Metaproteome analysis of the microbial communities in agricultural biogas plants

Energetically exploiting lignocellulose-rich residues in anaerobic digestion technologies: from bioreactors to proteogenomics

The biogas produced through anaerobic digestion (AD) of renewable feedstocks is one of the promising alternatives to replace fossil-derived energy. Even though lignocellulosic biomass is the most abundant biomass on earth, only a small fraction is being used towards resources recovery, leaving a great potential unexploited. In this study, the combination of state-of-art genomic techniques and engineered systems were used to further advance the knowledge on biogas production from lignocellulosic-rich residues and the microbiome involved in the anaerobic digestion hereof. A long-term adapted anaerobic microbiome capable of degrading wheat straw as the sole substrate was investigated using protein stable isotope probing (protein-SIP). The results indicated that a diverse microbial community, primarily composed of Firmicutes and Methanogens, played crucial roles in cellulose degradation and methane production. Notably, Defluviitoga tunisiensis, Syntrophothermus lipocalidus, and Pelobacter carbinolicus were identified as direct metabolizers of cellulose, while Dehalobacterium assimilated labelled carbon through cross-feeding. This study provides direct evidence of primary cellulose degraders and sheds light on their genomic composition. By harnessing the potential of lignocellulosic biomass and understanding the microbial communities involved, we can promote sustainable biogas production, contributing to energy security and environmental preservation.

Uncovering Microbiome Adaptations in a Full-Scale Biogas Plant: Insights from MAG-Centric Metagenomics and Metaproteomics

The current focus on renewable energy in global policy highlights the importance of methane production from biomass through anaerobic digestion (AD). To improve biomass digestion while ensuring overall process stability, microbiome-based management strategies become more important. In this study, metagenomes and metaproteomes were used for metagenomically assembled genome (MAG)-centric analyses to investigate a full-scale biogas plant consisting of three differentially operated digesters. Microbial communities were analyzed regarding their taxonomic composition, functional potential, as well as functions expressed on the proteome level. Different abundances of genes and enzymes related to the biogas process could be mostly attributed to different process parameters. Individual MAGs exhibiting different abundances in the digesters were studied in detail, and their roles in the hydrolysis, acidogenesis and acetogenesis steps of anaerobic digestion could be assigned. Methanoculleus thermohydrogenotrophicum was an active hydrogenotrophic methanogen in all three digesters, whereas Methanothermobacter wolfeii was more prevalent at higher process temperatures. Further analysis focused on MAGs, which were abundant in all digesters, indicating their potential to ensure biogas process stability. The most prevalent MAG belonged to the class Limnochordia; this MAG was ubiquitous in all three digesters and exhibited activity in numerous pathways related to different steps of AD.

Assessing labelled carbon assimilation from poly butylene adipate-co-terephthalate (PBAT) monomers during thermophilic anaerobic digestion

PBAT (poly butylene adipate-co-terephthalate) is a widely used biodegradable plastic, but the knowledge about its metabolization in anaerobic environments is very limited. In this study, the anaerobic digester sludge from a municipal wastewater treatment plant was used as inoculum to investigate the biodegradability of PBAT monomers in thermophilic conditions. The research employs a combination of ¹³C-labelled monomers and proteogenomics to track the labelled carbon and identify the microorganisms involved. A total of 122 labelled peptides of interest were identified for adipic acid (AA) and 1,4-butanedio (BD). Through the time-dependent isotopic enrichment and isotopic profile distributions, Bacteroides, Ichthyobacterium, and Methanosarcina were proven to be directly involved in the metabolization of at least one monomer. This study provides a first insight into the identity and genomic potential of microorganisms responsible for biodegradability of PBAT monomers during anaerobic digestion under thermophilic conditions.

In silico evaluation of a targeted metaproteomics strategy for broad screening of cellulolytic enzyme capacities in anaerobic microbiome bioreactors

Background

Microbial-driven solubilization of lignocellulosic material is a natural mechanism that is exploited in anaerobic digesters (ADs) to produce biogas and other valuable bioproducts. Glycoside hydrolases (GHs) are the main enzymes that bacterial and archaeal populations use to break down complex polysaccharides in these reactors. Methodologies for rapidly screening the physical presence and types of GHs can provide information about their functional activities as well as the taxonomical diversity within AD systems but are largely unavailable. Targeted proteomic methods could potentially be used to provide snapshots of the GHs expressed by microbial consortia in ADs, giving valuable insights into the functional lignocellulolytic degradation diversity of a community. Such observations would be essential to evaluate the hydrolytic performance of a reactor or potential issues with it.

Results

As a proof of concept, we performed an in silico selection and evaluation of groups of tryptic peptides from five important GH families derived from a dataset of 1401 metagenome-assembled genomes (MAGs) in anaerobic digesters. Following empirical rules of peptide-based targeted proteomics, we selected groups of shared peptides among proteins within a GH family while at the same time being unique compared to all other background proteins. In particular, we were able to identify a tractable unique set of peptides that were sufficient to monitor the range of GH families. While a few thousand peptides would be needed for comprehensive characterization of the main GH families, we found that at least 50% of the proteins in these families (such as the key families) could be tracked with only 200 peptides. The unique peptides selected for groups of GHs were found to be sufficient for distinguishing enzyme specificity or microbial taxonomy. These in silico results demonstrate the presence of specific unique GH peptides even in a highly diverse and complex microbiome and reveal the potential for development of targeted metaproteomic approaches in ADs or lignocellulolytic microbiomes. Such an approach could be valuable for estimating molecular-level enzymatic capabilities and responses of microbial communities to different substrates or conditions, which is a critical need in either building or utilizing constructed communities or defined cultures for bio-production.

Conclusions

This in silico study demonstrates the peptide selection strategy for quantifying relevant groups of GH proteins in a complex anaerobic microbiome and encourages the development of targeted metaproteomic approaches in fermenters. The results revealed that targeted metaproteomics could be a feasible approach for the screening of cellulolytic enzyme capacities for a range of anaerobic microbiome fermenters and thus could assist in bioreactor evaluation and optimization.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13068-022-02125-x.

Adaptation of a microbial community to demand-oriented biological methanation

BACKGROUND

Biological conversion of the surplus of renewable electricity and carbon dioxide (CO₂) from biogas plants to biomethane (CH₄) could support energy storage and strengthen the power grid. Biological methanation (BM) is linked closely to the activity of biogas-producing Bacteria and methanogenic Archaea. During reactor operations, the microbiome is often subject to various changes, e.g., substrate limitation or pH-shifts, whereby the microorganisms are challenged to adapt to the new conditions. In this study, various process parameters including pH value, CH₄ production rate, conversion yields and final gas composition were monitored for a hydrogenotrophic-adapted microbial community cultivated in a laboratory-scale BM reactor. To investigate the robustness of the BM process regarding power oscillations, the biogas microbiome was exposed to five hydrogen (H₂)-feeding regimes lasting several days.

