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Disentangling the syntrophic electron transfer mechanisms of Candidatus geobacter eutrophica through electrochemical stimulation and machine learning
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 potenti...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8302695/ https://www.ncbi.nlm.nih.gov/pubmed/34302023 http://dx.doi.org/10.1038/s41598-021-94628-0 |
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author | Yuan, Heyang Wang, Xuehao Lin, Tzu-Yu Kim, Jinha Liu, Wen-Tso |
author_facet | Yuan, Heyang Wang, Xuehao Lin, Tzu-Yu Kim, Jinha Liu, Wen-Tso |
author_sort | Yuan, Heyang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8302695 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83026952021-07-27 Disentangling the syntrophic electron transfer mechanisms of Candidatus geobacter eutrophica through electrochemical stimulation and machine learning Yuan, Heyang Wang, Xuehao Lin, Tzu-Yu Kim, Jinha Liu, Wen-Tso Sci Rep Article 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. Nature Publishing Group UK 2021-07-23 /pmc/articles/PMC8302695/ /pubmed/34302023 http://dx.doi.org/10.1038/s41598-021-94628-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Yuan, Heyang Wang, Xuehao Lin, Tzu-Yu Kim, Jinha Liu, Wen-Tso Disentangling the syntrophic electron transfer mechanisms of Candidatus geobacter eutrophica through electrochemical stimulation and machine learning |
title | Disentangling the syntrophic electron transfer mechanisms of Candidatus geobacter eutrophica through electrochemical stimulation and machine learning |
title_full | Disentangling the syntrophic electron transfer mechanisms of Candidatus geobacter eutrophica through electrochemical stimulation and machine learning |
title_fullStr | Disentangling the syntrophic electron transfer mechanisms of Candidatus geobacter eutrophica through electrochemical stimulation and machine learning |
title_full_unstemmed | Disentangling the syntrophic electron transfer mechanisms of Candidatus geobacter eutrophica through electrochemical stimulation and machine learning |
title_short | Disentangling the syntrophic electron transfer mechanisms of Candidatus geobacter eutrophica through electrochemical stimulation and machine learning |
title_sort | disentangling the syntrophic electron transfer mechanisms of candidatus geobacter eutrophica through electrochemical stimulation and machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8302695/ https://www.ncbi.nlm.nih.gov/pubmed/34302023 http://dx.doi.org/10.1038/s41598-021-94628-0 |
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