Format
Sort by
Items per page

Send to

Choose Destination

Search results

Items: 1 to 20 of 57

1.
F1000Res. 2018 Aug 6;7. pii: ELIXIR-1199. doi: 10.12688/f1000research.15161.1. eCollection 2018.

Common ELIXIR Service for Researcher Authentication and Authorisation.

Author information

1
CSC - IT Center for Science, Espoo, Finland.
2
Masaryk University, Brno, Czech Republic.
3
Bielefeld University, Bielefeld, Germany.
4
EMBL-EBI, Hinxton, UK.

Abstract

A common Authentication and Authorisation Infrastructure (AAI) that would allow single sign-on to services has been identified as a key enabler for European bioinformatics. ELIXIR AAI is an ELIXIR service portfolio for authenticating researchers to ELIXIR services and assisting these services on user privileges during research usage. It relieves the scientific service providers from managing the user identities and authorisation themselves, enables the researcher to have a single set of credentials to all ELIXIR services and supports meeting the requirements imposed by the data protection laws. ELIXIR AAI was launched in late 2016 and is part of the ELIXIR Compute platform portfolio. By the end of 2017 the number of users reached 1000, while the number of relying scientific services was 36. This paper presents the requirements and design of the ELIXIR AAI and the policies related to its use, and how it can be used for serving some example services, such as document management, social media, data discovery, human data access, cloud compute and training services.

KEYWORDS:

GA4GH; GDPR; IAM; authentication; authorisation; data access

Conflict of interest statement

No competing interests were disclosed.

2.
Biotechnol Biofuels. 2018 Jun 19;11:167. doi: 10.1186/s13068-018-1162-4. eCollection 2018.

Characterization of Bathyarchaeota genomes assembled from metagenomes of biofilms residing in mesophilic and thermophilic biogas reactors.

Author information

1
1Dept. Bioengineering, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam, Germany.
2
2Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstrasse 27, 33615 Bielefeld, Germany.
3
3Computational Metagenomics, Faculty of Technology, Bielefeld University, Universitätsstrasse 25, 33615 Bielefeld, Germany.
4
4Biochemical Conversion Department, Deutsches Biomasseforschungszentrum gemeinnützige GmbH, Torgauer Straße 116, 04347 Leipzig, Germany.
5
5Urban Water Management and Environmental Engineering, Faculty of Civil and Environmental Engineering, Ruhr University Bochum, Universitätsstraße 150, 44780 Bochum, Germany.
6
6Dept. Bioinformatics and Systems Biology, Justus-Liebig University Gießen, Heinrich-Buff-Ring 58, 35392 Giessen, Germany.
#
Contributed equally

Abstract

Background:

Previous studies on the Miscellaneous Crenarchaeota Group, recently assigned to the novel archaeal phylum Bathyarchaeota, reported on the dominance of these Archaea within the anaerobic carbohydrate cycle performed by the deep marine biosphere. For the first time, members of this phylum were identified also in mesophilic and thermophilic biogas-forming biofilms and characterized in detail.

Results:

Metagenome shotgun libraries of biofilm microbiomes were sequenced using the Illumina MiSeq system. Taxonomic classification revealed that between 0.1 and 2% of all classified sequences were assigned to Bathyarchaeota. Individual metagenome assemblies followed by genome binning resulted in the reconstruction of five metagenome-assembled genomes (MAGs) of Bathyarchaeota. MAGs were estimated to be 65-92% complete, ranging in their genome sizes from 1.1 to 2.0 Mb. Phylogenetic classification based on core gene sets confirmed their placement within the phylum Bathyarchaeota clustering as a separate group diverging from most of the recently known Bathyarchaeota clusters. The genetic repertoire of these MAGs indicated an energy metabolism based on carbohydrate and amino acid fermentation featuring the potential for extracellular hydrolysis of cellulose, cellobiose as well as proteins. In addition, corresponding transporter systems were identified. Furthermore, genes encoding enzymes for the utilization of carbon monoxide and/or carbon dioxide via the Wood-Ljungdahl pathway were detected.

Conclusions:

For the members of Bathyarchaeota detected in the biofilm microbiomes, a hydrolytic lifestyle is proposed. This is the first study indicating that Bathyarchaeota members contribute presumably to hydrolysis and subsequent fermentation of organic substrates within biotechnological biogas production processes.

KEYWORDS:

Anaerobic digestion; Archaea; Bathyarchaeota; Biomass conversion; Biomethanation; Genome binning; Hydrolysis; Metabolic pathway reconstruction; Metagenome-assembled genomes

3.
Gigascience. 2018 Jul 1;7(7). doi: 10.1093/gigascience/giy075.

Binning enables efficient host genome reconstruction in cnidarian holobionts.

Author information

1
Animal Ecology and Systematics, Justus Liebig University Giessen. Heinrich-Buff-Ring 26-32 (IFZ), 35392 Giessen, Germany.
2
Corporation Center of Excellence in Marine Sciences, Cra 54 No 106-18, Bogotá, Colombia.
3
Center for Biotechnology, Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany.
4
Evolutionary Biology and Ecology, Université libre de Bruxelles, Av. Franklin D. Roosevelt 50, CP 160/12, B-1050 Brussels, Belgium.
5
Bioinformatics and Systems Biology, Justus Liebig University Giessen, Heinrich-Buff-Ring 58, 35392 Giessen, Germany.

