Nitrogen fixation in the surface ocean impacts global marine nitrogen bioavailability and thus microbial primary productivity. Until now, cyanobacterial populations have been viewed as the main suppliers of bioavailable nitrogen in this habitat. Although PCR amplicon surveys targeting the nitrogenase reductase gene have revealed the existence of diverse non-cyanobacterial diazotrophic populations, subsequent quantitative PCR surveys suggest that they generally occur in low abundance. Here, we use state-of-the-art metagenomic assembly and binning strategies to recover nearly one thousand non-redundant microbial population genomes from the TARA Oceans metagenomes. Among these, we provide the first genomic evidence for non-cyanobacterial diazotrophs inhabiting surface waters of the open ocean, which correspond to lineages within the Proteobacteria and, most strikingly, the Planctomycetes. Members of the latter phylum are prevalent in aquatic systems, but have never been linked to nitrogen fixation previously. Moreover, using genome-wide quantitative read recruitment, we demonstrate that the discovered diazotrophs were not only widespread but also remarkably abundant (up to 0.3% of metagenomic reads for a single population) in both the Pacific Ocean and the Atlantic Ocean northwest. Our results extend decades of PCR-based gene surveys, and substantiate the importance of heterotrophic bacteria in the fixation of nitrogen in the surface ocean.
Marine microbial communities play a critical role in biogeochemical fluxes and regulating climate1,2,3, but their activity in the euphotic zone of low latitude oceans is often limited by the availability of inorganic fixed nitrogen4,5. Thus, biological fixation of gaseous dinitrogen in the surface ocean is a globally important process that contributes to the ocean’s productivity and can potentially enhance the sequestration of carbon through the biological pump6,7. Microbial populations that can fix nitrogen (termed diazotrophs) encompass a wide range of archaeal and bacterial lineages8,9. However, diazotrophs within the bacterial phylum Cyanobacteria, in particular, are considered to be responsible for a substantial portion of nitrogen input in the surface ocean10,11,12. Studies employing cultivation and flow cytometry13,14,15,16,17 have characterized multiple cyanobacterial diazotrophs and shed light on their functional lifestyles18,19,20. PCR amplicon surveys of the nitrogenase reductase nifH gene have indicated that the ability to fix nitrogen is also found in bacterial lineages that include the phyla Proteobacteria, Firmicutes and Spirochaetes9,21,22, suggesting the presence of heterotrophic bacterial diazotrophs (HBDs) that contribute to the introduction of fixed nitrogen in the surface ocean. Quantitative surveys of non-cyanobacterial nifH genes have indicated that HBDs are diverse and active, but relatively rare in the surface ocean23,24,25,26,27,28,29, and efforts to access genomic representatives through cultivation and culture-independent techniques have so far only been successful in coastal waters30,31, limiting our understanding of their ecophysiology in the open ocean.
Here, we have used metagenomic assembly, binning and curation strategies to create a non-redundant database of archaeal, bacterial and eukaryotic genomes from the TARA Oceans project32. We characterized nearly one thousand microbial genomes from the surface samples of four oceans and two seas, revealing nitrogen-fixing populations within the phylum Proteobacteria, as well as in the Planctomycetes, which is a widespread phylum33 that has never been linked to nitrogen fixation previously. These discoveries enable the genome-wide tracking of these populations, through which we determined that putative HBDs are orders of magnitude more abundant in surface seawater across large regions of the global open ocean compared to previous estimates that relied on PCR amplifications.
The 93 TARA Oceans metagenomes we analysed correspond to a size fraction targeting free-living microorganisms (0.2–3 μm) from 61 surface samples and 32 samples from the deep chlorophyll maximum layer of the water column (Supplementary Table 1). Presumed absent from this size fraction are the majority of those bacterial and archaeal cells that have a symbiotic relationship with eukaryotes, form large aggregates or attach to large particles. Of 33.7 billion metagenomic reads, 30.9 billion passed quality control criteria and were used as input for 12 metagenomic co-assemblies (1.14–5.33 billion reads per set) using geographically bounded samples (Supplementary Fig. 1). A total of 42,193,607 genes were identified in scaffolds longer than 1,000 nucleotides (see Supplementary Table 2 for a summary of the assembly statistics). A combination of automatic and manual binning was applied to each co-assembly output, which resulted in 957 manually curated, non-redundant metagenome-assembled genomes (MAGs) containing 2,288,202 genes (Supplementary Fig. 1; also see ref. 34 for an automatic binning effort that includes larger size fractions).
