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

Overview of attention for article published in Biotechnology for Biofuels, July 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

blogs
1 blog
twitter
4 tweeters

Citations

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32 Dimensions

Readers on

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75 Mendeley
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Title
Proteotyping of biogas plant microbiomes separates biogas plants according to process temperature and reactor type
Published in
Biotechnology for Biofuels, July 2016
DOI 10.1186/s13068-016-0572-4
Pubmed ID
Authors

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

Abstract

Methane 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. Liquid 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. This 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.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
Brazil 1 1%
Unknown 73 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 21%
Researcher 13 17%
Student > Master 9 12%
Student > Doctoral Student 8 11%
Student > Bachelor 8 11%
Other 13 17%
Unknown 8 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 24%
Biochemistry, Genetics and Molecular Biology 13 17%
Environmental Science 12 16%
Engineering 11 15%
Computer Science 5 7%
Other 5 7%
Unknown 11 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 December 2017.
All research outputs
#1,652,706
of 12,287,089 outputs
Outputs from Biotechnology for Biofuels
#122
of 953 outputs
Outputs of similar age
#48,399
of 266,894 outputs
Outputs of similar age from Biotechnology for Biofuels
#3
of 19 outputs
Altmetric has tracked 12,287,089 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 953 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 266,894 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.