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News Article | December 23, 2015
Site: www.nature.com

The inoculum for the Ca. N. inopinata enrichment culture was sampled from a microbial biofilm that grew on the metal surface of a pipe and was covered by hot water, which was raised from a 1,200 m deep oil exploration well. The water temperature was 56 °C and the pH 7.5. The well was located in Aushiger, North Caucasus, Russia (43°22′45.0′′ N, 43°43′26.1′′ E). The biofilm samples were taken in April 2011. Activated sludge, membrane biofilm, and foam (from a foaming event) samples were taken in August and October 2014 from a pilot-scale membrane bioreactor (MBR) performing nitrogen removal and enhanced biological phosphorus removal (EBPR) at the conventional full-scale WWTP Aalborg West, Aalborg, Denmark (57°02′59.9′′ N, 9°51′55.4′′ E). The influent wastewater for this MBR came from the primary settling tank of the full-scale plant, entering an anoxic/denitrification (2 m3) tank and going to an oxic/nitrification (2 m3) tank. An anaerobic tank (1.8 m3) used for return sludge sidestream hydrolysis provided easily degradable substrate for EBPR and denitrification. Activated sludge was also sampled from an aerated activated sludge basin (tank no. 2) of the full-scale WWTP of the University of Veterinary Medicine, Vienna, Austria (48°15′17.8′′ N, 16°25′45.6′′ E) in January 2015 (WWTP VetMed). The two continuously operated activated sludge tanks of this WWTP have a volume of 254 m3 each. The wastewater composition and nitrogen load vary with the amounts of animal faeces and other sewage. This WWTP was known to host a large diversity of Nitrospira18. Iron sludge samples were taken from groundwater well (GWW) no. 1 of the well field of the Wolfenbüttel waterworks (Wolfenbüttel, Germany) (52°08′55.9′′ N, 10°32′33.9′′ E). The well has a depth of 50 m below ground level (bgl) and a diameter of 600 mM. Groundwater is extracted through two well intake screens in 28 to 38 m bgl and 46 to 48 m bgl. The normal well capacity is 160 m3 h−1. Before sampling, the well had been out of operation for about three weeks. The well water is a mixture of aerobic and anaerobic groundwater from two different ground water storeys and is characterized by the following parameters (values from years 2012 to 2014): pH about 7.2, about 10 °C, 5 to 10 mg l−1 dissolved oxygen, 0.13 to 0.17 mg l−1 ammonium, <0.01 mg l−1 nitrite, 12 to 16 mg l−1 nitrate, 0.16 to 0.42 mg l−1 total iron, 0.03 to 0.08 mg l−1 manganese, 0.64 to 0.99 mg l−1 total organic carbon, 0.44 to 0.78 mg l−1 dissolved organic carbon, 71 to 81 mg l−1 dissolved inorganic carbon, 121 to 138 mg l−1 calcium. The drop pipe, through which the extracted water is pumped to ground level, was drawn out of the well on 27 April 2015 and had deposits of pasty iron sludge on the inner surface. A sample was taken from these deposits at several points corresponding to depths between 20 and 10 m bgl. A second sample consisted of suspended iron sludge deposits that had been flushed away from the upper well intake screen and retained on a fleece filter during pumping out of the turbid water on 28 April 2015. The biofilm used as inoculum was suspended and incubated at 46 °C with 0.5 mM NH Cl in a modified AOM medium51 containing (per litre): 50 mg KH PO ; 75 mg KCl; 50 mg MgSO  × 7H O; 584 mg NaCl; 4 g CaCO (mostly undissolved, acting as a solid buffering system and growth surface); 1 ml of specific trace element solution (TES); and 1 ml of selenium-wolfram solution (SWS)52. The composition of TES and SWS is described below. Both solutions were added to the autoclaved medium by sterile filtration using 0.2 μm pore-size cellulose acetate filters (Thermo Scientific). The pH of the medium was around 8.2 after autoclaving and was kept around 7.8 by the CaCO buffering system during growth of the enrichment. TES contained (per litre): 34.4 mg MnSO  × 1H O; 50 mg H BO ; 70 mg ZnCl ; 72.6 mg Na MoO  × 2H O; 20 mg CuCl  × 2H O; 24 mg NiCl  × 6H O; 80 mg CoCl  × 6H O; 1 g FeSO  × 7H O. All salts except FeSO  × 7H O were dissolved in 997.5 ml Milli-Q water and 2.5 ml of 37% HCl was added before dissolving the FeSO  × 7H O salt. SWS contained (per litre): 0.5 g NaOH; 3 mg Na SeO  × 5H O; 4 mg Na WO  × 2H O. The primary ammonium-consuming enrichment was subsequently treated with antibiotics (one treatment with 50 mg l−1 vancomycin, two treatments with 50 mg l−1 bacitracin). The ammonium concentration was increased to 1 mM NH Cl for these and all further cultivation steps. After these treatments and repeated serial dilutions in AOM medium without antibiotics, enrichment culture ENR4 was obtained that was characterized in this study. An aliquot of ENR4 was incubated at 50 °C for four weeks and then subjected to serial dilution at 46 °C. Propagation of the most diluted (10−8) ammonia-oxidizing culture was followed by serial dilution in AOM medium containing 1 mM urea instead of ammonium. The most diluted (10−7) urea-consuming (that is, nitrifying) culture was again cultivated in AOM medium with 1 mM NH Cl and subjected to repeated serial dilutions, which resulted in culture ENR6 that was also characterized in this study. Enrichments ENR4 and ENR6 were further cultivated in 100 ml or 250 ml Schott bottles in AOM medium containing 1 mM NH Cl. To obtain enough biomass for DNA extraction, enrichment ENR4 was up-scaled in 1 l and 2 l Schott bottles. The composition of enrichment cultures was analysed by phase contrast microscopy, electron microscopy, FISH with rRNA-targeted probes, amoA- and 16S rRNA-specific PCR, and metagenomics (see later for methodological details). To study nitrification by Ca. N. inopinata, an actively nitrifying ENR4 stock culture was harvested by centrifugation (9,300g, 30 min, 10 °C) and the biomass was suspended in AOM