Genomic, physiologic, and proteomic insights into metabolic versatility in Roseobacter clade bacteria isolated from deep-sea water

Tang, Kai; Yang, Yujie; Lin, Dan; Li, Shuhui; Zhou, Wenchu; Han, Yu; Liu, Keshao; Jiao, Nianzhi

doi:10.1038/srep35528

Genomic, physiologic, and proteomic insights into metabolic versatility in Roseobacter clade bacteria isolated from deep-sea water

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Published: 20 October 2016

Volume 6, article number 35528, (2016)
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Scientific Reports

Genomic, physiologic, and proteomic insights into metabolic versatility in Roseobacter clade bacteria isolated from deep-sea water

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Kai Tang¹,
Yujie Yang¹,
Dan Lin¹,
Shuhui Li¹,
Wenchu Zhou¹,
Yu Han¹,
Keshao Liu¹ &
…
Nianzhi Jiao¹

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Abstract

Roseobacter clade bacteria are ubiquitous in marine environments and now thought to be significant contributors to carbon and sulfur cycling. However, only a few strains of roseobacters have been isolated from the deep-sea water column and have not been thoroughly investigated. Here, we present the complete genomes of phylogentically closed related Thiobacimonas profunda JLT2016 and Pelagibaca abyssi JLT2014 isolated from deep-sea water of the Southeastern Pacific. The genome sequences showed that the two deep-sea roseobacters carry genes for versatile metabolisms with functional capabilities such as ribulose bisphosphate carboxylase-mediated carbon fixation and inorganic sulfur oxidation. Physiological and biochemical analysis showed that T. profunda JLT2016 was capable of autotrophy, heterotrophy, and mixotrophy accompanied by the production of exopolysaccharide. Heterotrophic carbon fixation via anaplerotic reactions contributed minimally to bacterial biomass. Comparative proteomics experiments showed a significantly up-regulated carbon fixation and inorganic sulfur oxidation associated proteins under chemolithotrophic conditions compared to heterotrophic conditions. Collectively, rosebacters show a high metabolic flexibility, suggesting a considerable capacity for adaptation to the marine environment.

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Introduction

Roseobacter clade bacteria (RCB) are one of the most abundant bacterioplanktonic groups in oceans worldwide, accounting for 10–25% and 2–15% of the total 16S ribosomal RNA microbial community in some surface ocean and sediment ecosystems, respectively^1,2,3,4,5,6. All RCB cluster closely together within the Rhodobacteraceae family of Alphaproteobacteria and are widely distributed in various marine habitats from coastal regions to open oceans, surface water to sediments, and polar ice to tropical latitudes^1,2,3,4,5,6. Moreover, RCB species are often found in symbiosis with marine algae⁷.

Marine RCB carry out critical carbon and sulfur biogeochemical transformations^6,8,9,10. RCB are a metabolically diverse group of bacterioplankton with opportunitrophic lifestyles¹¹. RCB may function to remineralize organic matter and recycle essential nutrients, especially in coastal environments and during phytoplankton blooms, where they often reach high abundance^12,13. Certain species of RCB, termed aerobic anoxygenic phototrophic bacteria (AAPB) contain a photosynthetic gene cluster with the capability for photoheterotrophy^{7,14,Full size image}

Major functions shared by two deep-sea roseobacters

The presence of gene sets in their chromosomes and plasmids assumed to be important for inhabiting marine environments within the genomes of the two strains were selected and summarized in Fig. 1 and Supplementary Table S1. T. profunda JLT2016 and P. abyssi JLT2014 shared major metabolic modules (Fig. 2).

Carbon fixation and sulfur oxidation

A striking genomic feature of the two strains is that they have the potential for CO₂ fixation via the CBB cycle and inorganic sulfur oxidation, suggesting potential chemoautotrophic ability (Fig. 2). The complete CBB operons in T. profunda JLT2016 and P. abyssi JLT2014 showed high sequence identity and conserved gene structure (Supplementary Fig. S1). Phylogenetic analysis showed that RuBisCO in T. profunda JLT2016 and P. abyssi JLT2014 belong to the IC class RuBisCO (Supplementary Fig. S2). The Sox enzyme complex (soxABXYZ) for oxidation of thiosulfate (S₂O₃²⁻) to elemental sulfur was found in their genomes (Supplementary Fig. S1). Sox is proposed to produce energy and reducing power for carbon fixation via a reverse and forward electron transfer of sulfur oxidation (Fig. 2)³². The components of forward and reverse electron transport chains involved in sulfur oxidation include cytochrome c proteins, cytochrome bc1 complex and NADH (or NADPH)-quinone oxidoreductases (Fig. 2)³³. Both genomes include terminal cytochrome oxidase genes for low (types aa3 and bo3) and high (types cbb3 and bd) affinity cytochrome c oxidases³⁴, suggesting adaptation to growth in a wide range of oxygen concentrations. Other genes encode enzymes for the oxidation of reduced sulfur compounds, including sulfide quinone oxidoreductase and reverse dissimilatory sulfite reductase complex, which are responsible for the oxidation of sulfide and sulfite, respectively (Supplementary Table S1).

