Improved biological methanation using tubular foam-bed reactor

BACKGROUND

Power-to-gas is the pivotal link between electricity and gas infrastructure, enabling the broader integration of renewable energy. Yet, enhancements are necessary for its full potential. In the biomethanation process, transferring H2 into the liquid phase is a rate-limiting step. To address this, we developed a novel tubular foam-bed reactor (TFBR) and investigated its performance at laboratory scale.

RESULTS

A non-ionic polymeric surfactant (Pluronic® F-68) at 1.5% w/v was added to the TFBR's culture medium to generate a stabilized liquid foam structure. This increased both the gas-liquid surface area and the bubble retention time. Within the tubing, cells predominantly traveled evenly suspended in the liquid phase or were entrapped in the thin liquid film of bubbles flowing inside the tube. Phase (I) of the experiment focused primarily on mesophilic (40 °C) operation of the tubular reactor, followed by phase (II), when Pluronic® F-68 was added. In phase (II), the TFBR exhibited 6.5-fold increase in biomethane production rate (MPR) to 15.1 ( L CH 4 /L R /d) , with a CH4 concentration exceeding 90% (grid quality), suggesting improved H2 transfer. Transitioning to phase (III) with continuous operation at 55 °C, the MPR reached 29.7L CH 4 /L R /d while maintaining the grid quality CH4. Despite, reduced gas-liquid solubility and gas-liquid mass transfer at higher temperatures, the twofold increase in MPR compared to phase (II) might be attributed to other factors, i.e., higher metabolic activity of the methanogenic archaea. To assess process robustness for phase (II) conditions, a partial H2 feeding regime (12 h 100% and 12 h 10% of the nominal feeding rate) was implemented. Results demonstrated a resilient MPR of approximately 14.8L CH 4 /L R /d even with intermittent, low H2 concentration.

CONCLUSIONS

Overall, the TFBR's performance plant sets the course for an accelerated introduction of biomethanation technology for the storage of volatile renewable energy. Robust process performance, even under H2 starvation, underscores its reliability. Further steps towards an optimum operation regime and scale-up should be initiated. Additionally, the use of TFBR systems should be considered for biotechnological processes in which gas-liquid mass transfer is a limiting factor for achieving higher reaction rates.

Biodegradation of selected aminophosphonates by the bacterial isolate Ochrobactrum sp. BTU1

Aminophosphonates, like glyphosate (GS) or metal chelators such as ethylenediaminetetra(methylenephosphonic acid) (EDTMP), are released on a large scale worldwide. Here, we have characterized a bacterial strain capable of degrading synthetic aminophosphonates. The strain was isolated from LC/MS standard solution. Genome sequencing indicated that the strain belongs to the genus Ochrobactrum. Whole-genome classification using pyANI software to compute a pairwise ANI and other metrics between Brucella assemblies and Ochrobactrum contigs revealed that the bacterial strain is designated as Ochrobactrum sp. BTU1. Degradation batch tests with Ochrobactrum sp. BTU1 and the selected aminophosphonates GS, EDTMP, aminomethylphosphonic acid (AMPA), iminodi(methylene-phosphonic) (IDMP) and ethylaminobis(methylenephosphonic) acid (EABMP) showed that the strain can use all phosphonates as sole phosphorus source during phosphorus starvation. The highest growth rate was achieved with AMPA, while EDTMP and GS were least supportive for growth. Proteome analysis revealed that GS degradation is promoted by C-P lyase via the sarcosine pathway, i.e., initial cleavage at the C-P bond. We also identified C-P lyase to be responsible for degradation of EDTMP, EABMP, IDMP and AMPA. However, the identification of the metabolite ethylenediaminetri(methylenephosphonic acid) via LC/MS analysis in the test medium during EDTMP degradation indicates a different initial cleavage step as compared to GS. For EDTMP, it is evident that the initial cleavage occurs at the C-N bond. The detection of different key enzymes at regulated levels, form the bacterial proteoms during EDTMP exposure, further supports this finding. This study illustrates that widely used and structurally more complex aminophosphonates can be degraded by Ochrobactrum sp. BTU1 via the well-known degradation pathways but with different initial cleavage strategy compared to GS.