RESULTS

Applying various "on-off" H₂-feeding regimes, the CH₄ production rate recovered quickly, demonstrating a significant resilience of the microbial community. Analyses of the taxonomic composition of the microbiome revealed a high abundance of the bacterial phyla Firmicutes, Bacteroidota and Thermotogota followed by hydrogenotrophic Archaea of the phylum Methanobacteriota. Homo-acetogenic and heterotrophic fermenting Bacteria formed a complex food web with methanogens. The abundance of the methanogenic Archaea roughly doubled during discontinuous H₂-feeding, which was related mainly to an increase in acetoclastic Methanothrix species. Results also suggested that Bacteria feeding on methanogens could reduce overall CH₄ production. On the other hand, using inactive biomass as a substrate could support the growth of methanogenic Archaea. During the BM process, the additional production of H₂ by fermenting Bacteria seemed to support the maintenance of hydrogenotrophic methanogens at non-H₂-feeding phases. Besides the elusive role of Methanothrix during the H₂-feeding phases, acetate consumption and pH maintenance at the non-feeding phase can be assigned to this species.

CONCLUSIONS

Taken together, the high adaptive potential of microbial communities contributes to the robustness of BM processes during discontinuous H₂-feeding and supports the commercial use of BM processes for energy storage. Discontinuous feeding strategies could be used to enrich methanogenic Archaea during the establishment of a microbial community for BM. Both findings could contribute to design and improve BM processes from lab to pilot scale.

Proteogenomics identification of TBBPA degraders in anaerobic bioreactor

Tetrabromobisphenol A (TBBPA) is the most used flame retardant worldwide and has become a threat to aquatic ecosystems. Previous research into the degradation of this micropollutant in anaerobic bioreactors has suggested several identities of putative TBBPA degraders. However, the organisms actively degrading TBBPA under in situ conditions have so far not been identified. Protein-stable isotope probing (protein-SIP) has become a cutting-edge technique in microbial ecology for enabling the link between identity and function under in situ conditions. Therefore, it was hypothesized that combining protein-based stable isotope probing with metagenomics could be used to identify and provide genomic insight into the TBBPA-degrading organisms. The identified ¹³C-labelled peptides were found to belong to organisms affiliated to Phytobacter, Clostridium, Sporolactobacillus, and Klebsilla genera. The functional classification of identified labelled peptides revealed that TBBPA is not only transformed by cometabolic reactions, but also assimilated into the biomass. By application of the proteogenomics with labelled micropollutants (protein-SIP) and metagenome-assembled genomes, it was possible to extend the current perspective of the diversity of TBBPA degraders in wastewater and predict putative TBBPA degradation pathways. The study provides a link to the active TBBPA degraders and which organisms to favor for optimized biodegradation.

Unravelling gradient layers of microbial communities, proteins, and chemical structure in aerobic granules

Most bacteria live in microbial assemblages like biofilms and granules, and each layer of these assemblages provides a niche for certain bacteria with specific metabolic functions. In this study, a gentle (non-destructive) extraction approach based on a cation exchange resin and defined shear was employed to gradually disintegrate biomass and collect single layers of aerobic granules from a full-scale municipal wastewater treatment plant. The microbial community composition of granule layers was characterized using next-generation sequencing (NGS) targeting the 16S rRNA gene, and protein composition was investigated using metaproteomics. The chemical composition of eroded layers was explored using Fourier Transformed Infrared Spectroscopy. On the surface of the granules, the microbial structure (flocculation-supporting Nannocystis sp.) as well as composition of extracellular polymers (extracellular DNA) and proteome (chaperonins and binding proteins) favored microbial aggregation. Extracellular polymeric substances in the granules were composed of mostly proteins and EPS-producers, such as Tetrasphaera sp. and Zoogloea sp., were evenly distributed throughout the granule structure. The interior of the granules harbored several denitrifiers (e.g., Thauera sp.), phosphate-accumulating denitrifiers (Candidatus Accumulibacter, Dechloromonas sp.) and nitrifiers (Candidatus Nitrotoga). Proteins associated with glycolytic activity were identified in the outer and middle granule layers, and proteins associated with phosphorus conversions, in the deeper layers. In conclusion, the use of an existing cation-exchange resin for gradual biomass disintegration, combined with NGS and metaproteomic analysis was demonstrated as a promising approach for simultaneously investigating the identity and functions of microbes in multilayered biofilm structures.

Lignocellulose Fermentation Products Generated by Giant Panda Gut Microbiomes Depend Ultimately on pH Rather than Portion of Bamboo: A Preliminary Study

Giant pandas feed almost exclusively on bamboo but miss lignocellulose-degrading genes. Their gut microbiome may contribute to their nutrition; however, the limited access to pandas makes experimentation difficult. In vitro incubation of dung samples is used to infer gut microbiome activity. In pandas, such tests indicated that green leaves are largely fermented to ethanol at neutral pH and yellow pith to lactate at acidic pH. Pandas may feed on either green leaves or yellow pith within the same day, and it is unclear how pH, dung sample, fermentation products and supplied bamboo relate to one another. Additionally, the gut microbiome contribution to solid bamboo digestion must be appropriately assessed. Here, gut microbiomes derived from dung samples with mixed colors were used to ferment green leaves, also by artificially adjusting the initial pH. Gut microbiomes digestion of solid lignocellulose accounted for 30–40% of the detected final fermentation products. At pH 6.5, mixed-color dung samples had the same fermentation profile as green dung samples (mainly alcohols), while adjusting the initial pH to 4.5 resulted in the profile of yellow dung samples (mainly lactate). Metaproteomics confirmed that gut microbiomes attacked hemicellulose, and that the panda’s alpha amylase was the predominant enzyme (up to 75%).

Characterisation of cellulose-degrading organisms in an anaerobic digester

The recalcitrant nature of lignocellulosic biomass hinders efficient exploitation of this fraction for energy production. A better understanding of the microorganisms able to convert plant-based feedstocks is needed to improve anaerobic digestion of lignocellulosic biomass. In this study, active thermophilic cellulose-degrading microorganisms were identified from a full-scale anaerobic digester fed with maize by using metagenome-resolved protein stable isotope probing (protein-SIP). ¹³C-cellulose was converted into ¹³C-methane with a ^13/12C isotope ratio of 0.127 after two days of incubation. Metagenomic analysis revealed 238 different genes coding for carbohydrate-active enzymes (CAZymes), six of which were directly associated with cellulose degradation. The protein-SIP analysis identified twenty heavily labelled peptides deriving from microorganisms actively assimilating labelled carbon from the degradation of ¹³C-cellulose, highlighting several members of the order Clostridiales. Corynebacterium was identified through CAZyme screening, amplicon analysis, and in the metagenome giving a strong identification of being a cellulose degrader.

