Advances in the clinical use of metaproteomics.

INTRODUCTION: Investigating the taxonomic and functional composition of human microbiomes can aid in the understanding of disease etiologies, diagnosis, and therapy monitoring for several diseases, including inflammatory bowel disease or obesity. One method for microbiome monitoring is metaproteomics, which assesses human and microbial proteins and thus enables the study of host-microbiome interactions. This advantage led to increased interest in metaproteome analyses and significant developments to introduce this method into a clinical context. AREAS COVERED: This review summarizes the recent progress from a technical side and an application-related point of view. EXPERT OPINION: Numerous publications imply the massive potential of metaproteomics to impact human health care. However, the key challenges of standardization and validation of experimental and bioinformatic workflows and accurate quantification methods must be overcome.

ConCysFind: a pipeline tool to predict conserved amino acids of protein sequences across the plant kingdom.

BACKGROUND: Post-translational modifications (PTM) of amino acid (AA) side chains in peptides control protein structure and functionality. PTMs depend on the specific AA characteristics. The reactivity of cysteine thiol-based PTMs are unique among all proteinaceous AA. This pipeline aims to ease the identification of conserved AA of polypeptides or protein families based on the phylogenetic occurrence in the plant kingdom. The tool is customizable to include any species. The degree of AA conservation is taken as indicator for structural and functional significance, especially for PTM-based regulation. Further, this pipeline tool gives insight into the evolution of these potentially regulatory important peptides. RESULTS: The web-based or stand-alone pipeline tool Conserved Cysteine Finder (ConCysFind) was developed to identify conserved AA such as cysteine, tryptophan, serine, threonine, tyrosin and methionine. ConCysFind evaluates multiple alignments considering the proteome of 21 plant species. This exemplar study focused on Cys as evolutionarily conserved target for multiple redox PTM. Phylogenetic trees and tables with the compressed results of the scoring algorithm are generated for each Cys in the query polypeptide. Analysis of 33 translation elongation and release factors alongside of known redox proteins from Arabidopsis thaliana for conserved Cys residues confirmed the suitability of the tool for identifying conserved and functional PTM sites. Exemplarily, the redox sensitivity of cysteines in the eukaryotic release factor 1-1 (eRF1-1) was experimentally validated. CONCLUSION: ConCysFind is a valuable tool for prediction of new potential protein PTM targets in a broad spectrum of species, based on conserved AA throughout the plant kingdom. The identified targets were successfully verified through protein biochemical assays. The pipeline is universally applicable to other phylogenetic branches by customization of the database.

Critical Assessment of Metagenome Interpretation-a benchmark of metagenomics software.

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.

Analyzing large scale genomic data on the cloud with Sparkhit.

Motivation: The increasing amount of next-generation sequencing data poses a fundamental challenge on large scale genomic analytics. Existing tools use different distributed computational platforms to scale-out bioinformatics workloads. However, the scalability of these tools is not efficient. Moreover, they have heavy run time overheads when pre-processing large amounts of data. To address these limitations, we have developed Sparkhit: a distributed bioinformatics framework built on top of the Apache Spark platform. Results: Sparkhit integrates a variety of analytical methods. It is implemented in the Spark extended MapReduce model. It runs 92-157 times faster than MetaSpark on metagenomic fragment recruitment and 18-32 times faster than Crossbow on data pre-processing. We analyzed 100 terabytes of data across four genomic projects in the cloud in 21 h, which includes the run times of cluster deployment and data downloading. Furthermore, our application on the entire Human Microbiome Project shotgun sequencing data was completed in 2 h, presenting an approach to easily associate large amounts of public datasets with reference data. Availability and implementation: Sparkhit is freely available at: https://rhinempi.github.io/sparkhit/. Contact: asczyrba@cebitec.uni-bielefeld.de. Supplementary information: Supplementary data are available at Bioinformatics online.

Insights into the phylogeny and coding potential of microbial dark matter.

