Skip to main content
Download PDF

Metagenome-assembled genomes reveal greatly expanded taxonomic and functional diversification of the abundant marine Roseobacter RCA cluster

Microbiome volume 11, Article number: 265 (2023) Cite this article

Abstract

Background

The RCA (Roseobacter clade affiliated) cluster belongs to the family Roseobacteracea and represents a major Roseobacter lineage in temperate to polar oceans. Despite its prevalence and abundance, only a few genomes and one described species, Planktomarina temperata, exist. To gain more insights into our limited understanding of this cluster and its taxonomic and functional diversity and biogeography, we screened metagenomic datasets from the global oceans and reconstructed metagenome-assembled genomes (MAG) affiliated to this cluster.

Results

The total of 82 MAGs, plus five genomes of isolates, reveal an unexpected diversity and novel insights into the genomic features, the functional diversity, and greatly refined biogeographic patterns of the RCA cluster. This cluster is subdivided into three genera: Planktomarina, Pseudoplanktomarina, and the most deeply branching Candidatus Paraplanktomarina. Six of the eight Planktomarina species have larger genome sizes (2.44–3.12 Mbp) and higher G + C contents (46.36–53.70%) than the four Pseudoplanktomarina species (2.26–2.72 Mbp, 42.22–43.72 G + C%). Cand. Paraplanktomarina is represented only by one species with a genome size of 2.40 Mbp and a G + C content of 45.85%. Three novel species of the genera Planktomarina and Pseudoplanktomarina are validly described according to the SeqCode nomenclature for prokaryotic genomes. Aerobic anoxygenic photosynthesis (AAP) is encoded in three Planktomarina species. Unexpectedly, proteorhodopsin (PR) is encoded in the other Planktomarina and all Pseudoplanktomarina species, suggesting that this light-driven proton pump is the most important mode of acquiring complementary energy of the RCA cluster. The Pseudoplanktomarina species exhibit differences in functional traits compared to Planktomarina species and adaptations to more resource-limited conditions. An assessment of the global biogeography of the different species greatly expands the range of occurrence and shows that the different species exhibit distinct biogeographic patterns. They partially reflect the genomic features of the species.

Conclusions

Our detailed MAG-based analyses shed new light on the diversification, environmental adaptation, and global biogeography of a major lineage of pelagic bacteria. The taxonomic delineation and validation by the SeqCode nomenclature of prominent genera and species of the RCA cluster may be a promising way for a refined taxonomic identification of major prokaryotic lineages and sublineages in marine and other prokaryotic communities assessed by metagenomics approaches.

Video Abstract

Introduction

The RCA (Roseobacter clade affiliated) cluster is one of the largest lineages of the Roseobacter group, recently reclassified as family Roseobacteraceae of Rhodobacterales [1]. Its abundance, distribution, and ecological significance in marine ecosystems have been investigated in the past 20 years [2,3,4,5,6,7]. Members of this cluster are widely distributed from temperate to polar regions of the global oceans and constitute ~ 10 to 35% of the total bacterial communities [3, 4, 8] but are absent from permanently stratified (sub)tropical regions [2,3,4, 8, 9]. The RCA cluster often dominates the Roseobacter group in marine pelagic systems [10,11,12,13], is closely associated with phytoplankton blooms, and is a major player in processing phytoplankton-derived organic matter [5, 6, 14, 15]. The 16S rRNA gene sequence similarity within the cluster is > 98% [6], but the further taxonomic and genomic substructure of the RCA cluster is unknown due to the few isolates and genomes available [16, 17].

The primary genome characteristics of the RCA cluster were previously analyzed in the type strain, Planktomarina temperata RCA23, isolated from the North Sea [6]. In addition to many functional genomic features typical for the Roseobacter group, this strain has a relatively small genome, encodes various modes of complementary energy acquisition such as aerobic anoxygenic photosynthesis (AAP), carbon monoxide (CO), and sulfur oxidation, but lacks plasmids, prophages, or complete GTAs (gene transfer agent) [6, 18, 19]. The other seven available isolates were also obtained from coastal waters [16, 17, 20], but genome sequences of these strains have not been published. Therefore, the genomic information on the RCA cluster is still scarce and hardly provides a comprehensive understanding of the genetic diversity and metabolic potential within the RCA cluster. Hence, we hypothesize that other members of the RCA cluster, dwelling exclusively in oceanic off shore regions, differ in their genomic features from the known isolates [2, 21].

For the last two decades, our understanding of the prokaryotic diversity and metabolic potential has greatly advanced by reconstructing genomes directly from environmental samples. Metagenome-assembled genomes (MAG) and single-cell amplified genomes (SAG) make it possible to gain insight into genomic and functional traits of uncultivated lineages, otherwise not accessible to genomic analyses [19, 22,23,24,25,26]. A nearly complete RCA genome from the Southern Ocean, co-assembled from three SAGs with identical 16S rRNA genes, was recently reported [21]. This genome, distinctly different from the genomes of the known RCA isolates, lacks many pathways for nitrogen (N) and phosphorus (P) uptake and metabolism and contains genes encoding proteorhodopsin (PR) and iron transporters. The latter genomic traits were proposed as a strategy of this RCA member to live in high-nutrient, low-chlorophyll regions [21]. We hypothesize that recruiting MAGs of the RCA cluster from different oceanic regions will be a valuable means to reveal the breadth of the functional potential of this important cluster and its phylogenomic diversity.

Therefore, we searched metagenomic and metatranscriptomic datasets of the Tara Ocean [27,28,29] and BioGEOTRACES expeditions [30], an Atlantic transect from 62°S to 47°N [31], cruises to the Arctic and Southern Ocean [32] and the North Sea, and collected MAGs affiliated to the RCA cluster from these metagenomes. This enabled us to carry out a comprehensive analysis of the genomic and functional traits of the RCA cluster, to assess its taxonomic diversity and the global biogeography of RCA sublineages.

Materials and methods

Metagenomic quality control, assembly and binning

We collected 127 and 7 RCA MAGs from the Ocean Microbiomics Database [29] and the North Sea (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA365016), respectively, based on the genome taxonomy database (GTDB) (Supplementary Table S1). Besides, 20 MAGs were reconstructed from samples of an Atlantic Ocean transect from 62°S to 47°N (n = 22, Supplementary Table S1) [31]. Metagenomics data of this transect were quality controlled using Trimmomatic 0.36 [33] and sequencing reads assembled with metaSPAdes [34, 35]. Contigs ≥ 210 bp and with an average coverage > 2 were kept for metagenomic binning. MetaBAT2 (v2.12.1) [36] was used to reconstruct MAGs which were classified taxonomically using GTDB-Tk [37]. The completeness and contamination for assessing the quality of genomes were examined by both CheckM and Anvi’o [38, 39]. Only MAGs with a mean completeness ≥ 70%, a mean contamination ≤ 5%, and having scaffolds with an N50 ≥ 10 Kb were kept for further analysis. Five genomes of isolates were also included, i.e., genomic sequences of four strains (Roseobacter bacterium LE17 [20], Rhodobacteraceae bacterium IMCC1909, Rhodobacteraceae bacterium IMCC1923, and Rhodobacteraceae bacterium IMCC1933 [16]) were sequenced by a combined approach using 454 pyrosequencing and Illumina sequencing. The genomes were assembled using Newbler v2.8 [40] and SPAdes v2.5.1 [34]. Gap closure was partially done by PCR and Sanger sequencing of the products. The genome of the isolate Planktomarina temperata RCA23 was obtained from Integrated Microbial Genomes and Microbiomes database [41, 42]. Three previously reported SAGs were excluded for analyses due to low completeness (66.21%, 42.48%, and 17.24) [21]. In total, we obtained a set of 87 MAGs and genomes after quality control (Table 1, Supplementary Table S2).

Table 1 Species ID, number of MAG per species (in parenthesis), approved names of the species and genera of the RCA cluster, the location of the MAGs used for denomination, mean genome size ± standard deviation (SD), mean number of coding sequences (CDS) ± SD and mean G + C content ± SD of 13 RCA species
Full size table

Genome annotation and metabolic pathway prediction

Gene open reading frames and gene function were predicted with Prokka (default setting) [43]. Metabolic reconstruction of MAGs was performed by Anvi’o v.7.0 [39]: First, annotation of genes with the KEGG KOfam database was done by calling “anvi-run-kegg-kofams” [44] and the “anvi-estimate-metabolism” was then called for metabolic capabilities based on the KEGG MODULE database [45, 46]. A module with a completeness score ≥ 75% was considered as “present” in a genome.

Phylogenetic analyses

Genome-based phylogeny of the RCA cluster

Overall, 87 RCA and two outgroup genomes (Rhodobacterales Bacterium HTCC2255 and Rhodobacteraceae bacterium HTCC2150) were included in the phylogenomic analysis. The phylogenetic tree was inferred from 120 bacterial marker genes [47]. The identification and alignment of marker genes and trimming of a concatenated alignment followed the GTDB-Tk workflow [37]. The concatenated alignment was applied to reconstruct a maximum likelihood (ML) phylogenetic tree using IQ-TREE [48] under the LG + R10 substitution model with 1000 ultrafast bootstraps. The tree was visualized using the Interactive Tree of Life view (iTOL) [49].

