scholarly journals Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems

2018 ◽  
Author(s):  
Alan R. Pacheco ◽  
Mauricio Moel ◽  
Daniel Segrè
Keyword(s):  
Microbial Interactions ◽  
Fitness Cost ◽  
Microbial Consortia ◽  
Interspecies Interactions ◽  
Large Space ◽  
Microbial Ecosystems ◽  
Scale Models ◽  
Genome Scale ◽  
Synthetic Microbial Consortia ◽  
Metabolic Exchange

ABSTRACTMetabolic exchange can mediate beneficial interactions among microbes, helping explain diversity in microbial communities. These interactions are often assumed to involve a fitness cost, prompting questions on how cooperative phenotypes can be stable and withstand the emergence of cheaters. Here we use genome-scale models of metabolism to investigate whether a radically different scenario, the pervasive release of “costless” metabolites (i.e. those that cause no fitness cost to the producing organism), can serve as a prominent mechanism for inter-microbial interactions. By carrying out over 1 million pairwise growth simulations for 14 microbial species in a combinatorial assortment of environmental conditions, we find that there is indeed a large space of metabolites that can be secreted at no cost, which can generate ample cross-feeding opportunities. In addition to providing an atlas of putative costless interdependencies, our modeling also demonstrates that oxygen availability significantly enhances mutualistic interactions by providing more opportunities for metabolic exchange through costless metabolites, resulting in an over-representation of specific ecological network motifs. In addition to helping explain natural diversity, we show how the exchange of costless metabolites can facilitate the engineering of stable synthetic microbial consortia.

Characterization of a designed synthetic autotrophic–heterotrophic consortia for fixing CO2 without light

RSC Advances ◽  
2016 ◽  
Vol 6 (81) ◽  
pp. 78161-78169 ◽  
Author(s):  
Jiajun Hu ◽  
Yiyun Xue ◽  
Jixiang Li ◽  
Lei Wang ◽  
Shiping Zhang ◽  
...  
Keyword(s):  
Microbial Interactions ◽  
Microbial Consortia ◽  
Synthetic Microbial Consortia

CO2 fixation efficiency of the devised synthetic microbial consortia with both autotrophic–autotrophic and autotrophic–heterotrophic microbial interactions were higher than the sum of theoretical CO2 fixation efficiency of the microbial components.

scholarly journals Cooperation Enhances Robustness of Coexistence in Spatially Structured Consortia

2018 ◽  
Author(s):  
Xinying Ren ◽  
Richard M. Murray
Keyword(s):  
Reaction Diffusion ◽  
Population Level ◽  
Microbial Consortia ◽  
Interspecies Interactions ◽  
Major Goal ◽  
E Coli ◽  
Cooperative Interactions ◽  
Multiple Species ◽  
The Stability ◽  
Synthetic Microbial Consortia

AbstractDesigning synthetic microbial consortia is an emerging area in synthetic biology and a major goal is to realize stable and robust coexistence of multiple species. Co-operation and competition are fundamental intra/interspecies interactions that shape population level behaviors, yet it is not well-understood how these interactions affect the stability and robustness of coexistence. In this paper, we show that communities with cooperative interactions are more robust to population disturbance, e.g., depletion by antibiotics, by forming intermixed spatial patterns. Meanwhile, competition leads to population spatial heterogeneity and more fragile coexistence in communities. Using reaction-diffusion and nonlocal PDE models and simulations of a two-species E. coli consortium, we demonstrate that cooperation is more beneficial than competition in maintaining coexistence in spatially structured consortia, but not in well-mixed environments. This also suggests a trade-off between constructing heterogeneous communities with localized functions and maintaining robust coexistence. The results provide general strategies for engineering spatially structured consortia by designing interspecies interactions and suggest the importance of cooperation for biodiversity in microbial community.

scholarly journals The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

2018 ◽  
Vol 35 (13) ◽  
pp. 2332-2334 ◽  
Author(s):  
Federico Baldini ◽  
Almut Heinken ◽  
Laurent Heirendt ◽  
Stefania Magnusdottir ◽  
Ronan M T Fleming ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Microbial Interactions ◽  
Microbial Genome ◽  
Metagenomic Data ◽  
Metabolic Interactions ◽  
Constraint Based Modeling ◽  
Genome Scale

Abstract Motivation The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. Results To address this gap, we created a comprehensive toolbox to model (i) microbe–microbe and host–microbe metabolic interactions, and (ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the constraint-based reconstruction and analysis toolbox. Availability and implementation The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.

scholarly journals Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

2020 ◽  
Author(s):  
Colton J. Lloyd ◽  
Jonathan Monk ◽  
Laurence Yang ◽  
Ali Ebrahim ◽  
Bernhard O. Palsson
Keyword(s):  
Amino Acids ◽  
Growth Conditions ◽  
Anaerobic Growth ◽  
Metabolic Phenotype ◽  
E Coli ◽  
Scale Models ◽  
Protein Components ◽  
K 12 ◽  
Genome Scale ◽  
Prosthetic Groups

AbstractSustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated genome-scale models of metabolism and gene expression (ME-models) have the unique ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we use the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME model mostly agree with the standard biomass objective function used in models of metabolism alone (M models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of prebiotic amino acids in the proteins used to sustain anaerobic growth (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

scholarly journals A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems

PLoS ONE ◽  
2017 ◽  
Vol 12 (2) ◽  
pp. e0171744 ◽  
Author(s):  
Marko Budinich ◽  
Jérémie Bourdon ◽  
Abdelhalim Larhlimi ◽  
Damien Eveillard
Keyword(s):  
Multi Objective ◽  
Microbial Ecosystems ◽  
Genome Scale

scholarly journals Deciphering the metabolic capabilities of Bifidobacteria using genome-scale metabolic models

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
N. T. Devika ◽  
Karthik Raman
Keyword(s):  
Short Chain Fatty Acids ◽  
Acetate Production ◽  
Scale Models ◽  
Powerful Approach ◽  
Metabolic Models ◽  
Commercial Applications ◽  
Different Strains ◽  
Genome Scale ◽  
Modelling Approach

AbstractBifidobacteria, the initial colonisers of breastfed infant guts, are considered as the key commensals that promote a healthy gastrointestinal tract. However, little is known about the key metabolic differences between different strains of these bifidobacteria, and consequently, their suitability for their varied commercial applications. In this context, the present study applies a constraint-based modelling approach to differentiate between 36 important bifidobacterial strains, enhancing their genome-scale metabolic models obtained from the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. By studying various growth and metabolic capabilities in these enhanced genome-scale models across 30 different nutrient environments, we classified the bifidobacteria into three specific groups. We also studied the ability of the different strains to produce short-chain fatty acids, finding that acetate production is niche- and strain-specific, unlike lactate. Further, we captured the role of critical enzymes from the bifid shunt pathway, which was found to be essential for a subset of bifidobacterial strains. Our findings underline the significance of analysing metabolic capabilities as a powerful approach to explore distinct properties of the gut microbiome. Overall, our study presents several insights into the nutritional lifestyles of bifidobacteria and could potentially be leveraged to design species/strain-specific probiotics or prebiotics.

scholarly journals Majority sensing in synthetic microbial consortia

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Razan N. Alnahhas ◽  
Mehdi Sadeghpour ◽  
Ye Chen ◽  
Alexis A. Frey ◽  
William Ott ◽  
...  
Keyword(s):  
Microbial Consortia ◽  
Synthetic Microbial Consortia
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