scholarly journals Steering ecological-evolutionary dynamics during artificial selection of microbial communities

2018 ◽  
Author(s):  
Li Xie ◽  
Wenying Shou
Keyword(s):  
Microbial Communities ◽  
Artificial Selection ◽  
Evolutionary Dynamics ◽  
Ecological Interactions ◽  
Evolutionary Trajectory ◽  
Community Function ◽  
Selection Strategies ◽  
Effective Selection ◽  
Orthogonal Components ◽  
Selection Of

AbstractMicrobial communities often perform important functions that arise from interactions among member species. Community functions can be improved via artificial selection: Many communities are repeatedly grown, mutations arise, and communities with the highest desired function are chosen to reproduce where each is partitioned into multiple offspring communities for the next cycle. Since selection efficacy is often unimpressive in published experiments and since multiple experimental parameters need to be tuned, we sought to use computer simulations to learn how to design effective selection strategies. We simulated community selection to improve a community function that requires two species and imposes a fitness cost on one of the species. This simplified case allowed us to distill community function down to two fundamental and orthogonal components: a heritable determinant and a nonheritable determinant. We then visualize a “community function landscape” relating community function to these two determinants, and demonstrate that the evolutionary trajectory on the landscape is restricted along a path designated by ecological interactions. This path can prevent the attainment of maximal community function, and trap communities in landscape locations where community function has low heritability. Exploiting these observations, we devise a species spiking approach to shift the path to improve community function heritability and consequently selection efficacy. We show that our approach is applicable to communities with complex and unknown function landscapes.

scholarly journals Steering ecological-evolutionary dynamics to improve artificial selection of microbial communities

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Li Xie ◽  
Wenying Shou
Keyword(s):  
Microbial Communities ◽  
Artificial Selection ◽  
Species Interactions ◽  
Evolutionary Dynamics ◽  
Species Coexistence ◽  
Evolutionary Trajectory ◽  
Community Function ◽  
Link Type ◽  
Selection Strategies ◽  
Effective Selection

AbstractMicrobial communities often perform important functions that depend on inter-species interactions. To improve community function via artificial selection, one can repeatedly grow many communities to allow mutations to arise, and “reproduce” the highest-functioning communities by partitioning each into multiple offspring communities for the next cycle. Since improvement is often unimpressive in experiments, we study how to design effective selection strategies in silico. Specifically, we simulate community selection to improve a function that requires two species. With a “community function landscape”, we visualize how community function depends on species and genotype compositions. Due to ecological interactions that promote species coexistence, the evolutionary trajectory of communities is restricted to a path on the landscape. This restriction can generate counter-intuitive evolutionary dynamics, prevent the attainment of maximal function, and importantly, hinder selection by trapping communities in locations of low community function heritability. We devise experimentally-implementable manipulations to shift the path to higher heritability, which speeds up community function improvement even when landscapes are high dimensional or unknown. Video walkthroughs: https://go.nature.com/3GWwS6j; https://online.kitp.ucsb.edu/online/ecoevo21/shou2/.

scholarly journals Interactions between strains govern the eco-evolutionary dynamics of microbial communities

2021 ◽  
Author(s):  
Akshit Goyal ◽  
Leonora S. Bittleston ◽  
Gabriel E. Leventhal ◽  
Lu Lu ◽  
Otto X. Cordero
Keyword(s):  
Microbial Communities ◽  
Evolutionary Dynamics ◽  
Metagenomic Sequencing ◽  
Ecological Interactions ◽  
Base Pairs ◽  
Pitcher Plant ◽  
Naturally Occurring ◽  
Relevant Level ◽  
Over Time

AbstractGenomic data has revealed that genotypic variants of the same species, i.e., strains, coexist and are abundant in natural microbial communities. However, it is not clear if strains are ecologically equivalent, or if they exhibit distinct interactions and dynamics. Here, we address this problem by tracking 10 microbial communities from the pitcher plant Sarracenia purpurea in the laboratory for more than 300 generations. Using metagenomic sequencing, we reconstruct their dynamics over time and across scales, from distant phyla to closely related genotypes. We find that interactions between naturally occurring strains govern eco-evolutionary dynamics. Surprisingly, even fine-scale variants differing only by 100 base pairs can exhibit vastly different dynamics. We show that these differences may stem from ecological interactions in the communities, which are specific to strains, not species. Finally, by analyzing genomic differences between strains, we identify major functional hubs such as transporters, regulators, and carbohydrate-catabolizing enzymes, which might be the basis for strain-specific interactions. Our work shows that strains are the relevant level of diversity at which to study the long-term dynamics of microbiomes.