Phylogenetic tree construction of the PR and pufM genes

For construction of the PR phylogenetic tree, 591 PR-encoding amino acid sequences were downloaded from the Reference Sequence (RefSeq) database at NCBI (https://www.ncbi.nlm.nih.gov) [50]. After clustering at 90% amino acid sequence similarity using CD-HIT [51], 338 PR representative reference sequences were kept. Sequences of 68 PR genes extracted from the analyzed RCA genomes, along with references, were aligned with MAFFT and trimmed with trimAL “-gappyout” [52, 53]. IQ-Tree was used for a maximum likelihood phylogenetic tree with 1000 ultrafast bootstraps using the LG + F + R8 model [48]. A similar workflow was applied for the pufM gene phylogeny. This analysis included 43 sequences of the pufM gene, consisting of 34 references from the NCBI database and nine sequences of three Planktomarina species. An alignment of the pufM gene sequences was constructed with MAFFT and trimmed with trimAL [52, 53]. The phylogenetic tree was built using IQ-Tree using the LG + I + G4 model with a bootstrap of 1000 replicates [48].

Taxonomic classification according to the SeqCode nomenclature

Reference MAGs of the newly identified species which passed the quality control criteria of the SeqCode nomenclature, ≥ 90% completeness, ≤ 5% contamination [54], were submitted to the SeqCode nomenclature and confirmed as new validly described species.

Global biogeography

To provide a global distribution pattern of the RCA cluster, we collected datasets including the Tara Ocean (370 metagenomic and 187 metatranscriptomic samples) [27, 28, 52], an Atlantic Ocean transect from 62°S to 47°N (22 metagenomic samples) [31], BioGEOTRACES expeditions (480 metagenomic samples) [30], and cruises to the Arctic and Southern Ocean (60 metagenomic samples) [32] and investigated the metagenomic operational taxonomic units (mOTUs) profiles of these samples [55]. Overall, we detected mOTUs corresponding to the RCA cluster in 282 samples (214 metagenomic, 68 metatranscriptomic) from the epipelagic zone (< 200 m), in 41 samples (36 metagenomic, 5 metatranscriptomic) from the mesopelagic zone (200–1000 m) and in 10 metagenomic samples from the bathypelagic zone (> 1000 m) (Supplementary Tables S3, S4).

The mOTUs based on housekeeping genes can identify microbial taxa at species-level resolution in metagenomes [55]. Therefore, we explored the distribution of the distinct RCA species in the global ocean based on mOTUs. Eleven mOTUs associated with members of the RCA cluster were collected in the mOTUs references database. Nine of the 11 mOTUs correspond to 10 of the 13 RCA species we newly identified as two species shared the same mOTUs. The remaining three species lack representatives in the mOTUs reference database.

Results and discussion

Diversity of the RCA cluster and genome characteristics

The phylogenomic analysis yielded three major clades within the RCA cluster (Fig. 1) Genomes of the three clades were relatively distinct with appr. < 70% average nucleotide identity (ANI), resulting in the proposal of three genera, the known genus Planktomarina, and two new genera without representative pure cultures, named Paraplanktomarina and Pseudoplanktomarina (Fig. 2). Pseudoplanktomarina is an approved new genus by the SeqCode nomenclature (https://disc-genomics.uibk.ac.at/seqcode/page/seqcode), whereas Paraplanktomarina is a candidate genus. To explore whether the MAGs/genomes presented novel species, we clustered 82 MAGs plus 5 genomes of the isolates on the basis of 95% ANI, following previous studies [56, 57] and delineated 13 species-level genome clusters among the three RCA genera (designated A1, B1-B4, C1-C8; Fig. 2). ANI was estimated based on the whole-genomes comparisons using the FastANI [58]. In species C3, MAGs 13 and 14 formed a separate cluster with an ANI of 95.4–97.9% when compared to the other MAGs of this species. The three strains IMCC1909, IMCC1923, and IMCC1933, isolated from the Korean Yellow Sea, formed one cluster within the species Planktomarina temperata, sharing ANI values of 94.8–95.8% with strain LE17 and the type strain of this species.

Fig. 1
figure 1

Phylogenetic tree and delineation of the three genera of the RCA cluster. Phylogenetic tree of the 82 MAGs and 5 genomes of isolates of the RCA cluster is based on 120 conserved genes and Rhodobacterales bacterium HTCC 2255 and Rhodobacteraceae bacterium HTCC 2150 were used as outgroups. Only bootstrap values ≥ 75 were shown with filled blue circles. The 13 species are shown in different colors. Heatmap of paired comparisons of Average Nucleotide Identity (ANI) among 87 RCA genomes/MAGs revealing three genera (ca. ANI > 70%), Cand. Paraplanktomarina, Pseudoplanktomarina, Planktomarina, and 13 species (A1, B1-B4, C1-C8) (ANI > 95%)

Full size image
Fig. 2
figure 2

Genome characteristics of 87 RCA MAGs/genomes of the RCA cluster. Deviation of each genome/MAG from the overall mean of the genome size, G + C content and CDS and presence/absence of genes for complementary energy acquisition by proteorhodopsin (PR), aerobic anoxygenic photosynthesis (pufM), carbon monoxide dehydrogenase (CODH, i.e., Form I coxMSL and Form II coxSLM), and sulfur oxidation (sox cluster). Deviation from the mean is indicated by bar length and presence/absence of genes by filled or empty circles

Full size image

Cand. Paraplanktomarina is the most deeply branching genus and represented by only one species with two MAGs. Pseudoplanktomarina encompasses four species with 43 MAGs, including species B2 with the largest number of MAGs, and Planktomarina encompasses eight species with 43 MAGs/genomes. The five genomes of the isolates, including the type strain P. temperata RCA23, and one MAG formed a monophyletic group assigned to Planktomarina C6 (Fig. 2). From nine RCA MAGs/genomes we obtained the 16S rRNA gene that has > 98% sequence similarity.

The genome size of the RCA species, corrected for contamination and completeness according to reference [59], ranges from 2.13 to 3.33 Mbp (Table 1). The genomes contain 1535 to 3310 coding DNA sequences (CDS) and the G + C content varies from 42.05 to 55.21% (Table 1). The genome size and G + C content of species A1 of the most deeply branching genus, Cand. Paraplanktomarina, are 2.40 Mbp and 45.85%, respectively, and the number of CDS is 1774 (Table 1, Supplementary Table S2). Comparing genomic characteristics with dRep [60], and using the means of all MAGs/genomes of each species, the number of CDS of all except one Planktomarina (C1) and Pseudoplanktomarina species (B3) is higher than that of Cand. Paraplanktomarina. Numbers of CDS of the other Planktomarina species range between 2112 and 3014, whereas numbers of CDS of the other Pseudoplanktomarina species did not exceed 2042 (Table 1). This indicates that the number of CDS of Planktomarina is significantly larger (p-value < 0.05) than that of Pseudoplanktomarin (supplementary Fig. S1). The genome size of the Pseudoplanktomarina species B1 to B3 ranges between 2.26 and 2.36 Mbp and that of species B4 is 2.72 Mbp. Hence, three of the four species of this genus have a reduced genome size relative to Cand. Paraplanktomarina (and the overall mean of 2.68 Mbp), despite an increase in CDS, and only species B4 has a larger genome size (Table 1, Supplementary Table S2). The genome sizes of seven Planktomarina species are larger than that of Cand. Paraplanktomarina (and the overall mean) and only that of species C1 was slightly smaller, features in line with the CDS data. The G + C content of all Planktomarina species is higher than that of Cand. Paraplanktomarina and ranges between 46.36 and 53.79% with the largest values in species C6 and C7 (Table 1). In contrast, the G + C content of all Pseudoplanktomarina species is lower than that of Cand. Paraplanktomarina and ranges from 42.22 to 44.10%. Interestingly, the co-assembled genome of the three SAGs retrieved from the Southern Ocean [21] affiliates most closely to species Cand. Paraplanktomarina A1 and ANI indicates that it is a separate species of this genus (Supplementary Fig. S2). Its estimated genome size is 3.47 Mbp and its G + C content 45.3% (Supplementary Table S5).

These findings show substantial differences among the three RCA genera regarding genome size, CDS and G + C content. Most Planktomarina species have a larger genome and more CDS and all have a higher G + C content than the species of both other genera. These are typical evolutionary genomic features for prokaryotes dwelling under relaxed growth conditions and largely driven by mutation and horizontal gene transfer [61,62,63,64]. In contrast, the reduced genome size of three and the lower G + C content of all Pseudoplanktomarina species relative to Cand. Paraplanktomarina are typical features of streamlining and resource-driven evolution towards saving N under strong N limitation such as in oligotrophic open ocean ecosystems [65,66,67,68]. Biogeographic patterns reflect these genomic adaptations of the different species (see below).

As A1 of Cand. Paraplanktomarina is the most deeply branching species of the RCA cluster, distinct in quite a few genomic features from the species of both other genera, we focus our analysis of functional features predominantly on those in the genera Pseudoplanktomarina and Planktomarina differing from Cand. Paraplanktomarina A1. As both MAGs of A1 are only to 77 and 84% complete, several genes in this species are likely missing due to genome incompleteness. General genomic features of the type strain P. temperata RCA23, affiliated to species C6, also in comparison to the entire Roseobacter group, have been published previously [6].