medium (see above) without ammonium. Aliquots (25 ml) of this suspension were distributed to 100 ml Schott bottles (all glassware was rinsed twice in 6 M HCl and three times in Milli-Q water, autoclaved, and dried at 60 °C before use). After addition of NH Cl to final concentrations of 1 mM, 0.1 mM, or 10 μm, respectively, or of NaNO to a final concentration of 0.5 mM, the biomass was incubated at 46 °C for 9 h (10 μm NH Cl) or 48 h (other experiments) without agitation in the dark. Samples (500 μl) for chemical analyses (see below) were taken directly after ammonium or nitrite addition and during the incubations. The samples were centrifuged (22,000g, 10 min, 4 °C) to remove cells and undissolved CaCO and 450 μl of the supernatant was transferred to plastic tubes and stored at −20 °C until analysis. Each incubation condition except 10 μm NH Cl was performed in parallel with four biological replicates (biological triplicates for 10 μm NH Cl), two dead biomass controls (cells were killed by autoclaving), and two abiotic controls that contained only medium and substrate, but no biomass. After the experiments, the remaining biomass was harvested by centrifugation (9,300g, 30 min, 10 °C), frozen immediately at −80 °C, and shipped on dry ice for proteome analysis. To quantify growth of Ca. N. inopinata by complete nitrification, culture ENR4 was incubated in mineral NOB medium, which has been used to cultivate nitrite-oxidizing Nitrospira21. In this experiment, the NOB medium was amended with ammonium instead of nitrite. The NOB medium was chosen because it contains less CaCO3, which can affect quantitative PCR (qPCR) efficiency and accuracy. Nitrifying activity of ENR4 in NOB medium was confirmed in preceding tests. Biomass from the supernatant (without undissolved CaCO ) from an ammonia-oxidizing culture was washed once in NOB medium, harvested by centrifugation (9,300g, 30 min, 10 °C), and prepared for incubation as described above. Following the addition of NH Cl to a final concentration of 0.6 mM, samples (100 μl) for quantitative PCR were taken immediately and after 4, 5, 7, and 8 days of incubation. Samples for chemical measurements (see below) were taken immediately and after 1, 4, 5, 7, and 8 days of incubation. All samples were stored at −20 °C until analysis. These incubation experiments were performed in biological triplicates. Copy numbers of the Ca. N. inopinata amoA gene were determined by qPCR using the newly designed Ca. Nitrospira inopinata amoA gene-specific primers Nino_amoA_19F (5′-ATAATCAAAGCCGCCAAGTTGC-3′) and Nino_amoA_252R (5′-AACGGCTGACGATAATTGACC-3′). The qPCR reactions were run with three technical replicates in a Bio-Rad C1000 CFX96 Real-Time PCR system, using the Bio-Rad iQ SYBR Green Supermix kit (Bio-Rad). Each qPCR reaction was performed in 20 μl reaction mix containing 10 μl SYBR Green Supermix, 2 μl of the sampled ENR4 cell suspension, 0.1 μl of each primer (50 μM), and 7.9 μl of autoclaved double-distilled ultrapure water. Cells were lysed and DNA was released for 10 min at 95 °C, followed by 43 PCR cycles of 40 s at 94 °C, 40 s at 52 °C, and 45 s at 72 °C. Plasmids carrying the Ca. N. inopinata amoA gene were obtained by PCR-amplifying the gene from the ENR4 culture and cloning the product into the pCR4-TOPO TA vector (Invitrogen). The M13-PCR product from these plasmids containing the amoA gene was used as standard for qPCR (the amoA copy number in the standard was calculated from DNA concentration). Tenfold serial dilutions of the standard were subjected to qPCR in triplicates to generate an external standard curve. The amplification efficiency was 92.6%, and the correlation coefficient (r2) of the standard curve was 0.999. A 1 ml aliquot of the ENR4 culture was transferred to 25 ml modified AOM medium (see above) containing 6 mM sodium acetate. After three weeks of incubation at 46 °C, a 1 ml aliquot of the betaproteobacterial primary enrichment was transferred into 25 ml of fresh modified AOM medium containing 6 mM sodium acetate. After three more weeks, a 5 ml aliquot of this culture was centrifuged (9,300g, 10 min, 10 °C) and the cells were resuspended in 25 ml NOB medium (see above) containing 1 ml of SWS and 4 mM sodium acetate. Thereafter, 1 ml of the betaproteobacterial enrichment was transferred into fresh NOB medium containing 4 mM sodium acetate every 2 weeks. The fourth transfer was checked for purity by FISH with the betaproteobacterium-specific probe Nmir1009, which showed 100% overlap with the EUB338 probe mix and DAPI signals. No Nitrospira cells were detected by FISH in the culture. To test whether the betaproteobacterium had the capability to nitrify, 20 ml of a dense pure culture of this organism was centrifuged (9,300g, 10 min, 10 °C), washed once in modified AOM medium without solid CaCO , and resuspended in modified AOM medium without ammonium and solid CaCO . Aliquots of this suspension were distributed into 100 ml Schott bottles, which had been rinsed twice in 6 M HCl, washed 3 times in Milli-Q water, closed with aluminium caps, autoclaved, and dried at 60 °C before use. Subsequently, the following substrates were added: 1 mM NH Cl; or 0.5 mM NaNO and 0.1 mM NH Cl; or 4 mM sodium acetate and 0.1 mM NH Cl (the 0.1 mM NH Cl was added to the nitrite and acetate incubations to provide the organism with a nitrogen source for assimilation). The biomass was incubated at 46 °C in the dark without agitation. All experiments were performed in parallel with biological triplicates. Samples (700 μl) for qPCR and chemical analyses (see below) were taken immediately after experimental set-up and after 19, 24, 30, 42, and 48 h of incubation. The samples were stored at −20 °C until analysis. Cell densities of the betaproteobacterium were quantified by qPCR targeting the soxB gene, which encodes the