scholarly journals Artificial selection of communities drives the emergence of structured interactions

2021 ◽  
Author(s):  
Jules Fraboul ◽  
Giulio Biroli ◽  
Silvia De Monte
Keyword(s):  
Artificial Selection ◽  
Evolutionary Dynamics ◽  
Interspecific Interactions ◽  
Evolutionary Process ◽  
Ecological Resilience ◽  
Interaction Matrix ◽  
Volterra Equations ◽  
Theoretical Understanding ◽  
Multiple Parameters ◽  
Selection Of

Species-rich communities, such as the microbiota or environmental microbial assemblages, provide key functions for human health and ecological resilience. Increasing effort is being dedicated to design experimental protocols for selecting community-level functions of interest. These experiments typically involve selection acting on populations of communities, each of which is composed of multiple species. Numerical explorations allowed to link the evolutionary dynamics to the multiple parameters involved in this complex, multi-scale evolutionary process. However, a comprehensive theoretical understanding of artificial selection of communities is still lacking. Here, we propose a general model for the evolutionary dynamics of species-rich communities, each described by disordered generalized Lotka-Volterra equations, that we study analytically and by numerical simulations. Our results reveal that a generic response to selection for larger total community abundance is the emergence of an isolated eigenvalue of the interaction matrix that can be understood as an effective cross-feeding term. In this way, selection imprints a structure on the community, which results in a global increase of both the level of mutualism and the diversity of interactions. Our approach moreover allows to disentangle the role of intraspecific competition, interspecific interactions symmetry and number of selected communities in the evolutionary process, and can thus be used as a guidance in optimizing artificial selection protocols.

scholarly journals Reducing fluctuations in species composition facilitates artificial selection of microbial community function

2018 ◽  
Author(s):  
Li Xie ◽  
Wenying Shou
Keyword(s):  
Microbial Community ◽  
Species Composition ◽  
Microbial Communities ◽  
Cell Sorting ◽  
Maturation Time ◽  
Experimental Procedure ◽  
Community Function ◽  
Stochastic Fluctuations ◽  
Total Product ◽  
Selection Of

AbstractMulti-species microbial communities often display functions - biochemical activities unattainable by member species alone, such as fighting pathogens. To improve community function, we can artificially select communities by growing “Newborn” communities over “maturation time” into “Adult” communities, and selecting highest-functioning Adults to “reproduce” by diluting each into multiple Newborns of the next cycle. Community selection has been attempted a few times on complex communities, often generating mixed results that are difficult to interpret. Here, we ask how costly community function may be improved via mutations and community selection. We simulate selection of two-species communities where Helpers digest Waste and generate Byproduct essential to Manufacturers; Manufacturers divert a fraction of their growth to make Product. Community function, the total Product in an “Adult”, is sub-optimal even when both species have been pre-optimized as monocultures. If we dilute an Adult into Newborns by pipetting (a common experimental procedure), stochastic fluctuations in Newborn composition prevents community function from improving. Reducing fluctuations via cell sorting allows selection to work. Our conclusions hold regardless of whether H and M are commensal or mutualistic, or variations in model assumptions.

scholarly journals Strong inter-population cooperation leads to partner intermixing in microbial communities

eLife ◽  
2013 ◽  
Vol 2 ◽  
Author(s):  
Babak Momeni ◽  
Kristen A Brileya ◽  
Matthew W Fields ◽  
Wenying Shou
Keyword(s):  
Microbial Communities ◽  
Environmental Variability ◽  
Initial Conditions ◽  
Ecological Interactions ◽  
Community Function ◽  
Yeast Community ◽  
Engineered Yeast ◽  
Fitness Benefits ◽  
Spatial Positioning ◽  
Natural Communities

Patterns of spatial positioning of individuals within microbial communities are often critical to community function. However, understanding patterning in natural communities is hampered by the multitude of cell–cell and cell–environment interactions as well as environmental variability. Here, through simulations and experiments on communities in defined environments, we examined how ecological interactions between two distinct partners impacted community patterning. We found that in strong cooperation with spatially localized large fitness benefits to both partners, a unique pattern is generated: partners spatially intermixed by appearing successively on top of each other, insensitive to initial conditions and interaction dynamics. Intermixing was experimentally observed in two obligatory cooperative systems: an engineered yeast community cooperating through metabolite-exchanges and a methane-producing community cooperating through redox-coupling. Even in simulated communities consisting of several species, most of the strongly-cooperating pairs appeared intermixed. Thus, when ecological interactions are the major patterning force, strong cooperation leads to partner intermixing.

