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Genome-resolved correlation mapping links microbial community structure to metabolic interactions driving methane production from wastewater

Anaerobic digestion of municipal mixed sludge produces methane that can be converted into renewable natural gas. To improve economics of this microbial mediated process, metabolic interactions catalyzing biomass conversion to energy need to be identified. Here, we present a two-year time series asso...

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Autores principales: Kieft, Brandon, Finke, Niko, McLaughlin, Ryan J., Nallan, Aditi N., Krzywinski, Martin, Crowe, Sean A., Hallam, Steven J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10477309/
https://www.ncbi.nlm.nih.gov/pubmed/37666802
http://dx.doi.org/10.1038/s41467-023-40907-5
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author Kieft, Brandon
Finke, Niko
McLaughlin, Ryan J.
Nallan, Aditi N.
Krzywinski, Martin
Crowe, Sean A.
Hallam, Steven J.
author_facet Kieft, Brandon
Finke, Niko
McLaughlin, Ryan J.
Nallan, Aditi N.
Krzywinski, Martin
Crowe, Sean A.
Hallam, Steven J.
author_sort Kieft, Brandon
collection PubMed
description Anaerobic digestion of municipal mixed sludge produces methane that can be converted into renewable natural gas. To improve economics of this microbial mediated process, metabolic interactions catalyzing biomass conversion to energy need to be identified. Here, we present a two-year time series associating microbial metabolism and physicochemistry in a full-scale wastewater treatment plant. By creating a co-occurrence network with thousands of time-resolved microbial populations from over 100 samples spanning four operating configurations, known and novel microbial consortia with potential to drive methane production were identified. Interactions between these populations were further resolved in relation to specific process configurations by mapping metagenome assembled genomes and cognate gene expression data onto the network. Prominent interactions included transcriptionally active Methanolinea methanogens and syntrophic benzoate oxidizing Syntrophorhabdus, as well as a Methanoregulaceae population and putative syntrophic acetate oxidizing bacteria affiliated with Bateroidetes (Tenuifilaceae) expressing the glycine cleavage bypass of the Wood–Ljungdahl pathway.
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spelling pubmed-104773092023-09-06 Genome-resolved correlation mapping links microbial community structure to metabolic interactions driving methane production from wastewater Kieft, Brandon Finke, Niko McLaughlin, Ryan J. Nallan, Aditi N. Krzywinski, Martin Crowe, Sean A. Hallam, Steven J. Nat Commun Article Anaerobic digestion of municipal mixed sludge produces methane that can be converted into renewable natural gas. To improve economics of this microbial mediated process, metabolic interactions catalyzing biomass conversion to energy need to be identified. Here, we present a two-year time series associating microbial metabolism and physicochemistry in a full-scale wastewater treatment plant. By creating a co-occurrence network with thousands of time-resolved microbial populations from over 100 samples spanning four operating configurations, known and novel microbial consortia with potential to drive methane production were identified. Interactions between these populations were further resolved in relation to specific process configurations by mapping metagenome assembled genomes and cognate gene expression data onto the network. Prominent interactions included transcriptionally active Methanolinea methanogens and syntrophic benzoate oxidizing Syntrophorhabdus, as well as a Methanoregulaceae population and putative syntrophic acetate oxidizing bacteria affiliated with Bateroidetes (Tenuifilaceae) expressing the glycine cleavage bypass of the Wood–Ljungdahl pathway. Nature Publishing Group UK 2023-09-04 /pmc/articles/PMC10477309/ /pubmed/37666802 http://dx.doi.org/10.1038/s41467-023-40907-5 Text en © The Author(s) 2023 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
Kieft, Brandon
Finke, Niko
McLaughlin, Ryan J.
Nallan, Aditi N.
Krzywinski, Martin
Crowe, Sean A.
Hallam, Steven J.
Genome-resolved correlation mapping links microbial community structure to metabolic interactions driving methane production from wastewater
title Genome-resolved correlation mapping links microbial community structure to metabolic interactions driving methane production from wastewater
title_full Genome-resolved correlation mapping links microbial community structure to metabolic interactions driving methane production from wastewater
title_fullStr Genome-resolved correlation mapping links microbial community structure to metabolic interactions driving methane production from wastewater
title_full_unstemmed Genome-resolved correlation mapping links microbial community structure to metabolic interactions driving methane production from wastewater
title_short Genome-resolved correlation mapping links microbial community structure to metabolic interactions driving methane production from wastewater
title_sort genome-resolved correlation mapping links microbial community structure to metabolic interactions driving methane production from wastewater
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10477309/
https://www.ncbi.nlm.nih.gov/pubmed/37666802
http://dx.doi.org/10.1038/s41467-023-40907-5
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