Microbial electrosynthesis (MES) has been gaining considerable interest as the next step in the evolution of microbial electrochemical technologies. Understanding the niche biocathode environment and microbial community is critical for further developing this technology as the biocathode is key to product formation and efficiency. MES is generally operated to enrich a specific functional group (e.g., methanogens or homoacetogens) from a mixed-culture inoculum. However, due to differences in H2 and CO2 availability across the cathode surface, competition and syntrophy may lead to overall variability and significant beta-diversity within and between replicate reactors, which can affect performance reproducibility. Therefore, this study aimed to investigate the distribution and potential spatial variability of the microbial communities in MES methanogenic biocathodes. Triplicate methanogenic biocathodes were enriched in microbial electrolysis cells for 5 months at an applied voltage of 0.7 V. They were then transferred to triplicate dual-chambered MES reactors and operated at -1.0 V vs. Ag/AgCl for six batches. At the end of the experiment, triplicate samples were taken at different positions (top, center, bottom) from each biocathode for a total of nine samples for total biomass protein analysis and 16S rRNA gene amplicon sequencing. Microbial community analyses showed that the biocathodes were highly enriched with methanogens, especially the hydrogenotrophic methanogen family Methanobacteriaceae, Methanobacterium sp., and the mixotrophic Methanosarcina sp., with an overall core community representing > 97% of sequence reads in all samples. There was no statistically significant spatial variability (p > 0.05) observed in the distribution of these communities within and between the reactors. These results suggest deterministic community assembly and indicate the reproducibility of electromethanogenic biocathode communities, with implications for larger-scale reactors.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): URF/1/2985-01-01
Acknowledgements: This work was funded by Competitive Research Grant (URF/1/2985-01-01) to PS from King Abdullah University of Science and Technology