Central carbon metabolism

Genes for a complete tricarboxylic acid cycle (TCA), Embden-Meyerhof-Parnas (EMP, glycolysis), Entner-Doudoroff (ED) and pentose phosphate pathway (PP) were identified in both deep-sea roseobacter genomes (Fig. 2). Additionally, both genomes contained genes encoding enzymes in anaplerosis (pyruvate carboxylase) and cataplerosis (malic enzyme and phosphoenolpyruvate carboxykinase) that work together to ensure the appropriate balance of carbon flow into and out of the TCA cycle³⁵. Moreover, malic enzyme may act as a major anaplerotic factor in Roseobacter denitrificans as well, which catalyzes the reversible oxidative decarboxylation of malate to pyruvate and CO₂¹⁷.

Transporter systems

RCB contain the genes for a relatively high number of ATP- binding cassette systems (ABC transporters) and tripartite ATP-independent periplasmic (TRAP) systems compared to other bacterial groups³⁶, which allow them to efficiently import organic matter. Consequently, they are adapted for dilute, heterogeneous growth substrates¹². The highest number of transporter systems were ABC transporters (92 and 78 for T. profunda JLT2016 and P. abyssi JLT2014, respectively), followed by TRAP transporters (28 and 22), and major facilitator superfamily transporters (21 and 18) (Supplementary Table S1), accounting for 9.2% and 8.7% of the total CDSs of T. profunda JLT2016 and P. abyssi JLT2014, respectively. Predicted uptaking substrates for the complete transporter systems in T. profunda JLT2016 comprised a variety of carbohydrates, carboxylic acids, amino acids, peptides, metals and other nutrients (Supplementary Table S2). Both genomes were found to possess abundant exporters involved in the efflux of polysaccharide, heavy metals and metabolites (Supplementary Table S2). Transporter genes in plasmids of T. profunda JLT2016 and P. abyssi JLT2014 account for 15.1% and 29.3% of the total of transporter systems, respectively. Their plasmids carried genes encoding transporters such as TRAP systems, ABC transporters for amino acid, di/oligopeptide, ribose, glycerol-3-phosphate, and heavy metal efflux (Supplementary Table S2). The type IV secretion systems are present in chromosomes of T. profunda JLT2016 or P. abyssi JLT2014 (Supplementary Fig. S3), which are commonly used for both DNA and protein transfer between bacteria and between bacteria and host³⁷.

Exopolysaccharide (EPS) biosynthesis

A number of genes associated with putative production and biosynthesis of exopolysaccharides present in P. abyssi JLT2014 and T. profunda JLT2016 (Supplementary Table S1) showed high sequence identity to those in an exopolysaccharide-producing RCB strain, Salipiger mucosus DSM16094, found in soil³⁸.

Two roseobacter bacteria share other genetic potentials for likely an adaption to the marine environments characterized by low-nutrient (“Nitrogen and phosphorus acquisition”, “Motility and chemotaxis” in the Supplementary Material), low temperature and high-pressure conditions (“Responses to stress” in the Supplementary Material).

Comparative genomics of marine roseobacters

T. profunda JLT2016 and P. abyssi JLT2014 show substantial genomic dissimilarity. For instance, T. profunda JLT2016 contains gene clusters associated with hydrogen oxidation and pectin utilization, whereas P. abyssi JLT2014 contains a CRISPR-Cas system (Fig. 1), indicating a high potential for individual adaptations (“Distinct functional gene clusters for two deep-sea roseobacters” in the Supplementary Material). The CRISPR-Cas system is also found in other RCB species, Dinoroseobacter shibae DFL-12. (Supplementary Table S3).

Similar to other many roseobacter genomes, two roseobacter genomes exhibit high plasticity with the rearrangement of large portions of its genome and gene acquisition, as evidenced by abundant transposases, and the presence of the conjugative plasmid, prophage-like elements and genomic islands (Supplementary Table S3, “Mobile genetic elements” and “Genomic islands” in the Supplementary Material). The genomic plasiticity might shape diverse metabolic adaptations to a wide diversity of ecological niches, as has been observed for RCB.

Sulfur oxidation gene clusters (30/47) were found most frequently in roseobacter genomes, followed by photosynthetic genes (12/47) (Fig. 3). Only five marine roseobacters harbor genes encoding form IC Rubisco enzymes and other CBB genes (Supplementary Fig. S2). They belong to the same subclade including Thiobacimonas, Pelagibaca, Oceanicola and Citreicella (Fig. 3). Certain species of this subclade contained the H₂ utilization gene cluster (Fig. 3). Most genes in the Sox, CBB and H₂ gene clusters had high sequence identity (>70%) and conserved structures (Supplementary Figs S1 and S4). T. profunda JLT2016 and P. abyssi JLT2014 have CO dehydrogenase for aerobic CO oxidation but lack AAPB genes and DMSP utilization genes frequently found in roseobacters isolated from the surface water and alga-associated samples. Heterotrophy is essential for RCB success in the costal ocean whereas photoheterotrophy provide a survival advantage for AAPB in RCB in surface waters of the oligotrophic open ocean^12,16. Pervious studies demonstrated that the Gammaproteobacteria (SUP05 clade and Oceanospirillales) and the Deltaproteobacteria (SAR324 clade) lineages that are ubiquitous in the dark oxygenated ocean are likely mixotrophs and have the potential for autotrophic CO2 fixation, coupled to the oxidation of reduced sulfur compounds²³. Similarly, RCB bacteria within the Alphabacteria have the versatile metabolic potentials (heterotrophy, lithotrophy and authotrophy) that are beneficial for thriving at lower nutrients in the deep-sea than those in the surface water³⁹.

