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Pathogens face strong selection from host immune responses, yet many host populations support pervasive pathogen populations. We investigated this puzzle in a model system ofBartonellaand rodents from Israel’s northwestern Negev Desert. We chose to study this system because, in this region, 75–100% of rodents are infected withBartonellaat any given time, despite an efficient immunological response. In this region,Bartonellaspecies circulate in three rodent species, and we tested the hypothesis that at least one of these hosts exhibits a waning immune response toBartonella, which allows reinfections.

We inoculated captive animals of all three rodent species with the sameBartonellastrain, and we quantified the bacterial dynamics andBartonella-specific immunoglobulin G antibody kinetics over a period of 139 days after the primary inoculation, and then for 60 days following reinoculation with the same strain.

Contrary to our hypothesis, we found a strong, long-lasting immunoglobulin G antibody response, with protective immunological memory in all three rodent species. That response prevented reinfection upon exposure of the rodents to the sameBartonellastrain.

This study constitutes an initial step toward understanding how the interplay between traits ofBartonellaand their hosts influences the epidemiological dynamics of these pathogens in nature.

 

Animals form complex symbiotic associations with their gut microbes, whose evolution is determined by an intricate network of host and environmental factors. In many insects, such asDrosophila melanogaster, the microbiome is flexible, environmentally determined, and less diverse than in mammals. In contrast, mammals maintain complex multispecies consortia that are able to colonize and persist in the gastrointestinal tract. Understanding the evolutionary and ecological dynamics of gut microbes in different hosts is challenging. This requires disentangling the ecological factors of selection, determining the timescales over which evolution occurs, and elucidating the architecture of such evolutionary patterns.

We employ experimental evolution to track the pace of the evolution of a common gut commensal,Lactiplantibacillus plantarum, within invertebrate (Drosophila melanogaster) and vertebrate (Mus musculus) hosts and their respective diets. We show that inDrosophila, the nutritional environment dictates microbial evolution, while the host benefitsL. plantarumgrowth only over short ecological timescales. By contrast, in a mammalian animal model,L. plantarumevolution results to be divergent between the host intestine and its diet, both phenotypically (i.e., host-evolved populations show higher adaptation to the host intestinal environment) and genomically. Here, both the emergence of hypermutators and the high persistence of mutated genes within the host’s environment strongly differed from the low variation observed in the host’s nutritional environment alone.

Our results demonstrate thatL. plantarumevolution diverges between insects and mammals. While the symbiosis betweenDrosophilaandL. plantarumis mainly determined by the host diet, in mammals, the host and its intrinsic factors play a critical role in selection and influence both the phenotypic and genomic evolution of its gut microbes, as well as the outcome of their symbiosis.

 

Parasites, including pathogens, can adapt to better exploit their hosts on many scales, ranging from within an infection of a single individual to series of infections spanning multiple host species. However, little is known about how the genomes of parasites in natural communities evolve when they face diverse hosts. We investigated howBartonellabacteria that circulate in rodent communities in the dunes of the Negev Desert in Israel adapt to different species of rodent hosts. We propagated 15Bartonellapopulations through infections of either a single host species (Gerbillus andersoniorGerbillus pyramidum) or alternating between the two. After 20 rodent passages, strains withde novomutations replaced the ancestor in most populations. Mutations in two mononucleotide simple sequence repeats (SSRs) that caused frameshifts in the same adhesin gene dominated the evolutionary dynamics. They appeared exclusively in populations that encounteredG.andersoniand altered the dynamics of infections of this host. Similar SSRs in other genes are conserved and exhibit ON/OFF variation inBartonellaisolates from the Negev Desert dunes. Our results suggest that SSR-based contingency loci could be important not only for rapidly and reversibly generating antigenic variation to escape immune responses but that they may also mediate the evolution of host specificity. 

