Skip to main content

Biogeographic traits of dimethyl sulfide and dimethylsulfoniopropionate cycling in polar oceans

A Correction to this article was published on 09 November 2021

This article has been updated



Dimethyl sulfide (DMS) is the dominant volatile organic sulfur in global oceans. The predominant source of oceanic DMS is the cleavage of dimethylsulfoniopropionate (DMSP), which can be produced by marine bacteria and phytoplankton. Polar oceans, which represent about one fifth of Earth’s surface, contribute significantly to the global oceanic DMS sea-air flux. However, a global overview of DMS and DMSP cycling in polar oceans is still lacking and the key genes and the microbial assemblages involved in DMSP/DMS transformation remain to be fully unveiled.


Here, we systematically investigated the biogeographic traits of 16 key microbial enzymes involved in DMS/DMSP cycling in 60 metagenomic samples from polar waters, together with 174 metagenome and 151 metatranscriptomes from non-polar Tara Ocean dataset. Our analyses suggest that intense DMS/DMSP cycling occurs in the polar oceans. DMSP demethylase (DmdA), DMSP lyases (DddD, DddP, and DddK), and trimethylamine monooxygenase (Tmm, which oxidizes DMS to dimethylsulfoxide) were the most prevalent bacterial genes involved in global DMS/DMSP cycling. Alphaproteobacteria (Pelagibacterales) and Gammaproteobacteria appear to play prominent roles in DMS/DMSP cycling in polar oceans. The phenomenon that multiple DMS/DMSP cycling genes co-occurred in the same bacterial genome was also observed in metagenome assembled genomes (MAGs) from polar oceans. The microbial assemblages from the polar oceans were significantly correlated with water depth rather than geographic distance, suggesting the differences of habitats between surface and deep waters rather than dispersal limitation are the key factors shaping microbial assemblages involved in DMS/DMSP cycling in polar oceans.


Overall, this study provides a global overview of the biogeographic traits of known bacterial genes involved in DMS/DMSP cycling from the Arctic and Antarctic oceans, laying a solid foundation for further studies of DMS/DMSP cycling in polar ocean microbiome at the enzymatic, metabolic, and processual levels.

Video Abstract


The volatile organosulfur compound dimethyl sulfide (DMS) is the main source of marine sulfate aerosols [1], a key player in the global sulfur cycle [2], and an important nutrient for many organisms (e.g., marine algae [3], coral reefs [4], and heterotrophic bacteria [5]). Although DMS can be produced and removed by a variety of abiotic processes, biological transformations, particularly bacterial production and consumption, exert great influence on the oceanic DMS budget [6].

The predominant source of oceanic DMS is bacterial cleavage of dimethylsulfoniopropionate (DMSP). DMS can also be produced by direct cleavage of intracellular DMSP in DMSP-producing phytoplankton [7]. DMSP cleavage is mediated via several known DMSP lyases, including an algal DMSP lyase (Alma1) [7] and 7 bacterial DMSP lyases (DddD, DddL, DddY, DddQ, DddK, DddW, and DddP) (Table 1, Fig. 1) [6]. Dimethylsulfoxide (DMSO) is another precursor of DMS, which is ubiquitous in surface ocean waters, the sea-ice zone and sediments [25,26,27]. DMSO can be reduced to DMS in marine algae although the enzymes involved remain to be identified [3]. In bacteria, DMSO reduction to DMS is carried out by the DMSO reductase, DMSOR (Fig. 1) [28]. Moreover, DMS production can also be mediated by the microbial transmethylation of methanethiol (MeSH) via a methyltransferase MddA (Fig. 1) [24]. Similar to DMS, MeSH is also a volatile organic sulfur compound [29]. The transformation of MeSH to DMS plays a role in DMS production in both marine and terrestrial environments [24, 30].

Table 1 A list of key enzymes involved in DMS/DMSP cycling
Fig. 1
figure 1

The conceptual sketch of known key proteins and pathways involved in microbial DMS/DMSP cycling. The different catabolic pathways are marked in different colours and distinguished using numbers 1–7. The dotted arrow indicated that a series of enzymatic reactions are required to form the end product

Bacterial oxidation is the primary process for DMS removal in the marine environment [31]. Microbial oxidation of DMS to DMSO represents a major sink of DMS in surface seawater [32]. Three enzymes capable of DMS oxidation have been identified, the multicomponent monooxygenase DsoABCDEF [21], the DMS dehydrogenase DdhABC [22], and the flavin-containing trimethylamine (TMA) monooxygenase Tmm [20]. DMS can also be converted to MeSH by the two-component DMS monooxygenase DmoAB (Fig. 1) [23].

The biosynthesis of DMSP is initiated from methionine (Met) through four different pathways, including two methylation pathways, a transamination pathway, and a decarboxylation pathway [6]. It is generally accepted that marine phytoplankton are likely the main producers [33] although marine bacteria are also known to produce DMSP [10, 11]. To date, two eukaryotic isozymes in the key step of the transamination pathway have been identified, methylthiohydroxybutryate (MTHB) methyltransferases DSYB [8] and TpMMT [9]. In bacteria, the dsyB gene encoding a MTHB methyltransferase in the transamination pathway [10] and the mmtN gene encoding a Met methyltransferase in the methylation pathway [11] have been identified very recently (Fig. 1). As the main precursor of DMS, DMSP is the most abundant organosulfur compound in the marine environment [34]. It is estimated that DMSP synthesis accounts for ~ 1 to 10% of global marine primary production [35]. In addition to the cleavage pathway which transforms DMSP to DMS, DMSP can be also metabolized to MeSH via a demethylation pathway [35]. It is estimated that between 50 and 90% of DMSP is metabolized by marine bacteria through this pathway [36]. The first step of the demethylation pathway is conducted by the DMSP demethylase DmdA, which converts DMSP to methylmercaptopropionate (MMPA); MMPA is subsequently transformed to MeSH through a series of catalytic reactions (Fig. 1) [19, 35]. DmdA is thought to be present in up to 20% of marine bacteria, mainly in the marine Roseobacter and SAR11 clades [37, 38]. Most recently, a structurally unusual metabolite, dimethylsulfoxonium propionate (DMSOP), has been reported, which is produced from DMSP and a previously undescribed biogenic source of DMSO [39]. However, enzymes involved in the metabolism of DMSOP have not yet been identified.

Although historically, each pathway in DMS/DMSP cycling is discovered independent, many of these genes co-exist in bacteria. For example, it is reported that Ruegeria pomeroyi DSS-3 has 3 different DMSP lyases, DddQ [40], DddW [17], and DddP [18], the DMSP demethylase DmdA [35] as well as the trimethylamine monooxygenase Tmm [32]. The SAR11 clade marine bacterium Pelagibacter sp. HTCC1062 contains DddK [41], DmdA [42], and Tmm [20]. It is also capable of catabolizing DMSP to DMS and MeSH [43] and DMS oxidation to DMSO [32]. The DMSP-producing bacterium Labrenzia aggregata LZB033 possessing methyltransferase DsyB can also carry out DMSP cleavage using the DMSP lyase DddL [10]. However, the reason why one bacterium carrying different types of enzymes involved in DMS/DMSP cycling, and its ecological function remain elusive.

The Arctic and Antarctic are two of the most geographically separated bioregions on Earth with extreme environmental conditions. High concentrations of DMS/DMSP have been detected in both the Arctic Ocean and the Southern Ocean (Table 2). Indeed, the world’s highest concentration of DMS in marine surface water was recorded in the Southern Ocean, which contributes significantly to the global oceanic DMS sea-air flux [60, 61]. Previous studies on Arctic and Antarctic DMS/DMSP cycling mainly focused on quantifying the spatial and temporal concentrations as well as the turnover rates of these compounds [44, 55]. Investigations on the abundance and diversity of potential genes involved in DMS/DMSP cycling in polar oceans are limited to a few selected genes involved in DMSP degradation, e.g., DmdA, DddD, DddL, and DddP, via metagenomics [62, 63], qPCR [37], or gene clone library analyses [64, 65]. With the global warming threat, the polar regions are experiencing rapid changes including sea ice melting [66, 67] that is known to correlate with the reduced production of DMS/DMSP [61, 68], and this, in turn, may feedback to the global climate. Thus, interpreting the biogeographic traits of DMS/DMSP cycling in Arctic and Antarctic oceans is an urgent task. We postulate that the biogeographic traits of DMS/DMSP cycling in polar oceans may be similar and are less affected by dispersal limitation since similar microbial community structure was observed in these regions [69]. Moreover, considering high concentrations and fast turnover rates of DMS/DMSP have been recorded in polar oceans [26, 27], we hypothesize that genes involved in DMS/DMSP cycling are common in polar ocean microbiome. In this study, we set out to systematically uncover the distribution and abundance of 16 functional microbial enzymes involved in DMS/DMSP cycling (Table 1) in the Arctic and Antarctic oceans via metagenomic and metatranscriptomic analyses in order to gain a global overview of microbial transformation of DMS/DMSP in polar oceans.

