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Bioreactor operation and samplingA continuous-flow MBR made from Plexiglass with a working volume of 12 L was used for enrichment (Supplementary Fig. S1). The reactor was installed with a submerged hollow fiber ultrafiltration membrane module (0.02 μm pore size, Litree, China) with a total membrane surface area of 0.03 m2. A level control system was set up to prevent liquid overflowing. The reactor was fed with diluted real urine with Total Kjeldahl Nitrogen (TKN) concentration of 140–405 mg N L−1 (for detailed influent composition see Supplementary Table S1). Initially, the reactor was inoculated with activated sludge taken from the aeration tank of a municipal wastewater treatment plant (Tsinghua Campus Water Reuse). The pH was maintained at 6.0 ± 0.1 by adding 1 M NaOH to buffer acidification by ammonia oxidation. The airflow was controlled at 2 L min−1, leading to the dissolved oxygen (DO) concentration above 4 mg O2 L−1 as regularly measured by a DO probe (WTW Multi 3420). The airflow also served to wash the membrane and mix the liquid. The temperature was controlled at 22–25 °C. The initial hydraulic retention time (HRT) was 3 days and was decreased to 1.5 days on day 222. The sludge retention time (SRT) was infinite as no biomass was discharged.The MBR was operated for 490 days. During this period, influent and effluent samples (10 mL each) were collected 1–3 times per week and used to determine the concentrations of TKN, total nitrite nitrogen (TNN = NO2−-N + HNO2-N), and nitrate nitrogen, according to standard methods.19 Mixed liquid samples (25 mL) were also taken weekly to measure mixed-liquor suspended solids (MLSS) and mixed-liquor volatile suspended solids (MLVSS).19 Biomass samples (10 mL) were regularly taken for qPCR and microbial community analyses (see below).Batch testsIn order to test urea hydrolysis and subsequent nitrification in the enrichment culture, short-term incubations were performed in a cylindrical batch reactor (8 ×18.5 cm [d × h], made from Plexiglass). 150 mL biomass was sampled from the reactor and washed three times in 1 x PBS buffer to remove any remaining nitrogen source. Subsequently, the biomass was resuspended in a 400 mL growth medium, which contained urea (about 40 mg N L−1), NaHCO3 (120 mg L−1), and 2 mL Hunter’s trace elements stock. Dissolved oxygen was controlled above 4 mg O2 L−1. Biotic and abiotic controls were performed under identical conditions with NH4Cl (~40 mg N L−1) instead of urea. The pH in all batch assays was maintained at 6.0 ± 0.1 by adding 1 M HCl or NaOH. According to the microbial activities during long-term operation, each batch assay lasted 6 to 8 h, and samples (5 mL) were taken every 20 to 60 min. Biomass was removed by sterile syringe filter (0.45 μm pore size, JINTENG, China), and urea, ammonium, nitrite, and nitrate concentrations were determined as described above. All experiments were performed in triplicate.DNA extractionBiomass (2 mL) for DNA extraction was collected on days 0, 53, 98, 131, 161, 189, 210, 238, 266, 301, 321, 358, 378, 449, and 471. DNA was extracted using the FastDNA™ SPIN Kit for Soil (MP Biomedicals, CA, U.S.) according to the manufacturer’s protocols. DNA purity and concentration were examined using agarose gel electrophoresis and spectrophotometrically on a NanoDrop 2000 (ThermoFisher Scientific, Waltham, MA, USA).16S rRNA gene amplicon sequencing and data analysisThe V4-V5 region of the 16 S rRNA gene was amplified using the universal primers 515F (5′-barcode-GTGCCAGCMGCCGCGG-3′) and 907 R (5′-CCGTCAATTCMTTTRAGTTT-3′).20 PCR products were purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to manufacturer’s instructions and quantified using the QuantiFluor™ -ST (Promega, USA). Amplicons were pooled in equimolar concentrations and sequenced using the Illumina MiSeq PE3000 sequencer as per the manufacturer’s protocol. Amplicon sequences were demultiplexed and quality filtered using QIIME (version 1.9.1).21 Reads 10 bp were assembled. UPARSE (version 7.0.1090 http://drive5.com/uparse/) was used to cluster operational units (OTUs) on a 97% similarity cut-off level, and UCHIME to identify and remove chimeric sequences. The taxonomy of each 16S rRNA gene sequence was assigned by the RDP Classifier algorithm (http://rdp.cme.msu.edu/) according to the SILVA (SSU132) 16S rRNA database using a confidence threshold of 70%.Quantification of various amoA by qPCRTo quantify the abundances of comammox Nitrospira, AOB and AOA in the bioreactor, qPCR targeting the functional marker gene amoA was performed on DNA extracted from the bioreactor at different time points. We used the specific primers Ntsp-amoA 162F/359R amplifying comammox Nitrospira clades A and clade B simultaneously,12 Arch-amoAF/amoAR targeting AOA amoA,22 and amoA-1F/amoA-2R for AOB amoA.23 Reactions were conducted on a Bori 9600plus fluorescence quantitative PCR instrument using previously reported thermal profiles (Supplementary Table S2). Triplicate PCR assays were performed the appropriately diluted samples (10–30 ng μL−1) and 10-fold serially diluted plasmid standards as described by Guo et al.24. Plasmid standards containing the different amoA variants were obtained by TA-cloning with subsequent