Evidence map›Paper›PMID 34302023›Full record

ArticleScientific reports2021

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

Heyang Yuan, Xuehao Wang, Tzu-Yu Lin, Jinha Kim, Wen-Tso Liu

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Heyang YuanDepartment of Civil and Environmental Engineering, University of Illinois, Urbana-Champaign, Urbana, IL, 61801, USA. heyang.yuan@temple.edu.
Xuehao WangDepartment of Civil and Environmental Engineering, University of Illinois, Urbana-Champaign, Urbana, IL, 61801, USA.
Tzu-Yu LinDepartment of Civil and Environmental Engineering, University of Illinois, Urbana-Champaign, Urbana, IL, 61801, USA.
Jinha KimDepartment of Civil and Environmental Engineering, University of Illinois, Urbana-Champaign, Urbana, IL, 61801, USA.
Wen-Tso LiuDepartment of Civil and Environmental Engineering, University of Illinois, Urbana-Champaign, Urbana, IL, 61801, USA. wtliu@illinois.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Bayes TheoremCytochromesElectronsElectron TransportGeobacterHydrogenMachine LearningMetabolic Networks and PathwaysMethaneMethanobacteriumCytochromesHydrogenMethane

Identifiers

PMID34302023
PMCPMC8302695

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.