In none of the RCA MAGs or genomes, we detected complete GTAs, any prophage, or plasmid except for one incomplete prophage in IMCC1909 (Supplementary Table S6). Even though we are aware of the difficulties to detect these features in MAGs [69, 70], these findings are in line with previous reports of features of RCA genomes. These features were interpreted as an adaptation to oligotrophic growth conditions in pelagic marine ecosystems [6, 17], in line with genome characteristics of other streamlined pelagic marine prokaryotes including the SAR11 and SAR116 clades [71, 72].

Complementary energy acquisition

Utilization of light energy

The Roseobacter group encompasses purely heterotrophic but quite a few subgroups including the RCA cluster carry out AAP whereas only one sublineage is known to encode the PR gene [6]. Therefore, it is of great interest to examine the MAGs of the RCA cluster for their genetic traits to acquire complementary light energy. Cand. Paraplanktomarina A1 is likely to be purely heterotrophic as the two MAGs do not encode any gene for acquiring light energy (Fig. 2). In contrast, all Pseudoplanktomarina and five Planktomarina species and in total 68 MAGs encode the PR gene (Fig. 2). In a few genomes of these species, we did not find a PR-encoding gene but detected several genes involved in the retinal biosynthesis, presumably because their completeness is only 71 to 91%, making it likely that these MAGs miss this gene. It is surprising that nine of the twelve species in both genera are able to acquire light energy via PR. This mode of complementary energy acquisition by a proton pump is widespread in several lineages of marine pelagic bacteria, including the alphaproteobacterial SAR11 (Pelagibacterales) and SAR116 (Puneispirillales) clades, several lineages of Gammaproteobacteria and Flavobacteriaceae [73]. In the Roseobacter group, PR has only been reported to be encoded in the genome-streamlined NAC11-7 lineage, dwelling in pelagic systems [74], and from two SAGs affiliated to the RCA cluster retrieved from the Southern Ocean [21]. As we identified the latter as a species of the genus Cand. Paraplanktomarina (see above, Supplementary Fig. S2), it is likely that this most deeply branching genus or sublineages of it also acquire complementary energy by this light-driven proton pump. Hence, our findings indicate that this mode of complementary energy acquisition is widespread in the RCA cluster. As the most common ancestor of the RCA cluster, Nereida ignava, does not encode the PR gene (Supplementary Fig. S3, [75]), it is likely that members of the RCA cluster gained the PR gene by horizontal transfer from other prokaryotic lineages. Horizontal transfer of the PR gene is a quite common phenomenon between bacteria, not only of closely related taxa but also between families and even phyla [73]. To identify source candidates of this gene, we calculated a phylogenetic tree of bacterial PR genes (Fig. 3). This analysis showed that the 68 PR genes associated with the RCA cluster formed one clade, phylogenetically most closely related to members of Betaproteobacteria and Cand. Puniceispirillum marinum of the SAR116 clade (Alphaproteobacteria). Betaproteobacterial lineages are prevalent in freshwater habitats and have low abundances in the ocean [76, 77]. By contrast, the SAR116 clade is globally distributed in the oceans, accounting for up to 30% of prokaryotic communities [72]. Hence, the co-occurrence of the deeply branching RCA species, dwelling in nutrient depleted oceanic environments, with members of the SAR116 clade such as Cand. P. marinum, suggests a higher likelihood of gene transfer between these two phylogenetic lineages. Furthermore, the presence of mobile genetic elements near the PR gene in the RCA cluster (Supplementary Fig. S4) and the dissimilarity in GC content of the PR gene and its corresponding genome of the RCA cluster (Supplementary Table S7) further imply that the PR gene was transferred from a member of the SAR116 clade to members of the RCA cluster [78,79,80].

Fig. 3
figure 3

Phylogenetic tree of the PR gene and gene sequence of the PR operon detected in the RCA cluster. A Phylogenetic tree based on PR genes. B Gene sequence of the PR operon detected in the RCA cluster; Blh: 15,15′-β-carotene dioxygenase; crtY: lycopene cyclase; crtB: phytoene synthase; crtI: phytoene dehydrogenase; crtE: putative geranylgeranyl pyrophosphate (GGPP)

Full size image

The PR gene becomes only functional when it is embedded in an operon including also genes encoding for the biosynthesis of different carotenoids and which can vary in their organization [73]. Hence, we analyzed the structure of the PR operon in the RCA MAGs and identified one type Blh-crtY-crtB-crtI-crtE-PR (Fig. 3). The analysis of the amino acid variants indicates that all PR genes affiliated with the RCA cluster are green-tuned [80], in line with the predominant occurrence of the RCA cluster in near surface waters [4].

All MAGs of Planktomarina C6, C7 and C8 encode the genes to synthesize bacteriochlorophyll a (BChl a) and the complete photosynthetic gene cluster (PGC) to carry out AAP (Fig. 2). Cand. Paraplanktomarina does not encode the PGC. Interestingly, the most closely related Roseobacter lineage of the RCA cluster, represented by Nereida ignava, and Nereida sp. MMG025, encodes the PGC as shown by its pufM marker genes (Supplementary Figs. S2, S4A [75]). However, it is encoded in a plasmid and not closely related to the PGC of the RCA cluster (Supplementary Fig. S5A, Supplementary Table S8). Hence, despite a PGC cluster being encoded in the most closely related lineage of the RCA cluster, it appears unlikely that the Planktomarina species C6, C7, and C8 conserved it from a common ancestor. It is more likely that they acquired it by horizontal gene transfer from another Roseobacter lineage with a more closely related PGC. A phylogenetic analysis of the pufM genes shows that they form a monophyletic branch in the Roseobacter group with a subcluster of the Planktomarina species (Supplementary Fig. S5A). Two types of PGC organization were present: one in a genome and a MAG of Planktomarina C6 and C8, isolated from the Korean Yellow Sea and North Sea, respectively; the other type in Planktomarina C7 and in the other genomes of Planktomarina C6 (Supplementary Fig. S5B). Both types have also been found in other species of the Roseobacter group. The AAP mode to acquire complementary energy is more cost-intense than the PR proton pump but conserves more energy than the latter [81]. The species carrying the PGC exhibit the highest G + C content (~ 53%) of all RCA species, suggesting that they are adapted to relatively relaxed resource conditions [67, 68] such as in coastal seas and the temperate and (sub)polar regions. These conditions are in line with distribution patterns of the RCA cluster reported previously, based on the detection of the 16S rRNA gene and genomic features of the isolate P. temperata RCA23 [2,3,4, 6, 11, 13].

The detection of genes encoding the PGC in MAGs of the RCA cluster was expected and is in line with previous reports and also with the fact that quite a few sublineages of the Roseobacter group encode AAP [6, 16]. The finding of PR in other species and in two of the three genera of the RCA cluster was unexpected as it was not detected in any RCA isolate and only reported in two SAGs (see above). To find both modes of complementary energy acquisition from light in one sublineage on the genus level of marine bacteria is unusual and to the best of our knowledge unprecedented. Our findings demonstrate that the RCA cluster subdivides into two fractions regarding acquisition of complementary light energy for adaptation to environmental conditions and resource limitation. In the sun-lit ocean, PR-based photoheterotrophic bacteria are more abundant than AAP bacteria in particular in oligotrophic regions [81]. It indicates that the majority of species of the RCA cluster are members of these photoheterotrophic prokaryotic communities dwelling predominantly in nutrient-poor oceanic environments. Surprisingly, only a small fraction of the RCA cluster belongs to the AAP bacterial communities of which the entire Roseobacter group can represent up to ~ 60% [8].

Oxidation of carbon monoxide and reduced sulfur compounds

Other ways to conserve complementary energy in the Roseobacter group are oxidation of CO and reduced sulfur compounds including thiosulfate [8, 19, 82,83,84]. The cox gene cluster, encoding for the CO dehydrogenase (CODH), has two distinct types, form I and II [84, 85]. Form I contains genes in the order coxMSL, whereas form II in the order coxSLM. Only those Roseobacter organisms oxidize CO and transcribe coxL which encode both forms [82]. Cand. Paraplanktomarina A1 encodes only CODH I (Fig. 2) and thus is predicted to be unable to oxidize CO. All Pseudoplanktomarina species and Planktomarina C1 lack CODH I as well as form II, whereas most MAGs/genomes of the other Planktomarina species encode CODH I (Fig. 2). All MAGs/genomes of the Planktomarina species C2 to C8 encode CODH II. Hence, species C3 to C6 are predicted to oxidize CO. The fact that several MAGs of these species lack form II may be due to the incomplete genomes. Planktomarina temperata RCA23 has been shown to be capable of oxidizing CO. In cultures grown in the dark and supplemented with CO, cell numbers in the stationary phase remained significantly higher than in an unsupplemented control [86]. However, cell yield in the stationary phase was much smaller as compared to cultures grown at light relative to a dark control indicating that complementary energy acquisition by CO oxidation is much lower than by AAP. This suggests that other Planktomarina species with both forms may also take advantage of CO oxidation as complementary energy source, however, presumably only under extremely resource-limited conditions.

The sox cluster (soxAXYZBCD), encoding genes for acquiring energy via the oxidation of reduced sulfur compounds such as thiosulfate, is neither found in the Cand. Paraplanktomarina nor in Pseudoplanktomarina species and also not in Planktomarina C2, C7, and C8. Most MAGs/genomes of the other Planktomarina species, however, encode the sox cluster, which is also present in the closely related Nereida ignava [75].

