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Acetate is a characteristic by-product of Escherichia coli K-12 growing in batch cultures with glucose, both under aerobic as well as anaerobic conditions. While the reason underlying aerobic acetate production is still under discussion, during anaerobic growth acetate production is important for ATP generation by substrate level phosphorylation. Under both conditions, acetate is produced by a pathway consisting of the enzyme phosphate acetyltransferase (Pta) producing acetyl-phosphate from acetyl-coenzyme A, and of the enzyme acetate kinase (AckA) producing acetate from acetyl-phosphate, a reaction that is coupled to the production of ATP. Mutants in the AckA-Pta pathway differ from each other in the potential to produce and accumulate acetyl-phosphate. In the publication at hand, we investigated different mutants in the acetate pathway, both under aerobic as well as anaerobic conditions. While under aerobic conditions only small changes in growth rate were observed, all acetate mutants showed severe reduction in growth rate and changes in the by-product pattern during anaerobic growth. The AckA– mutant showed the most severe growth defect. The glucose uptake rate and the ATP concentration were strongly reduced in this strain. This mutant exhibited also changes in gene expression. In this strain, the atoDAEB operon was significantly upregulated under anaerobic conditions hinting to the production of acetoacetate. During anaerobic growth, protein acetylation increased significantly in the ackA mutant. Acetylation of several enzymes of glycolysis and central metabolism, of aspartate carbamoyl transferase, methionine synthase, catalase and of proteins involved in translation was increased. Supplementation of methionine and uracil eliminated the additional growth defect of the ackA mutant. The data show that anaerobic, fermentative growth of mutants in the AckA-Pta pathway is reduced but still possible. Growth reduction can be explained by the lack of an important ATP generating pathway of mixed acid fermentation. An ackA deletion mutant is more severely impaired than pta or ackA-pta deletion mutants. This is most probably due to the production of acetyl-P in the ackA mutant, leading to increased protein acetylation.

Metaproteomics, the mass spectrometry-based analysis of proteins from multispecies samples faces severe challenges concerning data analysis and results interpretation. To overcome these shortcomings, we here introduce the MetaProteomeAnalyzer (MPA) Portable software. In contrast to the original server-based MPA application, this newly developed tool no longer requires computational expertise for installation and is now independent of any relational database system. In addition, MPA Portable now supports state-of-the-art database search engines and a convenient command line interface for high-performance data processing tasks. While search engine results can easily be combined to increase the protein identification yield, an additional two-step workflow is implemented to provide sufficient analysis resolution for further postprocessing steps, such as protein grouping as well as taxonomic and functional annotation. Our new application has been developed with a focus on intuitive usability, adherence to data standards, and adaptation to Web-based workflow platforms. The open source software package can be found at https://github.com/compomics/meta-proteome-analyzer .

The investigation of microbial proteins by mass spectrometry (metaproteomics) is a key technology for simultaneously assessing the taxonomic composition and the functionality of microbial communities in medical, environmental, and biotechnological applications. We present an improved metaproteomics workflow using an updated sample preparation and a new version of the MetaProteomeAnalyzer software for data analysis. High resolution by multidimensional separation (GeLC, MudPIT) was sacrificed to aim at fast analysis of a broad range of different samples in less than 24 h. The improved workflow generated at least two times as many protein identifications than our previous workflow, and a drastic increase of taxonomic and functional annotations. Improvements of all aspects of the workflow, particularly the speed, are first steps toward potential routine clinical diagnostics (i.e., fecal samples) and analysis of technical and environmental samples. The MetaProteomeAnalyzer is provided to the scientific community as a central remote server solution at www.mpa.ovgu.de.

