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Metabolic evolution and the self-organization of ecosystems.

ABSTRACT: Metabolism mediates the flow of matter and energy through the biosphere. We examined how metabolic evolution shapes ecosystems by reconstructing it in the globally abundant oceanic phytoplankter Prochlorococcus To understand what drove observed evolutionary patterns, we interpreted them in the context of its population dynamics, growth rate, and light adaptation, and the size and macromolecular and elemental composition of cells. This multilevel view suggests that, over the course of evolution, there was a steady increase in Prochlorococcus' metabolic rate and excretion of organic carbon. We derived a mathematical framework that suggests these adaptations lower the minimal subsistence nutrient concentration of cells, which results in a drawdown of nutrients in oceanic surface waters. This, in turn, increases total ecosystem biomass and promotes the coevolution of all cells in the ecosystem. Additional reconstructions suggest that Prochlorococcus and the dominant cooccurring heterotrophic bacterium SAR11 form a coevolved mutualism that maximizes their collective metabolic rate by recycling organic carbon through complementary excretion and uptake pathways. Moreover, the metabolic codependencies of Prochlorococcus and SAR11 are highly similar to those of chloroplasts and mitochondria within plant cells. These observations lead us to propose a general theory relating metabolic evolution to the self-amplification and self-organization of the biosphere. We discuss the implications of this framework for the evolution of Earth's biogeochemical cycles and the rise of atmospheric oxygen.

SUBMITTER: Braakman R

PROVIDER: S-EPMC5393222 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Metabolic evolution and the self-organization of ecosystems.

Braakman Rogier R Follows Michael J MJ Chisholm Sallie W SW

Proceedings of the National Academy of Sciences of the United States of America 20170327 15

Metabolism mediates the flow of matter and energy through the biosphere. We examined how metabolic evolution shapes ecosystems by reconstructing it in the globally abundant oceanic phytoplankter <i>Prochlorococcus</i> To understand what drove observed evolutionary patterns, we interpreted them in the context of its population dynamics, growth rate, and light adaptation, and the size and macromolecular and elemental composition of cells. This multilevel view suggests that, over the course of evol ...[more]

PMID: 28348231

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Project description:BackgroundComparison of metabolic networks across species is a key to understanding how evolutionary pressures shape these networks. By selecting taxa representative of different lineages or lifestyles and using a comprehensive set of descriptors of the structure and complexity of their metabolic networks, one can highlight both qualitative and quantitative differences in the metabolic organization of species subject to distinct evolutionary paths or environmental constraints.ResultsWe used a novel representation of metabolic networks, termed network of interacting pathways or NIP, to focus on the modular, high-level organization of the metabolic capabilities of the cell. Using machine learning techniques we identified the most relevant aspects of cellular organization that change under evolutionary pressures. We considered the transitions from prokarya to eukarya (with a focus on the transitions among the archaea, bacteria and eukarya), from unicellular to multicellular eukarya, from free living to host-associated bacteria, from anaerobic to aerobic, as well as the acquisition of cell motility or growth in an environment of various levels of salinity or temperature. Intuitively, we expect organisms with more complex lifestyles to have more complex and robust metabolic networks. Here we demonstrate for the first time that such organisms are not only characterized by larger, denser networks of metabolic pathways but also have more efficiently organized cross communications, as revealed by subtle changes in network topology. These changes are unevenly distributed among metabolic pathways, with specific categories of pathways being promoted to more central locations as an answer to environmental constraints.ConclusionsCombining methods from graph theory and machine learning, we have shown here that evolutionary pressures not only affects gene and protein sequences, but also specific details of the complex wiring of functional modules in the cell. This approach allows the identification and quantification of those changes, and provides an overview of the evolution of intracellular systems.

Project description:BACKGROUND: Resource cycling is a defining process in the maintenance of the biosphere. Microbial communities, ranging from simple to highly diverse, play a crucial role in this process. Yet the evolutionary adaptation and speciation of micro-organisms have rarely been studied in the context of resource cycling. In this study, our basic questions are how does a community evolve its resource usage and how are resource cycles partitioned? RESULTS: We design a computational model in which a population of individuals evolves to take up nutrients and excrete waste. The waste of one individual is another's resource. Given a fixed amount of resources, this leads to resource cycles. We find that the shortest cycle dominates the ecological dynamics, and over evolutionary time its length is minimized. Initially a single lineage processes a long cycle of resources, later crossfeeding lineages arise. The evolutionary dynamics that follow are determined by the strength of indirect selection for resource cycling. We study indirect selection by changing the spatial setting and the strength of direct selection. If individuals are fixed at lattice sites or direct selection is low, indirect selection result in lineages that structure their local environment, leading to 'smart' individuals and stable patterns of resource dynamics. The individuals are good at cycling resources themselves and do this with a short cycle. On the other hand, if individuals randomly change position each time step, or direct selection is high, individuals are more prone to crossfeeding: an ecosystem based solution with turbulent resource dynamics, and individuals that are less capable of cycling resources themselves. CONCLUSION: In a baseline model of ecosystem evolution we demonstrate different eco-evolutionary trajectories of resource cycling. By varying the strength of indirect selection through the spatial setting and direct selection, the integration of information by the evolutionary process leads to qualitatively different results from individual smartness to cooperative community structures.

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Metabolic evolution and the self-organization of ecosystems.

Publications

Metabolic evolution and the self-organization of ecosystems.

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