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 that are used to make these predictions 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 creating a common language for evolutionary predictions.

BackgroundExperimental 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.ResultsWe 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.ConclusionsAll 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.

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.

1Experimental evolution of microbes often involves a serial transfer protocol with repeated dilutions and transfers to 2 fresh media to start a new growth cycle. Here we study how in silico evolved Virtual Microbe "wild types" (WTs) 3 adapt to such a protocol, study the generic evolutionary features, and investigate how these features depend on prior 4 evolution. All WTs adopt a balance of growth and survival, therewith anticipating the regularity of the serial 5 transfer. We find that this anticipation can happen by means of a single lineage, or by coexisting lineages that 6 specialise on either the growth phase or the stationary phase. Parallel experiments of the same WT show similar 7 trajectories with respect to growth and yield, and similar biases towards diversification. In summary, all our in silico 8 WTs show the same anticipation effects -fitting the periodicity of serial transfer protocol -but prior adaptations 9 determines what solution is found by subsequent evolution. 10 15 adapt to such a simple protocol, we might one day be able to predict evolution in the lab and -ideally -also in 16 nature. Indeed, a lot of evolution in the lab seems remarkably reproducible, where microbes show parallel adaptations 17 both on the level of the phenotype as well as the genotype [4][5][6][7][8][9][10][11]. However, there also seems to be strong potential 18 for divergent evolution, leading to diversity both between and within replicate populations [12][13][14]. Diversification 19 events within populations often entail cross-feeding interactions [12, 13,[15][16][17][18], where species emerge that grow on 20 metabolic by-products. These cross-feeding interactions are increasingly well understood with the help of metabolic 21 modeling and digital evolution [19, 20]. A recent metagenomic study has revealed even more coexisting lineages in the 22 LTEE than were previously reported [21]. It is however not yet clear whether all these polymorphisms are the result 23 of uni-directional cross-feeding interactions, or if other mechanisms could drive coexistence in a simple experiment 24 such as a serial transfer protocol. 25Prior to being subjected to lab conditions, the microbes used in the aforementioned experimental studies all 26 evolved for billions of years in natural environments, experiencing harshly fluctuating and -more often than not -27 unfavorable conditions. While a serial transfer protocol such as that of the LTEE at a first glance selects mostly for 28 higher growth rates when resources are abundant (i.e. during the log phase), there is also selection to survive when 29 resources are depleted and the population no longer grows (i.e. during the stationary phase). In fact, given the 30 1/31 unpredictable conditions found in nature, some of the ancestors of Escherichia coli might have survived precisely 31 because they diverted resources away from growth. Indeed, E. coli does exactly this during the stationary phase by 32 means of the stringent response, regulating up to one third of all genes during starvation [2...

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 experimental approaches and analyses are necessary to advance our understanding beyond simple pairwise MGE-host interactions. To detect horizontal transfer, a bioinformatic pipeline (xenoseq) was developed to cross-compare metagenomic samples, which was then applied to metagenomic data from evolving compost communities. These communities were routinely exposed to an "MGE cocktail" derived from allopatric communities. We show that this results in horizontal transfer of a multitude of previously undetected MGEs, including bacteriophages, phage-plasmids, megaplasmids, and even nanobacteria. Sequences that spread from one community to another are shown to disproportionally carry characteristics of phages and insertion-sequences, i.e., traits of canonically parasitic MGEs. We also show that one particularly prolific mobile element - a 313 kb plasmid - correlates substantially with rates of ammonia production, which under nitrogen limitation is likely beneficial. Taken together, our data show that new experimental strategies combined with bioinformatic analyses of metagenomic data stand to provide insight into the drivers of microbial community evolution.