Table 2 DMS and DMSP concentrations in environmental samples obtained from the polar oceans

Materials and methods

Bioinformatic analyses of genes involved in DMS/DMSP cycling

The sampling locations (Fig. 2), sequencing, and assembly of 60 polar seawater samples (Table S1, NCBI BioProject accession no. PRJNA588686) and 214 metagenome assembled genomes (MAGs, Table S3, NCBI BioProject accession no. SUB7116349) have been described previously [69]. These polar seawater samples were prefiltered through 20-μm polycarbonate membrane filters (Millipore, MA, USA) and cells were then filtered onto 0.22-μm polycarbonate membrane filters, as such algal genes involved in DMSP/DMS metabolism were not analysed in this study. According to the sampling locations and depths, the 60 metagenomic samples were separated into four groups: Arctic-Surface (0–100 m, n = 16), Arctic-Deep (300–3800 m, n = 23), Antarctic-Surface (0 m, n = 12), and Antarctic-Deep (300–3500 m, n = 9). For comparison, metagenomes of 174 non-polar seawater samples (Table S4) were also analysed, including 139 surface seawater (5–188 m) and 35 deep seawater (250–1000 m) samples from the Tara Oceans project [70] (fraction size, 0.22–3 μm; Additionally, 151 metatranscriptomes (99 non-polar seawater samples and 52 polar seawater samples; Table S5) and all microbial genomes (as of March 9, 2021) in the IMG/M database [71] were also used for analysis.

Fig. 2
figure 2

Geographic distribution of the sampling locations of the metagenomic (blue symbols) and metatranscriptomic (purple symbols) samples from polar (indicated by stars) and non-polar (indicated by dots) ocean

The functionally ratified protein sequences (Table 3), namely MmtN, DsyB, DddD, DddK, DddP, DddQ, DddW, DddL, DddY, DmdA, DMSOR, Tmm, DsoB (a key catalytic subunit of monooxygenase DsoABCDEF), DdhA (the catalytic subunit of DMS dehydrogenase DdhABC), MddA, and DmoA (the catalytic subunit of DMS monooxygenase DmoAB), were obtained from the National Center for Biotechnology Information (NCBI) database ( or the IMG/M database [71]. Homologues of DsoB and DmoA in metagenomes/metatranscriptomes were obtained using BLASTP, since both of which only had one biochemically characterized protein (Table 3). For the other 14 proteins involved in DMS/DMSP cycling, hidden Markov models (HMM) were created for each enzyme using protein sequences that are biochemically or structurally characterized and their homologues from metagenomes/metatranscriptomes were obtained using hmmsearch ( The cutoff values used were selected based on established stringency cutoff values from previous reports (Table 3). The sequences retrieved from our bioinformatics pipeline were further scrutinized for the presence of key residues involved in substrate binding or catalysis and/or validated through protein purification and further biochemical characterization (see below). The amino acid sequences of 10 conserved bacterial marker genes [75] were retrieved from the NCBI database, and the average abundance of these marker genes was used to normalize the abundance of the genes involved in DMS/DMSP cycling in metagenomic and metatranscriptomic datasets as described previously [10].

Table 3 The functionally ratified protein homologues of DMS/DMSP cycling-related enzymes

Curation and validation of predicted DMS/DMSP cycling-related genes

To further validate the environmental sequences retrieved from these marine metagenomes/metatranscriptomes, several approaches were applied to curate these datasets. Firstly, all hits of the top 5 most abundant enzymes (DddD, DddP, DddK, DmdA, and Tmm) were retrieved from the metagenomes/metatranscriptomes and aligned by MUSCLE. Maximum-likelihood phylogenetic trees were created via FastTree [76] and visualized through EvolView [77]. Phytogenic affiliation of the predicted hits was assessed using other enzymes of the same protein family as outgroups (Table S2).

Second, for those enzymes involved in DMSP/DMS cycling whose structures are available (i.e., DddK [41], DddQ [40], DddY [78], Tmm [79], and DddP [80], DMSOR [81], and DmdA [82]), we performed multiple sequence alignment of environmental hits using MUSCLE [83] and analysed the conserved key resides involved in substrate-coordination and catalysis (Figure S1).

Finally, to validate the function of predicted hits of the top 5 most abundant enzymes from our datasets, we randomly selected several environmental sequences from each group, chemically synthesized these genes, and overexpressed them in recombinant Escherichia coli for functional characterization of their enzyme activities. These included DddD (2 sequences), DddP (2 sequences), DddK (2 sequences), DmdA (1 sequence), and Tmm (2 sequences) (Table S6). The nucleotide sequences of these 9 hits were synthesized by BGI (Beijing, China), cloned, and overexpressed using the pET22b plasmid in Escherichia coli BL21 (DE3). These proteins were purification as described previously [84] and their activities were measured following the protocols from previous reports (Table S6) [12, 32, 41, 72, 80]. The newly identified DddX was not analysed in this study [85]. DddX homologs returned from Tara Oceans datasets are usually short and do not always contain the full open reading frame, making it difficult for gene synthesis and overexpression in E. coli for functional validation.

Taxonomic profiling

The amino acid sequences of predicted DMS/DMSP cycling-related genes from these metagenomes/metatranscriptomes were extracted using scripts compiled in Python code and aligned against the non-redundant protein sequences (nr) database using BLASTP [86]. The best hit of each query sequence was retrieved, and its taxon was recorded. Taxonomic classification of the assembled MAGs was performed with GTDB-Tk v0.3.2 (the script classify wf was used) [87] using the Genome Taxonomy Database (GTDB) [88].

Data analysis and visualization via bioinformatics tools

The geographical distribution of sampling locations was constructed by Ocean Data View [89]. DMS/DMSP-related protein homologs retrieved from these marine metagenomes/metatranscriptomes were analysed and visualized using the R software package [90] with the following descriptions. Relative abundance and phylogenetic diversities of DMS/DMSP cycling-related genes in polar metagenomic samples were visualized using the ‘gplots’ and the ‘ggplot2’ package [91], respectively. The Sankey diagram of the taxonomic profiling of DMS/DMSP cycling-related genes was built using the ‘ggalluvial’ package [92]. For principal coordinates analysis (PCoA), gross relative abundance in each metagenomic sample was normalized to 1, and Bray-Curtis distances were generated using the ‘vegan’ packages [93], based on the percentages of DMS/DMSP-related genes. Redundancy analysis (RDA) was performed based on the relative abundance of DMS/DMSP-related genes using the ‘vegan’ package. Geographical distance was generated using the ‘geosphere’ package ( The relationship between Bray-Curtis dissimilarity of microbial communities [94] involved in DMSP/DMS cycling and geographic distance or water depth were analysed using the Mantel test. Alpha-diversity analysis was performed on polar microbiota involved in DMS/DMSP cycling. Shannon and Simpson index was calculated using the ‘vegan’ package and plotted via Origin 2018 ( The average abundance of DMS/DMSP-related genes in metagenomic and metatranscriptomic samples from polar and non-polar oceans was used for Pearson correlation analysis. Pearson correlation coefficients and P values were calculated using ‘ggcorrplot’ packages [91]. Data processing was performed via scripts compiled in Python code. All graphs were combined via Adobe Illustrator CS5.


Curation of the environmental sequences obtained from polar and non-polar oceans and abundance of genes involved in DMS/DMSP cycling

Wherever feasible, we built hidden Markov models (HMM) for each protein involved in DMSP/DMS cycling using ratified sequences obtained from literature (Table 1). These HMM models were then used to search the polar metagenomes and metagenomes/metatranscriptomes from the Tara Ocean datasets (Table 3). Homologs of all currently known bacterial enzymes in DMS/DMSP cycling (Table 1) were found in the Arctic and Antarctic seawater samples (Fig. 3a) although majority of the samples were dominated by five putative enzymes, i.e., DddD, DddP, DddK, DmdA, and Tmm. Most of these putative enzymes involved in DMSP/DMS cycling exhibited wide geographical distributions, several of which (e.g., DddD, DddP, DmdA, Tmm) were detected in all 60 polar ocean samples (Fig. 3a, Table S1).