Breakdown of hardly degradable carbohydrates (lignocellulose) in a two-stage anaerobic digestion plant is favored in the main fermenter

The yield and productivity of biogas plants depend on the degradation performance of their microbiomes. The spatial separation of the anaerobic digestion (AD) process into a separate hydrolysis and a main fermenter should improve cultivation conditions of the microorganisms involved in the degradation of complex substrates like lignocellulosic biomass (LCB) and, thus, the performance of anaerobic digesters. However, relatively little is known about such two-stage processes. Here, we investigated the process performance of a two-stage agricultural AD over one year, focusing on chemical and technical process parameters and metagenome-centric metaproteomics. Technical and chemical parameters indicated stable operation of the main fermenter but varying conditions for the open hydrolysis fermenter. Matching this, the microbiome in the hydrolysis fermenter has a higher dynamic than in the main fermenter. Metaproteomics-based microbiome analysis revealed a partial separation between early and common steps in carbohydrate degradation and primary fermentation in the hydrolysis fermenter but complex carbohydrate degradation, secondary fermentation, and methanogenesis in the main fermenter. Detailed metagenomics and metaproteomics characterization of the single metagenome-assembled genomes showed that the species focus on specific substrate niches and do not utilize their full genetic potential to degrade, for example, LCB. Overall, it seems that a separation of AD in a hydrolysis and a main fermenter does not improve the cleavage of complex substrates but significantly improves the overall process performance. In contrast, the remaining methanogenic activity in the hydrolysis fermenter may cause methane losses.

Generation of "OP7 chimera" defective interfering influenza A particle preparations free of infectious virus that show antiviral efficacy in mice

Influenza A virus (IAV) defective interfering particles (DIPs) are considered as new promising antiviral agents. Conventional DIPs (cDIPs) contain a deletion in the genome and can only replicate upon co-infection with infectious standard virus (STV), during which they suppress STV replication. We previously discovered a new type of IAV DIP "OP7" that entails genomic point mutations and displays higher antiviral efficacy than cDIPs. To avoid safety concerns for the medical use of OP7 preparations, we developed a production system that does not depend on infectious IAV. We reconstituted a mixture of DIPs consisting of cDIPs and OP7 chimera DIPs, in which both harbor a deletion in their genome. To complement the defect, the deleted viral protein is expressed by the suspension cell line used for production in shake flasks. Here, DIP preparations harvested are not contaminated with infectious virions, and the fraction of OP7 chimera DIPs depended on the multiplicity of infection. Intranasal administration of OP7 chimera DIP material was well tolerated in mice. A rescue from an otherwise lethal IAV infection and no signs of disease upon OP7 chimera DIP co-infection demonstrated the remarkable antiviral efficacy. The clinical development of this new class of broad-spectrum antiviral may contribute to pandemic preparedness.

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.

Advances in the clinical use of metaproteomics

INTRODUCTION

Investigating the taxonomic and functional composition of human microbiomes can aid in the understanding of disease etiologies, diagnosis, and therapy monitoring for several diseases, including inflammatory bowel disease or obesity. One method for microbiome monitoring is metaproteomics, which assesses human and microbial proteins and thus enables the study of host-microbiome interactions. This advantage led to increased interest in metaproteome analyses and significant developments to introduce this method into a clinical context.

AREAS COVERED

This review summarizes the recent progress from a technical side and an application-related point of view.

EXPERT OPINION

Numerous publications imply the massive potential of metaproteomics to impact human health care. However, the key challenges of standardization and validation of experimental and bioinformatic workflows and accurate quantification methods must be overcome.

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.