SoxB component of the periplasmic thiosulfate-oxidizing Sox enzyme complex. SoxB is a single-copy gene in the genome of the betaproteobacterium. The primers used to quantify the soxB gene were soxB_F1 (5′-GGACCAGACCGCCATCACTTACCC-3′) and soxB_R1 (5′-GCACCATGTCCCCGCCTTGCT-3′). The qPCR protocol and conditions were the same as described above. Ammonium levels were measured photometrically as described previously53, 54 with adjusted volumes of sample and reagents. Standards were prepared in AOM or NOB medium and ranged from 7.25 to 1,000 μm NH Cl. Nitrite concentrations were determined photometrically by the acidic Griess reaction55. Nitrate was reduced to nitrite by vanadium chloride and measured as NO by the Griess assay. Nitrate concentrations were calculated from the NO measurements as described elsewhere56. Standards were prepared in AOA or NOB medium and ranged from 7.8 to 1,000 μm for NO and from 3.9 to 500 μm for nitrite. The number of replications are detailed in the subsections for each specific experiment, and were mostly determined by the amount of biomass available for the different cultures. In all experiments, a minimum of three biological replications were used. No statistical methods were used to predetermine sample size. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment. FISH with rRNA-targeted oligonucleotide probes was performed as described elsewhere57 using the EUB338 probe mix58, 59 for the detection of Bacteria, probes Ntspa662 and Ntspa712 specific for Nitrospira10, and probes Nso1225, Nso190, and NEU specific for betaproteobacterial AOB22. The betaproteobacterium in ENR4 and ENR6 was detected by FISH with the specific probe Nmir1009 (5′-CACTCCCCCGTCTCCGGG-3′) with 35% of formamide in the hybridization buffer. If required, unlabelled competitor oligonucleotides were added in equimolar amounts as probes. Cells were counterstained by incubation for 5 min in a 0.1 μg ml−1 DAPI (4′,6-diamidino-2-phenylindole) solution. Fluorescence micrographs were recorded by using a Leica SP7 confocal laser scanning microscope equipped with a white light laser. To determine the relative abundances of Nitrospira and AOB in WWTP VetMed by quantitative FISH, 20 confocal images of FISH probe signals were taken at random positions in the sample and analysed as described elsewhere60 by using the digital image analysis software daime61. For whole-cell electron microscopy, cells were positively stained with 1% (w/v) uranyl acetate. Electron microscopy of thin sections was carried out as described elsewhere62. To check whether the Ca. N. inopinata enrichments contained known AOB or AOA, PCR tests were performed using primer sets amoA-1F/amoA-2R targeting betaproteobacterial amoA63, CamoA-19f/CamoA-616r targeting thaumarchaeal amoA33, 64, and 771F/957R for thaumarchaeal 16S rRNA genes65 and the respective published reaction conditions. DNA was extracted for these PCR assays by using the PowerSoil DNA Isolation Kit (MoBio) according to the manufacturer’s instructions. Protein extraction from concentrated ENR4 biomass, proteolytic digestion, analysis of peptide lysates by mass spectrometry (MS), processing of MS raw files, and analysis of MS spectra were carried out as described elsewhere20. MS spectra were searched against a database of predicted gene products on the ENR4 metagenome scaffolds containing 12,234 sequence entries and a common Repository of Adventitious Proteins (cRAP) database using the Sequest HT algorithm. The PROPHANE pipeline (http://www.prophane.de/index.php) was used to classify the lowest common phylogenetic ancestor of each protein group and to calculate the normalized spectral abundance factor (NSAF). Biomass of enrichment ENR4 was collected from three culture bottles (samples ENR4_A, ENR4_E, ENR4_F) by centrifugation and frozen over night at −80 °C before total nucleic acids were extracted by bead beating in the presence of phosphate buffer, 10% (w/v) SDS and phenol as described elsewhere66 (see ref. 67 for full protocol). Bead beating was repeated twice to break remaining intact cells, the supernatants from each step were pooled, and nucleic acids purified by phenol/chloroform/isoamyl alcohol and chloroform/isoamyl alcohol extraction. Nucleic acids were precipitated using 20% (w/v) polyethylene glycol, washed in ice-cold 75% (v/v) ethanol, and resuspended in sterile 10 mM TRIS buffer. RNA was digested with RNase I (Promega) and the purity of DNA assessed by spectrophotometry. The same protocol was used to extract DNA from concentrated biomass of enrichment ENR6 (sample ENR6_N3), with the modification that bead beating was not repeated, and from an activated sludge sample of WWTP VetMed collecting only the supernatants of the second and third bead beating steps (DNA extract Vetmed_23). DNA was extracted from a second aliquot of the WWTP VetMed sample (DNA extract Vetmed_Pskit), and from pasty (sample GWW_HP_F1) or suspended (sample GWW_HP_D) iron sludge from the GWW, by using the PowerSoil DNA Isolation Kit (MoBio). DNA was extracted from all MBR samples by using the FastDNA SPIN Kit for Soil (MP Biomedicals) following the manufacturer’s instructions. Sequencing libraries were prepared using the Nextera or TruSeq PCR free kits (Illumina Inc.) following the manufacturer’s recommendations. For the TruSeq PCR free kits, the 550 bp protocol was used with 1 μg of input DNA. The prepared libraries were sequenced using either an Illumina MiSeq with MiSeq Reagent Kit v3 (2x301 bp; Illumina Inc.) or an Illumina HiSeq2000 using the TruSeq PE Cluster Kit v3-cBot-HS and TruSeq SBS kit v.3-HS sequencing kit (Illumina Inc.). Nanopore sequencing was performed in addition to facilitate completion of the Ca. N. inopinata genome sequence. Library preparation was done using the Nanopore Sequencing kit (SQK-MAP005, Oxford Nanopore) following