Evidence for nontraditional <i>mcr</i>-containing archaea contributing to biological methanogenesis in geothermal springs

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Abstract

Recent discoveries of methyl-coenzyme M reductase–encoding genes ( mcr) in uncultured archaea beyond traditional euryarchaeotal methanogens have reshaped our view of methanogenesis. However, whether any of these nontraditional archaea perform methanogenesis remains elusive. Here, we report field and microcosm experiments based on ¹³C-tracer labeling and genome-resolved metagenomics and metatranscriptomics, revealing that nontraditional archaea are predominant active methane producers in two geothermal springs. Archaeoglobales performed methanogenesis from methanol and may exhibit adaptability in using methylotrophic and hydrogenotrophic pathways based on temperature/substrate availability. A five-year field survey found Candidatus Nezhaarchaeota to be the predominant mcr-containing archaea inhabiting the springs; genomic inference and mcr expression under methanogenic conditions strongly suggested that this lineage mediated hydrogenotrophic methanogenesis in situ. Methanogenesis was temperature-sensitive , with a preference for methylotrophic over hydrogenotrophic pathways when incubation temperatures increased from 65° to 75°C. This study demonstrates an anoxic ecosystem wherein methanogenesis is primarily driven by archaea beyond known methanogens, highlighting diverse nontraditional mcr-containing archaea as previously unrecognized methane sources.

Abstract

Nontraditional archaea beyond known methanogens can mediate methanogenesis in geothermal springs.

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Fast gapped-read alignment with Bowtie 2.

Ben Langmead, Steven L Salzberg (2023)

As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

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IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

Lam-Tung Nguyen, Heiko Schmidt, Arndt von Haeseler … (2014)

Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.

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SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.

Anton Bankevich, Sergey Nurk, Dmitry Antipov … (2012)

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

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Author and article information

Contributors

Jiajia Wang:

ORCID: https://orcid.org/0000-0002-5860-5016

Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Yan-Ni Qu: Role: ConceptualizationRole: Formal analysisRole: Writing - original draft

Paul N. Evans:

ORCID: https://orcid.org/0000-0001-5995-2602

Role: InvestigationRole: ValidationRole: Writing - review & editing

Qinghai Guo:

ORCID: https://orcid.org/0000-0001-6602-9664

Role: Conceptualization

Fengwu Zhou:

ORCID: https://orcid.org/0000-0002-1396-4103

Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: ResourcesRole: Visualization

Ming Nie:

ORCID: https://orcid.org/0000-0003-0702-8009

Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Qusheng Jin:

ORCID: https://orcid.org/0000-0002-0808-2122

Role: Formal analysisRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - review & editing

Yan Zhang:

ORCID: https://orcid.org/0000-0002-1895-326X

Role: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: Validation

Xiangmei Zhai:

ORCID: https://orcid.org/0000-0001-6532-7264

Role: Formal analysisRole: InvestigationRole: Validation

Ming Zhou:

ORCID: https://orcid.org/0009-0006-2055-7756

Role: InvestigationRole: Validation

Zhiguo Yu: Role: ConceptualizationRole: InvestigationRole: Methodology

Qing-Long Fu:

ORCID: https://orcid.org/0000-0002-7125-6877

Role: ValidationRole: VisualizationRole: Writing - original draft

Yuan-Guo Xie: Role: Formal analysis

Brian P. Hedlund:

ORCID: https://orcid.org/0000-0001-8530-0448

Role: ConceptualizationRole: Funding acquisitionRole: Writing - original draftRole: Writing - review & editing

Wen-Jun Li:

ORCID: https://orcid.org/0000-0002-1233-736X

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Zheng-Shuang Hua:

ORCID: https://orcid.org/0000-0002-2405-4228

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Zimeng Wang:

ORCID: https://orcid.org/0000-0002-4572-629X

Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing - review & editing

Yanxin Wang: Role: ConceptualizationRole: Formal analysisRole: Project administrationRole: Supervision

Journal

Journal ID (nlm-ta): Sci Adv

Journal ID (iso-abbrev): Sci Adv

Journal ID (publisher-id): sciadv

Journal ID (hwp): advances

Title: Science Advances

Publisher: American Association for the Advancement of Science

ISSN (Electronic): 2375-2548

Publication date Collection: June 2023

Publication date (Electronic, pub): 28 June 2023

Volume: 9

Issue: 26

Electronic Location Identifier: eadg6004

Affiliations

[ ¹ ]Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.

[ ² ]Chinese Academy of Sciences Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China.

[ ³ ]State Key Laboratory of Biocontrol, Guangdong Provincial Key Laboratory of Plant Resources and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, China.

[ ⁴ ]The Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, QLD, Australia.

[ ⁵ ]MOE Key Laboratory of Groundwater Quality and Health, State Environmental Protection Key Laboratory of Source Apportionment and Control of Aquatic Pollution, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China.

[ ⁶ ]College of Geography Science, Nanjing Normal University, Nanjing 210023, China.

[ ⁷ ]Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science and Institute of Eco-Chongming, School of Life Sciences, Fudan University, Shanghai 200438, China.

[ ⁸ ]National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Fudan University, Shanghai 200433, China.

[ ⁹ ]Department of Earth Sciences, University of Oregon, Eugene, OR 97403, USA.

[ ¹⁰ ]School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China.

[ ¹¹ ]School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA.

[ ¹² ]Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA.

[ ¹³ ]Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China.

Author notes

[* ]Corresponding author. Email: zimengw@ 123456fudan.edu.cn (Z.W.); hzhengsh@ 123456ustc.edu.cn (Z.-S.H.)

Author information

Jiajia Wang https://orcid.org/0000-0002-5860-5016

Paul N. Evans https://orcid.org/0000-0001-5995-2602

Qinghai Guo https://orcid.org/0000-0001-6602-9664

Fengwu Zhou https://orcid.org/0000-0002-1396-4103

Ming Nie https://orcid.org/0000-0003-0702-8009

Qusheng Jin https://orcid.org/0000-0002-0808-2122

Yan Zhang https://orcid.org/0000-0002-1895-326X

Xiangmei Zhai https://orcid.org/0000-0001-6532-7264

Ming Zhou https://orcid.org/0009-0006-2055-7756

Qing-Long Fu https://orcid.org/0000-0002-7125-6877

Brian P. Hedlund https://orcid.org/0000-0001-8530-0448

Wen-Jun Li https://orcid.org/0000-0002-1233-736X

Zheng-Shuang Hua https://orcid.org/0000-0002-2405-4228

Zimeng Wang https://orcid.org/0000-0002-4572-629X

Article

Publisher ID: adg6004

DOI: 10.1126/sciadv.adg6004

PMC ID: 10306296

PubMed ID: 37379385

SO-VID: f07bb986-ac9f-4849-89c4-d7e8c9d4d5de

Copyright © Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.