Growth experiments and physiological characteristics of T. profunda JLT2016

T. profunda JLT2016 could grow autotrophically (sodium bicarbonate as carbon source) and hetotropically (glucose as carbon source) by generating energy from chemolitotrophic sulfur oxidation (thiosulfate as an energy resource) (Fig. 4A–C). Moreover, T. profunda JLT2016 is able to grow with thiosulfate as an energy resource and using both sodium bicarbonate and glucose as carbon sources for growth (termed mixotrophy) (Fig. 4A–C). The new generations of sulfate concentrations under chemolithotrophic conditions are approximately 1.9-fold change relative to the added thiosulfate concentrations after exhaustion of thiosulfate, suggesting that bacteria could produce minimal amounts of elemental sulfur during thiosulfate oxidation, in contrast to the aerobic sulfur oxidizer Gammaproteobacteria SUP05 clade⁴⁰. There was a significant difference in bacterial abudance under five culture conditions (one-way ANOVA, p < 0.05). The maximum final cell density during chemolithoheterotrophic growth (mean ± SD, 9.26 ± 0.31 cells × 10⁷/mL) was higher than that in heterotrophic growth with glucose (4.53 ± 0.08 cells × 10⁷/mL) (Fig. 4A), suggesting that heterotrophic cells could regulate the energy metabolism and biosynthesis after being supplied with thiosulfate as an alternative energy resource. R. denitrificans Och 114 has been reported to use anaplerotic pathways mainly via malic enzyme to fix 10% to 15% of protein carbon¹⁷. In contrast, ¹³C content in T. profunda JLT2016 when grown with glucose and NaH¹³CO₃ is signifcantly less than that in autotrophic and mixotrophic cells (paired t-test, p < 0.001) (Fig. 4C), accounting for only 0.5–1.2% of the total carbon of cells. This finding suggests that the level of anaplerotic carbon fixation is low and bacterial growth relies primarily on heterotrophic metabolism (herein referred as heterotrophy). The ¹³C content in mixotrophic cells at 24 h was close to that in other growth phase, when cluture medium contained more than half of glucose added (Fig. 4B,C), indicating that autotrophic carbon fixation and heterotrophic metabolic pathways occur simultaneously. The average ¹³C content during the mixotrophic growth is only approximately 1/3 that during autotrophic growth (Fig. 4C), indicating that glucose was assimilated at the expense of chemolithotrophic energy, resulting in less carbon fixation.

The average cellular carbon (C) and nitrogen (N) ratio (4.09) of T. profunda JLT2016 is lower than the previously estimated for marine heterotrophic bacteria (4.31)⁴¹, but close to that estimated for marine dissolved organic carbon (4.13)⁴¹. Moreover, cellular C/N ratios varied very little among different trophic growth cells (Supplementary Fig. S5), suggesting that the trophic strategy does not have a great impact on cellular biochemical composition. Autotrophic growth cells yield the lowest amount of poly-3-hydroxybutyrate (PHB), which is a cellular carbon storage material (Fig. 4D). In contrast, autotrophic cells yield the highest amount of EPS, followed by cells grown on glucose and sodium bicarbonate. Specifically, their EPS accounted for 25.9% ± 2.9% and 25.5% ± 4.5% of dry cell weight, respectively, which was significantly higher than that observed for cells cultivated under other growth conditions (paired t-test, p < 0.05) (Fig. 4D). EPS biosynthesis consumed a great deal of energy and carbon, which may have been responsible for the relatively low cell densities in the presence of glucose and sodium bicarbonate mixture compared to those grown in the presence of glucose (Fig. 4A). EPS helps deep-sea bacteria endure extremes of temperature, salinity and nutrient availability⁴². Moreover, EPS can speed the rate of substance uptake and trap dissolved organic matter in the marine environment⁴². During T. profunda JLT2016 growth, microbial aggregates were detected in the liquid media and their aggregation ability was observed in by electron micrographs (Supplementary Fig. S6). Furthermore, aggregated bacterial cells surrounded by a continuous film of extracellular substances were observed by confocal laser scanning microscopy (Supplementary Fig. S7), indicating that T. profunda JLT2016 has biofilm-forming ability. Bacterial aggregates and biofilms enhance their competition and adaptation to the environment⁴³. EPS may be involved in cell aggregation and biofim formation as previously suggested⁴³.

Comparative proteomics of T. profunda JLT2016

In total, 2,656 iTRAQ-labeled proteins, including 283 proteins on plasmids, were identified. Of these, 1,621 displayed significant differential expression between at least two growth conditions at early stationary phase (fold change >1.2 or <0.8 adopted in most iTRAQ studies based on accuracy and resolution of iTRAQ quantitation^44,45. Overall, compared to heterotrophic growth, most of proteins in the CBB cycle and the Sox enzymes during autotrophic growth showed significantly higher abundance (Fig. 5). For example, the cbbL (ribulose-bisphosphate carboxylase large subunit) and soxB (sulfur-oxidizing protein soxB) proteins significantly increased by 3.8- and 4.2-fold, and 3.1- and 3.3-fold changes, respectively, in autotrophic cells relative to those in two heterotrophic cells grown with glucose, and glucose and sodium bicarbonate, whereas the mRNA abundance of the two genes relative to that of heterotrophic cells showed 8.2- and 5.5-fold changes, and 5.4- and 11.5-fold changes upon qRT-PCR, respectively (Supplementary Fig. S8). Moreover, cytochrome bc1 complexes, cytochrome c proteins, and a NAD(P)H-ubiquinone oxidoreductase (Fig. 5), which are predicted to be components of a reverse and forward electron transfer of sulfur oxidation, were up-regulated in autotrophic cells relative to those in heterotrophic growth. Cytochrome c oxidase type cbb3 in an electron transport was the only detected terminal oxidase under different conditions. Furthermore, most of proteins in the CBB cycle and the Sox enzymes in chemolithoheterotrophic and mixotrophic growth were present at higher levels than those in heterotrophic growth (Fig. 5). These findings combined with physiological evidence confirmed the existence of autotrophic and chemolithotrophic metabolic pathways in T. profunda JLT2016, as shown in Fig. 2.