Our MAGs belonged to the domains Bacteria (n = 820), Eukarya (n = 72) and Archaea (n = 65) (Supplementary Table 3), and recruited 2.11 billion quality controlled reads (6.84% of the data set) when we mapped the metagenomic data back to this collection. The genomic completion estimates for archaeal and bacterial MAGs based on domain-specific single-copy core genes averaged to 79% and 76.1%, respectively, and resolved to the phyla Proteobacteria (n = 432), Bacteroidetes (n = 113), Euryarchaeota (n = 65), Verrucomicrobia (n = 65), Planctomycetes (n = 43), Actinobacteria (n = 37), Chloroflexi (n = 34), Candidatus Marinimicrobia (n = 27), Acidobacteria (n = 6), Cyanobacteria (n = 6), Spirochaetes (n = 5), Firmicutes (n = 2), Ignavibacteriae (n = 1) and diverse members of the Candidate Phyla Radiation (n = 4). We could assign only 6.33% of the bacterial and archaeal MAGs to described genera. Eukaryotic MAGs were substantially larger than bacterial and archaeal MAGs (7.24 Mbp versus 2.26 Mbp and 1.47 Mbp on average, respectively) and were dominated by a small number of genera: Micromonas (n = 14), Emilliana (n = 14), Bathycoccus (n = 8) and Ostreococcus (n = 4). Recovery of these MAGs complements decades of cultivation efforts by providing genomic context for lineages missing in culture collections (for example, Euryarchaeota and Candidatus Marinimicrobia), and allowed us to search for diazotrophs within a large pool of marine microbial populations.
Genomic stability of a well-studied nitrogen-fixing symbiotic population at large scale
Our genomic collection included six cyanobacterial MAGs, one of which (ASW 00003) contained genes that encode the catalytic (nifHDK) and biosynthetic (nifENB) proteins required for nitrogen fixation8. This MAG, which we recovered from the Atlantic southwest metagenomic co-assembly, showed remarkable similarity to the genome of the symbiotic cyanobacterium ‘Candidatus Atelocyanobacterium thalassa’35,36 (previously known as UCYN-A) sorted by flow cytometry from the North Pacific gyre (GenBank accession no. CP001842.1). Besides their comparable size of 1.43 Mbp (MAG ASW 00003) and 1.46 Mbp (consensus genome from isolated cells), their average nucleotide identity was 99.96% over the 1.43 Mbp alignment. ‘Ca. A. thalassa’ is a diazotrophic taxon that lacks key metabolic pathways and lives in symbiosis with photosynthetic eukaryotic cells19,36. The high genomic similarity between ASW 00003 and the ‘Ca. A. thalassa’ genome sorted by flow cytometry demonstrates the accuracy of our metagenomic workflow.
Genomic evidence for nitrogen fixation by Proteobacteria and Planctomycetes
Besides the cyanobacterial MAG, we also identified seven Proteobacteria and two Planctomycetes MAGs in our collection that contained the complete set of genes for nitrogen fixation. To the best of our knowledge, these MAGs (HBD-01 to HBD-09) represent the first genomic evidence of putative HBDs inhabiting the surface of the open ocean (Table 1). They were obtained from the Pacific Ocean (n = 6), Atlantic Ocean (n = 2) and Indian Ocean (n = 1), and possessed relatively large genomes (up to 6 Mbp and 5,390 genes) and a GC content ranging from 50% to 58.7%. One of the Proteobacterial MAGs resolved to the genus Desulfovibrio (HBD-01). The remaining MAGs from this phylum correspond to lineages within the orders Desulfobacterales (HBD-02), Oceanospirillales (HBD-03, HBD-04, HBD-05) and Pseudomonadales (HBD-06, HBD-07) (Table 1). The phylogenetic assignment of one Planctomycetes MAG (HBD-08) with a low completion estimate (33.5%) could not be resolved beyond the phylum level, possibly due to missing phylogenetic marker genes for taxonomic inferences. However, the length of this MAG (4.03 Mbp) suggests that its completion may have been underestimated, as we have observed in previous studies37,38. The second Planctomycetes MAG (HBD-09) was affiliated with the family Planctomycetaceae (order Planctomycetales) based on its single-copy core genes. This MAG contained a large fragment of the 16S rRNA gene (1,188 nt; Supplementary Table 4) for which the best match to any characterized bacterium in the NCBI’s non-redundant database was Algisphaera agarilytica (strain 06SJR6-2, NR_125472) with 88% identity.
We placed the nine HBDs in a phylogenomic analysis of the 432 Proteobacteria and 43 Planctomycetes MAGs using a set of 37 marker gene families (Fig. 1a; for an interactive version see https://anvi-server.org/merenlab/tara_hbds). The two deltaproteobacterial HBDs were closely related to each other, but not adjacent in the phylogenomic tree. The HBDs within Oceanospirillales (n = 3), Pseudomonadales (n = 2) and Planctomycetes (n = 2) formed three distinct phylogenomic lineages. These results suggest that closely related populations of diazotrophs inhabit the surface ocean, and nitrogen fixation genes occur sporadically among diverse putatively heterotrophic marine microbial lineages, consistent with previous investigations39.
