Nanospray Desorption Electrospray Ionization (Nano-DESI) Mass Spectrometry Imaging with High Ion Mobility Resolution.

Untargeted separation of isomeric and isobaric species in mass spectrometry imaging (MSI) is challenging. The combination of ion mobility spectrometry (IMS) with MSI has emerged as an effective strategy for differentiating isomeric and isobaric species, which substantially enhances the molecular coverage and specificity of MSI experiments. In this study, we have implemented nanospray desorption electrospray ionization (nano-DESI) MSI on a trapped ion mobility spectrometry (TIMS) mass spectrometer. A new nano-DESI source was constructed, and a specially designed inlet extension was fabricated to accommodate the new source. The nano-DESI-TIMS-MSI platform was evaluated by imaging mouse brain tissue sections. We achieved high ion mobility resolution by utilizing three narrow mobility scan windows that covered the majority of the lipid molecules. Notably, the mobility resolution reaching up to 300 in this study is much higher than the resolution obtained in our previous study using drift tube IMS. High-resolution TIMS successfully separated lipid isomers and isobars, revealing their distinct localizations in tissue samples. Our results further demonstrate the power of high-mobility-resolution IMS for unraveling the complexity of biomolecular mixtures analyzed in MSI experiments.

Feature-based molecular networking in the GNPS analysis environment.

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.

MS Imaging-Guided Microproteomics for Spatial Omics on a Single Instrument.

Mass spectrometry imaging (MSI) allows investigating the spatial distribution of chemical compounds directly in biological tissues. As the analytical depth of MSI is limited, MSI needs to be coupled to more sensitive local extraction-based omics approaches to achieve a comprehensive molecular characterization. For this, it is important to retain the spatial information provided by MSI for follow-up omics studies. It has been shown that regiospecific MSI data can be used to guide a laser microdissection system for ultra-sensitive liquid chromatography-mass spectrometry (LC-MS) analyses. So far, this combination has required separate and specialized mass spectrometry (MS) instrumentation. Recent advances in dual-source instrumentation, harboring both matrix assisted laser/desorption ionization (MALDI) and electrospray ionization (ESI) sources, promise state-of-the-art MSI and liquid-based proteomic capabilities on the same MS instrument. This study demonstrates that such an instrument can offer both fast lipid-based MSI at high mass and high lateral resolution and sensitive LC-MS on local protein extracts from the exact same tissue section.

Unravelling the Distribution of Secondary Metabolites in Olea europaea L.: Exhaustive Characterization of Eight Olive-Tree Derived Matrices by Complementary Platforms (LC-ESI/APCI-MS and GC-APCI-MS).

In order to understand the distribution of the main secondary metabolites found in Olea europaea L., eight different samples (olive leaf, stem, seed, fruit skin and pulp, as well as virgin olive oil, olive oil obtained from stoned and dehydrated fruits and olive seed oil) coming from a Picudo cv. olive tree were analyzed. All the experimental conditions were selected so as to assure the maximum coverage of the metabolome of the samples under study within a single run. The use of LC and GC with high resolution MS (through different ionization sources, ESI and APCI) and the annotation strategies within MetaboScape 3.0 software allowed the identification of around 150 compounds in the profiles, showing great complementarity between the evaluated methodologies. The identified metabolites belonged to different chemical classes: triterpenic acids and dialcohols, tocopherols, sterols, free fatty acids, and several sub-types of phenolic compounds. The suitability of each platform and polarity (negative and positive) to determine each family of metabolites was evaluated in-depth, finding, for instance, that LC-ESI-MS (+) was the most efficient choice to ionize phenolic acids, secoiridoids, flavonoids and lignans and LC-APCI-MS was very appropriate for pentacyclic triterpenic acids (MS (-)) and sterols and tocopherols (MS (+)). Afterwards, a semi-quantitative comparison of the selected matrices was carried out, establishing their typical features (e.g., fruit skin was pointed out as the matrix with the highest relative amounts of phenolic acids, triterpenic compounds and hydroxylated fatty acids, and seed oil was distinctive for its high relative levels of acetoxypinoresinol and tocopherols).

Metabolic flux pattern of glucose utilization by Xanthomonas campestris pv. campestris: prevalent role of the Entner-Doudoroff pathway and minor fluxes through the pentose phosphate pathway and glycolysis.

