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Giant Hydrogen Sulfide Plume in the Oxygen Minimum Zone off Peru Supports Chemolithoautotrophy

  • Harald Schunck ,

    Contributed equally to this work with: Harald Schunck, Gaute Lavik, Dhwani K. Desai

    Affiliations Research Division Marine Biogeochemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany, Institute for General Microbiology, Christian-Albrechts-University, Kiel, Germany

  • Gaute Lavik ,

    Contributed equally to this work with: Harald Schunck, Gaute Lavik, Dhwani K. Desai

    Affiliation Department of Biogeochemistry, Max-Planck-Institute for Marine Microbiology, Bremen, Germany

  • Dhwani K. Desai ,

    Contributed equally to this work with: Harald Schunck, Gaute Lavik, Dhwani K. Desai

    Affiliations Research Division Marine Biogeochemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany, Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada

  • Tobias Großkopf,

    Affiliations Research Division Marine Biogeochemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom

  • Tim Kalvelage,

    Affiliation Department of Biogeochemistry, Max-Planck-Institute for Marine Microbiology, Bremen, Germany

  • Carolin R. Löscher,

    Affiliation Institute for General Microbiology, Christian-Albrechts-University, Kiel, Germany

  • Aurélien Paulmier,

    Affiliations Department of Biogeochemistry, Max-Planck-Institute for Marine Microbiology, Bremen, Germany, Laboratory for Studies in Geophysics and Spatial Oceanography, Institute of Research for Development, Toulouse, France, Dirección de Investigaciones Oceanográficas, Instituto del Mar del Perú, Callao, Peru

  • Sergio Contreras,

    Affiliations Department of Biogeochemistry, Max-Planck-Institute for Marine Microbiology, Bremen, Germany, Large Lakes Observatory, University of Minnesota Duluth, Duluth, Minnesota, United States of America

  • Herbert Siegel,

    Affiliation Physical Oceanography and Instrumentation, Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, Germany

  • Moritz Holtappels,

    Affiliation Department of Biogeochemistry, Max-Planck-Institute for Marine Microbiology, Bremen, Germany

  • Philip Rosenstiel,

    Affiliation Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany

  • Markus B. Schilhabel,

    Affiliation Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany

  • Michelle Graco,

    Affiliation Dirección de Investigaciones Oceanográficas, Instituto del Mar del Perú, Callao, Peru

  • Ruth A. Schmitz,

    Affiliation Institute for General Microbiology, Christian-Albrechts-University, Kiel, Germany

  • Marcel M. M. Kuypers,

    Affiliation Department of Biogeochemistry, Max-Planck-Institute for Marine Microbiology, Bremen, Germany

  •  [ ... ],
  • Julie LaRoche

    Affiliations Research Division Marine Biogeochemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany, Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada

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Giant Hydrogen Sulfide Plume in the Oxygen Minimum Zone off Peru Supports Chemolithoautotrophy

  • Harald Schunck, 
  • Gaute Lavik, 
  • Dhwani K. Desai, 
  • Tobias Großkopf, 
  • Tim Kalvelage, 
  • Carolin R. Löscher, 
  • Aurélien Paulmier, 
  • Sergio Contreras, 
  • Herbert Siegel, 
  • Moritz Holtappels


In Eastern Boundary Upwelling Systems nutrient-rich waters are transported to the ocean surface, fuelling high photoautotrophic primary production. Subsequent heterotrophic decomposition of the produced biomass increases the oxygen-depletion at intermediate water depths, which can result in the formation of oxygen minimum zones (OMZ). OMZs can sporadically accumulate hydrogen sulfide (H2S), which is toxic to most multicellular organisms and has been implicated in massive fish kills. During a cruise to the OMZ off Peru in January 2009 we found a sulfidic plume in continental shelf waters, covering an area >5500 km2, which contained ∼2.2×104 tons of H2S. This was the first time that H2S was measured in the Peruvian OMZ and with ∼440 km3 the largest plume ever reported for oceanic waters. We assessed the phylogenetic and functional diversity of the inhabiting microbial community by high-throughput sequencing of DNA and RNA, while its metabolic activity was determined with rate measurements of carbon fixation and nitrogen transformation processes. The waters were dominated by several distinct γ-, δ- and ε-proteobacterial taxa associated with either sulfur oxidation or sulfate reduction. Our results suggest that these chemolithoautotrophic bacteria utilized several oxidants (oxygen, nitrate, nitrite, nitric oxide and nitrous oxide) to detoxify the sulfidic waters well below the oxic surface. The chemolithoautotrophic activity at our sampling site led to high rates of dark carbon fixation. Assuming that these chemolithoautotrophic rates were maintained throughout the sulfidic waters, they could be representing as much as ∼30% of the photoautotrophic carbon fixation.

Postulated changes such as eutrophication and global warming, which lead to an expansion and intensification of OMZs, might also increase the frequency of sulfidic waters. We suggest that the chemolithoautotrophically fixed carbon may be involved in a negative feedback loop that could fuel further sulfate reduction and potentially stabilize the sulfidic OMZ waters.


Eastern Boundary Upwelling Systems are found along the westward shelves of the continents in both the Atlantic and the Pacific Ocean. They are characterized by high photoautotrophic primary production, which is driven by the upwelling of nutrient-rich waters [1]. The produced biomass supports large fish populations in these regions, underlining the importance of Eastern Boundary Upwelling Systems in providing a source of food for mankind [2][6]. However, a significant proportion of the produced biomass also sinks through the water column and is remineralized in subsurface waters, contributing to the oxygen (O2) depletion in intermediate water depths of these regions. Combined with large-scale ocean circulation patterns and poor ventilation of intermediate waters with the ocean surface, the remineralization of rich organic matter leads to oxygen-depleted zones of varying intensities [7][10]. These oxygen-depleted waters, also referred to as oxygen minimum zones (OMZs), are found in the eastern tropical North and South Pacific, and to a lesser extent in the eastern tropical North and South Atlantic [11]. In addition to OMZs found in regions of strong upwelling, oxygen-depleted waters are also present in the northern Indian Ocean and in enclosed basins like the Baltic and the Black Sea [12].

The OMZ off Peru, Chile and Ecuador in the South Pacific Ocean is the largest oceanic area where O2 concentrations are reported to fall below the detection limit of the most sensitive O2 sensors (∼10–100 nM) [13][16]. In the absence of O2, organic carbon degradation has been historically attributed to heterotrophic denitrification, the reduction of nitrate (NO3) to dinitrogen gas (N2) [17][19]. Some in situ experiments have shown active heterotrophic denitrification in OMZ waters [20], [21], however numerous studies have demonstrated that anammox, the anaerobic oxidation of ammonium (NH4+) with nitrite (NO2) to N2, is responsible for the major loss of fixed nitrogen from the OMZ off Namibia [22], Oman [15], Peru [23][25] and Chile [26]. As anammox is an autotrophic process, its dominance over heterotrophic denitrification questions our understanding of organic matter remineralization in OMZ waters. This is supported by the hypothesis that oxygen-depletion leads to a shift of the microbial community from organoheterotrophs to chemolithotrophs [27], [28].

In anoxic sediments, the main process for the degradation of organic carbon is considered to be microbial sulfate (SO42−) reduction to elemental sulfur (S0) and hydrogen sulfide (H2S) [29]. The release of large quantities of the toxic H2S from these underlying sediments can lead to the occasional build-up of high concentrations of H2S in bottom waters [30][33]. On the other hand, it has also been suggested that H2S build-up in oceanic waters could be caused by SO42− reduction of pelagic microorganisms within the water column [34], [35]. A recent study suggested that an active, but cryptic sulfur cycle is present in non-sulfidic subsurface waters in the eastern tropical South Pacific OMZ off northern Chile [14]. According to this hypothesis, SO42− reduction and consequently H2S formation would take place in the water column, even when thermodynamically more favourable electron acceptors like NO3 or NO2 are still present. However, the resulting H2S would be rapidly re-oxidized to S0 or SO42−, such that the two processes are in steady-state, thereby preventing an accumulation of H2S [31], [36].

The presence of chemolithoautotrophic γ-proteobacteria involved in sulfur cycling (e.g. related to the uncultured SUP05 cluster bacterium, referred to henceforth as SUP05) in non-sulfidic OMZ waters supports the hypothesis of a cryptic sulfur cycle [8], [14], [37][40]. Studies conducted during occurrences of sulfidic events in the Benguela Current upwelling OMZ and in a seasonally anoxic fjord in Canada demonstrated that γ-proteobacteria related to SUP05 are highly abundant in sulfidic waters and that they probably detoxified the waters via the chemolithotrophic oxidation of H2S coupled to the reduction of NO3 [31], [41], a pathway termed sulfur-driven autotrophic denitrification. In addition, chemolithoautotrophic α- and especially ε-proteobacteria detected in the sulfidic waters in the Benguela Current upwelling OMZ as well as in the sulfidic zones of the Baltic and the Black Sea could also be important members of microbial communities in sulfidic plumes [31], [42], [43].

The initiation, termination and frequency of sulfidic events in oceanic OMZs are so far poorly understood, and H2S in the water column has been mostly observed in enclosed basins like the Baltic Sea [43][45], the Black Sea [46][48], the Cariaco trench off Venezuela [49], [50] and the Saanich Inlet in Canada [41], [51]. With the exception of a few studies mentioning the characteristic odor of H2S and anecdotal reports of Peruvian fishermen on ‘black’ fishing gear in relation to the so-called ‘aquajes’ conditions for the OMZ off Peru, sulfidic waters have not been measured in the Pacific Ocean so far [31], [34], [35], [52].