Our initial binning results included 120 redundant MAGs that were observed multiple times in independent co-assemblies (Supplementary Table 5). Although they are not present in our final collection of 957 non-redundant MAGs (for an accurate assessment of the relative abundance of microbial populations), we used this redundancy to investigate the stability of the phylogeny and functional potential of populations recovered from multiple geographical regions. For instance, we characterized the genomic content of HBD-06 from the Atlantic northwest (5.49 Mbp) and from each of the three Pacific Ocean regions (5.56, 5.33 and 5.29 Mbp in regions PON, PSW and PSE, respectively) (Table 1 and Supplementary Table 5). Average nucleotide identities between the Atlantic MAG and three Pacific MAGs ranged from 99.89% to 99.97% over more than 97% of the genome length. We observed similar trends for HBD-07 and HBD-09 (Table 1 and Supplementary Table 5). The complete set of nitrogen fixation genes was present in all of the redundant MAGs, demonstrating the large-scale stability of this functional trait in these HBDs.
On average, the proportion of genes of unknown function was 27.6% (±2.63%) for the proteobacterial HBDs and 49.3% (±0.5%) for the Planctomycetes HBDs, reflecting our greater lack of functional understanding of the latter taxonomic group of diazotrophs. The 37,582 total genes identified in the nine HBDs encoded for 5,912 known functions (Supplementary Table 6), and a network analysis of HBDs based on known functions organized them into four distinct groups corresponding to Deltaproteobacteria, Oceanospirillales, Pseudomonadales and Planctomycetes (Fig. 1b), mirroring the results of our phylogenomic analysis. A large number of the functions identified in these HBDs (4,224 out of 5,912) were unique to one of the four groups (Fig. 1b and Supplementary Table 6). The relatively weak overlap of known functions between these groups indicates that the ability to fix nitrogen in marine populations may not be associated with a tightly defined functional lifestyle. The HBDs we characterized appeared to be involved in different steps of the nitrogen cycle (for example, denitrification for HBD-06) and possessed distinct strategies regulating nitrogen fixation (see section ‘Functional differences between HBDs’ in the Supplementary Information for additional functional insights), but shared traits related to energy conservation, motility, nutrient acquisition and gene regulatory processes. Swimming motility, which has previously been suggested as a potential mechanism to find anaerobic microniches favourable to nitrogen fixation28,40, was a common trait we observed in all the HBDs and may be an indication of particle-attached lifestyle rather than the symbiotic lifestyles observed in some cyanobacterial diazotrophs.
The taxonomy of HBDs is coherent with the phylogeny of nitrogen fixation genes
Our phylogenetic analysis of the catalytic nifH and nifD genes from a wide range of diazotrophs placed our HBDs in four distinct lineages (Fig. 2). Also included in this analysis were the genomic replicates that were removed from the non-redundant genomic collection. These replicates clustered with their representative MAGs in the phylogenetic tree, revealing near-identical nitrogen fixation genes in geographically distant HBDs. HBD-01 (Desulfovibrio) and HBD-02 (Desulfobacterales) were clustered with close taxonomic relatives. In addition, the gammaproteobacterial HBDs were most closely related to reference genomes of the genera Pseudomonas and Azotobacter from the same class. Finally, the nifD and nifH genes we identified in the Planctomycetes HBDs formed distinct clusters, which was particularly apparent for nifD (Fig. 2). All of the catalytic and biosynthetic genes for nitrogen fixation were located in a single operon in the Planctomycetes HBD-09 genome (HBD-08 was too fragmented to determine their organization). The agreement between the taxonomy of HBDs and their placement in the functional gene-based phylogeny, along with the synteny of genes involved in nitrogen fixation (see Supplementary Information), both favour a scenario where transmission of these genes is mainly vertical in the surface ocean, contributing to the ongoing debate regarding the extent of horizontal transmission for this key functionality9,30,41.
