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

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.

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An equation-free method reveals the ecological interaction networks within complex microbial ecosystems

10.1101/080697 ◽

2016 ◽

Author(s):

Kenta Suzuki ◽

Katsuhiko Yoshida ◽

Yumiko Nakanishi ◽

Shinji Fukuda

Keyword(s):

Time Series ◽

Microbial Communities ◽

Microbial Processes ◽

Interaction Networks ◽

List Type ◽

Metagenomic Sequencing ◽

Ecological Interactions ◽

Microbial Interaction ◽

Ecological Interaction ◽

Dataset Size

AbstractMapping the network of ecological interactions is key to understanding the composition, stability, function and dynamics of microbial communities. In recent years various approaches have been used to reveal microbial interaction networks from metagenomic sequencing data, such as time-series analysis, machine learning and statistical techniques. Despite these efforts it is still not possible to capture details of the ecological interactions behind complex microbial dynamics.We developed the sparse S-map method (SSM), which generates a sparse interaction network from a multivariate ecological time-series without presuming any mathematical formulation for the underlying microbial processes. The advantage of the SSM over alternative methodologies is that it fully utilizes the observed data using a framework of empirical dynamic modelling. This makes the SSM robust to non-equilibrium dynamics and underlying complexity (nonlinearity) in microbial processes.We showed that an increase in dataset size or a decrease in observational error improved the accuracy of SSM whereas, the accuracy of a comparative equation-based method was almost unchanged for both cases and equivalent to the SSM at best. Hence, the SSM outperformed a comparative equation-based method when datasets were large and the magnitude of observational errors were small. The results were robust to the magnitude of process noise and the functional forms of inter-specific interactions that we tested. We applied the method to a microbiome data of six mice and found that there were different microbial interaction regimes between young to middle age (4-40 week-old) and middle to old age (36-72 week-old) mice.The complexity of microbial relationships impedes detailed equation-based modeling. Our method provides a powerful alternative framework to infer ecological interaction networks of microbial communities in various environments and will be improved by further developments in metagenomics sequencing technologies leading to increased dataset size and improved accuracy and precision.

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Анализ навигационной аварии с использованием аппарата современной теории катастроф

MORSKIE INTELLEKTUAL`NYE TEHNOLOGII ◽

10.37220/mit.2020.47.1.052 ◽

2020 ◽

Author(s):

I.A. Yanchin

Keyword(s):

Dynamic Characteristic ◽

Catastrophe Theory ◽

Evolutionary Dynamics ◽

Geometric Interpretation ◽

Estimation Technique ◽

Long Term Effects ◽

The Future ◽

Over Time

В работе рассматривается метод оценки маршрута судна с точки зрения безопасности и оптимальности. Оценка выполняется за счёт вычисления характеристических коэффициентов, которые описывают свойства маршрута, важные для оценки этих качеств. Также в работе даётся описание метода прогнозирования дальнейшего маршрута судна на основе предписанного маршрута рейса и информации о существующем отклонении фактического маршрута от предписанного. На основе прогнозирования дальнейшего маршрута, и с использованием характеристических коэффициентов, становится возможным анализ долгосрочного влияния отклонения фактического маршрута от предписанного. Маршрут рассматривается как динамическая характеристика судна, эволюционирующая с течением времени. Обсуждаются особенности развития маршрута судна при стремлении к стабильному либо катастрофичному состоянию с позиций современной теории катастроф. Геометрическая интерпретация эволюционной динамики предусматривает использование когнитивных образов. В работе приводится анализ произошедшей аварии круизного судна Costa Concordia с использованием предложенного метода с целью исследования влияния изменения маршрута на безопасность рейса. Выполняется сравнение предписанного маршрута с фактическим.The paper presents a method to analyze a ships route in terms of safety and optimality. The route is examined using the characteristic coefficients that represent routes properties essential for assessment of these aspects. The paper also presents a method to estimate the future route based on the prescribed route and the existing discrepancy between the actual route and the prescribed one. With such estimation technique and using the characteristic coefficients it is possible to determine the long-term effects of the discrepancy. The route is considered as a dynamic characteristic of the ship, evolving over time. Peculiarities of the development of the ships route when striving for a stable or catastrophic state from the standpoint of the modern catastrophe theory are discussed. A geometric interpretation of evolutionary dynamics involves the use of cognitive images. As an example, the paper provides analysis of the Costa Concordias accident using the described method in order to determine the effects of the route discrepancy. A comparison is made of the prescribed route with the actual one that the crew planned to move.