Negative consequences on fish stocks and quality of life along the populated coastal upwelling regions are potentially severe, because H2S is highly toxic to animals and humans and has already been invoked as the cause for occasional but massive fish kills in African shelf waters [53][55]. The anticipated decrease in O2 concentrations and the increase in water column stratification, as predicted from global change [11], as well as local eutrophication [27], [35], might lead to more frequent and intense depletion of O2 and of alternate electron acceptors (e.g. NO3 and NO2), favouring the development of sulfidic waters within OMZs [31]. Given that the detoxification of sulfidic water is a microbial process, it is important to assess the phylogenetic structure and the metabolic response of the endemic microbial community to the accumulation of H2S.

We explored the microbial community structure and its transcriptional activity in such a sulfidic event off the coast of Peru with high-throughput metagenomic and metatranscriptomic sequencing. We present rate measurements of carbon dioxide (CO2) fixation and nitrogen transformation processes as well as total bacterial cell counts with flow-cytometry. Several of the proteobacterial taxa that were dominant in the sulfidic waters were expressing genes involved in the sulfur cycle, which reflected various metabolic strategies for H2S oxidation. Our data further suggests that the microbial communities were responsible for considerable light-independent CO2 fixation.

Results and Discussion

Description of the sampling site

During RV Meteor cruise M77/3 on the Peruvian shelf in January 2009 we found sulfidic waters stretching from Lima to Paracas National Reserve southwest of Pisco (Figure 1). O2 concentration in shelf waters in the study area were generally below the detection limit of our microsensor (0.5–1 µM) at water depth below 20 m (from 12° S to 14° S; Figure 1A), while NOx (the sum of NO3 and NO2) was heavily depleted in the water column throughout the transect from 20–60 m to the bottom (from 12°30′S to 13°50′S; Figure 1B), mirror-imaging the distribution of H2S (Figure 1C). Sulfidic waters were first detected on January 9th south of Lima and seemed to have persisted until the end of the cruise, when H2S-containing waters covered ∼5500 km2 of the shelf. The thickness of the sulfidic layer was about 80 m (Figure 1C), yielding the largest sulfidic plume (∼440 km3) ever reported for oceanic waters. We calculated a H2S content of ∼2.2×104 tons. The total area affected by H2S may have been even larger, as we did not map the extent of the sulfidic plume into the protected area of the Paracas National Reserve.

Figure 1. Extent of the sulfidic plume off the Peruvian coast.

(A) Vertical distribution of O2 concentrations. (B) Vertical distribution of NOx (the sum of NO3 and NO2) concentrations. (C) Vertical distribution of H2S concentrations. (D) Areal view of stations sampled along the transect off the Peruvian coast between Lima and Pisco. The station (19) that was analyzed in detail is marked with a red dot. (E) Satellite image (MODIS) showing a colloidal S0 plume (white circle) on May 8th, 2009.

Remote satellite sensing revealed large patches (50–150 km2) of turquoise discoloured surface-waters, attributable to the formation of colloidal S0 upon H2S oxidation [56], [57] off Lima as well as in Paracas National Reserve during our sampling campaign (Figure S1). The larger extension of H2S in deeper waters when compared to the colloidal S0 in the surface indicated that most of the H2S was oxidized in subsurface waters, similar to the observations from the Benguela upwelling system [31]. Colloidal S0 plumes measuring up to 500 km2 were observed in the same region again in May 2009, indicating that the occurrence of S0 plumes detected in January 2009 was not a unique event (Figure 1E). This suggests that sulfidic waters in the OMZ off Peru might be more frequent and persistent than originally thought.

A vertical profile of the sulfidic water column (station 19), sampled during the upcast with a pump-CTD on January 9th, 2009 at a site located ∼15 km offshore Lima (12°21.88′S, 77°0.00′W, ∼100 m water depth; Figure 1D) was the target of a detailed analysis. The surface mixed layer was shallow with the thermocline at about 10 m water depth (Figure 2D). The surface temperature (>16°C) was only ∼2°C warmer than the bottom waters and the salinity (34.95–34.97) changed merely slightly with depths, which indicated an active upwelling of subsurface waters. Even the surface waters were characterized by low O2 conditions down to 40 µM (or ∼15% saturation; Figure 2A). During the downcast, O2 decreased at the thermocline and dropped to about the detection limit of our amperometric O2 microsensor (0.5–1 µM) at around 20 m. Nevertheless, trace amounts of O2 (<1 µM) were still detected with some variability down to ∼40 m water depth. These low concentrations of O2 were close to detection limit of our sensor and we cannot rule out whether this was due to water advection caused by the CTD rosette or to a memory effect of the sensor. However, using a highly-sensitive self-calibrating Switchable Trace amount Oxygen (STOX) sensor with a detection limit of ∼50 nM [13], [16] during the upcast of the CTD rosette, O2 was undetectable below 20 m, and therefore we defined this zone as anoxic. We detected large vertical movement in the oxycline from 5–18 m due to internal waves, which may have caused non steady state conditions and induced a flux of O2 down into the anoxic waters.

Figure 2. Vertical distribution of physical, chemical and biological water properties.

(A) Concentrations of H2S and O2. (B) Concentrations of NO3, NO2 and N2O. (C) Concentrations of PO43− and NH4+. (D) In situ fluorescence (chlorophyll, relative units as measured with the pump-CTD) and temperature. (E) Salinity and density.

Phosphate (PO43−) and NH4+ concentrations remained high (both around 3 µM) and stable throughout the water column, with NH4+ only having a minor drop in concentrations near 50 m (Figure 2C). NO3 concentrations (detection limit ∼0.1 µM) were lower than those measured in the southern part of the study area and lower than expected from strong upwelling regions in general. Highest NO3 concentrations with ∼5 µM were found in surface waters, but dropped rapidly below1 µM, just beneath the oxycline at ∼19 m (Figure 2B). Detectable concentrations of NO3 (ranging from 0.1–0.2 µM) were measured down to 59 m with the exception of 52–55 m, where a small increase up to 0.7 µM was observed. NO2 (detection limit ∼0.01 µM) was high (1.5–3 µM) in surface waters down to 26 m. Trace concentrations (∼25 nM) were measurable down to 50 m and again from 67–81 m. Nitrous oxide (N2O) concentrations ranged between 20–40 nM from 15–80 m and dropped below the detection limit closer to the bottom of the water column (Figure 2B). H2S was first detected (with both microsensor and wet chemistry) at 26 m and increased steadily, reaching a concentration of 4.2 µM at 48 m (Figure 2A). This maximum was followed by a rapid drop in concentrations to below 0.1 µM at 52–53 m, before increasing again to about 2.6 µM at 95 m, approximately 5 m above the sediment. H2S concentrations directly at the sediment-water interface were probably even greater, but were not measured in this study.

Phylogenetic diversity of the microbial community

Based on the monitoring of O2 and H2S during the upcast, we defined three zones within the water column, where we carried out a detailed sampling: the oxic surface (5 m sample, where sampling was stopped when internal waves decreased O2 concentrations to below 30 µM), the upper boundary of the anoxic zone (15 and 20 m samples) and the sulfidic zone (30, 40, 50, 60, 80 and 100 m samples). Both a hierarchical clustering approach with a statistical analysis of taxonomic assignments and a non-parametric Multidimensional Scaling indicated that the selected sample groups were justified (an ANOSIM test using a Bray-Curtis distance measure showed a Global R value of 0.83 and a significance level of 0.1%, Figure S2).

Using a 98% similarity cut off, the metagenomes and metatranscriptomes accounted for an average of 263,606 (DNA) and 98,785 (RNA) unique sequences (clusters) per depths (Table S1). A total of 4809 (DNA) and 3872 (RNA) different taxa were identified using BLAST-searches, revealing a highly diverse microbial community at all depths (Table S2). The taxonomic composition based on all rRNA genes (5,923 sequences) using BLASTn-searches against the SILVA database is shown in Figure 3A and based on the metagenomes (1,882,842 sequences, excluding all rRNA genes) and the metatranscriptome (421,528 sequences, excluding all rRNAs) using BLASTx-searches against the non-redundant database of NCBI in Figure 3B and 3C. A large percentage of the sequences found in both the metagenomes and metatranscriptomes had no significant match against the non-redundant database of NCBI. On average, 49% of the sequences remained unidentified, which is comparable to other studies that utilized high-throughput sequencing technologies in marine habitats [37], [40], [58][60].

Figure 3. Vertical distribution of taxonomic assignments.

Shown on either domain, phylum or class level. (A) rRNA genes in percent of all rRNA genes (5,923 sequences). (B) Metagenomic sequences in percent of all metagenomic sequences (1,882,842 sequences, excluding rRNA genes). (C) Metatranscriptomic sequences in percent of all metatranscriptomic sequences (421,528 sequences, excluding rRNAs). ‘Other Bacteria’ include Acidobacteria, Actinobacteria, Aquificae, Chlamydiae, Chloroflexi, Deferribacteres, Deinococcus-Thermus, Dictyoglomi, Elusimicrobia, Fibrobacteres, Fusobacteria, Gemmatimonadetes, Lentisphaerae, Nitrospirae, Spirochaetes, Synergistetes, Tenericutes, Thermotogae and Verrucomicrobia. *Sequences with no significant match against the non-redundant database of NCBI or sequences with a match that lacks taxonomic information.

The community structure presented a stable and uniform distribution at the phylum-level, especially within the sulfidic zone (Figure 3). The metagenome data suggested that the microbial community was overall dominated by proteobacteria (16.6–34.1% of all DNA sequences, including all unidentified sequences); while the Bacteroidetes/Chlorobi-group was the second largest group we could identify (3.9–7.4%). In oxic and anoxic waters, both α- and γ-proteobacterial sequences were abundant (6.9–16.4%), similar to previous findings from the OMZs off northern Chile [38] and the Arabian Sea [61]. In sulfidic waters, γ-proteobacteria were clearly dominating (up to 23.2%) and we further found a significant increase in the frequencies of δ- and ε-proteobacterial sequences (1.9–4.0%), which were much less abundant in the 5 and 20 m samples. The δ- and ε-proteobacterial sequences were even more abundant within the rRNA gene dataset (Figure 3A) when compared to the metagenomes (Figure 3B), probably due to the lack of representatives genomes for these groups.

