Metagenomic analysis of an industrial-scale biogas plant by high throughput sequencing

Introduction Energy crisis and environmental pollution have led to an increasing interest in renewable energies. Biogas production from plant material, agricultural residual products and food wastes represents one of the most economically attractive alternative technology for biofuel production. In this regards, anaerobic digestion has been widely applied to produce methane for biofuel. Complex consortia of microorganisms are responsible for biomass degradation and biogas production involving several stages such as substrate hydrolysis, acidogenesis, acetogenesis and methanogenesis. In this sense, the next-generation high-throughput sequencing provides a powerful tool for dissecting microbial community structure and methane-producing pathways in anaerobic digestion. Here, a taxonomic and functional metagenomic analysis of microbial community residing in an industrial-scale biogas fermenter has been carried out at different steps of biogas production. Methods Sample were collected from an industrial-scale mesophilic plant, daily fed with maize silage, consisting of a three steps production taking place in a bioreactor, post-reactor and a storage tank. Total DNA was extracted from samples belonging to each stage of biogas production. Metagenomic analysis were carried out by 16S and shotgun sequencing approach. The 16S datasets were generated by sequencing the bacterial and archaeal V4 hypervariable region. Reads from 16S sequencing were aligned against SILVA ribosomal RNA sequence database by using MALT (1), while shotgun reads were aligned against NCBI-nr sequence database by using DIAMOND (2). Taxonomic binning and functional annotation were performed with MEGAN 6 software (3). Results Over 14.5 million high quality reads (about 3.4 gigabases) were generated on the Ion Torrent S5 Sequencing System. About 2.4 and 3 million reads were assigned for 16S and shotgun approach, respectively. Although the average number of assigned taxa for 16S analysis was considerably lower than shotgun analysis, the overall taxa distribution resulting from both sequencing strategies was conserved. In detail, metagenomic analysis revealed that the superkingdom of Bacteria was dominant (~93%) along the production steps, whereas Archaea were less represented (~4%). Within Bacteria the most abundant phyla were Firmicutes, mostly represented by Clostridia, followed by Bacteroidetes, Synergistetes and Proteobacteria. Within the superkingdom of Archaea, only microorganisms belonging to the phylum of Euryarchaeota were detected. Within Euryarchaeota the dominant genera were Methanosarcina and Methanoculleus, notably to be key microorganisms involved in methanogenesis. Data showed that during biogas production steps the abundance of Methanosarcina genus decreased from bioreactor to storage tank, with a simultaneous increase of Methanoculleus genus. Functional analysis of assigned reads also supported a shift from acetotrophic methanogens to hydrogenotrophic methanogens. In this regards, considering the key methanogenesis pathways, in bioreactor most of the assigned reads were related to genes encoding for acetate kinase, acetyl-CoA synthetase and phosphate acetyltransferase, whereas in the storage tank reads were mostly related to genes encoding for formylmethanofuran dehydrogenase, methyle-netetrahydromethanopterin dehydro-genase and methenyltetrahydromethanopterin cyclohydrolase. Conclusions In this work, a metagenomics analysis of an industrial-scale biogas plant has been carried out. The combination of both 16S and shotgun sequencing approach successfully addressed the taxonomical and functional analysis of microbial community, revealing new insights in microbial and functional dynamics during biogas production steps. Acknowledgments This work was supported by H2020-E.U.3.2-678781-MycoKey-Integrated and innovative key actions for mycotoxin management in the food and feed chain and Short Term Mobility Program (2016) of National Research Council. We thanks Austep S.p.A for providing samples. References 1.Herbig A.,Maixner F., Bos K.I., et al., 2016. MALT: fast alignment and analysis of metagenomic DNA sequence data applied to the Tyrolean Iceman. Preprint at http://biorxiv.org/content/early/2016/04/27/050559. 2.Buchfink B., Xie C., Huson D.H., 2015.Fast and sensitive protein alignment using DIAMOND. Nature Methods 12, 59-60. 3.Huson D.H., Beier S., Flade I., et al., 2016. MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data, PLoS Computational Biology 12(6): e1004957.