Philippa Kaur

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Study populations and sequencing strategyDNA libraries were prepared for 1261 D. sylvestris individuals from 115 populations (5–20 individuals per population) under a modified protocol49 of the Illumina Nextera DNA library preparation kit (Supplementary Methods S1.1, Supplementary Data 1). Individuals were indexed with unique dual-indexes (IDT Illumina Nextera 10nt UDI – 384 set) from Integrated DNA Technologies Co, to avoid index-hopping50. Libraries were sequenced (150 bp paired-end sequencing) in four lanes of an Illumina NovaSeq 6000 machine at Novogene Co. This resulted in an average coverage of ca. 2x per individual. Sequenced individuals were trimmed for adapter sequences (Trimmomatic version 0.3551), mapped (BWA-MEM version 0.7.1752,53) against a reference assembly54 (ca. 440 Mb), had duplicates marked and removed (Picard Toolkit version 2.0.1; http://broadinstitute.github.io/picard), locally realigned around indels (GATK version 3.555), recalibrated for base quality scores (ATLAS version 0.956) and had overlapping read pairs clipped (bamUtil version 1.0.1457) (Supplementary Methods S1.1). Population genetic analyses were performed on the resultant BAM files via genotype likelihoods (ANGSD version 0.93358 and ATLAS versions 0.9–1.056), to accommodate the propagation of uncertainty from the raw sequence data to population genetic inference.Population genetic structure and biogeographic barriersTo investigate the genetic structure of our samples (Fig. 2A, Supplementary Fig. S2), we performed principal component analyses (PCA) on all 1261 samples (“full” dataset) via PCAngsd version 0.9859, following conversion of the mapped sequence data to ANGSD genotype likelihoods in Beagle format (Supplementary Methods S1.2). To visualise PCA results in space (Supplementary Fig. S4), individuals’ principal components were projected on a map, spatially interpolated (linear interpolation, akima R package version 0.6.260) and had the first two principal components represented as green and blue colour channels. Given that uneven sampling can bias the inference of structure in PCA, PCA was also performed on a balanced dataset comprising a common, down-sampled size of 125 individuals per geographic region (“balanced” dataset; Fig. 2B, Supplementary Fig. S3; Supplementary Methods S1.2; Supplementary Data 1). Individual admixture proportions and ancestral allele frequencies were estimated using PCAngsd (-admix model) for K = 2–6, using the balanced dataset to avoid potential biases related to imbalanced sampling22,23 and an automatic search for the optimal sparseness regularisation parameter (alpha) soft-capped to 10,000 (Supplementary Methods S1.2). To visualise ancestry proportions in space, population ancestry proportions were spatially interpolated (kriging) via code modified from Ref. 61 (Supplementary Fig. S5).To test if between-lineage admixture underlies admixture patterns inferred by PCAngsd or if the data is better explained by alternative scenarios such as recent bottlenecks, we used chromosome painting and patterns of allele sharing to construct painting palettes via the programmes MixPainter and badMIXTURE (unlinked model)28 and compared this to the PCAngsd-inferred palettes (Fig. 2B, C; Supplementary Methods S1.2). We referred to patterns of residuals between these palettes to inform of the most likely underlying demographic scenario. For assessing Alpine–Balkan palette residuals (and hence admixture), 65 individuals each from the French Alps (inferred as pure Alpine ancestry in PCAngsd), Monte Baldo (inferred with both Alpine and Balkan ancestries in PCAngsd) and Julian Alps (inferred as pure Balkan ancestry in PCAngsd) were analysed under K = 2 in PCAngsd and badMIXTURE (Fig. 2C). For assessing Apennine–Balkan admixture, 22 individuals each from the French pre-Alps (inferred as pure Apennine ancestry in PCAngsd), Tuscany (inferred with both Apennine and Balkan ancestries in PCAngsd) and Julian Alps (inferred as pure Balkan ancestry in PCAngsd) were analysed under K = 2 in PCAngsd and badMIXTURE.To construct a genetic distance tree (Supplementary Fig. S1), we first calculated pairwise genetic distances between 549 individuals (5 individuals per population for all populations) using ATLAS, employing a distance measure (weight) reflective of the number of alleles differing between the genotypes (Supplementary Methods S1.2; Supplementary Data 1). A tree was constructed from the resultant distance matrix via an initial topology defined by the BioNJ algorithm with subsequent topological moves performed via Subtree Pruning and Regrafting (SPR) in FastME version 2.1.6.162. This matrix of pairwise genetic distances was also used as input for analyses of effective migration and effective diversity surfaces in EEMS25. EEMS was run setting the number of modelled demes to 1000 (Fig. 2A, Supplementary Fig. S8). For each case, ten independent Markov chain Monte Carlo (MCMC) chains comprising 5 million iterations each were run, with a 1 million iteration burn-in, retaining every 10,000th iteration. Biogeographic barriers (Fig. 2A, Supplementary Fig. S7) were further identified via applying Monmonier’s algorithm24 on a valuated graph constructed via Delauney triangulation of population geographic coordinates, with edge values reflecting population pairwise FST; via the adegenet R package version 2.1.163. FST between all population pairs were calculated via ANGSD, employing a common sample size of 5 individuals per population (Supplementary Fig. S6; Supplementary Methods S1.2; Supplementary Data 1). 