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

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Biogas from lignocellulosic feedstock: current status and challenges

The organic wastes and residues generated from agricultural, industrial, and domestic activities have the potential to be converted to bioenergy. One such energy is biogas, which has already been included in rural areas as an alternative cooking energy source and agricultural activities. It is produced via anaerobic digestion of a wide range of organic nutrient sources and is an essential renewable energy source. The factors influencing biogas yield, i.e., the various substrate, their characteristics, pretreatment methods involved, different microbial types, sources, and inoculum properties, are analyzed. Furthermore, the optimization of these parameters, along with fermentation media optimization, such as optimum pH, temperature, and anaerobic digestion strategies, is discussed. Novel approaches of bioaugmentation, co-digestion, phase separation, co-supplementation, nanotechnology, and biorefinery approach have also been explored for biogas production. Finally, the current challenges and prospects of the process are discussed in the review.

GroEL-Proteotyping of Bacterial Communities Using Tandem Mass Spectrometry

Profiling bacterial populations in mixed communities is a common task in microbiology. Sequencing of 16S small subunit ribosomal-RNA (16S rRNA) gene amplicons is a widely accepted and functional approach but relies on amplification primers and cannot quantify isotope incorporation. Tandem mass spectrometry proteotyping is an effective alternative for taxonomically profiling microorganisms. We suggest that targeted proteotyping approaches can complement traditional population analyses. Therefore, we describe an approach to assess bacterial community compositions at the family level using the taxonomic marker protein GroEL, which is ubiquitously found in bacteria, except a few obligate intracellular species. We refer to our method as GroEL-proteotyping. GroEL-proteotyping is based on high-resolution tandem mass spectrometry of GroEL peptides and identification of GroEL-derived taxa via a Galaxy workflow and a subsequent Python-based analysis script. Its advantage is that it can be performed with a curated and extendable sample-independent database and that GroEL can be pre-separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) to reduce sample complexity, improving GroEL identification while simultaneously decreasing the instrument time. GroEL-proteotyping was validated by employing it on a comprehensive raw dataset obtained through a metaproteome approach from synthetic microbial communities as well as real human gut samples. Our data show that GroEL-proteotyping enables fast and straightforward profiling of highly abundant taxa in bacterial communities at reasonable taxonomic resolution.

Label-Free Multiplex Proteotyping of Microbial Isolates

To meet clinical diagnostic needs and for general microbiological screening, it is essential to be able to accurately and rapidly identify any microorganisms from complex microbiota. To gain insight into the individual components of microbiota, culturomics has been proposed as a means to systematically test hundreds of possible cultivation conditions and generate numerous microbial isolates with very distinct characteristics. High-throughput identification methods must now be developed to quickly screen these isolates. Currently, most multiplexing methods involve labeling, which comes at a cost. In this paper, we present an innovative label-free multiplexing method for the identification of microorganisms using tandem mass spectrometry. The method is based on offline reversed-phase fractionation of individual peptidomes. Multiplexing is achieved by mixing fractions of staged hydrophobicity; thus, each sample is mapped to specific elution times. In this proof-of-concept study, multiplexed samples were analyzed by tandem mass spectrometry in a single run and microorganisms present in the mixture were resolved by phylopeptidomics proteotyping. Using this methodology, up to 21 microorganisms could be identified in a single 60 min run performed with a Q-Exactive HF high-resolution mass spectrometer, resulting in a rate of one microorganism identified per 3 min of mass spectrometry, without any need for the use of labeling reagents. This approach opens new perspectives for the application of high-throughput proteotyping of bacteria using tandem mass spectrometry in large culturomics projects.

Inter-kingdom interactions and stability of methanogens revealed by machine-learning guided multi-omics analysis of industrial-scale biogas plants

