Evidence map›Paper›PMID 27462366›Full record

ArticleBiotechnology for biofuels2016

Proteotyping of biogas plant microbiomes separates biogas plants according to process temperature and reactor type.

R Heyer, D Benndorf, F Kohrs, J De Vrieze, N Boon, M Hoffmann, E Rapp, Andreas Schlüter, Alexander Sczyrba, U Reichl

Abstract read
In one paragraph

Article in Biotechnology for biofuels, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

30 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Microbiome modeling: a beginner's guide.Frontiers in microbiology · 2024
    Review
  5. Article
  6. Review
  7. GroEL-Proteotyping of Bacterial Communities Using Tandem Mass Spectrometry.International journal of molecular sciences · 2023
    Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Review
  14. Review
  15. Microbial invasions in sludge anaerobic digesters.Applied microbiology and biotechnology · 2021
    Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
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

10 authors.

R HeyerBioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106 Magdeburg, Germany ; Bioprocess Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany.
D BenndorfBioprocess Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany.
F KohrsBioprocess Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany.
J De VriezeLaboratory of Microbial Technology and Ecology (LabMET), Ghent University, Coupure Links 653, 9000 Ghent, Belgium.
N BoonLaboratory of Microbial Technology and Ecology (LabMET), Ghent University, Coupure Links 653, 9000 Ghent, Belgium.
M HoffmannBioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106 Magdeburg, Germany.
E RappBioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106 Magdeburg, Germany.
Andreas SchlüterCenter for Biotechnology, Genome Research of Industrial Microorganisms, Universität Bielefeld, Universitätsstraße 25, 33615 Bielefeld, Germany.
Alexander SczyrbaCenter for Biotechnology, Computational Metagenomics, Universität Bielefeld, Universitätsstr. 25, 33615 Bielefeld, Germany.
U ReichlBioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106 Magdeburg, Germany ; Bioprocess Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMethane yield and biogas productivity of biogas plants (BGPs) depend on microbial community structure and function, substrate supply, and general biogas process parameters. So far, however, relatively little is known about correlations between microbial community function and process parameters. To close this knowledge gap, microbial communities of 40 samples from 35 different industrial biogas plants were evaluated by a metaproteomics approach in this study.

resultsLiquid chromatography coupled to tandem mass spectrometry (Orbitrap Elite™ Hybrid Ion Trap-Orbitrap Mass Spectrometer) of all 40 samples as triplicate enabled the identification of 3138 different metaproteins belonging to 162 biological processes and 75 different taxonomic orders. The respective database searches were performed against UniProtKB/Swiss-Prot and seven metagenome databases. Subsequent clustering and principal component analysis of these data allowed for the identification of four main clusters associated with mesophile and thermophile process conditions, the use of upflow anaerobic sludge blanket reactors and BGP feeding with sewage sludge. Observations confirm a previous phylogenetic study of the same BGP samples that was based on 16S rRNA gene sequencing by De Vrieze et al. (Water Res 75:312-323, 2015). In particular, we identified similar microbial key players of biogas processes, namely Bacillales, Enterobacteriales, Bacteriodales, Clostridiales, Rhizobiales and Thermoanaerobacteriales as well as Methanobacteriales, Methanosarcinales and Methanococcales. For the elucidation of the main biomass degradation pathways, the most abundant 1 % of metaproteins was assigned to the KEGG map 1200 representing the central carbon metabolism. Additionally, the effect of the process parameters (i) temperature, (ii) organic loading rate (OLR), (iii) total ammonia nitrogen (TAN), and (iv) sludge retention time (SRT) on these pathways was investigated. For example, high TAN correlated with hydrogenotrophic methanogens and bacterial one-carbon metabolism, indicating syntrophic acetate oxidation.

conclusionsThis is the first large-scale metaproteome study of BGPs. Proteotyping of BGPs reveals general correlations between the microbial community structure and its function with process parameters. The monitoring of changes on the level of microbial key functions or even of the microbial community represents a well-directed tool for the identification of process problems and disturbances.Graphical abstractCorrelation between the different orders and process parameter, as well as principle component analysis of all investigated biogas plants based on the identified metaproteins.

Indexed as

Anaerobic digestionBiogasBiogas plantClusteringCommunity functionMetaProteomeAnalyzerMetaproteomicsMicrobial resource managementNetwork analysis machine learningPrincipal component analysis

Identifiers

PMID27462366
PMCPMC4960849

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.