Diagnosing and Predicting Mixed-Culture Fermentations with Unicellular and Guild-Based Metabolic Models

ABSTRACT Multispecies microbial communities determine the fate of materials in the environment and can be harnessed to produce beneficial products from renewable resources. In a recent example, fermentations by microbial communities have produced medium-chain fatty acids (MCFAs). Tools to predict, a...

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Autores principales:	Matthew J. Scarborough, Joshua J. Hamilton, Elizabeth A. Erb, Timothy J. Donohue, Daniel R. Noguera
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	carboxylate platform community modeling medium-chain carboxylic acids medium-chain fatty acids microbiome mixed-culture fermentation Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/f2d79ed970cc48f5a60454d2b59db2cc
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spelling	oai:doaj.org-article:f2d79ed970cc48f5a60454d2b59db2cc2021-12-02T18:44:36ZDiagnosing and Predicting Mixed-Culture Fermentations with Unicellular and Guild-Based Metabolic Models10.1128/mSystems.00755-202379-5077https://doaj.org/article/f2d79ed970cc48f5a60454d2b59db2cc2020-10-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00755-20https://doaj.org/toc/2379-5077ABSTRACT Multispecies microbial communities determine the fate of materials in the environment and can be harnessed to produce beneficial products from renewable resources. In a recent example, fermentations by microbial communities have produced medium-chain fatty acids (MCFAs). Tools to predict, assess, and improve the performance of these communities, however, are limited. To provide such tools, we constructed two metabolic models of MCFA-producing microbial communities based on available genomic, transcriptomic, and metabolomic data. The first model is a unicellular model (iFermCell215), while the second model (iFermGuilds789) separates fermentation activities into functional guilds. Ethanol and lactate are fermentation products known to serve as substrates for MCFA production, while acetate is another common cometabolite during MCFA production. Simulations with iFermCell215 predict that low molar ratios of acetate to ethanol favor MCFA production, whereas the products of lactate and acetate coutilization are less dependent on the acetate-to-lactate ratio. In simulations of an MCFA-producing community fed a complex organic mixture derived from lignocellulose, iFermGuilds789 predicted that lactate was an extracellular cometabolite that served as a substrate for butyrate (C4) production. Extracellular hexanoic (C6) and octanoic (C8) acids were predicted by iFermGuilds789 to be from community members that directly metabolize sugars. Modeling results provide several hypotheses that can improve our understanding of microbial roles in an MCFA-producing microbiome and inform strategies to increase MCFA production. Further, these models represent novel tools for exploring the role of mixed microbial communities in carbon recycling in the environment, as well as in beneficial reuse of organic residues. IMPORTANCE Microbiomes are vital to human health, agriculture, and protecting the environment. Predicting behavior of self-assembled or synthetic microbiomes, however, remains a challenge. In this work, we used unicellular and guild-based metabolic models to investigate production of medium-chain fatty acids by a mixed microbial community that is fed multiple organic substrates. Modeling results provided insights into metabolic pathways of three medium-chain fatty acid-producing guilds and identified potential strategies to increase production of medium-chain fatty acids. This work demonstrates the role of metabolic models in augmenting multi-omic studies to gain greater insights into microbiome behavior.Matthew J. ScarboroughJoshua J. HamiltonElizabeth A. ErbTimothy J. DonohueDaniel R. NogueraAmerican Society for Microbiologyarticlecarboxylate platformcommunity modelingmedium-chain carboxylic acidsmedium-chain fatty acidsmicrobiomemixed-culture fermentationMicrobiologyQR1-502ENmSystems, Vol 5, Iss 5 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	carboxylate platform community modeling medium-chain carboxylic acids medium-chain fatty acids microbiome mixed-culture fermentation Microbiology QR1-502
spellingShingle	carboxylate platform community modeling medium-chain carboxylic acids medium-chain fatty acids microbiome mixed-culture fermentation Microbiology QR1-502 Matthew J. Scarborough Joshua J. Hamilton Elizabeth A. Erb Timothy J. Donohue Daniel R. Noguera Diagnosing and Predicting Mixed-Culture Fermentations with Unicellular and Guild-Based Metabolic Models