Multi-omics analysis is a powerful tool for the detection and study of inter-kingdom interactions, such as those between bacterial and archaeal members of complex biogas-producing microbial communities. In the present study, the microbiomes of three industrial-scale biogas digesters, each fed with different substrates, were analysed using a machine-learning guided genome-centric metagenomics framework complemented with metatranscriptome data. This data permitted us to elucidate the relationship between abundant core methanogenic communities and their syntrophic bacterial partners. In total, we detected 297 high-quality, non-redundant metagenome-assembled genomes (nrMAGs). Moreover, the assembled 16 S rRNA gene profiles of these nrMAGs showed that the phylum Firmicutes possessed the highest copy number, while the representatives of the archaeal domain had the lowest. Further investigation of the three anaerobic microbial communities showed characteristic alterations over time but remained specific to each industrial-scale biogas plant. The relative abundance of various microorganisms as revealed by metagenome data was independent from corresponding metatranscriptome activity data. Archaea showed considerably higher activity than was expected from their abundance. We detected 51 nrMAGs that were present in all three biogas plant microbiomes with different abundances. The core microbiome correlated with the main chemical fermentation parameters, and no individual parameter emerged as a predominant shaper of community composition. Various interspecies H₂/electron transfer mechanisms were assigned to hydrogenotrophic methanogens in the biogas plants that ran on agricultural biomass and wastewater. Analysis of metatranscriptome data revealed that methanogenesis pathways were the most active of all main metabolic pathways.

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.

Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects

Energy crisis and environmental pollution have become the bottleneck of human sustainable development. Therefore, there is an urgent need to develop new catalysts for energy production and environmental remediation. Due to the high cost caused by blind screening and limited valuable computing resources, the traditional experimental methods and theoretical calculations are difficult to meet with the requirements. In the past decades, computer science has made great progress, especially in the field of machine learning (ML). As a new research paradigm, ML greatly accelerates the theoretical calculation methods represented by first principal calculation and molecular dynamics, and establish the physical picture of heterogeneous catalytic processes for energy and environment. This review firstly summarized the general research paradigms of ML in the discovery of catalysts. Then, the latest progresses of ML in light-, electricity- and enzyme-mediated heterogeneous catalysis were reviewed from the perspective of catalytic performance, operating conditions and reaction mechanism. The general guidelines of ML for heterogeneous catalysis were proposed. Finally, the existing problems and future development trend of ML in heterogeneous catalysis mediated by light, electricity and enzyme were summarized. We highly expect that this review will facilitate the interaction between ML and heterogeneous catalysis, and illuminate the development prospect of heterogeneous catalysis.

Bioconversion of Lignocellulosic Biomass into Value Added Products under Anaerobic Conditions: Insight into Proteomic Studies

Production of biofuels and other value-added products from lignocellulose breakdown requires the coordinated metabolic activity of varied microorganisms. The increasing global demand for biofuels encourages the development and optimization of production strategies. Optimization in turn requires a thorough understanding of the microbial mechanisms and metabolic pathways behind the formation of each product of interest. Hydrolysis of lignocellulosic biomass is a bottleneck in its industrial use and often affects yield efficiency. The accessibility of the biomass to the microorganisms is the key to the release of sugars that are then taken up as substrates and subsequently transformed into the desired products. While the effects of different metabolic intermediates in the overall production of biofuel and other relevant products have been studied, the role of proteins and their activity under anaerobic conditions has not been widely explored. Shifts in enzyme production may inform the state of the microorganisms involved; thus, acquiring insights into the protein production and enzyme activity could be an effective resource to optimize production strategies. The application of proteomic analysis is currently a promising strategy in this area. This review deals on the aspects of enzymes and proteomics of bioprocesses of biofuels production using lignocellulosic biomass as substrate.

Increasing the power of interpretation for soil metaproteomics data

BACKGROUND

Soil and sediment microorganisms are highly phylogenetically diverse but are currently largely under-represented in public molecular databases. Their functional characterization by means of metaproteomics is usually performed using metagenomic sequences acquired for the same sample. However, such hugely diverse metagenomic datasets are difficult to assemble; in parallel, theoretical proteomes from isolates available in generic databases are of high quality. Both these factors advocate for the use of theoretical proteomes in metaproteomics interpretation pipelines. Here, we examined a number of database construction strategies with a view to increasing the outputs of metaproteomics studies performed on soil samples.

RESULTS

The number of peptide-spectrum matches was found to be of comparable magnitude when using public or sample-specific metagenomics-derived databases. However, numbers were significantly increased when a combination of both types of information was used in a two-step cascaded search. Our data also indicate that the functional annotation of the metaproteomics dataset can be maximized by using a combination of both types of databases.