The metatranscriptomes showed a more variable picture of the microbial community (Figure 3C). In surface waters eukaryotic sequences formed the largest identifiable group (5%), while at all other depths γ-proteobacteria were dominating (13.3–30.5%). In sulfidic waters ε-proteobacterial transcripts were further identified in relatively high, but also more variable proportions (5.0–13.2%), when compared to the metagenomes. Notably, ‘other bacteria’, summarizing 19 bacterial phyla, were present at all depth, but never exceeded 5.8% of the sequences, in both the metagenome and the metatranscriptome datasets.

A more detailed analysis of the metagenomes revealed that the oxic surface waters harboured several different photoheterotrophic organisms (Figure S3). Prokaryotes similar to Candidatus Pelagibacter sp. HTCC7211 accounted for 1.6% and relatives of Candidatus Pelagibacter sp. HTCC1002 made up 0.4% of all DNA sequences, which is in agreement with other studies conducted in OMZs [14], [37], [40], [61]. Additionally, photosynthetic Synechococcus spp., which are also known to be present in OMZ waters [62] accounted for about 0.4% of the sequences. However, the most abundant single taxon identified in the oxic surface metagenome had high similarity to the uncultured SUP05 cluster bacterium (1.9%), a chemolithoautotrophic sulfur oxidizer, which has been detected previously in oxygen-depleted [14], [37], [38], [40] and sulfidic waters [31], [41], [42], [63]. In the metagenome sample from the anoxic zone (20 m), SUP05 was also the dominant taxon with 6.6% of all DNA sequences (Figure 4A and S3). At 20 m and below, γ-proteobacterial sulfur oxidizers (GSO) related to gill symbionts of deep-sea hydrothermal-vent clams, Candidatus Ruthia magnifica str. Cm (2.6%) and Candidatus Vesicomyosocius okutanii HA (1.4%) [64], [65] became increasingly abundant. This indicated that the GSO-community at our sampling site was composed of at least three separate taxa. In the sulfidic zone, the dominance of the GSO-group was even higher, reaching a maximum of 17% of all DNA sequences at 50 m (Figure 4 and S3). Other common microorganisms in the metagenome were similar to the ε-proteobacterium Sulfurovum sp. NBC37- 1 (up to 1.7%) and to the δ-proteobacterium Desulfobacterium autotrophicum HRM2 (up to 1.4%) [66][68]. In all sulfidic depths, organisms related to SUP05, R. magnifica, V. okutanii, Sulfurovum and D. autotrophicum were the five most abundant organisms that we identified with BLAST-searches.

Figure 4. Vertical distribution of dominant proteobacterial taxa.

Shown in percent of all sequences (excluding rRNA genes and rRNAs). (A) DNA and (B) RNA sequence abundances for γ-proteobacteria. (C) DNA and (D) RNA sequence abundances for ε-proteobacteria. (E) DNA and (F) RNA sequence abundances for δ-proteobacteria.

In contrast to the metagenomes, the metatranscriptomes reflect the suite of genes that were expressed in the microbial community at the time of sampling and therefore displayed a more variable picture of the microbial community, showing to some extent its actual metabolic activity. In the oxic surface waters, we found RNA sequences similar to the archaeal ammonia-oxidizer Nitrosopumilus maritimus SCM1 and the α-proteobacterium Magnetospirillum gryphiswaldense to comprise the largest single taxa (0.7 and 0.4%). N. maritimus is considered a ‘classical’ inhabitant of the oxycline in OMZ waters [14], [37], [39], [40]. At 20 m, RNA sequences similar to the γ-proteobacteria Marinomonas sp. MWYL1 and Neptuniibacter caesariensis were most abundant (1.3 and 1.2%).

Similar to the metagenome assignments, the GSO-group was predominant in the metatranscriptomes throughout the sulfidic zone. However, the composition of the GSO-group was variable and changed with depths. While SUP05 was virtually undetectable using BLAST-searches in the 5, 20 and 40 m metatranscriptomes (0–0.1%; Figure 4B and S3), GSO-related RNA sequences at these depths could be almost exclusively assigned to the relatives of R. magnifica and V. okutanii. In all other metatranscriptomes from sulfidic waters (50, 60 and 80 m), sequences similar to SUP05 represented the most abundant identifiable taxon (5.5–11%).

Also similar to the metagenomes, the five most abundant organisms detected in the metatranscriptomes within sulfidic waters (except at 40 m) were related to SUP05, R. magnifica, V. okutanii, Sulfurovum and D. autotrophicum. We also identified several other taxa in the metatranscriptomes that were poorly represented in the metagenomes, some of which were closely related to members of hydrothermal vent communities. At 40 m, organisms related to the ε-proteobacterium Arcobacter butzleri RM4018 accounted for 2% of all RNA sequences and at 50 m the γ-proteobacterium Colwellia psychrerythraea 34H accounted for 3%. In deeper parts of the water column (60 and 80 m), organisms similar to the ε-proteobacteria Sulfurimonas gotlandica GD1 (1.1–1.3%) and Arcobacter nitrofigilis DSM 7299 (0.9–1%) were detected. Although sequences similar to the anammox-planctomycete Candidatus Kuenenia stuttgartiensis have been found in high abundances in OMZ waters [14], [37], [40], they were relatively rare within our samples, never exceeding 0.7% of all DNA and 0.8% of all RNA sequences.

Metabolic activity and functional diversity of the microbial community

General activity patterns.

To assess the functional diversity of the microbial community in detail, we used three different approaches to investigate our sequence data. The BLAST-searches were supplemented by scans of our sequences with profile hidden Markov models of the ModEnzA Enzyme Commission (EC) groups [69] and of the Pfam protein families [70]. Furthermore, we recruited the DNA and RNA sequences onto the genomes of the five organisms most often recognized by our BLAST-searches, SUP05, R. magnifica, V. okutanii, Sulfurovum and D. autotrophicum [41], [64][67]. For these genome recruitments, we calculated the expression-ratio, a measure of the enrichment of selected transcripts over the corresponding genes, normalized to the total pool of all protein-coding sequences (Figure 5 and S4). In general, the genome recruitment plots showed high expression-ratios for ribosomal proteins, DNA and RNA polymerases, cell division proteins and transcription and translation factors indicating a growing microbial community (data not shown). Similarly, the collection of all abundant EC numbers also suggested an overall active microbial community (Figure S5). Sequences encoding for ubiquitous proteins related to general metabolic activity like DNA and RNA polymerases, DNA topoisomerases and adenosinetriphosphatases (a general ATP-binding and hydrolyzing motif in sequences which lack further specific functional information) were among the most abundant at all depths.

Figure 5. Vertical distribution of sequences recruited onto the genomes of three γ-proteobacterial sulfur oxidizers.

Shown are selected genes encoding for enzymes involved in oxygen (blue), sulfur (yellow), nitrogen (red) and carbon metabolism (green) in the corresponding order of the genomes. The y-axis depicts the log of the expression-ratio, a measure for the selective enrichment of transcripts over the corresponding gene, normalized to the total pool of protein-coding sequences. A list of the start and end position of all genes and the full names of the corresponding enzymes are shown in Table S3. (A) Uncultured SUP05 cluster bacterium. (B) Candidatus Ruthia magnifica str. Cm. (C) Candidatus Vesicomyosocius okutanii HA. *This DsrM-like protein has also high similarity to a narG respiratory nitrate reductase.

Hydrogen sulfide sources.

H2S formation through microbial SO42− reduction commonly occurs in anoxic marine sediments, where it is considered to be the main heterotrophic process for the degradation of organic carbon [29]. The sedimentary flux has been shown to be the main source of H2S in the water column during sulfidic events in the Benguela Current upwelling system [30], [31]. However, at times water column SO42− reduction could also contribute significantly to the H2S accumulation in oceanic waters [34], [35], as demonstrated in the ∼2000 m thick anoxic water column in the Black Sea, where pelagic SO42− reduction rates ranged between 0.01–3.5 nmol l−1 d−1 [71]. Moreover, H2S formation from SO42− has been measured even in the presence of more favourable electron acceptors (NOx) in the OMZ waters off northern Chile after preincubations with H2S [14].

We investigated the sulfur cycling by identifying genes and transcripts indicative of specific metabolic functions related to sulfur transformation processes in our collection of sequences and used flux calculations to estimate the sedimentary source of H2S. D. autotrophicum, one of the most abundant organism we identified in our sequence dataset, is a metabolically versatile SO42− reducing marine δ-proteobacterium, which can completely oxidize organic carbon compounds to CO2, but is also capable of growing autotrophically on hydrogen (H2) [67]. The genome recruitment plots of D. autotrophicum (Figure S4B) show regions of the genome mostly related to energy metabolism and nutrient cycling. High expression-ratios for key sulfur metabolizing enzymes like the dissimilatory sulfite reductase (dsrABD), the adenylylsulfate reductase (aprAB) and the sulfate adenylyltransferase (sat2) suggest that D. autotrophicum could have been reducing SO42− and thus may have contributed to the formation of H2S at our sampling site. However, since many sulfur cycling proteins (e.g. dsr and apr) can function in both the oxidation and reduction of sulfur species [72], [73], other chemolithoautotrophic metabolic processes (e.g. the disproportionation of sulfur compounds resulting in the simultaneous formation of H2S and SO42−) could have been catalyzed by these enzymes at the time of sampling [74]. The presence of large plumes of colloidal S0 (Figure S1) in the study area would have supported this chemolithoautotrophic reaction.