Population bottlenecks can impact the rate of adaptation in evolving populations. On the one hand, each bottleneck reduces the genetic variation that fuels adaptation. On the other hand, each founder that survives a bottleneck can undergo more generations and leave more descendants in a resource-limited environment, which allows surviving beneficial mutations to spread more quickly. A theoretical model predicted that the rate of fitness gains should be maximized using ~8-fold dilutions. Here we investigate the impact of repeated bottlenecks on the dynamics of adaptation using numerical simulations and experimental populations ofEscherichia coli. Our simulations confirm the model’s prediction when populations evolve in a regime where beneficial mutations are rare and waiting times between successful mutations are long. However, more extreme dilutions maximize fitness gains in simulations when beneficial mutations are common and clonal interference prevents most of them from fixing. To examine these predictions, we propagated 48E. colipopulations with 2-, 8-, 100-, and 1000-fold dilutions for 150 days. Adaptation began earlier and fitness gains were greater with 100- and 1000-fold dilutions than with 8-fold dilutions, consistent with the simulations when beneficial mutations are common. However, the selection pressures in the 2-fold treatment were qualitatively different from the other treatments, violating a critical assumption of the model and simulations. Thus, varying the dilution factor during periodic bottlenecks can have multiple effects on the dynamics of adaptation caused by differential losses of diversity, different numbers of generations, and altered selection.

 

Viruses of microbes are ubiquitous biological entities that reprogram their hosts’ metabolisms during infection in order to produce viral progeny, impacting the ecology and evolution of microbiomes with broad implications for human and environmental health. Advances in genome sequencing have led to the discovery of millions of novel viruses and an appreciation for the great diversity of viruses on Earth. Yet, with knowledge of only“who is there?”we fall short in our ability to infer the impacts of viruses on microbes at population, community, and ecosystem-scales. To do this, we need a more explicit understanding“who do they infect?”Here, we developed a novel machine learning model (ML), Virus-Host Interaction Predictor (VHIP), to predict virus-host interactions (infection/non-infection) from input virus and host genomes. This ML model was trained and tested on a high-value manually curated set of 8849 virus-host pairs and their corresponding sequence data. The resulting dataset, ‘Virus Host Range network’ (VHRnet), is core to VHIP functionality. Each data point that underlies the VHIP training and testing represents a lab-tested virus-host pair in VHRnet, from which meaningful signals of viral adaptation to host were computed from genomic sequences. VHIP departs from existing virus-host prediction models in its ability to predict multiple interactions rather than predicting a single most likely host or host clade. As a result, VHIP is able to infer the complexity of virus-host networks in natural systems. VHIP has an 87.8% accuracy rate at predicting interactions between virus-host pairs at the species level and can be applied to novel viral and host population genomes reconstructed from metagenomic datasets.

 

Experimental evolution is an approach that allows researchers to study organisms as they evolve in controlled environments. Despite the growing popularity of this approach, there are conceptual gaps among projects that use different experimental designs. One such gap concerns the contributions to adaptation of genetic variation present at the start of an experiment and that of new mutations that arise during an experiment. The primary source of genetic variation has historically depended largely on the study organisms. In the long-term evolution experiment (LTEE) using Escherichia coli , for example, each population started from a single haploid cell, and therefore, adaptation depended entirely on new mutations. Most other microbial evolution experiments have followed the same strategy. By contrast, evolution experiments using multicellular, sexually reproducing organisms typically start with preexisting variation that fuels the response to selection. New mutations may also come into play in later generations of these experiments, but it is generally difficult to quantify their contribution in these studies. Here, we performed an experiment using E. coli to compare the contributions of initial genetic variation and new mutations to adaptation in a new environment. Our experiment had four treatments that varied in their starting diversity, with 18 populations in each treatment. One treatment depended entirely on new mutations, while the other three began with mixtures of clones, whole-population samples, or mixtures of whole-population samples from the LTEE. We tracked a genetic marker associated with different founders in two treatments. These data revealed significant variation in fitness among the founders, and that variation impacted evolution in the early generations of our experiment. However, there were no differences in fitness among the treatments after 500 or 2,000 generations in the new environment, despite the variation in fitness among the founders. These results indicate that new mutations quickly dominated, and eventually they contributed more to adaptation than did the initial variation. Our study thus shows that preexisting genetic variation can have a strong impact on early evolution in a new environment, but new beneficial mutations may contribute more to later evolution and can even drive some initially beneficial variants to extinction. 

