标题：Disentangling the syntrophic electron transfer mechanisms of Candidatus geobacter eutrophica through electrochemical stimulation and machine learning
摘要：Interspecies hydrogen transfer (IHT) and direct interspecies electron transfer (DIET) are two syntrophy models for methanogenesis. Their relative importance in methanogenic environments is still unclear. Our recent discovery of a novel species
Candidatus Geobacter eutrophica with the genetic potential of IHT and DIET may serve as a model species to address this knowledge gap. To experimentally demonstrate its DIET ability, we performed electrochemical enrichment of
Ca. G. eutrophica-dominating communities under 0 and 0.4 V vs. Ag/AgCl based on the presumption that DIET and extracellular electron transfer (EET) share similar metabolic pathways. After three batches of enrichment,
Geobacter OTU650, which was phylogenetically close to
Ca. G. eutrophica, was outcompeted in the control but remained abundant and active under electrochemical stimulation, indicating
Ca. G. eutrophica’s EET ability. The high-quality draft genome further showed high phylogenomic similarity with
Ca. G. eutrophica, and the genes encoding outer membrane cytochromes and enzymes for hydrogen metabolism were actively expressed. A Bayesian network was trained with the genes encoding enzymes for alcohol metabolism, hydrogen metabolism, EET, and methanogenesis from dominant fermentative bacteria,
Geobacter, and
Methanobacterium. Methane production could not be accurately predicted when the genes for IHT were in silico knocked out, inferring its more important role in methanogenesis. The genomics-enabled machine learning modeling approach can provide predictive insights into the importance of IHT and DIET.
