Metabolic modelling in a dynamic evolutionary framework predicts adaptive diversification of bacteria in a long-term evolution experiment

Großkopf, Tobias; Consuegra, Jessika; Gaffé, Joël; Willison, John C.; Lenski, Richard E.; Soyer, Orkun S.; Schneider, Dominique

doi:10.1186/s12862-016-0733-x

Cited by 75 publications

(84 citation statements)

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“…The idea of cellular tradeoffs driving the emergence of metabolic cross‐feeding has recently been evaluated in a combined in silico and experimental evolution study (Großkopf et al ., ). In that study, the authors have incorporated tradeoffs in a stoichiometric metabolic model of Escherichia coli by imposing global constraints on the total uptake rates.…”

Section: Microbial Cooperations Based On Metabolic Interactionsmentioning

confidence: 97%

“…C). These two models have distinct uptake fluxes suggestive of a cross‐feeding interaction; one model had increased glucose uptake and acetate excretion rate and the other had increased acetate uptake rate (Großkopf et al ., ). Further experimental analyses revealed that the two models show metabolic flux patterns that qualitatively match experimentally observed genotypes in one lineage of the long‐term experiments, indicating that this approach might provide useful insights into how ecological and evolutionary dynamics can shape metabolic systems.…”

Section: Microbial Cooperations Based On Metabolic Interactionsmentioning

confidence: 97%

“…Eco‐evo feedbacks can be modelled by adding notions of population dynamics to evolutionary game theory, leading to the framework of ecological public good games (Hauert et al ., ) that extend the standard evolutionary game theory (in which, usually, the focus of the analysis is the change in frequency of a certain trait). Combination of population dynamics with metabolic models at the level of individual species or genotypes (Harcombe et al ., ) with evolutionary dynamics (Großkopf et al ., ) is another promising route towards capturing eco‐evolutionary dynamics, especially when cooperative interactions involve metabolite secretions.…”

Section: Factors Contributing To the Stabilization Of Cooperative Intmentioning

confidence: 99%

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Cooperation in microbial communities and their biotechnological applications

Cavaliere

Feng

Soyer

et al. 2017

Environmental Microbiology

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168

117

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SummaryMicrobial communities are increasingly utilized in biotechnology. Efficiency and productivity in many of these applications depends on the presence of cooperative interactions between members of the community. Two key processes underlying these interactions are the production of public goods and metabolic cross‐feeding, which can be understood in the general framework of ecological and evolutionary (eco‐evo) dynamics. In this review, we illustrate the relevance of cooperative interactions in microbial biotechnological processes, discuss their mechanistic origins and analyse their evolutionary resilience. Cooperative behaviours can be damaged by the emergence of ‘cheating’ cells that benefit from the cooperative interactions but do not contribute to them. Despite this, cooperative interactions can be stabilized by spatial segregation, by the presence of feedbacks between the evolutionary dynamics and the ecology of the community, by the role of regulatory systems coupled to the environmental conditions and by the action of horizontal gene transfer. Cooperative interactions enrich microbial communities with a higher degree of robustness against environmental stress and can facilitate the evolution of more complex traits. Therefore, the evolutionary resilience of microbial communities and their ability to constraint detrimental mutants should be considered to design robust biotechnological applications.

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Section: Microbial Cooperations Based On Metabolic Interactionsmentioning

confidence: 97%

Section: Microbial Cooperations Based On Metabolic Interactionsmentioning

confidence: 97%

Section: Factors Contributing To the Stabilization Of Cooperative Intmentioning

confidence: 99%

See 1 more Smart Citation

Cooperation in microbial communities and their biotechnological applications

Cavaliere

Feng

Soyer

et al. 2017

Environmental Microbiology

Self Cite

168

117

View full text Add to dashboard Cite

show abstract

“…Diversification events within populations often entail cross-feeding interactions [12,13,[15][16][17][18], where species emerge that grow on metabolic by-products. These cross-feeding interactions are increasingly well understood with the help of metabolic modeling and digital evolution [19,20]. A recent metagenomic study has revealed even more coexisting lineages in the LTEE than were previously reported [21].…”

Section: Introductionmentioning

confidence: 99%

Virtual Microbes evolve multiple mechanisms to the same end: anticipating a serial transfer protocol

Dijk

Meijer

Cuypers

et al. 2019

Preprint

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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...

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“…However, ever larger datasets are being generated using high-throughput phenotyping and whole genome sequencing, and computational and modeling approaches are increasingly successful at combining such data across multiple organizational levels. For example, a recent model that integrated flux balance analysis with eco-evolutionary dynamics correctly predicted the evolution of cross-feeding in the E. coli long-term evolution experiment (Grosskopf et al 2016).…”

Section: Genetics Of Adaptationmentioning

confidence: 98%