Identifying and tracking mobile elements in evolving compost communities yields insights into the nanobiome.

Microbial evolution is driven by rapid changes in gene content mediated by horizontal gene transfer (HGT). While mobile genetic elements (MGEs) are important drivers of gene flux, the nanobiome-the zoo of Darwinian replicators that depend on microbial hosts-remains poorly characterised. New approaches are necessary to increase our understanding beyond MGEs shaping individual populations, towards their impacts on complex microbial communities. A bioinformatic pipeline (xenoseq) was developed to cross-compare metagenomic samples from microbial consortia evolving in parallel, aimed at identifying MGE dissemination, which was applied to compost communities which underwent periodic mixing of MGEs. We show that xenoseq can distinguish movement of MGEs from demographic changes in community composition that otherwise confounds identification, and furthermore demonstrate the discovery of various unexpected entities. Of particular interest was a nanobacterium of the candidate phylum radiation (CPR) which is closely related to a species identified in groundwater ecosystems (Candidatus Saccharibacterium), and appears to have a parasitic lifestyle. We also highlight another prolific mobile element, a 313 kb plasmid hosted by a Cellvibrio lineage. The host was predicted to be capable of nitrogen fixation, and acquisition of the plasmid coincides with increased ammonia production. Taken together, our data show that new experimental strategies combined with bioinformatic analyses of metagenomic data stand to provide insight into the nanobiome as a driver of microbial community evolution.

Evolution of genome fragility enables microbial division of labor.

Division of labor can evolve when social groups benefit from the functional specialization of its members. Recently, a novel means of coordinating the division of labor was found in the antibiotic-producing bacterium Streptomyces coelicolor, where specialized cells are generated through large-scale genomic re-organization. We investigate how the evolution of a genome architecture enables such mutation-driven division of labor, using a multiscale computational model of bacterial evolution. In this model, bacterial behavior-antibiotic production or replication-is determined by the structure and composition of their genome, which encodes antibiotics, growth-promoting genes, and fragile genomic loci that can induce chromosomal deletions. We find that a genomic organization evolves, which partitions growth-promoting genes and antibiotic-coding genes into distinct parts of the genome, separated by fragile genomic loci. Mutations caused by these fragile sites mostly delete growth-promoting genes, generating sterile, and antibiotic-producing mutants from weakly-producing progenitors, in agreement with experimental observations. This division of labor enhances the competition between colonies by promoting antibiotic diversity. These results show that genomic organization can co-evolve with genomic instabilities to enable reproductive division of labor.

Towards evolutionary predictions: Current promises and challenges.

Evolution has traditionally been a historical and descriptive science, and predicting future evolutionary processes has long been considered impossible. However, evolutionary predictions are increasingly being developed and used in medicine, agriculture, biotechnology and conservation biology. Evolutionary predictions may be used for different purposes, such as to prepare for the future, to try and change the course of evolution or to determine how well we understand evolutionary processes. Similarly, the exact aspect of the evolved population that we want to predict may also differ. For example, we could try to predict which genotype will dominate, the fitness of the population or the extinction probability of a population. In addition, there are many uses of evolutionary predictions that may not always be recognized as such. The main goal of this review is to increase awareness of methods and data in different research fields by showing the breadth of situations in which evolutionary predictions are made. We describe how diverse evolutionary predictions share a common structure described by the predictive scope, time scale and precision. Then, by using examples ranging from SARS-CoV2 and influenza to CRISPR-based gene drives and sustainable product formation in biotechnology, we discuss the methods for predicting evolution, the factors that affect predictability and how predictions can be used to prevent evolution in undesirable directions or to promote beneficial evolution (i.e. evolutionary control). We hope that this review will stimulate collaboration between fields by establishing a common language for evolutionary predictions.

Nutrition or nature: using elementary flux modes to disentangle the complex forces shaping prokaryote pan-genomes.

BACKGROUND: Microbial pan-genomes are shaped by a complex combination of stochastic and deterministic forces. Even closely related genomes exhibit extensive variation in their gene content. Understanding what drives this variation requires exploring the interactions of gene products with each other and with the organism's external environment. However, to date, conceptual models of pan-genome dynamics often represent genes as independent units and provide limited information about their mechanistic interactions. RESULTS: We simulated the stochastic process of gene-loss using the pooled genome-scale metabolic reaction networks of 46 taxonomically diverse bacterial and archaeal families as proxies for their pan-genomes. The frequency by which reactions are retained in functional networks when stochastic gene loss is simulated in diverse environments allowed us to disentangle the metabolic reactions whose presence depends on the metabolite composition of the external environment (constrained by "nutrition") from those that are independent of the environment (constrained by "nature"). By comparing the frequency of reactions from the first group with their observed frequencies in bacterial and archaeal families, we predicted the metabolic niches that shaped the genomic composition of these lineages. Moreover, we found that the lineages that were shaped by a more diverse metabolic niche also occur in more diverse biomes as assessed by global environmental sequencing datasets. CONCLUSION: We introduce a computational framework for analyzing and interpreting pan-reactomes that provides novel insights into the ecological and evolutionary drivers of pan-genome dynamics.

