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Open AccessFeature PaperArticle

In Silico Identification of Microbial Partners to Form Consortia with Anaerobic Fungi

1
Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA
2
Department of Computer Science, University of California, Santa Barbara, CA 93106, USA
*
Author to whom correspondence should be addressed.
Processes 2018, 6(1), 7; https://doi.org/10.3390/pr6010007
Received: 27 December 2017 / Revised: 10 January 2018 / Accepted: 12 January 2018 / Published: 15 January 2018
Lignocellulose is an abundant and renewable resource that holds great promise for sustainable bioprocessing. However, unpretreated lignocellulose is recalcitrant to direct utilization by most microbes. Current methods to overcome this barrier include expensive pretreatment steps to liberate cellulose and hemicellulose from lignin. Anaerobic gut fungi possess complex cellulolytic machinery specifically evolved to decompose crude lignocellulose, but they are not yet genetically tractable and have not been employed in industrial bioprocesses. Here, we aim to exploit the biomass-degrading abilities of anaerobic fungi by pairing them with another organism that can convert the fermentable sugars generated from hydrolysis into bioproducts. By combining experiments measuring the amount of excess fermentable sugars released by the fungal enzymes acting on crude lignocellulose, and a novel dynamic flux balance analysis algorithm, we screened potential consortia partners by qualitative suitability. Microbial growth simulations reveal that the fungus Anaeromyces robustus is most suited to pair with either the bacterium Clostridia ljungdahlii or the methanogen Methanosarcina barkeri—both organisms also found in the rumen microbiome. By capitalizing on simulations to screen six alternative organisms, valuable experimental time is saved towards identifying stable consortium members. This approach is also readily generalizable to larger systems and allows one to rationally select partner microbes for formation of stable consortia with non-model microbes like anaerobic fungi. View Full-Text
Keywords: anaerobic fungi; in silico modeling; microbial consortia; dynamic flux balance analysis; non-model organism; lignocellulose anaerobic fungi; in silico modeling; microbial consortia; dynamic flux balance analysis; non-model organism; lignocellulose
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Wilken, S.E.; Saxena, M.; Petzold, L.R.; O’Malley, M.A. In Silico Identification of Microbial Partners to Form Consortia with Anaerobic Fungi. Processes 2018, 6, 7.

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