Triangulation of microbial fingerprinting in anaerobic digestion reveals consistent fingerprinting profiles

The anaerobic digestion microbiome has been puzzling us since the dawn of molecular methods for mixed microbial community analysis. Monitoring of the anaerobic digestion microbiome can either take place via a non-targeted holistic evaluation of the microbial community through fingerprinting or by targeted monitoring of selected taxa. Here, we compared four different microbial community fingerprinting methods, i.e., amplicon sequencing, metaproteomics, metabolomics and cytomics, in their ability to characterise the full-scale anaerobic digestion microbiome. Cytometric fingerprinting through cytomics reflects a, for anaerobic digestion, novel, single cell-based approach of direct microbial community fingerprinting by flow cytometry. Three different digester types, i.e., sludge digesters, digesters treating agro-industrial waste and dry anaerobic digesters, each reflected different operational parameters. The α-diversity analysis yielded inconsistent results, especially for richness, across the different methods. In contrast, β-diversity analysis resulted in comparable profiles, even when translated into phyla or functions, with clear separation of the three digester types. In-depth analysis of each method's features i.e., operational taxonomic units, metaproteins, metabolites, and cytometric traits, yielded certain similar features, yet, also some clear differences between the different methods, which was related to the complexity of the anaerobic digestion process. In conclusion, cytometric fingerprinting through flow cytometry is a reliable, fast method for holistic monitoring of the anaerobic digestion microbiome, and the complementary identification of key features through other methods could give rise to a direct interpretation of anaerobic digestion process performance.

Microbiological Surveillance of Biogas Plants: Targeting Acetogenic Community

Acetogens play a very important role in anaerobic digestion and are essential in ensuring process stability. Despite this, targeted studies of the acetogenic community in biogas processes remain limited. Some efforts have been made to identify and understand this community, but the lack of a reliable molecular analysis strategy makes the detection of acetogenic bacteria tedious. Recent studies suggest that screening of bacterial genetic material for formyltetrahydrofolate synthetase (FTHFS), a key marker enzyme in the Wood-Ljungdahl pathway, can give a strong indication of the presence of putative acetogens in biogas environments. In this study, we applied an acetogen-targeted analyses strategy developed previously by our research group for microbiological surveillance of commercial biogas plants. The surveillance comprised high-throughput sequencing of FTHFS gene amplicons and unsupervised data analysis with the AcetoScan pipeline. The results showed differences in the acetogenic community structure related to feed substrate and operating parameters. They also indicated that our surveillance method can be helpful in the detection of community changes before observed changes in physico-chemical profiles, and that frequent high-throughput surveillance can assist in management towards stable process operation, thus improving the economic viability of biogas plants. To our knowledge, this is the first study to apply a high-throughput microbiological surveillance approach to visualise the potential acetogenic population in commercial biogas digesters.

Molecular Microbial Community Analysis as an Analysis Tool for Optimal Biogas Production

The microbial diversity in anaerobic digestion (AD) is important because it affects process robustness. High-throughput sequencing offers high-resolution data regarding the microbial diversity and robustness of biological systems including AD; however, to understand the dynamics of microbial processes, knowing the microbial diversity is not adequate alone. Advanced meta-omic techniques have been established to determine the activity and interactions among organisms in biological processes like AD. Results of these methods can be used to identify biomarkers for AD states. This can aid a better understanding of system dynamics and be applied to producing comprehensive models for AD. The paper provides valuable knowledge regarding the possibility of integration of molecular methods in AD. Although meta-genomic methods are not suitable for on-line use due to long operating time and high costs, they provide extensive insight into the microbial phylogeny in AD. Meta-proteomics can also be explored in the demonstration projects for failure prediction. However, for these methods to be fully realised in AD, a biomarker database needs to be developed.

Effective pretreatment of lignocellulosic co-substrates using barley straw-adapted microbial consortia to enhanced biomethanation by anaerobic digestion

Microbial pretreatments have been identified as a compatible and sustainable process with anaerobic digestion compared to energy-intensive physicochemical pretreatments. In this study, barley straw and hay co-substrate was pretreated with a microaerobic barley straw-adapted microbial (BSAM) consortium prior to anaerobic digestion. The improved digestibility was investigated through 16S rRNA gene sequencing, microbial counts and C:N ratios. BSAM pretreatment resulted in 15.2 L kg^-1 TS of methane yield after 35 days, almost 40 times more than the control. The methane content in total biogas produced were 58% (v/v) and 10% (v/v) in BSAM and control, respectively. This research demonstrated that BSAM-based pretreatment significantly increased the digestibility and surface area of the lignocellulosic material and considerably enhanced biomethanation. This study generates new potential bio-research opportunities in the emerging field of lignocellulosic anaerobic digestion-biorefineries.

A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane

Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.

Effect of granular activated carbon pore-size distribution on biological activated carbon filter performance

Proper granular activated carbon (GAC) selection could improve the performance of biological activated carbon (BAC) filters through a combination of adsorption and biodegradation, while the GACs used in BAC filters are now mainly selected according to adsorption function, ignoring biodegradation. In this study, sand filter effluent obtained from a drinking water treatment plant was fed into continuous-flow bench-scale BAC columns operated in parallel over 245 days to examine the effects of GAC pore-size distribution on BAC filter performance, in terms of the dissolved organic carbon (DOC) and disinfection byproduct (DBP) precursors. A metagenomic analysis indicated that bacterial community structure played an important role in BAC filter performance. A significant correlation was found between metabolism-related proteins and the volume of micro-level macropores based on metaproteomic analysis. It is suggested that the adsorption saturation was dynamic and that adsorption played a role in the performance of the BAC filters throughout the 245-day operating period. Renewed adsorption capacity, or bioregeneration, was driven by bacterial metabolic activity. Such activity largely depended on the organic matter adsorbed by the GAC, in which micro-level macropores, especially those with diameters of 0.2-10 μm, played an important but previously unrecognized role. The results suggest that more attention should be paid to well-developed pores and pore-size distribution in the production and selection of GAC used for full-scale drinking water biofilters.

