Impact of process temperature and organic loading rate on cellulolytic / hydrolytic biofilm microbiomes during biomethanation of ryegrass silage revealed by genome-centered metagenomics and metatranscriptomics.

Anaerobic digestion Bioconversion Biogas Integrated -omics Metabolic activity Metagenome assembled genomes Methane Microbial community structure Polyomics

Journal

Environmental microbiome
ISSN: 2524-6372
Titre abrégé: Environ Microbiome
Pays: England
ID NLM: 101768168

Informations de publication

Date de publication:
02 Mar 2020
Historique:
received: 20 09 2019
accepted: 14 02 2020
entrez: 27 4 2021
pubmed: 28 4 2021
medline: 28 4 2021
Statut: epublish

Résumé

Anaerobic digestion (AD) of protein-rich grass silage was performed in experimental two-stage two-phase biogas reactor systems at low vs. increased organic loading rates (OLRs) under mesophilic (37 °C) and thermophilic (55 °C) temperatures. To follow the adaptive response of the biomass-attached cellulolytic/hydrolytic biofilms at increasing ammonium/ammonia contents, genome-centered metagenomics and transcriptional profiling based on metagenome assembled genomes (MAGs) were conducted. In total, 78 bacterial and archaeal MAGs representing the most abundant members of the communities, and featuring defined quality criteria were selected and characterized in detail. Determination of MAG abundances under the tested conditions by mapping of the obtained metagenome sequence reads to the MAGs revealed that MAG abundance profiles were mainly shaped by the temperature but also by the OLR. However, the OLR effect was more pronounced for the mesophilic systems as compared to the thermophilic ones. In contrast, metatranscriptome mapping to MAGs subsequently normalized to MAG abundances showed that under thermophilic conditions, MAGs respond to increased OLRs by shifting their transcriptional activities mainly without adjusting their proliferation rates. This is a clear difference compared to the behavior of the microbiome under mesophilic conditions. Here, the response to increased OLRs involved adjusting of proliferation rates and corresponding transcriptional activities. The analysis led to the identification of MAGs positively responding to increased OLRs. The most outstanding MAGs in this regard, obviously well adapted to higher OLRs and/or associated conditions, were assigned to the order Clostridiales (Acetivibrio sp.) for the mesophilic biofilm and the orders Bacteroidales (Prevotella sp. and an unknown species), Lachnospirales (Herbinix sp. and Kineothrix sp.) and Clostridiales (Clostridium sp.) for the thermophilic biofilm. Genome-based metabolic reconstruction and transcriptional profiling revealed that positively responding MAGs mainly are involved in hydrolysis of grass silage, acidogenesis and / or acetogenesis. An integrated -omics approach enabled the identification of new AD biofilm keystone species featuring outstanding performance under stress conditions such as increased OLRs. Genome-based knowledge on the metabolic potential and transcriptional activity of responsive microbiome members will contribute to the development of improved microbiological AD management strategies for biomethanation of renewable biomass.

Sections du résumé

BACKGROUND BACKGROUND
Anaerobic digestion (AD) of protein-rich grass silage was performed in experimental two-stage two-phase biogas reactor systems at low vs. increased organic loading rates (OLRs) under mesophilic (37 °C) and thermophilic (55 °C) temperatures. To follow the adaptive response of the biomass-attached cellulolytic/hydrolytic biofilms at increasing ammonium/ammonia contents, genome-centered metagenomics and transcriptional profiling based on metagenome assembled genomes (MAGs) were conducted.
RESULTS RESULTS
In total, 78 bacterial and archaeal MAGs representing the most abundant members of the communities, and featuring defined quality criteria were selected and characterized in detail. Determination of MAG abundances under the tested conditions by mapping of the obtained metagenome sequence reads to the MAGs revealed that MAG abundance profiles were mainly shaped by the temperature but also by the OLR. However, the OLR effect was more pronounced for the mesophilic systems as compared to the thermophilic ones. In contrast, metatranscriptome mapping to MAGs subsequently normalized to MAG abundances showed that under thermophilic conditions, MAGs respond to increased OLRs by shifting their transcriptional activities mainly without adjusting their proliferation rates. This is a clear difference compared to the behavior of the microbiome under mesophilic conditions. Here, the response to increased OLRs involved adjusting of proliferation rates and corresponding transcriptional activities. The analysis led to the identification of MAGs positively responding to increased OLRs. The most outstanding MAGs in this regard, obviously well adapted to higher OLRs and/or associated conditions, were assigned to the order Clostridiales (Acetivibrio sp.) for the mesophilic biofilm and the orders Bacteroidales (Prevotella sp. and an unknown species), Lachnospirales (Herbinix sp. and Kineothrix sp.) and Clostridiales (Clostridium sp.) for the thermophilic biofilm. Genome-based metabolic reconstruction and transcriptional profiling revealed that positively responding MAGs mainly are involved in hydrolysis of grass silage, acidogenesis and / or acetogenesis.
CONCLUSIONS CONCLUSIONS
An integrated -omics approach enabled the identification of new AD biofilm keystone species featuring outstanding performance under stress conditions such as increased OLRs. Genome-based knowledge on the metabolic potential and transcriptional activity of responsive microbiome members will contribute to the development of improved microbiological AD management strategies for biomethanation of renewable biomass.