When compared to other growth, key enzymes of central metabolisms were significantly up-regulated in heterotrophic growth including edd (phosphogluconate dehydratase) for ED pathways, and zwf (glucose-6-phosphate dehydrogenase) and pgl (6-phosphogluconolactonase) shared by ED pathway and PP pathway (Fig. 5). Most of enzymes in the TCA cycle in heterotrophic bacteria were also significantly upregulated than other growth (Fig. 5). ATP, NADPH, and NADH concentrations were higher in hetrotrophic cells than that in chemolithoheterotrophic and mixotrophic cells at early stationary phase (Supplementary Fig. S9, paried t-test, p < 0.01), thereby indicating an enhanced energy and reducing power production from central metabolism in heterotrophic cells. Bacterial growth is crucially dependent on protein synthesis and thus on the number of ribosomes⁴⁶. Specifically, upregulated ribosomal protein expression was observed during chemolithoheterotrophic and mixotrophic growth (Supplementary Fig. S10), thereby indicating bacteria enhanced protein biosynthesis and futher increased cell abundance. Although chemolithoheterotrophic and mixotrophic growth required more energy for biosynthesis than heterotrophic growth, sulfur oxidation provides another ATP resource and reduces ATP production through oxidative phosphorylation, as indicated by the upregulation of the Sox enzymes and downregulation of the TCA cycle proteins (Fig. 5). These then gave rise to increased carbon flux toward biomolecules biosynthesis for cells under chemolithoheterotrophic and mixotrophic growth compared to heterotrophic growth. The expression of malic enzyme and pyruvate carboxylase was not affected by the switch from heterotrophic growth on glucose to heterotrophic growth on glucose and sodium carbonate (Fig. 5), indicating that anaplerotic carbon fixation in T. profunda JLT2016 did not appear to be important for bacterial growth. When compared to other types of growth, autotrophic growth upregulated phosphoenolpyruvate carboxykinase and the gluconeogenic enzyme fructose-1,6-bisphosphatase (fbp), which are required to bypass the irreversible step in glycolysis (Fig. 5). The increasing gluconeogenesis might lead to increased carbon flux to hexoses that are used in polysaccharides production. Most EPS biosynthesis proteins have relatively high abundance in autotrophic growth and heterotrophic growth in the presence of glucose and sodium bicarbonate mixture (Supplementary Fig. S10).

Transporter was the major functional category of detected proteins in all samples, accounting for 8.2% of the total. The most frequently observed transporters were ABC transporters (154/219) such as sugar, amino acid, and dipeptide/oligopeptide. The next most frequently observed transporter is TRAP (36/219). A SulP family transporter predicted to take up sodium bicarbonate was significantly up-regulated during autotrophic and mixotrophic growth, whereas glucose transporter was down-regulated in autotrophic growth (Supplementary Fig. S11). The transporters for ammonia, phosphate and sulfate were also detected (Supplementary Fig. S11). Bacteria expressed not only transporters for essential nutrients, but also transporters for other originally non-existent nutrients in media. For example, autotrophic growth cells have higher expression of a glycerol ABC transporter system in a plasmid (Supplementary Fig. S11). Even though, hydrogen gas, hydrogen sulfide and urea were not selected as growth substrates in this study, hydrogenase, sulfide quinone oxidoreductase and urease were detected in all samples (Supplementary Fig. S11). A previous study showed that approximately 50% of proteomes in several roseobacter strains represent an opportunistrophic gene pool, allowing rapid bacterial response to specific environmental changes¹¹. Similarly, T. profunda JLT2016 not only has the bare minimum proteome for survival under different trophic conditions, but also follows an opportunitrophic lifestyle of maintaining broad functional potential to exploit spatially and temporally variable substrates as carbon and energy resources.

Methods

Genome sequencing

T. profunda JLT2016 and P. abyssi JLT2014 DNA were extracted using a TIANamp Bacteria DNA Kit (Tiangen, Bei**g, China). The genome sequencing was performed at the Chinese National Human Genome Center at Shanghai.

The genome of T. profunda JLT2016 was sequenced using a massively parallel pyrosequencing technology (GS FLX+, Roche Diagnostics, Indianapolis, IN, USA). Briefly, 5 μg of sample DNA was sheared to 500–1000 bp by Covaris S2 (Covaris, Woburn, MA, USA) and purified using AMPure Beads (Beckman-Coulter, Fullerton, CA, USA). A DNA sequencing library was generated using a GS DNA Library Preparation Kit (Roche Diagnostics, USA) and a GS emPCR Kit (Roche Diagnostics, USA) for emPCR. A total of 248,283 reads counting up to 133.79 Mbp were obtained, covering 25.3-folds of the genome. Assembly was performed using Newbler (v2.7) and produced 136 contigs ranging from 500 bp to 383,719 bp. Relationships of the contigs were determined by multiplex PCR. Gaps were then filled in by sequencing PCR products using ABI 3730xl capillary sequencers (Applied Biosystems, Foster city, CA, USA). Finally, sequences were assembled using Phred, Phrap and Consed software packages (http://www.phrap.org/), and low quality regions of the genome were resequenced.

Whole genome sequencing of P. abyssi JLT2014 was accomplished using a hybrid approach, combining Illumina short read data with PacBio long read data⁴⁷. Briefly, 1 μg of sample DNA was sheared to 300 bp by sonication with Covaris S2 (Covaris, USA). NEBNext Ultra™ DNA Library Prep Kit for Illumina (Illumina, San Diego, CA, USA) was used to construct the library, after which 10 ng of the sequencing library was used generate clusters in cBot using a TruSeq PE Cluster Kit (Illumina, USA), and finally sequenced by Illumina HiseqTM 2500 to generate 2 × 125 bp reads. A 5 μg sample DNA was then sheared to 10 Kb by Covaris g-TUBE (Covaris, USA). A PacBio SMRT bell™ Template Prep Kit (Pacific Biosciences, Menlo Park, CA, USA) was used to construct the library. The sequencing primers were annealed using a PacBio DNA/Polymerase Kit and polymerase combined to the SMRTbell templates (Pacific Biosciences, USA). PacBio sequencing data were generated using a PacBio RS-II instrument, C2 chemistry and one SMRT cell per genome (Pacific Biosciences, USA). A total of 685.67 M bp (post-filter) with an average length of 5664 bp were obtained. The genome sequences were de novo assembled by the HGAP2 program in the SMRT analysis server (v2.3). Illumina pair end reads were mapped to the assembled contigs to improve the accuracy of genome sequences.

Bioinformatics analysis

The final assembled genomes were automatically annotated and analyzed through the IMG/ER (http://img.jgi.doe.gov). The comparison and visualization of multiple genomes was conducted with BRIG⁴⁸. The concatenated conserved 70 single copy genes⁴⁹ in the RCB were used for reconstruction of the maximum likelihood phylogenetic inference through the RAxML program (v7.4.2)⁵⁰ under a JTT plus GAMMA model. Other bioinformtaic analysis methods were available in the Supplementary Material.

Culture conditions

All reagents used in bacterial cultures were obtained from Sigma-Aldrich (St Louis, MO, USA) unless otherwise specified. Growth medium consisted of artificial seawater (ASW) base combined with substrates including glucose (Glc, 100 μM), NaHCO₃ (C, 2.5 mM), and Na₂S₂O₃ (S, 1 mM) in phosphate-buffered saline (PBS; pH 8.0) (autotrophic culture: C and S; mixotrophic culture: Glc, C and S; chemolithoheterotrophic culture: Glc and S; heterotrophic culture: Glc, or Glc and C). 2% cultures of T. profunda JLT2016 (2:100 dilution in PBS) in the expotential phase grown in the rich organic medium⁵¹ was used to inoculate the definite medium following 100-fold dilution by ASW to minimize the carryover of rich medium. Bacterial cells (approximately 1.8 × 10⁵ cells/mL) were then inoculated into 100-mL serum bottles (headspace: 75 mL volume) in an incubator (XMTE-8112, Sukun, China) with shaking (160 rpm) at 28 °C. Growth experiments were conducted in six replicates. pH and concentration of dissolved oxygen (DO) was monitored using a WTW-Multi3430 water quality checker (WTW, Weilheim, Germany), which revealed slight variations during culture growth. The ASW solution was prepared as follows: 20 g NaCl, 0.5 g MgSO₄, 7H₂O, 0.3 g KCl, 0.05 g, CaCl₂, 2H₂O, 0.3 g NH₄Cl, 0.3 g K₂HPO₄ per L supplemented with 1.0 ml·L⁻¹ trace elements and vitamins stock solution (trace elements: 3.15 g FeCl₃·6H₂O, 4.36 g EDTA-2Na·2H₂O, 0.18 g MnCl₂·4H₂O, 9.8 mg CuSO₄·5H₂O, 6.3 mg Na₂MoO₄·2H₂O, 22.0 mg ZnSO₄·7H₂O and 10.0 mg CoCl₂·6H₂O per L; vitamin: 0.02 g vitamin B₁₂, 0.2 g niacin, 0.08 g biotin and 0.4 g thiamine per L).

Flow cytometry measurements

Cells were stained with SYBR Green Ι (Invitrogen, Eugene, OR, USA) for 15 min, after which the abundance was monitored by EPICS ALTRA II flow cytometry (Beckman-Coulter, USA).

Biochemical measurements

Glucose concentration in growth media was determined using a Glucose Colorimetric/Fluorometric Assay Kit (Biovision, Milpitas, CA, USA). Concentrations of NADH, NADPH and ATP were chemically tested with NAD⁺/NADH and NADP⁺/NADPH quantification kits (Biovision, USA) and an ATP Assay Kit (Abcam, Cambridge, MA, USA) according to the manufacturer’s instructions. Sulphate and thiosulfate in the culture medium measurements were carried out using a Dionex ICS-2500 ion chromatography system (Dionex, Sunnyvale, CA, USA). To analyze the elemental composition (C and N) samples were evaluated using a Vario EL Cube (Elementar Analysen Syetem GmbH, Germany). The PHB content in cells was determined by acid hydrolysis⁵² with HPLC using a Dionex ASI-100 autosampler injector (Dionex, USA) equipped with an Aminex HPX-87 H ion exchange organic acids column (300 × 7.8 mm) (BioRad, Hercules, CA, USA). Exopolysaccharides were determined by the phenol-sulfuric acid method⁵³.

Carbon isotope measurements

For analysis, ¹³C-labeled NaHCO₃ (99% ¹³C, Cambridge Isotope Laboratories, MA, USA) was added into the culture medium as described above. Each sample was collected at different culture times using 0.3-μm glass fiber filters (GF-75, Advantec, Japan) that had been weighed and burned at 450 °C for 4 h. Sample filters were then freeze dried for about 2 days, after which they were weighed and packed into tin cans. The ¹³C content in cells was determined using a Flash EA 1112 Series elemental analyzer coupled with a ConfloIII interface to a Delta V Advantage isotope ratio mass spectrometer (Thermo Electron, San Jose, CA, USA). All defined samples were collected and analyzed in triplicate.

iTRAQ-based quantitative proteomic analysis

Isobaric tags for relative and absolute quantitative (iTRAQ) MS/MS were used to analyze the proteome of T. profunda JLT2016 under different growth conditions as described above. The iTRAQ-MS/MS was performed at the Rearch Center for Proteome Analysis, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences. Total proteins were extracted before iTRAQ analysis. Briefly, cell suspensions at early stationary phase (120 h for autotrophic cells, 48 h for mixotrophic cells and chemolithoheterotrophic cells, and 62 h for heterotrophic cells) were pelleted by centrifugation (7,000g, 4 °C, 15 min). After washing pellets with 0.2 M potassium phosphate buffer (pH 7.4), cells were added to STD buffer (4% SDS, 100 mM DTT, 150 mM Tris-HCl pH 8.0) and incubated in a water bath at 99 °C for 5 min. To improve cell lysis, samples were sonicated for 10 s, interval 15 s, and lasting for 2–3 min. After centrifugation (16,000g, 4 °C, 30 min), the supernatants were transferred into new tubes and prepared for subsequent analysis.

Protein digestion was performed according to a previously described FASP procedure⁵⁴, after which the resulting peptide mixture was labeled using the 8-plex iTRAQ reagent according to the manufacturer’s instructions (Applied Biosystems, USA). iTRAQ labeled peptides were fractionated by strong cation exchange chromatography (SCX) using the AKTA Purifier system (GE Healthcare Amersham Biosciences, USA) as previously described⁵⁵. The collected fractions were finally combined into 10 pools and desalted on SPE C18 Cartridges (Empore™ standard density bed I.D. 7 mm, volume 3 ml, Sigma-Aldrich, USA). Each fraction was concentrated by vacuum centrifugation and reconstituted in 40 μl of 0.1% (v/v) formic acid. Experiments were performed on a Q Exactive mass spectrometer (Thermo Electron, USA) coupled to an Easy nLC (Thermo Fisher Scientific, Waltham, MA, USA) according to a previous method⁵⁵.

MS/MS spectra were searched using the MASCOT engine (Matrix Science, London, UK; v2.2) embedded into Proteome Discoverer 1.3 (Thermo Electron, USA) against T. profunda JLT2016 protein sequences and the decoy database. The MASCOT parameters were set as follows: peptide mass tolerance, 20 ppm, MS/MS tolerance, 0.1 Da, trypsin enzyme with up to 2 missed cleavages, fixed modification of iTRAQ 8-plex (K), iTRAQ 8-plex (N-term), variable modification of oxidation (M), and decoy database pattern of Reverse. A protein quantified with at least three peptides in experimental replicates was kept for further analysis (p < 0.05). Final ratios of protein quantification were then normalized by the median average protein quantification ratio for unequally mixed differently labeled samples.

Additional Information

Accession codes: The complete genome sequence of T. profunda JLT2016 has been deposited in GenBank under the accession numbers CP014796 (chromosome), CP014797-CP014804 (plasmids pTPRO1- pTPRO8). The complete genome sequence of P. abyssi JLT2014 has been deposited in GenBank under the accession numbers CP015093 (chromosome), CP015091-CP015092 and CP015094-CP015097 (plasmids pPABY1- pPABY8). The complete genome sequences of T. profunda JLT2016 and P. abyssi JLT2014 have also been deposited at the through the Joint Genome Institute IMG/ER website (http://img.jgi.doe.gov) under the Genome ID 2630968259 and 2630968260, respectively.

How to cite this article: Tang, K. et al. Genomic, physiologic, and proteomic insights into metabolic versatility in Roseobacter clade bacteria isolated from deep-sea water. Sci. Rep. 6, 35528; doi: 10.1038/srep35528 (2016).

References

Selje, N., Simon, M. & Brinkhoff, T. A newly discovered Roseobacter cluster in temperate and polar oceans. Nature 427, 445–448 (2004).
CAS PubMed ADS Google Scholar
Buchan, A., González, J. M., Moran, M. A. & Gonza, M. Overview of the Marine Roseobacter Lineage. Appl. Environ. Microbiol. 71, 5665–5677 (2005).
CAS PubMed PubMed Central Google Scholar
Moran, M. A. et al. Ecological genomics of marine Roseobacters. Appl. Environ. Microbiol. 14, 4559–4569 (2007).
Google Scholar
Brinkhoff, T., Giebel, H.–A. & Simon, M. Diversity, ecology, and genomics of the Roseobacter clade: a short overview. Arch. Microbiol. 189, 531–539 (2008).
CAS PubMed Google Scholar
Giebel, H. et al. Distribution of Roseobacter RCA and SAR11 lineages in the North Sea and characteristics of an abundant RCA isolate. ISME J. 5, 8–19 (2010).
PubMed PubMed Central Google Scholar
Lenk, S. et al. Roseobacter clade bacteria are abundant in coastal sediments and encode a novel combination of sulfur oxidation genes. ISME J. 6, 2178–2187 (2012).
CAS PubMed PubMed Central Google Scholar
Wagner–do, I. et al. The complete genome sequence of the algal symbiont Dinoroseobacter shibae: a hitchhiker’s guide to life in the sea. ISME J. 4, 61–77 (2010).
Google Scholar
Wagner–do, I. & Biebl, H. Environmental biology of the marine Roseobacter lineage. Annu. Rev. Microbiol. 60, 255–280 (2006).
Google Scholar
Howard, E. C., Sun, S., Biers, E. J. & Moran, M. A. Abundant and diverse bacteria involved in DMSP degradation in marine surface waters. Environ. Microbiol. 10, 2397–2410 (2008).
CAS PubMed Google Scholar
Durham, B. P. et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc. Natl. Acad. Sci. USA 112, 453–457 (2015).
CAS PubMed ADS Google Scholar
Christie–Oleza, J. A., Fernandez, B., Nogales, B., Bosch, R. & Armengaud, J. Proteomic insights into the lifestyle of an environmentally relevant marine bacterium. ISME J. 6, 124–135 (2012).
PubMed Google Scholar
Poretsky, R. S., Sun, S., Mou, X. & Moran, M. A. Transporter genes expressed by coastal bacterioplankton in response to dissolved organic carbon. Environ. Microbiol. 12, 616–627 (2010).
CAS PubMed PubMed Central Google Scholar
Buchan, A., Lecleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).
CAS PubMed Google Scholar
Swingley, W. D. et al. The complete genome sequence of Roseobacter denitrificans reveals a mixotrophic rather than photosynthetic metabolism. J. Bacteriol. 189, 683–690 (2007).
CAS PubMed Google Scholar
Tang, K., Zong, R., Zhang, F., **ao, N. & Jiao, N. Characterization of the photosynthetic apparatus and proteome of Roseobacter denitrificans. Curr. Microbiol. 60, 124–133 (2010).
CAS PubMed Google Scholar
Jiao, N. et al. Distinct distribution pattern of abundance and diversity of aerobic anoxygenic phototrophic bacteria in the global ocean. Environ. Microbiol. 9, 3091–3099 (2007).
CAS PubMed Google Scholar
Tang, K. H., Feng, X., Tang, Y. J. & Blankenship, R. E. Carbohydrate metabolism and carbon fixation in Roseobacter denitrificans OCh114. PLoS One 4, e7233 (2009).
PubMed PubMed Central ADS Google Scholar
Bürgmann, H. et al. Transcriptional response of Silicibacter pomeroyi DSS–3 to dimethylsulfoniopropionate (DMSP). Environ. Microbiol. 9, 2742–2755 (2007).
PubMed Google Scholar
Lenk, S. et al. Novel groups of Gammaproteobacteria catalyse sulfur oxidation and carbon fixation in a coastal, intertidal sediment. Environ. Microbiol. 13, 758–774 (2011).
CAS PubMed Google Scholar
Thrash, J. C. et al. Genome sequences of Pelagibaca bermudensis HTCC2601^T and Maritimibacter alkaliphilus HTCC2654^T, the type strains of two Marine Roseobacter genera. J. Bacteriol. 192, 5552–5553 (2010).
CAS PubMed PubMed Central Google Scholar
Follett, C. L., Repeta, D. J., Rothman, D. H., Xu, L. & Santinelli, C. Hidden cycle of dissolved organic carbon in the deep ocean. Proc. Natl. Acad. Sci. USA 111, 16706–16711 (2014).
CAS PubMed ADS Google Scholar
Reinthaler, T., van Aken, H. M. & Herndl, G. J. Major contribution of autotrophy to microbial carbon cycling in the deep North Atlantic’s interior. Deep Sea Res. Part II Top Stud. Oceanogr 57, 1572–1580 (2010).
CAS ADS Google Scholar
Swan, B. K. et al. Potential for chemolithoautotrophy among ubiquitous bacteria lineages in the dark ocean. Science 333, 1296–1300 (2011).
CAS PubMed ADS Google Scholar
Li, S., Tang, K., Liu, K. & Jiao, N. Thiobacimonas profunda gen. nov., sp. nov., a member of the family Rhodobacteraceae isolated from deep–sea water. Int. J. Syst. Evol. Microbiol. 65, 359–364 (2015).
CAS PubMed Google Scholar
Lin, Y. et al. Pelagibaca abyssi sp. nov., of the family Rhodobacteraceae, isolated from deep–sea water. Antonie van Leeuwenhoek, Int. J. Gen. Mol. Microbiol. 106, 507–513 (2014).