Fig. 3
figure 3

Relative abundance of potential genes involved in bacterial DMS/DMSP cycling. a The relative abundances of DMS/DMSP cycling-related genes in 60 polar metagenomes (left panel) and 52 polar metatranscriptomes (right panel). b The relative abundances of DMS/DMSP cycling-related genes in 174 non-polar Tara Ocean samples (left panel) and 99 non-polar metatranscriptomes (right panel). Polar metagenomes were separated into Arctic-Surface (0–100 m), Arctic-Deep (300–3800 m), Antarctic-Surface (0 m), and Antarctic-Deep (300–3500 m). Polar metatranscriptomic samples were separated into two groups: Polar-surface (0–146 m) and Polar-deep (200–1000 m) groups. Tara metagenomes and metatranscriptomes were separated into Tara-Surface (5–188 m) and Tara-Deep (250–1000 m), and Non-polar surface (0–150 m) and Non-polar deep (200–3262 m), respectively. The relative abundance of each gene was normalized against the average abundance of the 10 selected bacterial marker genes. MetaG, metagenomes; MetaT, metatranscriptomes

To evaluate the validity of our approach, we used three complementary methods to curate these sequences. First, we analysed predicted hits for the occurrence of conserved amino acid resides involved in substrate coordination and catalysis guided by biochemical data and/or available protein structures (Figure S1). Our analyses suggest that the HMM model can successfully retrieve environmental sequences that largely retained the conserved sites necessary for performing corresponding enzyme activity (Figure S1). This is supported by further phylogenetic analyses performed for the top five most abundant genes in our datasets (i.e., DddD, DddP, DddK, DmdA, and Tmm), showing that the majority of the predicted hits are affiliated with ratified enzymes (Figure S2). To validate the function of these predicted proteins, we then randomly selected 9 environmental sequences from the aforementioned five protein groups and tested their corresponding enzyme activities using purified proteins from recombinant E. coli. Indeed, these proteins retrieved from environmental samples were functional (Table S6). Taken together, our approach appears capable of retrieving bona fide sequences involved in DMS/DMSP cycling from these polar and no-polar marine omics datasets.

In contrast to proteins involved in DMSP catabolism, bacterial DMSP biosynthesis pathway (e.g., dsyB, mmtN) did not appear to be prevalent in these polar samples (Table S1). In contrast, the DmdA-mediated DMSP demethylation pathway was more prevalent, consistent with previous reports of high abundance of DmdA from other oceans [37, 62, 95]. The DMSP cleavage pathway was also numerically abundant in polar oceans, and DddD, DddP, and DddK were more frequently observed than DddW/DddQ/DddL/DddY. Moreover, the potential genes involved in the transformation between DMS and DMSO were more abundant than those between DMS and MeSH (Fig. 3a). To compare the geographic distribution of DMS/DMSP cycling between the polar and non-polar oceans, the 174 non-polar metagenome samples and the 151 metatranscriptome samples from the Tara Oceans project were analysed. Among the 16 proteins analysed, DmdA, DddD, and DddP were also the most abundant genes involved in DMS/DMSP cycling in non-polar metagenomic samples (Fig. 3b). DMS/DMSP cycling in non-polar oceans appears to be primarily driven by the DMSP demethylation pathway (DmdA), and DddD and DddP mediated DMSP cleavage pathways (Fig. 3b). In addition, the relative abundance of potential transcripts involved in DMS/DMSP cycling in non-polar and polar metatranscriptomic samples were significantly correlated with the relative abundance of potential genes in non-polar (Pearson correlation coefficient = 0.84, P value < 0.0001) and polar metagenomic samples (Pearson correlation coefficient = 0.94, P value < 0.0001), respectively (Fig. 3).

Geographic distribution traits of DMS/DMSP cycling in polar and non-polar oceans

In the metagenomic samples from the Arctic Ocean, the average relative abundance of DMS/DMSP cycling-related genes in surface waters was higher than that in deep waters. However, the opposite appears to hold true in the Southern Ocean metagenomic samples (Fig. 4a, Table S1), which may be explained by the so-called ‘high nutrient, low chlorophyll’ paradox likely caused by iron limitation in the surface layer of the Southern Ocean [96, 97]. In addition, it is noticeable that a high relative abundance of DMS/DMSP cycling-related genes, especially DMSP lyases, was found in deep seawaters over 3000 m (Fig. 4a, Table S1), implying an important role of DMS/DMSP cycling in deep ocean sulfur cycle.

Fig. 4
figure 4

Analyses of inter-sample similarity among the polar and non-polar seawater samples. a Average relative abundance of DMS/DMSP-related genes in different metagenomic sample groups. Bray-Curtis dissimilarities of all (b), polar (c), and Tara (d) metagenomic samples illustrated by PCoA analysis based on the relative abundances of DMS/DMSP-related genes. The total abundance of each metagenomic sample was normalized to 1. The percentages of variation explained by the principal coordinates are indicated on the axes. RDA analyses of sampling sites and protein types of polar samples (e) and Tara samples (f). The ordination plot was constructed using the relative abundance of DMS/DMSP-related proteins. Proteins involved in DMS/DMSP cycling are indicated by black arrows. The percentages of variation are shown on the axes

To determine the distribution characteristics of DMS/DMSP-related genes in polar and non-polar oceans, principal coordinates analysis (PCoA) and redundancy analysis (RDA) were performed. These metagenomic samples were broadly grouped into three independent coordinates: polar surface waters, Tara surface waters, and deep waters (Fig. 4b). Polar and non-polar surface waters were less similar from their gene abundance. In contrast, deep waters in polar and non-polar oceans were more similar and displayed different distribution patterns compared with the surface waters (Fig. 4c, d). Hence, the distributions of DMS/DMSP-related genes were clustered primarily based on water depth rather than geographic distance.

Further RDA analysis demonstrated that the divergence of the ordinations is mostly driven by the differences of relative abundance of certain genes in DMS/DMSP cycling in surface and deep waters (Table S7). DddK was relatively more prevalent in polar surface waters, while DddD and DddP were more common in polar deep waters (Fig. 4a, e). In non-polar oceans, DmdA and DddK were the principal elements that influenced the distribution traits of surface DMS/DMSP cycling, whereas DddD and DdhA were more influential in deep waters (Fig. 4f). The high relative abundance (Fig. 4a) and wide distribution (Fig. 4e, f) of DddK in surface waters were consistent with the fact that it is primarily originated from the SAR11 clade (Pelagibacterales) which is numerically dominant in the surface ocean [43, 95], and the broad dispersion of DddD in deep waters suggests its importance in DMS/DMSP cycling in deep waters.

Phylogenetic diversity of DMS/DMSP cycling-related genes in polar oceans

To reveal the taxonomic diversity of DMS/DMSP cycling-related proteins in polar oceans, 17,189 protein sequences from polar oceans obtained through our pipeline were aligned against the NCBI-nr database, and the taxon of each best hit with the highest accuracy to species level was extracted. Thirty phyla (26 phyla from Bacteria domain, 2 phyla from Eukaryota domain and 2 phyla from Archaea domain) spanning over 38 classes, 72 orders, and 107 families were involved in polar DMS/DMSP cycling (Table S8). Among the phyla affiliated to Bacteria, Proteobacteria accounted for 84% of the total sequences, of which the dominant classes were Alphaproteobacteria (58%) and Gammaproteobacteria (23%) (Fig. 5a, b). Sequences of DddY, DsoB, DdhA, and DmoA were dominated by Gammaproteobacteria whereas the other 12 proteins were mainly affiliated with Alphaproteobacteria (Fig. 5b). In Alphaproteobacteria (9917 sequences), the Pelagibacterales (5016 sequences) were the most abundant (Fig. 5c), in which members of DmdA, DddK, and Tmm made great contributions. Indeed, Alphaproteobacteria participated in all 7 DMS/DMSP cycling pathways (Fig. 5b), in which Pelagibacterales were involved in 5 pathways (i.e., DsyB, DddD/DddK/DddP/DddQ, DmdA, DMSOR, and Tmm) indicating their role as generalists in DMS/DMSP cycling.