scholarly journals Species matter for predicting the functioning of evolving microbial communities

2019 ◽  
Author(s):  
Timothy Giles Barraclough
Keyword(s):  
Microbial Communities ◽  
Resource Use ◽  
Bacterial Communities ◽  
Evolutionary Dynamics ◽  
Ecological Interactions ◽  
Community Functioning ◽  
Trade Offs ◽  
As Species ◽  
Multiple Species ◽  
Digestive Health

ABSTRACTHumans depend on microbial communities for numerous ecosystem services such as global nutrient cycles, plant growth and their digestive health. Yet predicting dynamics and functioning of these complex systems is hard, making interventions to enhance functioning harder still. One simplifying approach is to assume that functioning can be predicted from the set of enzymes present in a community. Alternatively, ecological and evolutionary dynamics of species, which depend on how enzymes are packaged among species, might be vital for predicting community functioning. I investigate these alternatives by extending classical chemostat models of bacterial growth to multiple species that evolve in their use of chemical resources. Ecological interactions emerge from patterns of resource use, which change as species evolve in their allocation of metabolic enzymes. Measures of community functioning derive in turn from metabolite concentrations and bacterial density. Although the model shows considerable functional redundancy, species packaging does matter by introducing constraints on whether enzyme levels can reach optimum levels for the whole system. Evolution can either promote or reduce functioning compared to purely ecological models, depending on the shape of trade-offs in resource use. The model provides baseline theory for interpreting emerging data on evolution and functioning in real bacterial communities.

scholarly journals Artificial selection of microbial communities to enhance degradation of recalcitrant polymers

2018 ◽  
Author(s):  
Robyn J. Wright ◽  
Matthew I. Gibson ◽  
Joseph A. Christie-Oleza
Keyword(s):  
Microbial Communities ◽  
Artificial Selection ◽  
Selection Process ◽  
Polymer Degradation ◽  
Synthetic Polymers ◽  
Natural Polymers ◽  
Uncertain Distribution ◽  
First Time ◽  
Selection Of

AbstractRecalcitrant polymers are widely distributed in the environment. This includes natural polymers, such as chitin, but synthetic polymers are becoming increasingly abundant, for which biodegradation is uncertain. Distribution of labour in microbial communities commonly evolves in nature, particularly for arduous processes, suggesting a community may be better at degrading recalcitrant compounds than individual microorganisms. Artificial selection of microbial communities with better degradation potential has seduced scientists for over a decade, but the method has not been systematically optimised nor applied to polymer degradation. Using chitin as a case study, we successfully selected for microbial communities with enhanced chitinase activities but found that continuous optimisation of incubation times between selective generations was of utmost importance. The analysis of the community composition over the entire selection process revealed fundamental aspects in microbial ecology: when incubation times between generations were optimal, the system was dominated byGammaproteobacteria, main bearers of chitinase enzymes and drivers of chitin degradation, before being succeeded by cheating, cross-feeding and grazing organisms.ImportanceArtificial selection is a powerful and atractive technique that can enhance the biodegradation of a recalcitrant polymer and other pollutants by microbial communities. We show, for the first time, that the success of artificially selecting microbial communities requires an optimisation of the incubation times between generations when implementing this method. Hence, communities need to be transferred at the peak of the desired activity in order to avoid community drift and replacement of the efficient biodegrading community by cheaters, cross-feeders and grazers.