A numerical compilation of all sequences affiliated to known SO42− reducers and sulfur oxidizers is shown in Figure 6 [14], [72], [73]. Although the abundance of SO42− reducing organisms throughout sulfidic waters was not correlated with the H2S concentrations, it is likely that they contributed to SO42− reduction and H2S formation in the water column at the time of sampling. However, assuming the maximum SO42− reduction rates (1.3–12 nmol l−1 d−1) measured in OMZ waters off northern Chile [14], it would take more than one year (in the absence of any oxidation) to accumulate the H2S concentrations reported in our study. In contrast, SO42− reduction in sediments underlying the eastern tropical South Pacific OMZ is generally very high (10–30 mmol m−2 d−1) [75] and thus the most likely source of the H2S we measured in the water column.

Figure 6. Vertical distribution of organisms involved in the sulfur cycle and abundance and taxonomic affiliation of transcripts encoding for cytochrome c oxidases.

(A) H2S concentration and abundance of DNA sequences affiliated to organisms either capable of oxidizing or reducing inorganic sulfur species. Shown in percent of all DNA sequences (excluding rRNA genes) and summed according to their metabolic potentials. (B) Abundance of RNA sequences affiliated to organisms either capable of oxidizing or reducing inorganic sulfur species. Shown in percent of all RNA sequences (excluding rRNAs) and summed according to their metabolic potentials. (C) O2 concentrations and transcript abundance of the (low-affinity) cytochrome c oxidase and the (high-affinity) cbb3-type cytochrome c oxidase (both EC Shown in percent of all protein-coding RNA sequences. (D) Phylogenetic affiliation of the transcripts encoding for both types of the cytochrome c oxidase.

Assuming steady state conditions, we used the density structure and the H2S concentration gradient (Figure 2A) in the bottom water to estimate a turbulent diffusion of ∼10−4 m2 s−1 and, subsequently, a sedimentary efflux of ∼2 mmol H2S m−2 d−1. This is well within the estimates based on sedimentary flux calculations and SO42− reduction rate measurements (1–11 mmol m−2 d−1) from sediments directly underlying sulfidic events [76]. Moreover, the repeated observations of the H2S maxima in bottom waters along the transect (Figure 1C) point towards the sediment as the main H2S source.

We observed a second distinct H2S maximum in the water column at ∼48 m with no direct contact to the sediment. However, the salinity and the corresponding PO43− and NH4+ concentrations (Figure 2C and 2E) indicated that the upper H2S layer was most probably created by lateral advection of nearby bottom waters that had recently been in contact with sulfidic sediments rather than by production of H2S within the water column.

Sulfur oxidation.

The largest functional group of microorganisms detected in sulfidic waters were γ–proteobacterial sulfur oxidizers. Figure 5 shows the expression-ratio for selected genome regions of the three most abundant GSO-representatives. The recruitment of the sequences onto separate genomes supported the presence of at least three distinct taxa within the GSO-community (similar to SUP05, R. magnifica and V. okutanii) at our study site, which were actively growing and metabolizing. Figure S4A further depicts a genome recruitment plot for ε-proteobacterial Sulfurovum. The recruitment of sequences onto the genome of Sulfurovum was much less extensive than for the GSO-group, and showed low coverage especially in the oxic and anoxic depths (5 and 20 m).

A large number of transcripts were recruited onto genes (if present in the genomes) of the reverse dissimilatory sulfite reduction (dsr - oxidation of intracellular S0) and the periplasmic sulfur oxidation (sox - S2O32− oxidation) pathways, both encoding for enzymes involved in the oxidation of reduced sulfur compounds. Additionally, we found high expression-ratios for the sulfide∶quinone oxidoreductase (sqr - H2S oxidation to S0), the adenylylsulfate reductase (apr) and the sulfate adenylyltransferase (sat - latter both SO32− oxidation to SO42−). The abundance and coverage of transcripts matching these clusters for the GSO-group and Sulfurovum increased generally with depth, delivering the highest expression-ratios of genes encoding for sulfur oxidizing proteins within sulfidic waters. For Sulfurovum, which is thought to be capable of both sulfur oxidation and SO42− reduction [66], [77], [78], the transcript coverage for its sox- and sqr-genes suggested that it was likely acting as a sulfur oxidizer at the time of sampling.

R. magnifica, V. okutanii and Sulfurovum genomes harbour genes for different cytochrome c oxidases (cox) which are used in oxic respiration and are absent in the currently annotated version of the SUP05 genome [41], [64][66]. Of the transcripts recruiting to the R. magnifica genome, the cytochrome c oxidase was among the most abundant in the oxic surface. In anoxic and sulfidic waters, however, expression patterns changed and transcripts for a cbb3-type cytochrome c oxidase dominated instead. Transcripts for the cbb3-type cytochrome c oxidase recruiting to the genomes of V. okutanii and Sulfurovum were also among the most highly expressed in sulfidic waters.

The cbb3-type cytochrome c oxidase is thought to be involved in a specialized microaerobic respiration. This enzyme has an extremely high affinity to O2, with a KM value as low as 7 nM, allowing certain proteobacteria to colonize oxygen-limited or even presumed anoxic environments [79], [80], well below the detection limit of the microsensor (0.5–1 µM) and the STOX sensor (∼50 nM) we used in this study. Furthermore, it was shown that Escherichia coli cultures actively grew and respired O2 even below the detection limit of a highly sensitive STOX sensor (≤3 nM), probably using a high-affinity cytochrome bd oxidase [81].

A clear separation of the cytochrome c oxidase expression was visible between the oxic and the anoxic/sulfidic zone, directly reflecting the availability of O2 (Figure 6C). In oxic surface waters, the low-affinity cytochrome c oxidase was dominant and assigned mainly to eukaryotic sequences, as well as to diverse bacterial groups belonging to α- and γ-proteobacteria (Figure 6D). In contrast, in sulfidic waters where the high-affinity cbb3-type cytochrome c oxidase prevailed, as much as 80% of the transcripts could be assigned to either γ- and ε-proteobacteria. For the R. magnifica-like organism, which possesses both types of cytochrome oxidases, the switch in the expression from the low-affinity type in oxic surface waters (5 m) to the high-affinity type in anoxic and sulfidic waters (20–80 m) can be observed in the genome recruitment plots (Figure 5B).

Despite the presence of H2S, the oxygen microsensor showed trace amounts of O2 (<1 µM) down to 40 m water depth during the downcast, which might be an artefact from water advection caused by the CTD rosette (see section ‘description of the sampling site’). Assuming that the measured O2 concentrations were not an artifact, we calculated a vertical down flux of 0.07 mmol O2 m−2 d−1 between 17 and 27 m. Although this O2 flux could only account for the oxidation of ∼6% of the sedimentary H2S flux of 2 mmol m−2 d−1, it could partly sustain microaerobic activity in the sulfidic waters as suggested by the presence of the cbb3-type cytochrome c oxidase transcripts. Lateral advection of oxic waters and water exchange induced by internal waves may also have supported microaerobic activity at O2 concentrations that would remain below the detection limit of our STOX sensor (∼50 nM). However, mixing due to internal waves would have influenced only the upper part of the water column and the widespread anoxic conditions below 30 m gave little indication of lateral advection of oxic waters from 30–100 m, where the cbb3-type cytochrome c oxidase was nevertheless strongly expressed. Either, the O2 was consumed shortly before our sampling campaign or advective transport of oxic waters supplied O2 in concentrations below the detection limit of our STOX sensor. An alternative explanation for the high cbb3-type cytochrome c oxidase expression in the sulfidic waters could also be the use of nitric oxide (NO) as an alternate substrate instead of O2, as hypothesized for a γ-proteobacterial species [82] and supported by structural similarities of cbb3-type cytochrome c oxidases to bacterial nitric oxide reductases [83]. Although we did not measure NO in our study, a high expression-ratio of the genes encoding for nitric oxide reductases (norBC) were found in the genome recruitments for SUP05 (these genes are absent from currently available versions of the R. magnifica and V. okutanii genomes) in sulfidic depths, and at 60 m also for Sulfurovum. Furthermore, transcripts for the dissimilatory nitrite reductase (nirK) were highly expressed in SUP05-like organisms throughout the water column. Since this enzyme reduces NO2 to NO, minor concentrations of NO might have been available for respiration processes linked to sulfur oxidation.

Alternatively to O2 (and NO) utilization, SUP05, V. okutanii and Sulfurovum and several other ε-proteobacteria [84], [85] are also capable of coupling the oxidation of H2S to the reduction of NOx. Low but persistent concentrations of NOx were measurable at most depths down to 60 m, and nitrate as well as nitrite reductase (nar-, nap- and nir-genes) transcripts were expressed in all anoxic and sulfidic depths (Figure 5 and S4). The genome recruitment plots for SUP05 (the genome contains genes from all three clusters) show that nap- and especially nir-genes were expressed, while relatives of V. okutanii and Sulfurovum showed high expression-ratios mostly for nar- and nap-genes, respectively. In contrast, most of the genes mentioned above are absent from the currently available version of the R. magnifica genome and it is thought that R. magnifica reduces NOx for assimilatory reasons only [65].

We could not calculate fluxes from the trace amounts of NOx below 30 m. However, at the upper boundary of the second H2S peak (25–29 m) the NO2 and H2S profiles overlap. The ratio of the opposing concentration gradients of NO2 (−0.53 µM m−1) and H2S (0.22 µM m−1) between 25 and 29 m depth indicates that the NO2 flux was sufficient to oxidize the upward flux of H2S. A diffusion coefficient of ∼2×10−5 m2 s−1 can be estimated from the density gradient at this depth, resulting in an upward flux of 0.38 mmol H2S m−2 d−1 and an average oxidation rate of ∼100 nmol H2S l−1 d−1 within the ∼4 m thick overlapping layer. This is in the same range as the experimentally measured reduction of NO2 to N2 (126 nmol N l−1 d−1) at 30 m depth (see section ‘nitrogen cycling’ and Figure 7), suggesting that H2S oxidation was carried out partly via sulfur-driven autotrophic denitrification. The removal rate of NO2 calculated from the downward flux of NO2 was 230 nmol N l−1 d−1 and matched the experimentally measured NO2 removal of 255 nmol N l−1 d−1 from combined denitrification, anammox and dissimilatory nitrate reduction to ammonia (DNRA).

