Using proteins from soil or groundwater as functional biomarkers requires efficient extraction. We developed an extraction method in which the separation of proteins from the inorganic and organic constituents of the soil matrix was achieved by a combination of 0.1 m NaOH treatment and phenol extraction. Incubation with NaOH released humic acids and proteins from soil minerals, and simultaneously, disrupted microorganisms. The subsequent phenol extraction separated the proteins from the humic organic matter. Protein extracts were applied to sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and 2D-electrophoresis (2-DE). Spots and bands were excised and individual proteins identified by liquid chromatography online linked to mass spectrometry (MS) via electrospray ionization source (LC-ESI-MS). To assess the suitability of the method for the functional analysis of environmental metaproteomes, it was applied to soil that had been enriched in chlorophenoxy acid-degrading bacteria by incubation with 2,4-dichlorophenoxy acetic acid (2,4-D) for 22 days. The method was also used to analyze groundwater from the aquifer of a chlorobenzene-contaminated site. The identification of enzymes such as chlorocatechol dioxygenases was consistent with bacterial metabolic pathways expected to be expressed in these samples. The protocol enabled thus the analysis of the metaproteome of soil and groundwater samples. It thereby provides a means to study the diversity of environmental microbial communities while addressing functional aspects more directly than metagenome or even metatranscriptome analysis.

In nature microorganisms live in complex microbial communities. Comprehensive taxonomic and functional knowledge about microbial communities supports medical and technical application such as fecal diagnostics as well as operation of biogas plants or waste water treatment plants. Furthermore, microbial communities are crucial for the global carbon and nitrogen cycle in soil and in the ocean. Among the methods available for investigation of microbial communities, metaproteomics can approximate the activity of microorganisms by investigating the protein content of a sample. Although metaproteomics is a very powerful method, issues within the bioinformatic evaluation impede its success. In particular, construction of databases for protein identification, grouping of redundant proteins as well as taxonomic and functional annotation pose big challenges. Furthermore, growing amounts of data within a metaproteomics study require dedicated algorithms and software. This review summarizes recent metaproteomics software and addresses the introduced issues in detail.

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.