Substrate-Dependent Fermentation of Bamboo in Giant Panda Gut Microbiomes: Leaf Primarily to Ethanol and Pith to Lactate

The giant panda is known worldwide for having successfully moved to a diet almost exclusively based on bamboo. Provided that no lignocellulose-degrading enzyme was detected in panda's genome, bamboo digestion is believed to depend on its gut microbiome. However, pandas retain the digestive system of a carnivore, with retention times of maximum 12 h. Cultivation of their unique gut microbiome under controlled laboratory conditions may be a valid tool to understand giant pandas' dietary habits, and provide valuable insights about what component of lignocellulose may be metabolized. Here, we collected gut microbiomes from fresh fecal samples of a giant panda (either entirely green or yellow stools) and supplied them with green leaves or yellow pith (i.e., the peeled stem). Microbial community composition was substrate dependent, and resulted in markedly different fermentation profiles, with yellow pith fermented to lactate and green leaves to lactate, acetate and ethanol, the latter to strikingly high concentrations (∼3%, v:v, within 3.5 h). Microbial metaproteins pointed to hemicellulose rather than cellulose degradation. The alpha-amylase from the giant panda (E.C. 3.2.1.1) was the predominant identified metaprotein, particularly in reactors inoculated with pellets derived from fecal samples (up to 60%). Gut microbiomes assemblage was most prominently impacted by the change in substrate (either leaf or pith). Removal of soluble organics from inocula to force lignocellulose degradation significantly enriched Bacteroides (in green leaf) and Escherichia/Shigella (in yellow pith). Overall, different substrates (either leaf or pith) markedly shaped gut microbiome assemblies and fermentation profiles. The biochemical profile of fermentation products may be an underestimated factor contributing to explain the peculiar dietary behavior of giant pandas, and should be implemented in large scale studies together with short-term lab-scale cultivation of gut microbiomes.

Pseudomonas spp. are key players in agricultural biogas substrate degradation

Anaerobic degradation (AD) of heterogeneous agricultural substrates is a complex process involving a diverse microbial community. While microbial community composition of a variety of biogas plants (BPs) is well described, little is known about metabolic processes and microbial interaction patterns. Here, we analyzed 16 large-scale BPs using metaproteomics. All metabolic steps of AD were observed in the metaproteome, and multivariate analyses indicated that they were shaped by temperature, pH, volatile fatty acid content and substrate types. Biogas plants could be subdivided into hydrogenotrophic, acetoclastic or a mixture of both methanogenic pathways based on their process parameters, taxonomic and functional metaproteome. Network analyses showed large differences in metabolic and microbial interaction patterns. Both, number of interactions and interaction partners were highly dependent on the prevalent methanogenic pathway for most species. Nevertheless, we observed a highly conserved metabolism of different abundant Pseudomonas spp. for all BPs indicating a key role during AD in carbohydrate hydrolysis irrespectively of variabilities in substrate input and process parameters. Thus, Pseudomonas spp. are of high importance for robust and versatile AD food webs, which highlight a large variety of downstream metabolic processes for their respective methanogenic pathways.

Immediate Effects of Ammonia Shock on Transcription and Composition of a Biogas Reactor Microbiome

The biotechnological process of biogas production from organic material is carried out by a diverse microbial community under anaerobic conditions. However, the complex and sensitive microbial network present in anaerobic degradation of organic material can be disturbed by increased ammonia concentration introduced into the system by protein-rich substrates and imbalanced feeding. Here, we report on a simulated increase of ammonia concentration in a fed batch lab-scale biogas reactor experiment. Two treatment conditions were used simulating total ammonia nitrogen concentrations of 4.9 and 8.0 g/L with four replicate reactors. Each reactor was monitored concerning methane generation and microbial composition using 16S rRNA gene amplicon sequencing, while the transcriptional activity of the overall process was investigated by metatranscriptomic analysis. This allowed investigating the response of the microbial community in terms of species composition and transcriptional activity to a rapid upshift to high ammonia conditions. Clostridia and Methanomicrobiales dominated the microbial community throughout the entire experiment under both experimental conditions, while Methanosarcinales were only present in minor abundance. Transcription analysis demonstrated clostridial dominance with respect to genes encoding for enzymes of the hydrolysis step (cellulase, EC 3.2.1.4) as well as dominance of key genes for enzymes of the methanogenic pathway (methyl-CoM reductase, EC 2.8.4.1; heterodisulfide reductase, EC 1.8.98.1). Upon ammonia shock, the selected marker genes showed significant changes in transcriptional activity. Cellulose hydrolysis as well as methanogenesis were significantly reduced at high ammonia concentrations as indicated by reduced transcription levels of the corresponding genes. Based on these experiments we concluded that, apart from the methanogenic archaea, hydrolytic cellulose-degrading microorganisms are negatively affected by high ammonia concentrations. Further, Acholeplasma and Erysipelotrichia showed lower abundance under increased ammonia concentrations and thus might serve as indicator species for an earlier detection in order to counteract against ammonia crises.

A Robust and Universal Metaproteomics Workflow for Research Studies and Routine Diagnostics Within 24 h Using Phenol Extraction, FASP Digest, and the MetaProteomeAnalyzer

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.

Metaproteome analysis reveals that syntrophy, competition, and phage-host interaction shape microbial communities in biogas plants

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.

Metaproteomics of fecal samples of Crohn's disease and Ulcerative Colitis

Crohn's Disease (CD) and Ulcerative Colitis (UC) are chronic inflammatory bowel diseases (IBD) of the gastrointestinal tract. This study used non-invasive LC-MS/MS to find disease specific microbial and human proteins which might be used later for an easier diagnosis. Therefore, 17 healthy controls, 11 CD patients and 14 UC patients but also 13 Irritable Bowel Disease (IBS) patients, 8 Colon Adenoma (CA) patients, and 8 Gastric Carcinoma (GCA) patients were investigated. The proteins were extracted from the fecal samples with liquid phenol in a ball mill. Subsequently, the proteins were digested tryptically to peptides and analyzed by an Orbitrap LC-MS/MS. For protein identification and interpretation of taxonomic and functional results, the MetaProteomeAnalyzer software was used. Cluster analysis and non-parametric test (analysis of similarities) separated healthy controls from patients with CD and UC as well as from patients with GCA. Among others, CD and UC correlated with an increase of neutrophil extracellular traps and immune globulins G (IgG). In addition, a decrease of human IgA and the transcriptional regulatory protein RprY from Bacillus fragilis was found for CD and UC. A specific marker in feces for CD was an increased amount of the human enzyme sucrose-isomaltase. SIGNIFICANCE: Crohn's Disease and Ulcerative Colitis are chronic inflammatory diseases of the gastrointestinal tract, whose diagnosis required comprehensive medical examinations including colonoscopy. The impact of the microbial communities in the gut on the pathogenesis of these diseases is poorly understood. Therefore, this study investigated the impact of gut microbiome on these diseases by a metaproteome approach, revealing several disease specific marker proteins. Overall, this indicated that fecal metaproteomics has the potential to be useful as non-invasive tool for a better and easier diagnosis of both diseases.

