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Biogas production is performed anaerobically by complex microbial communities with key species driving the process. Hence, analyses of their insitu activities are crucial to understand the process. In a previous study, metagenome sequencing and subsequent genome binning for different production-scale biogas plants (BGPs) resulted in four genome bins of special interest, assigned to the phyla Thermotogae, Fusobacteria, Spirochaetes and Cloacimonetes, respectively, that were genetically analysed. In this study, metatranscriptome sequencing of the same BGP samples was conducted, enabling insitu transcriptional activity determination of these genome bins. For this, mapping of metatranscriptome reads on genome bin sequences was performed providing transcripts per million (TPM) values for each gene. This approach revealed an active sugar-based metabolism of the Thermotogae and Spirochaetes bins and an active amino acid-based metabolism of the Fusobacteria and Cloacimonetes bins. The data also hint at syntrophic associations of the four corresponding species with methanogenic Archaea. © 2017 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

This study describes the laboratory cultivation of ARMAN (Archaeal Richmond Mine Acidophilic Nanoorganisms). After 2.5 years of successive transfers in an anoxic medium containing ferric sulfate as an electron acceptor, a consortium was attained that is comprised of two members of the order Thermoplasmatales, a member of a proposed ARMAN group, as well as a fungus. The 16S rRNA identity of one archaeon is only 91.6% compared to the most closely related isolate Thermogymnomonas acidicola. Hence, this organism is the first member of a new genus. The enrichment culture is dominated by this microorganism and the ARMAN. The third archaeon in the community seems to be present in minor quantities and has a 100% 16S rRNA identity to the recently isolated Cuniculiplasma divulgatum. The enriched ARMAN species is most probably incapable of sugar metabolism because the key genes for sugar catabolism and anabolism could not be identified in the metagenome. Metatranscriptomic analysis suggests that the TCA cycle funneled with amino acids is the main metabolic pathway used by the archaea of the community. Microscopic analysis revealed that growth of the ARMAN is supported by the formation of cell aggregates. These might enable feeding of the ARMAN by or on other community members.

Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug‐resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and expression profiles, we generated predictive models and identified biomarkers of resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8–0.9) or very high (> 0.9) sensitivity and predictive values. For all drugs except for ciprofloxacin, gene expression information improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections., The spread of antibiotic resistance complicates infection treatment, requiring an optimization of current diagnostics. In this study, a machine learning approach identified a set of biomarkers suitable for the development of a molecular test system to determine antibiotic resistance profiles.

To study the metaproteome of a biogas-producing microbial community, fermentation samples were taken from an agricultural biogas plant for microbial cell and protein extraction and corresponding metagenome analyses. Based on metagenome sequence data, taxonomic community profiling was performed to elucidate the composition of bacterial and archaeal sub-communities. The community's cytosolic metaproteome was represented in a 2D-PAGE approach. Metaproteome databases for protein identification were compiled based on the assembled metagenome sequence dataset for the biogas plant analyzed and non-corresponding biogas metagenomes. Protein identification results revealed that the corresponding biogas protein database facilitated the highest identification rate followed by other biogas-specific databases, whereas common public databases yielded insufficient identification rates. Proteins of the biogas microbiome identified as highly abundant were assigned to the pathways involved in methanogenesis, transport and carbon metabolism. Moreover, the integrated metagenome/-proteome approach enabled the examination of genetic-context information for genes encoding identified proteins by studying neighboring genes on the corresponding contig. Exemplarily, this approach led to the identification of a Methanoculleus sp. contig encoding 16 methanogenesis-related gene products, three of which were also detected as abundant proteins within the community's metaproteome. Thus, metagenome contigs provide additional information on the genetic environment of identified abundant proteins. Copyright 2016 Elsevier B.V. All rights reserved.

Pseudomonas pseudoalcaligenes CECT5344 tolerates cyanide and is also able to utilize cyanide and cyano-derivatives as a nitrogen source under alkaline conditions. The strain is considered as candidate for bioremediation of habitats contaminated with cyanide-containing liquid wastes. Information on the genome sequence of the strain CECT5344 became available previously. The P. pseudoalcaligenes CECT5344 genome was now resequenced by applying the single molecule, real-time (SMRT()) sequencing technique developed by Pacific Biosciences. The complete and finished genome sequence of the strain consists of a 4,696,984 bp chromosome featuring a GC-content of 62.34%. Comparative analyses between the new and previous versions of the P. pseudoalcaligenes CECT5344 genome sequence revealed additional regions in the new sequence that were missed in the older version. These additional regions mostly represent mobile genetic elements. Moreover, five additional genes predicted to play a role in sulfoxide reduction are present in the newly established genome sequence. The P. pseudoalcaligenes CECT5344 genome sequence is highly related to the genome sequences of different Pseudomonas mendocina strains. Approximately, 70% of all genes are shared between P. pseudoalcaligenes and P. mendocina. In contrast to P. mendocina, putative pathogenicity genes were not identified in the P. pseudoalcaligenes CECT5344 genome. P. pseudoalcaligenes CECT5344 possesses unique genes for nitrilases and mercury resistance proteins that are of importance for survival in habitats contaminated with cyano- and mercury compounds. As an additional feature of the SMRT sequencing technology, the methylome of P. pseudoalcaligenes was established. Six sequence motifs featuring methylated adenine residues (m6A) were identified in the genome. The genome encodes several methyltransferases, some of which may be considered for methylation of the m6A motifs identified. The complete genome sequence of the strain CECT5344 now provides the basis for exploitation of genetic features for biotechnological purposes. Copyright 2016 Elsevier B.V. All rights reserved.