description	ABSTRACT Multispecies microbial communities determine the fate of materials in the environment and can be harnessed to produce beneficial products from renewable resources. In a recent example, fermentations by microbial communities have produced medium-chain fatty acids (MCFAs). Tools to predict, assess, and improve the performance of these communities, however, are limited. To provide such tools, we constructed two metabolic models of MCFA-producing microbial communities based on available genomic, transcriptomic, and metabolomic data. The first model is a unicellular model (iFermCell215), while the second model (iFermGuilds789) separates fermentation activities into functional guilds. Ethanol and lactate are fermentation products known to serve as substrates for MCFA production, while acetate is another common cometabolite during MCFA production. Simulations with iFermCell215 predict that low molar ratios of acetate to ethanol favor MCFA production, whereas the products of lactate and acetate coutilization are less dependent on the acetate-to-lactate ratio. In simulations of an MCFA-producing community fed a complex organic mixture derived from lignocellulose, iFermGuilds789 predicted that lactate was an extracellular cometabolite that served as a substrate for butyrate (C4) production. Extracellular hexanoic (C6) and octanoic (C8) acids were predicted by iFermGuilds789 to be from community members that directly metabolize sugars. Modeling results provide several hypotheses that can improve our understanding of microbial roles in an MCFA-producing microbiome and inform strategies to increase MCFA production. Further, these models represent novel tools for exploring the role of mixed microbial communities in carbon recycling in the environment, as well as in beneficial reuse of organic residues. IMPORTANCE Microbiomes are vital to human health, agriculture, and protecting the environment. Predicting behavior of self-assembled or synthetic microbiomes, however, remains a challenge. In this work, we used unicellular and guild-based metabolic models to investigate production of medium-chain fatty acids by a mixed microbial community that is fed multiple organic substrates. Modeling results provided insights into metabolic pathways of three medium-chain fatty acid-producing guilds and identified potential strategies to increase production of medium-chain fatty acids. This work demonstrates the role of metabolic models in augmenting multi-omic studies to gain greater insights into microbiome behavior.
format	article
author	Matthew J. Scarborough Joshua J. Hamilton Elizabeth A. Erb Timothy J. Donohue Daniel R. Noguera
author_facet	Matthew J. Scarborough Joshua J. Hamilton Elizabeth A. Erb Timothy J. Donohue Daniel R. Noguera
author_sort	Matthew J. Scarborough
title	Diagnosing and Predicting Mixed-Culture Fermentations with Unicellular and Guild-Based Metabolic Models
title_short	Diagnosing and Predicting Mixed-Culture Fermentations with Unicellular and Guild-Based Metabolic Models
title_full	Diagnosing and Predicting Mixed-Culture Fermentations with Unicellular and Guild-Based Metabolic Models
title_fullStr	Diagnosing and Predicting Mixed-Culture Fermentations with Unicellular and Guild-Based Metabolic Models
title_full_unstemmed	Diagnosing and Predicting Mixed-Culture Fermentations with Unicellular and Guild-Based Metabolic Models
title_sort	diagnosing and predicting mixed-culture fermentations with unicellular and guild-based metabolic models
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/f2d79ed970cc48f5a60454d2b59db2cc
work_keys_str_mv	AT matthewjscarborough diagnosingandpredictingmixedculturefermentationswithunicellularandguildbasedmetabolicmodels AT joshuajhamilton diagnosingandpredictingmixedculturefermentationswithunicellularandguildbasedmetabolicmodels AT elizabethaerb diagnosingandpredictingmixedculturefermentationswithunicellularandguildbasedmetabolicmodels AT timothyjdonohue diagnosingandpredictingmixedculturefermentationswithunicellularandguildbasedmetabolicmodels AT danielrnoguera diagnosingandpredictingmixedculturefermentationswithunicellularandguildbasedmetabolicmodels
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Diagnosing and Predicting Mixed-Culture Fermentations with Unicellular and Guild-Based Metabolic Models

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