100 bootstrap runs were performed to generate a heatmap of genetic boundaries in space, from which a weighted mean line was drawn (Supplementary Fig. S7). All analyses in ANGSD were performed with the GATK (-GL 2) model, as we noticed irregularities in the site frequency spectra (SFS) with the SAMtools (-GL 1) model similar to that reported in Ref. 58 with particular BAM files. All analyses described above were performed on the full genome.Ancestral sequence reconstructionTo acquire ancestral states and polarise site-frequency spectra for use in the directionality index ψ and demographic inference, we reconstructed ancestral genome sequences at each node of the phylogenetic tree of 9 Dianthus species: D. carthusianorum, D. deltoides, D. glacialis, D. sylvestris (Apennine lineage), D. lusitanus, D. pungens, D. superbus alpestris, D. superbus superbus, and D. sylvestris (Alpine lineage). This tree topology was extracted from a detailed reconstruction of Dianthus phylogeny based on 30 taxa by Fior et al. (Fior, Luqman, Scharmann, Zemp, Zoller, Pålsson, Gargano, Wegmann & Widmer; paper in preparation) (Supplementary Methods S1.3). For ancestral sequence reconstruction, one individual per species was sequenced at medium coverage (ca. 10x), trimmed (Trimmomatic), mapped against the D. sylvestris reference assembly (BWA-MEM) and had overlapping read pairs clipped (bamUtil) (Supplementary Methods S1.3). For each species, we then generated a species-specific FASTA using GATK FastaAlternateReferenceMaker. This was achieved by replacing the reference bases at polymorphic sites with species-specific variants as identified by freebayes64 (version 1.3.1; default parameters), while masking (i.e., setting as “N”) sites (i) with zero depth and (ii) that didn’t pass the applied variant filtering criteria (i.e., that are not confidently called as polymorphic; Supplementary Methods S1.3). Species FASTA files were then combined into a multi-sample FASTA. Using this, we probabilistically reconstructed ancestral sequences at each node of the tree via PHAST (version 1.4) prequel65, using a tree model produced by PHAST phylofit under a REV substitution model and the specified tree topology (Supplementary Methods S1.3). Ancestral sequence FASTA files were then generated from the prequel results using a custom script.Expansion signalTo calculate the population pairwise directionality index ψ for the Alpine lineage, we utilised equation 1b from Peter and Slatkin (2013)31, which defines ψ in terms of the two-population site frequency spectrum (2D-SFS) (Supplementary Methods S1.4). 2D-SFS between all population pairs (10 individuals per population; Supplementary Data 1) were estimated via ANGSD and realSFS66 (Supplementary Methods S1.4), for unfolded spectra. Unfolding of spectra was achieved via polarisation with respect to the ancestral state of sites defined at the D. sylvestris (Apennine lineage) – D. sylvestris (Alpine lineage) ancestral node. Correlation of pairwise ψ and (great-circle) distance matrices was tested via a Mantel test (10,000 permutations). To infer the geographic origin of the expansion (Fig. 3), we employed a time difference of arrival (TDOA) algorithm following Peter and Slatkin (2013);31 performed via the rangeExpansion R package version 0.0.0.900031,67. We further estimated the strength of the founder of this expansion using the same package.Demographic inferenceTo evaluate the demographic history of D. sylvestris, a set of candidate demographic models was formulated. To constrain the topology of tested models, we first inferred the phylogenetic tree of the three identified evolutionary lineages of D. sylvestris (Alpine, Apennine and Balkan) as embedded within the larger phylogeny of the Eurasian Dianthus clade (note that the phylogeny from Fior et al. (Fior, Luqman, Scharmann, Zemp, Zoller, Pålsson, Gargano, Wegmann & Widmer; paper in preparation) excludes Balkan representatives of D. sylvestris). Trees were inferred based on low-coverage whole-genome sequence data of 1–2 representatives from each D. sylvestris lineage, together with whole-genome sequence data of 7 other Dianthus species, namely D. carthusianorum, D. deltoides, D. glacialis, D. lusitanus, D. pungens, D. superbus alpestris and D. superbus superbus, that were used to root the D. sylvestris clade (Supplementary Methods S1.5). We estimated distance-based phylogenies using ngsDist68 that accommodates genotype likelihoods in the estimation of genetic distances (Supplementary Methods S1.5). Genetic distances were calculated via two approaches: (i) genome-wide and (ii) along 10 kb windows. For the former, 110 bootstrap replicates were calculated by re-sampling over similar-sized genomic blocks. For the alternative strategy based on 10 kb windows, window trees were combined using ASTRAL-III version 5.6.369 to generate a genome-wide consensus tree accounting for potential gene tree discordance (Supplementary Methods S1.5). Trees were constructed from matrices of genetic distances from initial topologies defined by the BioNJ algorithm with subsequent topological moves performed via Subtree Pruning and Regrafting (SPR) in FastME version 2.1.6.162. We rooted all resultant phylogenetic trees with D. deltoides as the outgroup70. Both approaches recovered a topology with the Balkan lineage diverging prior to the Apennine and Alpine lineages (Supplementary Fig. S9). This taxon topology for D. sylvestris was supported by high ASTRAL-III posterior probabilities ( >99%), ASTRAL-III quartet scores ( >0.5) and bootstrap values ( >99%). Topologies deeper in the tree were less well-resolved (with quartet scores More

Different picocyanobacterial communities exhibit distinct gene repertoiresTo analyze the distribution of Prochlorococcus and Synechococcus reads along the Tara Oceans transect, metagenomic reads corresponding to the bacterial size fraction were recruited against 256 picocyanobacterial reference genomes, including SAGs and MAGs representative of uncultured lineages (e.g., Prochlorococcus HLIII-IV, Synechococcus EnvA or EnvB). This yielded a total of 1.07 billion recruited reads, of which 87.7% mapped onto Prochlorococcus genomes and 12.3% onto Synechococcus genomes, which were then functionally assigned by mapping them onto the manually curated Cyanorak v2.1 CLOG database [19]. In order to identify picocyanobacterial genes potentially involved in niche adaptation, we analyzed the distribution across the oceans of flexible (i.e. non-core) genes. Clustering of Tara Oceans stations according to the relative abundance of flexible genes resulted in three well-defined clusters for Prochlorococcus (Fig. 1A), which matched those obtained when stations were clustered according to the relative abundance of Prochlorococcus ESTUs, as assessed using the high-resolution marker gene petB, encoding cytochrome b6 (Fig. 1A; [24]). Only a few discrepancies can be observed between the two trees, including