the manufacturer's recommendations (v. MN005_1124_revC_02Mar2015) with shearing in an Eppendorf MiniSpin plus centrifuge at 8,000 rpm and including the optional PreCR treatment step, as well as Ampure XP Bead purification after dA-tailing. The libraries were sequenced using nanopore flow cells (FLO-MAP003, Oxford Nanopore) using the MinION device (Oxford Nanopore) with the MinKNOW software (v. 0.50.1.15). Flow cells were primed twice with a mixture of 3 μl Fuel Mix, 75 μl 2 × Running Buffer, and 72 μl nuclease-free water for 10 min. Libraries were prepared for loading onto the flow cell by mixing 75 μl 2 × Running Buffer, 66 μl nuclease-free water, 3 μl Fuel Mix, and 6 μl Library (Pre-sequencing Mix). A sequencing run was started (MAP_48Hr_Sequencing_Run.py) after loading the library. Additional DNA library top-ups and restart of the run script was carried out to maximize yield by allowing a new selection of active pores. Base calling was carried out using Metrichor and the 2D Basecalling workflow (Rev 1.16). Details for each metagenome can be found in Supplementary Table 1. Paired-end Illumina reads were imported into CLC Genomics Workbench v. 8.0 (CLCBio, Qiagen) and trimmed using a minimum phred score of 20 and a minimum length of 50 bp, with allowing no ambiguous nucleotides and trimming off Illumina sequencing adaptors if found. FASTQ files for the Oxford Nanopore 2D reads were obtained using the R package poRe v. 0.668 and error corrected using Illumina reads through Proovread v. 2.1369. For each environment, all trimmed Illumina reads were co-assembled using CLCs de novo assembly algorithm, using a kmer of 63 and a minimum scaffold length of 1 kbp. Trimmed reads were mapped to the assembled scaffolds using CLCs map reads to reference algorithm, with a minimum similarity of 95% over 70% of the read length. Open reading frames (ORFs) were predicted in the assembled scaffolds using Prodigal70. A set of 107 hidden Markov models (HMMs) of essential single-copy genes71 were searched against the ORFs using HMMER3 (http://hmmer.janelia.org/) with default settings, except option (-cut_tc) was used. Identified proteins were taxonomically classified using BLASTP against the RefSeq (v. 52) protein database with a maximum e-value cutoff of 10−5. MEGAN72 was used to extract class-level taxonomic assignments from the BLAST output. The script network.pl (http://madsalbertsen.github.io/mmgenome/) was used to extract paired-end read connections between scaffolds. PhyloPythiaS+73 was used to taxonomically classify all scaffolds of selected samples. In addition, selected metagenome assemblies were binned based on ESOM maps74. After training the ESOM using scaffolds >5 kbp and large scaffolds chopped into 5 kbp pieces, all scaffolds were projected back to the ESOM map to retrieve a single coordinate for all scaffolds. Individual genome bins were extracted using the multi-metagenome principles23 implemented in the mmgenome R package (http://madsalbertsen.github.io/mmgenome/). All genome bins are fully reproducible from the raw metagenome assemblies using Rmarkdown files available on http://madsalbertsen.github.io/mmgenome/. The script extract.fastq.reassembly.pl was used to extract paired-end reads from the binned scaffolds, which were used for re-assembly using SPAdes75. For selected samples, error-corrected Oxford Nanopore 2D reads were used for scaffolding using SSPACE-LongRead76. For all genomes, quality was assessed using coverage plots through the mmgenome R package and through the use of QUAST77 and CheckM78. Details for each metagenome assembly can be found in Supplementary Table 2, and further details for the reconstructed bacterial genomes (including CheckM results) in Supplementary Tables 3–7. Relative genome sequence coverage was calculated as the fraction of sequence coverage of a reconstructed genome compared to the summed coverage of all genomes in these low-complexity metagenomes. The reconstructed bacterial genomes were uploaded to the MicroScope platform79 for automatic annotation and for manual annotation refinement17 of key pathways of Ca. N. inopinata. To test for the presence of additional organisms capable of nitrification, the raw reads for each enrichment ENR4 and ENR6 were mapped to the amoA, amoB, amoC, hao and nxrB sequences used to generate the trees in Extended Data Figs 5b,d, 8, and 9. Reads were required to align to any one member of a target data set over at least 70% of read length with BLASTN (word size = 7). Reads that mapped with >97% nucleotide identity were automatically classified. Reads with lower identity were placed with the Evolutionary Placement Algorithm (EPA) using RAxML80. Using this procedure, no indication was found for the presence of any nitrifier other than Ca. N. inopinata in these enrichments. For phylogenetic analyses of AMO and HAO, full amino acid data sets were downloaded from the Pfam81 site for bacterial (pfam02461) and archaeal (pfam12942) amoA. Additional amino acid sequences were identified from the NCBI GenBank82 and the Integrated Microbial Genomes databases (IMG-ER and -MER)83 that were returned using the search words ‘ammonia, methane, amo, pmo or monooxygenase’ (GenBank) or had been annotated with one of the target pfams (IMG). A BLASTP84 search was performed using the Ca. Nitrospira inopinata amoA sequence as a query, word size = 2, BLOSUM 45, E = 10 and the top 1,000 returned sequences were downloaded. Comparable procedures were performed to generate a comprehensive set of amoB (pfam04744) and amoC (pfam04896) sequences. For construction of the hao (pfam13447) data set, query words were changed to ‘hydroxylamine’ and ‘Hao’. For each gene set, amino acid sequences were filtered using hmmsearch (http://hmmer.janelia.org/) with the respective pfam HMMs, requiring an expect value < 0.001. Amino acid sequences were clustered at 75% identity using USEARCH85 and aligned using Mafft86. Phylogenetic trees were calculated using PhyloBayes87, running 5 independent chains for 21,000 cycles each, using 11,000 cycles for burn-in and sampling every 20 cycles. Sequences that mapped to centroids that clustered within the comammox clade were used for additional phylogenetic calculations along with an outgroup of 27 betaproteobacterial amoA and 29 diverse pmoA sequences. Corresponding nucleotide sequences for this set were aligned according to their amino acid translations using MUSCLE88 and manually corrected for frameshifts. Nucleotide alignments were then used for constructing consensus trees in Phylobayes, running 5 independent chains for 21,000 cycles each, using 11,000 cycles for burn-in and sampling every 20 cycles. To estimate relative abundances of amoA genes, comammox-type amoA sequences were identified from three publicly available Rifle soil metagenomic data sets (3300002121, 3300002122 and 3300002124) available within IMG. Functional profiles were generated within IMG using pfam12942 (archaeal amoA) and pfam02461 (bacterial amoA/pmoA) against the assembly and unassembled reads. All identified amoA/pmoA nucleotide sequences were downloaded as nucleic acid sequences and added to the existing amoA alignment used to generate Extended Data Fig. 8 with the -add option in Mafft. EPA in RAxML was used to assign downloaded sequences into the reference tree that is the basis for Extended Data Fig. 8. AmoA abundance for each amoA type (comammox, archaeal, betaproteobacterial AOB) was estimated by taking the sum of the estimated copy numbers of each assembled amoA gene of a given type as well as the number of unassembled reads assigned to a given amoA type. Comammox, betaproteobacterial, and archaeal amoA sequences from the metagenomes of WWTP VetMed and the GWW were identified using the same procedure as above. Comammox amoA read abundances were then used to calculate an estimate of the fraction of Nitrospira that are comammox. AmoA was assumed to be a single copy gene in all comammox (as it is in Ca. N. inopinata). Total Nitrospira were enumerated by mapping raw reads from metagenomic samples using the first 700 nucleotides of the predicted ATP-citrate lyase subunit beta (aclB) gene from Ca. N. inopinata. Reads were required to align to Ca. N. inopinata aclB over at least 70% of read length and with >60% alignment identity with BLASTN (word size = 7). AclB was chosen on the basis that this gene has a restricted taxonomic distribution, encodes a key enzyme of the reductive tricarboxylic acid cycle employed by all known Nitrospira for CO fixation, and is present in single copy within known Nitrospira genomes. To test its utility, all 150 nt segments (pos 1:150, 2:151…1,051:1,200) of the Ca. N. inopinata aclB gene was used as a query against the nr database (BLAST, word size = 7, 70% read length and 60% alignment identity). Over the first 700 nucleotides of the aclB gene, test fragments mapped only to reference Nitrospira organisms. Downstream of this region, the aclB mapping was less specific, mapping to Nitrospira and Chlorobi with high (>90%) identity. Coverage of each gene was calculated by dividing the number of mapped reads by gene length of the query (843 nt for comammox amoA and 700 nt for Nitrospira aclB). Adjusted coverage was calculated by dividing gene coverage by total number of reads in the metagenomic data set. Ratios discussed in the main text are then the adjusted coverage of comammox (as calculated from comammox amoA) divided by the adjusted coverage for all Nitrospira (as calculated from aclB). For phylogenetic analyses of NXR, the NxrA and nxrB sequences of Ca. N. inopinata were imported into existing NxrA17 and nxrB8 sequence databases using the software ARB89. NxrA sequences were aligned using Mafft, nxrB sequences were manually aligned according to their amino acid translations. Maximum likelihood trees were calculated using RAxML with the GAMMA model of rate heterogeneity using empirical base frequencies and the LG substitution model (NxrA) or with the GAMMA model of rate heterogeneity and the GTR substitution model (nxrB). Bayesian inference trees were calculated using PhyloBayes, running 3 independent chains for 32,200 cycles each, using 6,440 cycles for burn-in (NxrA) or 3 independent chains for 35,500 cycles each, using 7,000 cycles for burn-in (nxrB). Nitrospira 16S rRNA genes from this study were added to an existing Nitrospira 16S rRNA sequence database and aligned in ARB. Phylogenetic trees were calculated using RAxML with the GAMMA model of rate heterogeneity and the GTR substitution model, and using MrBayes90 v.3.2.1, running 4 independent chains for 5 million generations each, with 1.25 million cycles for burn-in and sampling every 100 generations. Pairwise average nucleotide identity (ANI) values were calculated for comammox Nitrospira genomes using BLAST (ANIb) in JSpecies91. Genome-wide tetranucleotide signatures were calculated for the forward and reverse strand for each genome with the oligonucleotideFrequency(width = 4) command using the Biostrings package in R92. Tetranucleotide patterns were also calculated across the length of the genome with a sliding window of 5 kb (step = 1 kb). The tetranucleotide pattern for each window was compared to the global tetranucleotide signature by calculating the Pearson correlation (r) of log(1+counts) of each window against the log(1+counts) of the global signature. P values, indicating a significantly low correlation for tetranucleotide signature of a window, were calculated by modelling 1 − r across all windows as a log-normal distribution. Multiple testing was accounted for by using the Benjamini–Hochberg procedure with a false discovery rate of 5%.