Abstract

Background:

Many cnidarians, including stony corals, engage in complex symbiotic associations, comprising the eukaryotic host, photosynthetic algae, and highly diverse microbial communities-together referred to as holobiont. This taxonomic complexity makes sequencing and assembling coral host genomes extremely challenging. Therefore, previous cnidarian genomic projects were based on symbiont-free tissue samples. However, this approach may not be applicable to the majority of cnidarian species for ecological reasons. We therefore evaluated the performance of an alternative method based on sequence binning for reconstructing the genome of the stony coral Porites rus from a hologenomic sample and compared it to traditional approaches.

Results:

Our results demonstrate that binning performs well for hologenomic data, producing sufficient reads for assembling the draft genome of P. rus. An assembly evaluation based on operational criteria showed results that were comparable to symbiont-free approaches in terms of completeness and usefulness, despite a high degree of fragmentation in our assembly. In addition, we found that binning provides sufficient data for exploratory k-mer estimation of genomic features, such as genome size and heterozygosity.

Conclusions:

Binning constitutes a powerful approach for disentangling taxonomically complex coral hologenomes. Considering the recent decline of coral reefs on the one hand and previous limitations to coral genome sequencing on the other hand, binning may facilitate rapid and reliable genome assembly. This study also provides an important milestone in advancing binning from the metagenomic to the hologenomic and from the prokaryotic to the eukaryotic level.

PMID:
29917104
PMCID:
PMC6049006
DOI:
10.1093/gigascience/giy075
[Indexed for MEDLINE]
Free PMC Article
Icon for Silverchair Information Systems Icon for PubMed Central
4.
Gigascience. 2018 Jun 1;7(6). doi: 10.1093/gigascience/giy069.

AMBER: Assessment of Metagenome BinnERs.

Author information

1
Department of Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany.
2
Braunschweig Integrated Centre of Systems Biology, Braunschweig, Germany.
3
Faculty of Technology, Bielefeld University, Bielefeld, Germany.
4
Center for Biotechnology, Bielefeld University, Bielefeld, Germany.
5
Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany.
6
Cluster of Excellence on Plant Sciences.

Abstract

Reconstructing the genomes of microbial community members is key to the interpretation of shotgun metagenome samples. Genome binning programs deconvolute reads or assembled contigs of such samples into individual bins. However, assessing their quality is difficult due to the lack of evaluation software and standardized metrics. Here, we present Assessment of Metagenome BinnERs (AMBER), an evaluation package for the comparative assessment of genome reconstructions from metagenome benchmark datasets. It calculates the performance metrics and comparative visualizations used in the first benchmarking challenge of the initiative for the Critical Assessment of Metagenome Interpretation (CAMI). As an application, we show the outputs of AMBER for 11 binning programs on two CAMI benchmark datasets. AMBER is implemented in Python and available under the Apache 2.0 license on GitHub.

PMID:
29893851
PMCID:
PMC6022608
DOI:
10.1093/gigascience/giy069
[Indexed for MEDLINE]
Free PMC Article
Icon for Silverchair Information Systems Icon for PubMed Central
5.
Bioinformatics. 2018 May 1;34(9):1457-1465. doi: 10.1093/bioinformatics/btx808.

Analyzing large scale genomic data on the cloud with Sparkhit.

Huang L1,2,3, Krüger J1,2, Sczyrba A1,2,3.

Author information

1
Faculty of Technology, Bielefeld University, Bielefeld 33615, Germany.
2
Center for Biotechnology - CeBiTec, Bielefeld University, Bielefeld 33615, Germany.
3
Computational Methods for the Analysis of the Diversity and Dynamics of Genomes, Bielefeld University, Bielefeld 33615, Germany.

Abstract

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.

6.
Microb Biotechnol. 2018 Jul;11(4):667-679. doi: 10.1111/1751-7915.12982. Epub 2017 Dec 4.

Targeted in situ metatranscriptomics for selected taxa from mesophilic and thermophilic biogas plants.

Author information

1
Center for Biotechnology - CeBiTec, Bielefeld University, Universitätsstraße 27, 33615, Bielefeld, Germany.
2
Faculty of Technology, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany.

Abstract

Biogas production is performed anaerobically by complex microbial communities with key species driving the process. Hence, analyses of their in situ 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 in situ 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.

7.
Biotechnol Biofuels. 2017 Nov 13;10:264. doi: 10.1186/s13068-017-0947-1. eCollection 2017.

Genomics and prevalence of bacterial and archaeal isolates from biogas-producing microbiomes.

