Robert Heyer

Metagenome and metaproteome analyses of microbial communities in mesophilic biogas-producing anaerobic batch fermentations indicate concerted plant carbohydrate degradation

Microbial communities in biogas batch fermentations, using straw and hay as co-substrates, were analyzed at the gene and protein level by metagenomic and metaproteomic approaches. The analysis of metagenomic data revealed that the Clostridiales and Bacteroidales orders were prevalent in the community. However, the number of sequences assigned to the Clostridiales order decreased during fermentation, whereas the number of sequences assigned to the Bacteroidales order increased. In addition, changes at the functional level were monitored and the metaproteomic analyses detected transporter proteins and flagellins, which were expressed mainly by members of the Bacteroidetes and Firmicutes phyla. A high number of sugar transporters, expressed by members of the Bacteroidetes, proved their potential to take up various glycans efficiently. Metagenome data also showed that methanogenic organisms represented less than 4% of the community, while 20-30% of the identified proteins were of archeal origin. These data suggested that methanogens were disproportionally active. In conclusion, the community studied was capable of digesting the recalcitrant co-substrate. Members of the Firmicutes phylum seemed to be the main degraders of cellulose, even though expression of only a few glycoside hydrolases was detected. The Bacteroidetes phylum expressed a high number of sugar transporters and seemed to specialize in the digestion of other polysaccharides. Finally, it was found that key enzymes of methanogenesis were expressed in high quantities, indicating the high metabolic activity of methanogens, although they only represented a minor group within the microbial community.

The MetaProteomeAnalyzer: a powerful open-source software suite for metaproteomics data analysis and interpretation.

The enormous challenges of mass spectrometry-based metaproteomics are primarily related to the analysis and interpretation of the acquired data. This includes reliable identification of mass spectra and the meaningful integration of taxonomic and functional meta-information from samples containing hundreds of unknown species. To ease these difficulties, we developed a dedicated software suite, the MetaProteomeAnalyzer, an intuitive open-source tool for metaproteomics data analysis and interpretation, which includes multiple search engines and the feature to decrease data redundancy by grouping protein hits to so-called meta-proteins. We also designed a graph database back-end for the MetaProteomeAnalyzer to allow seamless analysis of results. The functionality of the MetaProteomeAnalyzer is demonstrated using a sample of a microbial community taken from a biogas plant.

Metaproteome analysis of the microbial communities in agricultural biogas plants.

In biogas plants agricultural waste and energy crops are converted by complex microbial communities to methane for the production of renewable energy. In Germany, this process is widely applied namely in context of agricultural production systems. However, process disturbances, are one of the major causes for economic losses. In addition, the conversion of biomass, in particular of cellulose, is in most cases incomplete and, hence, insufficient. Besides technical aspects, a more profound characterization concerning the functionality of the microbial communities involved would strongly support the improvement of yield and stability in biogas production. To monitor these communities on the functional level, metaproteome analysis was applied in this study to full-scale agricultural biogas plants. Proteins were extracted directly from sludge for separation by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and subsequent identification with mass spectrometry. Protein profiles obtained with SDS-PAGE were specific for different biogas plants and often stable for several months. Differences of protein profiles were visualized by clustering, which allowed not only the discrimination between mesophilic and thermophilic operated biogas plants but also the detection of process disturbances such as acidification. In particular, acidification of a biogas plant was detected in advance by disappearance of major bands in SDS-PAGE. Identification of proteins from SDS-PAGE gels revealed that methyl CoM reductase, which is responsible for the release of methane during methanogenesis, from the order Methanosarcinales was significantly decreased. Hence, it is assumed that this enzyme might be a promising candidate to serve as a predictive biomarker for acidification.

Sample prefractionation with liquid isoelectric focusing enables in depth microbial metaproteome analysis of mesophilic and thermophilic biogas plants.

Biogas production from energy crops and biodegradable waste is one of the major sources for renewable energies in Germany. Within a biogas plant (BGP) a complex microbial community converts biomass to biogas. Unfortunately, disturbances of the biogas process occur occasionally and cause economic losses of varying extent. Besides technical failures the microbial community itself is commonly assumed as a reason for process instability. To improve the performance and efficiency of BGP, a deeper knowledge of the composition and the metabolic state of the microbial community is required and biomarkers for monitoring of process deviations or even the prediction of process failures have to be identified. Previous work based on 2D-electrophoresis demonstrated that the analysis of the metaproteome is well suited to provide insights into the apparent metabolism of the microbial communities. Using SDS-PAGE with subsequent mass spectrometry, stable protein patterns were evaluated for a number of anaerobic digesters. Furthermore, it was shown that severe changes in process parameters such as acidification resulted in significant modifications of the metaproteome. Monitoring of changing protein patterns derived from anaerobic digesters, however, is still a challenge due to the high complexity of the metaproteome. In this study, different combinations of separation techniques to reduce the complexity of proteomic BGP samples were compared with respect to the subsequent identification of proteins by tandem mass spectrometry (MS/MS): (i) 1D: proteins were tryptically digested and the resulting peptides were separated by reversed phase chromatography prior to MS/MS. (ii) 2D: proteins were separated by GeLC-MS/MS according to proteins molecular weights before tryptic digestion, (iii) 3D: proteins were separated by gel-free fractionation using isoelectric focusing (IEF) conducted before GeLC-MS/MS. For this study, a comparison of two anaerobic digesters operated at mesophilic and at thermophilic conditions was conducted. The addition of further separation dimensions before protein identification increased the number of identified proteins. On the other hand additional fractionation steps increased the experimental work load and the time required for LC-MS/MS measurement. The high resolution of the 3D-approach enabled the detection of approximately 750 to 1650 proteins covering the main pathways of hydrolysis, acidogenesis, acetogenesis and methanogenesis. Methanosarcinales dominated in the mesophilic BGP, whereas Methanomicrobiales were highly abundant in the thermophilic BGP. Pathway analysis confirmed the taxonomic results and revealed that the acetoclastic methanogenesis occurred preferentially at mesophilic conditions, whereas exclusively hydrogenotrophic methanogenesis was detected in thermophilic BGP. However, for the identification of process biomarkers by comprehensive screening of BGP it will be indispensable to find a balance between the experimental efforts and analytical resolution.