Figure 7. Vertical distribution of nitrogen transformation process rates and abundances of sequences encoding for involved enzymes.

Shown in percent of all protein-coding DNA and RNA sequences, respectively. (A) NO3 reduction to N2 (denitrification). (B) NO3 reduction to NO2, respiratory nitrate reductase (EC (C) NO2 reduction to N2, (NO forming) nitrite reductase (EC (D) N2O reduction to N2, nitrous-oxide reductase (EC (E) NO2 reduction to NH4+ (DNRA), (NH4+ forming) nitrite reductase (EC (F) NO2+NH4+ to N2 (anammox, based on the sole addition of NO2), hydrazine oxidoreductase (EC Please note that at 40 m only NO3 reduction to N2 (A) and NO3 reduction to NO2 (B) were measured.

Although our results suggest that H2S was oxidized by both microaerobic activity and sulfur-driven autotrophic denitrification well below the oxic zone (>1 µM O2), our flux calculations indicate that the larger part of the H2S was probably oxidized anaerobically with NOx.

Nitrogen cycling.

To shed light on the nitrogen cycling carried out by the microbial community, we measured potential rates of various nitrogen transformation processes using 15NO3, 15NO2, 15N2O or 15NH4+ incubations and compared them with corresponding abundances of functional genes and transcripts (the sum of BLAST-hits, EC number- and Pfam-assignments) involved in their turnover (Figure 7). Reduction of NO3 to NO2 was active throughout the anoxic and sulfidic zones, with the highest rates measured at 40 m (2500 nmol N l−1 d−1; Figure 7B). In comparison, the much lower rate measured at 30 m (150 nmol N l−1 d−1) might have been caused by limitations in reductants, as suggested by the much lower H2S concentrations at that depth. The transcript abundance for respiratory nitrate reductase (EC peaked in the anoxic zone at 20 m (0.35% of all protein-coding sequences) and then dropped within the sulfidic waters following the decrease in NO3 concentrations. Gene abundances however, increased with depth (∼0.5%) and it is possible that the addition of NO3 necessary for rate measurements stimulated a fast response of the microbial community yielding the actual potential for NO3 reduction to NO2 rather than the in situ rate. The majority of the genes and transcripts encoding for respiratory nitrate reductase at 5 and 20 m were similar to K. stuttgartiensis, while transcripts in sulfidic waters belonged to diverse groups of α-, β-, γ-, δ- and ε-proteobacteria, indicating a clear taxonomic separation with depth.

Measured rates for the subsequent steps in denitrification, the reduction of NO2 to N2 were highest close to bottom waters at 80 m (900 nmol N l−1 d−1; Figure 7C), whereas the transcript abundance for NO-forming cd-cytochrome nitrite reductase (EC was highest in the oxic and anoxic zones at 5 and 20 m (0.4% and 0.2%), mirroring the availability of NO2. The majority of the transcripts at 5 and 20 m were similar to the gene from N. maritimus, while those recovered from sulfidic depths were affiliated with diverse groups of proteobacteria. We also found high expression of ammonia monooxygenase (EC transcripts related to N. maritimus (1.5%, data not shown) in the oxic surface (5 m), which may be partly responsible for the high NO2 concentrations in the surface waters. Some of the NO2 reduction (N2 production) could be attributed to anammox activity, with the highest rates measured at 30 m (250 nmol N l−1 d−1 based on 15NO2 addition, Figure 7F and 96 nmol N l−1 d−1 based on 15NH4+ addition, data not shown). At 80 m anammox activity could only be detected by 15NO2 addition (152 nmol N l−1 d−1); an incubation with added 15NH4 did not stimulate any N2 production, most likely due to limitations in NOx. The abundance of genes encoding for hydrazine oxidoreductase (EC was generally very low at all depths (less than 0.015%; Figure 7F) while transcripts, mostly annotated as similar to K. stuttgartiensis, peaked in abundance at 20 and at 50 m (0.15 and 0.25%, respectively). The 50 m transcript maximum of hydrazine oxidoreductase (we did not measure rates at this depth) is in good agreement with a small peak in NO3 concentrations (to 0.7 µM) and a minor dip in NH4+ concentrations (Figure 2C). The transcript maximum might also be influenced by the much lower H2S concentrations at this depth, as anammox activity was shown to be inhibited by H2S [86].

N2O is an intermediate in denitrification (NOx to N2) and we measured the reduction of N2O to N2, which turned out to be of smaller magnitude (30 nmol N l−1 d−1) than the NO2 reduction to N2 (Figure 7D). The N2O concentrations, which ranged between 20 and 40 nM from surface to ∼80 m, dropped below detection limit at 80 m (Figure 2B), indicating either a complete reduction of N2O as previously observed for this area [87] or a lack of production due to the limitation in NOx. Gene and transcript abundance for nitrous oxide reductase (EC were highest in anoxic waters (20 m) reaching 0.3% and comparable in magnitude to the abundances of sequences encoding for nitrate and nitrite reductases from the same depths.

We also conducted rate measurements of complete denitrification (NO3 to N2; Figure 7A). The highest rate (490 nmol N l−1 d−1) was found at 40 m within the first H2S maximum. Much lower rates were observed for the other depths (26–41 nmol N l−1 d−1), which might be attributed to incomplete denitrification (e.g. NO3 reduction to NO2, NO or N2O) and potentially also to limitations in concentrations of the reductant (H2S).

Rates for DNRA were the lowest of the nitrogen transformations processes we measured, not exceeding 40 nmol N l−1 d−1 (Figure 7E). Gene abundance for cytochrome c nitrite reductase (EC was also lower than genes implicated in the other nitrogen transformation processes. Except for 50 m depth, the transcripts for this gene were even rarer than the gene abundances (less than 0.01%). This suggests only a minor role of DNRA in the microbial community metabolism at the time of sampling.

Although rate measurements do not provide information on the phylogenetic affiliation of organisms carrying out the nitrogen transformations, the predominance of sulfur oxidizing proteobacteria found in our sequence data suggests that the sulfur-driven autotrophic denitrification was most likely the dominant pathway for N-loss during our sampling campaign.

Carbon assimilation.

Eastern Boundary Upwelling Systems are characterized by high primary productivity due to photosynthetic (photoautotrophic) growth in surface waters, which in turn stimulates heterotrophic respiration processes in underlying waters. However, autotrophic lifestyles have also been found in these underlying waters, e.g. by organisms responsible for nitrification [28], [88], anammox [22][24], [26] and sulfur-driven autotrophic denitrification [31], [44], [84]. To investigate the magnitude of inorganic carbon assimilation of the microbial community, we conducted rate measurements at selected depths with 13C-bicarbonate incubations and compared them to the relative abundance of transcripts encoding for key carbon-fixing enzymes (Figure 8). High abundances of transcripts for ribulose-bisphosphate carboxylase/oxygenase (RuBisCo, EC were found at all depths, but especially in sulfidic waters. At 40, 60 and 80 m transcripts encoding for RuBisCo were the most abundant to be identified in the metatranscriptomes (1.1, 2.1 and 1.6% of all protein-coding sequences, respectively; Figure 8B and Figure S5). The phylogenetic diversity of the RuBisCo transcripts varied throughout the water column. Transcripts from photosynthetic organisms such as algae and diatoms (e.g. similar to Heterosigma akashiwo, Odontella sinensis and Thalassiosira spp.) as well as cyanobacteria dominated the 5 and 20 m samples. In the sulfidic zone (40–80 m), β- and especially γ-proteobacterial transcripts were most abundant (Figure 8C). Approximately 25% of all RuBisCo-transcripts were most similar to a bacterial artificial chromosome-clone of unknown bacterium 560, which also appears to belong to a SUP05 genome [89]. Altogether, transcripts from γ-proteobacteria contributed about 70% of all RuBisCo transcripts in sulfidic waters.

Figure 8. Vertical distribution of CO2 fixation rates, microbial cell counts and abundance of transcript encoding for key carbon-fixing enzymes.

(A) CO2 fixation rates and total microbial cell counts. (B) Abundances of transcripts encoding for carbon-fixing enzymes (in percent of all protein-coding RNA sequences): Ribulose-1,5-bisphosphate carboxylase oxygenase (RuBisCo, EC, Calvin-Benson-Bassham cycle), ATP citrate lyase (EC, Arnon-Buchanan cycle) and CO-dehydrogenase (EC, Wood-Ljungdahl pathway). (C) Phylogenetic affiliation of the transcripts encoding for RuBisCo.

While the high proportions of RuBisCo transcripts were indicative of an active Calvin-Benson-Bassham cycle, other CO2 fixation pathways were also represented by the presence of transcripts for key enzymes of the Arnon-Buchanan cycle (ATP citrate lyase, EC and the Wood-Ljungdahl pathway (CO-dehydrogenase, EC Transcripts encoding for these two enzymes accounted for up to 0.3% of all protein-coding sequences at 40 m depth (Figure 8B) and were also recruited onto the Sulfurovum and D. autotrophicum genomes, respectively (Figure S4).