In summary, the analyses of the different modes of complementary energy acquisition show a clear dichotomy in the speciation and diversification of the RCA cluster into the genera Planktomarina and Pseudoplanktomarina/Cand. Paraplanktomarina. Planktomarina has the highest G + C content, the largest genomes, five species encode the CODH I and II and sox genes, and three species harbor the PGC, whereas the other five species encode the PR gene. Pseudoplanktomarina does not encode CODH I and II nor sox genes but gained the PR gene. Cand. Paraplanktomarina does not encode CODH II but gained the PR gene in one sublineage [21].

Utilization of organic carbon, nitrogen, phosphorous, sulfur and iron

For glycolysis, the Entner-Doudoroff (ED) and the pentose phosphate (PP) pathways are encoded in all species of the RCA cluster, whereas the Embden-Meyerhof-Parnas (EMP) pathway is incomplete and presumably not functional (Fig. 4A). This is consistent with findings for many members of the Roseobacter group and other marine bacteria including the SAR11 clade [71, 87]. Furthermore, the De Ley-Doudoroff (DLD) pathway for galactose catabolism is encoded in all RCA species but to different degrees of completeness. In species B3, C2, and C4, it is less complete than in the others leading to the speculation that the different species vary in their galactose metabolism.

Fig. 4
figure 4

Overview of the genetically encoded metabolic potential of the 13 species and MAGs/genomes of the RCA cluster. A Carbon and nitrogen metabolisms. B Phosphorus, sulfur, and iron metabolism. For abbreviations of the listed genes, see Supplementary Table S9

Full size image

RCA members harbor various ABC transporter systems for carbohydrate uptake, but Paraplanktomarina A1 is most limited and only encodes a sorbitol/mannitol and a xylose transporter (Fig. 4A). Sorbitol and mannitol are osmoprotectants and transporters for both compounds are genetically encoded in many marine bacteria [88]. These transporters are encoded in MAGs/genomes of several Pseudoplanktomarina and Planktomarina species including species C6 to C8 and B2 (smoEFGK), whereas species C1, C2, C4, and B3 do not encode them. They must rely on other osmoprotectants, such as choline and glycine-betaine (see below). The gene of the transporter for xylose (xylFHG) is encoded in the MAGs/genomes of all RCA species except B4 (Fig. 4A). Xylose is a major constituent of phytoplankton-derived polysaccharides [89, 90] and a potentially important carbon source of many marine bacteria. Even though the Roseobacter group is deficient in polysaccharide hydrolysis [91] and relies on other polysaccharide-degrading bacteria such as Flavobacteriia and different lineages of Gammaproteobacteria [90], xylose can be utilized by various Roseobacter lineages [91] and is predicted to be utilized by all species of the RCA cluster except B4. This species, however, encodes a transporter for alpha-glucosides and glucose/mannose. Alpha-glucoside transporters (aglEFK) and glucose/mannose transporters (gtsABC, malK) were only encoded in MAGs/genomes of two other species, C3 and C6 (Fig. 4A). In addition, several other transporters for carbohydrates are encoded in a few MAGs of other RCA species but are not of general significance (Fig. 4A). These data indicate that the utilization of different monosaccharides is rather limited among the members of the RCA cluster. Several Pseudoplanktomarina and Planktomarina species encode transporters for one or a few monosaccharides but without a phylogenetic consistency. It appears that the transporters reflect an adaptation to species-specific environmental or biotic conditions.

Acquisition and utilization of organic N compounds by Cand. Paraplanktomarina A1 and most other species, based on genomic predictions, include the ammonium transporter (amtB), glutamate synthase (gltB, gltD), glutamine synthetase (glnA), and glutamate dehydrogenase (gdhA) (Fig. 4A). Furthermore, these species encode transporters for different amino acids (aapJQMP and livGFHMK) and polyamines (potABCD), but the latter are missing in a few MAGs of several species. Most species also encode genes for the metabolism of choline to glycine-betaine (betAB) and the glycine betaine transporter (proXWV) (Fig. 4A), presumably the major osmoprotectant of these organisms. Species C3, C6, and C8 additionally encode the choline sulfatase (betC), thus further widening the use of osmoprotectants of these species with the highest G + C content, presumably dwelling under relatively relaxed resource conditions and also at lower salinities [3]. Species B4, C1, C3, C6, and C7 encode also a urease (ureABCDEFG) and C3 and one or several MAGs of species B4, C1, C2, C4, C6, and C7 also a urea transporter (urtABCDE), indicating that most of these species are able to utilize urea as an N source (Fig. 4A). Urea has been shown to be an important source of organic N for marine pelagic prokaryotes [92]. Our findings show that this is also the case for the majority of RCA species. Interestingly, the isolates of Planktomarina C6 from the Korean Yellow Sea in addition encode a cyanate lyase (cynS) and nitrate transporters (nrtABC), indicating that these coastal species further broadened their potential for acquiring N compounds and thus their niche space.

Regarding utilization of phosphorous (P), it is interesting that Cand. Paraplanktomarina A1 encodes only genes of the high-affinity phosphate transporter (ptsBCS), detected; however, only in one of both MAGs of this species, and the phosphate regulon gene phoB (Fig. 4B). Hence, this species is predicted to be rather limited in its P-acquisition traits, whereas the species of the other genera exhibit additional transporters for inorganic and organic P. The low affinity P-transporter (TC.PIT), widely distributed in Bacteria [93], is encoded in all other species, except B1, even though detected only in two MAGs of Pseudoplanktomarina B3 (Fig. 4B). Genes encoding the high affinity P-transporter (ptsABCS) were detected in most species but not in species B1, B3 C2, C4, and C5. The phosphate regulon (phoBR) is also rather widely genetically encoded but not present in species B1, B3, C2, C5, C7, and C8 and only in one MAG of species C4 (Fig. 4B). In contrast, genes encoding the carbon-P lyase (phnABCDEWY) were only detected in MAGs/genomes of species C3-C6 (Fig. 4B). It was puzzling, though, that we did not detect any P-acquisition genes in Pseudoplanktomarina B3 except in two MAGs. We assume that due to the completeness of the genomes of this species of only 72 to 89% (Supplementary Table S2) these genes were not captured. Our findings indicate that species of the RCA cluster are able to acquire P from the two major pools of dissolved organic P in the ocean, organophosphoesters and organophosphonates [94], but there is a clear separation of species exploring either pool except Planktomarina C3 to C5, which can access both pools. Planktomarina species are more versatile in P-acquisition than species of the other two genera.

The Roseobacter group7 is known to be one of the most active players in marine environments in the cycling of organic sulfur compounds such as dimethylsulfonium propionate (DMSP) [19] and dihydroxypropane sulfonate (DHPS) [95]. Therefore, it was not surprising to find the key genes of the DMSP-demethylation and cleavage pathways (dmdA, dddP) and of DHPS-degradation and subsequent sulfonate oxidation (hpsN, comDE, soeABC) in many species of all three genera (Fig. 4B) [96, 97]. This indicates that they are involved in the breakdown of these important organic sulfur compounds. The methanethiol (MeSH) oxidase (mto) is encoded as well, indicating that these species are able to oxidize MeSH [98]. They further encode both enzymes for biosynthesis of cysteine from serine, cysteine synthase (cysK), and L-serine O-acetyltransferase (sat). In addition, the 3-mercaptopyruvate sulfurtransferase (mstA) is genetically encoded and involved in other metabolic processes of organic sulfur compounds. Taurine is an important C-, N-, and potentially S-source of marine bacterioplankton [99]. The proteins encoding taurine metabolism (tauD, xsc, tpa) [100] exist in almost all members of the RCA cluster, though the binding protein for taurine import (tauABC) is only encoded in MAGs/genomes of species C1, C2, C5, C6, and B3 (Fig. 4B). Hence, a limited number of Planktomarina species and one Pseudoplanktomarina species are predicted to be involved in the cycling of this important compound. Furthermore, a few MAGs of Planktomarina C3 contain the gene cysH, encoding a phosphoadenosine phosphosulfate reductase, and the genomes of Planktomarina C6 cuyA, encoding the desulfonation of cysteate to pyruvate and ammonium. Hence, several species of the RCA cluster have an even larger metabolic potential of metabolizing reduced and oxidized organic sulfur compounds.

All species of the RCA cluster encode genes for ferric iron transporters (afuABC), while genes encoding importers for ferrous iron (sitABCD) are missing in most species (Fig. 4B). Only Planktomarina C2, C4, and C5 and several MAGs of Pseudoplanktomarina B3 encode proteins for the import of ferrous iron (Fig. 4B), which suggests that they gained this trait by horizontal gene transfer.

Global biogeography of RCA species

All RCA MAGs were obtained from the epipelagic zone of either temperate or (sub)polar regions, except two MAGs of Pseudoplanktomarina B2 which were collected from the mesopelagic zone (Supplementary Table S2). Fifty-eight MAGs were collected from polar regions, of which 80% (47 MAGs) originated in the Arctic Ocean. Genomes/MAGs of species C6 and B4 came exclusively from temperate regions (Supplementary Table S2).

Based on taxonomic profiling of metagenomic reads, the relative abundance of the RCA cluster accounted for up to 16.9% and 5.3% of the total bacterial communities in the epipelagic and mesopelagic layers, respectively, with the highest percentages in the Southern Ocean (Fig. 5A, B, Supplementary Table S10). These proportions are in line with previous findings based on metagenomics and 16S rRNA marker gene studies [2,3,4, 6, 10]. Even at depths of more than 1000 m in the Southern Ocean, members of the RCA cluster account for up to 5% of the total bacterial community (Supplementary Table S10). Based on metatranscriptomic reads, the RCA cluster exhibits similar distribution patterns as on the basis of metagenomic reads, even though with fewer samples, indicating that this cluster is an active player of the resident prokaryotic communities (Fig. 5C, D, Supplementary Table S11).

Fig. 5
figure 5

Global biogeography of the RCA cluster. A, B Relative abundance of the RCA cluster (% of total metagenomic reads mapping to mOTUs marker genes) in the epipelagic (0–200 m) and mesopelagic zones (200–1000 m). C, D Relative abundance of the RCA cluster (% of total metatranscriptomic reads mapping to mOTUs marker genes) in the epipelagic (0–200 m) and mesopelagic zones (200–1000 m). The dots in different colors indicate the percent of the RCA cluster. Note the different scalings of the colored scale on the panels

Full size image

Three identified RCA species (C2, C7, C8) in the present study lack representatives in the mOTUs reference database, indicating that the proportion of the RCA cluster is underestimated. The global distribution and relative abundance of the other ten RCA species, however, could be assessed with mOTUs. Based on metagenomic and metatranscriptomic data, we identified eleven geographic regions of the Atlantic, Pacific, Arctic, and Southern Ocean and the Mediterranean Sea for the distribution patterns of these species (Fig. 6A, B). Most distinguished was Pseudoplanktomarina B4, which was abundant in temperate regions of the Atlantic and Pacific Ocean and the Mediterranean Sea and also detected at low relative abundance in Baffin Bay and the Southern Ocean but not at all in the Arctic Ocean (Fig. 6C, D, Supplementary Fig. S6). In the Mediterranean Sea, the northwest Atlantic and the southeast Pacific B4 was the only RCA species detected at most sampling stations (Supplementary Fig. S6, Supplementary Table S3). The high abundance of Pseudoplanktomarina B4 in temperate regions, accounting for more than 50% and up to > 90% of the total RCA cluster, corresponds to the MAGs of this species recovered from the temperate Atlantic, indicating that this RCA species inhabits temperate oceanic regions. The abundance of the RCA cluster in the Atlantic north-west, Pacific south-east, and Mediterranean Seas is generally lower than in the temperate and (sub)polar regions (Fig. 5) [2, 4].

Fig. 6
figure 6

Oceanic regions and global distribution patterns of 10 species of the RCA cluster. A, B Distribution patterns of 10 RCA species in metagenomic and metatranscriptomic data in 11 geographic oceanic regions: Arctic Ocean (ARC), Baffin Bay (BAF), Southern Ocean (SOC), Atlantic north-east (ANE), north-west (ANW), south-east (ASE), and south-west (ASW), Pacific north (PNO); south-east (PSE) and south-west (PSW), Mediterranean Sea (MED). C, D Relative abundances (% of total RCA reads mapping to mOTUs marker genes) of the different species in the pooled metagenomic and metatranscriptomic samples of the different oceanic regions. Circle size refers to relative abundances of 1–75%

Full size image

Species C1, C3 to C4/5, B3, and A1 were restricted to latitudes of > 40° in both hemispheres (Fig. 6C, D, Supplementary Fig. S6). Only B1, B2, and C6 occurred also at lower latitudes, co-occurring with Pseudoplanktomarina B4 in the south Atlantic but at lower abundances (Supplementary Fig. S6). Pseudoplanktomarina B1 and B4 were the only species detected in latitudes < 40° of the Pacific Ocean. As the Pacific Ocean was not well represented in our database, we are reluctant to conclude that the other species are really absent from the Pacific. All species except B4 exhibited a bipolar distribution (Fig. 6C, D, Supplementary Fig. S6). However, species B2, C1, and C3 exhibited relatively higher abundances in the Arctic Ocean, whereas species B3 and C4/5 were relatively more abundant in the Southern Ocean (Fig. 6C, D). Species Paraplanktomarina A1 exhibited rather similar abundances in both polar oceans. In every geographic region, at least two species co-occurred, but in Baffin Bay, the Southern Ocean and south-west Atlantic even ten species. Biogeographic distribution patterns based on metatranscriptomic data matched those based on metagenomic data, but fewer stations were available for this analysis (Fig. 6B, D, Supplementary Tables S10 and 11). These data specify that RCA species are active players of the resident prokaryotic communities.

Bipolar distribution patterns of prokaryotic genera such as Polaribacter, Octadecabacter, subcluster Ia.1 of the SAR11 clade and the RCA cluster have been reported previously [2, 8, 101,102,103]. Our MAG-based approach reveals that even several species of the RCA cluster co-occur in both polar oceans despite the huge geographic distance. It has to be kept in mind, though, that this species definition is based on an ANI of > 95% [47] and that it relates to a MAG/genomes-species, not to a species definition in the classical microbiological sense [104]. Several of these species exhibit some within-species genomic variability (Fig. 2) which suggests that they underwent subspecies diversification. This subspecies adaptation to the environmental and biotic conditions and diversification was expected considering the well-separated water masses in both polar oceans. The SAR11 subcluster Ia.1 exhibits also some diversification, suggesting within-cluster adaptation to specific environmental conditions [103]. On the other hand, we detected several co-occurring RCA species at the same location in different geographic regions. However, this is not uncommon and known for other marine and freshwater prokaryotes as they may occupy different co-existing ecological niches which exhibit spatio-temporal dynamics in the course of phytoplankton blooms and/or seasonally. It 7has been shown by SAG analyses that different species and ecotypes of Prochlorococcus and the SAR11 clade co-exist [105,106,107].

Proposal of species names

Considering the origin of the samples from which the MAGs originated and some biogeographic and functional features, we name the twelve new RCA species as indicated in Table 1. Six of the twelve species are validated by the SeqCode recently introduced to identify prokaryotes on the basis of a sequenced genome and other requirements [54]. Details and vouchers of the validly approved species are provided in the Supplementary text S1.

Concluding remarks

Genome expansion and reduction were shown to be important events during the evolution of the Roseobacter group [108]. In the most ancestral lineages, gene loss dominated and led to reduced genome sizes, whereas in other more advanced lineages, gain of gene families dominated. However, gene loss and reduction of genome size persisted in several sublineages and resulted in a cluster of Roseobacter organisms, the Pelagic Roseobacter Cluster including the RCA cluster, with a gene content distinct from other lineages with larger genomes and occurring in pelagic marine systems [109]. Our detailed analysis of the 82 MAGs and genomes of five isolates of in total 13 species of the RCA cluster reveals that diversification, genome reduction, and expansion occurred also in this globally important pelagic lineage. In comparison to the most deeply branching Paraplanktomarina A1, a few Pseudoplanktomarina species further reduced the genome size and G + C content, well-known adaptations to resource-limited pelagic environments [61]. The number of CDS increased and genetic adaptations included several features, indicated, e.g., by the gain of the PR gene. In contrast, all species except one of the genus Planktomarina exhibited larger genomes, a higher G + C content and more CDS than both other genera, and more and different modes of complementary energy acquisition. This suggests the genera Pseudoplanktonmarina and Planktomarina likely evolved from Cand. Paraplanktomarina, but more in-depth analyses are necessary to support this hypothesis. The global biogeographic distribution patterns of the different RCA species reflect their genomic features to a great extent. Such studies are important for a more refined and better understanding of the speciation, environmental adaptation, and successful performance of relevant prokaryotic players in global elemental cycles.

Availability of data and materials

The genomes of strains Rhodobacteraceae bacterium IMCC1909, Rhodobacteraceae bacterium IMCC1923, Rhodobacteraceae bacterium IMCC1933, and Roseobacter bacterium LE17 have been deposited at NCBI under the following accession numbers: SUB12126367 PRJNA887231 SAMN31157516 JAPDHM000000000 (IMCC1909), SUB12126368 PRJNA887233 SAMN31157519 JAPDHN000000000 (IMCC1923), SUB12126370 PRJNA887234 SAMN31157520 JAPDHO000000000 (IMCC1933), and SUB12126372 PRJNA887235 SAMN31157521 JAPDHP000000000 (LE17).

Genomes of 154 RCA MAGs that we collected are available on figshare (https://doi.org/10.6084/m9.figshare.21931311.v1). The results of gene function predicted with Prokka for the 87 RCA MAGs and genomes in this study are also deposited at figshare (https://doi.org/10.6084/m9.figshare.21931707.v1).

Genomes submitted to SeqCode are available at the European Nuncleotide Archive (ENA) under project no. PRJEB62088 and PRJNA934655 and at the SeqCode registry under the following URLs: https://seqco.de/i:24046, https://seqco.de/i:24058, https://seqco.de/i:24063, https://seqco.de/i:24060, https://seqco.de/i:24064, https://seqco.de/i:24065.

Sequences of PR and pufM genes for the phylogenetic tree are available on figshare (https://doi.org/10.6084/m9.figshare.21932034.v1; https://doi.org/10.6084/m9.figshare.21932100.v2).

Samples for studying the global distribution of the RCA cluster are from published datasets. 370 metagenomes and 187 metatranscriptomes have been deposited in the NCBI database, and the information of these samples is available at https://doi.org/10.5281/zenodo.3473199. Metagenomics from the Atlantic Ocean and Polar regions are deposited in the European Nucleotide Archive (INSDC accession no. PRJEB34453) and NCBI database (BioProject accession no. PRJNA588686), respectively. Sequence data of GEOTRACES cruises are available in NCBI (BioProject accession no. PRJNA385854 and PRJNA385855).

References

  1. Liang KYH, Orata FD, Boucher YF, Case RJ. Roseobacters in a Sea of Poly- and Paraphyly: whole genome-based taxonomy of the family Rhodobacteraceae and the proposal for the split of the “Roseobacter Clade” into a novel family Roseobacteraceae fam nov. Front Microbiol. 2021;12: 683109.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Selje N, Simon M, Brinkhoff T. A newly discovered Roseobacter cluster in temperate and polar oceans. Nature. 2004;427:445–8.

    Article  CAS  PubMed  Google Scholar 

  3. Giebel H-A, Kalhoefer D, Lemke A, Thole S, Gahl-Janssen R, Simon M, et al. Distribution of Roseobacter RCA and SAR11 lineages in the North Sea and characteristics of an abundant RCA isolate. ISME J. 2011;5:8–19.

    Article  PubMed  Google Scholar 

  4. Giebel H, Brinkhoff T, Zwisler W, Selje N, Simon M. Distribution of Roseobacter RCA and SAR11 lineages and distinct bacterial communities from the subtropics to the Southern Ocean. Environ Microbiol. 2009;11:2164–78.

    Article  CAS  PubMed  Google Scholar 

  5. Wemheuer B, Güllert S, Billerbeck S, Giebel H, Voget S, Simon M, et al. Impact of a phytoplankton bloom on the diversity of the active bacterial community in the southern North Sea as revealed by metatranscriptomic approaches. FEMS Microbiol Ecol. 2014;87:378–89.

    Article  CAS  PubMed  Google Scholar 

  6. Voget S, Wemheuer B, Brinkhoff T, Vollmers J, Dietrich S, Giebel H-A, et al. Adaptation of an abundant Roseobacter RCA organism to pelagic systems revealed by genomic and transcriptomic analyses. ISME J. 2015;9:371–84.

    Article  CAS  PubMed  Google Scholar 

  7. West NJ, Obernosterer I, Zemb O, Lebaron P. Major differences of bacterial diversity and activity inside and outside of a natural iron-fertilized phytoplankton bloom in the Southern Ocean. Environ Microbiol. 2008;10:738–56.

    Article  CAS  PubMed  Google Scholar 

  8. Zhang Y, Sun Y, Jiao N, Stepanauskas R, Luo H. Ecological genomics of the uncultivated marine Roseobacter lineage CHAB-I-5. Appl Environ Microb. 2016;82:2100–11.

    Article  CAS  Google Scholar 

  9. Malmstrom R, Straza T, Cottrell M, Kirchman D. Diversity, abundance, and biomass production of bacterial groups in the western Arctic Ocean. Aquat Microb Ecol. 2007;47:45–55.

    Article  CAS  Google Scholar 

  10. Landa M, Blain S, Christaki U, Monchy S, Obernosterer I. Shifts in bacterial community composition associated with increased carbon cycling in a mosaic of phytoplankton blooms. ISME J. 2016;10:39–50.

    Article  CAS  PubMed  Google Scholar 

  11. Bakenhus I, Dlugosch L, Billerbeck S, Giebel H-A, Milke F, Simon M. Composition of total and cell-proliferating bacterioplankton community in early summer in the North Sea – Roseobacters are the most active component. Front Microbiol. 2017;08:1771.

    Article  Google Scholar 

  12. Bakenhus I, Dlugosch L, Giebel H, Beardsley C, Simon M, Wietz M. Distinct biogeographic patterns of bacterioplankton composition and single-cell activity between the subtropics and Antarctica. Environ Microbiol. 2018;20:3100–8.

    Article  CAS  PubMed  Google Scholar 

  13. Giebel H-A, Arnosti C, Badewien TH, Bakenhus I, Balmonte JP, Billerbeck S, et al. Microbial growth and organic matter cycling in the Pacific Ocean along a latitudinal transect between subarctic and subantarctic waters. Front Mar Sci. 2021;8: 764383.

    Article  Google Scholar 

  14. Wemheuer B, Wemheuer F, Hollensteiner J, Meyer F-D, Voget S, Daniel R. The green impact: bacterioplankton response toward a phytoplankton spring bloom in the southern North Sea assessed by comparative metagenomic and metatranscriptomic approaches. Front Microbiol. 2015;6:805.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–11.

    Article  CAS  PubMed  Google Scholar 

  16. Giebel H-A, Kalhoefer D, Gahl-Janssen R, Choo Y-J, Lee K, Cho J-C, et al. Planktomarina temperata gen. nov., sp. nov., belonging to the globally distributed RCA cluster of the marine Roseobacter clade, isolated from the German Wadden Sea. Int J Syst Evol Micr. 2013;63:4207–4217.

  17. Zhang Z, Chen F, Chu X, Zhang H, Luo H, Qin F, et al. Diverse, abundant, and novel viruses infecting the marine Roseobacter RCA lineage. Msystems. 2019;4:e00494-e519.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Pradella S, Päuker O, Petersen J. Genome organisation of the marine Roseobacter clade member Marinovum algicola. Arch Microbiol. 2010;192:115–26.

    Article  CAS  PubMed  Google Scholar 

  19. Luo H, Moran MA. Evolutionary ecology of the marine Roseobacter clade. Microbiol Mol Biol R. 2014;78:573–87.

    Article  Google Scholar 

  20. Mayali X, Franks PJS, Azam F. Cultivation and ecosystem role of a marine Roseobacter clade-affiliated cluster bacterium▿. Appl Environ Microb. 2008;74:2595–603.

    Article  CAS  Google Scholar 

  21. Sun Y, Zhang Y, Hollibaugh JT, Luo H. Ecotype diversification of an abundant Roseobacter lineage. Environ Microbiol. 2017;19:1625–38.

    Article  CAS  PubMed  Google Scholar 

  22. Haro-Moreno JM, Rodriguez-Valera F, Rosselli R, Martinez-Hernandez F, Roda-Garcia JJ, Gomez ML, et al. Ecogenomics of the SAR11 clade. Environ Microbiol. 2020;22:1748–63.

    Article  CAS  PubMed  Google Scholar 

  23. Kraemer S, Ramachandran A, Colatriano D, Lovejoy C, Walsh DA. Diversity and biogeography of SAR11 bacteria from the Arctic Ocean. ISME J. 2020;14:79–90.

    Article  PubMed  Google Scholar 

  24. Dupont CL, Rusch DB, Yooseph S, Lombardo M-J, Richter RA, Valas R, et al. Genomic insights to SAR86, an abundant and uncultivated marine bacterial lineage. ISME J. 2012;6:1186–99.

    Article  CAS  PubMed  Google Scholar 

  25. Reji L, Francis CA. Metagenome-assembled genomes reveal unique metabolic adaptations of a basal marine Thaumarchaeota lineage. ISME J. 2020;14:2105–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Rinke C, Schwientek P, Sczyrba A, Ivanova NN, Anderson IJ, Cheng J-F, et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature. 2013;499:431–7.

    Article  CAS  PubMed  Google Scholar 

  27. Salazar G, Paoli L, Alberti A, Huerta-Cepas J, Ruscheweyh H-J, Cuenca M, et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell. 2019;179:1068-1083.e21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.

    Article  PubMed  Google Scholar 

  29. Paoli L, Ruscheweyh H-J, Forneris CC, Hubrich F, Kautsar S, Bhushan A, et al. Biosynthetic potential of the global ocean microbiome. Nature. 2022;607:111–8.

  30. Biller SJ, Berube PM, Dooley K, Williams M, Satinsky BM, Hackl T, et al. Marine microbial metagenomes sampled across space and time. Sci Data. 2018;5: 180176.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Dlugosch L, Poehlein A, Wemheuer B, Pfeiffer B, Badewien TH, Daniel R, et al. Significance of gene variants for the functional biogeography of the near-surface Atlantic Ocean microbiome. Nat Commun. 2022;13:456.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Cao S, Zhang W, Ding W, Wang M, Fan S, Yang B, et al. Structure and function of the Arctic and Antarctic marine microbiota as revealed by metagenomics. Microbiome. 2020;8:47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7: e7359.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019;36:1925–7.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3: e1319.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Miller JR, Koren S, Sutton G. Assembly algorithms for next-generation sequencing data. Genomics. 2010;95:315–27.

    Article  CAS  PubMed  Google Scholar 

  41. Mukherjee S, Stamatis D, Bertsch J, Ovchinnikova G, Sundaramurthi JC, Lee J, et al. Genomes OnLine Database (GOLD) v.8: overview and updates. Nucleic Acids Res. 2021;49:D723–D733.

  42. Chen I-MA, Chu K, Palaniappan K, Ratner A, Huang J, Huntemann M, et al. The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities. Nucleic Acids Res. 2021;49:D751–D763.

  43. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.

    Article  CAS  PubMed  Google Scholar 

  44. Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics. 2020;36:2251–2.

    Article  CAS  PubMed  Google Scholar 

  45. Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 2014;42:D199–205.

    Article  CAS  PubMed  Google Scholar 

  46. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45:D353–61.