A robust, cost-effective method for DNA, RNA and protein co-extraction from soil, other complex microbiomes and pure cultures

The soil microbiome is inherently complex with high biological diversity, and spatial heterogeneity typically occurring on the submillimetre scale. To study the microbial ecology of soils, and other microbiomes, biomolecules, that is, nucleic acids and proteins, must be efficiently and reliably co-recovered from the same biological samples. Commercial kits are currently available for the co-extraction of DNA, RNA and proteins but none has been developed for soil samples. We present a new protocol drawing on existing phenol-chloroform-based methods for nucleic acids co-extraction but incorporating targeted precipitation of proteins from the phenol phase. The protocol is cost-effective and robust, and easily implemented using reagents commonly available in laboratories. The method is estimated to be eight times cheaper than using disparate commercial kits for the isolation of DNA and/or RNA, and proteins, from soil. The method is effective, providing good quality biomolecules from a diverse range of soil types, with clay contents varying from 9.5% to 35.1%, which we successfully used for downstream, high-throughput gene sequencing and metaproteomics. Additionally, we demonstrate that the protocol can also be easily implemented for biomolecule co-extraction from other complex microbiome samples, including cattle slurry and microbial communities recovered from anaerobic bioreactors, as well as from Gram-positive and Gram-negative pure cultures.

Long-term investigation of microbial community composition and transcription patterns in a biogas plant undergoing ammonia crisis

Ammonia caused disturbance of biogas production is one of the most frequent incidents in regular operation of biogas reactors. This study provides a detailed insight into the microbial community of a mesophilic, full-scale biogas reactor (477 kWh h-1 ) fed with maize silage, dried poultry manure and cow manure undergoing initial process disturbance by increased ammonia concentration. Over a time period of 587 days, the microbial community of the reactor was regularly monitored on a monthly basis by high-throughput amplicon sequencing of the archaeal and bacterial 16S rRNA genes. During this sampling period, the total ammonia concentrations varied between 2.7 and 5.8 g l-1 [NH4 + -N]. To gain further inside into the active metabolic pathways, for selected time points metatranscriptomic shotgun analysis was performed allowing the quantification of marker genes for methanogenesis, hydrolysis and syntrophic interactions. The results obtained demonstrated a microbial community typical for a mesophilic biogas plant. However in response to the observed changing process conditions (e.g. increasing NH4 + levels, changing feedstock composition), the microbial community reacted highly flexible by changing and adapting the community composition. The Methanosarcina-dominated archaeal community was shifted to a Methanomicrobiales-dominated archaeal community in the presence of increased ammonia conditions. A similar trend as in the phylogenetic composition was observed in the transcription activity of genes coding for enzymes involved in acetoclastic methanogenesis and syntrophic acetate oxidations (Codh/Acs and Fthfs). In accordance, Clostridia simultaneously increased under elevated ammonia concentrations in abundance and were identified as the primary syntrophic interaction partner with the now Methanomicrobiales-dominated archaeal community. In conclusion, overall stable process performance was maintained during increased ammonia concentration in the studied reactor based on the microbial communities' ability to flexibly respond by reorganizing the community composition while remaining functionally stable.

Reduced TCA cycle rates at high hydrostatic pressure hinder hydrocarbon degradation and obligate oil degraders in natural, deep-sea microbial communities

Petroleum hydrocarbons reach the deep-sea following natural and anthropogenic factors. The process by which they enter deep-sea microbial food webs and impact the biogeochemical cycling of carbon and other elements is unclear. Hydrostatic pressure (HP) is a distinctive parameter of the deep sea, although rarely investigated. Whether HP alone affects the assembly and activity of oil-degrading communities remains to be resolved. Here we have demonstrated that hydrocarbon degradation in deep-sea microbial communities is lower at native HP (10 MPa, about 1000 m below sea surface level) than at ambient pressure. In long-term enrichments, increased HP selectively inhibited obligate hydrocarbon-degraders and downregulated the expression of beta-oxidation-related proteins (i.e., the main hydrocarbon-degradation pathway) resulting in low cell growth and CO₂ production. Short-term experiments with HP-adapted synthetic communities confirmed this data, revealing a HP-dependent accumulation of citrate and dihydroxyacetone. Citrate accumulation suggests rates of aerobic oxidation of fatty acids in the TCA cycle were reduced. Dihydroxyacetone is connected to citrate through glycerol metabolism and glycolysis, both upregulated with increased HP. High degradation rates by obligate hydrocarbon-degraders may thus be unfavourable at increased HP, explaining their selective suppression. Through lab-scale cultivation, the present study is the first to highlight a link between impaired cell metabolism and microbial community assembly in hydrocarbon degradation at high HP. Overall, this data indicate that hydrocarbons fate differs substantially in surface waters as compared to deep-sea environments, with in situ low temperature and limited nutrients availability expected to further prolong hydrocarbons persistence at deep sea.

SDS-PAGE fractionation to increase metaproteomic insight into the taxonomic and functional composition of microbial communities for biogas plant samples

Metaproteomics represent an important tool for the taxonomic and functional investigation of microbial communities in humans, environment, and technical applications. Due to the high complexity of the microbial communities, protein, and peptide fractionation is applied to improve the characterization of taxonomic and functional composition of microbial communities. In order to target scientific questions regarding taxonomic and functional composition adequately, a tradeoff between the number of fractions analyzed and the required depth of information has to be found. Two samples of a biogas plant were analyzed by either single LC-MS/MS measurement (1D) or LC-MS/MS measurements of fractions obtained after SDS-PAGE (2D) separation. Fractionation with SDS-PAGE increased the number of identified spectra by 273%, the number of peptides by 95%, and the number of metaproteins by 59%. Rarefaction plots of species and metaproteins against identified spectra showed that 2D separation was sufficient to identify most microbial families but not all metaproteins. More reliable quantitative comparison could be achieved with 2D. 1D separation enabled high-throughput analysis of samples, however, depth in functional descriptions and reliability of quantification were lost. Nevertheless, the proteotyping of multiple samples was still possible. 2D separations provided more reliable quantitative data combined with a deeper insight into the taxonomic and functional composition of the microbial communities. Regarding taxonomic and functional composition, metaproteomics based on 2D is just the tip of an iceberg.