stations TARA-070 that displayed one of the most disparate ESTU compositions and TARA-094, dominated by the rare HLID ESTU (Fig. 1A). Similarly, for Synechococcus, most of the eight assemblages of stations discriminated based on the relative abundance of ESTUs (Fig. 1B) were also retrieved in the clustering based on flexible gene abundance, except for a few intra-assemblage switches between stations, notably those dominated by ESTU IIA (Fig. 1B). Despite these few variations, four major clusters can be clearly delineated in both Synechococcus trees, corresponding to four broadly defined ecological niches, namely (i) cold, nutrient-rich, pelagic or coastal environments (blue and light red in Fig. 1B), (ii) Fe-limited environments (purple and grey), (iii) temperate, P-depleted, Fe-replete areas (yellow) and (iv) warm, N-depleted, Fe-replete regions (dark red). This correspondence between taxonomic and functional information was also confirmed by the high congruence between distance matrices based on ESTU relative abundance and on CLOG relative abundance (p-value  0.01) are marked by a cross. Φsat: index of iron limitation derived from satellite data. PAR30: satellite-derived photosynthetically available radiation at the surface, averaged on 30 days. DCM: depth of the deep chlorophyll maximum.Full size imageIdentification of individual genes potentially involved in niche partitioningTo identify genes relevant for adaptation to a specific set of environmental conditions and enriched in specific ESTU assemblages, we selected the most representative genes from each module (Dataset 5; Figs. 3, S2). Most genes retrieved this way encode proteins of unknown or hypothetical function (85.7% of 7,485 genes). However, among the genes with a functional annotation (Dataset 6), a large fraction seems to have a function related to their realized environmental niche (Figs. 3, S2). For instance, many genes involved in the transport and assimilation of nitrite and nitrate (nirA, nirX, moaA-C, moaE, mobA, moeA, narB, M, nrtP; [6]) as well as cyanate, an organic form of nitrogen (cynA, B, D, S), are enriched in the Prochlorococcus blue module, which is correlated with the HLIIA-D ESTU and to low inorganic N, P, and silica levels and anti-correlated with Fe availability (Fig. 2A–C). This is consistent with previous studies showing that while only a few Prochlorococcus strains in culture possess the nirA gene and even less the narB gene, natural Prochlorococcus populations inhabiting N-poor areas do possess one or both of these genes [40,41,42]. Similarly, numerous genes amongst the most representative of Prochlorococcus brown, red and turquoise modules are related to adaptation of HLIIIA/IVA, HLIA and LLIA ESTUs to Fe-limited, cold P-limited, and cold, mixed waters, respectively (Fig. 3). Comparable results were obtained for Synechococcus, although the niche delineation was less clear than for Prochlorococcus since genes within each module exhibited lower correlations with the module eigenvalue (Fig. S2). These results therefore constitute a proof of concept that this network analysis was able to retrieve niche-related genes from metagenomics data.Fig. 3: Violin plots highlighting the most representative genes of each Prochlorococcus module.For each module, each gene is represented as a dot positioned according to its correlation with the eigengene for each module, the most representative genes being localized on top of each violin plot. Genes mentioned in the text and/or in Dataset 6 have been colored according to the color of the corresponding module, indicated by a colored bar above each module. The text above violin plots indicates the most significant environmental parameter(s) and/or ESTU(s) for each module, as derived from Fig. 2.Full size imageIdentification of eCAGs potentially involved in niche partitioningIn order to better understand the function of niche-related genes, notably of the numerous unknowns, we then integrated global distribution data with gene synteny in reference genomes using a network approach (Datasets 7, 8). This led us to identify clusters of adjacent genes in reference genomes, and thus potentially involved in the same metabolic pathway (Figs. 4, S3, S4; Dataset 6). These clusters were defined within each module and thus encompass genes with similar distribution and abundance in situ. Hereafter, these environmental clusters of adjacent genes will be called “eCAGs”.Fig. 4: Delineation of Prochlorococcus eCAGs, defined as a set of genes that are both adjacent in reference genomes and share a similar in situ distribution.Nodes correspond to individual genes with their gene name (or significant numbers of the CK number, e.g. 1234 for CK_00001234) and are colored according to their WGCNA module. A link between two nodes indicates that these two genes are less than five genes apart in at least one genome. The bottom insert shows the most significant environmental parameter(s) and/or ESTU(s) for each module, as derived from Fig. 2.Full size imageeCAGs related to nitrogen metabolismThe well-known nitrate/nitrite gene cluster involved in uptake and assimilation of inorganic forms of N (see above), which is present in most Synechococcus genomes (Dataset 6), was expectedly not restricted to a particular niche in natural Synechococcus populations, as shown by its quasi-absence from WGCNA modules. In Prochlorococcus, this cluster is separated into two eCAGs enriched in low-N areas (Fig. S5A, B), most genes being included in Pro-eCAG_002, present in only 13 out of 118 Prochlorococcus genomes, while nirA and nirX form an independent eCAG (Pro-eCAG_001) due to their presence in many more genomes. The quasi-core ureA-G/urtB-E genomic region was also found to form a Prochlorococcus eCAG (Pro-eCAG_003) that was impoverished in low-Fe compared to other regions (Fig. S5C, D), in agreement with its presence in only two out of six HLIII/IV genomes. We also uncovered several other Prochlorococcus and Synechococcus eCAGs that seem to be involved in the transport and/or assimilation of more unusual and/or complex forms of nitrogen, which might either be degraded into elementary N molecules or possibly directly used by cells for e.g. the biosynthesis of proteins or DNA. Indeed, we detected in both genera an eCAG (Pro-eCAG_004 and Syn-eCAG_001; Fig. S6A, B; Dataset 6) that encompasses speB2, an ortholog of Synechocystis PCC 6803 sll1077, previously annotated as encoding an agmatinase [29, 43] and which was recently characterized as a guanidinase that degrades guanidine rather than agmatine to urea and ammonium [44]. E. coli produces guanidine under nutrient-poor conditions, suggesting that guanidine metabolism is biologically significant and potentially prevalent in natural environments [44, 45]. Furthermore, the ykkC riboswitch candidate, which was shown to specifically sense guanidine and to control the expression of a variety of genes involved in either guanidine metabolism or nitrate, sulfate, or bicarbonate transport, is located immediately upstream of this eCAG in Synechococcus reference genomes, all genes of this cluster being predicted by RegPrecise 3.0 to be regulated by this riboswitch (Fig. S6C; [45, 46]). The presence of hypA and B homologs within this eCAG furthermore suggests that, in the presence of guanidine, these homologs could be involved in the insertion of Ni2+, or another metal cofactor, in the active site of guanidinase. The next three genes of this eCAG, which encode an ABC transporter similar to the TauABC taurine transporter in E. coli (Fig. S6C), could be involved in guanidine transport in low-N areas. Of note, the presence in most Synechococcus/Cyanobium genomes possessing this eCAG of a gene encoding a putative Rieske Fe-sulfur protein (CK_00002251) downstream of this gene cluster, seems to constitute a specificity compared to the homologous gene cluster in Synechocystis sp. PCC 6803. The presence of this Fe-S protein suggests that Fe is used as a cofactor in this system and might explain why this gene cluster is absent from picocyanobacteria thriving in low-Fe areas, while it is present in a large proportion of the population in most other oceanic areas (Fig. S6A, B).Another example of the use of organic N forms concerns compounds containing a cyano radical (C ≡ N). The cyanate transporter genes (cynABD) were indeed found in a Prochlorococcus eCAG (Pro-eCAG_005, also including the conserved hypothetical gene CK_00055128; Fig. S7A, B). While only a small proportion of the Prochlorococcus community possesses this eCAG in warm, Fe-replete waters, it is absent from other oceanic areas in accordance with its low frequency in Prochlorococcus genomes (present in only two HLI and five HLII genomes). In Synechococcus these genes were not included in a module, and thus are not in an eCAG (Dataset 6; Fig. S7C), but seem widely distributed despite their presence in only a few Synechococcus genomes (mostly in clade III strains; [6, 47, 48]). Interestingly, we also uncovered a 7-gene eCAG (Pro-eCAG_006 and Syn-eCAG_002), encompassing a putative nitrilase gene (nitC), which also suggests that most Synechococcus cells and a more variable fraction of the Prochlorococcus population could use nitriles or cyanides in warm, Fe-replete waters and more particularly in low-N areas such as the Indian Ocean (Fig. 5A, B). The whole operon (nitHBCDEFG; Fig. 5C), called Nit1C, was shown to be upregulated in the presence of cyanide and to trigger an increase in the rate of ammonia accumulation in the heterotrophic bacterium Pseudomonas fluorescens [49], suggesting that like cyanate, cyanide could constitute an alternative nitrogen source in marine picocyanobacteria as well. However, given the potential toxicity of these C ≡ N-containing compounds [50], we cannot exclude that these eCAGs could also be devoted to cell detoxification [45, 47]. Such an example of detoxification has been described for arsenate and chromate that, as analogs of phosphate and sulfate respectively, are toxic to marine phytoplankton and must be actively exported out of the cells [51, 52].Fig. 5: Global distribution map of the eCAG involved in nitrile or cyanide transport and assimilation.A Prochlorococcus Pro-eCAG_006. B Synechococcus Syn-eCAG_002. C The genomic region in Prochlorococcus marinus MIT9301. The size of the circle is proportional to relative abundance of each genus as estimated based on the single-copy core gene petB and this gene was also used to estimate the relative abundance of other genes in the population. Black dots represent Tara Oceans stations for which Prochlorococcus or Synechococcus read abundance was too low to reach the threshold limit.Full size imageWe detected the presence of an eCAG encompassing asnB, pyrB2, and pydC (Pro-eCAG_007, Syn-eCAG_003, Fig. S8), which could contribute to an alternative pyrimidine biosynthesis pathway and thus provide another way for cells to recycle complex nitrogen forms. While this eCAG is found in only one fifth of HLII genomes and in quite specific locations for Prochlorococcus, notably in the Red Sea, it is found in most Synechococcus cells in warm, Fe-replete, N and P-depleted niches, consistent with its phyletic pattern showing its absence only from most clade I, IV, CRD1, and EnvB genomes (Fig. S8; Dataset 6). More generally, most N-uptake and assimilation genes in both genera were specifically absent from Fe-depleted areas, including the nirA/narB eCAG for Prochlorococcus, as mentioned by Kent et al. [36] as well as guanidinase and nitrilase eCAGs. In contrast, picocyanobacterial populations present in low-Fe areas possess, in addition to the core ammonium transporter amt1, a second transporter amt2, also present in cold areas for Synechococcus (Fig. S9). Additionally, Prochlorococcus populations thriving in HNLC areas also possess two amino acid-related eCAGs that are present in most Synechococcus genomes, the first one involved in polar amino acid N-II transport (Pro-eCAG_008; natF-G-H-bgtA; [53]; Fig. S10A, B) and the second one (leuDH-soxA-CK_00001744, Pro-eCAG_009, Fig. S10C, D) that notably encompasses a leucine dehydrogenase, able to produce ammonium from branched-chain amino acids. This highlights the profound difference in N acquisition mechanisms between HNLC regions and Fe-replete, N-deprived areas: the primary nitrogen sources for picocyanobacterial populations dwelling in HNLC areas seem to be ammonium and amino acids, while N acquisition mechanisms are more diverse in N-limited, Fe-replete regions.eCAGs related to phosphorus metabolismAdaptation to P