plasmid DNA extraction using the Easy Pure Plasmid MiniPrep Kit (TransGen Biotech, China). Standard curves covered three to eight orders of magnitude with R2 greater than 0.999. The efficiency of qPCR was about 95%.Library construction and metagenomic sequencingThe extracted DNA was fragmented to an average size of about 400 bp using Covaris M220 (Gene Company Limited, China) for paired-end library construction. A paired-end library was constructed using NEXTFLEX Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA). Adapters containing the full complement of sequencing primer hybridization sites were ligated to the blunt-end of fragments. Paired-end sequencing was performed on Illumina NovaSeq PE150 (Illumina Inc., San Diego, CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) using NovaSeq Reagent Kits according to the manufacturer’s instructions (www.illumina.com).Metagenomic assembly and genome binningRaw metagenomic sequencing reads (in PE150 mode) were trimmed and quality filtered with in-house Perl scripts as described previously.25 Briefly, duplicated reads caused by the PCR bias during the amplification step were dereplicated. Reads were eliminated if both paired-end reads contained >10% ambiguous bases (that is, “N”). Low-quality bases with phred values 2.5 kbp were retained for later analysis. Genome binning was conducted for each sample using sequencing depth and tetranucleotide frequency. To calculate coverage, high-quality reads from all samples were mapped to the contigs using BBMap v38.85 (http://sourceforge.net/projects/bbmap/) with minimal identity set to 90%. The generated bam files were sorted using samtools v1.3.1.27 Then, sequencing depth was calculated using the script “jgi_summarize_bam_contig_depths” in MetaBAT.28 Metagenome-assembled genomes (MAGs) were obtained in MetaBAT. MAG quality, including completeness, contamination, and heterogeneity, was estimated using CheckM v1.0.12.29 To optimize the MAGs, emergent self-organizing maps30 were used to visualize the bins, and contigs with abnormal coverage or discordant tetranucleotide frequencies were removed manually. Finally, all MAGs were reassembled using SPAdes with the following parameters: –careful –k 21,33,55,77,99,127. The reads used for reassembly were recruited by mapping all high-quality reads to each MAG using BBMap with the same parameter settings as described above.Functional annotation of metagenomic assemblies and metagenome-assembled genomesGene calling was conducted for the complete metagenomic assemblies and all retrieved MAGs using Prodigal v2.6.3.31 For the MAGs, predicted protein-coding sequences (CDSs) were subsequently aligned to a manually curated database containing amoCAB, hao, and nxrAB genes collected from public database using DIAMOND v0.7.9 (E-values < 1e−5 32) MAGs found to contain all these genes were labeled as comammox Nitrospira MAGs and kept for later analysis. Functional annotations were obtained by searching all CDSs in the complete metagenomic assemblies and the retrieved MAGs against the NCBI-nr, eggNOG, and KEGG databases using DIAMOND (E-values < 1e−5).Phylogenetic analysisPhylogenomic treeThe taxonomic assignment of the three identified comammox Nitrospira MAGs was determined using GTDB-tk v0.2.2.33 To reveal the phylogenetic placement of these MAGs within the Nitrospirae, 296 genomes from this phylum were downloaded from the NCBI-RefSeq database. The download genomes were dereplicated using dRep v2.3.234 (-con 10 -comp 80) to reduce the complexity and redundancy of the phylogenetic tree, which resulted in the removal of 166 genomes. In the remaining genomes, the three comammox Nitrospira MAGs and 25 genomes from phylum Thermotogae which were treated as outgroups, a set of 16 ribosomal proteins were identified using AMPHORA2.35 Each gene set was aligned separately using MUSCLE v3.8.31 with default parameters,36 and poorly aligned regions were filtered by TrimAl v1.4.rev22 (-gt 0.95 –cons 5037) The individual alignments of the 16 marker genes were concatenated, resulting in an alignment containing 118 species and 2665 amino acid positions. Subsequently, the best phylogenetic model LG + F + R8 was determined using ModelFinder38 integrated into IQ-tree v1.6.10.39 Finally, a phylogenetic tree was reconstructed using IQ-tree with the following options: -bb 1000 –alrt 1000. The generated tree in newick format was visualized by iTOL v3.40 amoA treeReference amoA sequences of AOB, AOA, and comammox Nitrospira were obtained from NCBI. Together with the amoA genes from the present study, all sequences were aligned and trimmed as described above. IQ-tree was used to generate the phylogenetic tree, with “LG + G4” determined as the best model.ureABC gene treeureABC gene sequences detected in this study were extracted and used to build a database using “hmmbuild” command in HMMER.41 ureABC gene sequences from genomes in NCBI-RefSeq database (downloaded on July 1st, 2019) were identified by searching against the built database using AMPHORA2. The same procedures as above were conducted to construct the phylogenetic tree of concatenated ureABC genes, except for the sequence collection step. To reduce the complexity of the phylogenetic tree, the alignment of concatenated ureABC genes was clustered using CD-HIT42 with the following parameters: -aS 1 -c 0.8 -g 1. Only representative sequences were kept for phylogeny reconstruction, which resulted in an alignment containing 858 sequences and 1263 amino acids positions. “LG + R10” was determined as the best model and used to build the phylogenetic tree. Regarding the Nitrospirae-specific ureABC gene tree, ureABC gene sequences were recruited from the genomes as described above, but without the sequence clustering step. The final Nitrospirae-specific phylogeny of ureABC genes was built on an alignment containing 62 sequences and 1015 amino acid positions with “LG + F + I + G4” as the best model. More