CONCLUSIONS

A two-step strategy combining sample-specific metagenome database and public databases such as the non-redundant NCBI database and a massive soil gene catalog allows maximizing the metaproteomic interpretation both in terms of ratio of assigned spectra and retrieval of function-derived information. Video abstract.

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.

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.

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.

Combined whole cell wall analysis and streamlined in silico carbohydrate-active enzyme discovery to improve biocatalytic conversion of agricultural crop residues

The production of biofuels as an efficient source of renewable energy has received considerable attention due to increasing energy demands and regulatory incentives to reduce greenhouse gas emissions. Second-generation biofuel feedstocks, including agricultural crop residues generated on-farm during annual harvests, are abundant, inexpensive, and sustainable. Unlike first-generation feedstocks, which are enriched in easily fermentable carbohydrates, crop residue cell walls are highly resistant to saccharification, fermentation, and valorization. Crop residues contain recalcitrant polysaccharides, including cellulose, hemicelluloses, pectins, and lignin and lignin-carbohydrate complexes. In addition, their cell walls can vary in linkage structure and monosaccharide composition between plant sources. Characterization of total cell wall structure, including high-resolution analyses of saccharide composition, linkage, and complex structures using chromatography-based methods, nuclear magnetic resonance, -omics, and antibody glycome profiling, provides critical insight into the fine chemistry of feedstock cell walls. Furthermore, improving both the catalytic potential of microbial communities that populate biodigester reactors and the efficiency of pre-treatments used in bioethanol production may improve bioconversion rates and yields. Toward this end, knowledge and characterization of carbohydrate-active enzymes (CAZymes) involved in dynamic biomass deconstruction is pivotal. Here we overview the use of common "-omics"-based methods for the study of lignocellulose-metabolizing communities and microorganisms, as well as methods for annotation and discovery of CAZymes, and accurate prediction of CAZyme function. Emerging approaches for analysis of large datasets, including metagenome-assembled genomes, are also discussed. Using complementary glycomic and meta-omic methods to characterize agricultural residues and the microbial communities that digest them provides promising streams of research to maximize value and energy extraction from crop waste streams.

Microbial invasions in sludge anaerobic digesters

Among processes that control microbial community assembly, microbial invasion has received little attention until recently, especially in the field of anaerobic digestion. However, knowledge of the principles regulating the taxonomic and functional stability of microbial communities is key to truly develop better predictive models and effective management strategies for the anaerobic digestion process. To date, available studies focus on microbial invasions in digesters feed with activated sludge from municipal wastewater treatment plants. Herein, this review summarizes the importance of invasions for anaerobic digestion management, the ecological theories about microbial invasions, the traits of activated sludge microorganisms entering the digesters, and the resident communities of anaerobic reactors that are relevant for invasions and the current knowledge about the success and impacts of invasions, and discusses the research needs on this topic. The initial data indicate that the impact of invasions is low and only a small percentage of the mostly aerobic microorganisms present in the activated sludge feed are able to become stablished in the anaerobic digesters. However, there are still numerous unknowns about microbial invasions in anaerobic digestion including the influence of anaerobic feedstocks or process perturbances that new approaches on microbial ecology could unveil. KEY POINTS: • Microbial invasions are key processes to develop better strategies for digesters management. • Knowledge on pathogen invasions can improve anaerobic digestion microbial safety. • To date, the number of successful invasions on anaerobic digesters from activated sludge organisms is low. • Feed organisms detected in digesters are mostly inactive residual populations. • Need to expand the range of invaders and operational scenarios studied.

Integrating independent microbial studies to build predictive models of anaerobic digestion inhibition by ammonia and phenol

Anaerobic digestion (AD) is a process that can efficiently degrade organic waste into renewable energies. AD failure is however common as the underpinning microbial mechanisms are highly vulnerable to a wide range of inhibitory compounds. Sequencing technologies enable the identification of microbial indicators of digesters inhibition, but existing studies are limited. They used different inocula, substrates, sites and types of reactors and reported different or contradictory indicators. Our aim was to identify a robust signature of microbial indicators of phenol and ammonia inhibitions across four independent AD microbial studies. To identify such signature, we applied an original multivariate integrative method on two in-house studies, then validated our approach by predicting the inhibitory status of samples from two other studies with more than 90% accuracy. Our approach shows how we can efficiently leverage on existing studies to extract reproducible microbial community patterns and predict AD inhibition to improve AD microbial management.