Absolute quantification of viral proteins during single-round replication of MDCK suspension cells

Madin-Darby canine kidney (MDCK) cells are widely used in basic research and for the propagation of influenza A viruses (IAV) for vaccine production. To identify targets for antiviral therapies and to optimize vaccine manufacturing, a detailed understanding of the viral life cycle is important. This includes the characterization of virus entry, the synthesis of the various viral RNAs and proteins, the transfer of viral compounds in the cell and virus budding. In case quantitative information is available, the analysis can be complemented by mathematical modelling approaches. While comprehensive studies focusing on IAV entry as well as viral mRNA, vRNA and cRNA accumulation in the nucleus of cells have been performed, quantitative data regarding IAV protein synthesis and accumulation was mostly lacking. In this study, we present a mass spectrometry (MS)-based method to evaluate whether an absolute quantification of viral proteins is possible for single-round replication in suspension MDCK cells. Using influenza A/PR/8/34 (H1N1, RKI) as a model strain at a multiplicity of infection of ten, defined amounts of isotopically labelled peptides of synthetic origin of four IAV proteins (hemagglutinin, neuraminidase, nucleoprotein, matrix protein 1) were added as an internal standard before tryptic digestion of samples for absolute quantification (AQUA). The first intracellular protein detected was NP at 1 h post infection (hpi). A maximum extracellular concentration of 7.7E+12 copies/mL was achieved. This was followed by hemagglutinin (3 hpi, maximum 4.1E+12 copies/mL at 13 hpi), matrix protein 1 (5 hpi, maximum 2.2E+12 copies/mL at 13 hpi) and neuraminidase (5 hpi, 6.0E+11 copies/mL at 13 hpi). In sum, for the first time absolute IAV protein copy numbers were quantified by a MS-based method for infected MDCK cells providing important insights into viral protein dynamics during single-round virus replication. SIGNIFICANCE: Influenza A virus is a significant human pathogen worldwide. To improve therapies against influenza and overcome bottlenecks in vaccine production in cell culture, it is critical to gain a detailed understanding of the viral life cycle. In addition to qPCR-based models, this study will examine the dynamics of influenza virus proteins during infection of producer cells to gain initial insights into changes in absolute copy numbers.

Pout2Prot: An Efficient Tool to Create Protein (Sub)groups from Percolator Output Files

In metaproteomics, the study of the collective proteome of microbial communities, the protein inference problem is more challenging than in single-species proteomics. Indeed, a peptide sequence can be present not only in multiple proteins or protein isoforms of the same species, but also in homologous proteins from closely related species. To assign the taxonomy and functions of the microbial species, specialized tools have been developed, such as Prophane. This tool, however, is not directly compatible with post-processing tools such as Percolator. In this manuscript we therefore present Pout2Prot, which takes Percolator Output (.pout) files from multiple experiments and creates protein group and protein subgroup output files (.tsv) that can be used directly with Prophane. We investigated different grouping strategies and compared existing protein grouping tools to develop an advanced protein grouping algorithm that offers a variety of different approaches, allows grouping for multiple files, and uses a weighted spectral count for protein (sub)groups to reflect abundance. Pout2Prot is available as a web application at https://pout2prot.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the Apache License 2.0 and is available at https://github.com/compomics/pout2prot.

The Metaproteomics Initiative: a coordinated approach for propelling the functional characterization of microbiomes

Through connecting genomic and metabolic information, metaproteomics is an essential approach for understanding how microbiomes function in space and time. The international metaproteomics community is delighted to announce the launch of the Metaproteomics Initiative (www.metaproteomics.org), the goal of which is to promote dissemination of metaproteomics fundamentals, advancements, and applications through collaborative networking in microbiome research. The Initiative aims to be the central information hub and open meeting place where newcomers and experts interact to communicate, standardize, and accelerate experimental and bioinformatic methodologies in this field. We invite the entire microbiome community to join and discuss potential synergies at the interfaces with other disciplines, and to collectively promote innovative approaches to gain deeper insights into microbiome functions and dynamics. Video Abstract.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

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

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

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

MPA_Pathway_Tool: User-Friendly, Automatic Assignment of Microbial Community Data on Metabolic Pathways

Taxonomic and functional characterization of microbial communities from diverse environments such as the human gut or biogas plants by multi-omics methods plays an ever more important role. Researchers assign all identified genes, transcripts, or proteins to biological pathways to better understand the function of single species and microbial communities. However, due to the versality of microbial metabolism and a still-increasing number of newly biological pathways, linkage to standard pathway maps such as the KEGG central carbon metabolism is often problematic. We successfully implemented and validated a new user-friendly, stand-alone web application, the MPA_Pathway_Tool. It consists of two parts, called 'Pathway-Creator' and 'Pathway-Calculator'. The 'Pathway-Creator' enables an easy set-up of user-defined pathways with specific taxonomic constraints. The 'Pathway-Calculator' automatically maps microbial community data from multiple measurements on selected pathways and visualizes the results. The MPA_Pathway_Tool is implemented in Java and ReactJS.

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.

Indicative Marker Microbiome Structures Deduced from the Taxonomic Inventory of 67 Full-Scale Anaerobic Digesters of 49 Agricultural Biogas Plants

There are almost 9500 biogas plants in Germany, which are predominantly operated with energy crops and residues from livestock husbandry over the last two decades. In the future, biogas plants must be enabled to use a much broader range of input materials in a flexible and demand-oriented manner. Hence, the microbial communities will be exposed to frequently varying process conditions, while an overall stable process must be ensured. To accompany this transition, there is the need to better understand how biogas microbiomes respond to management measures and how these responses affect the process efficiency. Therefore, 67 microbiomes originating from 49 agricultural, full-scale biogas plants were taxonomically investigated by 16S rRNA gene amplicon sequencing. These microbiomes were separated into three distinct clusters and one group of outliers, which are characterized by a specific distribution of 253 indicative taxa and their relative abundances. These indicative taxa seem to be adapted to specific process conditions which result from a different biogas plant operation. Based on these results, it seems to be possible to deduce/assess the general process condition of a biogas digester based solely on the microbiome structure, in particular on the distribution of specific indicative taxa, and without knowing the corresponding operational and chemical process parameters. Perspectively, this could allow the development of detection systems and advanced process models considering the microbial diversity.

smORFer: a modular algorithm to detect small ORFs in prokaryotes

Emerging evidence places small proteins (≤50 amino acids) more centrally in physiological processes. Yet, their functional identification and the systematic genome annotation of their cognate small open-reading frames (smORFs) remains challenging both experimentally and computationally. Ribosome profiling or Ribo-Seq (that is a deep sequencing of ribosome-protected fragments) enables detecting of actively translated open-reading frames (ORFs) and empirical annotation of coding sequences (CDSs) using the in-register translation pattern that is characteristic for genuinely translating ribosomes. Multiple identifiers of ORFs that use the 3-nt periodicity in Ribo-Seq data sets have been successful in eukaryotic smORF annotation. They have difficulties evaluating prokaryotic genomes due to the unique architecture (e.g. polycistronic messages, overlapping ORFs, leaderless translation, non-canonical initiation etc.). Here, we present a new algorithm, smORFer, which performs with high accuracy in prokaryotic organisms in detecting putative smORFs. The unique feature of smORFer is that it uses an integrated approach and considers structural features of the genetic sequence along with in-frame translation and uses Fourier transform to convert these parameters into a measurable score to faithfully select smORFs. The algorithm is executed in a modular way, and dependent on the data available for a particular organism, different modules can be selected for smORF search.

Fecal Metaproteomics Reveals Reduced Gut Inflammation and Changed Microbial Metabolism Following Lifestyle-Induced Weight Loss

Gut microbiota-mediated inflammation promotes obesity-associated low-grade inflammation, which represents a hallmark of metabolic syndrome. To investigate if lifestyle-induced weight loss (WL) may modulate the gut microbiome composition and its interaction with the host on a functional level, we analyzed the fecal metaproteome of 33 individuals with metabolic syndrome in a longitudinal study before and after lifestyle-induced WL in a well-defined cohort. The 6-month WL intervention resulted in reduced BMI (-13.7%), improved insulin sensitivity (HOMA-IR, -46.1%), and reduced levels of circulating hsCRP (-39.9%), indicating metabolic syndrome reversal. The metaprotein spectra revealed a decrease of human proteins associated with gut inflammation. Taxonomic analysis revealed only minor changes in the bacterial composition with an increase of the families Desulfovibrionaceae, Leptospiraceae, Syntrophomonadaceae, Thermotogaceae and Verrucomicrobiaceae. Yet we detected an increased abundance of microbial metaprotein spectra that suggest an enhanced hydrolysis of complex carbohydrates. Hence, lifestyle-induced WL was associated with reduced gut inflammation and functional changes of human and microbial enzymes for carbohydrate hydrolysis while the taxonomic composition of the gut microbiome remained almost stable. The metaproteomics workflow has proven to be a suitable method for monitoring inflammatory changes in the fecal metaproteome.

Tracking changes in adaptation to suspension growth for MDCK cells: cell growth correlates with levels of metabolites, enzymes and proteins

Abstract

Adaptations of animal cells to growth in suspension culture concern in particular viral vaccine production, where very specific aspects of virus-host cell interaction need to be taken into account to achieve high cell specific yields and overall process productivity. So far, the complexity of alterations on the metabolism, enzyme, and proteome level required for adaptation is only poorly understood. In this study, for the first time, we combined several complex analytical approaches with the aim to track cellular changes on different levels and to unravel interconnections and correlations. Therefore, a Madin-Darby canine kidney (MDCK) suspension cell line, adapted earlier to growth in suspension, was cultivated in a 1-L bioreactor. Cell concentrations and cell volumes, extracellular metabolite concentrations, and intracellular enzyme activities were determined. The experimental data set was used as the input for a segregated growth model that was already applied to describe the growth dynamics of the parental adherent cell line. In addition, the cellular proteome was analyzed by liquid chromatography coupled to tandem mass spectrometry using a label-free protein quantification method to unravel altered cellular processes for the suspension and the adherent cell line. Four regulatory mechanisms were identified as a response of the adaptation of adherent MDCK cells to growth in suspension. These regulatory mechanisms were linked to the proteins caveolin, cadherin-1, and pirin. Combining cell, metabolite, enzyme, and protein measurements with mathematical modeling generated a more holistic view on cellular processes involved in the adaptation of an adherent cell line to suspension growth.

Key points

• Less and more efficient glucose utilization for suspension cell growth

• Concerted alteration of metabolic enzyme activity and protein expression

• Protein candidates to interfere glycolytic activity in MDCK cells

Supplementary Information

The online version contains supplementary material available at 10.1007/s00253-021-11150-z.

The structure and function of soil archaea across biomes

We lack a predictive understanding of the environmental drivers determining the structure and function of archaeal communities as well as the proteome associated with these important soil organisms. Here, we characterized the structure (by 16S rRNA gene sequencing) and function (by metaproteomics) of archaea from 32 soil samples across terrestrial ecosystems with contrasting climate and vegetation types. Our multi-"omics" approach unveiled that genes from Nitrosophaerales and Thermoplasmata dominated soils collected from four continents, and that archaea comprise 2.3 ± 0.3% of microbial proteins in these soils. Aridity positively correlated with the proportion of Nitrosophaerales genes and the number of archaeal proteins. The interaction of climate x vegetation shaped the functional profile of the archaeal community. Our study provides novel insights into the structure and function of soil archaea across climates, and highlights that these communities may be influenced by increasing global aridity.

The Role of Petrimonas mucosa ING2-E5AT in Mesophilic Biogas Reactor Systems as Deduced from Multiomics Analyses

Members of the genera Proteiniphilum and Petrimonas were speculated to represent indicators reflecting process instability within anaerobic digestion (AD) microbiomes. Therefore, Petrimonas mucosa ING2-E5AT was isolated from a biogas reactor sample and sequenced on the PacBio RSII and Illumina MiSeq sequencers. Phylogenetic classification positioned the strain ING2-E5AT in close proximity to Fermentimonas and Proteiniphilum species (family Dysgonomonadaceae). ING2-E5AT encodes a number of genes for glycosyl-hydrolyses (GH) which are organized in Polysaccharide Utilization Loci (PUL) comprising tandem susCD-like genes for a TonB-dependent outer-membrane transporter and a cell surface glycan-binding protein. Different GHs encoded in PUL are involved in pectin degradation, reflecting a pronounced specialization of the ING2-E5AT PUL systems regarding the decomposition of this polysaccharide. Genes encoding enzymes participating in amino acids fermentation were also identified. Fragment recruitments with the ING2-E5AT genome as a template and publicly available metagenomes of AD microbiomes revealed that Petrimonas species are present in 146 out of 257 datasets supporting their importance in AD microbiomes. Metatranscriptome analyses of AD microbiomes uncovered active sugar and amino acid fermentation pathways for Petrimonas species. Likewise, screening of metaproteome datasets demonstrated expression of the Petrimonas PUL-specific component SusC providing further evidence that PUL play a central role for the lifestyle of Petrimonas species.

OP7, a novel influenza A virus defective interfering particle: production, purification, and animal experiments demonstrating antiviral potential

Abstract

The novel influenza A virus (IAV) defective interfering particle “OP7” inhibits IAV replication in a co-infection and was previously suggested as a promising antiviral agent. Here, we report a batch-mode cell culture-based production process for OP7. In the present study, a seed virus containing standard virus (STV) and OP7 was used. The yield of OP7 strongly depended on the production multiplicity of infection. To inactivate infectious STV in the OP7 material, which may cause harm in a potential application, UV irradiation was used. The efficacy of OP7 in this material was preserved, as shown by an in vitro interference assay. Next, steric exclusion chromatography was used to purify and to concentrate (~ 13-fold) the UV-treated material. Finally, administration of produced OP7 material in mice did not show any toxic effects. Furthermore, all mice infected with a lethal dose of IAV survived the infection upon OP7 co-treatment. Thus, the feasibility of a production workflow for OP7 and its potential for antiviral treatment was demonstrated.

Key points

• OP7 efficacy strongly depended on the multiplicity of infection used for production

• Purification by steric exclusion chromatography increased OP7 efficacy

• OP7-treated mice were protected against a lethal infection with IAV

Supplementary Information

The online version contains supplementary material available at 10.1007/s00253-020-11029-5.

Impact of feeding and stirring regimes on the internal stratification of microbial communities in the fermenter of anaerobic digestion plants

In anaerobic digestion plants (ADs), homogenization of the feed, fermenter content and microbial communities is crucial for efficient and robust biogas production. However, mixing also requires a significant amount of energy. For an 850 m³ agricultural AD equipped with eight sampling ports, we investigated whether different feeding and stirring regimes enable a sufficient homogenization of the microbial community using metaproteomics and terminal restriction fragment length polymorphism (TRFLP) analysis. Systematic comparison of samples taken at the top and the bottom as well as at the rim and the center of the AD using scatter plots and students t-test revealed only a small number of differences in metaproteins, taxonomies and biological processes. Obviously, the applied stirring and feeding conditions were sufficient to largely homogenize the content of the AD.

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.

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.

The Impact of ackA, pta, and ackA-pta Mutations on Growth, Gene Expression and Protein Acetylation in Escherichia coli K-12

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.

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.

Augmenting Biogas Process Modeling by Resolving Intracellular Metabolic Activity

The process of anaerobic digestion in which waste biomass is transformed to methane by complex microbial communities has been modeled for more than 16 years by parametric gray box approaches that simplify process biology and do not resolve intracellular microbial activity. Information on such activity, however, has become available in unprecedented detail by recent experimental advances in metatranscriptomics and metaproteomics. The inclusion of such data could lead to more powerful process models of anaerobic digestion that more faithfully represent the activity of microbial communities. We augmented the Anaerobic Digestion Model No. 1 (ADM1) as the standard kinetic model of anaerobic digestion by coupling it to Flux-Balance-Analysis (FBA) models of methanogenic species. Steady-state results of coupled models are comparable to standard ADM1 simulations if the energy demand for non-growth associated maintenance (NGAM) is chosen adequately. When changing a constant feed of maize silage from continuous to pulsed feeding, the final average methane production remains very similar for both standard and coupled models, while both the initial response of the methanogenic population at the onset of pulsed feeding as well as its dynamics between pulses deviates considerably. In contrast to ADM1, the coupled models deliver predictions of up to 1,000s of intracellular metabolic fluxes per species, describing intracellular metabolic pathway activity in much higher detail. Furthermore, yield coefficients which need to be specified in ADM1 are no longer required as they are implicitly encoded in the topology of the species’ metabolic network. We show the feasibility of augmenting ADM1, an ordinary differential equation-based model for simulating biogas production, by FBA models implementing individual steps of anaerobic digestion. While cellular maintenance is introduced as a new parameter, the total number of parameters is reduced as yield coefficients no longer need to be specified. The coupled models provide detailed predictions on intracellular activity of microbial species which are compatible with experimental data on enzyme synthesis activity or abundance as obtained by metatranscriptomics or metaproteomics. By providing predictions of intracellular fluxes of individual community members, the presented approach advances the simulation of microbial community driven processes and provides a direct link to validation by state-of-the-art experimental techniques.