Transposable elements promote the evolution of genome streamlining.

Eukaryotes and prokaryotes have distinct genome architectures, with marked differences in genome size, the ratio of coding/non-coding DNA, and the abundance of transposable elements (TEs). As TEs replicate independently of their hosts, the proliferation of TEs is thought to have driven genome expansion in eukaryotes. However, prokaryotes also have TEs in intergenic spaces, so why do prokaryotes have small, streamlined genomes? Using an in silico model describing the genomes of single-celled asexual organisms that coevolve with TEs, we show that TEs acquired from the environment by horizontal gene transfer can promote the evolution of genome streamlining. The process depends on local interactions and is underpinned by rock-paper-scissors dynamics in which populations of cells with streamlined genomes beat TEs, which beat non-streamlined genomes, which beat streamlined genomes, in continuous and repeating cycles. Streamlining is maladaptive to individual cells, but improves lineage viability by hindering the proliferation of TEs. Streamlining does not evolve in sexually reproducing populations because recombination partially frees TEs from the deleterious effects they cause. This article is part of the theme issue 'The secret lives of microbial mobile genetic elements'.

Can mobile genetic elements rescue genes from extinction?

Bacteria and other prokaryotes evolve primarily through rapid changes in their gene content by quickly losing and gaining genes whenever an ecological opportunity emerges. As gene loss and horizontal gene transfer (HGT) appear to be the most common events across the prokaryotic tree of life, we need to think beyond gradual sequence evolution if we wish to understand the microbial world. Especially genes that reside on mobile genetic elements (MGEs) may spread much more rapidly through a microbial population than genes that reside on the bacterial chromosome. This raises the question: why are some genes associated with MGEs, while others are not? Here, I briefly review a recently proposed class of genes for which we have coined the term "rescuable genes". The fitness effect of carrying these genes is so small, either constantly or on average, that they are prone to be lost from a microbial population. I argue that HGT, even when costly to the individual cells, may play an important role in maintaining these rescuable genes in microbial communities.

Contingent evolution of alternative metabolic network topologies determines whether cross-feeding evolves.

Metabolic exchange is widespread in natural microbial communities and an important driver of ecosystem structure and diversity, yet it remains unclear what determines whether microbes evolve division of labor or maintain metabolic autonomy. Here we use a mechanistic model to study how metabolic strategies evolve in a constant, one resource environment, when metabolic networks are allowed to freely evolve. We find that initially identical ancestral communities of digital organisms follow different evolutionary trajectories, as some communities become dominated by a single, autonomous lineage, while others are formed by stably coexisting lineages that cross-feed on essential building blocks. Our results show how without presupposed cellular trade-offs or external drivers such as temporal niches, diverse metabolic strategies spontaneously emerge from the interplay between ecology, spatial structure, and metabolic constraints that arise during the evolution of metabolic networks. Thus, in the long term, whether microbes remain autonomous or evolve metabolic division of labour is an evolutionary contingency.

Slightly beneficial genes are retained by bacteria evolving DNA uptake despite selfish elements.

Horizontal gene transfer (HGT) and gene loss result in rapid changes in the gene content of bacteria. While HGT aids bacteria to adapt to new environments, it also carries risks such as selfish genetic elements (SGEs). Here, we use modelling to study how HGT of slightly beneficial genes impacts growth rates of bacterial populations, and if bacterial collectives can evolve to take up DNA despite selfish elements. We find four classes of slightly beneficial genes: indispensable, enrichable, rescuable, and unrescuable genes. Rescuable genes - genes with small fitness benefits that are lost from the population without HGT - can be collectively retained by a community that engages in costly HGT. While this 'gene-sharing' cannot evolve in well-mixed cultures, it does evolve in a spatial population like a biofilm. Despite enabling infection by harmful SGEs, the uptake of foreign DNA is evolutionarily maintained by the hosts, explaining the coexistence of bacteria and SGEs.