Fig. 5
figure 5

Phylogenetic diversity of DMS/DMSP cycling-related genes in the Arctic and Antarctic oceans. The taxonomic compositions of microbiota involved in DMS/DMSP cycling are displayed at the class level for sample groups (a) and proteins (b). c Taxonomic profiling of DMS/DMSP cycling-related microbiota in Alphaproteobacteria (Alpha) and Gammaproteobacteria (Gamma) classes. Pro, Proteobacteria; Act, Actinobacteria; Chl, Chloroflexi. d Alpha-diversity analyses of the polar microbiomes involved in DMS/DMSP cycling. Shannon and Simpson diversity was calculated based on the taxonomic composition of DMS/DMSP-related genes, with higher values representing higher biodiversity. e Bray-Curtis dissimilarities of polar metagenomic samples illustrated by PCoA analysis based on the taxonomic compositions of DMS/DMSP-related genes. The DMS/DMSP-related community composition of each metagenomic sample was normalized to 1. f Correlation between dissimilarity of DMS/DMSP-related bacterial community and water depth in polar oceans. The Bray-Curtis dissimilarity index was used. The correlation coefficients (r) and Spearman’s correlation P value (P) were indicated

Regardless of the abundance of the potential genes, DddD, DddP, and MddA exhibited high phylogenetic diversities (Fig. 5d). In contrast, MmtN (100% from Sphingomonadales), DddW (100% from Rhodobacterales), DddY (100% from Alteromonadales), and DddK (99% from Pelagibacterales) were highly conserved at the order level (Table S8). Similarly, the biogeographic patterns of DMS/DMSP cycling in polar oceans were mainly driven by water depth (Fig. 5e) rather than geographical distance. In addition, the dissimilarity of community composition of DMS/DMSP-related genes among polar seawater samples was in line with a depth-decay relationship (Fig. 5f) instead of a distance-decay relationship (Figure S3). Thus, environmental conditions were likely more important than dispersal limitation in determining community composition of DMS/DMSP-related genes.

DMS/DMSP cycling traits in MAGs obtained from polar oceans

In the majority of the metagenomic samples from both polar and non-polar oceans, the cumulative relative abundance of DMS/DMSP-related genes exceeded 1 (Fig. 3a, b), suggesting that some bacteria may harbour more than one key gene in one or more DMS/DMSP metabolic pathways. We thus carried out co-occurrence analyses of key genes involved in DMS/DMSP metabolic pathways using MAGs assembled from these polar ocean metagenomes. Two hundred and fourteen microbial MAGs (> 80% completeness and < 2% potential contamination) belonging to 23 classes (Table S3) were recovered from these 60 polar metagenomes [69]. One hundred and forty-three MAGs affiliated with 15 classes including 70 families (Table S3) were found to contain at least one gene involved in DMS/DMSP cycling (Fig. 6a). Of these 143 MAGs, 63 MAGs had more than one key gene in the DMS/DMSP metabolic pathways. Overall, at the gene level, these MAGs had 13 different genes (as indicated by the nodes) and 28 co-occurrence combinations (as indicated by the edges, Fig. 6b). At the pathway level, the genes in these MAGs contributed to 7 different DMS/DMSP pathways with 12 co-occurrence combinations (Fig. 6c). According to the biological network analysis, the DMSP demethylation pathway (DmdA) and DMSP cleavage pathway (DddD) maintained the most frequent coexistence relationship (Fig. 6b, c), which also formed a close clustering relationship with genes responsible for the transformation between DMS and DMSO (Fig. 6b, c).

Fig. 6
figure 6

The gene frequency and taxonomic composition of polar metagenome assembled genomes (MAGs) involved in DMS/DMSP cycling in polar oceans. a Frequency of DMS/DMSP cycling-related genes in 143 (out of 214) polar MAGs. MAGs are separated into four groups with MAGs in groups 1 to 3 carrying 1 to 3 types of DMS/DMSP cycling-related genes, MAGs in group 4 contains more than 3 types of DMS/DMSP cycling-related genes. The co-occurrence networks of protein-protein (b, d) and pathway-pathway (c, e) coexistence modes in DMS/DMSP cycling in MAGs obtained from polar oceans (b, c) compared to all microbial genomes in the IMG/M database (d, e). The cluster of each network was shown in its lower left corner. Each node in the networks indicates one protein (b, d) or one pathway (c, e) involved in DMS/DMSP cycling. The proteins in the same catabolic pathway (as indicated in Table 1) are marked using the same colour, and different pathways are distinguished using numbers 1–7. The size of nodes and the thickness of edges represent the frequencies of genes and MAGs carrying multiple genes involved in DMS/DMSP cycling, respectively.

To uncover the co-occurrence of these genes in DMS/DMSP metabolism, we carried out a comprehensive co-occurrence network analysis of all microbial genomes in the IMG/M database, which contained genomes of 10285 isolates, 2120 Single cell Amplified Genomes (SAGs) and 5267 MAGs. At the time of the analysis (March 9, 2021), IMG/M included 2428 genomes, of which 412 genomes had more than one gene involved in DMS/DMSP metabolism (Table S9). At the gene level, these combinations yielded 50 one-to-one gene configuration modes (Fig. 6d), with DddP being the most frequent enzyme present in these genome-sequenced microbial strains, while DddL being the most connected gene coexisting with other genes involved in DMS/DMSP metabolism. At the pathway level, 14 different pathway co-occurrence patterns were observed (Fig. 6e). Interestingly, strong co-existence clustering among various DMSP-degradation pathways were observed in both MAGs from polar oceans and microbial genomes from the IMG/M, suggesting marine microbes likely employ multiple routes for DMSP catabolism. However, DMSP cleavage pathway and DMSP biosynthesis pathway showed stronger connection in microbial genomes from the IMG/M than MAGs from polar oceans metagenomes.


Here, we investigated bacteria mediated DMS/DMSP cycling in 60 seawater metagenomes and 214 MAGs obtained from polar oceans and compared them with metagenomes and metatranscriptomes from the Tara Ocean datasets. The relative abundance and phylogenetic analyses of these potential genes involved in DMS/DMSP cycling in polar oceans suggested that there appears to be an intense and integrated DMS/DMSP cycle in polar oceans (Fig. 7). DmdA, DddD, DddP DddK, and Tmm appear to be the dominant genes involved in DMS/DMSP cycling, and Alpha- and Gamma-proteobacteria made the largest contributions. Globally, the geographic distribution of DMS/DMSP cycling was significantly influenced by water depth, which may be due to the differences in microbial assemblages caused by environmental selections. Furthermore, the coexistence of DMS/DMSP-related proteins in marine bacterial genomes was not a rare trait in polar oceans.

Fig. 7
figure 7

The conceptual diagram of bacterial DMS/DMSP metabolism in polar and non-polar oceans based on the analysis of the relative abundance of the potential genes involved in DMS/DMSP cycles. The thickness of the edge represents the relative abundance of the potential genes in each pathway. The arrowheads indicate the flow directions of organic sulfur compounds. Potential genes contributing more than 20% of the total relative abundance in each pathway are shown

Met is the sulfocompound for the initiation of DMSP biosynthesis [34]. Given the presence of a low abundance of bacterial DMSP biosynthesis genes, DMSP in polar and non-polar oceans may largely be produced by phytoplankton in surface waters [27, 48, 58, 98, 99], which can then be transported to the deep ocean [100] through sinking particles.

Based on our analysis of the relative abundance of potential genes, a large proportion of DMSP may act as intermediates, while most of the sulfur from Met may ultimately be channelled into the production of DMS and especially MeSH. Considerable MeSH may thus accumulate in the polar oceans, which certainly warrants further investigation by measuring its in situ concentration in these polar environments. Our hypothesis is indeed supported by the high abundance and active transcription of DmdA in situ in metatranscriptomic samples (Fig. 3). Thus, the produced MeSH may provide a substantial budget for other physiological processes, such as MeSH oxidation to hydrogen sulfide by the MeSH oxidase (MTO) enzyme [74]. MTO was found to be abundant and widely distributed in both metagenomic and especially in metatranscriptomic samples in this study (Table S10).