Metaproteomics of complex microbial communities in biogas plants

Production of biogas from agricultural biomass or organic wastes is an important source of renewable energy. Although thousands of biogas plants (BGPs) are operating in Germany, there is still a significant potential to improve yields, e.g. from fibrous substrates. In addition, process stability should be optimized. Besides evaluating technical measures, improving our understanding of microbial communities involved into the biogas process is considered as key issue to achieve both goals. Microscopic and genetic approaches to analyse community composition provide valuable experimental data, but fail to detect presence of enzymes and overall metabolic activity of microbial communities. 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. In the following, the workflow and potential pitfalls for metaproteomics of samples from full‐scale BGP are discussed. In addition, the value of metaproteomics to contribute to the further advancement of microbial ecology is evaluated. Finally, synergistic effects expected when metaproteomics is combined with advanced imaging techniques, metagenomics, metatranscriptomics and metabolomics are addressed.

Metaproteomics of activated sludge from a wastewater treatment plant- a pilot study

In this study, the impact of protein fractionation techniques prior to LC/MS analysis was investigated on activated sludge samples derived at winter and summer condition from a full‐scale wastewater treatment plant (WWTP). For reduction of the sample complexity, different fractionation techniques including RP‐LC (1D‐approach), SDS‐PAGE and RP‐LC (2D‐approach) as well as RP‐LC, SDS‐PAGE and liquid IEF (3D‐approach) were carried out before subsequent ion trap MS analysis. The derived spectra were identified by MASCOT search using a combination of the public UniProtKB/Swiss‐Prot protein database and metagenome data from a WWTP. The results showed a significant increase of identified spectra, enabled by applying IEF and SDS‐PAGE to the proteomic workflow. Based on meta‐proteins, a core metaproteome and a corresponding taxonomic profile of the wastewater activated sludge were described. Functional aspects were analyzed using the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway library by plotting KEGG Orthology identifiers (KO numbers) of protein hits into pathway maps of the central carbon (map01200) and nitrogen metabolism (map00910). Using the 3D‐approach, most proteins involved in glycolysis and citrate cycle and nearly all proteins of the nitrogen removal were identified, qualifying this approach as most promising for future studies. All MS data have been deposited in the ProteomeXchange with identifier PXD001547 (http://proteomecentral.proteomexchange.org/dataset/PXD001547).

Fractionation of biogas plant sludge material improves metaproteomic characterization to investigate metabolic activity of microbial communities

With the development of high resolving mass spectrometers, metaproteomics evolved as a powerful tool to elucidate metabolic activity of microbial communities derived from full‐scale biogas plants. Due to the vast complexity of these microbiomes, application of suitable fractionation methods are indispensable, but often turn out to be time and cost intense, depending on the method used for protein separation. In this study, centrifugal fractionation has been applied for fractionation of two biogas sludge samples to analyze proteins extracted from (i) crude fibers, (ii) suspended microorganisms, and (iii) secreted proteins in the supernatant using a gel‐based approach followed by LC‐MS/MS identification. This fast and easy method turned out to be beneficial to both the quality of SDS‐PAGE and the identification of peptides and proteins compared to untreated samples. Additionally, a high functional metabolic pathway coverage was achieved by combining protein hits found exclusively in distinct fractions. Sample preparation using centrifugal fractionation influenced significantly the number and the types of proteins identified in the microbial metaproteomes. Thereby, comparing results from different proteomic or genomic studies, the impact of sample preparation should be considered. All MS data have been deposited in the ProteomeXchange with identifier PXD001508 (http://proteomecentral.proteomexchange.org/dataset/PXD001508).

Challenges and perspectives of metaproteomic data 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.

Interactive Chord Visualization for Metaproteomics

Metaproteomics is an analytic approach to research microorganisms that live in complex microbial communities. A key aspect of understanding microbial communities is to link the functions of proteins identified by metaproteomics to their taxonomy. In this paper we demonstrate the interactive chord visualization as a powerful tool to explore such data. To evaluate the tools efficacy, we use the relation data between functions and taxonomies from a large metaproteomics experiment. We evaluated the work flow in comparison to previous methods of data analysis and showed that interactive exploration of data using the chord diagram is significantly faster in four of five tasks. Therefore, the chord visualization improves the users ability to discover complex biological relationships.

Protein Identification as a Suitable Application for Fast Data Architecture

Metaproteomics is a field of biology research that relies on mass spectrometry to characterize the protein complement of microbiological communities. Since only identified data can be analyzed, identification algorithms such as X!Tandem, OMSSA and Mascot are essential in the domain, to get insights into the biological experimental data. However, protein identification software has been developed for proteomics. Metaproteomics, in contrast, involves large biological communities, gigabytes of experimental data per sample, and greater amounts of comparisons, given the mixed culture of species in the protein database. Furthermore, the file-based nature of current protein identification tools makes them ill-suited for future metaproteomics research. In addition, possible medical use cases of metaproteomics require near real-time identification. From the technology perspective, Fast Data seems promising to increase throughput and performance of protein identification in a metaproteomics workflow. In this paper we analyze the core functions of the established protein identification engine X!Tandem and show that streaming Fast Data architectures are suitable for protein identification. Furthermore, we point out the bottlenecks of the current algorithms and how to remove them with our approach.