Author information

1
Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstrasse 27, 33615 Bielefeld, Germany.
2
Faculty of Technology, Bielefeld University, Universitätsstrasse 25, 33615 Bielefeld, Germany.
3
Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Inhoffenstraße 7, 38124 Brunswick, Germany.
4
German Center for Infection Research (DZIF), partner site Hannover-Braunscheig, Inhoffenstraße 7, 38124 Brunswick, Germany.
5
Department Bioengineering, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam, Germany.
6
Johannes Gutenberg-University, Institute of Microbiology and Wine Research, Johann-Joachim Becherweg 15, 55128 Mainz, Germany.
7
Department of Microbiology, Technische Universität München, Emil-Ramann-Str. 4, 85354 Freising-Weihenstephan, Germany.
8
Faculty Life Sciences/Research Center 'Biomass Utilization Hamburg', University of Applied Sciences Hamburg (HAW), Ulmenliet 20, 21033 Hamburg-Bergedorf, Germany.
9
Institut für Forensische Genetik GmbH, Im Derdel 8, 48168 Münster, Germany.
10
Institute of Molecular Genetics, Russian Academy of Science, Kurchatov Sq. 2, Moscow, 123182 Russia.
#
Contributed equally

Abstract

Background:

To elucidate biogas microbial communities and processes, the application of high-throughput DNA analysis approaches is becoming increasingly important. Unfortunately, generated data can only partialy be interpreted rudimentary since databases lack reference sequences.

Results:

Novel cellulolytic, hydrolytic, and acidogenic/acetogenic Bacteria as well as methanogenic Archaea originating from different anaerobic digestion communities were analyzed on the genomic level to assess their role in biomass decomposition and biogas production. Some of the analyzed bacterial strains were recently described as new species and even genera, namely Herbinix hemicellulosilytica T3/55T, Herbinix luporum SD1DT, Clostridium bornimense M2/40T, Proteiniphilum saccharofermentans M3/6T, Fermentimonas caenicola ING2-E5BT, and Petrimonas mucosa ING2-E5AT. High-throughput genome sequencing of 22 anaerobic digestion isolates enabled functional genome interpretation, metabolic reconstruction, and prediction of microbial traits regarding their abilities to utilize complex bio-polymers and to perform specific fermentation pathways. To determine the prevalence of the isolates included in this study in different biogas systems, corresponding metagenome fragment mappings were done. Methanoculleus bourgensis was found to be abundant in three mesophilic biogas plants studied and slightly less abundant in a thermophilic biogas plant, whereas Defluviitoga tunisiensis was only prominent in the thermophilic system. Moreover, several of the analyzed species were clearly detectable in the mesophilic biogas plants, but appeared to be only moderately abundant. Among the species for which genome sequence information was publicly available prior to this study, only the species Amphibacillus xylanus, Clostridium clariflavum, and Lactobacillus acidophilus are of importance for the biogas microbiomes analyzed, but did not reach the level of abundance as determined for M. bourgensis and D. tunisiensis.

Conclusions:

Isolation of key anaerobic digestion microorganisms and their functional interpretation was achieved by application of elaborated cultivation techniques and subsequent genome analyses. New isolates and their genome information extend the repository covering anaerobic digestion community members.

KEYWORDS:

Anaerobic digestion; Biomethanation; Defluviitoga tunisiensis; Fragment recruitment; Genome sequencing; Methanoculleus bourgensis

8.
Nat Methods. 2017 Nov;14(11):1063-1071. doi: 10.1038/nmeth.4458. Epub 2017 Oct 2.

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

Author information

1
Faculty of Technology, Bielefeld University, Bielefeld, Germany.
2
Center for Biotechnology, Bielefeld University, Bielefeld, Germany.
3
Formerly Department of Algorithmic Bioinformatics, Heinrich Heine University (HHU), Duesseldorf, Germany.
4
Department of Computational Biology of Infection Research, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany.
5
Braunschweig Integrated Centre of Systems Biology (BRICS), Braunschweig, Germany.
6
Mathematics Department, Oregon State University, Corvallis, Oregon, USA.
7
Department of Pediatrics, University of California, San Diego, California, USA.
8
Department of Computer Science and Engineering, University of California, San Diego, California, USA.
9
German Center for Infection Research (DZIF), partner site Hannover-Braunschweig, Braunschweig, Germany.
10
Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany.
11
Cluster of Excellence on Plant Sciences (CEPLAS).
12
Department of Environmental Science, Section of Environmental microbiology and Biotechnology, Aarhus University, Roskilde, Denmark.
13
Department of Microbiology, University of Copenhagen, Copenhagen, Denmark.
14
Department of Science and Environment, Roskilde University, Roskilde, Denmark.
15
Department of Energy, Joint Genome Institute, Walnut Creek, California, USA.
16
Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
17
Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, Saint Petersburg State University, Saint Petersburg, Russia.
18
Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria.
19
The ithree institute, University of Technology Sydney, Sydney, New South Wales, Australia.
20
Department of Computer Science, Research Center in Computer Science (CRIStAL), Signal and Automatic Control of Lille, Lille, France.
21
National Centre of the Scientific Research (CNRS), Rennes, France.
22
Department of Computational and Systems Biology, Genome Institute of Singapore, Singapore.
23
Department of Microbiology and Infection, Warwick Medical School, University of Warwick, Coventry, UK.
24
Department of Computer Science, University of Tuebingen, Tuebingen, Germany.
25
Intel Corporation, Hillsboro, Oregon, USA.
26
GenScale-Bioinformatics Research Team, Inria Rennes-Bretagne Atlantique Research Centre, Rennes, France.
27
Institute of Research in Informatics and Random Systems (IRISA), Rennes, France.
28
Department of Molecular Infection Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany.
29
Algorizk-IT consulting and software systems, Paris, France.
30
Joint BioEnergy Institute, Emeryville, California, USA.
31
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
32
Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
33
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
34
Energy Engineering and Geomicrobiology, University of Calgary, Calgary, Alberta, Canada.
35
Department of Bioinformatics, Institute for Microbiology and Genetics, University of Goettingen, Goettingen, Germany.
36
Genevention GmbH, Goettingen, Germany.
37
Institute of Population Health Sciences, National Health Research Institutes, Zhunan Town, Taiwan.
38
Computational Science Research Center, San Diego State University, San Diego, California, USA.
39
Boyce Thompson Institute for Plant Research, New York, New York, USA.
40
Research Group Bioinformatics (NG4), Robert Koch Institute, Berlin, Germany.
41
Coordination for the Improvement of Higher Education Personnel (CAPES) Foundation, Ministry of Education of Brazil, Brasília, Brazil.
42
Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
43
Department of Computer Science, University of Maryland, College Park, Maryland, USA.
44
School of Biology, Newcastle University, Newcastle upon Tyne, UK.
45
Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.
46
Institute of Microbiology, ETH Zurich, Zurich, Switzerland.

