Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics - PubMed (original) (raw)

. 2014 May 22;7(4):1104-15.

doi: 10.1016/j.celrep.2014.03.070. Epub 2014 May 1.

William J Riehl 2, Ilija Dukovski 2, Brian R Granger 2, Alex Betts 1, Alex H Lang 3, Gracia Bonilla 2, Amrita Kar 2, Nicholas Leiby 4, Pankaj Mehta 5, Christopher J Marx 6, Daniel Segrè 7

Affiliations

Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics

William R Harcombe et al. Cell Rep. 2014.

Abstract

The interspecies exchange of metabolites plays a key role in the spatiotemporal dynamics of microbial communities. This raises the question of whether ecosystem-level behavior of structured communities can be predicted using genome-scale metabolic models for multiple organisms. We developed a modeling framework that integrates dynamic flux balance analysis with diffusion on a lattice and applied it to engineered communities. First, we predicted and experimentally confirmed the species ratio to which a two-species mutualistic consortium converges and the equilibrium composition of a newly engineered three-member community. We next identified a specific spatial arrangement of colonies, which gives rise to what we term the "eclipse dilemma": does a competitor placed between a colony and its cross-feeding partner benefit or hurt growth of the original colony? Our experimentally validated finding that the net outcome is beneficial highlights the complex nature of metabolic interactions in microbial communities while at the same time demonstrating their predictability.

Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

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Figures

Figure 1

Figure 1

A schematic representation of the key steps of COMETS simulations, from the level of individual boxes (top) to a whole lattice (bottom). Within each box, dFBA is solved for each species, with uptake set by Michaelis-Menten equations (top right). These calculations amount to piecewise linear approximations of the growth and environmental metabolites as a function of time. Classical discretization of the diffusion equation gives local rules for updating biomass and nutrients in each box (middle). The ensuing algorithm computes ecosystem dynamics (bottom) as a function of intracellular metabolism of individual species.

Figure 2

Figure 2

COMETS predictions of E. coli colony growth on various carbon sources. A) COMETS predicts that colony diameter increases linearly on glucose (diamonds), lactate (squares) and acetate (triangles). B) COMETS predictions of the rate of colony expansion (white bars) compared to the values reported by Wimpenny (black bars, no error estimate available). Predicted colony expansion on glucose was 16.7% slower than observed, while predicted growth on lactate and acetate deviated by 2.7% and 2.2% respectively.

Figure 3

Figure 3

COMETS predictions of growth for a two-species synthetic consortium. A) The consortium consists of a mutant S. enterica that provides methionine to an auxotrophic E. coli, obtaining carbon byproducts in return. B) COMETS correctly predicts that co-culture is necessary for growth on lactose minimal medium. C) Predicted and observed species frequencies before and after 48 hours of growth. Blue bars correspond to E. coli, red bars to S. enterica. COMETS ratios (left) represent biomass; observed values (right) are based on CFU. Error bars are standard deviations.

Figure 4

Figure 4

COMETS predictions of growth for a novel 3-species consortium. A) A mutant M. extorquens AM1 was added to the 2-species system. The M. extorquens lacks hprA, so it relies on carbon from E. coli, while providing the other two species with a source of nitrogen. B) COMETS correctly predicts that all three members of the consortium are necessary for growth on lactose/methylamine minimal medium. C) Species frequencies before and after 5 transfers. Blue bars correspond to E. coli, red bars to S. enterica and green bars to M. extorquens. COMETS ratios (left) represent biomass; observed values (right) are based on CFU. Error bars are standard deviations.

Figure 5

Figure 5

The impact of competition on focal colony growth. A) Naïve expectation that growth of focal S. enterica colonies (red circles next to numerals) would be reduced by placement of a competitor between the focal S. enterica and its obligate mutualistic partner E. coli (blue circles). Relative anticipated growth is represented by grey arrows, methionine diffusing from S. enterica colonies is displayed in red, carbon byproducts diffusing from E. coli are displayed in blue. B) COMETS predicted that wildtype S. enterica between E. coli and the focal colony would reduce its growth (line ii) as compared to the no competitor scenario (line i). However, if a second colony of the same methionine-excreting S. enterica is placed in the middle, it increases growth of the distal colony (line iii). C) Growth of the distal colonies standardized to scenario i for the case with no competitor (i), wildtype competitor (ii), and mutualistic competitor (iii). COMETS ratios (left three bars) represent biomass; experimental values (right three bars) are based on CFU. Error bars are standard deviations. D) Acetate uptake of distal colonies i and iii in COMETS. The fraction of acetate is the total uptake of the distal colony divided by the total acetate excretion of its partner. The amount of acetate is the total moles taken up by each colony during the first 89 hours (i.e. before any E. coli start to utilize acetate).

Figure 6

Figure 6

Heatmaps of the spatial distributions of exchange fluxes (left-side, 3 by 5 set of heatmaps), metabolite concentrations (right-side, copper-toned, 3 by 5 set of heatmaps) and growth rates (top-right, gray-shaded heatmaps) are shown at different time points during the “metabolic eclipse” simulation described in Fig. 5. The legend in the top-left corner shows the relative positions of the simulated colonies (E = E. coli; S = S. enterica). Fluxes (left) are scaled from excretion (blue) to uptake (red) for each lattice box at each of five time points for three key metabolites (acetate, methionine and oxygen). Metabolite concentrations scale from low (dark) to high (bright). Fluxes are normalized across all time points; metabolites are normalized within each time point (to make early low concentration levels visible).

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