News Article | December 23, 2015
Site: www.nature.com

Nitrification, the aerobic oxidation of ammonium to nitrate is divided into two subsequent reactions: ammonium oxidation to nitrite (equation (1)) and nitrite oxidation to nitrate (equation (2)). These two reactions are catalysed by physiologically distinct clades of microorganisms. Even though the existence of a single microorganism capable of oxidizing ammonium to nitrate (equation (3)) was not previously reported, it was proposed that such a microorganism could have a competitive advantage in biofilms and other microbial aggregates with low substrate concentrations3. In this study, to characterize the microorganisms responsible for nitrogen transformations in an ammonium-oxidizing biofilm, we sampled the anaerobic compartment of a trickling filter connected to a recirculation aquaculture system4 with an ammonium effluent of less than 100 μM. To enrich for the N-cycling community, a bioreactor was inoculated and supplied with low concentrations of ammonium, nitrite and nitrate under hypoxic conditions (≤3.1 μM O ). Within 12 months, we obtained a stable enrichment culture that efficiently removed ammonium and nitrite from the medium (Extended Data Fig. 1). The culture showed anaerobic ammonium-oxidizing (anammox) activity (Fig. 1a), and fluorescence in situ hybridization (FISH) revealed that anammox organisms of the genus Brocadia constituted approximately 45% of all FISH-detectable bacteria. Surprisingly, Nitrospira-like nitrite-oxidizing bacteria accounted for approximately 15% of the community and co-occurred with the Brocadia species in flocs (Fig. 2a). This tight clustering with anammox bacteria was unexpected as both microorganisms require nitrite for growth. Together with the presence of Nitrospira at very low oxygen concentrations, this indicated that there could be a functional link between these organisms. To determine the function of Nitrospira in the community, we extracted and sequenced total DNA from the enrichment culture biomass. In total 4.95 gigabase pairs of trimmed metagenomic sequence were obtained and used for de novo assembly. By differential coverage and sequence composition-based binning5 it was possible to extract high-quality draft genomes of two Nitrospira species. The two strains had genomic pairwise average nucleotide identities (ANI)6 of 75% and thus clearly represented different species (Nitrospira sp.1 and sp.2, Extended Data Fig. 2 and Extended Data Table 1). Surprisingly, both genomes contained the full set of AMO and hydroxylamine dehydrogenase (HAO) genes for ammonia oxidation, in addition to the nitrite oxidoreductase (NXR) subunits necessary for nitrite oxidation in Nitrospira7. In both species all these genes were localized on a single contiguous genomic fragment, along with general housekeeping genes that allowed reliable phylogenetic assignment. Consequently, these Nitrospira species had the genetic potential for the complete oxidation of ammonia to nitrate. No AMO of canonical ammonia-oxidizing bacteria or archaea could be detected in the trimmed metagenomic reads or by amoA-specific PCR8, 9 on DNA extracted from reactor biomass, and no other indications for the presence of ammonia-oxidizing microorganisms were found in the metagenome or by FISH analyses. The AMO structural genes (amoCAB) of both Nitrospira species, along with the putative additional AMO subunits amoEDD210, 11, formed one gene cluster with haoAB-cycAB (encoding HAO, the putative membrane anchor protein HaoB, electron transfer protein cytochrome c and quinone reducing cytochrome c , respectively)12 and showed highest similarities to their counterparts in betaproteobacterial ammonia-oxidizing bacteria (60% average amino acid identity to the Nitrosomonas europaea genes; Fig. 3 and Supplementary Table 1). The same genomic region also contained genes for copper and haem transport, cytochrome c biosynthesis, and iron storage. These accessory genes were highly conserved in ammonia-oxidizing bacteria but not in other Nitrospira7, 13, indicating their involvement in AMO and HAO biosynthesis or activation. Nitrospira sp.1 encoded three discrete amoC genes, one of which was clustered with a second, almost identical copy of amoA (97.7% amino acid identity). Nitrospira sp.2 lacked the second amoA, but contained four additional amoC and a second haoA gene (Supplementary Table 1). Unlike other Nitrospira7, 13, both species lacked enzymes for assimilatory nitrite reduction, indicating adaptation to ammonium-containing habitats. For ammonium uptake, they encoded low-affinity Rh-type transporters most closely related to Rh50 found in Nitrosomonas europea14, in contrast to most ammonia-oxidizing and nitrite-oxidizing bacteria that have the high-affinity AmtB-type proteins. Both species encoded ureases and the corresponding ABC transport systems, indicating that urea could be used as an alternative ammonium source. Interestingly, Candidatus Nitrospira inopinata, the moderately thermophilic ammonia-oxidizing Nitrospira described by Daims et al.15, encoded a similar set of AMO, HAO and urease proteins, and also lacked genes for assimilatory nitrite reduction. Unlike the two species described here, however, it contained a periplasmic cytochrome c nitrite reductase (NrfA) that could allow it to conserve energy by dissimilatory nitrite reduction to ammonium (DNRA), but might also provide ammonium for assimilation. The evolutionary divergence of these organisms was also reflected in the low ANI values of 70.3–71.6% between Candidatus N. inopinata and the two species described here. Concerning their genetic repertoire for nitrite oxidation, sp.2 had four almost identical (>99% amino acid identity) NXR alpha and beta (NxrAB) subunits. Sp.1 had two nxrAB copies encoding identical NxrB subunits, but NxrA subunits with amino acid identities of 89.6%, which were separated into distinct clusters in phylogenetic analyses. One homologue branched with sequences from Nitrospira moscoviensis, while the other formed a novel sequence cluster together with the sequences from sp.2 (Extended Data Fig. 3). To ascertain that ammonia oxidation occurred under hypoxic conditions in the enrichment culture, we supplied the bioreactor with 15N-labelled ammonium. While the anammox bacteria consumed 15NH + and converted it into 29N , a steady increase of 30N was also observed (Fig. 1a). This formation of 30N could only be explained by the production of 15N-labelled nitrite derived through aerobic ammonium oxidation. As metagenomic analyses confirmed that the Nitrospira species were the only organism in the enrichment harbouring AMO and HAO, this clearly showed that they were able to perform this reaction even at O concentrations lower than 3.1 μM. To unambiguously link this activity to Nitrospira, we visualized the