The well-studied plant pathogenic bacterium Xanthomonas campestris pv. campestris (Xcc) synthesizes the biotechnologically important polysaccharide xanthan gum, which is also regarded as a virulence factor in plant interactions. In Xcc, sugars like glucose are utilized as a source to generate energy and biomass for growth and pathogenicity. In this study, we used [1-(13)C]glucose as a tracer to analyze the fluxes in the central metabolism of the bacterium growing in a minimal medium. (13)C-Metabolic flux analysis based on gas chromatography-mass spectrometry (GC-MS) confirmed the prevalent catabolic role of the Entner-Doudoroff pathway. Comparative nuclear magnetic resonance (NMR)-based isotopologue profiling of a mutant deficient in glycolysis gave evidence for a moderate flux via glycolysis in the wild-type. In addition to reconfirming the Entner-Doudoroff pathway as a catabolic main route, this approach affirmed a numerically minor but important flux via the pentose phosphate pathway.

Metabolite profiling on wheat grain to enable a distinction of samples from organic and conventional farming systems.

BACKGROUND: Identification of biomarkers capable of distinguishing organic and conventional products would be highly welcome to improve the strength of food quality assurance. Metabolite profiling was used for biomarker search in organic and conventional wheat grain (Triticum aestivum L.) of 11 different old and new bread wheat cultivars grown in the DOK system comparison trial. Metabolites were extracted using methanol and analysed by gas chromatography-mass spectrometry. RESULTS: Altogether 48 metabolites and 245 non-identified metabolites (TAGs) were detected in the cultivar Runal. Principal component analysis showed a sample clustering according to farming systems and significant differences in peak areas between the farming systems for 10 Runal metabolites. Results obtained from all 11 cultivars indicated a greater influence of the cultivar than the farming system on metabolite concentrations. Nevertheless, a t-test on data of all cultivars still detected 5 metabolites and 11 TAGs with significant differences between the farming systems. CONCLUSION: Based on individual cultivars, metabolite profiling showed promising results for the categorization of organic and conventional wheat. Further investigations are necessary with wheat from more growing seasons and locations before definite conclusions can be drawn concerning the feasibility to evolve a combined set of biomarkers for organically grown wheat using metabolite profiles.

MeltDB 2.0-advances of the metabolomics software system.

MOTIVATION: The research area metabolomics achieved tremendous popularity and development in the last couple of years. Owing to its unique interdisciplinarity, it requires to combine knowledge from various scientific disciplines. Advances in the high-throughput technology and the consequently growing quality and quantity of data put new demands on applied analytical and computational methods. Exploration of finally generated and analyzed datasets furthermore relies on powerful tools for data mining and visualization. RESULTS: To cover and keep up with these requirements, we have created MeltDB 2.0, a next-generation web application addressing storage, sharing, standardization, integration and analysis of metabolomics experiments. New features improve both efficiency and effectivity of the entire processing pipeline of chromatographic raw data from pre-processing to the derivation of new biological knowledge. First, the generation of high-quality metabolic datasets has been vastly simplified. Second, the new statistics tool box allows to investigate these datasets according to a wide spectrum of scientific and explorative questions. AVAILABILITY: The system is publicly available at https://meltdb.cebitec.uni-bielefeld.de. A login is required but freely available.

Combining peak- and chromatogram-based retention time alignment algorithms for multiple chromatography-mass spectrometry datasets.

BACKGROUND: Modern analytical methods in biology and chemistry use separation techniques coupled to sensitive detectors, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These hyphenated methods provide high-dimensional data. Comparing such data manually to find corresponding signals is a laborious task, as each experiment usually consists of thousands of individual scans, each containing hundreds or even thousands of distinct signals. In order to allow for successful identification of metabolites or proteins within such data, especially in the context of metabolomics and proteomics, an accurate alignment and matching of corresponding features between two or more experiments is required. Such a matching algorithm should capture fluctuations in the chromatographic system which lead to non-linear distortions on the time axis, as well as systematic changes in recorded intensities. Many different algorithms for the retention time alignment of GC-MS and LC-MS data have been proposed and published, but all of them focus either on aligning previously extracted peak features or on aligning and comparing the complete raw data containing all available features. RESULTS: In this paper we introduce two algorithms for retention time alignment of multiple GC-MS datasets: multiple alignment by bidirectional best hits peak assignment and cluster extension (BIPACE) and center-star multiple alignment by pairwise partitioned dynamic time warping (CeMAPP-DTW). We show how the similarity-based peak group matching method BIPACE may be used for multiple alignment calculation individually and how it can be used as a preprocessing step for the pairwise alignments performed by CeMAPP-DTW. We evaluate the algorithms individually and in combination on a previously published small GC-MS dataset studying the Leishmania parasite and on a larger GC-MS dataset studying grains of wheat (Triticum aestivum). CONCLUSIONS: We have shown that BIPACE achieves very high precision and recall and a very low number of false positive peak assignments on both evaluation datasets. CeMAPP-DTW finds a high number of true positives when executed on its own, but achieves even better results when BIPACE is used to constrain its search space. The source code of both algorithms is included in the OpenSource software framework Maltcms, which is available from http://maltcms.sf.net. The evaluation scripts of the present study are available from the same source.

Root metabolic response of rice (Oryza sativa L.) genotypes with contrasting tolerance to zinc deficiency and bicarbonate excess.

Plants are routinely subjected to multiple environmental stresses that constrain growth. Zinc (Zn) deficiency and high bicarbonate are two examples that co-occur in many soils used for rice production. Here, the utility of metabolomics in diagnosing the effect of each stress alone and in combination on rice root function is demonstrated, with potential stress tolerance indicators identified through the use of contrasting genotypes. Responses to the dual stress of combined Zn deficiency and bicarbonate excess included greater root solute leakage, reduced dry matter production, lower monosaccharide accumulation and increased concentrations of hydrogen peroxide, phenolics, peroxidase and N-rich metabolites in roots. Both hydrogen peroxide concentration and root solute leakage were correlated with higher levels of citrate, allantoin and stigmasterol. Zn stress resulted in lower levels of the tricarboxylic acid (TCA) cycle intermediate succinate and the aromatic amino acid tyrosine. Bicarbonate stress reduced shoot iron (Fe) concentrations, which was reflected by lower Fe-dependent ascorbate peroxidase activity. Bicarbonate stress also favoured the accumulation of the TCA cycle intermediates malate, fumarate and succinate, along with the non-polar amino acid tyrosine. Genotypic differentiation revealed constitutively higher levels of D-gluconate, 2-oxoglutarate and two unidentified compounds in the Zn-efficient line RIL46 than the Zn-inefficient cultivar IR74, suggesting a possible role for these metabolites in overcoming oxidative stress or improving metal re-distribution.

The complete genome sequence of the dominant Sinorhizobium meliloti field isolate SM11 extends the S. meliloti pan-genome.

Isolates of the symbiotic nitrogen-fixing species Sinorhizobium meliloti usually contain a chromosome and two large megaplasmids encoding functions that are absolutely required for the specific interaction of the microsymbiont with corresponding host plants leading to an effective symbiosis. The complete genome sequence, including the megaplasmids pSmeSM11c (related to pSymA) and pSmeSM11d (related to pSymB), was established for the dominant, indigenous S. meliloti strain SM11 that had been isolated during a long-term field release experiment with genetically modified S. meliloti strains. The chromosome, the largest replicon of S. meliloti SM11, is 3,908,022bp in size and codes for 3785 predicted protein coding sequences. The size of megaplasmid pSmeSM11c is 1,633,319bp and it contains 1760 predicted protein coding sequences whereas megaplasmid pSmeSM11d is 1,632,395bp in size and comprises 1548 predicted coding sequences. The gene content of the SM11 chromosome is quite similar to that of the reference strain S. meliloti Rm1021. Comparison of pSmeSM11c to pSymA of the reference strain revealed that many gene regions of these replicons are variable, supporting the assessment that pSymA is a major hot-spot for intra-specific differentiation. Plasmids pSymA and pSmeSM11c both encode unique genes. Large gene regions of pSmeSM11c are closely related to corresponding parts of Sinorhizobium medicae WSM419 plasmids. Moreover, pSmeSM11c encodes further novel gene regions, e.g. additional plasmid survival genes (partition, mobilisation and conjugative transfer genes), acdS encoding 1-aminocyclopropane-1-carboxylate deaminase involved in modulation of the phytohormone ethylene level and genes having predicted functions in degradative capabilities, stress response, amino acid metabolism and associated pathways. In contrast to Rm1021 pSymA and pSmeSM11c, megaplasmid pSymB of strain Rm1021 and pSmeSM11d are highly conserved showing extensive synteny with only few rearrangements. Most remarkably, pSmeSM11b contains a new gene cluster predicted to be involved in polysaccharide biosynthesis. Compilation of the S. meliloti SM11 genome sequence contributes to an extension of the S. meliloti pan-genome.

Size exclusion chromatography: an improved method to harvest Corynebacterium glutamicum cells for the analysis of cytosolic metabolites.

The efficient separation of Corynebacterium glutamicum cells from culture medium by size exclusion chromatography (SEC) is presented. Residue analysis demonstrated that this method effectively depletes extracellular compounds. For evaluation, SEC was compared with the common methods cold methanol treatment, fast centrifugation and fast filtration. For this purpose, samples of C. glutamicum cells from fermenter cultures were harvested and subjected to a metabolome analysis. In particular, the wild type strain C. glutamicum ATCC13032 and the lysine production strain C. glutamicum DM1730 were grown in a minimal or in a complex medium. Comparison of metabolite pool sizes after harvesting C. glutamicum cells by the methods mentioned above by gas chromatography coupled to mass spectrometry (GC-MS) revealed that SEC is the most suitable method when intracellular metabolite pools are to be measured during growth in complex media or in the presence of significant amounts of secreted metabolites. In contrast to the other methods tested, the SEC method turned out to be fast and able to remove extracellular compounds almost completely.

RAPYD--rapid annotation platform for yeast data.

Lower eukaryotes of the kingdom Fungi include a variety of biotechnologically important yeast species that are in the focus of genome research for more than a decade. Due to the rapid progress in ultra-fast sequencing technologies, the amount of available yeast genome data increases steadily. Thus, an efficient bioinformatics platform is required that covers genome assembly, eukaryotic gene prediction, genome annotation, comparative yeast genomics, and metabolic pathway reconstruction. Here, we present a bioinformatics platform for yeast genomics named RAPYD addressing the key requirements of extensive yeast sequence data analysis. The first step is a comprehensive regional and functional annotation of a yeast genome. A region prediction pipeline was implemented to obtain reliable and high-quality predictions of coding sequences and further genome features. Functions of coding sequences are automatically determined using a configurable prediction pipeline. Based on the resulting functional annotations, a metabolic pathway reconstruction module can be utilized to rapidly generate an overview of organism-specific features and metabolic blueprints. In a final analysis step shared and divergent features of closely related yeast strains can be explored using the comparative genomics module. An in-depth application example of the yeast Meyerozyma guilliermondii illustrates the functionality of RAPYD. A user-friendly web interface is available at https://rapyd.cebitec.uni-bielefeld.de.

Visualization of omics data for systems biology.

High-throughput studies of biological systems are rapidly accumulating a wealth of 'omics'-scale data. Visualization is a key aspect of both the analysis and understanding of these data, and users now have many visualization methods and tools to choose from. The challenge is to create clear, meaningful and integrated visualizations that give biological insight, without being overwhelmed by the intrinsic complexity of the data. In this review, we discuss how visualization tools are being used to help interpret protein interaction, gene expression and metabolic profile data, and we highlight emerging new directions.

Qupe--a Rich Internet Application to take a step forward in the analysis of mass spectrometry-based quantitative proteomics experiments.

MOTIVATION: The goal of present -omics sciences is to understand biological systems as a whole in terms of interactions of the individual cellular components. One of the main building blocks in this field of study is proteomics where tandem mass spectrometry (LC-MS/MS) in combination with isotopic labelling techniques provides a common way to obtain a direct insight into regulation at the protein level. Methods to identify and quantify the peptides contained in a sample are well established, and their output usually results in lists of identified proteins and calculated relative abundance values. The next step is to move ahead from these abstract lists and apply statistical inference methods to compare measurements, to identify genes that are significantly up- or down-regulated, or to detect clusters of proteins with similar expression profiles. RESULTS: We introduce the Rich Internet Application (RIA) Qupe providing comprehensive data management and analysis functions for LC-MS/MS experiments. Starting with the import of mass spectra data the system guides the experimenter through the process of protein identification by database search, the calculation of protein abundance ratios, and in particular, the statistical evaluation of the quantification results including multivariate analysis methods such as analysis of variance or hierarchical cluster analysis. While a data model to store these results has been developed, a well-defined programming interface facilitates the integration of novel approaches. A compute cluster is utilized to distribute computationally intensive calculations, and a web service allows to interchange information with other -omics software applications. To demonstrate that Qupe represents a step forward in quantitative proteomics analysis an application study on Corynebacterium glutamicum has been carried out. AVAILABILITY AND IMPLEMENTATION: Qupe is implemented in Java utilizing Hibernate, Echo2, R and the Spring framework. We encourage the usage of the RIA in the sense of the 'software as a service' concept, maintained on our servers and accessible at the following location: http://qupe.cebitec.uni-bielefeld.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Visualizing post genomics data-sets on customized pathway maps by ProMeTra-aeration-dependent gene expression and metabolism of Corynebacterium glutamicum as an example.

