Microbial electrosynthesis is a renewable energy and chemical production platform that employs microbial communities to generate industrially important chemical products (such as biofuels or commodity chemicals) by fixing carbon dioxide using an electric current as the electron donor. To optimize electrosynthesis systems, it is valuable to understand the overall metabolic capability of multispecies microbial communities on electrosynthetic systems. In a recent study published in Scientific Reports, researchers applied KBase annotation and metabolic modeling pipelines to analyze three major contributors in a 13-species electrosynthetic community that captures electrons from a cathode and fixes carbon dioxide. The results demonstrated that a diverse set of microorganisms could be active in limited niche space with carbon dioxide as the sole carbon source and an electrode as the only electron donor. Metabolic models of the predominant community members revealed that Acetobacterium is the primary carbon fixer for the community, excreting large amounts of acetate, which serves as the main carbon source for the rest of the community.
Environmental implications of this work include the elucidation of ecological aspects of one-carbon metabolism and extracellular electron transfer relevant to global biogeochemical cycling. The analysis showed that electrosynthetic microbiomes could potentially provide a valuable ecosystem service by scrubbing oxygen, as well as sustainably synthesizing important chemical products such as biofuels.
Marshall CW, Ross DE, Handley KM, Weisenhorn PB, Edirisinghe JN, Henry CS, Gilbert JA, May HD and Norman S. Metabolic Reconstruction and Modeling Microbial Electrosynthesis. Scientific Reports. 2017;7. doi:10.1038/s41598-017-08877
The analysis workflow for this study, including trophic interactions predicted between the three species based on metabolic model analyses, can be found in https://narrative.kbase.us/narrative/ws.15248.obj.1.
Core metabolic models representing 48 major phylogenetic microbial groups were constructed based on a core model template consisting of a highly curated set of biochemical reactions derived from a diverse set of model organisms. Researchers selected nearly 200 unique reactions comprising 12 key energy biosynthesis pathways linked to central metabolism and variations of bacterial electron transport chains. The models produced by the new pipeline had minimal need for gapfilling, demonstrating their value as functional models that align as closely as possible with raw annotation output, minimizing the number of model-driven conjectures. This study demonstrates that core metabolic models can be used to quickly and accurately (1) determine and predict microbial respiration types and energy (ATP) yields; (2) identify electron acceptors that can be reduced during anaerobic respiration; (3) determine the presence or absence of functional pathways in central metabolism and the phylogenetic distribution of these key pathways; (4) evaluate a microbe’s fermentation capabilities; and (5) assess its ability to produce key pathway intermediates in central metabolism that are precursors of essential biomass compounds.
Edirisinghe, J. N., et al. “Modeling central metabolism and energy biosynthesis across microbial life.” BMC Genomics 17, 568 (2016). [DOI:10.1186/s12864-016-2887-8].
The authors’ core metabolic model construction pipeline and supporting commentary can be accessed through KBase’s Narrative Interface at narrative.kbase.us/narrative/ws.15253.obj.1.
Life as we know it depends on the existence of microbial communities (microbiomes), in which multiple species of microbes function collaboratively with each other to extract nutrients and energy from their environment. A team of scientists from Argonne National Laboratory (ANL) and Pacific Northwest National Laboratory (PNNL), developed a novel way to model and predict these cross-species metabolic interactions using KBase tools and datasets. They demonstrated various strategies for constructing genome-scale metabolic networks that simulate two species in a microbial consortium exchanging metabolites to sustain life. Their work is described in the cover article of the November 2016 issue of the Journal of Cellular Physiology.
Henry, C. S., Bernstein, H. C., Weisenhorn, P., Taylor, R. C., Lee, J.-Y., Zucker, J. and Song, H.-S. (2016), Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction. J. Cell. Physiol. doi: 10.1002/jcp.25428
The authors’ analysis workflow and supporting commentary can be accessed through KBase’s Narrative Interface at https://narrative.kbase.us/narrative/ws.13807.obj.1.