Metagenome, metatranscriptome, and metaproteome approaches unraveled compositions and functional relationships of microbial communities residing in biogas plants

The production of biogas by anaerobic digestion (AD) of agricultural residues, organic wastes, animal excrements, municipal sludge, and energy crops has a firm place in sustainable energy production and bio-economy strategies. Focusing on the microbial community involved in biomass conversion offers the opportunity to control and engineer the biogas process with the objective to optimize its efficiency. Taxonomic profiling of biogas producing communities by means of high-throughput 16S rRNA gene amplicon sequencing provided high-resolution insights into bacterial and archaeal structures of AD assemblages and their linkages to fed substrates and process parameters. Commonly, the bacterial phyla Firmicutes and Bacteroidetes appeared to dominate biogas communities in varying abundances depending on the apparent process conditions. Regarding the community of methanogenic Archaea, their diversity was mainly affected by the nature and composition of the substrates, availability of nutrients and ammonium/ammonia contents, but not by the temperature. It also appeared that a high proportion of 16S rRNA sequences can only be classified on higher taxonomic ranks indicating that many community members and their participation in AD within functional networks are still unknown. Although cultivation-based approaches to isolate microorganisms from biogas fermentation samples yielded hundreds of novel species and strains, this approach intrinsically is limited to the cultivable fraction of the community. To obtain genome sequence information of non-cultivable biogas community members, metagenome sequencing including assembly and binning strategies was highly valuable. Corresponding research has led to the compilation of hundreds of metagenome-assembled genomes (MAGs) frequently representing novel taxa whose metabolism and lifestyle could be reconstructed based on nucleotide sequence information. In contrast to metagenome analyses revealing the genetic potential of microbial communities, metatranscriptome sequencing provided insights into the metabolically active community. Taking advantage of genome sequence information, transcriptional activities were evaluated considering the microorganism's genetic background. Metaproteome studies uncovered enzyme profiles expressed by biogas community members. Enzymes involved in cellulose and hemicellulose decomposition and utilization of other complex biopolymers were identified. Future studies on biogas functional microbial networks will increasingly involve integrated multi-omics analyses evaluating metagenome, transcriptome, proteome, and metabolome datasets.

Metagenome, metatranscriptome, and metaproteome approaches unraveled compositions and functional relationships of microbial communities residing in biogas plants

The production of biogas by anaerobic digestion (AD) of agricultural residues, organic wastes, animal excrements, municipal sludge, and energy crops has a firm place in sustainable energy production and bio-economy strategies. Focusing on the microbial community involved in biomass conversion offers the opportunity to control and engineer the biogas process with the objective to optimize its efficiency. Taxonomic profiling of biogas producing communities by means of high-throughput 16S rRNA gene amplicon sequencing provided high-resolution insights into bacterial and archaeal structures of AD assemblages and their linkages to fed substrates and process parameters. Commonly, the bacterial phyla Firmicutes and Bacteroidetes appeared to dominate biogas communities in varying abundances depending on the apparent process conditions. Regarding the community of methanogenic Archaea, their diversity was mainly affected by the nature and composition of the substrates, availability of nutrients and ammonium/ammonia contents, but not by the temperature. It also appeared that a high proportion of 16S rRNA sequences can only be classified on higher taxonomic ranks indicating that many community members and their participation in AD within functional networks are still unknown. Although cultivation-based approaches to isolate microorganisms from biogas fermentation samples yielded hundreds of novel species and strains, this approach intrinsically is limited to the cultivable fraction of the community. To obtain genome sequence information of non-cultivable biogas community members, metagenome sequencing including assembly and binning strategies was highly valuable. Corresponding research has led to the compilation of hundreds of metagenome-assembled genomes (MAGs) frequently representing novel taxa whose metabolism and lifestyle could be reconstructed based on nucleotide sequence information. In contrast to metagenome analyses revealing the genetic potential of microbial communities, metatranscriptome sequencing provided insights into the metabolically active community. Taking advantage of genome sequence information, transcriptional activities were evaluated considering the microorganism's genetic background. Metaproteome studies uncovered enzyme profiles expressed by biogas community members. Enzymes involved in cellulose and hemicellulose decomposition and utilization of other complex biopolymers were identified. Future studies on biogas functional microbial networks will increasingly involve integrated multi-omics analyses evaluating metagenome, transcriptome, proteome, and metabolome datasets.

Anaerobic digestion of food waste: A review focusing on process stability

Food waste (FW) is rich in biomass energy, and increasing numbers of national programs are being established to recover energy from FW using anaerobic digestion (AD). However process instability is a common operational issue for AD of FW. Process monitoring and control as well as microbial management can be used to control instability and increase the energy conversion efficiency of anaerobic digesters. Here, we review research progress related to these methods and identify existing limitations to efficient AD; recommendations for future research are also discussed. Process monitoring and control are suitable for evaluating the current operational status of digesters, whereas microbial management can facilitate early diagnosis and process optimization. Optimizing and combining these two methods are necessary to improve AD efficiency.

A Structural and Functional Elucidation of the Rumen Microbiome Influenced by Various Diets and Microenvironments

The structure and function of the microbiome inhabiting the rumen are, amongst other factors, mainly shaped by the animal's feed intake. Describing the influence of different diets on the inherent community arrangement and associated metabolic activities of the most active ruminal fractions (bacteria and archaea) is of great interest for animal nutrition, biotechnology, and climatology. Samples were obtained from three fistulated Jersey cows rotationally fed with corn silage, grass silage or grass hay, each supplemented with a concentrate mixture. Samples were fractionated into ruminal fluid, particle-associated rumen liquid, and solid matter. DNA, proteins and metabolites were analyzed subsequently. DNA extracts were used for Illumina sequencing of the 16S rRNA gene and the metabolomes of rumen fluids were determined by 500 MHz-NMR spectroscopy. Tryptic peptides derived from protein extracts were measured by LC-ESI-MS/MS and spectra were processed by a two-step database search for quantitative metaproteome characterization. Data are available via ProteomeXchange with the identifier PXD006070. Protein- and DNA-based datasets revealed significant differences between sample fractions and diets and affirmed similar trends concerning shifts in phylogenetic composition. Ribosomal genes and proteins belonging to the phylum of Proteobacteria, particularly Succinivibrionaceae, exhibited a higher abundance in corn silage-based samples while fiber-degraders of the Lachnospiraceae family emerged in great quantities throughout the solid phase fractions. The analysis of 8163 quantified bacterial proteins revealed the presence of 166 carbohydrate active enzymes in varying abundance. Cellulosome affiliated proteins were less expressed in the grass silage, glycoside hydrolases appeared in slightest numbers in the corn silage. Most expressed glycoside hydrolases belonged to families 57 and 2. Enzymes analogous to ABC transporters for amino acids and monosaccharides were more abundant in the corn silage whereas oligosaccharide transporters showed a higher abundance in the fiber-rich diets. Proteins involved in carbon metabolism were detected in high numbers and identification of metabolites like short-chain fatty acids, methylamines and phenylpropionate by NMR enabled linkage between producers and products. This study forms a solid basis to retrieve deeper insight into the complex network of microbial adaptation in the rumen.

From grass to gas: microbiome dynamics of grass biomass acidification under mesophilic and thermophilic temperatures

BACKGROUND

Separating acidification and methanogenic steps in anaerobic digestion processes can help to optimize the process and contribute to producing valuable sub-products such as methane, hydrogen and organic acids. However, the full potential of this technology has not been fully explored yet. To assess the underlying fermentation process in more detail, a combination of high-throughput sequencing and proteomics on the acidification step of plant material (grass) at both mesophilic and thermophilic temperatures (37 and 55 °C, respectively) was applied for the first time.