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In a paper in Nature, Terrer et al.1 reveal an unexpected trade-off between the effects of rising atmospheric carbon dioxide levels on plant biomass and on stocks of soil carbon. Contrary to the assumptions encoded in most computational models of terrestrial ecosystems, the accrual of soil carbon is not positively related to the amount of carbon taken up by plants for biomass growth when CO2 concentrations increase. Instead, the authors show that carbon accumulates in soils when there is a small boost in plant biomass growth in response to CO2, and declines when the growth of biomass is high. Terrer et al. propose that associations of plants with mycorrhizal soil fungi are a key factor in this relationship between the above- and below-ground responses to elevated CO2 levels.
Read the paper: A trade-off between plant and soil carbon storage under elevated CO2
Rising levels of atmospheric CO2 are thought to have driven an increase in the amount of carbon absorbed globally by land ecosystems over the past few decades, a phenomenon known as the CO2 fertilization effect2. This occurs because, at the scale of leaves, higher CO2 levels enhance photosynthesis and the efficiency with which resources (water, light and nutrients such as nitrogen) are used to assimilate CO2 and support biomass growth3. Evidence supporting the existence of the CO2 fertilization effect has been observed in experiments in which the atmosphere around plants or plant communities is enriched with CO2. But at the level of whole ecosystems, responses to CO2 enrichment are more difficult to track, because the effects are diluted throughout a chain of connected processes. Constraining estimates of the response of the global land carbon sink to rising CO2 levels therefore remains a major challenge (see go.nature.com/3vgvhj).Changes in soil carbon are inherently difficult to detect, and studies that assess the effects of elevated CO2 levels on soil-carbon stocks have been equivocal4. Terrer and colleagues set out to investigate these effects by carrying out a meta-analysis of 108 CO2-enrichment experiments. The authors estimate that, in these studies, soil-carbon stocks increased in non-forest sites but remained almost unchanged in forests. By evaluating the effects of multiple environmental variables, the authors found that, surprisingly, the best explanation for the observed patterns is that the changes in soil carbon stocks are inversely related to the changes in above-ground plant biomass: high accumulation of carbon in biomass was associated with soil-carbon loss, whereas low biomass accumulation was associated with soil-carbon gain. This relationship was evident only in experiments in which no nutrients had been added to the studied systems, leading the authors to propose that plant nutrient-acquisition strategies are responsible — which, in turn, depend on the mycorrhizal soil fungi associated with the plants.
Soils linked to climate change
A previous study reported5 that only a small increase in above-ground biomass occurs in CO2-enriched plants that associate with a particular family of mycorrhizae (arbuscular mycorrhizae; AM). AM-associated plants benefit from the fungi’s extensive network of hyphae (branching filaments that aid vegetative growth), which support the plants’ uptake of nitrogen from the soil solution. However, AM have only a limited ability to ‘mine’ nitrogen from organic matter in the soil. The availability of soil nitrogen therefore limits the increase of biomass growth of AM-associated plants in response to elevated CO2 levels. By contrast, plant species that associate with a different group of soil fungi (the ectomycorrhizae; ECM) exhibit a greater increase in above-ground biomass in CO2-enrichment studies, because some of their carbon is allocated to ECM that can mine for nitrogen5. Mining for nutrients by ECM is, however, thought to accelerate the decomposition of organic matter in soil.Terrer et al. now find that AM-associated plants produce a bigger increase in soil-carbon stocks in CO2-enrichment experiments than do ECM-associated plants. The authors suggest that this is because AM-associated plants allocate more carbon to fine roots and to compounds exuded by the roots, resulting in soil-carbon accrual (Fig. 1a). By contrast, nutrient acquisition by ECM-associated plants results in increased turnover — and therefore loss — of soil organic matter (Fig. 1b). Overall, this would lead to an ecosystem-scale trade-off between the amount of carbon sequestered in plants and that sequestered in soil, in a CO2-enriched atmosphere.

