Computational protocol: Microbial Community Structure and Functions in Ethanol-Fed Sulfate Removal Bioreactors for Treatment of Mine Water

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[…] The microbiology of the inoculum and reactors (Day 101 and 140) was determined using DNA-based high throughput (HTP) sequencing techniques. Microbial DNA was extracted from 0.5 g inoculum sludge and from 2 mL samples of effluent from the bioreactors. Excess liquid was first removed from the solids by centrifugation for 10 min at 13,000 rpm in a table-top centrifuge (Eppendorf, Hamburg, Germany), whereafter the supernatant was carefully decanted. The DNA extraction was performed with the NucleoSpin Soil DNA extraction kit (Macherey-Nagel, Düren, Germany) and proceeded according to the manufacturer’s instructions using the SL1 lysis buffer and Enhancer solution. The DNA was eluted in 100 μL elution buffer. The total number of bacteria and SRB in the inoculum and reactors was determined by quantitative PCR (qPCR) targeting the bacterial 16S rRNA genes and the dsrB genes of the SRB. The bacteria were targeted by amplifying an approximately 200 bp fragment of the 16S rRNA gene using primers P1 and P2 [] and the dsrB genes by using the primers dsr2060F and dsr4R [,] as previously described [,].The microbial community composition in the inoculum and bioreactor effluents was determined by characterizing the whole community profiles targeting the 16S rRNA genes of the bacteria and archaea using primer pair S-D-Bact-0341-b-S-17/S-D-Bact-0785-a-A-21 for bacteria and primer pair S-D-Arch-0349-a-S-17/S-D-Arch-0787-a-A-20 for archaea [] targeting the v3 and v4 regions of the gene, and the ITS region for the fungi using primers ITS1 and ITS2 []. PCR amplification was performed in parallel 25 μL reactions for every sample containing 1× MyTaqTM Red Mix (Bioline, London, UK), 20 pmol of each primer, up to 25 μL molecular-biology-grade water (Sigma-Aldrich, Munich, Germany) and 2 μL of DNA. The PCR program consisted of an initial denaturation step at 95 °C for 3 min, 35 and 40 cycles of 15 s at 95 °C, 15 s at 50 °C, and 15 s at 72 °C, for bacteria and archaea, respectively, and a final elongation step of 30 s was performed at 72 °C. The PCR products were verified with agarose gel electrophoresis. Amplicons were sent to Ion Torrent sequencing on the PGM platform (Bioser, Oulu, Finland) and amplicons were purified before sequencing at Bioser.The sequence reads obtained from Ion Torrent sequencing were subjected to quality control using the QIIME software version 1.9 [] using a minimum quality score of 20, minimum and maximum sequence length of 200 bp and 600 bp, respectively, maximum primer mismatch of 2 nucleotides (nt) and maximum homopolymer stretches of 8 nt. Adapters, barcodes, and primers were removed from the sequence reads, and chimeric sequence reads were removed from the data set with the USEARCH algorithm [] by de novo detection and through similarity searches against the Greengenes reference dataset (version gg_13_8) [] with bacterial and archaeal sequences, and UNITE reference dataset (version sh_taxonomy_qiime_ver7_97_s_31.01.2016) [] with fungal sequences.The sequences were grouped into Operational Taxonomic Units (OTUs), following the open-reference OTU-picking protocol of QIIME, using UCLUST [] to cluster sequence reads at 97% sequence similarity. Taxonomiy from the domain- to species-level was assigned to OTUs via representative OTU sequences with the RDP classifier algorithm at minimum confidence threshold of 80% [] for bacterial and archaeal sequences. Taxonomic assignments for the fungal ITS sequences were made using the BLAST algorithm with a maximum E-value of 0.001 []. Sequence reads obtaining no taxonomical assignments in the analyses were excluded from the datasets.Microbial metabolic pathways were estimated using the PICRUSt software, version 1.0.0 []. The principle of the PICRUSt analysis is thoroughly described by the developers [], and will only shortly be explained here for clarity. PICRUSt employs the ancestral state reconstruction (ASR) method to predict the gene content of uncultured microorganisms, for which a genome is not yet available. Despite genome plasticity due to gene loss, duplication, or transfer, the assumption is that the gene content between closely related taxa is more similar than between distantly related taxa. Nevertheless, uncertainties arise because microbial genomes may change rapidly over evolutionary time, a fact that should be considered when interpreting these predictions, and the prediction are accompanied by a 95% confidence interval. PICRUSt links 16S rRNA gene sequences of complete reference genomes and genomes identified from uncultured bacteria and archaea (environmental samples) to the annotated genomes. The 16S rRNA gene sequences of the reference genomes are contained in a phylogenetic tree and linked to the functional annotations (e.g., KEGG orthologs) of the gene content of the reference genomes. This analysis is based on 16S rRNA gene data that has been analyzed using the closed OTU picking method in QIIME using the Greengenes reference dataset version gg_13_5 database [] for taxonomic assignments. The number of taxa present in the samples was normalized by predicting the number of 16S rRNA gene copies of each identified taxon using PICRUSt, i.e., the resulting OTU table corresponds to the relative number of microorganisms, not 16S rRNA genes. The metagenome was then predicted for the normalized data using PICRUSt’s pre-calculated KEGG ortholog database files, which estimates the number of gene copies of each gene family per microorganism. The most common bacterial OTUs responsible for specific enzymes or metabolic processes were identified using the metagenome_contribution.py command in PICRUSt [] and the abundances of these bacterial groups were plotted separately for each function in R [].The Nearest Sequenced Taxon Index (NST) for evaluating the novelty of the organisms included in an OTU table (described in []) with respect to previously sequenced genomes was calculated for each sample. The NSTI is the sum of branch lengths between an OTU in the Greengenes tree to the nearest tip in the tree with a sequenced genome weighted by the relative abundance of that OTU. All OTU scores are then summed to give a single NSTI value per microbial community sample.The sequence data has been submitted to the European Nucleotides Archives (ENA, http://www.ebi.ac.uk/ena) under study number PRJEB21687 (Accession number ERS1812840-ERS1812860).The viability of the microorganisms in the reactors was examined using the LIVE/DEAD® BacLight™ Bacterial Viability (L/D) (Thermo Fisher Scientific, Waltham, MA, USA) staining kit as recommended by the manufacturer. Bioreactor effluent samples of 5 mL were obtained at the end of the experiment and were stained with the L/D reagents for 30 min after which the microbial cells were concentrated on black 0.2 μm pore-size polycarbonate membrane filters (Isopore™ Membrane filters, 0.2 μm GTBP, Millipore, MA, USA) with a Millipore 1225 Sampling Manifold (Millipore, MA, USA) using low vacuum suction. The filters were mounted on microscopy slides, covered with a cover glass, and examined under UV light with an epifluorescence microscope (Olympus BX60, Olympus Optical Ltd., Tokyo, Japan) at 100× magnification. […]

Pipeline specifications

Software tools QIIME, USEARCH, UCLUST, RDP Classifier, PICRUSt
Databases Greengenes
Applications Phylogenetics, 16S rRNA-seq analysis
Chemicals Acetaldehyde, Ethanol, Ethanolamine