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.

MetaFS: Performance assessment of biomarker discovery in metaproteomics

Metaproteomics suffers from the issues of dimensionality and sparsity. Data reduction methods can maximally identify the relevant subset of significant differential features and reduce data redundancy. Feature selection (FS) methods were applied to obtain the significant differential subset. So far, a variety of feature selection methods have been developed for metaproteomic study. However, due to FS's performance depended heavily on the data characteristics of a given research, the well-suitable feature selection method must be carefully selected to obtain the reproducible differential proteins. Moreover, it is critical to evaluate the performance of each FS method according to comprehensive criteria, because the single criterion is not sufficient to reflect the overall performance of the FS method. Therefore, we developed an online tool named MetaFS, which provided 13 types of FS methods and conducted the comprehensive evaluation on the complex FS methods using four widely accepted and independent criteria. Furthermore, the function and reliability of MetaFS were systematically tested and validated via two case studies. In sum, MetaFS could be a distinguished tool for discovering the overall well-performed FS method for selecting the potential biomarkers in microbiome studies. The online tool is freely available at https://idrblab.org/metafs/.

Performance of a novel IAHD-DSR process with methane and sulfide as co-electron donors

A novel integrated autotrophic and heterotrophic denitrification- denitrifying sulfide removal (IAHD-DSR) process was established in this study for biogas desulfurization to simultaneously remove nitrogen in wastewater. The study demonstrated that the system could utilize methane and sulfide as co-electron donors to replace organic carbon source in IAHD process. Three batch tests (B1, B2 and B3) were set up with IAHD sludge to explore how the novel process works. According to mass balance in B2, methane oxidation and sulfide oxidation contributed 18.75 % and 71.25 % to nitrate removal, respectively; however, the contribution of methane oxidation to total nitrogen (TN) removal reached 84.36 %. Sulfide was mainly responsible for the reduction of nitrate to nitrite, while the methane was for nitrite to nitrogen gas in the presence of insufficient sulfide as electron donors. The TN removal in B2 was almost the same as in normal IAHD-DSR process B3-C. The functional genes mcrA and pmoA responsible for methane oxidation were detected in all three batches, with the abundance of 2.23 ×10⁶ copies/(g dry soil) for mcrA in B1 being the highest in three batches. The sulfide addition in B2 increased the abundance of gene pmoA, indicating the enhancement of nitrite reduction coupled with methane oxidation.

Development of an alkaline/acid pre-treatment and anaerobic digestion (APAD) process for methane generation from waste activated sludge

Anaerobic sludge digesters are biorefineries for energy recovery from waste activated sludge (WAS) via methane production, in which disintegration of floc structure and microbial cells is a major challenge in releasing extracellular polymeric substances (EPS) and cytoplasmic macromolecules for subsequent hydrolysis and fermentation. Here, we developed a new process combining alkaline/acid pre-treatments and anaerobic digestion (APAD) to improve sludge digestion. Both alkaline and acid pre-treatments effectively disintegrated the floc structure and microbial cells to release sludge organic contents. Under the optimized alkaline/acid pre-treatment condition, carbon removal achieved 52.8 ± 1.7% in APAD digesters, in contrast to 30.9 ± 2.2% and 42.4 ± 1.6% in anaerobic digesters fed with fresh WAS (control-AD) and thermal pre-treated sludge (thermal-AD), respectively. Both alkaline/acid and thermal pre-treatments largely shifted sludge community composition and function, but in distinct ways, possibly due to their different sludge constitutes (i.e., dissolved organic matter and NaCl). Correspondingly, microbial network analysis identified three modules with varied keystone taxa and interaction patterns in the three digesters. Life cycle assessment showed the comparable environmental impacts of APAD, thermal-AD and control-AD. In all, this study provided a new solution for WAS treatment and insights into impact of sludge pre-treatments on sludge digestion microbiome.