Similarly, the relative abundance of Tmm and DMSOR in polar oceans suggested that the production DMS and DMSO were likely unbalanced, which may result in DMSO accumulation. Indeed, high concentrations of DMSO have been detected in both polar oceans waters and sea ice [25, 27, 47, 101], where they may act as cryoprotectants, osmoregulants, or cellular anti-oxidants in bacteria to cope with the extreme environments of the polar regions [102]. Besides, Tmm is also responsible for TMA oxidation to trimethylamine N-oxide (TMAO) [20] and the Tmm-mediated DMS oxidation to DMSO is a methylamine-dependent process [32], which suggests the presence of an inter-connected nitrogen-sulfur cycle through Tmm-mediated DMS oxidation.

Overall, the relative abundance of genes involved in DMS/DMSP cycling in polar oceans appears to be higher than that in non-polar oceans (Fig. 6). Interestingly, this corroborates with the fact that higher concentrations of DMS/DMSP were recorded at poles (Table 2) and turnover of DMS/DMSP at poles also appeared faster [26, 27] according to previous studies. Our results suggested that the dissimilarity of biogeographic traits of DMS/DMSP cycling was barely affected by dispersal limitation [103]. Instead, the similarities of environment conditions (i.e., illumination, temperature and salinity) at the same water layers may play a leading role [104]. The biogeographic traits tended to be more similarity in polar oceans which is consistent with bipolar distribution of marine bacteria [105, 106]. It is intriguing that biogeographic pattern of genes involved in DMS/DMSP cycling appears more similar in deep waters than surface waters. This may be due to the long-term stability and connectivity of deep waters [105]. However, there is still divergence between polar and non-polar surface waters, where the microbial communities suffered from short-term changing environmental conditions (e.g., changes in illumination and weather), consistent with the ecological theory that states ‘Everything is everywhere but the environment selects’ [107]. Future work on standing concentrations and turnover rates of these organic sulfurs and their response to environmental changes may shed new light on our understanding of their cycling in a changing climate.


Overall, this study provides a global overview of the biogeographic traits of known bacterial genes involved in DMS/DMSP cycling from the Arctic and Antarctic oceans, laying a solid foundation for further studies of DMS/DMSP cycling in polar ocean microbiome at the enzymatic, metabolic, and processual levels.

Change history



Dimethyl sulfide




Metagenome assembled genomes








Trimethylamine N-oxide








Dimethylsulfoxonium propionate


Basic local alignment search tool


Genome taxonomy database


National center for biotechnology information


Hidden markov models


Principal coordinates analysis


Redundancy analysis


High nutrient, low chlorophyll


  1. Gondwe M, Krol M, Gieskes W, Klaassen W, de Baar H. The contribution of ocean-leaving DMS to the global atmospheric burdens of DMS, MSA, SO2, and NSS SO4=. Glob Biogeochem Cycles. 2003;17:25.

    Google Scholar 

  2. Mahajan AS, Fadnavis S, Thomas MA, Pozzoli L, Gupta S, Royer S-J, et al. Quantifying the impacts of an updated global dimethyl sulfide climatology on cloud microphysics and aerosol radiative forcing. J Geophys Res Atmos. 2015;120:2524–36.

    Article  CAS  Google Scholar 

  3. Spiese CE, Kieber DJ, Nomura CT, Kiene RP. Reduction of dimethylsulfoxide to dimethylsulfide by marine phytoplankton. Limnol Oceanogr. 2009;54:560–70.

    Article  CAS  Google Scholar 

  4. Deschaseaux E, Jones GB, Swan HB. Dimethylated sulfur compounds in coral-reef ecosystems. Environ Chem. 2016;13:239–51.

    Article  CAS  Google Scholar 

  5. Bennett B, Benson N, McEwan AG, Bray RC. Multiple states of the molybdenum centre of dimethylsulphoxide reductase from Rhodobacter capsulatus revealed by EPR spectroscopy. Eur J Biochem. 1994;225:321–31.

    Article  CAS  PubMed  Google Scholar 

  6. Zhang X-H, Liu J, Liu J, Yang G, Xue C-X, Curson ARJ, et al. Biogenic production of DMSP and its degradation to DMS—their roles in the global sulfur cycle. Sci China Life Sci. 2019;62:1296–319.

  7. Alcolombri U, Ben-Dor S, Feldmesser E, Levin Y, Tawfik DS, Vardi A. Marine sulfur cycle. Identification of the algal dimethyl sulfide-releasing enzyme: a missing link in the marine sulfur cycle. Science. 2015;348:1466–9.

    Article  CAS  PubMed  Google Scholar 

  8. Curson A, Williams B, Pinchbeck B, Sims L, Martínez A, Rivera P, et al. DSYB catalyses the key step of dimethylsulfoniopropionate biosynthesis in many phytoplankton. Nat Microbiol. 2018;3:430–39.

  9. Kageyama H, Tanaka Y, Shibata A, Waditee-Sirisattha R, Takabe T. Dimethylsulfoniopropionate biosynthesis in a diatom Thalassiosira pseudonana: identification of a gene encoding MTHB-methyltransferase. Arch Biochem Biophys. 2018;645:100–6.

    Article  CAS  PubMed  Google Scholar 

  10. Curson AR, Liu J, Bermejo Martinez A, Green RT, Chan Y, Carrion O, et al. Dimethylsulfoniopropionate biosynthesis in marine bacteria and identification of the key gene in this process. Nat Microbiol. 2017;2:17009.

    Article  CAS  PubMed  Google Scholar 

  11. Williams BT, Cowles K, Bermejo Martinez A, Curson ARJ, Zheng Y, Liu J, et al. Bacteria are important dimethylsulfoniopropionate producers in coastal sediments. Nat Microbiol. 2019;4:1815–25.

    Article  CAS  PubMed  Google Scholar 

  12. Alcolombri U, Laurino P, Lara-Astiaso P, Vardi A, Tawfik DS. DddD is a CoA-transferase/lyase producing dimethyl sulfide in the marine environment. Biochemistry. 2014;53:5473–5.

    Article  CAS  PubMed  Google Scholar 

  13. Sullivan MJ, Curson AR, Shearer N, Todd JD, Green RT, Johnston AW. Unusual regulation of a leaderless operon involved in the catabolism of dimethylsulfoniopropionate in Rhodobacter sphaeroides. PLoS One. 2011;6:e15972.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ansede JH, Pellechia PJ, Yoch DC. Metabolism of acrylate to beta-hydroxypropionate and its role in dimethylsulfoniopropionate lyase induction by a salt marsh sediment bacterium, Alcaligenes faecalis M3A. Appl Environ Microbiol. 1999;65:5075–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Brummett AE, Dey M. New mechanistic insight from substrate- and product-bound structures of the metal-dependent dimethylsulfoniopropionate lyase DddQ. Biochemistry. 2016;55:6162–74.

    Article  CAS  PubMed  Google Scholar 

  16. Schnicker NJ, De Silva SM, Todd JD, Dey M. Structural and biochemical insights into dimethylsulfoniopropionate cleavage by cofactor-bound DddK from the prolific marine bacterium Pelagibacter. Biochemistry. 2017;56:2873–85.

    Article  CAS  PubMed  Google Scholar 

  17. Brummett AE, Schnicker NJ, Crider A, Todd JD, Dey M. Biochemical, kinetic, and spectroscopic characterization of Ruegeria pomeroyi DddW--a mononuclear iron-dependent DMSP lyase. PLoS One. 2015;10:e0127288.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Todd JD, Curson ARJ, Dupont CL, Nicholson P, Johnston AWB. The dddP gene, encoding a novel enzyme that converts dimethylsulfoniopropionate into dimethyl sulfide, is widespread in ocean metagenomes and marine bacteria and also occurs in some Ascomycete fungi. Environ Microbiol. 2009;11:1624–5.

    Article  Google Scholar 

  19. Shao X, Cao HY, Zhao F, Peng M, Wang P, Li CY, et al. Mechanistic insight into 3-methylmercaptopropionate metabolism and kinetical regulation of demethylation pathway in marine dimethylsulfoniopropionate-catabolizing bacteria. Mol Microbiol. 2019;111:1057–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Chen Y, Patel NA, Crombie A, Scrivens JH, Murrell JC. Bacterial flavin-containing monooxygenase is trimethylamine monooxygenase. Proc Natl Acad Sci U S A. 2011;108:17791–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Horinouchi M, Yoshida T, Nojiri H, Yamane H, Omori T. Polypeptide requirement of multicomponent monooxygenase DsoABCDEF for dimethyl sulfide oxidizing activity. Biosci Biotechnol Biochem. 1999;63:1765–71.