Growth–defense trade-offs shape population genetic composition in an iconic forest tree species

2021 ◽  
Vol 118 (37) ◽  
pp. e2103162118 ◽  
Author(s):  
Olivia L. Cope ◽  
Ken Keefover-Ring ◽  
Eric L. Kruger ◽  
Richard L. Lindroth
Keyword(s):  
Evolutionary Dynamics ◽  
Evolutionary Ecology ◽  
Functional Trait ◽  
Forest Tree ◽  
Ecological Interactions ◽  
Trait Variation ◽  
Trade Offs ◽  
Spatiotemporal Scales ◽  
Evolutionary Consequences ◽  
Selection Of

All organisms experience fundamental conflicts between divergent metabolic processes. In plants, a pivotal conflict occurs between allocation to growth, which accelerates resource acquisition, and to defense, which protects existing tissue against herbivory. Trade-offs between growth and defense traits are not universally observed, and a central prediction of plant evolutionary ecology is that context-dependence of these trade-offs contributes to the maintenance of intraspecific variation in defense [Züst and Agrawal, Annu. Rev. Plant Biol., 68, 513–534 (2017)]. This prediction has rarely been tested, however, and the evolutionary consequences of growth–defense trade-offs in different environments are poorly understood, especially in long-lived species [Cipollini et al., Annual Plant Reviews (Wiley, 2014), pp. 263–307]. Here we show that intraspecific trait trade-offs, even when fixed across divergent environments, interact with competition to drive natural selection of tree genotypes corresponding to their growth–defense phenotypes. Our results show that a functional trait trade-off, when coupled with environmental variation, causes real-time divergence in the genetic architecture of tree populations in an experimental setting. Specifically, competitive selection for faster growth resulted in dominance by fast-growing tree genotypes that were poorly defended against natural enemies. This outcome is a signature example of eco-evolutionary dynamics: Competitive interactions affected microevolutionary trajectories on a timescale relevant to subsequent ecological interactions [Brunner et al., Funct. Ecol. 33, 7–12 (2019)]. Eco-evolutionary drivers of tree growth and defense are thus critical to stand-level trait variation, which structures communities and ecosystems over expansive spatiotemporal scales.

The predator-dependent replicator dynamics

2021 ◽  
Author(s):  
Ian Magalhaes Braga ◽  
Lucas Wardil
Keyword(s):  
Time Scales ◽  
Evolutionary Dynamics ◽  
Replicator Dynamics ◽  
Capture Rate ◽  
Evolutionary Game ◽  
Ecological Interactions ◽  
Evolutionary Time ◽  
Predator Prey ◽  
Environmental Interactions ◽  
Selection Of

Abstract Ecological interactions are central to understanding evolution. For example, Darwin noticed that the beautiful colours of the male peacock increase the chance of successful mating. However, the colours can be a threat because of the increased probability of being caught by predators. Eco-evolutionary dynamics takes into account environmental interactions to model the process of evolution. The selection of prey types in the presence of predators may be subjected to pressure on both reproduction and survival. Here, we analyze the evolutionary game dynamics of two types of prey in the presence of predators. We call this model \textit{the predator-dependent replicator dynamics}. If the evolutionary time scales are different, the number of predators can be assumed constant, and the traditional replicator dynamics is recovered. However, if the time scales are the same, we end up with sixteen possible dynamics: the combinations of four reproduction’s games with four predation’s games. We analyze the dynamics and calculate conditions for the coexistence of prey and predator. The main result is that predators can change the equilibrium of the traditional replicator dynamics. For example, the presence of predators can induce polymorphism in prey if one type of prey is more attractive than the other, with the prey ending with a lower capture rate in this new equilibrium. Lastly, we provide two illustrations of the dynamics, which can be seen as rapid feedback responses in a predator-prey evolutionary arm’s race.

Organizational Evolution and Dynamic Capabilities

2016 ◽  
Author(s):  
Daniel A. Levinthal ◽  
Alessandro Marino
Keyword(s):  
Dynamic Capabilities ◽  
Evolutionary Dynamics ◽  
Organizational Adaptation ◽  
Dynamic Capability ◽  
Organizational Evolution ◽  
Hierarchical Learning ◽  
Learning Capabilities ◽  
Effective Selection ◽  
Variation And Selection ◽  
Selection Of

This article examines the importance of plasticity and diversity in organizational adaptation and with respect to dynamic capabilities. It begins by conceptualizing what elements comprise a dynamic capability within an evolving organization using the contrast between templates (genotypes) and realized practices (phenotypes). It then introduces a model of hierarchical learning inside organizations with the goal of elucidating the interrelationships underlying the adaptive properties of the internal ecology of organizational evolution. In particular, it compares higher-level selection/recombination mechanisms with lower-level learning capabilities. This article highlights the importance of recognizing the interplay among the mechanisms and forces at work in the evolutionary dynamics of organizations. It shows that processes of variation and selection can be a valuable substitute for the capacity to adapt individual behaviors and that plasticity of templates may mitigate the effective selection of more or less promising templates.

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