HBDs are not only diverse but are also abundant in the surface ocean
The cumulative relative abundance of the Planctomycetes and Proteobacteria HBDs in the metagenomic data set averaged 0.01% and 0.05%, respectively. In particular, HBD-06, the diazotrophic population that recruited the largest number of reads with an average and maximum relative abundance of 0.025% and 0.33% across all metagenomes, ranked 47th in our database of 957 MAGs (Supplementary Table 3). The relative abundance of Proteobacteria and Planctomycetes HBDs was very low in the Mediterranean Sea and Red Sea (0.00064% on average). In contrast, they were substantially enriched in metagenomes from the Pacific Ocean (0.14% on average) compared to the other regions (Fig. 3). In fact, the Pacific Ocean metagenomes contained 81.4% of the 17.8 million reads that were recruited by the HBD MAGs from the entire metagenomic data set. In particular, the two most abundant Proteobacteria and Planctomycetes HBDs (HBD-06 and HBD-09) showed a broad distribution (Fig. 3) and were significantly enriched in this ocean (Welch’s test, P < 0.005). HBD-06 was also abundant in the northwest region of the Atlantic Ocean and to a lesser extent in the Southern Ocean, revealing that the ecological niche of a single HBD population can encompass multiple oceans and a wide range of temperatures (Supplementary Table 3). Interestingly, HBD-07 and HBD-08, which are phylogenetically and functionally closely related to HBD-06 and HBD-09, respectively, were not only less abundant, but also exhibited a different geographical distribution (Fig. 3). We could not explain the increased signal for the nine HBDs in a few geographic regions using temperature, salinity or the concentration of essential inorganic chemicals including oxygen, phosphate and nitrate (Supplementary Table 1).
To reconcile the abundance of nitrogen-fixing populations in the surface of the open ocean with the inclusion of HBDs described in this study, we used the previous PCR-based estimations of the abundance of non-cyanobacterial nifH gene phylotypes. Quantitative PCR (qPCR) surveys have estimated that non-cyanobacterial nifH gene phylotypes generally range from 10 to 1,000 copies, and rarely reach 0.1 million copies per litre23,24,25,26,27,42. We translated genome-wide quantitative read recruitment of our HBDs into cells per litre (see Supplementary Information for details). Our estimates suggest that the nine populations of HBDs characterized in this study collectively correspond to 0.72 million cells per litre on average (and up to 3.16 million cells) in the surface of the Pacific Ocean, and 0.077 million cells per litre in the other regions. HBD-06 alone might contribute about 0.31 million cells per litre in the Pacific Ocean. These results indicate that HBD populations are orders of magnitude more abundant than previously thought in metagenomes covering large regions of the surface ocean.
PCR assays confirm the occurrence of Planctomycetes nifH genes in the surface ocean
We tracked HBDs at the long-term field study of Station ALOHA (22° 45′ N, 158° 00′ W) in the oligotrophic North Pacific Subtropical Gyre to compare the sensitivity of metagenomics and PCR surveys. The nine HBDs were below the detection limit in a data set of 624.2 million metagenomic reads originating from Station ALOHA43, indicating that HBDs are not as abundant at this location as they are in other regions of the Pacific Ocean (Supplementary Table 7). We developed digital droplet (dd)PCR assays for the two Planctomycetes nifH genes, and could detect HBD-08 at ~750 copies per litre in samples from Station ALOHA44 (Supplementary Table 7). We could also detect HBD-09 at levels near the limit of detection, confirming the occurrence of Planctomycetes nifH genes in the surface ocean.
Reconstructed nifH genes are more abundant than previously characterized nifH genes in surface ocean metagenomes
The non-redundant collection of 957 curated MAGs in which we searched for HBDs encompassed only 5.42% of the genes in our metagenomic assembly outputs. To identify more nifH genes, we also investigated those occurring in the remaining ‘orphan’ scaffolds (39,510,139 genes). Our search based on amino acid similarity with the HBD database resulted in the recovery of nine additional non-redundant nifH genes (Fig. 4 and Supplementary Table 8). Eight of them originated from the Pacific Ocean metagenomic co-assemblies, substantiating the unequal distribution patterns for nitrogen fixation genes we observed at the MAG level (Fig. 3). Phylogenetic analysis on these nifH genes affiliated them with Elusimicrobia (n = 2), Firmicutes (n = 2), Proteobacteria (n = 1), Spirochaeta (n = 1), Verrucomicrobia (n = 1), a group of uncultured bacteria (n = 1), and Euryarchaeota (n = 1) (Supplementary Fig. 1). This primer-independent survey identified a wide range of nifH gene lineages that spanned all four of the previously described phylogenetic clusters45 (Supplementary Table 8). The average nucleotide identity of short metagenomic reads each nifH gene recruited was between 97.4% and 100%, and above 99% for each of the nine HBDs (Supplementary Table 8), suggesting that these nifH gene sequences correspond to highly homogeneous phylotypes. Despite their high abundance in the surface ocean, most of these nifH genes were not in the NCBI non-redundant database, or reference nifH collections46,47, and none of them occurred in a large-scale amplicon survey of the surface ocean39, even when considering the subtle variations these phylotypes maintain in the environment (Supplementary Table 8). Our in silico analysis of widely used primer sequences (see Methods) revealed mismatches to these nifH genes, which is a likely reason for this discrepancy (Supplementary Table 8).