    Article  CAS  PubMed  Google Scholar 

  22. McDevitt CA, Hanson GR, Noble CJ, Cheesman MR, McEwan AG. Characterization of the redox centers in dimethyl sulfide dehydrogenase from Rhodovulum sulfidophilum. Biochemistry. 2002;41:15234–44.

    Article  CAS  PubMed  Google Scholar 

  23. Boden R, Murrell JC, Schafer H. Dimethylsulfide is an energy source for the heterotrophic marine bacterium Sagittula stellata. FEMS Microbiol Lett. 2011;322:188–93.

    Article  CAS  PubMed  Google Scholar 

  24. Carrión O, Pratscher J, Curson ARJ, Williams BT, Rostant WG, Murrell JC, et al. Methanethiol-dependent dimethylsulfide production in soil environments. ISME J. 2017;11:2379–90.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Hatton AD. DMSP removal and DMSO production in sedimenting particulate matter in the northern North Sea. Deep Sea Res 2 Top Stud Oceanogr. 2002;49:3053–65.

    Article  CAS  Google Scholar 

  26. Asher EC, Dacey JWH, Mills MM, Arrigo KR, Tortell PD. High concentrations and turnover rates of DMS, DMSP and DMSO in Antarctic sea ice. Geophys Res Lett. 2011;38:L23609.

  27. Asher E, Dacey JW, Ianson D, Peña A, Tortell PD. Concentrations and cycling of DMS, DMSP, and DMSO in coastal and offshore waters of the Subarctic Pacific during summer, 2010-2011. J Geophys Res Oceans. 2017;122:3269–86.

    Article  CAS  Google Scholar 

  28. Bray RC, Adams B, Smith AT, Richards RL, Lowe DJ, Bailey S. Reactions of dimethylsulfoxide reductase in the presence of dimethyl sulfide and the structure of the dimethyl sulfide-modified enzyme. Biochemistry. 2001;40:9810–20.

    Article  CAS  PubMed  Google Scholar 

  29. Sievert S, Kiene R, Schulz-Vogt H. The sulfur cycle. Oceanogr. 2007;20:117–23.

  30. Carrión O, Pratscher J, Richa K, Rostant WG, Farhan Ul Haque M, Murrell JC, et al. Methanethiol and dimethylsulfide cycling in Stiffkey Saltmarsh. Front Microbiol. 2019;10:1040.

  31. Kiene RP, Bates TS. Biological removal of dimethyl sulphide from sea water. Nature. 1990;345:702–5.

    Article  CAS  Google Scholar 

  32. Lidbury I, Krober E, Zhang Z, Zhu Y, Murrell JC, Chen Y, et al. A mechanism for bacterial transformation of dimethylsulfide to dimethylsulfoxide: a missing link in the marine organic sulfur cycle. Environ Microbiol. 2016;18:2754–66.

    Article  CAS  PubMed  Google Scholar 

  33. Stefels J. Physiological aspects of the production and conversion of DMSP in marine algae and higher plants. J Sea Res. 2000;43:183–97.

    Article  CAS  Google Scholar 

  34. Bullock HA, Luo HW, Whitman WB. Evolution of dimethylsulfoniopropionate metabolism in marine phytoplankton and bacteria. Front Microbiol. 2017;8:17.

    Article  Google Scholar 

  35. Howard EC, Henriksen JR, Buchan A, Reisch CR, Burgmann H, Welsh R, et al. Bacterial taxa that limit sulfur flux from the ocean. Science. 2006;314:649–52.

    Article  CAS  PubMed  Google Scholar 

  36. Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, Eisen JA, et al. Environmental genome shotgun sequencing of the Sargasso Sea. Science. 2004;304:66–74.

    Article  CAS  PubMed  Google Scholar 

  37. Cui Y, Suzuki S, Omori Y, Wong SK, Ijichi M, Kaneko R, et al. Abundance and distribution of dimethylsulfoniopropionate degradation genes and the corresponding bacterial community structure at dimethyl sulfide hot spots in the tropical and subtropical pacific ocean. Appl Environ Microbiol. 2015;81:4184–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Varaljay VA, Howard EC, Sun S, Moran MA. Deep sequencing of a dimethylsulfoniopropionate-degrading gene (dmdA) by using PCR primer pairs designed on the basis of marine metagenomic data. Appl Environ Microbiol. 2010;76:609–17.

    Article  CAS  PubMed  Google Scholar 

  39. Thume K, Gebser B, Chen L, Meyer N, Kieber DJ, Pohnert G. The metabolite dimethylsulfoxonium propionate extends the marine organosulfur cycle. Nature. 2018;563:412–5.

    Article  CAS  PubMed  Google Scholar 

  40. Li CY, Wei TD, Zhang SH, Chen XL, Gao X, Wang P, et al. Molecular insight into bacterial cleavage of oceanic dimethylsulfoniopropionate into dimethyl sulfide. Proc Natl Acad Sci U S A. 2014;111:1026–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Peng M, Chen XL, Zhang D, Wang XJ, Wang N, Wang P, et al. Structure-function analysis indicates that an active-site water molecule participates in dimethylsulfoniopropionate cleavage by DddK. Appl Environ Microbiol. 2019;85:e03127–18.

  42. Reisch CR, Moran MA, Whitman WB. Dimethylsulfoniopropionate-dependent demethylase (DmdA) from Pelagibacter ubique and Silicibacter pomeroyi. J Bacteriol. 2008;190:8018–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Sun J, Todd JD, Thrash JC, Qian Y, Qian MC, Temperton B, et al. The abundant marine bacterium Pelagibacter simultaneously catabolizes dimethylsulfoniopropionate to the gases dimethyl sulfide and methanethiol. Nat Microbiol. 2016;1:16065.

    Article  CAS  PubMed  Google Scholar 

  44. Luce M, Levasseur M, Scarratt MG, Michaud S, Royer SJ, Kiene R, et al. Distribution and microbial metabolism of dimethylsulfoniopropionate and dimethylsulfide during the 2007 Arctic ice minimum. J Geophys Res. 2011;116:C00G06.

  45. Matrai PA, Vernet M. Dynamics of the vernal bloom in the marginal ice zone of the Barents Sea: dimethyl sulfide and dimethylsulfoniopropionate budgets. J Geophys Res. 1997;102:22965–79.

    Article  CAS  Google Scholar 

  46. Motard-Côté J, Levasseur M, Scarratt MG, Michaud S, Gratton Y, Rivkin RB, Keats K, Gosselin M, Tremblay JÉ, Kiene RP, Lovejoy C. Distribution and metabolism of dimethylsulfoniopropionate (DMSP) and phylogenetic affiliation of DMSP-assimilating bacteria in northern Baffin Bay/Lancaster Sound. J Geophys Res Oceans. 2012;117:C00G11.

  47. Bouillon R-C, Lee PA, de Mora SJ, Levasseur M, Lovejoy C. Vernal distribution of dimethylsulphide, dimethylsulphoniopropionate, and dimethylsulphoxide in the North Water in 1998. Deep Sea Res 2 Top Stud Oceanogr. 2002;49:5171–89.

    Article  CAS  Google Scholar 

  48. Lee PA, De Mora S, Gosselin M, Levasseur M, Bouillon R, Nozais C, et al. Particulate dimethylsulfoxide in Arctic sea-ice algal communities: the cryoprotectant hypothesis revisited. J Phycol. 2001;37:488–99.

    Article  Google Scholar 

  49. Leck C, Persson C. The central Arctic Ocean as a source of dimethyl sulfide seasonal variability in relation to biological activity. Tellus B. 1996;48:156–77.

    Article  Google Scholar 

  50. Damm E, Kiene RP, Schwarz J, Falck E, Dieckmann G. Methane cycling in Arctic shelf water and its relationship with phytoplankton biomass and DMSP. Mar Chem. 2008;109:45–59.

    Article  CAS  Google Scholar 

  51. Tortell PD, Gueguen C, Long MC, Payne CD, Lee P, DiTullio GR. Spatial variability and temporal dynamics of surface water pCO2, ΔO2/Ar and dimethylsulfide in the Ross Sea. Antarctica. Deep Sea Res 1 Oceanogr Res Pap. 2011;58:241–59.

