Profiling the metabolically active community from a production scale biogas plant by means of high throughput metatranscriptome sequencing

Computational Genomics, Center for Biotechnology-CeBiTec, Bielefeld University, Bielefeld, Germany.
Journal of Biotechnology (Impact Factor: 2.87). 02/2012; 158(4):248-58. DOI: 10.1016/j.jbiotec.2012.01.020
Source: PubMed

ABSTRACT Structural composition and gene content of a biogas-producing microbial community from a production-scale biogas plant fed with renewable primary products was recently analyzed by means of a metagenome sequencing approach. To determine the transcriptionally active part of the same biogas community and to identify key transcripts for the biogas production process, the metatranscriptome of the microorganisms was sequenced for the first time. The metatranscriptome sequence dataset generated on the Genome Sequencer FLX platform is represented by 484,920 sequence reads. Taxonomic profiling of the active part of the community by classification of 16S ribosomal sequence tags revealed that members of the Euryarchaeota and Firmicutes account for the dominant phyla. Only smaller fractions of the 16S ribosomal sequence tags were assigned to the phyla Bacteroidetes, Actinobacteria and Synergistetes. Among the mRNA-derived sequence tags from the metatranscriptome dataset, transcripts encoding enzymes involved in substrate hydrolysis, acidogenesis, acetate formation and methanogenesis could be identified. Transcripts for enzymes functioning in methanogenesis are among the most abundant mRNA tags indicating that the corresponding pathway is very active in the methanogenic sub-community. As a frame of reference for evaluation of metatranscriptome sequence data, the 16S rDNA-based taxonomic profile of the community was analyzed by means of high-throughput 16S rDNA amplicon sequencing. Processing of the obtained amplicon reads resulted in 18,598 high-quality 16S rDNA sequences covering the V3-V4 hypervariable region of the 16S rRNA gene. Comparison of the taxonomic profiles deduced from 16S rDNA amplicon sequences and the metatranscriptome dataset indicates a high transcriptional activity of archaeal species. Overall, it was shown that the most abundant species dominating the community also contributed the majority of the transcripts. In the future, key transcripts for the biogas production process will provide valuable markers for evaluation of the performance of biogas-producing microbial communities with the objective to optimize the biotechnology of this process.

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Available from: Waleed Abu Al-Soud, Sep 28, 2015
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    • "Advances in high-throughput sequencing and bulk extraction of mRNA allow for RNA-based studies of organismal functioning in complex environments. Despite certain technological challenges, microbial metatranscriptomics has already helped elucidate microbial responses to oil spills and the resulting deep-sea hydrocarbon plumes, differences between anoxic and oxic paddy soils, and metabolic activity of methane-producing microbes, to name a few [31] [41] [47]. Similarly, metatranscriptomic approaches have been used to investigate the functional diversity of the eukaryotic microorganisms within the rumen of muskoxen [37] and in forest soils [6] [44]. "
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    ABSTRACT: RNA-Seq was used to examine the microbial, eukaryotic, and viral communities in water catchments (‘tanks’) formed by tropical bromeliads from Costa Rican. In total, transcripts with taxonomic affiliation to a wide array of bacteria, archaea, and eukaryotes, were observed, as well as RNA-viruses that appeared related to the specific presence of eukaryotes. Bacteria from 25 phyla appeared to comprise the majority of transcripts in one tank (Wg24), compared to only 14 phyla in the other (Wg25). Conversely, eukaryotes from only 16 classes comprised the majority of transcripts in Wg24, compared to 24 classes in the Wg25, revealing a greater eukaryote diversity in the latter. Given that these bromeliads had tanks of similar size (i.e. vertical oxygen gradient), and were neighboring with presumed similar light regime and acquisition of leaf litter through-fall, it is possible that pH was the factor governing these differences in bacterial and eukaryotic communities (Wg24 had a tank pH of 3.6 and Wg25 had a tank pH of 6.2). Archaeal diversity was similar in both tanks, represented by 7 orders, with the exception of Methanocellales transcripts uniquely recovered from Wg25. Based on measures of FPKG (fragments mapped per kilobase of gene length), genes involved in methanogenesis, in addition to a spirochaete flagellin gene, were among those most highly expressed in Wg25. Conversely, aldehyde dehydrogenase and monosaccharide-binding protein were among genes most highly expressed in Wg24. The ability to observe specific presence of insect, plant, and fungi-associated RNA-viruses was unexpected. As with other techniques, there are inherent biases in the use of RNA-Seq, however, these data suggest the possibility of understanding the entire community, including ecological interactions, via simultaneous analysis of microbial, eukaryotic, and viral transcripts.Keywordsbromeliadmetatranscriptomemethanogenrainforestdiversity
    Computational and Structural Biotechnology Journal 12/2015; 13. DOI:10.1016/j.csbj.2014.12.001
    • "Rademacher et al. 2012), the denaturing gradient gel electrophoresis (DGGE) (e.g. Liu et al. 2009) or the microarray technology (Franke-Whittle et al. 2009; Goberna et al. 2010) and also the high-throughput metagenome or amplicon sequencing (Schlüter et al. 2008; Krause et al. 2008; Jaenicke et al. 2011; Zakrzewski et al. 2012). These analyses mainly base on the investigation of the 16S rRNA gene as this gene gives the most reliable information about the phylogenetic relation of organisms and is therefore useful for a description of microbial communities (Lane et al. 1985; Talbot et al. 2008; Tringe and Hugenholtz 2008; Kim et al. 2011). "
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    • "Standard databases for protein identification are NCBInr (Acland et al., 2014), UniProtKB/Swiss-Prot or UniProtKB/TrEMBL (Consortium, 2012). With respect to metaproteomics, more specific databases or searches against metagenomes from the same or similar samples [e.g. for BGP samples (Schlüter et al., 2008; Rademacher et al., 2012; Wirth et al., 2012; Zakrzewski et al., 2012)] resulted in the identification of more proteins and are strongly recommended. "
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