Engineering microbial consortia by division of labor

Roell, Garrett W.; Zha, Jian; Carr, Rhiannon R.; Koffas, Mattheos A.; Fong, Stephen S.; Tang, Yinjie J.

doi:10.1186/s12934-019-1083-3

Title: Engineering microbial consortia by division of labor

Journal Article · Fri Feb 08 00:00:00 EST 2019 · Microbial Cell Factories

DOI:https://doi.org/10.1186/s12934-019-1083-3· OSTI ID:1618897

Roell, Garrett W.; Zha, Jian; Carr, Rhiannon R.; Koffas, Mattheos A.; Fong, Stephen S.;

During microbial applications, metabolic burdens can lead to a signifcant drop in cell performance. Novel synthetic biology tools or multi-step bioprocessing (e.g., fermentation followed by chemical conversions) are therefore needed to avoid compromised biochemical productivity from over-burdened cells. A possible solution to address metabolic burden is Division of Labor (DoL) via natural and synthetic microbial consortia. In particular, consolidated bioprocesses and metabolic cooperation for detoxifcation or cross feeding (e.g., vitamin C fermentation) have shown numerous successes in industrial level applications. However, distributing a metabolic pathway among proper hosts remains an engineering conundrum due to several challenges: complex subpopulation dynamics/interactions with a short time-window for stable production, suboptimal cultivation of microbial communities, proliferation of cheaters or low-producers, intermediate metabolite dilution, transport barriers between species, and breaks in metabolite channeling through biosynthesis pathways. To develop stable consortia, optimization of strain inoculations, nutritional divergence and crossing feeding, evolution of mutualistic growth, cell immobilization, and biosensors may potentially be used to control cell populations. Another opportunity is direct integration of non-bioprocesses (e.g., microbial electrosynthesis) to power cell metabolism and improve carbon efciency. Additionally, metabolic modeling and ¹³C-metabolic fux analysis of mixed culture metabolism and cross-feeding ofers a computational approach to complement experimental research for improved consortia performance.

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Research Organization:: Washington Univ., St. Louis, MO (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Contributing Organization:: Rensselaer Polytechnic Institute, Virginia Commonwealth University

Grant/Contract Number:: DESC0018324; SC0018324

OSTI ID:: 1618897

Alternate ID(s):: OSTI ID: 1529583

Journal Information:: Microbial Cell Factories, Journal Name: Microbial Cell Factories Vol. 18 Journal Issue: 1; ISSN 1475-2859

Publisher:: Springer Science + Business MediaCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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Cited by: 112 works

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