Robust Accurate Identification and Biomass Estimates of Microorganisms via Tandem Mass Spectrometry

Rapid and accurate identification of microorganisms and estimation of their biomasses are of extreme importance to public health. Mass spectrometry has become an important technique for these purposes. Previously we published a workflow named Microorganism Classification and Identification (MiCId v.12.26.2017) that was shown to perform no worse than other workflows. This manuscript presents MiCId v.12.13.2018 that, in comparison with the earlier version v.12.26.2017, allows for biomass estimates, provides more accurate microorganism identifications (better controls the number of false positives), and is robust against database size increase. This significant advance is made possible by several new ingredients introduced: first, we apply a modified expectation-maximization method to compute for each taxon considered a prior probability, which can be used for biomass estimate; second, we introduce a new concept called ownership, through which the participation ratio is computed and use it as the number of taxa to be kept within a cluster of closely related taxa; third, based on confidently identified peptides, we calculate for each taxon its degree of independence from the rest of taxa considered to determine whether or not to split this taxon off the cluster. Using 270 data files, each containing a large number of MS/MS spectra, we show that, in comparison with v.12.26.2017, version v.12.13.2018 yields superior retrieval results. We also show that MiCId v.12.13.2018 can estimate species biomass reasonably well. The new MiCId v.12.13.2018, designed to run in Linux environment, is freely available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.

Genome Analyses and Genome-Centered Metatranscriptomics of Methanothermobacter wolfeii Strain SIV6, Isolated from a Thermophilic Production-Scale Biogas Fermenter

In the thermophilic biogas-producing microbial community, the genus Methanothermobacter was previously described to be frequently abundant. The aim of this study was to establish and analyze the genome sequence of the archaeal strain Methanothermobacter wolfeii SIV6 originating from a thermophilic industrial-scale biogas fermenter and compare it to related reference genomes. The circular chromosome has a size of 1,686,891 bases, featuring a GC content of 48.89%. Comparative analyses considering three completely sequenced Methanothermobacter strains revealed a core genome of 1494 coding sequences and 16 strain specific genes for M. wolfeii SIV6, which include glycosyltransferases and CRISPR/cas associated genes. Moreover, M. wolfeii SIV6 harbors all genes for the hydrogenotrophic methanogenesis pathway and genome-centered metatranscriptomics indicates the high metabolic activity of this strain, with 25.18% of all transcripts per million (TPM) belong to the hydrogenotrophic methanogenesis pathway and 18.02% of these TPM exclusively belonging to the mcr operon. This operon encodes the different subunits of the enzyme methyl-coenzyme M reductase (EC: 2.8.4.1), which catalyzes the final and rate-limiting step during methanogenesis. Finally, fragment recruitment of metagenomic reads from the thermophilic biogas fermenter on the SIV6 genome showed that the strain is abundant (1.2%) within the indigenous microbial community. Detailed analysis of the archaeal isolate M. wolfeii SIV6 indicates its role and function within the microbial community of the thermophilic biogas fermenter, towards a better understanding of the biogas production process and a microbial-based management of this complex process.

Different response of bacteria, archaea and fungi to process parameters in nine full-scale anaerobic digesters

Biogas production is a biotechnological process realized by complex bacterial, archaeal and likely fungal communities. Their composition was assessed in nine full-scale biogas plants with distinctly differing feedstock input and process parameters. This study investigated the actually active microbial community members by using a comprehensive sequencing approach based on ribosomal 16S and 28S rRNA fragments. The prevailing taxonomical units of each respective community were subsequently linked to process parameters. Ribosomal rRNA of bacteria, archaea and fungi, respectively, showed different compositions with respect to process parameters and supplied feedstocks: (i) bacterial communities were affected by the key factors temperature and ammonium concentration; (ii) composition of archaea was mainly related to process temperature; and (iii) relative abundance of fungi was linked to feedstocks supplied to the digesters. Anaerobic digesters with a high methane yield showed remarkably similar bacterial communities regarding identified taxonomic families. Although archaeal communities differed strongly on genus level from each other, the respective digesters still showed high methane yields. Functional redundancy of the archaeal communities may explain this effect. 28S rRNA sequences of fungi in all nine full-scale anaerobic digesters were primarily classified as facultative anaerobic Ascomycota and Basidiomycota. Since the presence of ribosomal 28S rRNA indicates that fungi may be active in the biogas digesters, further research should be carried out to examine to which extent they are important players in anaerobic digestion processes.