Abstract

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.

PMID:
28967888
PMCID:
PMC5903868
DOI:
10.1038/nmeth.4458
[Indexed for MEDLINE]
Free PMC Article
Icon for Nature Publishing Group Icon for PubMed Central
9.
J Biotechnol. 2017 Nov 10;261:10-23. doi: 10.1016/j.jbiotec.2017.08.012. Epub 2017 Aug 18.

Bioinformatics for NGS-based metagenomics and the application to biogas research.

Author information

1
Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany; Faculty of Technology, Bielefeld University, Bielefeld, Germany. Electronic address: jueneman@cebitec.uni-bielefeld.de.
2
Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany.
3
Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany; Bioinformatics and Systems Biology, Justus-Liebig-Universität, Gießen, Germany.
4
Bioinformatics and Systems Biology, Justus-Liebig-Universität, Gießen, Germany.
5
Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany; Faculty of Technology, Bielefeld University, Bielefeld, Germany.

Abstract

Metagenomics has proven to be one of the most important research fields for microbial ecology during the last decade. Starting from 16S rRNA marker gene analysis for the characterization of community compositions to whole metagenome shotgun sequencing which additionally allows for functional analysis, metagenomics has been applied in a wide spectrum of research areas. The cost reduction paired with the increase in the amount of data due to the advent of next-generation sequencing led to a rapidly growing demand for bioinformatic software in metagenomics. By now, a large number of tools that can be used to analyze metagenomic datasets has been developed. The Bielefeld-Gießen center for microbial bioinformatics as part of the German Network for Bioinformatics Infrastructure bundles and imparts expert knowledge in the analysis of metagenomic datasets, especially in research on microbial communities involved in anaerobic digestion residing in biogas reactors. In this review, we give an overview of the field of metagenomics, introduce into important bioinformatic tools and possible workflows, accompanied by application examples of biogas surveys successfully conducted at the Center for Biotechnology of Bielefeld University.

KEYWORDS:

Anaerobic digestion; Biogas; High-throughput 16S rRNA gene amplicon sequencing; Metagenomics; Next-generation sequencing; de.NBI

PMID:
28823476
DOI:
10.1016/j.jbiotec.2017.08.012
[Indexed for MEDLINE]
Free full text
Icon for Elsevier Science
10.
J Biotechnol. 2017 Sep 10;257:58-60. doi: 10.1016/j.jbiotec.2017.02.020. Epub 2017 Feb 21.

Rapid protein alignment in the cloud: HAMOND combines fast DIAMOND alignments with Hadoop parallelism.

Author information

1
Int. Research Training Group 1906 (DiDy), Bielefeld University, Bielefeld, 33501, Germany. Electronic address: yujia@cebitec.uni-bielefeld.de.
2
Bioinformatics and Systems Biology, Justus-Liebig-University Giessen, Giessen, 35392, Germany.
3
Faculty of Technology and Center for Biotechnology, Bielefeld University, Bielefeld, 33501, Germany.

Abstract

The introduction of next generation sequencing has caused a steady increase in the amounts of data that have to be processed in modern life science. Sequence alignment plays a key role in the analysis of sequencing data e.g. within whole genome sequencing or metagenome projects. BLAST is a commonly used alignment tool that was the standard approach for more than two decades, but in the last years faster alternatives have been proposed including RapSearch, GHOSTX, and DIAMOND. Here we introduce HAMOND, an application that uses Apache Hadoop to parallelize DIAMOND computation in order to scale-out the calculation of alignments. HAMOND is fault tolerant and scalable by utilizing large cloud computing infrastructures like Amazon Web Services. HAMOND has been tested in comparative genomics analyses and showed promising results both in efficiency and accuracy.