CO2 fixation rates measured at selected depths with 13C-bicarbonate incubations (Figure 8A) were highest in the nutrient-rich oxic surface waters (23 µmol C l−1 d−1), reflecting the dominance of large-sized eukaryotic phytoplankton. However, dark incubations with 13C-bicarbonate yielded chemolithoautotrophic CO2 fixation rates ranging from 0.9 to 1.4 µmol C l−1 d−1 at depths of 30, 80 and 100 m. These measured rates are comparable to chemolithoautotrophic activity found in the redoxclines and the sulfidic zones of the Baltic and the Black Sea [42], [43], [90][93]. In comparison, carbon assimilation rates of 0.3 µmol C l−1 d−1 by heterotrophic bacteria, after the addition of 13C-glucose (data not shown), was only about one third when compared with the chemolithoautotrophic CO2 fixation. Based on total microbial cell counts, each cell fixed ∼0.3 fmol C d−1, a magnitude that was also observed in the sulfidic zone of the Baltic Sea [92].

Integrating the CO2 fixation rates over the predicted predominantly photic zone (0–20 m) we estimated ∼288 mmol C m−2 d−1 being fixed. This is in good agreement with the highest primary production rates modelled for the study area during the same period of time (250 mmol C m−2 d−1) [25]. On the other hand, the integrated light-independent CO2 fixation rates in the predicted predominantly aphotic zone (20–100 m) reached ∼96 mmol C m−2 d−1. Consequently, ∼25% of the total CO2 fixation at our study site was carried out by chemolithoautotrophic microorganisms. This is similar to the dark CO2 fixation in the redoxcline of the Baltic Sea, which was shown to contribute ∼30% of the surface productivity and to the dark CO2 fixation rates as reported for Chilean waters, which accounted for an average of ∼20% of total CO2 fixation [88], [94]. The chemolithoautotrophic CO2 fixation at our study site could represent as much as 33–53% of the estimated average CO2 fixation rate per square meter for the Humboldt Current System (182–290 mmol C m−2 d−1) [2], [4]. Assuming the measured rates were maintained throughout the entire sulfidic plume (∼5500 km2), the CO2 fixed through chemolithoautotrophy would have been ∼6.3×103 tons C per day. In comparison, in this area an estimate of the total primary production by remote sensing is ∼5.5×105 tons C per day [2]. Consequently, the chemolithoautotrophic activity during this sulfidic event would have contributed ∼1.2% of total CO2 fixed in the Humboldt Current System off the Peruvian coast at the given time. This is intriguing, since the Humboldt Current System is one of the most productive marine systems world-wide and supports the production of more fish per unit area than anywhere else in the world [2][4], [95]. Moreover, since the sulfidic plume may have been considerably larger than the extent we recorded and was either recurrent or prevailed for several months, the chemolithoautotrophic growth is significant in terms of carbon retention. Considering that only 15–30% of the photosynthetic surface production is actually exported to OMZ waters [25] the chemolithoautotrophic growth may act as an important, but up to now neglected factor promoting SO42− reduction and thus stabilizing sulfidic conditions in OMZ waters.


The Eastern Boundary Upwelling System off the Peruvian coast is one of the world's most productive oceanic regions and comprises one of the largest OMZs world-wide [2], [4], [11]. We reported here the detection of a sulfidic plume within continental shelf waters of the Peruvian OMZ in January 2009. The sulfidic plume covered an area >5500 km2 and contained ∼2.2×104 tons of toxic H2S, representing with ∼440 km3 the largest sulfidic plume ever observed in oceanic waters and the first time that H2S was measured in Peruvian OMZ waters.

The microbial community was largely dominated by several distinct γ- and ε-proteobacteria related to SUP05, R. magnifica, V. okutanii and Sulfurovum, which transcribed a broad range of genes involved in sulfur (H2S, S2O32−, S0 and SO32−) oxidation. Our data suggested that these sulfur oxidizing proteobacteria probably utilized several different oxidants ranging from O2, NO3, NO2, NO to N2O to oxidize the H2S well below the oxic surface. While sequences related to SUP05 indicated that genes involved in the reduction of NOx and NO were being expressed, R. magnifica-, V. okutanii- and Sulfurovum-like transcripts related to a microaerophilic cbb3-type cytochrome c oxidase also pointed to the use of O2 for H2S oxidation.

High-throughput sequencing data further showed a high abundance and transcriptional activity of δ-proteobacterial SO42− reducing D. autotrophicum. Transcripts recruiting to the SO42− reduction genes of D. autotrophicum were found suggesting that SO42− reduction may have occurred in the water column. However, our flux calculation indicated that the main source for H2S in the water column was sedimentary SO42− reduction. Given the fact that many sulfur cycling enzymes can both oxidize and reduce sulfur species [72], [73] and the presence of colloidal S0 plumes within the sampling area as observed with remote satellite sensing, the disproportionation of sulfur compounds could also be a way of energy acquisition for some of the proposed SO42− reducers [74].

High CO2 fixation rates were measured in photic surface waters (5.8–23 µmol C l−1 d−1), but also in the dark sulfidic zone, ranging from 0.9 to 1.4 µmol C l−1 d−1. Many identified microorganisms, both sulfur oxidizers and SO42− reducers were expressing transcripts encoding for key carbon-fixing enzymes. The light-independent, chemolithoautotrophic CO2 fixation is similar to observations made in permanently stratified systems like the Baltic and the Black Sea [42], [43], [90][93]. If the rates prevailed throughout the entire sulfidic zone, they would have represented as much as ∼30% of the photoautotrophic CO2 fixation at that site and would have been of similar magnitude as the photosynthetic surface production that is exported to OMZ waters [25].

The presence of colloidal S0 plumes in the study area again in May 2009 indicated that sulfidic waters in the OMZ off Peru might be more frequent and persistent than originally thought. Although the frequency and duration of H2S accumulations cannot be estimated from direct observations, their occurrence might increase in future resulting from eutrophication and global warming [27], [31], [35]. In addition, the carbon retention due to the chemolithoautotrophic activity presented in this study may enhance SO42− reduction and consequently H2S formation and thus could act as an important mechanism to stabilize sulfidic conditions in OMZ waters, potentially reducing the liveable habitat of many higher organisms.

Materials and Methods

Sample Collection

All waters samples were collected during RV Meteor cruise M77/3 (December 27th, 2008 to January 24th, 2009) on the Peruvian continental shelf. Station 19 (January 9th, 2009; 12°21.88′S, 77°0.00′W), located approximately 15 km off the coast of Lima was selected for detailed analysis. During the upcast, water was pumped from depth directly on board using a pump-conductivity-temperature-depth (pump-CTD) water sampler. We monitored density as well as O2 and H2S to account for internal waves and moved the pump-CTD accordingly.

Samples for nucleic acid extraction were filled (oxygen-free) in 4.5 l polycarbonate bottles. For each sample, 1.5–2 l of the water was prefiltered through 10 µm pore size filters (Whatman Nuclepore Track-Etch) and then collected upon 0.22 µm pore size filters (Millipore Durapore Membrane) using a vacuum pump. Less than 18 minutes elapsed from the time when the water was pumped on board until the filters were put in microcentrifuge reaction tubes and flash frozen in liquid nitrogen. Samples for incubation experiments and nutrient analysis were taken in parallel (see description below).

RNA extraction and cDNA synthesis

DNA and RNA were extracted using the DNA/RNA-Allprep kit (Qiagen) with minor modifications in the protocol for the lyses step: The frozen filters were crushed using a disposable pestle and incubated with 200 µl lysozyme (10 µg/µl) and 1 mM EDTA at ambient temperatures for 5 minutes. Subsequently, 40 µl of Proteinase K (10 µg/µl) were added, followed by another incubation of 5 minutes at ambient temperatures. After adding 500 µl buffer RLT-Plus (containing 10 µl/ml β-mercaptoethanol) the manufacturer's instructions were followed. DNA was eluted in 150 µl elution buffer; RNA in 50 µl nuclease-free water, followed by a subsequent step of DNA digestion (Turbo DNA-free kit, Ambion). Prokaryotic rRNA was removed with mRNA only prokaryotic mRNA isolation kit (Epicentre). Further depletion of bacterial rRNA was achieved by using the Ambion MicrobExpress kit (see also section ‘sequencing statistics’). Cleaned and rRNA-depleted mRNA was subjected to an in vitro transcription (amplification) step using Ambion MessageAmp. Finally, cDNA was synthesised using the Invitrogen superscript III cDNA synthesis kit with random hexameric primers (Qiagen). Throughout the procedure all DNA and RNA samples were subsequently quantified with a NanoDrop spectrophotometer and checked for degradation with a BioRad Experion Automated Electrophoresis System. Leftover reactants and reagents were removed using the PCR Mini Elute Kit (Qiagen). DNA and RNA was stored at −80°C until pyrosequencing. Throughout the whole procedure nuclease-free plastic consumables and nuclease-free water and reagents were used to hinder any possible degradation of DNA or RNA.


For both DNA and RNA (cDNA), 50 µl were sequenced with the GS-FLX (Roche) pyrosequencer at the Institute of Clinical Molecular Biology in Kiel. Each sample was loaded on one quarter of a PicoTiter plate (except the 5 m RNA sample, which was loaded on two quarters of a PicoTiter plate). This resulted in 1,888,768 (DNA) and 1,560,959 (RNA) sequences with an average length of 392 base pairs, accounting for 757,439,211 and 599,103,110 base pairs of sequence information, respectively (Table S1).