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Toward a dynamical understanding of microbial communities

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2019.0248 ◽

2020 ◽

Vol 375 (1798) ◽

pp. 20190248 ◽

Cited By ~ 4

Author(s):

Paul B. Rainey ◽

Steven D. Quistad

Keyword(s):

Microbial Community ◽

Horizontal Gene Transfer ◽

Microbial Communities ◽

Dynamic Systems ◽

Evolutionary Dynamics ◽

Microbial Community Composition ◽

System Function ◽

Theme Issue ◽

Microbial Community Ecology

The challenge of moving beyond descriptions of microbial community composition to the point where understanding underlying eco-evolutionary dynamics emerges is daunting. While it is tempting to simplify through use of model communities composed of a small number of types, there is a risk that such strategies fail to capture processes that might be specific and intrinsic to complexity of the community itself. Here, we describe approaches that embrace this complexity and show that, in combination with metagenomic strategies, dynamical insight is increasingly possible. Arising from these studies is mounting evidence of rapid eco-evolutionary change among lineages and a sense that processes, particularly those mediated by horizontal gene transfer, not only are integral to system function, but are central to long-term persistence. That such dynamic, systems-level insight is now possible, means that the study and manipulation of microbial communities can move to new levels of inquiry. This article is part of the theme issue ‘Conceptual challenges in microbial community ecology’.

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Phenotypic and molecular evolution across 10,000 generations in laboratory budding yeast populations

10.1101/2020.10.09.330191 ◽

2020 ◽

Author(s):

Milo S. Johnson ◽

Shreyas Gopalakrishnan ◽

Juhee Goyal ◽

Megan E. Dillingham ◽

Christopher W. Bakerlee ◽

...

Keyword(s):

Experimental Evolution ◽

Evolutionary Dynamics ◽

Evolutionary Process ◽

Mutation Rates ◽

Historical Contingency ◽

E Coli ◽

Term Evolution ◽

Changes Over Time ◽

Over Time

AbstractLaboratory experimental evolution provides a window into the details of the evolutionary process. To investigate the consequences of long-term adaptation, we evolved 205 S. cerevisiae populations (124 haploid and 81 diploid) for ∼10,000 generations in three environments. We measured the dynamics of fitness changes over time, finding repeatable patterns of declining adaptability. Sequencing revealed that this phenotypic adaptation is coupled with a steady accumulation of mutations, widespread genetic parallelism, and historical contingency. In contrast to long-term evolution in E. coli, we do not observe long-term coexistence or populations with highly elevated mutation rates. We find that evolution in diploid populations involves both fixation of heterozygous mutations and frequent loss-of-heterozygosity events. Together, these results help distinguish aspects of evolutionary dynamics that are likely to be general features of adaptation across many systems from those that are specific to individual organisms and environmental conditions.

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Trait Evolution in Microbial Communities

10.1101/2020.12.15.422935 ◽

2020 ◽

Author(s):

Lihong Zhao ◽

Benjamin J Ridenhour ◽

Christopher H Remien

Keyword(s):

Microbial Communities ◽

Population Dynamic ◽

Population Genetic ◽

Evolutionary Dynamics ◽

Relative Fitness ◽

Dynamic Parameters ◽

Population Dynamic Model ◽

Host Health ◽

And Shifts ◽

Over Time

Understanding the evolutionary dynamics of microbial communities is a key step towards the goal of predicting and manipulating microbiomes to promote beneficial states. While interactions within microbiomes and between microbes and their environment collectively determine the community composition and population dynamics, we are often concerned with traits or functions of a microbiome that link more directly to host health. To study how traits of a microbiome are impacted by eco-evolutionary dynamics, we recast a classic resource-mediated population dynamic model into a population genetic framework which incorporates traits. The relative fitness of each group of microbes can be explicitly written in terms of population dynamic parameters, and corresponding evolutionary dynamics emerge. Using several example systems, we demonstrate how natural selection, mutation, and shifts in the environment work together to produce changes in traits over time.

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Species matter for predicting the functioning of evolving microbial communities

10.1101/666685 ◽

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.

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Individual differences determine the strength of ecological interactions

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2000635117 ◽

2020 ◽

Vol 117 (29) ◽

pp. 17068-17073 ◽

Cited By ~ 1

Author(s):

Jason I. Griffiths ◽

Dylan Z. Childs ◽

Ronald D. Bassar ◽

Tim Coulson ◽

David N. Reznick ◽

...

Keyword(s):

Body Size ◽

Evolutionary Dynamics ◽

Size Structure ◽

Empirical Studies ◽

Biotic Interactions ◽

Ecological Interactions ◽

Intraspecific Interactions ◽

Different Populations ◽

The Impact

Biotic interactions are central to both ecological and evolutionary dynamics. In the vast majority of empirical studies, the strength of intraspecific interactions is estimated by using simple measures of population size. Biologists have long known that these are crude metrics, with experiments and theory suggesting that interactions between individuals should depend on traits, such as body size. Despite this, it has been difficult to estimate the impact of traits on competitive ability from ecological field data, and this explains why the strength of biotic interactions has empirically been treated in a simplistic manner. Using long-term observational data from four different populations, we show that large Trinidadian guppies impose a significantly larger competitive pressure on conspecifics than individuals that are smaller; in other words, competition is asymmetric. When we incorporate this asymmetry into integral projection models, the predicted size structure is much closer to what we see in the field compared with models where competition is independent of body size. This difference in size structure translates into a twofold difference in reproductive output. This demonstrates how the nature of ecological interactions drives the size structure, which, in turn, will have important implications for both the ecological and evolutionary dynamics.