KEYWORDS:

Cloud computing; Parallel computing; Sequence alignment

PMID:
28232083
DOI:
10.1016/j.jbiotec.2017.02.020
[Indexed for MEDLINE]
Free full text
Icon for Elsevier Science
11.
BMC Bioinformatics. 2016 Dec 20;17(1):543. doi: 10.1186/s12859-016-1397-7.

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

Author information

1
Computational Methods for the Analysis of the Diversity and Dynamics of Genomes, Bielefeld University, Universitätsstr. 25, Bielefeld, 33615, Germany. mlux@techfak.uni-bielefeld.de.
2
Center for Biotechnology - CeBiTec, Bielefeld University, Universitätsstr. 27, Bielefeld, 33615, Germany.
3
Australian Centre for Ecogenomics, University of Queensland, ST LUCIA, Brisbane, QLD 4072, Australia.
4
, 2800 Mitchell Drive, Walnut Creek, 94598, CA, USA.
5
CITEC centre of excellence, Bielefeld University, Inspiration 1, Bielefeld, 33619, Germany.

Abstract

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.

KEYWORDS:

Binning; Clustering; Contamination detection; Machine learning; Quality control; Single-cell sequencing

PMID:
27998267
PMCID:
PMC5168860
DOI:
10.1186/s12859-016-1397-7
[Indexed for MEDLINE]
Free PMC Article
Icon for BioMed Central Icon for PubMed Central
12.
Biotechnol Biofuels. 2016 Aug 11;9:171. doi: 10.1186/s13068-016-0581-3. eCollection 2016.

Unraveling the microbiome of a thermophilic biogas plant by metagenome and metatranscriptome analysis complemented by characterization of bacterial and archaeal isolates.

Author information

1
Center for Biotechnology (CeBiTec), Institute for Genome Research and Systems Biology, Bielefeld University, Universitätsstr. 27, 33615 Bielefeld, Germany.
2
Department of Microbiology, Technische Universität München, Emil-Ramann-Str. 4, 85354 Freising-Weihenstephan, Germany.
3
Institute of Microbiology and Wine Research, Johannes Gutenberg-University, Becherweg 15, 55128 Mainz, Germany.
4
Dept. Bioengineering, Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V. (ATB), Max-Eyth-Allee 100, 14469 Potsdam, Germany.
5
Faculty Life Sciences/Research Center ''Biomass Utilization Hamburg', University of Applied Sciences Hamburg (HAW), Ulmenliet 20, 21033 Hamburg-Bergedorf, Germany.
6
RIPAC-LABOR GmbH, Am Mühlenberg 11, 14476 Potsdam-Golm, Germany.
7
Center for Biotechnology (CeBiTec), Institute for Genome Research and Systems Biology, Bielefeld University, Universitätsstr. 27, 33615 Bielefeld, Germany ; Faculty of Technology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany.
8
Department of Bioinformatics and Systems Biology, Justus-Liebig University Gießen, Heinrich-Buff-Ring 58, 35392 Giessen, Germany.

Abstract

BACKGROUND:

One of the most promising technologies to sustainably produce energy and to mitigate greenhouse gas emissions from combustion of fossil energy carriers is the anaerobic digestion and biomethanation of organic raw material and waste towards biogas by highly diverse microbial consortia. In this context, the microbial systems ecology of thermophilic industrial-scale biogas plants is poorly understood.

RESULTS:

The microbial community structure of an exemplary thermophilic biogas plant was analyzed by a comprehensive approach comprising the analysis of the microbial metagenome and metatranscriptome complemented by the cultivation of hydrolytic and acido-/acetogenic Bacteria as well as methanogenic Archaea. Analysis of metagenome-derived 16S rRNA gene sequences revealed that the bacterial genera Defluviitoga (5.5 %), Halocella (3.5 %), Clostridium sensu stricto (1.9 %), Clostridium cluster III (1.5 %), and Tepidimicrobium (0.7 %) were most abundant. Among the Archaea, Methanoculleus (2.8 %) and Methanothermobacter (0.8 %) were predominant. As revealed by a metatranscriptomic 16S rRNA analysis, Defluviitoga (9.2 %), Clostridium cluster III (4.8 %), and Tepidanaerobacter (1.1 %) as well as Methanoculleus (5.7 %) mainly contributed to these sequence tags indicating their metabolic activity, whereas Hallocella (1.8 %), Tepidimicrobium (0.5 %), and Methanothermobacter (<0.1 %) were transcriptionally less active. By applying 11 different cultivation strategies, 52 taxonomically different microbial isolates representing the classes Clostridia, Bacilli, Thermotogae, Methanomicrobia and Methanobacteria were obtained. Genome analyses of isolates support the finding that, besides Clostridium thermocellum and Clostridium stercorarium, Defluviitoga tunisiensis participated in the hydrolysis of hemicellulose producing ethanol, acetate, and H2/CO2. The latter three metabolites are substrates for hydrogentrophic and acetoclastic archaeal methanogenesis.