Identifiants

pubmed: 33902713
doi: 10.1186/s40793-020-00354-x
pii: 10.1186/s40793-020-00354-x
pmc: PMC8067321
doi:

Types de publication

Journal Article

Langues

eng

Pagination

7

Subventions

Organisme : Bundesministerium für Bildung und Forschung
ID : 03SF0440

Références

Nat Ecol Evol. 2018 Jun;2(6):936-943
pubmed: 29662222
J Biotechnol. 2016 Aug 20;232:50-60
pubmed: 27165504
Bioinformatics. 2011 Nov 1;27(21):2957-63
pubmed: 21903629
J Biotechnol. 2017 Nov 10;261:10-23
pubmed: 28823476
Bioresour Technol. 2016 Sep;216:260-6
pubmed: 27243603
Microb Biotechnol. 2014 May;7(3):257-64
pubmed: 24612643
Bioresour Technol. 2013 Oct;146:408-415
pubmed: 23954246
Appl Microbiol Biotechnol. 2012 Oct;96(2):565-76
pubmed: 22899497
Anaerobe. 2017 Aug;46:146-154
pubmed: 28254264
Bioinformatics. 2015 Jan 15;31(2):166-9
pubmed: 25260700
Microb Biotechnol. 2015 Sep;8(5):749-63
pubmed: 25874383
Nucleic Acids Res. 2013 Jan 7;41(1):e1
pubmed: 22933715
FEMS Microbiol Ecol. 2013 Sep;85(3):612-26
pubmed: 23678985
Sci Total Environ. 2018 Jul 1;628-629:94-102
pubmed: 29428864
J Environ Manage. 2019 Oct 15;248:109297
pubmed: 31376610
Anaerobe. 2014 Oct;29:44-51
pubmed: 24342346
Biotechnol Biofuels. 2018 Jun 19;11:167
pubmed: 29951113
Biotechnol Biofuels. 2017 Nov 13;10:264
pubmed: 29158776
Bioresour Technol. 2015 Feb;177:34-40
pubmed: 25479391
Appl Microbiol Biotechnol. 2018 Jun;102(12):5045-5063
pubmed: 29713790
Bioinformatics. 2015 May 15;31(10):1674-6
pubmed: 25609793
Bioresour Technol. 2018 Aug;262:184-193
pubmed: 29705610
Biotechnol Biofuels. 2016 Feb 02;9:26
pubmed: 26839589
Genome Biol. 2014;15(12):550
pubmed: 25516281
Microb Biotechnol. 2015 Sep;8(5):764-75
pubmed: 25712194
Environ Sci Technol. 2009 Nov 15;43(22):8496-508
pubmed: 20028043
Int J Syst Evol Microbiol. 2012 Jun;62(Pt 6):1377-1382
pubmed: 21828011
PLoS One. 2013 Oct 16;8(10):e77265
pubmed: 24146974
Nat Methods. 2012 Mar 04;9(4):357-9
pubmed: 22388286
Biotechnol Biofuels. 2016 Jul 26;9:156
pubmed: 27462367
J Microbiol Biotechnol. 2017 Feb 28;27(2):321-334
pubmed: 27780961
Appl Microbiol Biotechnol. 2010 Jan;85(4):849-60
pubmed: 19777226
mBio. 2010 Oct 05;1(4):
pubmed: 20941329
Bioresour Technol. 2019 Nov;292:121968
pubmed: 31430671
Genome Res. 2011 Sep;21(9):1552-60
pubmed: 21690186
Anaerobe. 2017 Aug;46:23-32
pubmed: 28219787
Microbiome. 2018 May 24;6(1):94
pubmed: 29793532
Int J Mol Sci. 2015 Sep 25;16(10):23210-26
pubmed: 26404240
Genome Res. 2015 Jul;25(7):1043-55
pubmed: 25977477
mBio. 2011 Jul 26;2(4):
pubmed: 21791581
Nat Methods. 2015 Jan;12(1):59-60
pubmed: 25402007
Bioinformatics. 2012 Sep 1;28(17):2223-30
pubmed: 22796954
Nucleic Acids Res. 2012 Jul;40(Web Server issue):W445-51
pubmed: 22645317
Bioresour Technol. 2013 Sep;144:80-5
pubmed: 23867528
J Biotechnol. 2012 Apr 30;158(4):248-58
pubmed: 22342600
Bioinformatics. 2009 Aug 15;25(16):2078-9
pubmed: 19505943
Bioresour Technol. 2019 Sep;288:121597
pubmed: 31176202
Sci Rep. 2017 May 4;7(1):1510
pubmed: 28473726
Gigascience. 2015 Jul 30;4:33
pubmed: 26229594
Appl Microbiol Biotechnol. 2015 Sep;99(18):7791-803
pubmed: 25998656
PeerJ. 2015 Aug 27;3:e1165
pubmed: 26336640
Biotechnol Biofuels. 2016 Aug 11;9:171
pubmed: 27525040