Field denitrification beds containing polymeric plant material are increasingly used to eliminate nitrate from agricultural drainage water. They mirror a number of anoxic ecosystems. However, knowledge of the microbial composition, the interaction of microbial species, and the carbon degradation processes within these denitrification systems is sparse. This study revealed several new aspects of the carbon and nitrogen cycle, and these findings can be correlated with the dynamics of the microbial community composition and the activity of key species. Members of the order Pseudomonadales seem to be important players in denitrification at low nitrate concentrations, while a switch to higher nitrate concentrations seems to select for members of the orders Rhodocyclales and Rhizobiales. We observed that high nitrate loading rates lead to an unpredictable transition of the community’s activity from denitrification to dissimilatory reduction of nitrate to ammonium (DNRA). This transition is mirrored by an increase in transcripts of the nitrite reductase gene nrfAH and the increase correlates with the activity of members of the order Ignavibacteriales. Denitrification reactors sustained the development of an archaeal community consisting of members of the Bathyarchaeota and methanogens belonging to the Euryarchaeota. Unexpectedly, the activity of the methanogens positively correlated with the nitrate loading rates.

Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions., Nature Methods, 14 (11), ISSN:1548-7105, ISSN:1548-7091

Microorganisms play a primary role in regulating biogeochemical cycles and are a valuable source of enzymes that have biotechnological applications, such as carbohydrate-active enzymes (CAZymes). However, the inability to culture the majority of microorganisms that exist in natural ecosystems using common culture-dependent techniques restricts access to potentially novel cellulolytic bacteria and beneficial enzymes. The development of molecular-based culture-independent methods such as metagenomics enables researchers to study microbial communities directly from environmental samples, and presents a platform from which enzymes of interest can be sourced. We outline key methodological stages that are required as well as describe specific protocols that are currently used for metagenomic projects dedicated to CAZyme discovery.

Summary: We present a new tool, MeCorS, to correct chimeric reads and sequencing errors in Illumina data generated from single amplified genomes (SAGs). It uses sequence information derived from accompanying metagenome sequencing to accurately correct errors in SAG reads, even from ultra-low coverage regions. In evaluations on real data, we show that MeCorS outperforms BayesHammer, the most widely used state-of-the-art approach. MeCorS performs particularly well in correcting chimeric reads, which greatly improves both accuracy and contiguity of de novo SAG assemblies. Availability and implementation: https://github.com/metagenomics/MeCorS Contact: abremges@cebitec.uni-bielefeld.de Supplementary information: Supplementary data are available at Bioinformatics online.

The genome sequence of Defluviitoga tunisiensis L3 originating from a thermophilic biogas-production plant was established and recently published as Genome Announcement by our group. The circular chromosome of D. tunisiensis L3 has a size of 2,053,097bp and a mean GC content of 31.38%. To analyze the D. tunisiensis L3 genome sequence in more detail, a phylogenetic analysis of completely sequenced Thermotogae strains based on shared core genes was performed. It appeared that Petrotoga mobilis DSM 10674(T), originally isolated from a North Sea oil-production well, is the closest relative of D. tunisiensis L3. Comparative genome analyses of P. mobilis DSM 10674(T) and D. tunisiensis L3 showed moderate similarities regarding occurrence of orthologous genes. Both genomes share a common set of 1351 core genes. Reconstruction of metabolic pathways important for the biogas production process revealed that the D. tunisiensis L3 genome encodes a large set of genes predicted to facilitate utilization of a variety of complex polysaccharides including cellulose, chitin and xylan. Ethanol, acetate, hydrogen (H2) and carbon dioxide (CO2) were found as possible end-products of the fermentation process. The latter three metabolites are considered to represent substrates for methanogenic Archaea, the key organisms in the final step of the anaerobic digestion process. To determine the degree of relatedness between D. tunisiensis L3 and dominant biogas community members within the thermophilic biogas-production plant, metagenome sequences obtained from the corresponding microbial community were mapped onto the L3 genome sequence. This fragment recruitment revealed that the D. tunisiensis L3 genome is almost completely covered with metagenome sequences featuring high matching accuracy. This result indicates that strains highly related or even identical to the reference strain D. tunisiensis L3 play a dominant role within the community of the thermophilic biogas-production plant. Copyright 2016 Elsevier B.V. All rights reserved.