Genome sequencing enhances our understanding of the biological world by providing blueprints for the evolutionary and functional diversity that shapes the biosphere. However, microbial genomes that are currently available are of limited phylogenetic breadth, owing to our historical inability to cultivate most microorganisms in the laboratory. We apply single-cell genomics to target and sequence 201 uncultivated archaeal and bacterial cells from nine diverse habitats belonging to 29 major mostly uncharted branches of the tree of life, so-called 'microbial dark matter'. With this additional genomic information, we are able to resolve many intra- and inter-phylum-level relationships and to propose two new superphyla. We uncover unexpected metabolic features that extend our understanding of biology and challenge established boundaries between the three domains of life. These include a novel amino acid use for the opal stop codon, an archaeal-type purine synthesis in Bacteria and complete sigma factors in Archaea similar to those in Bacteria. The single-cell genomes also served to phylogenetically anchor up to 20% of metagenomic reads in some habitats, facilitating organism-level interpretation of ecosystem function. This study greatly expands the genomic representation of the tree of life and provides a systematic step towards a better understanding of biological evolution on our planet.

MeCorS: Metagenome-enabled error correction of single cell sequencing reads.

UNLABELLED: 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.

acdc - Automated Contamination Detection and Confidence estimation for single-cell genome data.

BACKGROUND: A major obstacle in single-cell sequencing is sample contamination with foreign DNA. To guarantee clean genome assemblies and to prevent the introduction of contamination into public databases, considerable quality control efforts are put into post-sequencing analysis. Contamination screening generally relies on reference-based methods such as database alignment or marker gene search, which limits the set of detectable contaminants to organisms with closely related reference species. As genomic coverage in the tree of life is highly fragmented, there is an urgent need for a reference-free methodology for contaminant identification in sequence data. RESULTS: We present acdc, a tool specifically developed to aid the quality control process of genomic sequence data. By combining supervised and unsupervised methods, it reliably detects both known and de novo contaminants. First, 16S rRNA gene prediction and the inclusion of ultrafast exact alignment techniques allow sequence classification using existing knowledge from databases. Second, reference-free inspection is enabled by the use of state-of-the-art machine learning techniques that include fast, non-linear dimensionality reduction of oligonucleotide signatures and subsequent clustering algorithms that automatically estimate the number of clusters. The latter also enables the removal of any contaminant, yielding a clean sample. Furthermore, given the data complexity and the ill-posedness of clustering, acdc employs bootstrapping techniques to provide statistically profound confidence values. Tested on a large number of samples from diverse sequencing projects, our software is able to quickly and accurately identify contamination. Results are displayed in an interactive user interface. Acdc can be run from the web as well as a dedicated command line application, which allows easy integration into large sequencing project analysis workflows. CONCLUSIONS: Acdc can reliably detect contamination in single-cell genome data. In addition to database-driven detection, it complements existing tools by its unsupervised techniques, which allow for the detection of de novo contaminants. Our contribution has the potential to drastically reduce the amount of resources put into these processes, particularly in the context of limited availability of reference species. As single-cell genome data continues to grow rapidly, acdc adds to the toolkit of crucial quality assurance tools.

Intraspecies Transfer of the Chromosomal Acinetobacter baumannii blaNDM-1 Carbapenemase Gene.

The species Acinetobacter baumannii is one of the most important multidrug-resistant human pathogens. To determine its virulence and antibiotic resistance determinants, the genome of the nosocomial blaNDM-1-positive A. baumannii strain R2090 originating from Egypt was completely sequenced. Genome analysis revealed that strain R2090 is highly related to the community-acquired Australian A. baumannii strain D1279779. The two strains belong to sequence type 267 (ST267). Isolate R2090 harbored the chromosomally integrated transposon Tn125 carrying the carbapenemase gene blaNDM-1 that is not present in the D1279779 genome. To test the transferability of the metallo-ß-lactamase (MBL) gene region, the clinical isolate R2090 was mated with the susceptible A. baumannii recipient CIP 70.10, and the carbapenem-resistant derivative R2091 was obtained. Genome sequencing of the R2091 derivative revealed that it had received an approximately 66-kb region comprising the transposon Tn125 embedding the blaNDM-1 gene. This region had integrated into the chromosome of the recipient strain CIP 70.10. From the four known mechanisms for horizontal gene transfer (conjugation, outer membrane vesicle-mediated transfer, transformation, and transduction), conjugation could be ruled out, since strain R2090 lacks any plasmid, and a type IV secretion system is not encoded in its chromosome. However, strain R2090 possesses three putative prophages, two of which were predicted to be complete and therefore functional. Accordingly, it was supposed that the transfer of the resistance gene region from the clinical isolate R2090 to the recipient occurred by general transduction facilitated by one of the prophages present in the R2090 genome. Hence, phage-mediated transduction has to be taken into account for the dissemination of antibiotic resistance genes within the species A. baumannii.