Figure 1 | Proposed effects of elevation of atmospheric carbon dioxide levels. Terrer et al.1 suggest that associations of plants with different types of mycorrhizal soil fungi affect plant and soil responses to increases in atmospheric carbon dioxide levels. a, Plants that associate with arbuscular mycorrhizal fungi (grasses and some trees, in this study) do not ‘mine’ nitrogen (N, a nutrient) from the soil, and therefore do not produce much extra above-ground biomass when CO2 levels rise. Instead, they allocate carbon to fine roots and to root-exuded substances, resulting in soil-carbon accrual. Carbon dioxide produced from the respiration of soil microorganisms returns carbon to the atmosphere. b, Plants that associate with ectomycorrhizal fungi (only trees in this study) mine the soil for nitrogen, the uptake of which supports a bigger increase in biomass growth than in a. However, nutrient mining increases the rate of decomposition of organic matter in soil. The amount of carbon in the soil therefore decreases in response to elevated CO2 levels; microbial soil respiration is greater than in a.

Most Earth-system models that account for land carbon-cycling processes assume that rising levels of atmospheric CO2 will increase plant growth, thus producing more plant litter and thereby increasing stocks of soil carbon6. The authors compared the changes in soil carbon and above-ground plant biomass predicted by various models, both in simulations of six open-air CO2-enrichment experiments, and in global simulations of historical and future increases in atmospheric CO2. None of the models reproduced the negative relationship between carbon sequestration by soil and growth in plant biomass that was observed in the current study.Terrer and co-workers’ findings thus provide another urgent warning that current climate models overestimate the amount of carbon that will be sequestered by land ecosystems as atmospheric CO2 levels increase — not only because the models largely ignore the effects of nutrient limitations, but also because they overestimate the amount of carbon that could be sequestered in soil, particularly in forest ecosystems7. But the new study also reveals that grasslands, shrublands and other ecosystems that already have high soil-carbon stocks have great potential to accumulate more soil carbon as CO2 levels increase. These results thus add weight to previous calls to protect existing soil-carbon stocks to mitigate the effects of climate change8.
Carbon dioxide loss from tropical soils increases on warming
There are some limitations to the set of CO2-enrichment experiments included in Terrer and colleagues’ meta-analysis. The experiments are biased towards temperate systems, and most of the forests studied are associated with ECM, whereas the grasslands are all AM-associated. The authors did not find that the type of ecosystem had a substantial effect on the observed responses to CO2, but it remains to be seen whether the reported trade-off between above- and below-ground carbon sequestration for AM- compared with ECM-associated plants applies to forests alone9. Further experiments, especially in tropical ecosystems, are now needed to address these issues.Tropical ecosystems are large contributors to the global terrestrial carbon sink10, but they are notoriously under-studied. Field observations are scarce and few manipulation experiments — such as CO2 enrichment or nutrient additions — have been carried out in these ecosystems11,12. Below-ground processes are particularly challenging to assess in the tropics, where the effects of multiple nutrient scarcities often come into play12. Terrer and colleagues’ study provides a promising framework that can be elaborated to describe diverse plant–soil interactions in various terrestrial ecosystems in the future.CO2-enrichment experiments generally last for just a few years, or just over a decade at most13. Such timescales are unlikely to capture the effects of elevated CO2 levels on plant mortality, plant-species composition and soil-carbon turnover time, all of which can affect the sequestration of carbon by ecosystems in different ways in the longer term. Mechanistic understanding gained from experiments about the coupling between carbon and nutrient cycling can, however, be integrated into computational models. And this will allow us to constrain estimates of the size of the terrestrial carbon sink in the coming decades. The interactions between plants and their associated soil fungi, as well as other crucial below-ground agents and processes such as microbial communities, are already stirring up modelling efforts14,15. Terrer and colleagues’ study now invites researchers to test hypotheses about the processes that drive coordinated above- and below-ground responses to rising CO2 levels. Such studies could be a real step forwards in our understanding of the fate of the terrestrial carbon sink. More