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Metaproteomics of complex microbial communities in biogas plants.

Heyer R, Kohrs F, Reichl U, Benndorf D - Microb Biotechnol (2015)

Bottom Line: Therefore, metaproteomics can significantly contribute to elucidate critical steps in the conversion of biomass to methane as it delivers combined functional and phylogenetic data.Although metaproteomics analyses are challenged by sample impurities, sample complexity and redundant protein identification, and are still limited by the availability of genome sequences, recent studies have shown promising results.Finally, synergistic effects expected when metaproteomics is combined with advanced imaging techniques, metagenomics, metatranscriptomics and metabolomics are addressed.

View Article: PubMed Central - PubMed

Affiliation: Bioprocess Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, Magdeburg, 39106, Germany.

No MeSH data available.


Related in: MedlinePlus

Carbon metabolism of a mesophilic biogas plant, based on the data of Kohrs and colleagues (2014). KEGG pathway map of the carbon metabolism with the identified proteins for methanogenesis from different Archaea (red: Methanosarcinales, blue: Methanomicrobiales, gold: both groups).
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fig03: Carbon metabolism of a mesophilic biogas plant, based on the data of Kohrs and colleagues (2014). KEGG pathway map of the carbon metabolism with the identified proteins for methanogenesis from different Archaea (red: Methanosarcinales, blue: Methanomicrobiales, gold: both groups).

Mentions: Shifting to protein functions, overview plots, such as a Voronoi Treemap (Bernhardt et al., 2013) or a common pie chart, based on gene ontologies (Ashburner et al., 2000) or UniProt Keywords (Consortium, 2012) are beneficial. Even more important is the assignment of identified proteins to biochemical pathways. A straightforward mapping to MetaCyc pathways (Caspi et al., 2014) or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (Figure 3, Fig. S2) (Kanehisa and Goto, 2000) can be achieved using KEGG ontologies or enzyme commission numbers (Bairoch, 2000). Often, proteome studies result in long lists of upregulated and downregulated proteins confirmed by statistical tests (Karp and Lilley, 2007). For better exploitation of data, correlation analysis between taxa, functions or process parameters can reveal unexpected functional relationships improving the knowledge about the microbial community. Moreover, differences between BGPs can be monitored by principal component analysis or cluster analysis of protein or taxonomic profiles, e.g. cluster of different BGPs based on SDS-PAGE profiles (Heyer et al., 2013). In the future, the use of machine learning algorithms (Kelchtermans et al., 2014) might result in further improvements.


Metaproteomics of complex microbial communities in biogas plants.

Heyer R, Kohrs F, Reichl U, Benndorf D - Microb Biotechnol (2015)

Carbon metabolism of a mesophilic biogas plant, based on the data of Kohrs and colleagues (2014). KEGG pathway map of the carbon metabolism with the identified proteins for methanogenesis from different Archaea (red: Methanosarcinales, blue: Methanomicrobiales, gold: both groups).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4554464&req=5

fig03: Carbon metabolism of a mesophilic biogas plant, based on the data of Kohrs and colleagues (2014). KEGG pathway map of the carbon metabolism with the identified proteins for methanogenesis from different Archaea (red: Methanosarcinales, blue: Methanomicrobiales, gold: both groups).
Mentions: Shifting to protein functions, overview plots, such as a Voronoi Treemap (Bernhardt et al., 2013) or a common pie chart, based on gene ontologies (Ashburner et al., 2000) or UniProt Keywords (Consortium, 2012) are beneficial. Even more important is the assignment of identified proteins to biochemical pathways. A straightforward mapping to MetaCyc pathways (Caspi et al., 2014) or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (Figure 3, Fig. S2) (Kanehisa and Goto, 2000) can be achieved using KEGG ontologies or enzyme commission numbers (Bairoch, 2000). Often, proteome studies result in long lists of upregulated and downregulated proteins confirmed by statistical tests (Karp and Lilley, 2007). For better exploitation of data, correlation analysis between taxa, functions or process parameters can reveal unexpected functional relationships improving the knowledge about the microbial community. Moreover, differences between BGPs can be monitored by principal component analysis or cluster analysis of protein or taxonomic profiles, e.g. cluster of different BGPs based on SDS-PAGE profiles (Heyer et al., 2013). In the future, the use of machine learning algorithms (Kelchtermans et al., 2014) might result in further improvements.

Bottom Line: Therefore, metaproteomics can significantly contribute to elucidate critical steps in the conversion of biomass to methane as it delivers combined functional and phylogenetic data.Although metaproteomics analyses are challenged by sample impurities, sample complexity and redundant protein identification, and are still limited by the availability of genome sequences, recent studies have shown promising results.Finally, synergistic effects expected when metaproteomics is combined with advanced imaging techniques, metagenomics, metatranscriptomics and metabolomics are addressed.

View Article: PubMed Central - PubMed

Affiliation: Bioprocess Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, Magdeburg, 39106, Germany.

No MeSH data available.


Related in: MedlinePlus