CONCLUSIONS:

Obtained results showed that high abundance of microorganisms as deduced from metagenome analysis does not necessarily indicate high transcriptional or metabolic activity, and vice versa. Additionally, it appeared that the microbiome of the investigated thermophilic biogas plant comprised a huge number of up to now unknown and insufficiently characterized species.

KEYWORDS:

Acetogenic Bacteria; Acidogenic Bacteria; Anaerobic digestion; Biomethanation; Cellulolytic Bacteria; Culturomics; Fragment recruitment; Methanogenic Archaea; Microbial community structure; Polyphasic characterization

13.
Biotechnol Biofuels. 2016 Jul 26;9:156. doi: 10.1186/s13068-016-0565-3. eCollection 2016.

Identification and genome reconstruction of abundant distinct taxa in microbiomes from one thermophilic and three mesophilic production-scale biogas plants.

Author information

1
Center for Biotechnology, Bielefeld University, 33615 Bielefeld, Germany.
2
Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany.
#
Contributed equally

Abstract

BACKGROUND:

Biofuel production from conversion of biomass is indispensable in the portfolio of renewable energies. Complex microbial communities are involved in the anaerobic digestion process of plant material, agricultural residual products and food wastes. Analysis of the genetic potential and microbiology of communities degrading biomass to biofuels is considered to be the key to develop process optimisation strategies. Hence, due to the still incomplete taxonomic and functional characterisation of corresponding communities, new and unknown species are of special interest.

RESULTS:

Three mesophilic and one thermophilic production-scale biogas plants (BGPs) were taxonomically profiled using high-throughput 16S rRNA gene amplicon sequencing. All BGPs shared a core microbiome with the thermophilic BGP featuring the lowest diversity. However, the phyla Cloacimonetes and Spirochaetes were unique to BGPs 2 and 3, Fusobacteria were only found in BGP3 and members of the phylum Thermotogae were present only in the thermophilic BGP4. Taxonomic analyses revealed that these distinctive taxa mostly represent so far unknown species. The only exception is the dominant Thermotogae OTU featuring 16S rRNA gene sequence identity to Defluviitoga tunisiensis L3, a sequenced and characterised strain. To further investigate the genetic potential of the biogas communities, corresponding metagenomes were sequenced in a deepness of 347.5 Gbp in total. A combined assembly comprised 80.3 % of all reads and resulted in the prediction of 1.59 million genes on assembled contigs. Genome binning yielded genome bins comprising the prevalent distinctive phyla Cloacimonetes, Spirochaetes, Fusobacteria and Thermotogae. Comparative genome analyses between the most dominant Thermotogae bin and the very closely related Defluviitoga tunisiensis L3 genome originating from the same BGP revealed high genetic similarity. This finding confirmed applicability and reliability of the binning approach. The four highly covered genome bins of the other three distinct phyla showed low or very low genetic similarities to their closest phylogenetic relatives, and therefore indicated their novelty.

CONCLUSIONS:

In this study, the 16S rRNA gene sequencing approach and a combined metagenome assembly and binning approach were used for the first time on different production-scale biogas plants and revealed insights into the genetic potential and functional role of so far unknown species.

KEYWORDS:

16S rRNA gene; Anaerobic digestion; Biogas; Cloacimonetes (WWE1); Fusobacteria; Genome binning; Metagenomics; Microbial community; Spirochaetes; Thermotogae

14.
Biotechnol Biofuels. 2016 Jul 26;9:155. doi: 10.1186/s13068-016-0572-4. eCollection 2016.

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

Author information

1
Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106 Magdeburg, Germany ; Bioprocess Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany.
2
Bioprocess Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany.
3
Laboratory of Microbial Technology and Ecology (LabMET), Ghent University, Coupure Links 653, 9000 Ghent, Belgium.
4
Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106 Magdeburg, Germany.
5
Center for Biotechnology, Genome Research of Industrial Microorganisms, Universität Bielefeld, Universitätsstraße 25, 33615 Bielefeld, Germany.
6
Center for Biotechnology, Computational Metagenomics, Universität Bielefeld, Universitätsstr. 25, 33615 Bielefeld, Germany.

Abstract

BACKGROUND:

Methane yield and biogas productivity of biogas plants (BGPs) depend on microbial community structure and function, substrate supply, and general biogas process parameters. So far, however, relatively little is known about correlations between microbial community function and process parameters. To close this knowledge gap, microbial communities of 40 samples from 35 different industrial biogas plants were evaluated by a metaproteomics approach in this study.