UGA is an additional glycine codon in uncultured SR1 bacteria from the human microbiota.

The composition of the human microbiota is recognized as an important factor in human health and disease. Many of our cohabitating microbes belong to phylum-level divisions for which there are no cultivated representatives and are only represented by small subunit rRNA sequences. For one such taxon (SR1), which includes bacteria with elevated abundance in periodontitis, we provide a single-cell genome sequence from a healthy oral sample. SR1 bacteria use a unique genetic code. In-frame TGA (opal) codons are found in most genes (85%), often at loci normally encoding conserved glycine residues. UGA appears not to function as a stop codon and is in equilibrium with the canonical GGN glycine codons, displaying strain-specific variation across the human population. SR1 encodes a divergent tRNA(Gly)UCA with an opal-decoding anticodon. SR1 glycyl-tRNA synthetase acylates tRNA(Gly)UCA with glycine in vitro with similar activity compared with normal tRNA(Gly)UCC. Coexpression of SR1 glycyl-tRNA synthetase and tRNA(Gly)UCA in Escherichia coli yields significant ß-galactosidase activity in vivo from a lacZ gene containing an in-frame TGA codon. Comparative genomic analysis with Human Microbiome Project data revealed that the human body harbors a striking diversity of SR1 bacteria. This is a surprising finding because SR1 is most closely related to bacteria that live in anoxic and thermal environments. Some of these bacteria share common genetic and metabolic features with SR1, including UGA to glycine reassignment and an archaeal-type ribulose-1,5-bisphosphate carboxylase (RubisCO) involved in AMP recycling. UGA codon reassignment renders SR1 genes untranslatable by other bacteria, which impacts horizontal gene transfer within the human microbiota.

Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean.

Planktonic bacteria dominate surface ocean biomass and influence global biogeochemical processes, but remain poorly characterized owing to difficulties in cultivation. Using large-scale single cell genomics, we obtained insight into the genome content and biogeography of many bacterial lineages inhabiting the surface ocean. We found that, compared with existing cultures, natural bacterioplankton have smaller genomes, fewer gene duplications, and are depleted in guanine and cytosine, noncoding nucleotides, and genes encoding transcription, signal transduction, and noncytoplasmic proteins. These findings provide strong evidence that genome streamlining and oligotrophy are prevalent features among diverse, free-living bacterioplankton, whereas existing laboratory cultures consist primarily of copiotrophs. The apparent ubiquity of metabolic specialization and mixotrophy, as predicted from single cell genomes, also may contribute to the difficulty in bacterioplankton cultivation. Using metagenome fragment recruitment against single cell genomes, we show that the global distribution of surface ocean bacterioplankton correlates with temperature and latitude and is not limited by dispersal at the time scales required for nucleotide substitution to exceed the current operational definition of bacterial species. Single cell genomes with highly similar small subunit rRNA gene sequences exhibited significant genomic and biogeographic variability, highlighting challenges in the interpretation of individual gene surveys and metagenome assemblies in environmental microbiology. Our study demonstrates the utility of single cell genomics for gaining an improved understanding of the composition and dynamics of natural microbial assemblages.

Fractionation of biogas plant sludge material improves metaproteomic characterization to investigate metabolic activity of microbial communities.

With the development of high resolving mass spectrometers, metaproteomics evolved as a powerful tool to elucidate metabolic activity of microbial communities derived from full-scale biogas plants. Due to the vast complexity of these microbiomes, application of suitable fractionation methods are indispensable, but often turn out to be time and cost intense, depending on the method used for protein separation. In this study, centrifugal fractionation has been applied for fractionation of two biogas sludge samples to analyze proteins extracted from (i) crude fibers, (ii) suspended microorganisms, and (iii) secreted proteins in the supernatant using a gel-based approach followed by LC-MS/MS identification. This fast and easy method turned out to be beneficial to both the quality of SDS-PAGE and the identification of peptides and proteins compared to untreated samples. Additionally, a high functional metabolic pathway coverage was achieved by combining protein hits found exclusively in distinct fractions. Sample preparation using centrifugal fractionation influenced significantly the number and the types of proteins identified in the microbial metaproteomes. Thereby, comparing results from different proteomic or genomic studies, the impact of sample preparation should be considered. All MS data have been deposited in the ProteomeXchange with identifier PXD001508 (http://proteomecentral.proteomexchange.org/dataset/PXD001508).