We used previously characterized reference and amplicon nifH gene sequences to recruit reads from metagenomes to estimate their relative abundance (see Methods). The large majority of these sequences were undetected in the TARA Oceans metagenomes (Supplementary Table 9), and the few sequences recruiting reads were less abundant than the nifH genes we reconstructed, confirming the remarkable abundance of the HBDs we characterized (Fig. 4). A notable exception was the ‘Ca. A. thalassa’ sequence, which was also present in our MAG database. Finally, the number of reads orphan nifH genes recruited suggests that HBDs abundant in the surface ocean might not be limited to Planctomycetes and Proteobacteria (Fig. 4).
A binomial naming for the HBDs characterized from multiple geographic regions
Most of the MAGs we characterized in our study correspond to unknown genera, but the lack of cultured representatives prevents a formal taxonomic characterization of these lineages. Here we suggest tentative names for the HBDs we independently characterized from multiple geographic regions (that is, those for which we have genomic replicates) using the candidatus status and binomial naming system: ‘Candidatus Azoaequarella praevalens’ gen. nov., sp. nov. (HBD-06) and ‘Ca. Azopseudomonas oceani’ gen. nov., sp. nov. (HBD-07) within the order Pseudomonadales (unknown family), and ‘Ca. Azoplanctomyces absconditus’ gen. nov., sp. nov. (HBD-09) within the phylum Planctomycetes (unknown order and family).
The nine HBDs we describe in this study represent the first genomic insights into nitrogen-fixing surface ocean populations that are not affiliated with Cyanobacteria, and their high-resolution niche partitioning through genome-wide read recruitment. These HBDs include two Planctomycetes populations, which is the first observation of diazotrophy in this phylum. Seawater samples analysed from Station ALOHA in the Pacific Ocean substantiated the presence of Planctomycetes nifH genes using metagenomic-guided ddPCR. These findings complement decades of PCR amplicon surveys, and corroborate the relevance of metagenomic assembly and binning strategies to improve our understanding of microbial communities inhabiting the largest biome on Earth. For instance, HBDs were mostly enriched in regions of the Pacific Ocean where iron bioavailability is known to be a limiting factor for cyanobacterial diazotrophs7. Iron bioavailability is required for nitrogen fixation but is also particularly important for photosynthesis48. Thus, marine systems co-limited by nitrogen and iron may represent appropriate ecological niches for HBDs, where they could be the main sources of inorganic fixed nitrogen input into the surface ocean.
Our study reveals that populations of HBDs within Proteobacteria and Planctomycetes, as well as putative diazotrophs within other archaeal and bacterial phyla, can be abundant in the surface ocean, occasionally across wide ecological niches spanning a large range of temperatures. Our investigation takes advantage of unprecedented amount of shotgun metagenomic sequencing data to investigate the diversity of nifH genes without primer bias, which led to the identification of a mismatch in nifH4, a widely used degenerate PCR primer targeting the nifH gene39,49,50. Although these findings substantiate the previous observations made through PCR amplicon surveys regarding the diversity of HBDs in the surface of the ocean23,24,25,26,27,39, they also demonstrate that amplicon surveys may have underestimated the abundance of HBDs by multiple orders of magnitude, and provide a potential explanation for the paradox between high nitrogen fixation rates in the Pacific Ocean and the low abundance of diazotrophs previously estimated in this region50. Overall, our investigation emphasizes the need to reassess the role of HBDs in oceanic primary production. As their contribution to the nitrogen cycle has yet to be demonstrated, additional environmental surveys, transcriptomic analyses and cultivation efforts will be essential to establish the lifestyles of HBDs in the open ocean, and to determine the mechanisms and environmental conditions supporting nitrogen fixation in the water column.
The URL https://merenlab.org/data/2017_Delmont_et_al_HBDs/ contains a reproducible workflow that extends the descriptions and parameters of the programs used here for (1) metagenomic binning, (2) identification and curation of MAGs, (3) identification of Candidate Phyla Radiation MAGs and (4) profiling of MAGs and nifH genes in the entire metagenomic data set.
TARA Oceans metagenomes
We acquired 93 metagenomes from the European Bioinformatics Institute (EBI) repository under project ID ERP001736, and quality filtered the reads using the illumina-utils library51 v1.4.1 (available from https://github.com/meren/illumina-utils). Noisy sequences were removed using the program ‘iu-filter-quality-minoche’ with default parameters, which implements a noise filtering as described in ref. 52. Supplementary Table 1 reports accession numbers and additional information (including the number of reads and environmental metadata) for each metagenome.