depletion has been well documented in marine picocyanobacteria showing that while in P-replete waters Prochlorococcus and Synechococcus essentially rely on inorganic phosphate acquired by core transporters (PstSABC), strains isolated from low-P regions and natural populations thriving in these areas additionally contain a number of accessory genes related to P metabolism, located in specific genomic islands [6, 14, 30,31,32, 54]. Here, we indeed found in Prochlorococcus an eCAG containing the phoBR operon (Pro-eCAG_010) that encodes a two-component system response regulator, as well as an eCAG including the alkaline phosphatase phoA (Pro-eCAG_011), both present in virtually the whole Prochlorococcus population from the Mediterranean Sea, the Gulf of Mexico and the Western North Atlantic Ocean, which are known to be P-limited [30, 55] (Fig. S11A, B). By comparison, in Synechococcus, we only identified the phoBR eCAG (Syn-eCAG_005, Fig. S11C) that is systematically present in warm waters whatever the limiting nutrient, in agreement with its phyletic pattern in reference genomes showing its specific absence from cold thermotypes (clades I and IV, Dataset 6). Furthermore, although our analysis did not retrieve them within eCAGs due to the variability of gene content and synteny in this genomic region, even within each genus, several other P-related genes were enriched in low-P areas but partially differed between Prochlorococcus and Synechococcus (Figs. 3, S2, S11; Dataset 6). While the genes putatively encoding a chromate transporter (ChrA) and an arsenate efflux pump ArsB were present in both genera in different proportions, a putative transcriptional phosphate regulator related to PtrA (CK_00056804; [56]) was specific to Prochlorococcus. Synechococcus in contrast harbors a large variety of alkaline phosphatases (PhoX, CK_00005263 and CK_00040198) as well as the phosphate transporter SphX (Fig. S11).Phosphonates, i.e. reduced organophosphorus compounds containing C–P bonds that represent up to 25% of the high-molecular-weight dissolved organic P pool in the open ocean, constitute an alternative P form for marine picocyanobacteria [57]. We indeed identified, in addition to the core phosphonate ABC transporter (phnD1-C1-E1), a second previously unreported putative phosphonate transporter phnC2-D2-E2-E3 (Pro-eCAG_012; Fig. 6A). Most of the Prochlorococcus population in strongly P-limited areas of the ocean harbored these genes, while they were absent from other areas, consistent with their presence in only a few Prochlorococcus and no Synechococcus genomes. Furthermore, as previously described [58,59,60], we found a Prochlorococcus eCAG encompassing the phnYZ operon involved in C-P bond cleavage, the putative phosphite dehydrogenase ptxD, and the phosphite and methylphosphonate transporter ptxABC (Pro-eCAG_0013, Dataset 6; Fig. 6B, [60,61,62]). Compared to these previous studies that mainly reported the presence of these genes in Prochlorococcus cells from the North Atlantic Ocean, here we show that they actually occur in a much larger geographic area, including the Mediterranean Sea, the Gulf of Mexico, and the ALOHA station (TARA_132) in the North Pacific, even though they were present in a fairly low fraction of Prochlorococcus cells. These genes occurred in an even larger proportion of the Synechococcus population, although not found in an eCAG for this genus (Fig. S12; Dataset 6). Synechococcus cells from the Mediterranean Sea, a P-limited area dominated by clade III [24], seem to lack phnYZ, in agreement with the phyletic pattern of these genes in reference genomes, showing the absence of this two-gene operon in the sole clade III strain that possesses the ptxABDC gene cluster. In contrast, the presence of the complete gene set (ptxABDC-phnYZ) in the North Atlantic, at the entrance of the Mediterranean Sea, and in several clade II reference genomes rather suggests that it is primarily attributable to this clade. Altogether, our data indicate that part of the natural populations of both Prochlorococcus and Synechococcus would be able to assimilate phosphonate and phosphite as alternative P-sources in low-P areas using the ptxABDC-phnYZ operon. Yet, the fact that no picocyanobacterial genome except P. marinus RS01 (Fig. 6C) possesses both phnC2-D2-E2-E3 and phnYZ, suggests that the phosphonate taken up by the phnC2-D2-E2-E3 transporter could be incorporated into cell surface phosphonoglycoproteins that may act to mitigate cell mortality by grazing and viral lysis, as recently suggested [63].Fig. 6: Global distribution map of eCAGs putatively involved in phosphonate and phosphite transport and assimilation.A Prochlorococcus Pro-eCAG_012 putatively involved in phosphonate transport. B Prochlorococcus Pro-eCAG_013, involved in phosphonate/phosphite uptake and assimilation and phosphonate C-P bond cleavage. C The genomic region encompassing both phnC2-D2-E2-E3 and ptxABDC-phnYZ specific to P. marinus RS01. The size of the circle is proportional to relative abundance of Prochlorococcus as estimated based on the single-copy core gene petB and this gene was also used to estimate the relative abundance of other genes in the population. Black dots represent Tara Oceans stations for which Prochlorococcus read abundance was too low to reach the threshold limit.Full size imageeCAGs related to iron metabolismAs for macronutrients, it has been hypothesized that the survival of marine picocyanobacteria in low-Fe regions was made possible through several strategies, including the loss of genes encoding proteins that contain Fe as a cofactor, the replacement of Fe by another metal cofactor, and the acquisition of genes involved in Fe uptake and storage [14, 15, 36, 39, 64]. Accordingly, several eCAGs encompassing genes encoding proteins interacting with Fe were found in modules anti-correlated to HNLC regions in both genera. These include three subunits of the (photo)respiratory complex succinate dehydrogenase (SdhABC, Pro-eCAG_014, Syn-eCAG_006, Fig. S13; [65]) and Fe-containing proteins encoded in most abovementioned eCAGs involved in N or P metabolism, such as the guanidinase (Fig. S6), the NitC1 (Fig. 5), the pyrB2 (Fig. S8), the phosphonate (Fig. 6, S12), and the urea and inorganic nitrogen eCAGs (Fig. S5). Most Synechococcus cells thriving in Fe-replete areas also possess the sodT/sodX eCAG (Syn-eCAG_007, Fig. S14A, B) involved in nickel transport and maturation of the Ni-superoxide dismutase (SodN), these three genes being in contrast core in Prochlorococcus. Additionally, Synechococcus from Fe-replete areas, notably from the Mediterranean Sea and the Indian Ocean, specifically possess two eCAGs (Syn-eCAG_008 and 009; Fig. S14C, D), involved in the biosynthesis of a polysaccharide capsule that appear to be most similar to the E. coli groups 2 and 3 kps loci [66]. These extracellular structures, known to provide protection against biotic or abiotic stress, were recently shown in Klebsiella to provide a clear fitness advantage in nutrient-poor conditions since they were associated with increased growth rates and population yields [67]. However, while these authors suggested that capsules may play a role in Fe uptake, the significant reduction in the relative abundance of kps genes in low-Fe compared to Fe-replete areas (t-test p-value  More

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Screening for viral AMGsViral protein annotation using VIBRANT and DRAM-v software combined with manual proofreading identified the viral AMGs in Napahai plateau wetland, including the viral AMGs phoH, phoU and pstS, which were associated with phosphorus metabolism.Phylogenetic analysis of AMGs associated with phosphorus metabolism in Napahai plateau wetlandThere were 24 amino acid sequences of phoH gene in Napahai plateau wetland (Fig. 1A). They were divided into 5 clusters, the largest of which had 10 sequences, while the smallest cluster had only 1 sequence. The remaining 3 clusters contained 6, 5 and 2 sequences, respectively. The phoH gene was genetically diverse in Napahai plateau wetland, which might be related to the different host origins. A total of 74 sequences of phoU gene could be found in seven clusters (Fig. 1B), with the largest cluster containing 27 sequences and the smallest cluster having two sequences. Similar to phoH, phoU was also genetically diverse, but richer than that of phoH. There were 71 pstS sequences forming 9 clusters, with the largest cluster of 23 sequences and the smallest cluster only 1 sequence (Fig. 1C). It could be seen that the genetic diversity of pstS was better than that of phoH and phoU, which might be related to the unique geographical location. Napahai plateau wetland is located in the Three Parallel Rivers of Yunnan protected areas, which forms a complex landscape, and then controls the evolution and characteristics of organisms, thus showing abundant biodiversity. Li et al. obtained 58 phoH gene sequences from Northeastern wetland sediments of China, which were 22%–99% consistent at the amino acid level, and found that the phoH gene could regulate phosphate uptake and metabolism under the low phosphate or phosphate limitation conditions16. However, the exact function remained unclear. The phoH gene clustered into five clusters in Napahai plateau wetland, indicating high genetic diversity. Additionally, water and soil samples were collected from eight separate sampling sites, and there were differences between samples environments, which might also have an impact on the genetic diversity of the three genes.Figure 1Phylogenetic analysis of phosphorus metabolism AMGs in Napahai plateau wetland, different colors represent different branches. (A) Phylogenetic analysis of phoH genes. (B) Phylogenetic analysis of phoU genes. (C) Phylogenetic analysis of pstS genes.Full size imagePhylogenetic analysis and PCoA analysis of AMGs associated with phosphorus metabolism from different habitats and host originsIn order to understand the genetic diversity of viral AMGs (phoH, phoU, pstS) associated with phosphorus metabolism in Napahai plateau wetland, a phylogenetic tree of phosphorus metabolism AMGs from different habitats was constructed, and PCoA analysis was performed (Fig. 2). The results showed that most sequences of phoH, phoU and pstS genes in Napahai plateau wetland clustered individually, especially phoU and pstS genes, and only a few sequences were closely related to those of other habitats. In Fig. 2A, 14 sequences clustered individually and were relatively far from sequences of other habitats, whereas 7 sequences were close to those from freshwater lakes, and other 3 sequences were close to those from rice fields, oceans and other wetlands, respectively. Therefore, the genetic diversity of phoH in Napahai plateau wetland was independent of the habitat. Moreover, some of the phoH sequences were clustered with those of other habitats and distributed in the fourth quadrants (Fig. 2D). From Fig. 2B, apart from 3 sequences which clustered with those from the marine habitats and freshwater lakes, the rest were clustered separately. Whereas in Fig. 2E, apart from only a few sequences, most sequences of phoU were far away from those of different habitats, which was consistent with Fig. 2B. Thus, the genetic diversity of phoU gene in Napahai wetland was also independent of habitat, where the separately clustered sequences may be unique. From Fig. 2C, we can seen that apart from 8 sequences which more closely related to those from the freshwater lake, ocean, rice field, and other wetlands, all the rest were individually clustered. The result was consistent with that of Fig. 2F. Therefore, the genetic diversity of the pstS gene was also habitat-independent.Figure 2Phylogenetic analysis and PCoA of phosphorus metabolism AMGs in different habitats, different colors represent different habitats. (A) Phylogenetic analysis of phoH genes in different habitats. (B) Phylogenetic analysis of phoU genes in different habitats. (C) Phylogenetic analysis of pstS genes in different habitats. (D) PCoA analysis of phoH genes in different habitats. (E) PCoA analysis of phoU genes in different habitats. (F) PCoA analysis of pstS genes in different habitats.Full size imageTo study whether the genetic diversity was related to host origins, three AMGs associated with phosphorus metabolism were selected for phylogenetic and PCoA analyses with AMGs sequences from different host origins (Fig. 3). It showed that some sequences of all three genes were similar to those from different host origins, while