Humans have long known how to co-opt evolutionary processes for their own benefit. Carefully choosing which individuals to breed so that beneficial traits would take hold, they have domesticated dogs, wheat, cows and many other species to fulfil their needs. Biologists have recently refined these ‘artificial selection’ approaches to focus on microorganisms. The hope is to obtain microbes equipped with desirable features, such as the ability to degrade plastic or to produce valuable molecules. However, existing ways of using artificial selection on microbes are limited and sometimes not effective. Computer scientists have also harnessed evolutionary principles for their own purposes, developing highly effective artificial selection protocols that are used to find solutions to challenging computational problems. Yet because of limited communication between the two fields, sophisticated selection protocols honed over decades in evolutionary computing have yet to be evaluated for use in biological populations. In their work, Lalejini et al. compared popular artificial selection protocols developed for either evolutionary computing or work with microorganisms. Two computing selection methods showed promise for improving directed evolution in the laboratory. Crucially, these selection protocols differed from conventionally used methods by selecting for both diversity and performance, rather than performance alone. These promising approaches are now being tested in the laboratory, with potentially far-reaching benefits for medical, biotech, and agricultural applications. While evolutionary computing owes its origins to our understanding of biological processes, it has much to offer in return to help us harness those same mechanisms. The results by Lalejini et al. help to bridge the gap between computational and biological communities who could both benefit from increased collaboration. 

Short Abstract for evolutionary computing community on selection schemes working for directed evolution experiments in microbes. 

ABSTRACT Iron acquisition is essential for almost all living organisms. In certain environments, ferrous iron is the most prevalent form of this element. Feo is the most widespread system for ferrous iron uptake in bacteria and is critical for virulence in some species. The canonical architecture of Feo consists of a large transmembrane nucleoside triphosphatase (NTPase) protein, FeoB, and two accessory cytoplasmic proteins, FeoA and FeoC. The role of the latter components and the mechanism by which Feo orchestrates iron transport are unclear. In this study, we conducted a comparative analysis of Feo protein sequences to gain insight into the evolutionary history of this transporter. We identified instances of how horizontal gene transfer contributed to the evolution of Feo. Also, we found that FeoC, while absent in most lineages, is largely present in the Gammaproteobacteria group, although its sequence is poorly conserved. We propose that FeoC, which may couple FeoB NTPase activity with pore opening, was an ancestral element that has been dispensed with through mutations in FeoA and FeoB in some lineages. We provide experimental evidence supporting this hypothesis by isolating and characterizing FeoC-independent mutants of the Vibrio cholerae Feo system. Also, we confirmed that the closely related species Shewanella oneidensis does not require FeoC; thus, Vibrio FeoC sequences may resemble transitional forms on an evolutionary pathway toward FeoC-independent transporters. Finally, by combining data from our bioinformatic analyses with this experimental evidence, we propose an evolutionary model for the Feo system in bacteria. IMPORTANCE Feo, a ferrous iron transport system composed of three proteins (FeoA, -B, and -C), is the most prevalent bacterial iron transporter. It plays an important role in iron acquisition in low-oxygen environments and some host-pathogen interactions. The large transmembrane protein FeoB provides the channel for the transport of iron into the bacterial cell, but the functions of the two small, required accessory proteins FeoA and FeoC are not well understood. Analysis of the evolution of this transporter shows that FeoC is poorly conserved and has been lost from many bacterial lineages. Experimental evidence indicates that FeoC may have different functions in different species that retain this protein, and the loss of FeoC is promoted by mutations in FeoA or by the fusion of FeoA and FeoB. 

Most of Earth’s diversity has been produced in rounds of adaptive radiation, but the ecological drivers of diversification, such as abiotic complexity (i.e., ecological opportunity ) or predation and parasitism (i.e., ecological necessity ), are hard to disentangle. However, most of these radiations occurred hundreds of thousands if not millions of years ago, and the mechanisms promoting contemporary coexistence are not necessarily the same mechanisms that drove diversification in the first place. Experimental evolution has been one fruitful approach used to understand how different ecological mechanisms promote diversification in simple microbial microcosms, but these microbial systems come with their own limitations. To test how ecological necessity and opportunity interact, we use an unusual system of self-replicating computer programs that diversify to fill niches in a virtual environment. These organisms are subject to ecological pressures just like their natural counterparts. They experience biotic interactions from digital parasites, which steal host resources to replicate their own code and spread in the population. With the control afforded by experimenting with computational ecologies, we begin to unweave the complex interplay between ecological drivers of diversification. In particular, we find that the complexity of the abiotic environment and the size of the phenotypic space in which organisms are able to interact play different roles depending on the ecological driver of diversification. We find that in some situations, both ecological opportunity and necessity drive similar levels of diversity. However, the phenotypes that hosts uncover while coevolving with parasites are dramatically more complex than hosts evolving alone.