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.

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.

RedCom: A strategy for reduced metabolic modeling of complex microbial communities and its application for analyzing experimental datasets from anaerobic digestion

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.

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.

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.

MPA Portable: A Stand-Alone Software Package for Analyzing Metaproteome Samples on the Go

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.

FMNH2-dependent monooxygenases initiate catabolism of sulfonamides in Microbacterium sp. strain BR1 subsisting on sulfonamide antibiotics

We report a cluster of genes encoding two monooxygenases (SadA and SadB) and one FMN reductase (SadC) that enable Microbacterium sp. strain BR1 and other Actinomycetes to inactivate sulfonamide antibiotics. Our results show that SadA and SadC are responsible for the initial attack of sulfonamide molecules resulting in the release of 4-aminophenol. The latter is further transformed into 1,2,4-trihydroxybenzene by SadB and SadC prior to mineralization and concomitant production of biomass. As the degradation products lack antibiotic activity, the presence of SadA will result in an alleviated bacteriostatic effect of sulfonamides. In addition to the relief from antibiotic stress this bacterium gains access to an additional carbon source when this gene cluster is expressed. As degradation of sulfonamides was also observed when Microbacterium sp. strain BR1 was grown on artificial urine medium, colonization with such strains may impede common sulfonamide treatment during co-infections with pathogens of the urinary tract. This case of biodegradation exemplifies the evolving catabolic capacity of bacteria, given that sulfonamide bacteriostatic are purely of synthetic origin. The wide distribution of this cluster in Actinomycetes and the presence of traA encoding a relaxase in its vicinity suggest that this cluster is mobile and that is rather alarming.

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.

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.

Predicting compositions of microbial communities from stoichiometric models with applications for the biogas process

BACKGROUND

Microbial communities are ubiquitous in nature and play a major role in ecology, medicine, and various industrial processes. In this study, we used stoichiometric metabolic modeling to investigate a community of three species, Desulfovibrio vulgaris, Methanococcus maripaludis, and Methanosarcina barkeri, which are involved in acetogenesis and methanogenesis in anaerobic digestion for biogas production.

RESULTS

We first constructed and validated stoichiometric models of the core metabolism of the three species which were then assembled to community models. The community was simulated by applying the previously described concept of balanced growth demanding that all organisms of the community grow with equal specific growth rate. For predicting community compositions, we propose a novel hierarchical optimization approach: first, similar to other studies, a maximization of the specific community growth rate is performed which, however, often leads to a wide range of optimal community compositions. In a secondary optimization, we therefore also demand that all organisms must grow with maximum biomass yield (optimal substrate usage) reducing the range of predicted optimal community compositions. Simulating two-species as well as three-species communities of the three representative organisms, we gained several important insights. First, using our new optimization approach we obtained predictions on optimal community compositions for different substrates which agree well with measured data. Second, we found that the ATP maintenance coefficient influences significantly the predicted community composition, especially for small growth rates. Third, we observed that maximum methane production rates are reached under high-specific community growth rates and if at least one of the organisms converts its substrate(s) with suboptimal biomass yield. On the other hand, the maximum methane yield is obtained at low community growth rates and, again, when one of the organisms converts its substrates suboptimally and thus wastes energy. Finally, simulations in the three-species community clarify exchangeability and essentiality of the methanogens in case of alternative substrate usage and competition scenarios.

CONCLUSIONS

In summary, our study presents new methods for stoichiometric modeling of microbial communities in general and provides valuable insights in interdependencies of bacterial species involved in the biogas process.

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).