of the easily accessible metaorganism research, visualization plays an essential role to keep complex processes understandable and memorable. Meet the Metaorganism is freely available online and can be accessed here: www.metaorganism.app.

Phylogroup-specific variation shapes the clustering of antimicrobial resistance genes and defence systems across regions of genome plasticity in Pseudomonas aeruginosa.

BACKGROUND: Pseudomonas aeruginosa is an opportunistic pathogen consisting of three phylogroups (hereafter named A, B, and C). Here, we assessed phylogroup-specific evolutionary dynamics across available and also new P. aeruginosa genomes. METHODS: In this genomic analysis, we first generated new genome assemblies for 18 strains of the major P. aeruginosa clone type (mPact) panel, comprising a phylogenetically diverse collection of clinical and environmental isolates for this species. Thereafter, we combined these new genomes with 1991 publicly available P. aeruginosa genomes for a phylogenomic and comparative analysis. We specifically explored to what extent antimicrobial resistance (AMR) genes, defence systems, and virulence genes vary in their distribution across regions of genome plasticity (RGPs) and "masked" (RGP-free) genomes, and to what extent this variation differs among the phylogroups. FINDINGS: We found that members of phylogroup B possess larger genomes, contribute a comparatively larger number of pangenome families, and show lower abundance of CRISPR-Cas systems. Furthermore, AMR and defence systems are pervasive in RGPs and integrative and conjugative/mobilizable elements (ICEs/IMEs) from phylogroups A and B, and the abundance of these cargo genes is often significantly correlated. Moreover, inter- and intra-phylogroup interactions occur at the accessory genome level, suggesting frequent recombination events. Finally, we provide here the mPact panel of diverse P. aeruginosa strains that may serve as a valuable reference for functional analyses. INTERPRETATION: Altogether, our results highlight distinct pangenome characteristics of the P. aeruginosa phylogroups, which are possibly influenced by variation in the abundance of CRISPR-Cas systems and are shaped by the differential distribution of other defence systems and AMR genes. FUNDING: German Science Foundation, Max-Planck Society, Leibniz ScienceCampus Evolutionary Medicine of the Lung, BMBF program Medical Infection Genomics, Kiel Life Science Postdoc Award.

Metabolic model predictions enable targeted microbiome manipulation through precision prebiotics.

The microbiome is increasingly receiving attention as an important modulator of host health and disease. However, while numerous mechanisms through which the microbiome influences its host have been identified, there is still a lack of approaches that allow to specifically modulate the abundance of individual microbes or microbial functions of interest. Moreover, current approaches for microbiome manipulation such as fecal transfers often entail a non-specific transfer of entire microbial communities with potentially unwanted side effects. To overcome this limitation, we here propose the concept of precision prebiotics that specifically modulate the abundance of a microbiome member species of interest. In a first step, we show that defining precision prebiotics by compounds that are only taken up by the target species but no other species in a community is usually not possible due to overlapping metabolic niches. Subsequently, we present a metabolic modeling network framework that allows us to define precision prebiotics for a two-member C. elegans microbiome model community comprising the immune-protective Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71. Thus, we predicted compounds that specifically boost the abundance of the host-beneficial MYb11, four of which were experimentally validated in vitro (L-serine, L-threonine, D-mannitol, and γ-aminobutyric acid). L-serine was further assessed in vivo, leading to an increase in MYb11 abundance also in the worm host. Overall, our findings demonstrate that constraint-based metabolic modeling is an effective tool for the design of precision prebiotics as an important cornerstone for future microbiome-targeted therapies.

The ESKAPE mobilome contributes to the spread of antimicrobial resistance and CRISPR-mediated conflict between mobile genetic elements.

