Engineering microbial consortia by division of labor

被引:0
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作者
Garrett W. Roell
Jian Zha
Rhiannon R. Carr
Mattheos A. Koffas
Stephen S. Fong
Yinjie J. Tang
机构
[1] Washington University,Department of Energy, Environmental and Chemical Engineering
[2] Rensselaer Polytechnic Institute,Department of Chemical and Biological Engineering
[3] Virginia Commonwealth University,Department of Chemical and Life Science Engineering
来源
关键词
C-metabolic flux analysis; Cross-feeding; Metabolite channeling; Reporter protein; Subpopulations;
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摘要
During microbial applications, metabolic burdens can lead to a significant 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 detoxification 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 efficiency. Additionally, metabolic modeling and 13C-metabolic flux analysis of mixed culture metabolism and cross-feeding offers a computational approach to complement experimental research for improved consortia performance.
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