RESULTS

High-strength liquor from acidified grass biomass exhibited a low biodiversity, which differed greatly depending on temperature. It was dominated by Bacteroidetes and Firmicutes at 37 °C, and by Firmicutes and Proteobacteria at 55 °C. At the methane stage, Methanosaeta, Methanomicrobium and Methanosarcina proved to be highly sensitive to environmental changes as their abundance in the seed sludges dropped dramatically after transferring the seed sludges from the respective reactors into the experimental setup. Further, an increase in Actinobacteria coincided with reduced biogas production at the end of the experiment. Over 1700 proteins were quantified from the first cycle of acidification samples using label-free quantitative proteome analysis and searching protein databases. The most abundant proteins included an almost complete set of glycolytic enzymes indicating that the microbial population is basically engaged in the degradation and catabolism of sugars. Differences in protein abundances clearly separated samples into two clusters corresponding to culture temperature. More differentially expressed proteins were found under mesophilic (120) than thermophilic (5) conditions.

CONCLUSION

Our results are the first multi-omics characterisation of a two-stage biogas production system with separated acidification and suggest that screening approaches targeting specific taxa such as Methanosaeta, Methanomicrobium and Methanosarcina could be useful diagnostic tools as indicators of environmental changes such as temperature or oxidative stress or, as in the case of Actinobacteria, they could be used as a proxy of the gas production potential of anaerobic digesters. Metaproteome analyses only detected significant expression differences in mesophilic samples, whereas thermophilic samples showed more stable protein composition with an abundance of chaperones suggesting a role in protein stability under thermal stress.

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.

Metaproteomics-guided selection of targeted enzymes for bioprospecting of mixed microbial communities

BACKGROUND

Hitherto, the main goal of metaproteomic analyses has been to characterize the functional role of particular microorganisms in the microbial ecology of various microbial communities. Recently, it has been suggested that metaproteomics could be used for bioprospecting microbial communities to query for the most active enzymes to improve the selection process of industrially relevant enzymes. In the present study, to reduce the complexity of metaproteomic samples for targeted bioprospecting of novel enzymes, a microbial community capable of producing cellulases was maintained on a chemically defined medium in an enzyme suppressed metabolic steady state. From this state, it was possible to specifically and distinctively induce the desired cellulolytic activity. The extracellular fraction of the protein complement of the induced sample could thereby be purified and compared to a non-induced sample of the same community by differential gel electrophoresis to discriminate between constitutively expressed proteins and proteins upregulated in response to the inducing substance.

RESULTS

Using the applied approach, downstream analysis by mass spectrometry could be limited to only proteins recognized as upregulated in the cellulase-induced sample. Of 39 selected proteins, the majority were found to be linked to the need to degrade, take up, and metabolize cellulose. In addition, 28 (72%) of the proteins were non-cytosolic and 17 (44%) were annotated as carbohydrate-active enzymes. The results demonstrated both the applicability of the proposed approach for identifying extracellular proteins and guiding the selection of proteins toward those specifically upregulated and targeted by the enzyme inducing substance. Further, because identification of interesting proteins was based on the regulation of enzyme expression in response to a need to hydrolyze and utilize a specific substance, other unexpected enzyme activities were able to be identified.

CONCLUSIONS

The described approach created the conditions necessary to be able to select relevant extracellular enzymes that were extracted from the enzyme-induced microbial community. However, for the purpose of bioprospecting for enzymes to clone, produce, and characterize for practical applications, it was concluded that identification against public databases was not sufficient to identify the correct gene or protein sequence for cloning of the identified novel enzymes.

The rumen microbial metaproteome as revealed by SDS-PAGE

BACKGROUND

Ruminal digestion is carried out by large numbers of bacteria, archaea, protozoa and fungi. Understanding the microbiota is important because ruminal fermentation dictates the efficiency of feed utilisation by the animal and is also responsible for major emissions of the greenhouse gas, methane. Recent metagenomic and metatranscriptomic studies have helped to elucidate many features of the composition and activity of the microbiota. The metaproteome provides complementary information to these other -omics technologies. The aim of this study was to explore the metaproteome of bovine and ovine ruminal digesta using 2D SDS-PAGE.

RESULTS

Digesta samples were taken via ruminal fistulae and by gastric intubation, or at slaughter, and stored in glycerol at -80 °C. A protein extraction protocol was developed to maximise yield and representativeness of the protein content. The proteome of ruminal digesta taken from dairy cows fed a high concentrate diet was dominated by a few very highly expressed proteins, which were identified by LC-MS/MS to be structural proteins, such as actin and α- and β-tubulins, derived from ciliate protozoa. Removal of protozoa from digesta before extraction of proteins revealed the prokaryotic metaproteome, which was dominated by enzymes involved in glycolysis, such as glyceraldehyde-3-phosphate dehydrogenase, phosphoenolpyruvate carboxykinase, phosphoglycerate kinase and triosephosphate isomerase. The enzymes were predominantly from the Firmicutes and Bacteroidetes phyla. Enzymes from methanogenic archaea were also abundant, consistent with the importance of methane formation in the rumen. Gels from samples from dairy cows fed a high proportion of grass silage were consistently obscured by co-staining of humic compounds. Samples from beef cattle and fattening lambs receiving a predominantly concentrate diet produced clearer gels, but the pattern of spots was inconsistent between samples, making comparisons difficult.

CONCLUSION

This work demonstrated for the first time that 2D-PAGE reveals key structural proteins and enzymes in the rumen microbial community, despite its high complexity, and that taxonomic information can be deduced from the analysis. However, technical issues associated with feed material contamination, which affects the reproducibility of electrophoresis of different samples, limits its value.

Identification of syntrophic acetate-oxidizing bacteria in anaerobic digesters by combined protein-based stable isotope probing and metagenomics

Inhibition of anaerobic digestion through accumulation of volatile fatty acids occasionally occurs as the result of unbalanced growth between acidogenic bacteria and methanogens. A fast recovery is a prerequisite for establishing an economical production of biogas. However, very little is known about the microorganisms facilitating this recovery. In this study, we investigated the organisms involved by a novel approach of mapping protein-stable isotope probing (protein-SIP) onto a binned metagenome. Under simulation of acetate accumulation conditions, formations of (13)C-labeled CO2 and CH4 were detected immediately following incubation with [U-(13)C]acetate, indicating high turnover rate of acetate. The identified (13)C-labeled peptides were mapped onto a binned metagenome for improved identification of the organisms involved. The results revealed that Methanosarcina and Methanoculleus were actively involved in acetate turnover, as were five subspecies of Clostridia. The acetate-consuming organisms affiliating with Clostridia all contained the FTFHS gene for formyltetrahydrofolate synthetase, a key enzyme for reductive acetogenesis, indicating that these organisms are possible syntrophic acetate-oxidizing (SAO) bacteria that can facilitate acetate consumption via SAO, coupled with hydrogenotrophic methanogenesis (SAO-HM). This study represents the first study applying protein-SIP for analysis of complex biogas samples, a promising method for identifying key microorganisms utilizing specific pathways.

Proteotyping of biogas plant microbiomes separates biogas plants according to process temperature and reactor type

BACKGROUND

Methane yield and biogas productivity of biogas plants (BGPs) depend on microbial community structure and function, substrate supply, and general biogas process parameters. So far, however, relatively little is known about correlations between microbial community function and process parameters. To close this knowledge gap, microbial communities of 40 samples from 35 different industrial biogas plants were evaluated by a metaproteomics approach in this study.

RESULTS

Liquid chromatography coupled to tandem mass spectrometry (Orbitrap Elite™ Hybrid Ion Trap-Orbitrap Mass Spectrometer) of all 40 samples as triplicate enabled the identification of 3138 different metaproteins belonging to 162 biological processes and 75 different taxonomic orders. The respective database searches were performed against UniProtKB/Swiss-Prot and seven metagenome databases. Subsequent clustering and principal component analysis of these data allowed for the identification of four main clusters associated with mesophile and thermophile process conditions, the use of upflow anaerobic sludge blanket reactors and BGP feeding with sewage sludge. Observations confirm a previous phylogenetic study of the same BGP samples that was based on 16S rRNA gene sequencing by De Vrieze et al. (Water Res 75:312-323, 2015). In particular, we identified similar microbial key players of biogas processes, namely Bacillales, Enterobacteriales, Bacteriodales, Clostridiales, Rhizobiales and Thermoanaerobacteriales as well as Methanobacteriales, Methanosarcinales and Methanococcales. For the elucidation of the main biomass degradation pathways, the most abundant 1 % of metaproteins was assigned to the KEGG map 1200 representing the central carbon metabolism. Additionally, the effect of the process parameters (i) temperature, (ii) organic loading rate (OLR), (iii) total ammonia nitrogen (TAN), and (iv) sludge retention time (SRT) on these pathways was investigated. For example, high TAN correlated with hydrogenotrophic methanogens and bacterial one-carbon metabolism, indicating syntrophic acetate oxidation.

CONCLUSIONS

This is the first large-scale metaproteome study of BGPs. Proteotyping of BGPs reveals general correlations between the microbial community structure and its function with process parameters. The monitoring of changes on the level of microbial key functions or even of the microbial community represents a well-directed tool for the identification of process problems and disturbances.Graphical abstractCorrelation between the different orders and process parameter, as well as principle component analysis of all investigated biogas plants based on the identified metaproteins.

Metaproteomics Applied to Activated Sludge for Industrial Wastewater Treatment Revealed a Dominant Methylotrophic Metabolism of Hyphomicrobium zavarzinii

In biological wastewater treatments, microbial populations of the so-called activated sludge work together in the abatement of pollutants. In this work, the metabolic behavior of the biomass of a lab-scale plant treating industrial pharmaceutical wastewater was investigated through a metaproteomic approach. The complete treatment process included a membrane biological reactor (MBR) coupled with an advanced oxidation process (AOP) for partial breakdown of non-biodegradable molecules. Proteins from biomass samples collected pre- and post-AOP application were investigated by two-dimensional gel electrophoresis (2DE), mass spectrometry (MS), and finally identified by database search. Results showed that most proteins remained constant between pre- and post-AOP. Methanol dehydrogenase (MDH) belonging to Hyphomicrobium zavarzinii appeared as the most constantly expressed protein in the studied consortium. Other identified proteins belonging to Hyphomicrobium spp. revealed a predominant methylotrophic metabolism, and H. zavarzinii appeared as a key actor in the studied microbial community.

An integrated metagenome and -proteome analysis of the microbial community residing in a biogas production plant

To study the metaproteome of a biogas-producing microbial community, fermentation samples were taken from an agricultural biogas plant for microbial cell and protein extraction and corresponding metagenome analyses. Based on metagenome sequence data, taxonomic community profiling was performed to elucidate the composition of bacterial and archaeal sub-communities. The community's cytosolic metaproteome was represented in a 2D-PAGE approach. Metaproteome databases for protein identification were compiled based on the assembled metagenome sequence dataset for the biogas plant analyzed and non-corresponding biogas metagenomes. Protein identification results revealed that the corresponding biogas protein database facilitated the highest identification rate followed by other biogas-specific databases, whereas common public databases yielded insufficient identification rates. Proteins of the biogas microbiome identified as highly abundant were assigned to the pathways involved in methanogenesis, transport and carbon metabolism. Moreover, the integrated metagenome/-proteome approach enabled the examination of genetic-context information for genes encoding identified proteins by studying neighboring genes on the corresponding contig. Exemplarily, this approach led to the identification of a Methanoculleus sp. contig encoding 16 methanogenesis-related gene products, three of which were also detected as abundant proteins within the community's metaproteome. Thus, metagenome contigs provide additional information on the genetic environment of identified abundant proteins.

Enhancing metaproteomics--The value of models and defined environmental microbial systems

Metaproteomics--the large-scale characterization of the entire protein complement of environmental microbiota at a given point in time--has provided new features to study complex microbial communities in order to unravel these "black boxes." New technical challenges arose that were not an issue for classical proteome analytics before that could be tackled by the application of different model systems. Here, we review different current and future model systems for metaproteome analysis. Following a short introduction to microbial communities and metaproteomics, we introduce model systems for clinical and biotechnological research questions including acid mine drainage, anaerobic digesters, and activated sludge. Model systems are useful to evaluate the challenges encountered within (but not limited to) metaproteomics, including species complexity and coverage, biomass availability, or reliable protein extraction. The implementation of model systems can be considered as a step forward to better understand microbial community responses and ecological functions of single member organisms. In the future, improvements are necessary to fully explore complex environmental systems by metaproteomics.

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.

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).

Community shifts in a well-operating agricultural biogas plant: how process variations are handled by the microbiome

This study provides a comprehensive, long-term microbiological study of a continuously operated, mesophilic, agricultural biogas plant fed with whole-crop silages of maize and rye, cattle manure and cattle slurry. The microbial community structure was accessed by high-throughput 16S rRNA gene amplicon sequencing. For the characterisation of the microbial dynamics, the community profiling method terminal restriction fragment length polymorphism (TRFLP) in combination with a cloning-sequencing approach as well as a LC-MS/MS approach for protein identification were applied. Our results revealed that the anaerobic digestion is a highly sensitive process: small variations in the process performance induce fluctuations in the microbial community composition and activity. In this context, it could be proven that certain microbial species were better adapted to changing process condition such as temperature (interspecies competition) and that there is a physiological compensation between different microorganisms so that the reactor efficiency was not adversely affected despite of structural and functional changes within the microbial community.