AMO protein in situ using batch incubations with reactor biomass and FTCP (fluorescein thiocarbamoylpropargylamine), a fluorescently labelled acetylene analogue that acts as suicide substrate for AMO16 and covalently binds to the enzyme17. When counterstained with Nitrospira-specific FISH probes, including a newly designed probe specifically targeting the 16S ribosomal RNA-defined phylogenetic group comprising spp.1 and 2 (Extended Data Table 2 and Extended Data Fig. 4), strong FTCP labelling of Nitrospira cells was observed, providing strong support for the presence of the ammonia-oxidizing enzyme at the single-cell level (Fig. 2b and Extended Data Fig. 5). Batch incubations were performed at ambient oxygen concentrations to determine conversion rates of ammonium and nitrite, the level of inhibition by allylthiourea (ATU; a potent inhibitor of bacterial ammonia oxidation18, 19), and the use of urea as ammonium source for nitrification. Flocs were mechanically disrupted to ensure complete exposure of the biomass to oxygen, which inhibits the anammox and denitrification processes20, 21. This inhibition was confirmed by the lack of labelled N formation in incubations with 15NH +. In these incubations (Fig. 1 and Extended Data Fig. 6), the culture oxidized ammonium (6.0 ± 1.0 μM h−1 NH +) and nitrite (23 ± 4.7 μM h−1 NO −) to nitrate. ATU selectively inhibited ammonia oxidation, but did not affect nitrite oxidation rates. Urea was converted to ammonium, which was subsequently oxidized to nitrate (7.8 ± 1.1 μM h−1 NO −), suggesting that these Nitrospira species were capable of using urea as source of ammonia to drive nitrification, as was also reported for some ammonia-oxidizing archaea22 and bacteria23. This trait could enable them to thrive in environments like fertilized soils, wastewater treatment plants, and many aquatic systems where urea is often present at micromolar levels24. However, it should be noted that the two Nitrospira spp. were not the only organisms in the enrichment culture that encoded ureases. To investigate substrate-dependent inorganic carbon fixation as a proxy for energy conservation from ammonia and nitrite oxidation, we used FISH in combination with microautoradiography (FISH-MAR)25. Aerobic incubations with mechanically disrupted flocs were performed in the presence of 500 μM ammonium, 500 μM ammonium with 100 μM ATU, or 500 μM nitrite. Nitrospira incorporated carbon from 14C-labelled bicarbonate in the presence of either ammonium or nitrite, and ammonia-dependent carbon fixation was strongly inhibited by the addition of ATU (Fig. 2c and Extended Data Fig. 7). Only flocs containing Nitrospira were labelled during all incubations, suggesting that these were the only chemolithoautotrophic nitrifying organisms present in the culture and indeed could conserve energy from the oxidation of ammonia and nitrite. In 16S rRNA-based phylogenetic analyses, the two ammonia-oxidizing Nitrospira species from our enrichment culture formed two separate lineages within one strongly supported sequence cluster affiliated with Nitrospira sublineage II26 (Extended Data Fig. 4). They both grouped with highly similar sequences (>99% nucleotide identity) from a diverse range of habitats, including soil, groundwater, recirculation aquaculture systems, wastewater treatment plants and drinking water distribution systems. The formation of distinct clusters containing sp.1 and sp.2 indicated that the last common ancestor encoded genes for complete nitrification and that this pathway might be conserved in most organisms affiliated with this sequence group. To explore the environmental relevance of these Nitrospira, we searched the NCBI nr database27 for closely related amoA genes. Surprisingly, we found the AmoA proteins of the two Nitrospira species to be phylogenetically divergent from the described bacterial AmoA sequences. Nitrospira sp.2 AmoA was 97–98% identical to the so-called “unusual” methane monooxygenase (PMO) proteins of Crenothrix polyspora28. The two AmoA copies from Nitrospira sp.1 had lower similarities to Crenothrix PmoA (90–91% identity), but also affiliated with this group (Fig. 4). Sequences within this group cannot be amplified by standard amoA primers, but only by pmoA primers when used at reduced stringency29. Therefore the public databases only contain few closely related sequences, which were mainly derived from habitats studied for their bacterial methane-oxidizing communities. Highly similar sequences derived from wastewater treatment plants and drinking water systems, however, indicated occurrence of ammonia-oxidizing Nitrospira in a range of engineered and natural environments. We furthermore screened all publicly available shotgun data sets on MG-RAST30. Indeed, 168 metagenomes (out of 6,255) and 28 metatranscriptomes (out of 1,051) contained at least two reads affiliated with this amoA group, yielding a total of 3,727 reads that were obtained mainly from soil, sediments and wastewater treatment plants (Extended Data Table 3). Thus, our results showed that the Crenothrix sequence group consists of so far unrecognized AMO sequences overlooked in nitrification studies based on amoA gene detection. Based on these findings, it is highly likely that the PCR-based determination of the Crenothrix pmoA gene from an environmental sample28 was erroneous, and this cluster only contains genes encoding AMOs. Nevertheless, with the currently available information it cannot be excluded that certain Crenothrix species attained an amoA gene through lateral gene transfer and use the encoded protein as a surrogate PMO. In conclusion, here we demonstrated the existence of complete nitrification in a single organism (comammox) and identified two Nitrospira species capable of catalysing this process (equation (3)). In 16S rRNA or amoA/pmoA-based studies these organisms would have been classified as nitrite-oxidizing or methane-oxidizing bacteria, respectively. Hence, our results show that a whole group of ammonia-oxidizing organisms was previously overlooked. Our findings furthermore disprove the long-held assumption that nitrification (ammonia oxidation via nitrite to nitrate) is catalysed by two distinct functional groups, thus redefining a key process of the biogeochemical nitrogen cycle. Based on their physiology, differences in genome content, and separation in different phylogenetic groups in 16S rRNA-based analyses, we propose tentative names for both Nitrospira species present in our enrichment: Candidatus Nitrospira nitrosa (etymology: L. fem. adj. nitrosa, nitrous; the nitrite and nitrate forming Nitrospira) for sp.1 and Candidatus Nitrospira nitrificans (N.L. part. adj. nitrificans, nitrifying; the nitrifying Nitrospira) for sp.2. Both species are chemolithoautotrophic and fully oxidize ammonia via nitrite to nitrate.