Constraint-based modeling (CBM) is increasingly used to analyze the metabolism of complex microbial communities involved in ecology, biomedicine, and various biotechnological processes. While CBM is an established framework for studying the metabolism of single species with linear stoichiometric models, CBM of communities with balanced growth is more complicated, not only due to the larger size of the multi-species metabolic network but also because of the bilinear nature of the resulting community models. Moreover, the solution space of these community models often contains biologically unrealistic solutions, which, even with model linearization and under application of certain objective functions, cannot easily be excluded. Here we present RedCom, a new approach to build reduced community models in which the metabolisms of the participating organisms are represented by net conversions computed from the respective single-species networks. By discarding (single-species) net conversions that violate a minimality criterion in the exchange fluxes, it is ensured that unrealistic solutions in the community model are excluded where a species altruistically synthesizes large amounts of byproducts (instead of biomass) to fulfill the requirements of other species. We employed the RedCom approach for modeling communities of up to nine organisms involved in typical degradation steps of anaerobic digestion in biogas plants. Compared to full (bilinear and linearized) community models, we found that the reduced community models obtained with RedCom are not only much smaller but allow, also in the largest model with nine species, extensive calculations required to fully characterize the solution space and to reveal key properties of communities with maximum methane yield and production rates. Furthermore, the predictive power of the reduced community models is significantly larger because they predict much smaller ranges of feasible community compositions and exchange fluxes still being consistent with measurements obtained from enrichment cultures. For an enrichment culture for growth on ethanol, we also used metaproteomic data to further constrain the solution space of the community models. Both model and proteomic data indicated a dominance of acetoclastic methanogens (Methanosarcinales) and Desulfovibrionales being the least abundant group in this microbial community., Author summary Microbial communities are involved in many fundamental processes in nature, health and biotechnology. The elucidation of interdependencies between the involved players of microbial communities and how the interactions shape the composition, behavior and characteristic features of the consortium has become an important branch of microbiology research. Many communities are based on the exchange of metabolites between the species and constraint-based metabolic modeling has become an important approach for a formal description and quantitative analysis of these metabolic dependencies. However, the complexity of the models rises quickly with a growing number of organisms and the space of predicted feasible behaviors often includes unrealistic solutions. Here we present RedCom, a new approach to build reduced stoichiometric models of balanced microbial communities based on net conversions of the single-species models. We demonstrate the applicability of our RedCom approach by modeling communities of up to nine organisms involved in degradation steps of anaerobic digestion in biogas plants. As one of the first studies in this field, we compare simulation results from the community models with experimental data of laboratory-scale biogas reactors for growth on ethanol and glucose-cellulose media. The results also demonstrate a higher predictive power of the RedCom vs. the full models.

Background In biogas plants, complex microbial communities produce methane and carbon dioxide by anaerobic digestion of biomass. For the characterization of the microbial functional networks, samples of 11 reactors were analyzed using a high-resolution metaproteomics pipeline. Results Examined methanogenesis archaeal communities were either mixotrophic or strictly hydrogenotrophic in syntrophy with bacterial acetate oxidizers. Mapping of identified metaproteins with process steps described by the Anaerobic Digestion Model 1 confirmed its main assumptions and also proposed some extensions such as syntrophic acetate oxidation or fermentation of alcohols. Results indicate that the microbial communities were shaped by syntrophy as well as competition and phage-host interactions causing cell lysis. For the families Bacillaceae, Enterobacteriaceae, and Clostridiaceae, the number of phages exceeded up to 20-fold the number of host cells. Conclusion Phage-induced cell lysis might slow down the conversion of substrates to biogas, though, it could support the growth of auxotrophic microbes by cycling of nutrients.

With today's growing focus on on-demand energy production, the dynamic operation of biogas plants is becoming more and more important. Detecting unacceptable load changes and so preventing process failures is vital. In the present contribution, we propose an operating scheme that includes the detection of disturbances and intervention. This is then evaluated systematically with the help of a mathematical model for step changes in the organic loading rate. We summarize the qualitative results for each diagnosis variable in an operation diagram, enabling an intuitive, comprehensive comparison. Of the diagnosis variables analyzed, we find that the most promising for identifying incipient process failures in biogas plants are the volatile fatty acids to total alkalinity ratio (VFA/TA), followed by the ratio of methane to carbon dioxide γ in the biogas. In addition, the combination of the online measured variables pH value and gas composition can reveal disturbances early on in a wide range of different conditions.