Most animals, including humans, inherit genes from their parents. However, bacteria and other microorganisms can also acquire genes from members of the same generation. This process, called horizontal gene transfer (HGT for short), allows bacteria to quickly adapt to new environments. For example, rather than waiting for rare mutations to arise, bacteria can pick up 'tried and true' genes from their neighbours which allow them to exploit new resources or become resistant to antibiotics. But gene sharing comes at a cost. For instance, taking up DNA is an energetically costly process and exposes bacteria to so-called selfish genes which replicate at the expense of other more useful genes in the genome. Given the costs and the threat of selfish genes, it remained unclear whether HGT is still beneficial in a stable environment where no new resources or antibiotics are present. Here, van Dijk et al. used mathematical modelling to examine how gene sharing affects the growth rate of bacterial colonies living in a stable environment. The experiments showed that bacteria are able to take up new sequences of DNA even in the presence of selfish genes. This allows communities of bacteria to retain genes that provide a small benefit that would otherwise be lost from the population, even when taking up DNA imposes a cost upon the individual. van Dijk et al. found that this collective behaviour cannot evolve in well-mixed bacterial populations, but readily emerged in more structured populations, such as biofilms. This work demonstrates how HGT, a key component of bacterial evolution, has allowed bacteria to coexist with harmful selfish genes. It also provides insights into how genes persist and spread through bacterial communities, which has implications for our understanding of antibiotic resistance.

Informing, simulating experience, or both: A field experiment on phishing risks.

Cybersecurity cannot be ensured with mere technical solutions. Hackers often use fraudulent emails to simply ask people for their password to breach into organizations. This technique, called phishing, is a major threat for many organizations. A typical prevention measure is to inform employees but is there a better way to reduce phishing risks? Experience and feedback have often been claimed to be effective in helping people make better decisions. In a large field experiment involving more than 10,000 employees of a Dutch ministry, we tested the effect of information provision, simulated experience, and their combination to reduce the risks of falling into a phishing attack. Both approaches substantially reduced the proportion of employees giving away their password. Combining both interventions did not have a larger impact.

Trusting the hand that feeds: microbes evolve to anticipate a serial transfer protocol as individuals or collectives.

BACKGROUND: Experimental evolution of microbes often involves a serial transfer protocol, where microbes are repeatedly diluted by transfer to a fresh medium, starting a new growth cycle. This has revealed that evolution can be remarkably reproducible, where microbes show parallel adaptations both on the level of the phenotype as well as the genotype. However, these studies also reveal a strong potential for divergent evolution, leading to diversity both between and within replicate populations. We here study how in silico evolved Virtual Microbe "wild types" (WTs) adapt to a serial transfer protocol to investigate generic evolutionary adaptations, and how these adaptations can be manifested by a variety of different mechanisms. RESULTS: We show that all WTs evolve to anticipate the regularity of the serial transfer protocol by adopting a fine-tuned balance of growth and survival. This anticipation is done by evolving either a high yield mode, or a high growth rate mode. We find that both modes of anticipation can be achieved by individual lineages and by collectives of microbes. Moreover, these different outcomes can be achieved with or without regulation, although the individual-based anticipation without regulation is less well adapted in the high growth rate mode. CONCLUSIONS: All our in silico WTs evolve to trust the hand that feeds by evolving to anticipate the periodicity of a serial transfer protocol, but can do so by evolving two distinct growth strategies. Furthermore, both these growth strategies can be accomplished by gene regulation, a variety of different polymorphisms, and combinations thereof. Our work reveals that, even under controlled conditions like those in the lab, it may not be possible to predict individual evolutionary trajectories, but repeated experiments may well result in only a limited number of possible outcomes.

In Silico Gene-Level Evolution Explains Microbial Population Diversity through Differential Gene Mobility.

Microbial communities can show astonishing ecological and phylogenetic diversity. What is the role of pervasive horizontal gene transfer (HGT) in shaping this diversity in the presence of clonally expanding "killer strains"? Does HGT of antibiotic production and resistance genes erase phylogenetic structure? To answer these questions, we study a spatial eco-evolutionary model of prokaryotes, inspired by recent findings on antagonistic interactions in Vibrionaceae populations. We find toxin genes evolve to be highly mobile, whereas resistance genes minimize mobility. This differential gene mobility is a requirement to maintain a diverse and dynamic ecosystem. The resistance gene repertoire acts as a core genome that corresponds to the phylogeny of cells, whereas toxin genes do not follow this phylogeny and have a patchy distribution. We also show that interstrain HGT makes the emergent phylogenetic structure robust to selective sweeps. Finally, in this evolved ecosystem we observe antagonistic interactions between, rather than within, spatially structure subpopulations, as has been previously observed for prokaryotes in soils and oceans. In contrast to ascribing the diversification and evolution of microbial communities to clonal dynamics, we show that multilevel evolution can elegantly explain the observed phylogenetic structure and ecosystem diversity.