the remaining were separately clustered. In Fig. 3A, apart from 14 sequences which clustered with those from fungi, bacteria, non-culturable phages, phages and viruses, all the rest were clustered separately. Whereas, most sequences were clustered with those from different host origins together, and only six sequences were far from other sequences of different host origins based on PCoA analysis (Fig. 3D). Only three sequences were clustered with those of archaea and uncultured archaea, and the rest were clustered together to form independent clusters (Fig. 3B). A small amount of sequences were gathered with bacteria, uncultured bacteria, archaea and uncultured archaea, and the rest were clustered individually (Fig. 3E). As can be seen in Fig. 3C, six sequences were clustered with those of archaea, fungi, bacteria, while the rest were clustered separately. Some sequences were gathered with bacteria, uncultured bacteria, archaea and uncultured archaea, and others were clustered separately (Fig. 3F). PCoA analysis was largely consistent with phylogenetic analysis. So the genetic diversity of phoH, phoU and pstS genes in Napahai plateau wetland was independent of the host origins.Figure 3Phylogenetic analysis and PCoA of phosphorus metabolism AMGs from different host origins, different colors represent different host origins. (A) Phylogenetic analysis of phoH gene from different host origins. (B) Phylogenetic analysis of phoU gene from different host origins. (C) Phylogenetic analysis of pstS gene from different host origins. (D) PCoA analysis of phoH genes from different host origins. (E) PCoA analysis of phoU genes from different host origins. (F) PCoA analysis of pstS genes from different host origins.Full size imageOverall, the genetic diversity of phoH, phoU and pstS genes associated with phosphorus metabolism in Napahai plateau wetland was independent of both the habitats and host origins based on phylogenetic and PCoA analyses. It suggested that three genes showed relatively rich genetic diversity and were not genetically limited by differences in habitats or host origins. Han et al. showed that phoH sequences were widely distributed in soil, freshwater, and seawater environments in different locations around the world, indicating the genetic diversity independent of the environment17, which corroborated the conclusions in our study. Phylogenetic analysis of the 58 viral phoH gene sequences in Northeastern wetland of China revealed that some sequences were clustered with bacterial sequences and others clustered with phages sequences16. In Napahai plateau wetland, some phoH gene sequences were clustered with fungal, bacterial, phage, uncultured phage, and viruses. Hence, the genetic diversity of phoH gene was independent of the host origins in either Northeastern wetland or Napahai plateau wetland. Compared with Northeastern wetland, the phoH genes in Napahai plateau wetland showed more abundant genetic diversity, which may be related to geographical location and climate. Additionally, compared with sequences from different habitats and host sources, partial sequences from Napahai plateau wetland were clustered individually, thus they were unique, which might be related to the unique geography. Napahai plateau wetland is located in the Three Parallel Rivers with low latitude and high altitude, and shows specific characteristics which not found in other habitats, and then the species very different, thus providing the possibility for the emergence of unique genetic sequences. Of course, it would require further verification by subsequent study.As far as the current studies are concerned, most reports on phosphorus AMGs focused on the function. Wang et al. mentioned that the phoH gene regulated phosphate uptake or metabolism under the low phosphorus or phosphate limitation conditions18. Kelly et al. isolated several phages from oligotrophic water bodies with low phosphorus condition, found that they contained the phosphate binding transporter gene pstS by sequencing, which enhanced the host cell with increasing the infection cycle of phages by increasing phosphate utilization19. Gardner et al. studied the PhoR-PhoB two-component regulatory system in E. coli, which regulated the expression of relevant genes according to environmental phosphate concentration and enabled cells to adapt the phosphate starvation20. The phoU existed in many bacteria and was identified as an auxiliary protein of the phosphate-specific transporter system, regulating phosphate metabolism in the host cell acting as phosphate regulators21. Few studies had been conducted on its genetic diversity, therefore, the information on the genetic diversity was relatively scarce.α diversity analysis of phosphorus metabolism AMGs in different habitats and different host originsChao, Shannon and Simpson diversity indices are common mathematical measure of species alpha diversity in the community. Chao focuses on species richness. Shannon index and Simpson index measure species richness and evenness. Simpson reinforces evenness and Shannon reinforces richness22.Sequences from different habitats, such as Napahai plateau wetland, Pacific Ocean, Lake Baikal, Northeast rice fields, glaciers, and wetlands, were selected for α-diversity analysis (Fig. 4). The genetic diversity indices, such as Chao, Shannon and Simpson, calculated based on the OUT dataset, were used to characterize the alpha diversity. Among them, larger Chao values, smaller Simpson values or larger Shannon values indicate higher genetic diversity. Only at the level of Chao values (Fig. 4A,D,G) and Shannon values (Fig. 4B,E,H), the values of phoH, phoU, and pstS in Napahai plateau wetland were greater than those from other habitats, indicating better heritable, which might be related to the unique geographical location and abundant water resources. The geographical location made it unique and less influenced by external factors, and abundant water resources created a rich biodiversity, thus providing a good genetic environment. From the Simpson values (Fig. 4C,F,I), the values of phoU and pstS genes were smaller than those of other habitats, indicating better inherited. For the phoH gene, the Simpson value was closer in magnitude and lower than those in Antarctic Lake and