    Article  CAS  Google Scholar 

  52. Mctaggart AR, Burton HR. Dimethyl sulfide concentrations in the surface waters of the Australasian Antarctic and Subantarctic oceans during an austral summer. J Geophys Res. 1992;97:14407–12.

    Article  CAS  Google Scholar 

  53. Gibson JAE, Garrick RC, Burton HR, McTaggart AR. Dimethylsulfide and the alga Phaeocystis pouchetii in antarctic coastal waters. Mar Biol. 1990;104:339–46.

    Article  CAS  Google Scholar 

  54. Delille B, Jourdain B, Borges AV, Tison J-L, Delille D. Biogas (CO2, O2, dimethylsulfide) dynamics in spring Antarctic fast ice. Limnol Oceanogr. 2007;52:1367–79.

    Article  CAS  Google Scholar 

  55. Jones GB, Fortescue D, King S, Williams GD, Wright SW. Dimethylsulphide and dimethylsulphoniopropionate in the South-West Indian Ocean sector of East Antarctica from 30° to 80°E during BROKE-West. Deep Sea Res 2 Top Stud Oceanogr. 2010;57:863–76.

    Article  CAS  Google Scholar 

  56. Rellinger AN, Kiene RP, del Valle DA, Kieber DJ, Slezak D, Harada H, et al. Occurrence and turnover of DMSP and DMS in deep waters of the Ross Sea, Antarctica. Deep Sea Res 1 Oceanogr Res Pap. 2009;56:686–702.

    Article  CAS  Google Scholar 

  57. Berresheim H, Huey JW, Thorn RP, Eisele FL, Tanner DJ, Jefferson A. Measurements of dimethyl sulfide, dimethyl sulfoxide, dimethyl sulfone, and aerosol ions at Palmer Station, Antarctica. J Geophys Res. 1998;103:1629–37.

    Article  CAS  Google Scholar 

  58. Kirst GO, Thiel C, Wolff H, Nothnagel J, Wanzek M, Ulmke R. Dimethylsulfoniopropionate (DMSP) in ice-algae and its possible biological role. Mar Chem. 1991;35:381–8.

    Article  CAS  Google Scholar 

  59. Turner SM, Nightingale PD, Broadgate W, Liss PS. The distribution of dimethyl sulphide and dimethylsulphoniopropionate in Antarctic waters and sea ice. Deep Sea Res 2 Top Stud Oceanogr. 1995;42:1059–80.

    Article  CAS  Google Scholar 

  60. Lana A, Bell TG, Simó R, Vallina SM, Ballabrera-Poy J, Kettle AJ, et al. An updated climatology of surface dimethlysulfide concentrations and emission fluxes in the global ocean. Global Biogeochem Cy. 2011;25:GB1004.

  61. Stefels J, van Leeuwe MA, Jones EM, Meredith MP, Venables HJ, Webb AL, et al. Impact of sea-ice melt on dimethyl sulfide (sulfoniopropionate) inventories in surface waters of Marguerite Bay, West Antarctic Peninsula. Philos Trans A Math Phys. Eng Sci. 2018;376:20170169.

  62. Howard EC, Sun S, Biers EJ, Moran MA. Abundant and diverse bacteria involved in DMSP degradation in marine surface waters. Environ Microbiol. 2008;10:2397–410.

    Article  CAS  PubMed  Google Scholar 

  63. Yau S, Lauro FM, Williams TJ, Demaere MZ, Brown MV, Rich J, et al. Metagenomic insights into strategies of carbon conservation and unusual sulfur biogeochemistry in a hypersaline Antarctic lake. ISME J. 2013;7:1944–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Zeng YX, Qiao ZY, Yu Y, Li HR, Luo W. Diversity of bacterial dimethylsulfoniopropionate degradation genes in surface seawater of Arctic Kongsfjorden. Sci Rep. 2016;6:33031.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Zeng YX, Qiao ZY. Diversity of dimethylsulfoniopropionate degradation genes reveals the significance of marine Roseobacter clade in sulfur metabolism in coastal areas of Antarctic Maxwell Bay. Curr Microbiol. 2019;76:967–74.

    Article  CAS  PubMed  Google Scholar 

  66. Pritchard HD, Ligtenberg SRM, Fricker HA, Vaughan DG, Den Broeke MRV, Padman L. Antarctic ice-sheet loss driven by basal melting of ice shelves. Nature. 2012;484:502–5.

    Article  CAS  PubMed  Google Scholar 

  67. Rignot E, Jacobs SS, Mouginot J, Scheuchl B. Ice-shelf melting around Antarctica. Science. 2013;341:266–70.

    Article  CAS  PubMed  Google Scholar 

  68. Kameyama S, Otomaru M, McMinn A, Suzuki K. Ice melting can change dmsp production and photosynthetic activity of the Haptophyte Phaeocystis antarctica. J Phycol. 2020;56:761–74.

  69. 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  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  71. Chen IA, Chu K, Palaniappan K, Pillay M, Ratner A, Huang J, et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 2019;47:D666–77.

    Article  CAS  PubMed  Google Scholar 

  72. Carrión O, Curson A, Kumaresan D, Fu Y, Lang A, Mercadé E, et al. A novel pathway producing dimethylsulphide in bacteria is widespread in soil environments. Nat Commun. 2015;6:6579.

    Article  PubMed  Google Scholar 

  73. Gonzalez JM, Hernandez L, Manzano I, Pedros-Alio C. Functional annotation of orthologs in metagenomes: a case study of genes for the transformation of oceanic dimethylsulfoniopropionate. ISME J. 2019;13:1183–97.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Eyice O, Myronova N, Pol A, Carrion O, Todd JD, Smith TJ, et al. Bacterial SBP56 identified as a Cu-dependent methanethiol oxidase widely distributed in the biosphere. ISME J. 2018;12:145–60.

    Article  CAS  PubMed  Google Scholar 

  75. Sunagawa S, Mende DR, Zeller G, Izquierdo-Carrasco F, Berger SA, Kultima JR, et al. Metagenomic species profiling using universal phylogenetic marker genes. Nat Methods. 2013;10:1196–9.

    Article  CAS  PubMed  Google Scholar 

  76. Price MN, Dehal PS, Arkin AP. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol. 2009;26:1641–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Zhang H, Gao S, Lercher MJ, Hu S, Chen W-H. EvolView, an online tool for visualizing, annotating and managing phylogenetic trees. Nucleic Acids Res. 2012;40:W569–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Li C-Y, Zhang D, Chen X-L, Wang P, Shi W-L, Li P-Y, et al. Mechanistic insights into dimethylsulfoniopropionate lyase DddY, a new member of the cupin superfamily. J Mol Biol. 2017;429:3850–62.

    Article  CAS  PubMed  Google Scholar 

  79. Li CY, Chen XL, Zhang D, Wang P, Sheng Q, Peng M, et al. Structural mechanism for bacterial oxidation of oceanic trimethylamine into trimethylamine N-oxide. Mol Microbiol. 2017;103:992–1003.

    Article  CAS  PubMed  Google Scholar 

  80. Wang P, Chen XL, Li CY, Gao X, Zhu DY, Xie BB, et al. Structural and molecular basis for the novel catalytic mechanism and evolution of DddP, an abundant peptidase-like bacterial dimethylsulfoniopropionate lyase: a new enzyme from an old fold. Mol Microbiol. 2015;98:289–301.

    Article  CAS  PubMed  Google Scholar 

  81. Bray RC, Adams B, Smith AT, Bennett B, Bailey S. Reversible dissociation of thiolate ligands from molybdenum in an enzyme of the dimethyl sulfoxide reductase family. Biochemistry. 2000;39:11258–69.

    Article  CAS  PubMed  Google Scholar 

  82. Schuller DJ, Reisch CR, Moran MA, Whitman WB, Lanzilotta WN. Structures of dimethylsulfoniopropionate-dependent demethylase from the marine organism Pelagabacter ubique. Protein Sci. 2012;21:289–98.

    Article  CAS  PubMed  Google Scholar 

  83. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Lei L, Cherukuri KP, Alcolombri U, Meltzer D, Tawfik DS. The dimethylsulfoniopropionate (DMSP) lyase and lyase-like cupin family consists of bona fide DMSP lyases as well as other enzymes with unknown function. Biochemistry. 2018;57:3364–77.