    Article  CAS  PubMed  Google Scholar 

  47. Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.

    Article  CAS  PubMed  Google Scholar 

  48. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol. 2020;37:1530–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:W256–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44:D733–45.

    Article  PubMed  Google Scholar 

  51. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28:3150–2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Katoh K, Standley DM. MAFFT Multiple Sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–3.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Hedlund BP, Chuvochina M, Hugenholtz P, Konstantinidis KT, Murray AE, Palmer M, et al. SeqCode: a nomenclatural code for prokaryotes described from sequence data. Nat Microbiol. 2022;7:1702–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Milanese A, Mende DR, Paoli L, Salazar G, Ruscheweyh H-J, Cuenca M, et al. Microbial abundance, activity and population genomic profiling with mOTUs2. Nat Commun. 2019;10:1014.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Goris J, Konstantinidis KT, Klappenbach JA, Coenye T, Vandamme P, Tiedje JM. DNA–DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Micr. 2007;57:81–91.

    Article  CAS  Google Scholar 

  57. Konstantinidis KT, Tiedje JM. Genomic insights that advance the species definition for prokaryotes. Proc Natl Acad Sci USA. 2005;102:2567–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.

    Article  CAS  PubMed  Google Scholar 

  60. Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Giovannoni SJ, Thrash JC, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8:1553–65.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Mira A, Ochman H, Moran NA. Deletional bias and the evolution of bacterial genomes. Trends Genet. 2001;17:589–96.

    Article  CAS  PubMed  Google Scholar 

  63. Treangen TJ, Rocha EPC. Horizontal transfer, not duplication, drives the expansion of protein families in prokaryotes. PLoS Genet. 2011;7: e1001284.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Bohlin J, Sekse C, Skjerve E, Brynildsrud O. Positive correlations between genomic %AT and genome size within strains of bacterial species. Env Microbiol Rep. 2014;6:278–86.

    Article  CAS  Google Scholar 

  65. Grzymski JJ, Dussaq AM. The significance of nitrogen cost minimization in proteomes of marine microorganisms. ISME J. 2012;6:71–80.

    Article  CAS  PubMed  Google Scholar 

  66. Moore CM, Mills MM, Arrigo KR, Berman-Frank I, Bopp L, Boyd PW, et al. Processes and patterns of oceanic nutrient limitation. Nat Geosci. 2013;6:701–10.

    Article  CAS  Google Scholar 

  67. Hellweger FL, Huang Y, Luo H. Carbon limitation drives GC content evolution of a marine bacterium in an individual-based genome-scale model. ISME J. 2018;12:1180–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Mende DR, Bryant JA, Aylward FO, Eppley JM, Nielsen T, Karl DM, et al. Environmental drivers of a microbial genomic transition zone in the ocean’s interior. Nat Microbiol. 2017;2:1367–73.

    Article  CAS  PubMed  Google Scholar 

  69. Meyer F, Fritz A, Deng Z-L, Koslicki D, Lesker TR, Gurevich A, et al. Critical assessment of metagenome interpretation: the second round of challenges. Nat Methods. 2022;19:429–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S, Dröge J, et al. Critical assessment of metagenome interpretation—a benchmark of metagenomics software. Nat Methods. 2017;14:1063–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Giovannoni SJ, Tripp HJ, Givan S, Podar M, Vergin KL, Baptista D, et al. Genome streamlining in a cosmopolitan oceanic bacterium. Science. 2005;309:1242–5.

    Article  CAS  PubMed  Google Scholar 

  72. Roda-Garcia JJ, Haro-Moreno JM, Huschet LA, Rodriguez-Valera F, López-Pérez M. Phylogenomics of SAR116 clade reveals two subclades with different evolutionary trajectories and an important role in the ocean sulfur cycle. Msystems. 2021;6:e00944-e1021.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Pinhassi J, DeLong EF, Béjà O, González JM, Pedrós-Alió C. Marine bacterial and archaeal ion-pumping rhodopsins: genetic diversity, physiology, and ecology. Microbiol Mol Biol Rev Mmbr. 2016;80:929–54.

    Article  CAS  PubMed  Google Scholar 

  74. Newton RJ, Griffin LE, Bowles KM, Meile C, Gifford S, Givens CE, et al. Genome characteristics of a generalist marine bacterial lineage. ISME J. 2010;4:784–98.

    Article  CAS  PubMed  Google Scholar 

  75. Arahal DR, Pujalte MJ, Rodrigo-Torres L. Draft genomic sequence of Nereida ignava CECT 5292T, a marine bacterium of the family Rhodobacteraceae. Stand Genomic Sci. 2016;11:21.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Yilmaz P, Yarza P, Rapp JZ, Glöckner FO. Expanding the world of marine bacterial and archaeal clades. Front Microbiol. 2016;6:1524.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Glöckner FO, Fuchs BM, Amann R. Bacterioplankton compositions of lakes and oceans: a first comparison based on fluorescence in situ hybridization. Appl Environ Microb. 1999;65:3721–6.

    Article  Google Scholar 

  78. Popa O, Hazkani-Covo E, Landan G, Martin W, Dagan T. Directed networks reveal genomic barriers and DNA repair bypasses to lateral gene transfer among prokaryotes. Genome Res. 2011;21:599–609.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Luo H, Löytynoja A, Moran MA. Genome content of uncultivated marine Roseobacters in the surface ocean. Environ Microbiol. 2011;14:41–51.

    Article  PubMed  Google Scholar 

  80. Olson DK, Yoshizawa S, Boeuf D, Iwasaki W, DeLong EF. Proteorhodopsin variability and distribution in the North Pacific Subtropical Gyre. ISME J. 2018;12:1047–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Kirchman DL, Hanson TE. Bioenergetics of photoheterotrophic bacteria in the oceans. Env Microbiol Rep. 2013;5:188–99.

    Article  CAS  Google Scholar 

  82. Tolli JD, Sievert SM, Taylor CD. Unexpected diversity of bacteria capable of carbon monoxide oxidation in a coastal marine environment, and contribution of the Roseobacter-associated clade to total CO oxidation. Appl Environ Microb. 2006;72:1966–73.

    Article  CAS  Google Scholar 

  83. Lenk S, Moraru C, Hahnke S, Arnds J, Richter M, Kube M, et al. Roseobacter clade bacteria are abundant in coastal sediments and encode a novel combination of sulfur oxidation genes. ISME J. 2012;6:2178–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Cunliffe M. Correlating carbon monoxide oxidation with cox genes in the abundant Marine Roseobacter Clade. ISME J. 2011;5:685–91.

    Article  CAS  PubMed  Google Scholar 

  85. King GM, Weber CF. Distribution, diversity and ecology of aerobic CO-oxidizing bacteria. Nat Rev Microbiol. 2007;5:107–18.

    Article  CAS  PubMed  Google Scholar 

  86. Giebel H-A, Wolterink M, Brinkhoff T, Simon M. Complementary energy acquisition via aerobic anoxygenic photosynthesis and carbon monoxide oxidation by Planktomarina temperata of the Roseobacter group. FEMS Microbiol Ecol. 2019;95:fiz050.

  87. Klingner A, Bartsch A, Dogs M, Wagner-Döbler I, Jahn D, Simon M, et al. Large-scale 13C flux profiling reveals conservation of the Entner-Doudoroff pathway as a glycolytic strategy among marine bacteria that use glucose. Appl Environ Microb. 2015;81:2408–22.

    Article  CAS  Google Scholar 

  88. McParland EL, Alexander H, Johnson WM. The osmolyte ties that bind: genomic insights into synthesis and breakdown of organic osmolytes in marine microbes. Front Mar Sci. 2021;8: 689306.

    Article  Google Scholar 

  89. Biersmith A, Benner R. Carbohydrates in phytoplankton and freshly produced dissolved organic matter. Mar Chem. 1998;63:131–44.

    Article  CAS  Google Scholar 

  90. Arnosti C, Wietz M, Brinkhoff T, Hehemann J-H, Probandt D, Zeugner L, et al. The biogeochemistry of marine polysaccharides: sources, inventories, and bacterial drivers of the carbohydrate cycle. Annu Rev Mar Sci. 2021;13:81–108.

    Article  CAS  Google Scholar 

  91. Hahnke S, Brock NL, Zell C, Simon M, Dickschat JS, Brinkhoff T. Physiological diversity of Roseobacter clade bacteria co-occurring during a phytoplankton bloom in the North Sea. Syst Appl Microbiol. 2013;36:39–48.

    Article  CAS  PubMed  Google Scholar 

  92. Berman T, Bronk D. Dissolved organic nitrogen: a dynamic participant in aquatic ecosystems. Aquat Microb Ecol. 2003;31:279–305.

    Article  Google Scholar 

  93. van Veen HW. Phosphate transport in prokaryotes: molecules, mediators and mechanisms. Antonie Van Leeuwenhoek. 1997;72:299–315.

    Article  PubMed  Google Scholar 

  94. Karl DM. Microbially mediated transformations of phosphorus in the sea: new views of an old cycle. Annu Rev Mar Sci. 2014;6:279–337.

    Article  Google Scholar 

  95. Chen X, Liu L, Gao X, Dai X, Han Y, Chen Q, et al. Metabolism of chiral sulfonate compound 2,3-dihydroxypropane-1-sulfonate (DHPS) by Roseobacter bacteria in marine environment. Environ Int. 2021;157: 106829.

