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An Economic Framework of Microbial Trade.

Tasoff J, Mee MT, Wang HH - PLoS ONE (2015)

Bottom Line: Our biotic GET (BGET) model provides an a priori theory of the growth benefits of microbial trade, yielding several novel insights relevant to understanding microbial ecology and engineering synthetic communities.Furthermore, we find that species engaged in trade exhibit a fundamental tradeoff between growth rate and relative population abundance, and that different environments that put greater pressure on group selection versus individual selection will promote varying strategies along this growth-abundance spectrum.This framework provides a foundation to study natural and engineered microbial communities through a new lens based on economic theories developed over the past century.

View Article: PubMed Central - PubMed

Affiliation: Department of Economics, Claremont Graduate University, Claremont, California, United States of America.

ABSTRACT
A large fraction of microbial life on earth exists in complex communities where metabolic exchange is vital. Microbes trade essential resources to promote their own growth in an analogous way to countries that exchange goods in modern economic markets. Inspired by these similarities, we developed a framework based on general equilibrium theory (GET) from economics to predict the population dynamics of trading microbial communities. Our biotic GET (BGET) model provides an a priori theory of the growth benefits of microbial trade, yielding several novel insights relevant to understanding microbial ecology and engineering synthetic communities. We find that the economic concept of comparative advantage is a necessary condition for mutualistic trade. Our model suggests that microbial communities can grow faster when species are unable to produce essential resources that are obtained through trade, thereby promoting metabolic specialization and increased intercellular exchange. Furthermore, we find that species engaged in trade exhibit a fundamental tradeoff between growth rate and relative population abundance, and that different environments that put greater pressure on group selection versus individual selection will promote varying strategies along this growth-abundance spectrum. We experimentally tested this tradeoff using a synthetic consortium of Escherichia coli cells and found the results match the predictions of the model. This framework provides a foundation to study natural and engineered microbial communities through a new lens based on economic theories developed over the past century.

No MeSH data available.


Related in: MedlinePlus

Experimental measurement of growth-abundance tradeoffs.All data are averages from experiments in biological replicates (n = 3). (a) Auxotrophic E. coli strains ∆R and ∆F grow by exchange of metabolites arginine R and phenylalanine F in a co-culture (black growth curve). Bar graph shows 24-hr population density of co-culture variants with R and F amino acid exporters argO and yddG respectively. (b) Growth rate, 24-hr cell density, and population ratio (top, middle, bottom panels) of co-cultures of ∆R-xF and ∆F-xR where argO expression is increased on a relative scale of 0 to 1. Asterisks highlight statistical significance (p<0.001 by t-test). Error bars indicate standard deviations of data acquired in biological replicates (n = 3).
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pone.0132907.g006: Experimental measurement of growth-abundance tradeoffs.All data are averages from experiments in biological replicates (n = 3). (a) Auxotrophic E. coli strains ∆R and ∆F grow by exchange of metabolites arginine R and phenylalanine F in a co-culture (black growth curve). Bar graph shows 24-hr population density of co-culture variants with R and F amino acid exporters argO and yddG respectively. (b) Growth rate, 24-hr cell density, and population ratio (top, middle, bottom panels) of co-cultures of ∆R-xF and ∆F-xR where argO expression is increased on a relative scale of 0 to 1. Asterisks highlight statistical significance (p<0.001 by t-test). Error bars indicate standard deviations of data acquired in biological replicates (n = 3).

Mentions: To further explore the growth-relative-abundance tradeoff predicted by BGET, we developed a simple experimental bacterial consortium using two auxotrophic E. coli strains that engage in metabolic exchange. Each strain is unable to synthesize an essential amino acid (phenylalanine F or arginine R) and thus cannot grow individually in minimal media (M9-glucose). However, both auxotrophic strains (designated ΔF or ΔR), when combined, are able to grow by intercellular metabolic exchange of F and R (Fig 6A). To modulate the degree of metabolic exchange, we built ΔF and ΔR variants that increase their amino acid contributions by activating specific amino acid efflux pumps. The ΔF-xR strain possesses increased R export through a tunable argO gene [40] while the ΔR-xF strain possesses increased F export through the yddG gene [41]. Co-cultures of these variants showed significant increases in growth rate when compared to their ancestral strains (ΔF and ΔR) (Fig 6A). We then systematically generated ΔF-xR variants with increased R contribution by tuning argO gene expression (see Methods). While using a ΔR-xF strain with a constant F contribution, we separately co-cultured this ΔR-xF with each of the ΔF-xR variants and measured the population growth rate and relative abundance of each species. We find that increasing R contribution by ΔF-xR variants significantly increased the overall population growth rate up to a peak before declining, but always reduced the relative population abundance of the ΔF-xR variant (Fig 6B). We therefore confirmed the same type of grow-relative-abundance tradeoffs in our experimental system as what was predicted by the BGET model. At low contribution rates, any increase in contribution leads to an initial increased population growth, but higher contribution rates always lead to a lower relative abundance and a lower overall growth rate. Interestingly, we also find an initial tradeoff between relative and absolute abundance. For example, even though ΔF-xR relative abundance at contribution level 0.1 is lower than at contribution level ~0 (relative abundance of 59% vs. 73%), its absolute abundance is actually higher (3.8x108 vs. 1.4x108 cells after 24 hours).


An Economic Framework of Microbial Trade.

Tasoff J, Mee MT, Wang HH - PLoS ONE (2015)

Experimental measurement of growth-abundance tradeoffs.All data are averages from experiments in biological replicates (n = 3). (a) Auxotrophic E. coli strains ∆R and ∆F grow by exchange of metabolites arginine R and phenylalanine F in a co-culture (black growth curve). Bar graph shows 24-hr population density of co-culture variants with R and F amino acid exporters argO and yddG respectively. (b) Growth rate, 24-hr cell density, and population ratio (top, middle, bottom panels) of co-cultures of ∆R-xF and ∆F-xR where argO expression is increased on a relative scale of 0 to 1. Asterisks highlight statistical significance (p<0.001 by t-test). Error bars indicate standard deviations of data acquired in biological replicates (n = 3).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4519184&req=5

pone.0132907.g006: Experimental measurement of growth-abundance tradeoffs.All data are averages from experiments in biological replicates (n = 3). (a) Auxotrophic E. coli strains ∆R and ∆F grow by exchange of metabolites arginine R and phenylalanine F in a co-culture (black growth curve). Bar graph shows 24-hr population density of co-culture variants with R and F amino acid exporters argO and yddG respectively. (b) Growth rate, 24-hr cell density, and population ratio (top, middle, bottom panels) of co-cultures of ∆R-xF and ∆F-xR where argO expression is increased on a relative scale of 0 to 1. Asterisks highlight statistical significance (p<0.001 by t-test). Error bars indicate standard deviations of data acquired in biological replicates (n = 3).
Mentions: To further explore the growth-relative-abundance tradeoff predicted by BGET, we developed a simple experimental bacterial consortium using two auxotrophic E. coli strains that engage in metabolic exchange. Each strain is unable to synthesize an essential amino acid (phenylalanine F or arginine R) and thus cannot grow individually in minimal media (M9-glucose). However, both auxotrophic strains (designated ΔF or ΔR), when combined, are able to grow by intercellular metabolic exchange of F and R (Fig 6A). To modulate the degree of metabolic exchange, we built ΔF and ΔR variants that increase their amino acid contributions by activating specific amino acid efflux pumps. The ΔF-xR strain possesses increased R export through a tunable argO gene [40] while the ΔR-xF strain possesses increased F export through the yddG gene [41]. Co-cultures of these variants showed significant increases in growth rate when compared to their ancestral strains (ΔF and ΔR) (Fig 6A). We then systematically generated ΔF-xR variants with increased R contribution by tuning argO gene expression (see Methods). While using a ΔR-xF strain with a constant F contribution, we separately co-cultured this ΔR-xF with each of the ΔF-xR variants and measured the population growth rate and relative abundance of each species. We find that increasing R contribution by ΔF-xR variants significantly increased the overall population growth rate up to a peak before declining, but always reduced the relative population abundance of the ΔF-xR variant (Fig 6B). We therefore confirmed the same type of grow-relative-abundance tradeoffs in our experimental system as what was predicted by the BGET model. At low contribution rates, any increase in contribution leads to an initial increased population growth, but higher contribution rates always lead to a lower relative abundance and a lower overall growth rate. Interestingly, we also find an initial tradeoff between relative and absolute abundance. For example, even though ΔF-xR relative abundance at contribution level 0.1 is lower than at contribution level ~0 (relative abundance of 59% vs. 73%), its absolute abundance is actually higher (3.8x108 vs. 1.4x108 cells after 24 hours).

Bottom Line: Our biotic GET (BGET) model provides an a priori theory of the growth benefits of microbial trade, yielding several novel insights relevant to understanding microbial ecology and engineering synthetic communities.Furthermore, we find that species engaged in trade exhibit a fundamental tradeoff between growth rate and relative population abundance, and that different environments that put greater pressure on group selection versus individual selection will promote varying strategies along this growth-abundance spectrum.This framework provides a foundation to study natural and engineered microbial communities through a new lens based on economic theories developed over the past century.

View Article: PubMed Central - PubMed

Affiliation: Department of Economics, Claremont Graduate University, Claremont, California, United States of America.

ABSTRACT
A large fraction of microbial life on earth exists in complex communities where metabolic exchange is vital. Microbes trade essential resources to promote their own growth in an analogous way to countries that exchange goods in modern economic markets. Inspired by these similarities, we developed a framework based on general equilibrium theory (GET) from economics to predict the population dynamics of trading microbial communities. Our biotic GET (BGET) model provides an a priori theory of the growth benefits of microbial trade, yielding several novel insights relevant to understanding microbial ecology and engineering synthetic communities. We find that the economic concept of comparative advantage is a necessary condition for mutualistic trade. Our model suggests that microbial communities can grow faster when species are unable to produce essential resources that are obtained through trade, thereby promoting metabolic specialization and increased intercellular exchange. Furthermore, we find that species engaged in trade exhibit a fundamental tradeoff between growth rate and relative population abundance, and that different environments that put greater pressure on group selection versus individual selection will promote varying strategies along this growth-abundance spectrum. We experimentally tested this tradeoff using a synthetic consortium of Escherichia coli cells and found the results match the predictions of the model. This framework provides a foundation to study natural and engineered microbial communities through a new lens based on economic theories developed over the past century.

No MeSH data available.


Related in: MedlinePlus