Mobile genetic elements (MGEs) mediate the shuffling of genes among organisms. They contribute to the spread of virulence and antibiotic resistance (AMR) genes in human pathogens, such as the particularly problematic group of ESKAPE pathogens. Here, we performed the first systematic analysis of MGEs, including plasmids, prophages, and integrative and conjugative/mobilizable elements (ICEs/IMEs), across all ESKAPE pathogens. We found that different MGE types are asymmetrically distributed across these pathogens, and that most horizontal gene transfer (HGT) events are restricted by phylum or genus. We show that the MGEs proteome is involved in diverse functional processes and distinguish widespread proteins within the ESKAPE context. Moreover, anti-CRISPRs and AMR genes are overrepresented in the ESKAPE mobilome. Our results also underscore species-specific trends shaping the number of MGEs, AMR, and virulence genes across pairs of conspecific ESKAPE genomes with and without CRISPR-Cas systems. Finally, we observed that CRISPR spacers found on prophages, ICEs/IMEs, and plasmids have different targeting biases: while plasmid and prophage CRISPRs almost exclusively target other plasmids and prophages, respectively, ICEs/IMEs CRISPRs preferentially target prophages. Overall, our study highlights the general importance of the ESKAPE mobilome in contributing to the spread of AMR and mediating conflict among MGEs.

Isolation and Characterization of the Natural Microbiota of the Model Nematode Caenorhabditis elegans.

The nematode Caenorhabditis elegans interacts with a large diversity of microorganisms in nature. In general, C. elegans is commonly found in rotten plant matter, especially rotten fruits like apples or on compost heaps. It is also associated with certain invertebrate hosts such as slugs and woodlice. These habitats are rich in microbes, which serve as food for C. elegans and which can also persistently colonize the nematode gut. To date, the exact diversity and consistency of the native C. elegans microbiota across habitats and geographic locations is not fully understood. Here, we describe a suitable approach for isolating C. elegans from nature and characterizing the microbiota of worms. Nematodes can be easily isolated from compost material, rotting apples, slugs, or attracted by placing apples on compost heaps. The prime time for finding C. elegans in the Northern Hemisphere is from September until November. Worms can be washed out of collected substrate material by immersing the substrate in buffer solution, followed by the collection of nematodes and their transfer onto nematode growth medium or PCR buffer for subsequent analysis. We further illustrate how the samples can be used to isolate and purify the worm-associated microorganisms and to process worms for 16S ribosomal RNA analysis of microbiota community composition. Overall, the described methods may stimulate new research on the characterization of the C. elegans microbiota across habitats and geographic locations, thereby helping to obtain a comprehensive understanding of the diversity and stability of the nematode's microbiota as a basis for future functional research.

Exploring Effects of C. elegans Protective Natural Microbiota on Host Physiology.

The Caenorhabditis elegans natural microbiota was described only recently. Thus, our understanding of its effects on nematode physiology is still in its infancy. We previously showed that the C. elegans natural microbiota isolates Pseudomonas lurida MYb11 and P. fluorescens MYb115 protect the worm against pathogens such as Bacillus thuringiensis (Bt). However, the overall effects of the protective microbiota on worm physiology are incompletely understood. Here, we investigated how MYb11 and MYb115 affect C. elegans lifespan, fertility, and intestinal colonization. We further studied the capacity of MYb11 and MYb115 to protect the worm against purified Bt toxins. We show that while MYb115 and MYb11 affect reproductive timing and increase early reproduction only MYb11 reduces worm lifespan. Moreover, MYb11 aggravates killing upon toxin exposure. We conclude that MYb11 has a pathogenic potential in some contexts. This work thus highlights that certain C. elegans microbiota members can be beneficial and costly to the host in a context-dependent manner, blurring the line between good and bad.

Gene sharing among plasmids and chromosomes reveals barriers for antibiotic resistance gene transfer.

The emergence of antibiotic resistant bacteria is a major threat to modern medicine. Rapid adaptation to antibiotics is often mediated by the acquisition of plasmids carrying antibiotic resistance (ABR) genes. Nonetheless, the determinants of plasmid-mediated ABR gene transfer remain debated. Here, we show that the propensity of ABR gene transfer via plasmids is higher for accessory chromosomal ABR genes in comparison with core chromosomal ABR genes, regardless of the resistance mechanism. Analysing the pattern of ABR gene occurrence in the genomes of 2635 Enterobacteriaceae isolates, we find that 33% of the 416 ABR genes are shared between chromosomes and plasmids. Phylogenetic reconstruction of ABR genes occurring on both plasmids and chromosomes supports their evolution by lateral gene transfer. Furthermore, accessory ABR genes (encoded in less than 10% of the chromosomes) occur more abundantly in plasmids in comparison with core ABR genes (encoded in greater than or equal to 90% of the chromosomes). The pattern of ABR gene occurrence in plasmids and chromosomes is similar to that in the total Escherichia genome. Our results thus indicate that the previously recognized barriers for gene acquisition by lateral gene transfer apply also to ABR genes. We propose that the functional complexity of the underlying ABR mechanism is an important determinant of ABR gene transferability. This article is part of the theme issue 'The secret lives of microbial mobile genetic elements'.