BACKGROUND: The rapid progress of post-genomic analyses, such as transcriptomics, proteomics, and metabolomics has resulted in the generation of large amounts of quantitative data covering and connecting the complete cascade from genotype to phenotype for individual organisms. Various benefits can be achieved when these "Omics" data are integrated, such as the identification of unknown gene functions or the elucidation of regulatory networks of whole organisms. In order to be able to obtain deeper insights in the generated datasets, it is of utmost importance to present the data to the researcher in an intuitive, integrated, and knowledge-based environment. Therefore, various visualization paradigms have been established during the last years. The visualization of "Omics" data using metabolic pathway maps is intuitive and has been applied in various software tools. It has become obvious that the application of web-based and user driven software tools has great potential and benefits from the use of open and standardized formats for the description of pathways. RESULTS: In order to combine datasets from heterogeneous "Omics" sources, we present the web-based ProMeTra system that visualizes and combines datasets from transcriptomics, proteomics, and metabolomics on user defined metabolic pathway maps. Therefore, structured exchange of data with our "Omics" applications Emma 2, Qupe and MeltDB is employed. Enriched SVG images or animations are generated and can be obtained via the user friendly web interface. To demonstrate the functionality of ProMeTra, we use quantitative data obtained during a fermentation experiment of the L-lysine producing strain Corynebacterium glutamicum DM1730. During fermentation, oxygen supply was switched off in order to perturb the system and observe its reaction. At six different time points, transcript abundances, intracellular metabolite pools, as well as extracellular glucose, lactate, and L-lysine levels were determined. CONCLUSION: The interpretation and visualization of the results of this complex experiment was facilitated by the ProMeTra software. Both transcriptome and metabolome data were visualized on a metabolic pathway map. Visual inspection of the combined data confirmed existing knowledge but also delivered novel correlations that are of potential biotechnological importance.

The Sequence Analysis and Management System -- SAMS-2.0: data management and sequence analysis adapted to changing requirements from traditional sanger sequencing to ultrafast sequencing technologies.

DNA sequencing plays a more and more important role in various fields of genetics. This includes sequencing of whole genomes, libraries of cDNA clones and probes of metagenome communities. The applied sequencing technologies evolve permanently. With the emergence of ultrafast sequencing technologies, a new era of DNA sequencing has recently started. Concurrently, the needs for adapted bioinformatics tools arise. Since the ability to process current datasets efficiently is essential for modern genetics, a modular bioinformatics platform providing extensive sequence analysis methods, is designated to achieve well the constantly growing requirements. The Sequence Analysis and Management System (SAMS) is a bioinformatics software platform with a database backend designed to support the computational analysis of (1) whole genome shotgun (WGS) bacterial genome sequencing, (2) cDNA sequencing by reading expressed sequence tags (ESTs) as well as (3) sequence data obtained by ultrafast sequencing. It provides extensive bioinformatics analysis of sequenced single reads, sequencing libraries and fragments of arbitrary DNA sequences such as assembled contigs of metagenome reads for instance. The system has been implemented to cope with several thousands of sequences, efficiently processing them and storing the results for further analysis. With the project setup, SAMS automatically recognizes the data type.

EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data.

BACKGROUND: Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems. RESULTS: The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services. CONCLUSION: Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.

MeltDB: a software platform for the analysis and integration of metabolomics experiment data.