Metagenomic co-assemblies, gene calling and binning
We organized the data set into 12 ‘metagenomic sets’ based on the geographic coordinates of metagenomes (Supplementary Table 1). We co-assembled reads from each metagenomic set using MEGAHIT53 v1.0.3, with a minimum scaffold length of 1 kbp, and simplified the scaffold header names in the resulting assembly outputs using anvi’o38 v2.3.0 (available from https://merenlab.org/software/anvio). For each metagenomic set, we then binned scaffolds >2.5 kbp (>5 kbp for the Southern Ocean) following the workflow outlined in ref. 38. Briefly, (1) anvi’o was used to profile the scaffolds using Prodigal54 v2.6.3 with default parameters to identify genes (Supplementary Table 2), and HMMER55 v3.1b2 to identify genes matching to archaeal56 and bacterial57,58,59,60 single-copy core gene collections; (2) Centrifuge61 was used with NCBI's NT database to infer the taxonomy of genes (as described in https://merenlab.org/2016/06/18/importing-taxonomy); (3) short reads were mapped from the metagenomic set to the scaffolds using Bowtie262 v2.0.5 and the recruited reads stored as BAM files using samtools63; (4) anvi’o was used to profile each BAM file to estimate the coverage and detection statistics of each scaffold, and to combine mapping profiles into a merged profile database for each metagenomic set. We then clustered scaffolds with the automatic binning algorithm CONCOCT57 by constraining the number of clusters per metagenomic set to 100 to minimize the ‘fragmentation error’ (when multiple clusters describe one population), with the exception of the Southern Ocean (25 clusters) and the Pacific Ocean southeast (150 clusters) metagenomic sets. Finally, we manually binned each CONCOCT cluster (n = 1,175) using the anvi’o interactive interface. Supplementary Table 10 reports the genomic features (including completion and redundancy values) of the characterized bins.
Identification and curation of MAGs
We defined all bins with >70% completeness or >2 Mbp in length as MAGs (Supplementary Table 2). We then individually refined each MAG as outlined in ref. 64, and renamed scaffolds they contained accordingly to their MAG ID to ensure that the names of all scaffolds in MAGs we characterized from the 12 metagenomic sets were unique.
Taxonomic and functional inference of MAGs
We used CheckM65 to infer the taxonomy of MAGs based on the proximity of 43 single-copy gene markers within a reference genomic tree. We also used Centrifuge, RAST66 and manual BLAST searches of single-copy core genes against the NCBI's non-redundant database to manually refine the CheckM taxonomic inferences, especially regarding the archaeal and eukaryotic MAGs. We also used the occurrence of bacterial single-copy core genes to identify MAGs affiliated to the Candidate Phyla Radiation (as described in https://merenlab.org/2016/04/17/predicting-CPR-Genomes/). Supplementary Table 4 reports our curated taxonomic inference of MAGs. We used KEGG (the 14 April 2014 release) to identify functions and pathways in MAGs. We also used RAST to identify functions in 15 MAGs that contained the complete set of nitrogen fixation genes (originally identified from the KEGG pathways). Supplementary Tables 6 and 11 report the RAST and KEEG results, respectively. We used Gephi67 v0.8.2 to generate a functional network using the Force Atlas 2 algorithm to connect MAGs and RAST functions. Node sizes were correlated to the number of edges they contained, which resulted in larger nodes for MAGs compared to functions.
Characterization of a non-redundant database of MAGs
We concatenated all scaffolds from the genomic database of MAGs into a single FASTA file and used Bowtie2 and samtools to recruit and store reads from the 93 metagenomes. We used anvi’o to determine the coverage values, detection and relative distribution of MAGs and individual genes across metagenomes (Supplementary Table 12). The Pearson correlation coefficient of each pair of MAGs was calculated based on their relative distribution across the 93 metagenomes using the function ‘cor’ in R68 (Supplementary Table 5). Finally, NUCmer69 was used to determine the average nucleotide identity (ANI) of each pair of MAGs affiliated to the same phylum for improved performance (the Proteobacteria MAGs were further split at the class level) (Supplementary Table 5). MAGs were considered redundant when their ANI reached 99% (minimum alignment of >75% of the smaller genome in each comparison) and the Pearson correlation coefficient was above 0.9. We then selected a single MAG to represent a group of redundant MAGs based on the largest ‘completion minus redundancy’ value from single-copy core genes for Archaea and Bacteria, or longer genomic length for Eukarya. This analysis provided a non-redundant genomic database of MAGs. We performed a final mapping of all metagenomes to calculate the mean coverage and detection of these MAGs (Supplementary Table 3 reproducible workflow).