Population size impacts host-pathogen coevolution.

Ongoing host-pathogen interactions are characterized by rapid coevolutionary changes forcing species to continuously adapt to each other. The interacting species are often defined by finite population sizes. In theory, finite population size limits genetic diversity and compromises the efficiency of selection owing to genetic drift, in turn constraining any rapid coevolutionary responses. To date, however, experimental evidence for such constraints is scarce. The aim of our study was to assess to what extent population size influences the dynamics of host-pathogen coevolution. We used Caenorhabditus elegans and its pathogen Bacillus thuringiensis as a model for experimental coevolution in small and large host populations, as well as in host populations which were periodically forced through a bottleneck. By carefully controlling host population size for 23 host generations, we found that host adaptation was constrained in small populations and to a lesser extent in the bottlenecked populations. As a result, coevolution in large and small populations gave rise to different selection dynamics and produced different patterns of host-pathogen genotype-by-genotype interactions. Our results demonstrate a major influence of host population size on the ability of the antagonists to co-adapt to each other, thereby shaping the dynamics of antagonistic coevolution.

The genetics of gene expression in a Caenorhabditis elegans multiparental recombinant inbred line population.

Studying genetic variation of gene expression provides a powerful way to unravel the molecular components underlying complex traits. Expression quantitative trait locus (eQTL) studies have been performed in several different model species, yet most of these linkage studies have been based on the genetic segregation of two parental alleles. Recently, we developed a multiparental segregating population of 200 recombinant inbred lines (mpRILs) derived from four wild isolates (JU1511, JU1926, JU1931, and JU1941) in the nematode Caenorhabditis elegans. We used RNA-seq to investigate how multiple alleles affect gene expression in these mpRILs. We found 1789 genes differentially expressed between the parental lines. Transgression, expression beyond any of the parental lines in the mpRILs, was found for 7896 genes. For expression QTL mapping almost 9000 SNPs were available. By combining these SNPs and the RNA-seq profiles of the mpRILs, we detected almost 6800 eQTLs. Most trans-eQTLs (63%) co-locate in six newly identified trans-bands. The trans-eQTLs found in previous two-parental allele eQTL experiments and this study showed some overlap (17.5-46.8%), highlighting on the one hand that a large group of genes is affected by polymorphic regulators across populations and conditions, on the other hand, it shows that the mpRIL population allows identification of novel gene expression regulatory loci. Taken together, the analysis of our mpRIL population provides a more refined insight into C. elegans complex trait genetics and eQTLs in general, as well as a starting point to further test and develop advanced statistical models for detection of multiallelic eQTLs and systems genetics studying the genotype-phenotype relationship.

Bottleneck size and selection level reproducibly impact evolution of antibiotic resistance.

During antibiotic treatment, the evolution of bacterial pathogens is fundamentally affected by bottlenecks and varying selection levels imposed by the drugs. Bottlenecks-that is, reductions in bacterial population size-lead to an increased influence of random effects (genetic drift) during bacterial evolution, and varying antibiotic concentrations during treatment may favour distinct resistance variants. Both aspects influence the process of bacterial evolution during antibiotic therapy and thereby treatment outcome. Surprisingly, the joint influence of these interconnected factors on the evolution of antibiotic resistance remains largely unexplored. Here we combine evolution experiments with genomic and genetic analyses to demonstrate that bottleneck size and antibiotic-induced selection reproducibly impact the evolutionary path to resistance in pathogenic Pseudomonas aeruginosa, one of the most problematic opportunistic human pathogens. Resistance is favoured-expectedly-under high antibiotic selection and weak bottlenecks, but-unexpectedly-also under low antibiotic selection and severe bottlenecks. The latter is likely to result from a reduced probability of losing favourable variants through drift under weak selection. Moreover, the absence of high resistance under low selection and weak bottlenecks is caused by the spread of low-resistance variants with high competitive fitness under these conditions. We conclude that bottlenecks, in combination with drug-induced selection, are currently neglected key determinants of pathogen evolution and outcome of antibiotic treatment.

High potency of sequential therapy with only ß-lactam antibiotics.

Evolutionary adaptation is a major source of antibiotic resistance in bacterial pathogens. Evolution-informed therapy aims to constrain resistance by accounting for bacterial evolvability. Sequential treatments with antibiotics that target different bacterial processes were previously shown to limit adaptation through genetic resistance trade-offs and negative hysteresis. Treatment with homogeneous sets of antibiotics is generally viewed to be disadvantageous as it should rapidly lead to cross-resistance. We here challenged this assumption by determining the evolutionary response of Pseudomonas aeruginosa to experimental sequential treatments involving both heterogenous and homogeneous antibiotic sets. To our surprise, we found that fast switching between only ß-lactam antibiotics resulted in increased extinction of bacterial populations. We demonstrate that extinction is favored by low rates of spontaneous resistance emergence and low levels of spontaneous cross-resistance among the antibiotics in sequence. The uncovered principles may help to guide the optimized use of available antibiotics in highly potent, evolution-informed treatment designs.

Overuse of antibiotic drugs is leading to the appearance of antibiotic-resistant bacteria; this is, bacteria with mutations that allow them to survive treatment with specific antibiotics. This has made some bacterial infections difficult or impossible to treat. Learning more about how bacteria evolve resistance to antibiotics could help scientists find ways to prevent it and develop more effective treatments. Changing antibiotics frequently may be one way to prevent bacteria from evolving resistance. That way if a bacterium acquires mutations that allow it to escape one antibiotic, another antibiotic will kill it, stopping it from dividing and preventing the appearance of descendants with resistance to several antibiotics. In order to use this approach, testing is needed to find the best sequences of antibiotics to apply and the optimal timings of treatment. To find out more, Batra, Roemhild et al. grew bacteria in the laboratory and exposed them to different sequences of antibiotics, switching antibiotics at different time intervals. This showed that sequential treatments with different antibiotics can limit bacterial evolution, especially when antibiotics are switched quickly. Unexpectedly, one of the most effective sequences used very similar antibiotics. This was surprising because using similar antibiotics should lead to the evolution of cross-resistance, which is when a drug causes changes that make the bacterium less sensitive to other treatments. However, in the tested case, cross-resistance did not evolve when antibiotics were switched quickly, thereby ensuring efficiency of treatment. Batra et al. show that alternating sequences of antibiotics may be an effective strategy to prevent drug resistance. Because the experiments were done in a laboratory setting it will be important to verify the results in studies in animals and humans before the approach can be used in medical or veterinary settings. If the results are confirmed, it could reduce the need to develop new antibiotics, which is expensive and time consuming.

Modeling host-associating microbes under selection.

The concept of fitness is often reduced to a single component, such as the replication rate in a given habitat. For species with multi-step life cycles, this can be an unjustified oversimplification, as every step of the life cycle can contribute to the overall reproductive success in a specific way. In particular, this applies to microbes that spend part of their life cycles associated to a host. In this case, there is a selection pressure not only on the replication rates, but also on the phenotypic traits associated to migrating from the external environment to the host and vice-versa (i.e., the migration rates). Here, we investigate a simple model of a microbial lineage living, replicating, migrating and competing in and between two compartments: a host and an environment. We perform a sensitivity analysis on the overall growth rate to determine the selection gradient experienced by the microbial lineage. We focus on the direction of selection at each point of the phenotypic space, defining an optimal way for the microbial lineage to increase its fitness. We show that microbes can adapt to the two-compartment life cycle through either changes in replication or migration rates, depending on the initial values of the traits, the initial distribution across the two compartments, the intensity of competition, and the time scales involved in the life cycle versus the time scale of adaptation (which determines the adequate probing time to measure fitness). Overall, our model provides a conceptual framework to study the selection on microbes experiencing a host-associated life cycle.