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

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

Reekeekee- and roodoodooviruses, two different Microviridae clades constituted by the smallest DNA phages.

Small circular single-stranded DNA viruses of the Microviridae family are both prevalent and diverse in all ecosystems. They usually harbor a genome between 4.3 and 6.3 kb, with a microvirus recently isolated from a marine Alphaproteobacteria being the smallest known genome of a DNA phage (4.248 kb). A subfamily, Amoyvirinae, has been proposed to classify this virus and other related small Alphaproteobacteria-infecting phages. Here, we report the discovery, in meta-omics data sets from various aquatic ecosystems, of sixteen complete microvirus genomes significantly smaller (2.991-3.692 kb) than known ones. Phylogenetic analysis reveals that these sixteen genomes represent two related, yet distinct and diverse, novel groups of microviruses-amoyviruses being their closest known relatives. We propose that these small microviruses are members of two tentatively named subfamilies Reekeekeevirinae and Roodoodoovirinae. As known microvirus genomes encode many overlapping and overprinted genes that are not identified by gene prediction software, we developed a new methodology to identify all genes based on protein conservation, amino acid composition, and selection pressure estimations. Surprisingly, only four to five genes could be identified per genome, with the number of overprinted genes lower than that in phiX174. These small genomes thus tend to have both a lower number of genes and a shorter length for each gene, leaving no place for variable gene regions that could harbor overprinted genes. Even more surprisingly, these two Microviridae groups had specific and different gene content, and major differences in their conserved protein sequences, highlighting that these two related groups of small genome microviruses use very different strategies to fulfill their lifecycle with such a small number of genes. The discovery of these genomes and the detailed prediction and annotation of their genome content expand our understanding of ssDNA phages in nature and are further evidence that these viruses have explored a wide range of possibilities during their long evolution.

Abundance, classification and genetic potential of Thaumarchaeota in metagenomes of European agricultural soils: a meta-analysis.

BACKGROUND: For a sustainable production of food, research on agricultural soil microbial communities is inevitable. Due to its immense complexity, soil is still some kind of black box. Soil study designs for identifying microbiome members of relevance have various scopes and focus on particular environmental factors. To identify common features of soil microbiomes, data from multiple studies should be compiled and processed. Taxonomic compositions and functional capabilities of microbial communities associated with soils and plants have been identified and characterized in the past few decades. From a fertile Loess-Chernozem-type soil located in Germany, metagenomically assembled genomes (MAGs) classified as members of the phylum Thaumarchaeota/Thermoproteota were obtained. These possibly represent keystone agricultural soil community members encoding functions of relevance for soil fertility and plant health. Their importance for the analyzed microbiomes is corroborated by the fact that they were predicted to contribute to the cycling of nitrogen, feature the genetic potential to fix carbon dioxide and possess genes with predicted functions in plant-growth-promotion (PGP). To expand the knowledge on soil community members belonging to the phylum Thaumarchaeota, we conducted a meta-analysis integrating primary studies on European agricultural soil microbiomes. RESULTS: Taxonomic classification of the selected soil metagenomes revealed the shared agricultural soil core microbiome of European soils from 19 locations. Metadata reporting was heterogeneous between the different studies. According to the available metadata, we separated the data into 68 treatments. The phylum Thaumarchaeota is part of the core microbiome and represents a major constituent of the archaeal subcommunities in all European agricultural soils. At a higher taxonomic resolution, 2074 genera constituted the core microbiome. We observed that viral genera strongly contribute to variation in taxonomic profiles. By binning of metagenomically assembled contigs, Thaumarchaeota MAGs could be recovered from several European soil metagenomes. Notably, many of them were classified as members of the family Nitrososphaeraceae, highlighting the importance of this family for agricultural soils. The specific Loess-Chernozem Thaumarchaeota MAGs were most abundant in their original soil, but also seem to be of importance in other agricultural soil microbial communities. Metabolic reconstruction of Switzerland_1_MAG_2 revealed its genetic potential i.a. regarding carbon dioxide (CO[Formula: see text]) fixation, ammonia oxidation, exopolysaccharide production and a beneficial effect on plant growth. Similar genetic features were also present in other reconstructed MAGs. Three Nitrososphaeraceae MAGs are all most likely members of a so far unknown genus. CONCLUSIONS: On a broad view, European agricultural soil microbiomes are similarly structured. Differences in community structure were observable, although analysis was complicated by heterogeneity in metadata recording. Our study highlights the need for standardized metadata reporting and the benefits of networking open data. Future soil sequencing studies should also consider high sequencing depths in order to enable reconstruction of genome bins. Intriguingly, the family Nitrososphaeraceae commonly seems to be of importance in agricultural microbiomes.

