Microbial experimental evolution in a massively multiplexed and high-throughput era

Evolving a plant-beneficial bacterium in soil vs. nutrient-rich liquid culture has contrasting effects on in-soil fitness

Inoculation of plant-beneficial microbes into agricultural soils can improve crop growth, but such outcomes depend on microbial survival. Here, we assessed how exposure to prior environmental conditions impacts microbial in-soil fitness, particularly focusing on incubation in liquid culture as an unavoidable phase of inoculant production and on pre-incubation in target soils as a potential method to improve performance. We conducted experimental evolution on a phosphorus-solubilizing bacterial species, Priestia megaterium, in (i) soil only, (ii) liquid media only, and (iii) soil followed by liquid media, using population metagenomic sequencing to track mutations over time. Several typical in vitro evolutionary phenomena were observed in liquid media-incubated populations, including clonal interference, genetic hitchhiking, and mutation parallelism between replicate populations, particularly in the sporulation transcription factor spo0A. Liquid media-incubated populations also developed a clear fitness reduction in soil compared to the ancestral isolate. However, soil-incubated populations grew slowly, experienced far fewer generations despite longer absolute time, and accumulated minimal mutational changes. Correspondingly, soil-incubated populations did not display improved survival compared to the ancestral isolate in their target soils, though there did appear to be minor fitness reductions in unfamiliar soil. This work demonstrates that adaptation to liquid media and/or a native soil can impact bacterial fitness in new soil and that bacterial evolution in more complex real-world habitats does not closely resemble bacterial evolution in liquid media.

IMPORTANCE

Innovative solutions are needed to address emerging challenges in agriculture while reducing its environmental footprint. Management of soil microbiomes could contribute to this effort, as plant growth-promoting microorganisms provide key ecosystem services that support crops. Yet, inoculating beneficial microbes into farm soils yields unreliable results. We require a greater knowledge of the ecology of these taxa to improve their functioning in sustainable agroecosystems. In this report, we demonstrate that exposure to laboratory media and lingering adaptation to another soil can negatively impact the in-soil survival of a phosphorus-solubilizing bacterial species. We go further to highlight the underlying mutations that give rise to these patterns. These insights can be leveraged to improve our understanding of how soil-dwelling beneficial microorganisms adapt to different evolutionary pressures.

Development of specialized devices for microbial experimental evolution

Experimental evolution of microbial cells provides valuable information on evolutionary dynamics, such as mutations that contribute to fitness gain under given selection pressures. Although experimental evolution is a promising tool in evolutionary biology and bioengineering, long-term culture experiments under multiple environmental conditions often impose an excessive workload on researchers. Therefore, the development of automated systems significantly contributes to the advancement of experimental evolutionary research. This review presents several specialized devices designed for experimental evolution studies, such as an automated system for high-throughput culture experiments, a culture device that generate a temperature gradient, and an automated ultraviolet (UV) irradiation culture device. The ongoing development of such specialized devices is poised to continually expand new frontiers in experimental evolution research.

Experimental Evolution in a Warming World: The Omics Era

A comprehensive understanding of the genetic mechanisms that shape species responses to thermal variation is essential for more accurate predictions of the impacts of climate change on biodiversity. Experimental evolution with high-throughput resequencing approaches (evolve and resequence) is a highly effective tool that has been increasingly employed to elucidate the genetic basis of adaptation. The number of thermal evolve and resequence studies is rising, yet there is a dearth of efforts to integrate this new wealth of knowledge. Here, we review this literature showing how these studies have contributed to increase our understanding on the genetic basis of thermal adaptation. We identify two major trends: highly polygenic basis of thermal adaptation and general lack of consistency in candidate targets of selection between studies. These findings indicate that the adaptive responses to specific environments are rather independent. A review of the literature reveals several gaps in the existing research. Firstly, there is a paucity of studies done with organisms of diverse taxa. Secondly, there is a need to apply more dynamic and ecologically relevant thermal environments. Thirdly, there is a lack of studies that integrate genomic changes with changes in life history and behavioral traits. Addressing these issues would allow a more in-depth understanding of the relationship between genotype and phenotype. We highlight key methodological aspects that can address some of the limitations and omissions identified. These include the need for greater standardization of methodologies and the utilization of new technologies focusing on the integration of genomic and phenotypic variation in the context of thermal adaptation.

Evolution of haploid and diploid populations reveals common, strong, and variable pleiotropic effects in non-home environments

Adaptation is driven by the selection for beneficial mutations that provide a fitness advantage in the specific environment in which a population is evolving. However, environments are rarely constant or predictable. When an organism well adapted to one environment finds itself in another, pleiotropic effects of mutations that made it well adapted to its former environment will affect its success. To better understand such pleiotropic effects, we evolved both haploid and diploid barcoded budding yeast populations in multiple environments, isolated adaptive clones, and then determined the fitness effects of adaptive mutations in 'non-home' environments in which they were not selected. We find that pleiotropy is common, with most adaptive evolved lineages showing fitness effects in non-home environments. Consistent with other studies, we find that these pleiotropic effects are unpredictable: they are beneficial in some environments and deleterious in others. However, we do find that lineages with adaptive mutations in the same genes tend to show similar pleiotropic effects. We also find that ploidy influences the observed adaptive mutational spectra in a condition-specific fashion. In some conditions, haploids and diploids are selected with adaptive mutations in identical genes, while in others they accumulate mutations in almost completely disjoint sets of genes.

Bayesian inference of relative fitness on high-throughput pooled competition assays

The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.

Barcoding Populations of Pseudomonas fluorescens SBW25

In recent years, evolutionary biologists have developed an increasing interest in the use of barcoding strategies to study eco-evolutionary dynamics of lineages within evolving populations and communities. Although barcoded populations can deliver unprecedented insight into evolutionary change, barcoding microbes presents specific technical challenges. Here, strategies are described for barcoding populations of the model bacterium Pseudomonas fluorescens SBW25, including the design and cloning of barcoded regions, preparation of libraries for amplicon sequencing, and quantification of resulting barcoded lineages. In so doing, we hope to aid the design and implementation of barcoding methodologies in a broad range of model and non-model organisms.

Identifying Targets of Selection in Laboratory Evolution Experiments

Adaptive evolution navigates a balance between chance and determinism. The stochastic processes of mutation and drift generate phenotypic variation; however, once mutations reach an appreciable frequency in the population, their fate is governed by the deterministic action of selection, enriching for favorable genotypes and purging the less-favorable ones. The net result is that replicate populations will traverse similar-but not identical-pathways to higher fitness. This parallelism in evolutionary outcomes can be leveraged to identify the genes and pathways under selection. However, distinguishing between beneficial and neutral mutations is challenging because many beneficial mutations will be lost due to drift and clonal interference, and many neutral (and even deleterious) mutations will fix by hitchhiking. Here, we review the best practices that our laboratory uses to identify genetic targets of selection from next-generation sequencing data of evolved yeast populations. The general principles for identifying the mutations driving adaptation will apply more broadly.

Scaling up and down: movement ecology for microorganisms

Movement is critical for the fitness of organisms, both large and small. It dictates how individuals acquire resources, evade predators, exchange genetic material, and respond to stressful environments. Movement also influences ecological and evolutionary dynamics at higher organizational levels, such as populations and communities. However, the links between individual motility and the processes that generate and maintain microbial diversity are poorly understood. Movement ecology is a framework linking the physiological and behavioral properties of individuals to movement patterns across scales of space, time, and biological organization. By synthesizing insights from cell biology, ecology, and evolution, we expand theory from movement ecology to predict the causes and consequences of microbial movements.

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.