wetlands, indicating better heritable.Figure 4Plots of genetic diversity indices analysis of phosphorus metabolism AMGs in different habitats, different colors represent different genetic diversity indices. (A, D, G) Represent respectively the Chao values of phoH, phoU, and pstS genes in different habitats. (B, E, H) Represent respectively the Shannon values of phoH, phoU, and pstS genes in different habitats. (C, F, I) Represent respectively the Simpson values of phoH, phoU, and pstS genes in different habitats.Full size imageThree AMGs associated with phosphorus metabolism in Napahai plateau wetland were selected for α-diversity analysis with AMGs sequences from different host origins (Fig. 5). In Fig. 5A, the Chao values of phoH gene from bacteria, phages, uncultured phages and uncultured viruses in Napahai plateau wetland were smaller than those of bacteria, phages, uncultured phages and uncultured viruses, indicating the poor genetic diversity. In addition, compared to the genetic diversity of sequences from other host sources, the genetic diversity of phoH gene from bacteria in Napahai plateau wetland was better. As can be seen in Fig. 5D, G, the Chao values of phoU and pstS genes from bacteria in Napahai plateau wetland were greater than those of other host origins, indicating better genetic diversity, while the Chao values of pstS genes from archaea in Napahai plateau wetland were smaller than those of other host origins, indicating poor genetic diversity.Figure 5Plots of genetic diversity indices analysis of phosphorus metabolism AMGs from different host origins, different colors represent different genetic diversity indices. (A, D, G) Represent respectively the Chao values of phoH, phoU, and pstS genes from different host origins. (B, E, H) Represent respectively the Shannon values of phoH, phoU, and pstS genes from different host origins. (C, F, I) Represent respectively the Simpson values of phoH, phoU, and pstS genes from different host origins.Full size imageThe Shannon value of phoH gene from bacteria in Napahai plateau wetland was smaller than that of bacteria and uncultured viruses, indicating poor diversity, but larger than other host sources, indicating better genetic diversity (Fig. 5B). The Shannon values of phoH gene from phages and uncultured phages in Napahai plateau wetland were lower than those of other host origins, indicating poor diversity. The Shannon value of phoH genes from uncultured viruses in Napahai plateau wetland was 0, probably due to sample size too small to calculate the Shannon value. In Fig. 5E, H, the Chao values of phoU and pstS genes from bacteria in Napahai plateaus wetland were greater than those from other host sources, indicating better diversity, while the Shannon value of pstS gene from archaea in the Napahai plateau wetland was 0, probably small sample size.The Simpson values of phoH genes from phage, uncultured phage and uncultured virus in Napahai plateau wetland were smaller than those of other host origins (except uncultured virus), indicating better diversity. The smaller Simpson values of phoH genes related to fungi, phages, uncultured phages, and viruses indicated better diversity, while the larger Simpson values compared to bacteria, phages, and uncultured viruses indicated poor diversity (Fig. 5C). As can be seen in Figs. 5F,I, the Simpson values of phoU genes from bacteria and pstS genes from bacteria and archaea in Napahai plateau wetland were smaller than those of other host origins, indicating better genetic diversity.Currently, most studies on phosphorus AMGs employed phylogenetic analysis16,23. In contrast, relatively few AMGs associated with phosphorus had been reported based on α-diversity analysis, so it was difficult to obtain specific values of α-diversity indices in other studies.Biogeochemical cycling of AMGs associated with phosphorus metabolism in Napahai plateau wetlandViruses are the gene carriers in susceptible hosts, and AMGs introduced by viruses into new hosts can enhance viral replication and/or influence key microbial metabolic pathways of the biogeochemical cycles24. It is well known that phosphorus is an essential nutrient and plays essential roles in cells25. Phosphorus deficiency leads to restricted cell division, down-regulation of photosynthesis, reduced protein and nitrogen content and chlorophyll synthesis26. To study the effect of AMGs associated with phosphorus metabolism, a phosphorus metabolic pathway containing phoH, phoU and pstS genes was constructed based on metagenomic data (Fig. 6). When phosphorus deficiency occurs in the host, it leads to the expression of phoH, phoU and pstS genes. phoH is a phosphate starvation inducible gene, while pstS acts as a phosphate transport gene and phoU belongs to a phosphate regulatory gene that produces dissolved inorganic phosphorus (DIPs), which then undergoes a series of reactions to produce ATP. The generated ATP becomes PolyP under the action of ppK which encoding polyphosphate kinase, or is used in Calvin cycle to provide energy for Ru5P to produce RuBP, or is used for DNA biosynthesis to provide energy. PolyP is regenerate into DIP with ppX which encoding exopolyphosphatase, and also involves in the biosynthesis process of DNA as Pi to provide phosphate for the nucleic acids synthesis. Thus, phosphorus metabolism of AMGs invoved plays a significant role in the life process of the virus and host. In addition, phoE and ugpQ genes also are identified in Napahai plateau wetland, but their roles in the phosphorus cycling are currently unknown and need further study.Figure 6Biogeochemical cycling of AMGs associated with phosphorus metabolism in Napahai plateau wetland. Red line indicates the process of phosphorus metabolism.Full size imageBased on the phylogenetic and PCoA analyses, we found that the phoH, phoU, and pstS genes all showed unique sequences, which might be drive the microorganisms to produce the phosphorus metabolic pathway in Napahai plateau wetland. Of course, in order to prove this pathway, further validation might be done by metabolomics or metabolic flow method. Furthermore, the phosphorus metabolic pathway was poorly reported, so we could not compare with the phosphorus pathway from other environment to find commonalities and differences. More