Literature DB >> 30336343

Novel Geobacter species and diverse methanogens contribute to enhanced methane production in media-added methanogenic reactors.

Ran Mei1, Masaru K Nobu2, Takashi Narihiro2, Jimmy Yu3, Arun Sathyagal3, Eric Willman3, Wen-Tso Liu4.   

Abstract

To determine whether the addition of conductive materials could enhance methane production by direct interspecies electron transfer (DIET), we operated three anaerobic reactors amended with non-conductive (ceramic) or conductive materials (anthracite and granular activated carbon (GAC)). Throughout eight months of operation, ethanol was consistently detected as the major fermentation product. The specific yield in the anthracite and GAC-added reactors increased by 31.5% and 43.3%, respectively, compared to the ceramic-added reactor. 16S rRNA gene sequencing results indicated Geobacter was dominant (up to 55% of total sequences), whereas acids-degrading syntrophic bacteria were low in abundance (<2%). Using metagenomic analysis, the draft genome of the dominant Geobacter population (bin GAC1) was reconstructed and observed to possess genetic abilities of ethanol oxidation, hydrogen production, and extracellular electron transfer, and represented a phylogenetically novel Geobacter species. While Methanosaeta was the dominant methanogen, reactors containing conductive materials harbored more diverse and abundant archaeal populations, as revealed by FISH, qPCR, and metagenomics. Our findings suggested that a novel Geobacter population could oxidize ethanol and employed both hydrogen transfer and DIET depending on the accessibility of conductive materials. Thermodynamic advantages of DIET over hydrogen production could lead to enhanced methane production in reactors with conductive materials.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Conductive materials; Direct interspecies electron transfer; Diverse methanogens; Novel Geobacter

Mesh:

Substances:

Year:  2018        PMID: 30336343     DOI: 10.1016/j.watres.2018.10.026

Source DB:  PubMed          Journal:  Water Res        ISSN: 0043-1354            Impact factor:   11.236


  9 in total

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2.  Identification of parameters needed for optimal anaerobic co-digestion of chicken manure and corn stover.

Authors:  Yilong Yan; Ziwen Du; Liqiu Zhang; Li Feng; Dezhi Sun; Yan Dang; Dawn E Holmes; Jessica A Smith
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3.  Coupling growth kinetics modeling with machine learning reveals microbial immigration impacts and identifies key environmental parameters in a biological wastewater treatment process.

Authors:  Ran Mei; Jinha Kim; Fernanda P Wilson; Benjamin T W Bocher; Wen-Tso Liu
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Review 4.  Quantifying the contribution of microbial immigration in engineered water systems.

Authors:  Ran Mei; Wen-Tso Liu
Journal:  Microbiome       Date:  2019-11-06       Impact factor: 14.650

5.  Ecogenomics-Based Mass Balance Model Reveals the Effects of Fermentation Conditions on Microbial Activity.

Authors:  Jinha Kim; Ran Mei; Fernanda P Wilson; Heyang Yuan; Benjamin T W Bocher; Wen-Tso Liu
Journal:  Front Microbiol       Date:  2020-12-02       Impact factor: 5.640

6.  Enhanced anaerobic digestion of dairy wastewater in a granular activated carbon amended sequential batch reactor.

Authors:  Mohanakrishnan Logan; Lea Chua Tan; Corine Orline Nzeteu; Piet N L Lens
Journal:  Glob Change Biol Bioenergy       Date:  2022-05-02       Impact factor: 5.957

7.  Metagenomic and Metatranscriptomic Analyses Revealed Uncultured Bacteroidales Populations as the Dominant Proteolytic Amino Acid Degraders in Anaerobic Digesters.

Authors:  Ran Mei; Masaru K Nobu; Takashi Narihiro; Wen-Tso Liu
Journal:  Front Microbiol       Date:  2020-10-30       Impact factor: 5.640

8.  Soil Bacterial Community Structure in Turfy Swamp and Its Response to Highway Disturbance.

Authors:  Yuanyuan He; Yan Xu; Yan Lv; Lei Nie; Hong Wang
Journal:  Int J Environ Res Public Health       Date:  2020-10-26       Impact factor: 3.390

9.  Disentangling the syntrophic electron transfer mechanisms of Candidatus geobacter eutrophica through electrochemical stimulation and machine learning.

Authors:  Heyang Yuan; Xuehao Wang; Tzu-Yu Lin; Jinha Kim; Wen-Tso Liu
Journal:  Sci Rep       Date:  2021-07-23       Impact factor: 4.379

  9 in total

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