Sequence annotation pipeline

The sequence data was organized and analyzed with the Meta2Pro annotation pipeline [96]. All raw sequences were clustered using Cd-hit [97] with a sequence identity threshold of 98% and a word length of 8, delivering about 1,581,637 (DNA) and 592,711 (RNA) cluster representative sequences in total. The rRNA genes and rRNAs in these cluster representatives were identified by a BLASTn-search [98] against the SILVA database [99] including both prokaryotic and eukaryotic small and large ribosomal subunit sequences with a bit score cut off of 86. The bit score cut off of 86 as described earlier [100] was validated using a simulation exercise. All sequences deposited in the SILVA database (477,749 sequences) were randomly fragmented into one million sequences to simulate a pyrosequencing run. The generated fragments had a normal length distribution with mean and standard deviation values similar to those from our own dataset (mean: 420 base pairs and standard deviation: 150 base pairs). This simulated dataset was Cd-hit-clustered (as described above) and BLAST-searches against the SILVA database itself were carried out. All queries which hit themselves in the database (e.g. the sequence of origin of the query) or hit a sequence belonging to the same taxonomic lineage (allowing a mismatch of up to two taxonomic levels) were considered true positives, while those fragments that hit sequences from other taxonomic lineages were false positives. The bit score distributions for the true and false positives were binned and a threshold sweep was used to calculate the sensitivity and specificity value at each bit score threshold. A bit score value of 86 in the resulting plot of the sensitivity and specificity distributions for this threshold sweep gave a specificity of 99.35% and a sensitivity of 99.85% respectively. Hence, this cut off was used for all further analysis of the sequences against the SILVA database and also in MEGAN to make taxonomic assignments (using a minimum support of 5 and a 10% score range for its LCA algorithm) [101]. The cluster representative sequences without a hit in the SILVA database were compared against the non-redundant database from NCBI using BLASTx with a bit score cut off of 35. The top hit of each BLASTx-search was used for the functional assignment of the cluster representatives.

The cluster representative sequences without a hit in the SILVA database were further scanned with profile hidden Markov models of the ModEnzA EC groups [69] and the Pfam protein families [70]. The Pfam-hits were converted to EC numbers and along with the ModEnzA EC-hits mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) reference pathways using the FROMP pathway mapping and visualization tool [96]. For ease of data analysis all cluster representative sequences, including clustering information from Cd-hit (cluster identification number, cluster size and all clustered nucleotide sequences), results from BLAST-, Pfam- and EC-searches as well as the taxonomic assignment from MEGAN were added to a MySQL database [96].

Sequencing statistics

Combining all DNA sequences, 0.3% of them were of rRNA gene origin. For the RNA sequences, the percentage of rRNA sequences varied between 58 and 76% with the exception of the surface sample collected at 5 m, which was dominated by eukaryotic rRNA sequences (>80%). The high count of eukaryotic rRNA sequences in the surface indicates that a further depletion of eukaryotic rRNAs supplementing the depletion of prokaryotic rRNAs would have been worthwhile. Nevertheless, also the depletion of prokaryotic rRNA still resulted in relatively high counts of bacterial rRNAs (7–55%), whereas the number of archaeal rRNAs was mostly negligible (2–11%). The rRNAs and rRNA genes were excluded from further analysis (except for Figure 3A), leaving 1,882,842 DNA and 421,528 RNA sequences. From this pool, 54% (DNA) and 53% (RNA) of the sequences could be identified as protein-coding (matching either the non-redundant database of NCBI or profile hidden Markov model scans of the ModEnzA EC groups or the Pfam protein families); the remainder could not be assigned. Of all protein-coding sequences, 82% of the DNA and 99% of the RNA sequences could be taxonomically identified (matched the non-redundant database of NCBI).

Hierarchical clustering of sequences using the taxonomic profiles

The taxonomic profiles of the metagenomes and the metatranscriptomes (the occurrence of the microbial taxa in the samples as a percentage of the total number of sequences having a BLASTx-hit) as presented in Figure 3 were used for a hierarchical clustering with the PRIMER 6 program [102]. The samples were grouped into 6 categories according to the depths and a multivariate statistical test (ANOSIM) was used to determine if the groupings were distinct from each other in terms of their microbial communities. The relationships between the depth groups were visualized in a non-parametric Multidimensional Scaling plot using PRIMER 6.

Metabolic and taxonomic diversity measures

The EC activity matrix (with sample sizes equalized to the smallest sample) was exported from the FROMP pathway mapping tool [96] and the EC counts for each sample were used to calculate the inverse of Simpson's index (1/D) where D = Σ Pi2 and Pi representing the proportional abundance of species i, and the Evenness E = (1/D)/S with S being the number of unique species. Similar calculations were also performed for the taxonomic assignments (as presented in Figure 3) from the BLASTx-searches normalized to total number of sequences having a BLASTx-hit.

Sequence recruitment onto reference genomes

The sequence data was recruited onto the reference genomes of the five most abundant organisms (as detected with BLAST-searches) SUP05, R. magnifica, V. okutanii, Sulfurovum and D. autotrophicum using the MUMmer program [103]. For SUP05, the draft genome (the metagenome with an ordered assembly of the contigs) as provided by Walsh and colleagues [41] was used and treated like the other genomes. The recruited sequences were re-assessed using a BLAST-search against the reference genomes. Sequences that hit more than one genome with a bit score difference of less than 5% between the first and second hits were discarded, giving rise to a non-overlapping set of sequences for each genome. These sequences were then recruited onto the genomes again to calculate the average coverage over non-overlapping windows of 300 base pairs. The coverage of RNA sequences in every reference genome window was normalized by the total number of BLASTx-hits for that metatranscriptome and divided by the coverage of DNA sequences (which have also been normalized by the total number of BLASTx-hits in that metagenome) for the same window. This value, the expression-ratio, was subsequently corrected for differences in sizes of the metatranscriptome and metagenome and selected regions of the reference genomes were plotted using customized R and PERL scripts.

Flow-cytometry cell counts

Samples for flow-cytometry were fixed with a final concentration of 1% paraformaldehyde and stored at −80°C until analysis. Total microbial cell counts were performed on a FACSCalibur flow cytometer (BD Biosciences). After 20 minutes staining of the samples with SybrGreen (Qiagen) at 4°C, cells were counted for 2 minutes or until a total count of 50,000 was reached. Sample flow rate was calibrated with standard beads (Trucount, BD Biosciences) and cell numbers were calculated via the time of measurement.

Chemical analysis and microsensor measurements

Our pump-CTD was equipped with a custom-built amperometric O2 microsensor to obtain vertical profiles of dissolved O2 (detection limit 0.5–1 µM). In addition, the recently developed self-calibrating Switchable Trace amount Oxygen (STOX) sensor was deployed, which allows high-accuracy O2 measurements in near anoxic environments (detection limit ∼50 nM during our measurements) [13], [16]. After a minimum of ten minute sensor equilibration at a given sampling depth, at least five measuring cycles were used to calculate O2 concentrations.

Water samples for nutrient analysis were taken with a depth resolution of 1 to 2 m. NH4+ was measured fluorometrically [104] and NO2 was analyzed spectrophotometrically [105] on board. Water samples for NO3 and PO43− were stored frozen until spectrophotometric determination [105] with an autoanalyzer (TRAACS 800, Bran & Lubbe). Detection limits for NH4+, NO2, NO3 and PO43− were 10, 10, 100 and 100 nmol l−1, respectively. Dissolved N2O concentrations were determined on board in triplicates measurements using the GC headspace equilibration method as described elsewhere [106].

H2S concentrations were measured continuously on water sampled from the pump-CTD using a custom-built microsensor with a detection limit of ∼0.5 µM [107]. Chemically determined H2S concentration (both H2S and HS; detection limit ∼0.5 µM) on discrete water samples were used to calibrate the microsensor [108]. Although the sulfidic waters contained a composite of H2S and HS, we use the term H2S throughout the manuscript to avoid unnecessary complexity.

Using a 125 m depth cut off and a grid resolution of 1′ (bathymetrical data was obtained from the National Geophysical Data Center) along the ∼200 km cruise track on the Peruvian shelf where H2S was detected in the water column, a total area of ∼5500 km2 was calculated to be affected by the sulfidic plume. The shelf contained mean H2S-maxima close to the bottom of the water column of 3.4 µM. H2S was extending vertically over the water column with an averaged depth of ∼80 m, yielding ∼440 km3 of H2S-containing waters. Based on an average H2S concentration of 1.5 µM, we estimated a total content of ∼2.2×104 tons H2S within the plume.

Flux calculations

The density was calculated using the data processing program SeaSoft (Sea-Bird Electronics). The stability of the water column was expressed using the Brunt-Väisälä frequency N, defined as: N2 = −(g/ρ)×(∂ρ/∂z) where g is the gravitational acceleration, ρ the water density and z the water depth. The density gradient was calculated over 4 m bins. The turbulent diffusivity Ez was calculated as described earlier [109] from the Brunt-Väisälä frequency and the dissipation rate of turbulent kinetic energy ε: Ez = γε/N2 with the mixing coefficient γ = 0.2. We applied a mean ε of 1.85×10−9 W kg−1 [110]. This value was measured for the open-ocean thermocline [110] and was applied in several rate diffusion models [111], [112]. Vertical concentration gradients for O2, H2S, and NO3 were calculated over 4 m bins. Fluxes of O2, H2S, and NO3 at respective depths were calculated according to Fick's law: Ji = Ez×(∂C/∂z).

Satellite images

Data of the sensors MODIS (Moderate Resolution Imaging Spectroradiometer) aboard the satellites Aqua and Terra (NASA) as well as MERIS (Medium Resolution Imaging Spectrometer) aboard the satellite Envisat (ESA) were implemented to study milky turquoise discolouration in waters off the Peruvian coast. Cloudy weather north of Pisco during most of our cruise made remote sensing difficult, but turquoise discolorations were observed off Lima on January 20–21st (Figure S1A) and 27–28th (Figure S1B). The estimation of the extent of the plumes requires data of higher spatial resolution; the algorithm for the identification of colloidal S0 plumes is based on the high spectral resolution of MERIS data [57]. Since full resolution MERIS data is not available for this region we present a high resolution MODIS true colour image in our main figure (Figure 1E).