STAMP70 and Welch’s test were used to identify non-redundant MAGs that were significantly enriched in the Pacific Ocean compared to all the other regions combined. Supplementary Table 3 reports the P values for each MAG.
We used the ggplot271 package for R to visualize the metagenomic sets and relative distribution of MAGs in the world map.
Phylogenomic analysis of MAGs
We used PhyloSift72 v1.0.1 with default parameters to infer associations between MAGs in a phylogenomic context. Briefly, PhyloSift (1) identifies a set of 37 marker gene families in each genome, (2) concatenates the alignment of each marker gene family across genomes, and (3) computes a phylogenomic tree from the concatenated alignment using FastTree73 v2.1. We rooted the phylogenomic tree to the phylum Planctomycetes with FigTree74 v1.4.3, and used anvi’o to visualize it with additional data layers.
Binomial naming of HBDs
The following is a brief explanation of the binomial naming of three populations of HBDs we characterized from multiple geographic regions:
Azoaequarella praevalens (N.L. n. azotum [from Fr. n. azote (from Gr. prep. a, not; Gr. n. zôê, life; N.Gr. n. azôê, not sustaining life)], nitrogen; N.L. pref. azo-, pertaining to nitrogen; L. v. aequare, to equalize; N.L. fem. n. Azoaequarella, the nitrogen equalizer; L. part. adj. praevalens, very powerful, very strong, here prevalent).
Azopseudomonas oceani (N.L. n. azotum [from Fr. n. azote (from Gr. prep. a, not; Gr. n. zôê, life; N.Gr. n. azôê, not sustaining life)], nitrogen; N.L. pref. azo-, pertaining to nitrogen; Gr. adj. pseudês, false; Gr. fem. n. monas, a unit, monad; N.L. fem. n. Azopseudomonas, nitrogen-fixing false monad; L. gen. n. oceani, of the ocean).
Azoplanctomyces absconditus (N.L. n. azotum [from Fr. n. azote (from Gr. prep. a, not; Gr. n. zôê, life; N.Gr. n. azôê, not sustaining life)], nitrogen; N.L. pref. azo-, pertaining to nitrogen; Gr. adj. planktos, wandering, floating; Gr. masc. n. mukês, fungus; N.L. masc. n. Azoplanctomyces, nitrogen-fixing floating fungus; L. part. adj. absconditus, hidden).
Identification of additional nifH sequences in orphan scaffolds
DIAMOND75 was used to generate a database of nifH genes we identified in the nine HBDs, and to search for additional nifH amino acid sequences within the genes Prodigal identified in scaffolds longer than 1,000 nucleotides. We considered only hits with an e-value of <1e-50, and defined them as nifH genes only when (1) ‘nitrogenase’ was the top blastx hit against the NCBI’s nr database, and (2) the characteristic [4Fe-4S]-binding site (Prosite signature PDOC00580) was present in their amino acid sequence.
Variation of metagenomic reads the nifH genes recruit
We concatenated all nifH genes (orphan genes, as well as those in HBDs) into a single FASTA file. To study their variation in the environment, we used this FASTA file to recruit reads from all metagenomes, and profiled the resulting mapping results with anvi’o as described in the section ‘Metagenomic co-assemblies, gene calling and binning’. We created an anvi’o collection linking each gene to a unique bin ID, and then used the program ‘anvi-get-short-reads-from-bam’ to extract from the BAM files metagenomic reads each nifH gene recruited. Finally, we used blastn to estimate the average nucleotide identity of metagenomic reads to the nifH genes. Supplementary Table 8 reports the search results.
Affiliating nifH genes with predetermined phylogenetic clusters
We affiliated the TARA Oceans nifH genes with predetermined phylogenetic clusters and subclusters using a classification and regression tree method76.
Searching nifH genes in existing sequence databases and amplicons
We searched TARA Oceans nifH genes in three databases, and a large amplicon survey. These databases included (1) ‘NCBI nr’, NCBI non-redundant database, (2) the ‘FunGene database’, nifH genes curated from NCBI GenBank database and stored in the FunGene database46 (available from https://fungene.cme.msu.edu/), and (3) the ‘Zehr Database’, a nifH gene repository curated from the NCBI GenBank database and maintained by the Zehr Laboratory47 (June 2017 release, available from https://www.jzehrlab.com). We also used the amplicon sequences from a large-scale survey of the nifH genes in the surface ocean using nested degenerate primers39. To search for our sequences in these resources, we used blastn77 with default parameters and only considered matches with a minimum alignment length of 100 nt.
Identifying mismatches between nifH genes and degenerate primers
We created a program (see Code availability) to determine all sequence combinations of 12 commonly used degenerate primers and compare them to the TARA Oceans nifH genes to assess their compatibility.
Mean coverage of nifH gene sequences from reference collections and amplicons
We included the 18 non-redundant nifH genes we recovered in our study in each of the three non-redundant reference collections: the FunGene database (genes that were not affiliated with nifH based on their functional annotation were removed), the ‘Zehr Database’, and the nifH amplicon sequences from ref. 39. We then used CD-HIT78 with a 99% sequence similarity cutoff to independently remove redundancy in these three collections. To estimate the mean coverage of all nucleotide sequences from these three non-redundant collections, we recruited reads from all metagenomes and profiled the resulting mapping results with anvi’o as described in the section ‘Metagenomic co-assemblies, gene calling and binning’. For the analysis of the amplicon sequences, we used blastn to search amplicon sequences that recruited any read from the metagenomes in the FunGene database (with a minimum alignment of 100 nt) to identify those that correspond to nifH. We then used blast to combine all nifH amplicons that match to the nifH gene of ‘Ca. A. thalassa’, and combined all matches into a single unit corresponding to this population. We used the R package ggplot271 to display the interquartile range of the mean coverage of nifH genes across metagenomes as violin plots, and finalized this figure and others using the open-source vector graphics editor Inkscape (https://inkscape.org/).
Phylogenetic analysis of nifD and nifH genes
We built a database using the amino acid sequences of nifD and nifH genes identified in this study, as well as the protein reference sequences for nifD and nifH genes we identified in the NCBI’s non-redundant database, and imported it into ARB v.5.5-org-916779. In ARB, we aligned sequences to each other using ClustalW80, manually refined alignments, and calculated phylogenetic trees with PhyML81 using the ‘WAG’ amino acid substitution model, and a 10% conservation filter.
Quantification using ddPCR analysis of nifH genes
We designed primers specifically targeting the two Planctomycetes population nifH genes using primer382 (Supplementary Table 7), and analysed samples from the ALOHA station in the Pacific Ocean44 with ddPCR on a Bio-Rad QX200 Droplet Digital PCR system in a reaction volume of 20 μl following the protocols of the manufacturer (Bio-Rad Laboratories). The samples were also tested using the previously described primers for the ϒ-24774A11 target27. Artificial constructs of each expected amplicon served as positive controls. We verified ddPCR results for the HBD-09 target using endpoint PCR employing forward and reverse primers and gel visualization (if sample material was available).
The URL https://merenlab.org/data/2017_Delmont_et_al_HBDs serves a reproducible bioinformatics workflow, and https://goo.gl/fZPvWw serves the ad hoc program to identify mismatches between nifH genes assembled from metagenomes and commonly used degenerate primers.
All data our study used or generated are publicly available. Accession ID ERP001736 serves TARA Oceans metagenomes through the European Bioinformatics Institute. We stored scaffolds of >2.5 kbp generated from the 12 metagenomic co-assemblies in NCBI Bioproject PRJNA326480. We have also made publicly available the raw assembly results that include scaffolds > 1 kbp (https://doi.org/10.6084/m9.figshare.4902920), amino acid sequences for 42.2 million genes identified in raw assembly results (https://doi.org/10.6084/m9.figshare.4902917), the FASTA files for our final collection of 957 non-redundant MAGs (https://doi.org/10.6084/m9.figshare.4902923), the anvi’o summary of non-redundant MAGs and their distribution across metagenomes (https://doi.org/10.6084/m9.figshare.4902926), the self-contained anvi’o split profiles for each non-redundant MAG (https://doi.org/10.6084/m9.figshare.4902941), short reads our nifH genes recruited from TARA Oceans metagenomes along with their identity statistics to the consensus gene sequence (https://doi.org/10.6084/m9.figshare.5259424), and the redundant and non-redundant versions of the FunGene database, Zehr database and nifH amplicon sequences we used in our study (https://doi.org/10.6084/m9.figshare.5259421).
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The authors acknowledge the TARA Oceans consortium for generating metagenomic data sets of great legacy. The authors thank M.D. Lee, B.J. Tully and E.D. Graham for helpful discussions, M. Bootsma for assistance with implementing ddPCR experiments, and our computer systems administrator R. Fox for his patience and help. This study was supported by the Frank R. Lillie Research Innovation Award and start-up funds from the University of Chicago to A.M.E. S.L. was funded by the Netherlands Organization for Scientific Research grant (VENI 863.14.019), and C.Q. was funded by an EPSRC Career Acceleration Fellowship (EP/H003851/1) and an MRC fellowship (MR/M50161X/1) as part of the CLoud Infrastructure for Microbial Genomics (CLIMB) consortium (MR/L015080/1).