Auteurs

Irena Maus (I)

Bielefeld University, Center for Biotechnology (CeBiTec), Genome Research of Industrial Microorganisms, Universitätsstr. 27, 33615, Bielefeld, Germany.

Michael Klocke (M)

Department Bioengineering, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469, Potsdam, Germany.

Jaqueline Derenkó (J)

Department Bioengineering, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469, Potsdam, Germany.

Yvonne Stolze (Y)

Bielefeld University, Center for Biotechnology (CeBiTec), Genome Research of Industrial Microorganisms, Universitätsstr. 27, 33615, Bielefeld, Germany.

Michael Beckstette (M)

Helmholtz Centre for Infection Research, Microbial Infection Biology / Experimental Immunology, Inhoffenstrasse 7, 38124, Braunschweig, Germany.

Carsten Jost (C)

Department Bioengineering, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469, Potsdam, Germany.

Daniel Wibberg (D)

Bielefeld University, Center for Biotechnology (CeBiTec), Genome Research of Industrial Microorganisms, Universitätsstr. 27, 33615, Bielefeld, Germany.

Jochen Blom (J)

Department Bioinformatics and Systems Biology, Justus-Liebig University Gießen, Heinrich-Buff-Ring 58, 35392, Giessen, Germany.

Christian Henke (C)

Faculty of Technology, Bielefeld University, Universitätsstr. 25, 33615, Bielefeld, Germany.

Katharina Willenbücher (K)

Department Bioengineering, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469, Potsdam, Germany.

Madis Rumming (M)

Faculty of Technology, Bielefeld University, Universitätsstr. 25, 33615, Bielefeld, Germany.

Antje Rademacher (A)

Department Bioengineering, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469, Potsdam, Germany.

Alfred Pühler (A)

Bielefeld University, Center for Biotechnology (CeBiTec), Genome Research of Industrial Microorganisms, Universitätsstr. 27, 33615, Bielefeld, Germany.

Alexander Sczyrba (A)

Bielefeld University, Center for Biotechnology (CeBiTec), Genome Research of Industrial Microorganisms, Universitätsstr. 27, 33615, Bielefeld, Germany.
Faculty of Technology, Bielefeld University, Universitätsstr. 25, 33615, Bielefeld, Germany.

Andreas Schlüter (A)

Bielefeld University, Center for Biotechnology (CeBiTec), Genome Research of Industrial Microorganisms, Universitätsstr. 27, 33615, Bielefeld, Germany. aschluet@cebitec.uni-bielefeld.de.

Classifications MeSH