    Article  CAS  PubMed  Google Scholar 

  96. Moran MA, Durham BP. Sulfur metabolites in the pelagic ocean. Nat Rev Microbiol. 2019;17:665–78.

    Article  CAS  PubMed  Google Scholar 

  97. Boughanemi S, Infossi P, Giudici-Orticoni M-T, Schoepp-Cothenet B, Guiral M. Sulfite oxidation by the quinone-reducing molybdenum sulfite dehydrogenase SoeABC from the bacterium Aquifex aeolicus. Biochimica Et Biophysica Acta Bba - Bioenergetics. 2020;1861: 148279.

    Article  CAS  PubMed  Google Scholar 

  98. Reisch CR, Moran MA, Whitman WB. Bacterial catabolism of dimethylsulfoniopropionate (DMSP). Front Microbiol. 2011;2:172.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Clifford EL, Varela MM, Corte DD, Bode A, Ortiz V, Herndl GJ, et al. Taurine Is a major carbon and energy source for marine prokaryotes in the North Atlantic Ocean off the Iberian peninsula. Microbial Ecol. 2019;78:299–312.

    Article  CAS  Google Scholar 

  100. Gorzynska AK, Denger K, Cook AM, Smits THM. Inducible transcription of genes involved in taurine uptake and dissimilation by Silicibacter pomeroyi DSS-3T. Arch Microbiol. 2006;185:402.m

  101. Gosink JJ, Herwig RP, Staley JT. Octadecabacter arcticus gen. nov., sp. nov., and O. antarcticus, sp. nov., nonpigmented, psychrophilic gas vacuolate bacteria from polar sea ice and water. Syst Appl Microbiol. 1997;20:356–365.

  102. Ghiglione J-F, Galand PE, Pommier T, Pedrós-Alió C, Maas EW, Bakker K, et al. Pole-to-pole biogeography of surface and deep marine bacterial communities. Proc National Acad Sci USA. 2012;109:17633–8.

    Article  CAS  Google Scholar 

  103. Brown MV, Lauro FM, DeMaere MZ, Muir L, Wilkins D, Thomas T, et al. Global biogeography of SAR11 marine bacteria. Mol Syst Biol. 2012;8:595–595.

    Article  PubMed  PubMed Central  Google Scholar 

  104. Achtman M, Wagner M. Microbial diversity and the genetic nature of microbial species. Nat Rev Microbiol. 2008;6:431–40.

    Article  CAS  PubMed  Google Scholar 

  105. Malmstrom RR, Rodrigue S, Huang KH, Kelly L, Kern SE, Thompson A, et al. Ecology of uncultured Prochlorococcus clades revealed through single-cell genomics and biogeographic analysis. ISME J. 2013;7:184–98.

    Article  CAS  PubMed  Google Scholar 

  106. Berube PM, Biller SJ, Hackl T, Hogle SL, Satinsky BM, Becker JW, et al. Single cell genomes of Prochlorococcus, Synechococcus, and sympatric microbes from diverse marine environments. Sci Data. 2018;5: 180154.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Garcia SL, Stevens SLR, Crary B, Martinez-Garcia M, Stepanauskas R, Woyke T, et al. Contrasting patterns of genome-level diversity across distinct co-occurring bacterial populations. ISME J. 2018;12:742–55.

    Article  CAS  PubMed  Google Scholar 

  108. Luo H, Csűros M, Hughes AL, Moran MA. Evolution of divergent life history strategies in marine Alphaproteobacteria. mBio. 2013;4:e00373–13.

  109. Billerbeck S, Wemheuer B, Voget S, Poehlein A, Giebel H-A, Brinkhoff T, et al. Biogeography and environmental genomics of the Roseobacter-affiliated pelagic CHAB-I-5 lineage. Nat Microbiol. 2016;1:16063.

Download references

Acknowledgements

We want to thank all researchers and crew members of research vessels who collected the samples in the global oceans which were the basis for metagenomics analyses and enabled us to recruit MAGs of the RCA cluster.

Funding

Open Access funding enabled and organized by Projekt DEAL. This work was supported by Deutsche Forschungsgemeinschaft within the Collaborative Research Center Roseobacter (TRR51). SS was supported by funding from the ETH and the Swiss National Science Foundation (SNSF) through project grants 205321_184955 and the NCCR Microbiomes (51NF40_180575). YL was supported by grants from the German Academic Exchange Service (DAAD), the Chinese Scholarship Program, and the Max-Plank Society.

Author information

Authors and Affiliations

  1. Institute for Chemistry and Biology of the Marine Environment, University of Oldenburg, Carl Von Ossietzky Str. 9-11, 26129, Oldenburg, Germany

    Yanting Liu, Thorsten Brinkhoff, Martine Berger & Meinhard Simon

  2. Max Planck Institute for Marine Microbiology, Bremen, Germany

    Yanting Liu & Rudolf Amann

  3. State Key Laboratory for Marine Environmental Science, Institute of Marine Microbes and Ecospheres, Xiamen University, Xiamen, People’s Republic of China

    Yanting Liu

  4. Department of Genomic and Applied Microbiology & Göttingen Genomics Laboratory, Georg-August University Göttingen, Grisebachstr. 8, 37077, Göttingen, Germany

    Anja Poehlein & Sonja Voget

  5. Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zurich, Switzerland

    Lucas Paoli & Shinichi Sunagawa

  6. Helmholtz Institute for Functional Marine Biodiversity at the University of Oldenburg (HIFMB), Ammerländer Heerstr. 231, 26129, Oldenburg, Germany

    Meinhard Simon

Authors
  1. Yanting Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thorsten Brinkhoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martine Berger
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anja Poehlein
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sonja Voget
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lucas Paoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shinichi Sunagawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Rudolf Amann
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Meinhard Simon
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YL carried out the analyses and wrote the draft of the manuscript. MS and TB designed the study and finished writing the manuscript. TB processed and submitted the MAG information to the SeqCode. SV carried out genome sequencing of RCA strains LE-17, IMCC 1909, 1923 and 1933 and AP the bioinformatic analyses of the RCA species. SS and LP conducted the bioinformatics to reconstruct, annotate and profile the MAGs. All authors contributed to the interpretation and understanding of the findings and commented on and read the manuscript.

Corresponding authors

Correspondence to Yanting Liu, Thorsten Brinkhoff or Meinhard Simon.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

Supplementary text S1. Table S1. Basic information of 154 MAGs of the RCA cluster. Table S2. Basic sequencing and genomic information of 87 analysed RCA MAGs/genomes (82 MAGs and 5 genomes from isolates) and locations where the metagenomic samples and isolates were collected. Table S3. Metadata for metagenomic data used to estimate the proportion of the RCA cluster and 10 of the 13 RCA species. Table S4. Metadata for metatranscriptomic data used to estimate the proportion of the RCA cluster and 10 of the 13 RCA species. Table S5. Genome data of Southern Ocean SAG [21]. Table S6. Annotation of genes encoded on PGC-containing extrachromosomal replicons of two Nereidia strains. Table S6. The incomplete prophage in the Rhodobacteraceae bacterium IMCC1909. Table S7. The GC content of genomes and PR genes in the RCA cluster. Table S8. Annotation of genes encoded on PGC-containing extrachromosomal replicons of two Nereidia strains. Figure S1. The variation of the genome size, GC content and CDS number in Planktomarina and Pseudoplanktomarina species. Values at the top indicate significant p-values. The significant difference was tested using the Wilcoxon test using the R package "rstatix" v0.7.2. Figure S2. Phylogenetic tree of southern Ocean SAG [21] and the RCA cluster. Figure S3. Phylogenetic tree of the RCA cluster with two Nereida strains as most closely related non-RCA species. Figure S4. Organization and structure of the PR operon present in genomes of RCA species. Blh: 15,15′-β-carotene dioxygenase; crt: carotenoid biosynthesis genes. Figure S5. Phylogenetic tree of the pufM gene and structure and arrangements of PGC operons detected in the RCA cluster. A: Phylogenetic tree based on 43 pufM genes. B: structure and arrangements of PGC operons detected in genomes of Planktomarina C6, C7 and C8. The tree was constructed using IQ-TREE under the LG+R10 substitution model with 1000 ultrafast bootstraps. Only bootstrap values ≥75 were shown with filled blue circles. Figure S6. Geographic distribution and relative abundance of ten of the thirteen RCA species (C1, C3-C6, B1-B4, A1) in different layers based on metagenomic and metatranscriptomic data: epipelagic (EPI, 0-200 m), mesopelagic (MES, 200-1000 m) and bathypelagic (BAT >1000 m). The dots in different colors indicated the relative abundance of the species as percent of the RCA cluster. Figure S6A - Metagenomics species A1, B1, B2. Figure S6B – Metagenomics species B3, B4/5. Figure S6C – Metagenomics species C1, C3, C4/C5, C6. Figure S6D – Metatranscriptomics species A1, B1, B2, B3, B4. Figure S6E – Metatranscriptomics species C1, C3, C4/5, C6.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Brinkhoff, T., Berger, M. et al. Metagenome-assembled genomes reveal greatly expanded taxonomic and functional diversification of the abundant marine Roseobacter RCA cluster. Microbiome 11, 265 (2023). https://doi.org/10.1186/s40168-023-01644-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40168-023-01644-5

Keywords

Microbiome

ISSN: 2049-2618

Contact us