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Steering ecological-evolutionary dynamics during artificial selection of microbial communities

10.1101/264697 ◽

2018 ◽

Cited By ~ 4

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.

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Phenotypic and molecular evolution across 10,000 generations in laboratory budding yeast populations

eLife ◽

10.7554/elife.63910 ◽

2021 ◽

Vol 10 ◽

Author(s):

Milo S Johnson ◽

Shreyas Gopalakrishnan ◽

Juhee Goyal ◽

Megan E Dillingham ◽

Christopher W Bakerlee ◽

...

Keyword(s):

Experimental Evolution ◽

Evolutionary Dynamics ◽

Evolutionary Process ◽

Mutation Rates ◽

Historical Contingency ◽

E Coli ◽

Term Evolution ◽

Changes Over Time ◽

Over Time

Laboratory experimental evolution provides a window into the details of the evolutionary process. To investigate the consequences of long-term adaptation, we evolved 205 Saccharomyces cerevisiae populations (124 haploid and 81 diploid) for ~10,000 generations in three environments. We measured the dynamics of fitness changes over time, finding repeatable patterns of declining adaptability. Sequencing revealed that this phenotypic adaptation is coupled with a steady accumulation of mutations, widespread genetic parallelism, and historical contingency. In contrast to long-term evolution in E. coli, we do not observe long-term coexistence or populations with highly elevated mutation rates. We find that evolution in diploid populations involves both fixation of heterozygous mutations and frequent loss-of-heterozygosity events. Together, these results help distinguish aspects of evolutionary dynamics that are likely to be general features of adaptation across many systems from those that are specific to individual organisms and environmental conditions.

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Predominance of Anaerobic, Spore-Forming Bacteria in Metabolically Active Microbial Communities from Ancient Siberian Permafrost

Applied and Environmental Microbiology ◽

10.1128/aem.00560-19 ◽

2019 ◽

Vol 85 (15) ◽

Cited By ~ 6

Author(s):

Renxing Liang ◽

Maggie Lau ◽

Tatiana Vishnivetskaya ◽

Karen G. Lloyd ◽

Wei Wang ◽

...

Keyword(s):

Microbial Community ◽

Microbial Diversity ◽

Microbial Communities ◽

Aspartic Acid ◽

Early Pleistocene ◽

Metagenomic Sequencing ◽

Metabolic Functions ◽

Northeastern Siberia ◽

Spore Forming Bacteria

ABSTRACTThe prevalence of microbial life in permafrost up to several million years (Ma) old has been well documented. However, the long-term survivability, evolution, and metabolic activity of the entombed microbes over this time span remain underexplored. We integrated aspartic acid (Asp) racemization assays with metagenomic sequencing to characterize the microbial activity, phylogenetic diversity, and metabolic functions of indigenous microbial communities across a ∼0.01- to 1.1-Ma chronosequence of continuously frozen permafrost from northeastern Siberia. Although Asp in the older bulk sediments (0.8 to 1.1 Ma) underwent severe racemization relative to that in the youngest sediment (∼0.01 Ma), the much lowerd-Asp/l-Asp ratio (0.05 to 0.14) in the separated cells from all samples suggested that indigenous microbial communities were viable and metabolically active in ancient permafrost up to 1.1 Ma. The microbial community in the youngest sediment was the most diverse and was dominated by the phylaActinobacteriaandProteobacteria. In contrast, microbial diversity decreased dramatically in the older sediments, and anaerobic, spore-forming bacteria withinFirmicutesbecame overwhelmingly dominant. In addition to the enrichment of sporulation-related genes, functional genes involved in anaerobic metabolic pathways such as fermentation, sulfate reduction, and methanogenesis were more abundant in the older sediments. Taken together, the predominance of spore-forming bacteria and associated anaerobic metabolism in the older sediments suggest that a subset of the original indigenous microbial community entrapped in the permafrost survived burial over geological time.IMPORTANCEUnderstanding the long-term survivability and associated metabolic traits of microorganisms in ancient permafrost frozen millions of years ago provides a unique window into the burial and preservation processes experienced in general by subsurface microorganisms in sedimentary deposits because of permafrost’s hydrological isolation and exceptional DNA preservation. We employed aspartic acid racemization modeling and metagenomics to determine which microbial communities were metabolically active in the 1.1-Ma permafrost from northeastern Siberia. The simultaneous sequencing of extracellular and intracellular genomic DNA provided insight into the metabolic potential distinguishing extinct from extant microorganisms under frozen conditions over this time interval. This in-depth metagenomic sequencing advances our understanding of the microbial diversity and metabolic functions of extant microbiomes from early Pleistocene permafrost. Therefore, these findings extend our knowledge of the survivability of microbes in permafrost from 33,000 years to 1.1 Ma.

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