Sample Preservation and Storage Significantly Impact Taxonomic and Functional Profiles in Metaproteomics Studies of the Human Gut Microbiome

With the technological advances of the last decade, it is now feasible to analyze microbiome samples, such as human stool specimens, using multi-omic techniques. Given the inherent sample complexity, there exists a need for sample methods which preserve as much information as possible about the biological system at the time of sampling. Here, we analyzed human stool samples preserved and stored using different methods, applying metagenomics as well as metaproteomics. Our results demonstrate that sample preservation and storage have a significant effect on the taxonomic composition of identified proteins. The overall identification rates, as well as the proportion of proteins from Actinobacteria were much higher when samples were flash frozen. Preservation in RNAlater overall led to fewer protein identifications and a considerable increase in the share of Bacteroidetes, as well as Proteobacteria. Additionally, a decrease in the share of metabolism-related proteins and an increase of the relative amount of proteins involved in the processing of genetic information was observed for RNAlater-stored samples. This suggests that great care should be taken in choosing methods for the preservation and storage of microbiome samples, as well as in comparing the results of analyses using different sampling and storage methods. Flash freezing and subsequent storage at −80 °C should be chosen wherever possible.

Proteomic Investigations of In Vitro and In Vivo Models of Periodontal Disease

Proteomics has currently been a developing field in periodontal diseases to obtain protein information of certain samples. Periodontal disease is an inflammatory disorder that attacks the teeth, connective tissues, and alveolar bone within the oral cavity. Proteomics information can provide proteins that are differentially expressed in diseased or healthy samples. This review provides insight into approaches researching single species, multi species, bacteria, non-human, and human models of periodontal disease for proteomics information. The approaches that have been taken include gel electrophoresis and qualitative and quantitative mass spectrometry. This review is carried out by extracting information about in vitro and in vivo studies of proteomics in models of periodontal diseases that have been carried out in the past two decades. The research has concentrated on a relatively small but well-known group of microorganisms. A wide range of models has been reviewed and conclusions across the breadth of these studies are presented in this review.

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.

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.

More Is Not Always Better: Evaluation of 1D and 2D-LC-MS/MS Methods for Metaproteomics

Metaproteomics, the study of protein expression in microbial communities, is a versatile tool for environmental microbiology. Achieving sufficiently high metaproteome coverage to obtain a comprehensive picture of the activities and interactions in microbial communities is one of the current challenges in metaproteomics. An essential step to maximize the number of identified proteins is peptide separation via liquid chromatography (LC) prior to mass spectrometry (MS). Thorough optimization and comparison of LC methods for metaproteomics are, however, currently lacking. Here, we present an extensive development and test of different 1D and 2D-LC approaches for metaproteomic peptide separations. We used fully characterized mock community samples to evaluate metaproteomic approaches with very long analytical columns (50 and 75 cm) and long gradients (up to 12 h). We assessed a total of over 20 different 1D and 2D-LC approaches in terms of number of protein groups and unique peptides identified, peptide spectrum matches (PSMs) generated, the ability to detect proteins of low-abundance species, the effect of technical replicate runs on protein identifications and method reproducibility. We show here that, while 1D-LC approaches are faster and easier to set up and lead to more identifications per minute of runtime, 2D-LC approaches allow for a higher overall number of identifications with up to >10,000 protein groups identified. We also compared the 1D and 2D-LC approaches to a standard GeLC workflow, in which proteins are pre-fractionated via gel electrophoresis. This method yielded results comparable to the 2D-LC approaches, however with the drawback of a much increased sample preparation time. Based on our results, we provide recommendations on how to choose the best LC approach for metaproteomics experiments, depending on the study aims.

Intermittent fasting for microbes: how discontinuous feeding increases functional stability in anaerobic digestion

BACKGROUND

Demand-driven biogas production could play an important role for future sustainable energy supply. However, feeding a biogas reactor according to energy demand may lead to organic overloading and, thus, to process failures. To minimize this risk, digesters need to be actively steered towards containing more robust microbial communities. This study focuses on acetogenesis and methanogenesis as crucial process steps for avoiding acidification. We fed lab-scale anaerobic digesters with volatile fatty acids under various feeding regimes and disturbances. The resulting microbial communities were analyzed on DNA and RNA level by terminal restriction fragment length polymorphism of the mcrA gene, 16S rRNA gene amplicon sequencing, and a [2-13C]-acetate assay. A modified Anaerobic Digestion Model 1 (ADM1) that distinguishes between the acetoclastic methanogens Methanosaeta and Methanosarcina was developed and fitted using experimental abiotic and biotic process parameters.