Patent
Gao, Xu, Hao and Wiesenfeld Hallin | Date: 2015-03-25

Provided are an analgesic pharmaceutical composition and a method for relieving pain. Specifically, by combination sinomenine with gamma - aminobutyric acid (GABA) drug or analgesic medicament such as paracetamol, the analgesic composition has excellent synergistic effect on analgesia.


An apparatus for AC physical signals measurement and data acquisition and the method for the same are provided. The apparatus for AC physical signals measurement and data acquisition comprises an analog sampling channel for inputting an AC signal and outputting an analog sampling value; a sampling switch for performing re-sampling to obtain data frequency as required by the receiving side; a register for storing a re-sampling value from the sampling switch; a bus for outputting the re-sampling value in the register to the receiving side; a timing controller for controlling the analog sampling channel and the re-sampling frequency of the sampling switch; and a digital low-pass filter, which has an input connected with the analog sampling value outputted by the analog sampling channel and an output connected with the sampling switch, filters out high frequency components from the sampling value, and has a cut-off frequency that should be lower than 0.5 times the re-sampling frequency of the sampling switch. The apparatus and method for AC physical signals measurement and data acquisition improve accuracy of remote measurement for electric power physical quantities. Not only waveform values are outputted by re-sampling, effective values, steady state values and their fundamental/harmonic wave effective values and steady state values are also outputted. Thus, various requirements by the receiving side on remote measurement data are satisfied.


The invention discloses a method and system for identifying an element parameter and a power correction factor of an electric power system. The method comprises: inputting an active power telemetering steady state value P, a reactive power telemetering steady state value Q, and a voltage telemetering state value of a power grid with n elements, to establish +j+jQ+G(,P,Q) for telemetering power, where G(,P,Q) is power correction function regarding P and Q, and is power correction factor; establishing F(Y,)=0 , where Y is an admittance matrix of the n elements; setting F(Y,)= and J=, minimizing J, and determining Y and ; restoring element parameters including resistance R, reactance X and susceptance B from Y; and outputting R,X,B and . The method can identify element parameters and power correction factor with high identification accuracy improve qualified rate of state estimation and improve accuracy of applications such as stability analysis, stability checking and stability control, etc.


The present invention relates to a data sampling method: sampling a physical quantity according to a sampling frequency f f, f= / being the upper limit of the sampling frequency; and a data sampling method and system: sampling a physical quantity with a sampling frequency satisfying Nyquist theorem, firstly performing digital low-pass filtering on an obtained sampled sequence, and then re-sampling, the re-sampling frequency f / , where is the Z-domain error of a sampling system, and is the maximum error of an S-domain. The present invention also relates to a parameter identification method and system which firstly adopt the above data sampling method and system to obtain sampled data, and then utilize the sampled data to perform dynamic and/or static parameter identification. The data sampling method and system and the parameter identification method and system of the present invention solve technical difficulties such as relatively large errors in sampled data, digital control instability, and parameter identification failure.


The invention discloses a method for identifying full parameters of an electric element by a fault recording data, comprising steps: inputting fault recording data related to an electric element; conducting data processing on the fault recording data; identifying full parameters of the element by intercepted data and a differential equation of the full parameters of the element; and outputting an identified result. Further proposed are a system for identifying full parameters of a power generator by fault recording data and a method for locating a line fault point with fault recording data. With the implementation of the invention, a fault resistance and full parameters of an element such as an electric line and a transformer, etc. can be identified. The invention can obtain full parameters of a fault element and also a non-fault element, and the parameters precision would be increased from the current 20% to less than 1%.


Patent
Hao | Date: 2010-09-15

A grinding device of vertical grinder, comprising a driving device (1); a vertical grinding barrel (2) which is driven by the driving device; a grinding roller (3); and a pressure applying device (4) acting on the grinding roller (3); a running-in surface is composed of the roller surface of the grinding roller and the grinding surface (6) of the inner lining of the vertical grinding barrel, and the angular separation between the grinding surface (6) of the inner lining of the vertical grinding barrel and the vertical line is 40 degrees to -5 degrees. The device is also provided with a material-shaving device (9).


Provided is a method for acquiring continuous physical signal such as temperature, pressure and the like. The method comprises the following steps: inputting a voltage signal u representing continuous physical signal; obtaining a sampled signal u_(k) of an analog voltage through an analog sampling channel (1), wherein the sampling frequency is fh; performing digital low-pass filtering on the u_(k) (6) to obtain a voltage signal u_(k) subjected to low-pass filtering, and resampling the u_(k) to obtain a resample signal u_(j), wherein the resampling frequency fy is the same as the sampling frequency required by an application terminal and the sampling frequency fh is M times of the resampling frequency fy; and storing and outputting the resample signal u_(j) to the application terminal. Provided also is a corresponding device. The cost of the analog sampling channel is lowered; the u_(j) can be directly applied to industrial automation for substitution of the u_(j), especially output signals do not contain transient values, the requirements of a stable model on input quantity can be met, the random disturbance can be inhibited, and the measurement accuracy can be improved.


The present invention provides a hydraulic cylinder, an oil pumping unit, an oil pumping module and an oil pumping system. The hydraulic cylinder provided in the present invention comprises a hydraulic cylinder body, a piston, a first piston rod and a second piston rod; said second piston rod extending out of the hydraulic cylinder in a direction opposite to that of the first piston rod. Said oil pumping unit comprises said hydraulic cylinder and a first oil-well pump that engages the first piston rod. Said oil pumping module comprises such an oil pumping unit, a control mechanism and a hydraulic driving mechanism. Said oil pumping system is composed of one or a plurality of above recited oil pumping modules, each is connected with the final oil outlet through an oil pipeline that is connected with its own oil outlet opening. Compared with prior arts, the oil pumping system of the present invention comprises less components and its configuration is much simpler; it also characterized of simpler transmission mechanism and more efficient transmission mechanism, further, since the present invention employs groupware/module assembly, therefore, the installation is much simpler and highly flexible, and the reliability and safety of the system have been markedly improved.

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