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

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

Personalized cloud-based bioinformatics services for research and education: use cases and the elasticHPC package.

BACKGROUND: Bioinformatics services have been traditionally provided in the form of a web-server that is hosted at institutional infrastructure and serves multiple users. This model, however, is not flexible enough to cope with the increasing number of users, increasing data size, and new requirements in terms of speed and availability of service. The advent of cloud computing suggests a new service model that provides an efficient solution to these problems, based on the concepts of "resources-on-demand" and "pay-as-you-go". However, cloud computing has not yet been introduced within bioinformatics servers due to the lack of usage scenarios and software layers that address the requirements of the bioinformatics domain. RESULTS: In this paper, we provide different use case scenarios for providing cloud computing based services, considering both the technical and financial aspects of the cloud computing service model. These scenarios are for individual users seeking computational power as well as bioinformatics service providers aiming at provision of personalized bioinformatics services to their users. We also present elasticHPC, a software package and a library that facilitates the use of high performance cloud computing resources in general and the implementation of the suggested bioinformatics scenarios in particular. Concrete examples that demonstrate the suggested use case scenarios with whole bioinformatics servers and major sequence analysis tools like BLAST are presented. Experimental results with large datasets are also included to show the advantages of the cloud model. CONCLUSIONS: Our use case scenarios and the elasticHPC package are steps towards the provision of cloud based bioinformatics services, which would help in overcoming the data challenge of recent biological research. All resources related to elasticHPC and its web-interface are available at http://www.elasticHPC.org.

Adaptation of a microbial community to demand-oriented biological methanation.

BACKGROUND: Biological conversion of the surplus of renewable electricity and carbon dioxide (CO2) from biogas plants to biomethane (CH4) could support energy storage and strengthen the power grid. Biological methanation (BM) is linked closely to the activity of biogas-producing Bacteria and methanogenic Archaea. During reactor operations, the microbiome is often subject to various changes, e.g., substrate limitation or pH-shifts, whereby the microorganisms are challenged to adapt to the new conditions. In this study, various process parameters including pH value, CH4 production rate, conversion yields and final gas composition were monitored for a hydrogenotrophic-adapted microbial community cultivated in a laboratory-scale BM reactor. To investigate the robustness of the BM process regarding power oscillations, the biogas microbiome was exposed to five hydrogen (H2)-feeding regimes lasting several days. RESULTS: Applying various "on-off" H2-feeding regimes, the CH4 production rate recovered quickly, demonstrating a significant resilience of the microbial community. Analyses of the taxonomic composition of the microbiome revealed a high abundance of the bacterial phyla Firmicutes, Bacteroidota and Thermotogota followed by hydrogenotrophic Archaea of the phylum Methanobacteriota. Homo-acetogenic and heterotrophic fermenting Bacteria formed a complex food web with methanogens. The abundance of the methanogenic Archaea roughly doubled during discontinuous H2-feeding, which was related mainly to an increase in acetoclastic Methanothrix species. Results also suggested that Bacteria feeding on methanogens could reduce overall CH4 production. On the other hand, using inactive biomass as a substrate could support the growth of methanogenic Archaea. During the BM process, the additional production of H2 by fermenting Bacteria seemed to support the maintenance of hydrogenotrophic methanogens at non-H2-feeding phases. Besides the elusive role of Methanothrix during the H2-feeding phases, acetate consumption and pH maintenance at the non-feeding phase can be assigned to this species. CONCLUSIONS: Taken together, the high adaptive potential of microbial communities contributes to the robustness of BM processes during discontinuous H2-feeding and supports the commercial use of BM processes for energy storage. Discontinuous feeding strategies could be used to enrich methanogenic Archaea during the establishment of a microbial community for BM. Both findings could contribute to design and improve BM processes from lab to pilot scale.

The novel oligopeptide utilizing species Anaeropeptidivorans aminofermentans M3/9T, its role in anaerobic digestion and occurrence as deduced from large-scale fragment recruitment analyses.

Research on biogas-producing microbial communities aims at elucidation of correlations and dependencies between the anaerobic digestion (AD) process and the corresponding microbiome composition in order to optimize the performance of the process and the biogas output. Previously, Lachnospiraceae species were frequently detected in mesophilic to moderately thermophilic biogas reactors. To analyze adaptive genome features of a representative Lachnospiraceae strain, Anaeropeptidivorans aminofermentans M3/9T was isolated from a mesophilic laboratory-scale biogas plant and its genome was sequenced and analyzed in detail. Strain M3/9T possesses a number of genes encoding enzymes for degradation of proteins, oligo- and dipeptides. Moreover, genes encoding enzymes participating in fermentation of amino acids released from peptide hydrolysis were also identified. Based on further findings obtained from metabolic pathway reconstruction, M3/9T was predicted to participate in acidogenesis within the AD process. To understand the genomic diversity between the biogas isolate M3/9T and closely related Anaerotignum type strains, genome sequence comparisons were performed. M3/9T harbors 1,693 strain-specific genes among others encoding different peptidases, a phosphotransferase system (PTS) for sugar uptake, but also proteins involved in extracellular solute binding and import, sporulation and flagellar biosynthesis. In order to determine the occurrence of M3/9T in other environments, large-scale fragment recruitments with the M3/9T genome as a template and publicly available metagenomes representing different environments was performed. The strain was detected in the intestine of mammals, being most abundant in goat feces, occasionally used as a substrate for biogas production.

Simultaneous Metabarcoding and Quantification of Neocallimastigomycetes from Environmental Samples: Insights into Community Composition and Novel Lineages.

Anaerobic fungi from the herbivore digestive tract (Neocallimastigomycetes) are primary lignocellulose modifiers and hold promise for biotechnological applications. Their molecular detection is currently difficult due to the non-specificity of published primer pairs, which impairs evolutionary and ecological research with environmental samples. We developed and validated a Neocallimastigomycetes-specific PCR primer pair targeting the D2 region of the ribosomal large subunit suitable for screening, quantifying, and sequencing. We evaluated this primer pair in silico on sequences from all known genera, in vitro with pure cultures covering 16 of the 20 known genera, and on environmental samples with highly diverse microbiomes. The amplified region allowed phylogenetic differentiation of all known genera and most species. The amplicon is about 350 bp long, suitable for short-read high-throughput sequencing as well as qPCR assays. Sequencing of herbivore fecal samples verified the specificity of the primer pair and recovered highly diverse and so far unknown anaerobic gut fungal taxa. As the chosen barcoding region can be easily aligned and is taxonomically informative, the sequences can be used for classification and phylogenetic inferences. Several new Neocallimastigomycetes clades were obtained, some of which represent putative novel lineages such as a clade from feces of the rodent Dolichotis patagonum (mara).

Effect of Growth Media on the Diversity of Neocallimastigomycetes from Non-Rumen Habitats.

Anaerobic fungi (AF), belonging to the phylum Neocallimastigomycota, are a pivotal component of the digestive tract microbiome of various herbivorous animals. In the last decade, the diversity of AF has rapidly expanded due to the exploration of numerous (novel) habitats. Studies aiming at understanding the role of AF require robust and reliable isolation and cultivation techniques, many of which remained unchanged for decades. Using amplicon sequencing, we compared three different media: medium with rumen fluid (RF), depleted rumen fluid (DRF), and no rumen fluid (NRF) to enrich the AF from the feces of yak, as a rumen control; and Przewalski's horse, llama, guanaco, and elephant, as a non-rumen habitats. The results revealed the selective enrichment of Piromyces and Neocallimastix from the feces of elephant and llama, respectively, in the RF medium. Similarly, the enrichment culture in DRF medium explicitly manifested Piromyces-related sequences from elephant feces. Five new clades (MM1-5) were defined from llama, guanaco, yak, and elephant feces that could as well be enriched from llama and elephant samples using non-conventional DRF and NRF media. This study presents evidence for the selective enrichment of certain genera in medium with RF and DRF from rumen as well as from non-rumen samples. NRF medium is suggested for the isolation of AF from non-rumen environments.