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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 approachesmore »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.« less

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 speciesmore »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.« less

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 inmore »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.« less

Laboratory experiments in which blood-borne parasitic microbes evolve in their animal hosts offer an opportunity to study parasite evolution and adaptation in real time and under natural settings. The main challenge of these experiments is to establish a protocol that is both practical over multiple passages and accurately reflects natural transmission scenarios and mechanisms. We provide a guide to the steps that should be considered when designing such a protocol, and we demonstrate its use via a case study. We highlight the importance of choosing suitable ancestral genotypes, treatments, number of replicates per treatment, types of negative controls, dependent variables, covariates, and the timing of checkpoints for the experimental design. We also recommend specific preliminary experiments to determine effective methods for parasite quantification, transmission, and preservation. Although these methodological considerations are technical, they also often have conceptual implications. To this end, we encourage other researchers to design and conduct in vivo evolution experiments with blood-borne parasitic microbes, despite the challenges that the work entails.

Symbiosis, the living together of unlike organisms as symbionts, is ubiquitous in the natural world. Symbioses occur within and across all scales of life, from microbial to macro-faunal systems. Further, the interactions between symbionts are multimodal in both strength and type, can span from parasitic to mutualistic within one partnership, and persist over generations. Studying the ecological and evolutionary dynamics of symbiosis in natural or laboratory systems poses a wide range of challenges, including the long time scales at which symbioses evolve de novo , the limited capacity to experimentally control symbiotic interactions, the weak resolution at which we can quantify interactions, and the idiosyncrasies of current model systems. These issues are especially challenging when seeking to understand the ecological effects and evolutionary pressures on and of a symbiosis, such as how a symbiosis may shift between parasitic and mutualistic modes and how that shift impacts the dynamics of the partner population. In digital evolution, populations of computational organisms compete, mutate, and evolve in a virtual environment. Digital evolution features perfect data tracking and allows for experimental manipulations that are impractical or impossible in natural systems. Furthermore, modern computational power allows experimenters to observe thousands of generations of evolution inmore »minutes (as opposed to several months or years), which greatly expands the range of possible studies. As such, digital evolution is poised to become a keystone technique in our methodological repertoire for studying the ecological and evolutionary dynamics of symbioses. Here, we review how digital evolution has been used to study symbiosis, and we propose a series of open questions that digital evolution is well-positioned to answer.« less

The addition of parasites to a host population can drive an escalation in the host population's phenotypic complexity – even in the absence of a direct fitness advantage for this increase. Parasites restrict certain regions of the genotype space, decreasing the fitness and the probability of survival of particular host phenotypes. While many artificial life frameworks model a direct correlation between genotype and fitness, the structure of genotype-phenotype maps can have important effects on evolutionary dynamics. Using a simple coarse-grained model for phenotypic transitions during evolution, we show that the escalation in phenotypic complexity under neutral co-evolution is dependent on the structure of the genotype-phenotype map. We discuss these results using the metaphor of evolutionary spandrels and highlight how these structural considerations might allow us to capture biological phenomena more accurately.