However, the reflectance spectra derived from reduced resolution MERIS data (Figure S1C), revealed that the turquoise plume southwest of Pisco conformed the criteria for S0 in the identification algorithm distinguishable from optically similar coccolithophore blooms [113]. Differences in the shape and appearance of the colloidal S0 plume on the MERIS image (Figure S1C) and the MODIS images (Figure 1E) from May 7th and 8th demonstrate the temporal variability, which is also visible in changes in the cloud structure between the two images.

Rate measurements of nitrogen transformation processes

Rates of microbial nitrogen transformations were measured at three or four depths in 15N-labeling experiments as described previously [114], [115]. Briefly, nitrogen-loss via anammox and denitrification as well as dissimilatory NO3 reduction to NO2 and NH4+ were measured in short-term incubation experiments amended with either 15NO3, 15NO2, 15N2O or 15NH4+ (20, 10, 1 and 5 µmol l−1, respectively; isotopes: Campro Scientific). Time-series incubations were carried out in 12 ml Exetainers (Labco) and biological activity was stopped in one replicate Exetainer at each time interval (0, 6, 12, 24 and 48 h) by the addition of saturated mercuric chloride. Anammox and denitrification was measured as the production of 15N-labeled N2 in 15NO2 and 15NO3, 15NO2 and 15N2O incubations, respectively. The N-isotopic composition of N2 gas produced in these experiments was determined by gas chromatography isotope-ratio mass spectrometry (GC/IRMS, Fisons VG Optima) [115]. NO2 produced from 15NO3 and NH4+ produced from 15NO2 was determined by GC/IRMS after conversion of NO2 and NH4+ to N2 by sulfamic acid [114] and alkaline hypobromite [24], respectively. Production rates were calculated from the increase of 15N-concentrations over time and only significant and linear productions of 15N-species without a lag-phase were considered (t-tests, p<0.05; R2>0.8). Rates are presented as net production rates corrected for the mole fractions of 14N in the original substrate pools.

Rate measurements of carbon fixation

Triplicate incubations of 4.5 l seawater were set up with water from 5, 15, 30, 80 and 100 m. To each bottle 4.5 ml of 13C bicarbonate solution (1 g 13C bicarbonate in 50 ml water) was added and bottles were incubated in on-deck incubators shaded to 25% surface irradiance with blue lagoon light foil (Lee Filters) and continuously cooled with surface seawater (5 and 15 m) or incubated at 12°C in the dark (30, 80 and 100 m) for 24 hours. At the end of the incubation 1–2 l were filtered on precombusted (450°C for 6 hours) Whatman GF/F filters (as these filters have an average pore size of 0.7 µm and small microorganisms may have passed through, the calculated CO2 fixation rates we present here have to be considered as minimal rates). The filters were oven dried (50°C for 24 hours) and stored for later analysis at room temperature. Filters were smoked overnight with 37% HCl to remove inorganic carbon retained on the filters, dried for 2 hours at 50°C and then analyzed in a CHN analyzer coupled to an isotope ratio monitoring mass spectrometer. The CO2 fixation rate was calculated according to the enrichment of 13C in the samples relative to unlabeled background values: Cfix = (At%sample−tAt%background)/(At%label−At%background)×(POC/time) where At%sample is the ratio of 13C/12C times 100 in the particulate organic carbon pool (POC), At%background is the same ratio in unlabeled POC and At%label is the final ratio of 13C/12C in the incubation bottle after label addition. The resulting ratio is multiplied with the concentration of POC and divided by the incubation time in days. Since we did not perform killed controls, we cannot exclude or estimate the contribution of anapleurotic carbon fixation in our samples. We averaged the CO2 fixation over the predicted predominantly photic (0–20 m, ∼14.4 µmol C l−1 d−1) and aphotic (20–100 m, ∼1.2 µmol C l−1 d−1, dark incubations) zones by multiplying the rates with the respective water depths. A photic zone CO2 fixation of ∼288 mmol C m−2 d−1 and a light-independent CO2 fixation of ∼96 mmol C m−2 d−1 was estimated.

When compared to the overall mean CO2 fixation rate of the Humboldt Current System off Peru (2.18 g C m−2 d−1 or 182 mmol C m−2 d−1 [2] and 3.5 g C m−2 d−1 or 292 mmol C m−2 d−1 [4]), our measured dark CO2 fixation (over an 80 m deep aphotic zone) contributed 33–53% of the total CO2 fixation. Extrapolating the dark CO2 fixation rates over the entire sulfidic plume (∼5500 km2), we calculated a total CO2 fixation of ∼5.3×108 mol C d−1 or ∼6.3×103 tons C d−1. This CO2 fixation estimate would contribute 1.2% of the total primary production of the Humboldt Current System off the Peruvian coast as presented by Carr, 2002 (∼2×108 tons C y−1 or ∼5.5×105 tons C d−1). The average CO2 fixation rates per cell were calculated from total microbial cell counts as obtained from flow-cytometry.

Accession numbers of sequence data

Metagenomic and metatranscriptomic sequences have been deposited in the metagenomics analysis server (MG-RAST) under accession numbers 4460677.3, 4450892.3, 4450891.3, 4460736.3, 4461588.3, 4460676.3, 4452038.3, 4460734.3, 4452039.3, 4452042.3, 4460735.3, 4460734.3 and 4452043.3.

Supporting Information

Figure S1.

Satellite images of the Peruvian coast. The red circles mark colloidal S0 plumes. (A) Satellite image (MODIS) of the area around Lima on January, 29th, 2009. (B) Satellite image (MODIS) of the area around Pisco on January, 27th, 2009. (C) Satellite image (MERIS) of the area around Pisco on May 7th, 2009.



Figure S2.

Multivariate statistical analysis and clustering of all protein-coding sequences based on shared taxonomic categories. Taxonomic categories are chosen according to Figure 3. (A) Hierarchical clustering. (B) Non-parametric Multidimensional Scaling. Plot is labelled by prior groupings of the samples. The solid green circles mark the hierarchical clusters obtained using a similarity cut off of 86%. (C) ANOSIM test for significance of difference between the prior groupings.



Figure S3.

Vertical distribution of the most abundant taxa. Shown are the eight most abundant organisms (on the highest taxonomic level possible) in percent of all sequences in the DNA and RNA datasets (excluding rRNA genes and rRNAs); ordered descending according to DNA counts and supplemented with the remainder of the top eight organisms from the RNA dataset if not already present in the DNA dataset. Please note that no RNA sequences were identified as similar to the SUP05 cluster bacterium at 20 and 40 m with BLASTx-searches against the non-redundant database of NCBI.



Figure S4.

Vertical distribution of sequences recruited onto the genomes of (A) Sulfurovum sp. NBC37- 1 and (B) Desulfobacterium autotrophicum HRM2. Shown are selected genes encoding for enzymes involved in oxygen- (blue), sulfur- (yellow), nitrogen- (red), carbon- (green) and hydrogen-metabolism (purple) in the corresponding order of the genomes. The y-axis depicts the log of the expression-ratio, a measure for the selective enrichment of transcripts over the corresponding gene, normalized to the total pool of protein-coding sequences. A list of the start and end position of each gene and the full name of the corresponding enzyme are shown in Table S3.



Figure S5.

Vertical distribution of the most abundant functional assignments. Shown are the top five most abundant EC numbers in percent of all protein-coding sequences in the DNA and RNA datasets; ordered descending according to the DNA counts and supplemented with the remainder of the top five EC numbers from the RNA dataset if not already present in the DNA dataset. Please note that the data presented here is based only on EC number- and Pfam-assignments; BLAST-hits are not included.



Table S1.

Sequencing statistics. aas obtained with Cd-hit. bas obtained by BLASTn-searches against the SILVA database. cas obtained by BLASTx-searches against the non-redundant database of NCBI and by scans with profile hidden Markov models of the ModEnzA EC groups and of the Pfam protein families. daverage.



Table S2.

Metabolic and taxonomic evenness and diversity in all protein-coding sequences. A collection of all EC number- and Pfam-assignments was used to determine the metabolic diversity, while the taxonomic diversity was calculated using all hits from BLASTx-searches. Shown are the evenness and the diversity (inverse of the Simpson's index) for both the metagenomic and metatranscriptomic datasets.



Table S3.

Genomic regions of the sequences recruited onto proteobacterial genomes as plotted in Figures 5 and S4. Shown are the start and end position of each gene and the corresponding enzyme name for the uncultured SUP05 cluster bacterium, Candidatus Ruthia magnifica str. Cm, Candidatus Vesicomyosocius okutanii HA, Sulfurovum sp. NBC37- 1 and Desulfobacterium autotrophicum HRM2.




We thank the government of Peru for permitting research in their territorial waters, the chief scientist Martin Frank (GEOMAR, Kiel) and the captain and crew of RV Meteor for their support at sea. We acknowledge Gabriele Klockgether, Andreas Ellrott (MPIMM, Bremen), Violeta V León Fernández (IMARPE, Callao) and Hermann Bange (GEOMAR, Kiel) for technical and analytical assistance. Additionally, we would like to thank Mary Ann Moran, James T Hollibaugh, Nasreen Bano (University of Georgia, Athens) and Rachel S Poretsky (Georgia Institute of Technology, Atlanta) for methodological help with the nucleic acid sample preparation.

Author Contributions

Conceived and designed the experiments: H. Schunck GL JL. Performed the experiments: H. Schunck GL DKD TG TK CRL AP SC JL. Analyzed the data: H. Schunck GL DKD TG TK CRL AP MH H. Siegel JL. Contributed reagents/materials/analysis tools: PR MBS MG RAS MMMK. Wrote the paper: H. Schunck GL DKD JL. Authors contributed equally to the work: H. Schunck GL DKD.


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