RESULTS:

Liquid chromatography coupled to tandem mass spectrometry (Orbitrap Elite™ Hybrid Ion Trap-Orbitrap Mass Spectrometer) of all 40 samples as triplicate enabled the identification of 3138 different metaproteins belonging to 162 biological processes and 75 different taxonomic orders. The respective database searches were performed against UniProtKB/Swiss-Prot and seven metagenome databases. Subsequent clustering and principal component analysis of these data allowed for the identification of four main clusters associated with mesophile and thermophile process conditions, the use of upflow anaerobic sludge blanket reactors and BGP feeding with sewage sludge. Observations confirm a previous phylogenetic study of the same BGP samples that was based on 16S rRNA gene sequencing by De Vrieze et al. (Water Res 75:312-323, 2015). In particular, we identified similar microbial key players of biogas processes, namely Bacillales, Enterobacteriales, Bacteriodales, Clostridiales, Rhizobiales and Thermoanaerobacteriales as well as Methanobacteriales, Methanosarcinales and Methanococcales. For the elucidation of the main biomass degradation pathways, the most abundant 1 % of metaproteins was assigned to the KEGG map 1200 representing the central carbon metabolism. Additionally, the effect of the process parameters (i) temperature, (ii) organic loading rate (OLR), (iii) total ammonia nitrogen (TAN), and (iv) sludge retention time (SRT) on these pathways was investigated. For example, high TAN correlated with hydrogenotrophic methanogens and bacterial one-carbon metabolism, indicating syntrophic acetate oxidation.

CONCLUSIONS:

This is the first large-scale metaproteome study of BGPs. Proteotyping of BGPs reveals general correlations between the microbial community structure and its function with process parameters. The monitoring of changes on the level of microbial key functions or even of the microbial community represents a well-directed tool for the identification of process problems and disturbances.Graphical abstractCorrelation between the different orders and process parameter, as well as principle component analysis of all investigated biogas plants based on the identified metaproteins.

KEYWORDS:

Anaerobic digestion; Biogas; Biogas plant; Clustering; Community function; MetaProteomeAnalyzer; Metaproteomics; Microbial resource management; Network analysis machine learning; Principal component analysis

15.
J Biotechnol. 2016 Aug 10;231:268-279. doi: 10.1016/j.jbiotec.2016.06.014. Epub 2016 Jun 14.

An integrated metagenome and -proteome analysis of the microbial community residing in a biogas production plant.

Author information

1
Genome Research of Industrial Microorganisms, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany; Microbial Genomics and Biotechnology, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany.
2
Genome Research of Industrial Microorganisms, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany.
3
Computational Metagenomics, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany.
4
Bioinformatics Resource Facility, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany.
5
Bioinformatics and Systems Biology, Justus Liebig University, 35392 Giessen, Germany.
6
Fraunhofer Institute for Molecular Biology and Applied Ecology, Department of Bioresources, Winchester Strasse, D-35394 Giessen, Germany.
7
Genome Research of Industrial Microorganisms, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany. Electronic address: aschluet@CeBiTec.Uni-Bielefeld.de.

Abstract

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.

KEYWORDS:

Biogas microbial community; Contig context information; Database impact on protein identification; Integrated metagenome/-proteome study; Taxonomic profile

PMID:
27312700
DOI:
10.1016/j.jbiotec.2016.06.014
[Indexed for MEDLINE]
Icon for Elsevier Science
16.
J Biotechnol. 2016 Aug 20;232:50-60. doi: 10.1016/j.jbiotec.2016.05.001. Epub 2016 May 7.

Genomic characterization of Defluviitoga tunisiensis L3, a key hydrolytic bacterium in a thermophilic biogas plant and its abundance as determined by metagenome fragment recruitment.

Author information

1
Center for Biotechnology, Bielefeld University, 33615 Bielefeld, Germany.
2
Institute of Microbiology and Wine Research, Johannes Gutenberg University Mainz, 55122 Mainz, Germany.
3
Center for Biotechnology, Bielefeld University, 33615 Bielefeld, Germany; Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany.
4
Department of Bioinformatics and Systems Biology, Justus-Liebig-University Gießen, 35390 Gießen, Germany.
5
Center for Biotechnology, Bielefeld University, 33615 Bielefeld, Germany. Electronic address: aschluet@cebitec.uni-bielefeld.de.

Abstract

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.

KEYWORDS:

Comparative genome analyses; Sugar utilization; Thermophilic Bacteria; Thermotogae

PMID:
27165504
DOI:
10.1016/j.jbiotec.2016.05.001
[Indexed for MEDLINE]
Icon for Elsevier Science
17.
Bioinformatics. 2016 Jul 15;32(14):2199-201. doi: 10.1093/bioinformatics/btw144. Epub 2016 Mar 15.

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

Author information

1
Center for Biotechnology and Faculty of Technology, Bielefeld University, Bielefeld 33615, Germany U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA.
2
U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA.

Abstract

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.

PMID:
27153586
PMCID:
PMC4937190
DOI:
10.1093/bioinformatics/btw144
[Indexed for MEDLINE]
Free PMC Article
Icon for Silverchair Information Systems Icon for PubMed Central
18.
J Biotechnol. 2016 Aug 20;232:61-8. doi: 10.1016/j.jbiotec.2016.04.008. Epub 2016 Apr 6.

Finished genome sequence and methylome of the cyanide-degrading Pseudomonas pseudoalcaligenes strain CECT5344 as resolved by single-molecule real-time sequencing.