One option to utilize excess electric energy is its conversion to hydrogen and the subsequent methanation. An alternative to the classical chemical Sabatier process is the biological methanation (methanogenesis) within biogas plants. In conventional biogas plants methane and carbon dioxide is produced. The latter can be directly converted to methane by feeding hydrogen into the reactor, since hydrogenotrophic bacteria are present. In the present contribution, a comprehensive simulation study with respect to stationary operating conditions and disturbances is presented. It reveals two qualitative different limitations, namely a biological limit (appr. at 4 m H 2 3 / m CO 2 3 corresponds to 4.2 m H 2 , STP 3 / m liq 3 /d) as well as a transfer limit. A parameter region for a safe operation was defined. The temporary operation with stationary unfeasible conditions was analysed and thereby three qualitatively different disturbances can be distinguished. In one of these the operation for several days is possible. On the basis of these results, a controller was proposed and tested that meets the demands on the conversion of hydrogen and also prevents the washout of the microbial community due to hydrogen overload.

Complex microbial communities are the functional core of anaerobic digestion processes taking place in biogas plants (BGP). So far, however, a comprehensive characterization of the microbiomes involved in methane formation is technically challenging. As an alternative, enriched communities from laboratory-scale experiments can be investigated that have a reduced number of organisms and are easier to characterize by state of the art mass spectrometric-based (MS) metaproteomic workflows. Six parallel laboratory digesters were inoculated with sludge from a full-scale BGP to study the development of enriched microbial communities under defined conditions. During the first three month of cultivation, all reactors (R1-R6) were functionally comparable regarding biogas productions (375–625 NL L reactor volume −1 d −1 ), methane yields (50–60%), pH values (7.1–7.3), and volatile fatty acids (VFA, 1 gNH 3 L −1 ) showed an increase to pH 7.5–8.0, accumulation of acetate (>10 mM), and decreasing biogas production ( reactor volume −1 d −1 ). Tandem MS (MS/MS)-based proteotyping allowed the identification of taxonomic abundances and biological processes. Although all reactors showed similar performances, proteotyping and terminal restriction fragment length polymorphisms (T-RFLP) fingerprinting revealed significant differences in the composition of individual microbial communities, indicating multiple steady-states. Furthermore, cellulolytic enzymes and cellulosomal proteins of Clostridium thermocellum were identified to be specific markers for the thermophilic reactors (R3, R4). Metaproteins found in R3 indicated hydrogenothrophic methanogenesis, whereas metaproteins of acetoclastic methanogenesis were identified in R4. This suggests not only an individual evolution of microbial communities even for the case that BGPs are started at the same initial conditions under well controlled environmental conditions, but also a high compositional variance of microbiomes under extreme conditions.

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.

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). 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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).

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.

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.

© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.cit-journal.com P6.02 Methanisierung von erneuerbarem Wasserstoff in Biogas-Anlagen Dr. R. Hanke-Rauschenbach (E-Mail: hanke-rauschenbach@mpi-magdeburg.mpg.de), A. Bensmann, F. Kohrs, Dr. D. Benndorf, Prof. U. Reichl, Prof. K. Sundmacher Max-Planck-Institut fur Dynamik komplexer technischer Systeme, Sandtorstrase 1, D-39106 Magdeburg, Germany Otto-von-Guericke-Universitat Magdeburg, Lehrstuhl fur Systemverfahrenstechnik, Universitatsplatz 2, D-39106 Magdeburg, Germany Otto-von-Guericke-Universitat Magdeburg, Lehrstuhl fur Bioprozesstechnik, Universitatsplatz 2, D-39106 Magdeburg, Germany DOI: 10.1002/cite.201450220