    Article  CAS  PubMed  Google Scholar 

  85. Li CY, Wang XJ, Chen XL, Sheng Q, Zhang S, Wang P, et al. A novel ATP dependent dimethylsulfoniopropionate lyase in bacteria that releases dimethyl sulfide and acryloyl-CoA. Elife. 2021;10:e64045.

  86. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.

    Article  CAS  PubMed  Google Scholar 

  87. Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019;36:1925–27.

  88. Kitts PA, Church DM, Thibaud-Nissen F, Choi J, Hem V, Sapojnikov V, et al. Assembly: a resource for assembled genomes at NCBI. Nucleic Acids Res. 2016;44:D73–80.

    Article  CAS  PubMed  Google Scholar 

  89. Schlitzer R. Interactive analysis and visualization of geoscience data with Ocean Data View. Comput Geosci. 2002;28:1211–8.

    Article  Google Scholar 

  90. Team RC. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2018. Available online at

  91. Ginestet C. ggplot2: Elegant graphics for data analysis. J R Stat Soc Ser A-Stat Soc. 2011;174:245.

    Article  Google Scholar 

  92. Brunson JC. ggalluvial: Layered grammar for alluvial plots. J Open Source Softw. 2017;5:49.

    Google Scholar 

  93. Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.

    Article  Google Scholar 

  94. Clark DR, Underwood GJC, McGenity TJ, Dumbrell AJ. What drives study-dependent differences in distance–decay relationships of microbial communities? Glob Ecol Biogeogr. 2021;30:811–25.

    Article  Google Scholar 

  95. Nowinski B, Motard-Côté J, Landa M, Preston CM, Scholin CA, Birch JM, Kiene RP, Moran MA. Microdiversity and temporal dynamics of marine bacterial dimethylsulfoniopropionate genes. Environ Microbiol. 2019;21:1687–701.

  96. Boyd PW, Watson AJ, Law CS, Abraham ER, Trull T, Murdoch R, et al. A mesoscale phytoplankton bloom in the polar Southern Ocean stimulated by iron fertilization. Nature. 2000;407:695–702.

    Article  CAS  PubMed  Google Scholar 

  97. Pollard RT, Salter I, Sanders RJ, Lucas MI, Moore CM, Mills RA, et al. Southern Ocean deep-water carbon export enhanced by natural iron fertilization. Nature. 2009;457:577–80.

    Article  CAS  PubMed  Google Scholar 

  98. Tison J-L, Brabant F, Dumont I, Stefels J. High-resolution dimethyl sulfide and dimethylsulfoniopropionate time series profiles in decaying summer first-year sea ice at Ice Station Polarstern, western Weddell Sea, Antarctica. J Geophys Res Biogeosci. 2010;115:G04044.

  99. Levasseur M, Gosselin M, Michaud S. A new source of dimethylsulfide (DMS) for the arctic atmosphere: ice diatoms. Mar Biol. 1994;121:381–7.

  100. Mestre M, Ruiz-Gonzalez C, Logares R, Duarte CM, Gasol JM, Sala MM. Sinking particles promote vertical connectivity in the ocean microbiome. Proc Natl Acad Sci U S A. 2018;115:E6799–807.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Hatton AD, Wilson ST. Particulate dimethylsulphoxide and dimethylsulphoniopropionate in phytoplankton cultures and Scottish coastal waters. Aquat Sci. 2007;69:330–40.

    Article  CAS  Google Scholar 

  102. Sunda W, Kieber DJ, Kiene RP, Huntsman S. An antioxidant function for DMSP and DMS in marine algae. Nature. 2002;418:317–20.

    Article  CAS  PubMed  Google Scholar 

  103. Carvajal-Endara S, Hendry AP, Emery NC, Davies TJ. Habitat filtering not dispersal limitation shapes oceanic island floras: species assembly of the Galápagos archipelago. Ecol Lett. 2017;20:495–504.

    Article  PubMed  Google Scholar 

  104. Hanson CA, Fuhrman JA, Horner-Devine MC, Martiny JB. Beyond biogeographic patterns: processes shaping the microbial landscape. Nat Rev Microbiol. 2012;10:497–506.

    Article  CAS  PubMed  Google Scholar 

  105. Ghiglione JF, 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 Natl Acad Sci U S A. 2012;109:17633–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Sul WJ, Oliver TA, Ducklow HW, Amaral-Zettler LA, Sogin ML. Marine bacteria exhibit a bipolar distribution. Proc Natl Acad Sci U S A. 2013;110:2342–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Fondi M, Karkman A, Tamminen MV, Bosi E, Virta M, Fani R, et al. "Every gene is everywhere but the environment selects": global geolocalization of gene sharing in environmental samples through network analysis. Genome Biol Evol. 2016;8:1388–400.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


We thank Caiyun Sun from State Key laboratory of Microbial Technology of Shandong University for her help in data analyses.


This work was supported by the National Key Research and Development Program of China (2016YFA0601303, 2018YFC1406700), the National Science Foundation of China (grants 91851205, 31630012, U1706207, 42076229, 31870052, 31800107, 91751101, 41706152, and 41676180), the Fundamental Research Funds for the Central Universities (202172002), the Major Scientific and Technological Innovation Project (MSTIP) of Shandong Province (2019JZZY010817), the Program of Shandong for Taishan Scholars (tspd20181203), and AoShan Talents Cultivation Program Supported by Qingdao National Laboratory for Marine Science and Technology (2017ASTCP-OS14 and QNLM2016ORP0310).

Availability of data and materials

All metagenomic datasets have been deposited in the NCBI database (BioProject accession no. PRJNA588686). The 214 MAGs have been submitted to figshare ( and the GenBank database (BioProject accession no. SUB7116349).

Author information

Authors and Affiliations



Z.-J.T. conceived the study and performed the majority of data analyses and activity assay. C.-Y.L. and Q.-L.Q. directed the study and data analyses. W.P.Z. processed metagenomic raw data from polar oceans and helped in data analyses. J.L. helped in protein purification and data interpretation. H.-H.F and P.W. helped in data interpretation. M.-S.L., G.-F.L, and J.-F.H. collected samples from polar oceans. Q.-L.Q., C.-Y.L., A.M., M.W., and X.-L.C. critically revised the manuscript. Y.-Z.Z. conceived the project, directed the study and critically revised the manuscript. Y.C. directed the study and did a critical revision of the manuscript for important intellectual content. The authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yin Chen or Chun-Yang Li.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

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

The original online version of this article was revised: The name of the 8th co-author of this article should be "Guangfu Luo", not "Guangfu Lu".

Supplementary Information

Additional file 1: Figure S1

. Analysis of conserved amino acid residues involved in substrate binding and catalysis of DddK (a), DddQ (b), DddY (c), Tmm (d), DddP (e), DmdA (f) and DMSOR (g) retrieved from polar metagenomic samples. Figure S2. Maximum likelihood trees of the predicted hits of the top five most abundant genes (DddP, DddK, DddK, DmdA, Tmm) involved in DMSP/DMS cycling which were retrieved from the polar metagenomes, Tara metagenomes/metatranscriptomes datasets. Figure S3. Correlation between dissimilarity of DMS/DMSP related bacterial community and water depth in polar oceans.

Additional file 2: Table S1

. Raw abundances of DMS/DMSP related genes in Arctic and Antarctic seawater samples. Table S2. A list of enzymes selected as outgroups for phylogenetic analyses. Table S3. Raw abundances and taxonomic composition of DMS/DMSP related genes in MAGs. Table S4. Raw abundances of DMS/DMSP related genes in Tara Ocean samples. Table S5. Raw abundances of DMS/DMSP related genes in metatranscriptomes. Table S6. The predicted hits with enzymatic activity. Table S7. Biplot scores for constraining variables. Table S8. Taxonomy composition of DMS/DMSP related genes in polar ocean samples. Table S9. Occurrence of DMS/DMSP related genes in genomes from IMG/M database. Table S10. Raw abundances of MTO in metagenomic and metatranscriptomic samples.

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 The Creative Commons Public Domain Dedication waiver ( 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

Teng, ZJ., Qin, QL., Zhang, W. et al. Biogeographic traits of dimethyl sulfide and dimethylsulfoniopropionate cycling in polar oceans. Microbiome 9, 207 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: