Metaproteome Analysis of Complex Microbial Communities

Microorganisms represent a significant proportion of biomass on earth and drive many natural cycles. Several analytical techniques allow the description of the structural and functional microbial diversity in ecosystems. Nucleic acids and phospholipid fatty acids are often used as biomarkers for microbial identity and metabolic potential in environmental samples. However, these approaches are not suitable to detect actual biological activities. In contrast to genes and lipids proteins are directly involved in metabolism and represent biological activities. Thus this project is dedicated to establish the analysis of proteins extracted from natural and technical microbial communities [1]. Protocols for extraction and gel-based (2D-electrophoresis) or gel-free (LC-MS/MS) analysis of proteins from wastewater treatment [2] and biogas [3-5] plants are currently improved. These protocols are being applied to laboratory as well as industrial scale plants in to screen for biomarkers that can be used to increase the performance of these bioprocesses. Furthermore, suitable software to handle comprehensive MS data sets (MetaProteomeAnalyzer) is programmed to analyze community composition and to identify metabolic pathways [6].

Workflow of metaproteome analysis.

© Otto-von-Guericke-University Magdeburg (Bioprocess Engineering)

Workflow of metaproteome analysis.

© Otto-von-Guericke-University Magdeburg (Bioprocess Engineering)

References

[1] Benndorf, D.; Balcke, G.U.; Harms, H; von Bergen, M. Functional metaproteome analysis of protein extracts from contaminated soil and groundwater. The ISME Journal 1 pp. 224-234 (2007).

[2] Püttker, S.; Kohrs, F.; Benndorf, D.; Heyer, R.; Rapp, E.; Reichl, U. Metaproteomics of activated sludge from a wastewater treatment plant - A pilot study. Proteomics (2015).

[3] Theuerl, S.; Kohrs, F.; Benndorf, D.; Maus, I.; Wibberg, D.; Schlüter, A.; Kausmann, R.; Heiermann, M.; Rapp, E.; Reichl, U.; Pühler, A.; Klocke, M. Community shifts in a well-operating agricultural biogas plant: how process variations are handled by the microbiome. Appl Microbiol Biotechnol. 99, pp. 7791-7803 (2015).

[4] Heyer, R.; Kohrs, F.; Reichl, U.; Benndorf, D. Metaproteomics of complex microbial communities in biogas plants. Microb. Biotechnol. 8, pp. 749–763 (2015).

[5] Kohrs, F.; Wolter, S.; Benndorf, D.; Heyer, R.; Hoffmann, M.; Rapp, E.; Bremges, A.; Sczyrba, A.; Schlüter, A.; Reichl, U. Fractionation of biogas plant sludge material improves metaproteomic characterization to investigate metabolic activity of microbial communities. Proteomics (2015).

[6] Muth, T.; Behne, A.; Heyer, R.; Kohrs, F.; Benndorf, D.; Hoffmann, M.; Lehtevä, M.; Reichl, U.; Martens, L.; Rapp, E. The MetaProteomeAnalyzer: a powerful open-source software suite for metaproteomics data analysis and interpretation. J Proteome Res.14, pp.1557-1565 (2015).