RESULTS

Discontinuous feeding led to more functional resilience than continuous feeding, without loss in process efficiency. This was attributed to a different microbial community composition. Methanosaeta dominated the continuously fed reactors, while its competitor Methanosarcina was washed out. With discontinuous feeding, however, the fluctuating acetic acid concentrations provided niches to grow and co-exist for both organisms as shown by transcription analysis of the mcrA gene. Our model confirmed the higher functional resilience due to the higher abundance of Methanosarcina based on its higher substrate uptake rate and higher resistance to low pH values. Finally, we applied our model to maize silage as a more complex and practically relevant substrate and showed that our model is likely transferable to the complete AD process.

CONCLUSIONS

The composition of the microbial community determined the AD functional resilience against organic overloading in our experiments. In particular, communities with higher share of Methanosarcina showed higher process stability. The share of these microorganisms can be purposefully increased by discontinuous feeding. A model was developed that enables derivation of the necessary feeding regime for a more robust community with higher share of Methanosarcina.

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.

Linking Microbial Community Structure and Function During the Acidified Anaerobic Digestion of Grass

Harvesting valuable bioproducts from various renewable feedstocks is necessary for the critical development of a sustainable bioeconomy. Anaerobic digestion is a well-established technology for the conversion of wastewater and solid feedstocks to energy with the additional potential for production of process intermediates of high market values (e.g., carboxylates). In recent years, first-generation biofuels typically derived from food crops have been widely utilized as a renewable source of energy. The environmental and socioeconomic limitations of such strategy, however, have led to the development of second-generation biofuels utilizing, amongst other feedstocks, lignocellulosic biomass. In this context, the anaerobic digestion of perennial grass holds great promise for the conversion of sustainable renewable feedstock to energy and other process intermediates. The advancement of this technology however, and its implementation for industrial applications, relies on a greater understanding of the microbiome underpinning the process. To this end, microbial communities recovered from replicated anaerobic bioreactors digesting grass were analyzed. The bioreactors leachates were not buffered and acidic pH (between 5.5 and 6.3) prevailed at the time of sampling as a result of microbial activities. Community composition and transcriptionally active taxa were examined using 16S rRNA sequencing and microbial functions were investigated using metaproteomics. Bioreactor fraction, i.e., grass or leachate, was found to be the main discriminator of community analysis across the three molecular level of investigation (DNA, RNA, and proteins). Six taxa, namely Bacteroidia, Betaproteobacteria, Clostridia, Gammaproteobacteria, Methanomicrobia, and Negativicutes accounted for the large majority of the three datasets. The initial stages of grass hydrolysis were carried out by Bacteroidia, Gammaproteobacteria, and Negativicutes in the grass biofilms, in addition to Clostridia in the bioreactor leachates. Numerous glycolytic enzymes and carbohydrate transporters were detected throughout the bioreactors in addition to proteins involved in butanol and lactate production. Finally, evidence of the prevalence of stressful conditions within the bioreactors and particularly impacting Clostridia was observed in the metaproteomes. Taken together, this study highlights the functional importance of Clostridia during the anaerobic digestion of grass and thus research avenues allowing members of this taxon to thrive should be explored.

Assessing species biomass contributions in microbial communities via metaproteomics

Microbial community structure can be analyzed by quantifying cell numbers or by quantifying biomass for individual populations. Methods for quantifying cell numbers are already available (e.g., fluorescence in situ hybridization, 16S rRNA gene amplicon sequencing), yet high-throughput methods for assessing community structure in terms of biomass are lacking. Here we present metaproteomics-based methods for assessing microbial community structure using protein abundance as a measure for biomass contributions of individual populations. We optimize the accuracy and sensitivity of the method using artificially assembled microbial communities and show that it is less prone to some of the biases found in sequencing-based methods. We apply the method to communities from two different environments, microbial mats from two alkaline soda lakes, and saliva from multiple individuals. We show that assessment of species biomass contributions adds an important dimension to the analysis of microbial community structure.

Convenient methods for assessing microbial community structure in terms of biomass are lacking. Here, the authors present a metaproteomics-based approach for assessing microbial community structure using protein abundance as a measure for biomass contributions of individual populations.

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.