MOTIVATION: The recent advances in metabolomics have created the potential to measure the levels of hundreds of metabolites which are the end products of cellular regulatory processes. The automation of the sample acquisition and subsequent analysis in high-throughput instruments that are capable of measuring metabolites is posing a challenge on the necessary systematic storage and computational processing of the experimental datasets. Whereas a multitude of specialized software systems for individual instruments and preprocessing methods exists, there is clearly a need for a free and platform-independent system that allows the standardized and integrated storage and analysis of data obtained from metabolomics experiments. Currently there exists no such system that on the one hand supports preprocessing of raw datasets but also allows to visualize and integrate the results of higher level statistical analyses within a functional genomics context. RESULTS: To facilitate the systematic storage, analysis and integration of metabolomics experiments, we have implemented MeltDB, a web-based software platform for the analysis and annotation of datasets from metabolomics experiments. MeltDB supports open file formats (netCDF, mzXML, mzDATA) and facilitates the integration and evaluation of existing preprocessing methods. The system provides researchers with means to consistently describe and store their experimental datasets. Comprehensive analysis and visualization features of metabolomics datasets are offered to the community through a web-based user interface. The system covers the process from raw data to the visualization of results in a knowledge-based background and is integrated into the context of existing software platforms of genomics and transcriptomics at Bielefeld University. We demonstrate the potential of MeltDB by means of a sample experiment where we dissect the influence of three different carbon sources on the gram-negative bacterium Xanthomonas campestris pv. campestris on the level of measured metabolites. Experimental data are stored, analyzed and annotated within MeltDB and accessible via the public MeltDB web server. AVAILABILITY: The system is publicly available at http://meltdb.cebitec.uni-bielefeld.de.

The metagenome of a biogas-producing microbial community of a production-scale biogas plant fermenter analysed by the 454-pyrosequencing technology.

Composition and gene content of a biogas-producing microbial community from a production-scale biogas plant fed with renewable primary products was analysed by means of a metagenomic approach applying the ultrafast 454-pyrosequencing technology. Sequencing of isolated total community DNA on a Genome Sequencer FLX System resulted in 616,072 reads with an average read length of 230 bases accounting for 141,664,289 bases sequence information. Assignment of obtained single reads to COG (Clusters of Orthologous Groups of proteins) categories revealed a genetic profile characteristic for an anaerobic microbial consortium conducting fermentative metabolic pathways. Assembly of single reads resulted in the formation of 8752 contigs larger than 500 bases in size. Contigs longer than 10kb mainly encode house-keeping proteins, e.g. DNA polymerase, recombinase, DNA ligase, sigma factor RpoD and genes involved in sugar and amino acid metabolism. A significant portion of contigs was allocated to the genome sequence of the archaeal methanogen Methanoculleus marisnigri JR1. Mapping of single reads to the M. marisnigri JR1 genome revealed that approximately 64% of the reference genome including methanogenesis gene regions are deeply covered. These results suggest that species related to those of the genus Methanoculleus play a dominant role in methanogenesis in the analysed fermentation sample. Moreover, assignment of numerous contig sequences to clostridial genomes including gene regions for cellulolytic functions indicates that clostridia are important for hydrolysis of cellulosic plant biomass in the biogas fermenter under study. Metagenome sequence data from a biogas-producing microbial community residing in a fermenter of a biogas plant provide the basis for a rational approach to improve the biotechnological process of biogas production.

Taxonomic composition and gene content of a methane-producing microbial community isolated from a biogas reactor.

A total community DNA sample from an agricultural biogas reactor continuously fed with maize silage, green rye, and small proportions of chicken manure has recently been sequenced using massively parallel pyrosequencing. In this study, the sample was computationally characterized without a prior assembly step, providing quantitative insights into the taxonomic composition and gene content of the underlying microbial community. Clostridiales from the phylum Firmicutes is the most prevalent phylogenetic order, Methanomicrobiales are dominant among methanogenic archaea. An analysis of Operational Taxonomic Units (OTUs) revealed that the entire microbial community is only partially covered by the sequenced sample, despite that estimates suggest only a moderate overall diversity of the community. Furthermore, the results strongly indicate that archaea related to the genus Methanoculleus, using CO2 as electron acceptor and H2 as electron donor, are the main producers of methane in the analyzed biogas reactor sample. A phylogenetic analysis of glycosyl hydrolase protein families suggests that Clostridia play an important role in the digestion of polysaccharides and oligosaccharides. Finally, the results unveiled that most of the organisms constituting the sample are still unexplored.