Author information

1
Institute for Genome Research and Systems Biology, Center for Biotechnology (CeBiTec), Bielefeld University, D-33615 Bielefeld, Germany.
2
Faculty of Technology, Center for Biotechnology (CeBiTec), Bielefeld University, D-33615 Bielefeld, Germany.
3
Departamento de Bioquímica y Biología Molecular, Universidad de Córdoba, Córdoba, Spain.
4
Pacific Biosciences Germany GmbH, Germany.
5
Departamento de Bioquímica y Biología Molecular y Genética, Facultad de Veterinaria, Universidad de Extremadura, Avenida de la Universidad SN, E-10071Cáceres, Spain.
6
Institute for Genome Research and Systems Biology, Center for Biotechnology (CeBiTec), Bielefeld University, D-33615 Bielefeld, Germany. Electronic address: aschluet@cebitec.uni-bielefeld.de.

Abstract

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.

KEYWORDS:

Bioremediation; Core genome; Mercury resistance; Methylome; Nitrilase; Restriction/modification system

PMID:
27060556
DOI:
10.1016/j.jbiotec.2016.04.008
[Indexed for MEDLINE]
Icon for Elsevier Science
19.
J Biotechnol. 2016 May 10;225:31-43. doi: 10.1016/j.jbiotec.2016.03.028. Epub 2016 Mar 19.

miRNA profiling of high, low and non-producing CHO cells during biphasic fed-batch cultivation reveals process relevant targets for host cell engineering.

Author information

1
Institute of Applied Biotechnology, University of Applied Sciences Biberach, Hubertus-Liebrecht-Strasse 35, 88400 Biberach, Germany; University of Ulm, Faculty of Medicine, Albert-Einstein-Allee 11, 89079 Ulm, Germany. Electronic address: stiefel@hochschule-bc.de.
2
Institute of Applied Biotechnology, University of Applied Sciences Biberach, Hubertus-Liebrecht-Strasse 35, 88400 Biberach, Germany; University of Ulm, Faculty of Medicine, Albert-Einstein-Allee 11, 89079 Ulm, Germany. Electronic address: simon.fischer@boehringer-ingelheim.com.
3
University of Bielefeld, Center of Biotechnology (CeBiTec), 33501 Bielefeld, Germany. Electronic address: asczyrba@techfak.uni-bielefeld.de.
4
Institute of Applied Biotechnology, University of Applied Sciences Biberach, Hubertus-Liebrecht-Strasse 35, 88400 Biberach, Germany. Electronic address: otte@hochschule-bc.de.
5
Institute of Applied Biotechnology, University of Applied Sciences Biberach, Hubertus-Liebrecht-Strasse 35, 88400 Biberach, Germany. Electronic address: hesse@hochschule-bc.de.

Abstract

Fed-batch cultivation of recombinant Chinese hamster ovary (CHO) cell lines is one of the most widely used production modes for commercial manufacturing of recombinant protein therapeutics. Furthermore, fed-batch cultivations are often conducted as biphasic processes where the culture temperature is decreased to maximize volumetric product yields. However, it remains to be elucidated which intracellular regulatory elements actually control the observed pro-productive phenotypes. Recently, several studies have revealed microRNAs (miRNAs) to be important molecular switches of cell phenotypes. In this study, we analyzed miRNA profiles of two different recombinant CHO cell lines (high and low producer), and compared them to a non-producing CHO DG44 host cell line during fed-batch cultivation at 37°C versus a temperature shift to 30°C. Taking advantage of next-generation sequencing combined with cluster, correlation and differential expression analyses, we could identify 89 different miRNAs, which were differentially expressed in the different cell lines and cultivation phases. Functional validation experiments using 19 validated target miRNAs confirmed that these miRNAs indeed induced changes in process relevant phenotypes. Furthermore, computational miRNA target prediction combined with functional clustering identified putative target genes and cellular pathways, which might be regulated by these miRNAs. This study systematically identified novel target miRNAs during different phases and conditions of a biphasic fed-batch production process and functionally evaluated their potential for host cell engineering.

KEYWORDS:

CHO; Fed-Batch; Next generation sequencing; miRNA profiles

PMID:
27002234
DOI:
10.1016/j.jbiotec.2016.03.028
[Indexed for MEDLINE]
Icon for Elsevier Science
20.
Antimicrob Agents Chemother. 2016 Apr 22;60(5):3032-40. doi: 10.1128/AAC.00124-16. Print 2016 May.

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

Author information

1
Institute for Genome Research and Systems Biology, Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany.
2
Emerging Antibiotic Resistance Unit, Medical and Molecular Microbiology, Department of Medicine, Faculty of Science, University of Fribourg, Fribourg, Switzerland.
3
Computational Metagenomics, Faculty of Technology, Bielefeld University, Bielefeld, Germany.
4
Emerging Antibiotic Resistance Unit, Medical and Molecular Microbiology, Department of Medicine, Faculty of Science, University of Fribourg, Fribourg, Switzerland HFR - Hôpital Cantonal, Fribourg, Switzerland.
5
Institute for Genome Research and Systems Biology, Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany aschluet@cebitec.uni-bielefeld.de.

Abstract

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-β-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.

PMID:
26953198
PMCID:
PMC4862468
DOI:
10.1128/AAC.00124-16
[Indexed for MEDLINE]
Free PMC Article
Icon for HighWire Icon for PubMed Central

Supplemental Content

Loading ...
Support Center