Google Scholar
Dai, X., Shi, X., Gao, X., Liang, J. & Zhang, X. H. Salipiger nanhaiensis sp. nov., a bacterium isolated from deep sea water. Int. J. Syst. Evol. Microbiol. 65, 1122–1126 (2015).
CAS PubMed Google Scholar
Oh, Y. S. et al. Roseovarius halotolerans sp. nov., isolated from deep seawater. Int. J. Syst. Evol. Microbiol. 59, 2718–2723 (2009).
CAS PubMed Google Scholar
Lai, Q. et al. Roseovarius indicus sp. nov., isolated from deep–sea water of the Indian Ocean. Int. J. Syst. Evol. Microbiol. 61, 2040–2044 (2011).
CAS PubMed Google Scholar
Albuquerque, L. et al. Palleronia abyssalis sp. nov., isolated from the deep Mediterranean Sea and the emended description of the genus Palleronia and of the species Palleronia marisminoris. Antonie van Leeuwenhoek, Int. J. Gen. Mol. Microbiol. 107, 633–642 (2015).
CAS Google Scholar
Dai, X., Shi, X., Gao, X., Liu, J. & Zhang, X. H. Roseivivax marinus sp. nov., isolated from deep water. Int. J. Syst. Evol. Microbiol. 64, 2540–2544 (2014).
CAS PubMed Google Scholar
**an, S. et al. Seohaeicola westpacificensis sp. nov., a novel, member of genera Seohaeicola isolated from deep West Pacific sea water. Curr. Microbiol. 69, 32–36 (2014).
CAS PubMed Google Scholar
Pradella, S., Päuker, O. & Petersen, J. Genome organisation of the marine Roseobacter clade member Marinovum algicola. Arch. Microbiol. 192, 115–126 (2010).
CAS PubMed Google Scholar
Scott, K. M. et al. The genome of deep–sea vent chemolithoautotroph Thiomicrospira crunogena XCL–2. PLoS Biol. 4, e383 (2006).
PubMed PubMed Central Google Scholar
Morris, R. L. & Schmidt, T. M. Shallow breathing: bacterial life at low O(2) Nat. Rev. Microbiol. 11, 205–212 (2013).
CAS PubMed PubMed Central Google Scholar
Owen, O. E., Kalhan, S. C. & Hanson, R. W. The key role of anaplerosis and cataplerosis for citric acid cycle function. J. Biol. Chem. 277, 30409–30412 (2002).
CAS PubMed Google Scholar
Tang, K., Jiao, N., Liu, K., Zhang, Y. & Li, S. Distribution and functions of TonB–dependent transporters in marine bacteria and environments: implications for dissolved organic matter utilization. PLoS One 7, e41204 (2012).
CAS PubMed PubMed Central ADS Google Scholar
Wallden, K., Rivera–Calzada, A. & Waksman, G. Type IV secretion systems: Versatility and diversity in function. Cell Microbiol. 12, 1203–1212 (2010).
CAS PubMed PubMed Central Google Scholar
Riedel, T. et al. Genome sequence of the exopolysaccharide–producing Salipiger mucosus type strain (DSM 16094^T), a moderately halophilic member of the Roseobacter clade. Stand. Genomic Sci. 9, 1331–1343 (2014).
PubMed Google Scholar
Jiao, N. et al. Microbial production of recalcitrant dissolved organic matter: long–term carbon storage in the global ocean. Nat. Rev. Microbiol. 8, 593–599 (2010).
CAS PubMed Google Scholar
Marshall, K. T. & Morris, R. M. Isolation of an aerobic sulfur oxidizer from the SUP05/Arctic96BD–19 clade. ISME J. 7, 452–455 (2013).
CAS PubMed Google Scholar
Suttle, C. A. Marine viruses–major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812 (2007).
CAS PubMed Google Scholar
Poli, A., Anzelmo, G. & Nicolaus, B. Bacterial exopolysaccharides from extreme marine habitats: Production, characterization and biological activities. Mar. Drugs 8, 1779–1802 (2010).
CAS PubMed PubMed Central Google Scholar
Flemming, H. C. & Wingender, J. The biofilm matrix. Nat. Rev. Microbiol. 8, 623–633 (2010).
CAS PubMed Google Scholar
Bantscheff, M. et al. Robust and sensitive iTRAQ quantification on an LTQ Orbitrap mass spectrometer. Mol. Cell Proteomics 7, 1702–1713 (2008).
CAS PubMed PubMed Central Google Scholar
Karp, N. A. et al. Addressing accuracy and precision issues in iTRAQ quantitation. Mol. Cell Proteomics 9, 1885–1897 (2010).
CAS PubMed PubMed Central Google Scholar
Klumpp, S., Scott, M., Pedersen, S. & Hwa, T. Molecular crowding limits translation and cell growth. Proc. Nat.l Acad. Sci. USA 110, 16754–16759 (2013).
CAS ADS Google Scholar
Koren, S. et al. Hybrid error correction and de novo assembly of single–molecule sequencing reads. Nat. Biotechnol. 30, 693–700 (2012).
CAS PubMed PubMed Central Google Scholar
Alikhan, N.–F., Petty, N. K., Ben Zakour, N. L. & Beatson, S. A. BLAST Ring Image Generator (BRIG): simple prokaryote genome comparisons. BMC Genomics 12, 402 (2011).
CAS PubMed PubMed Central Google Scholar
Newton, R. J. et al. Genome characteristics of a generalist marine bacterial lineage. ISME J. 4, 784–798 (2010).
CAS PubMed Google Scholar
Stamatakis, A. RAxML–VI–HPC: Maximum likelihood–based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22, 2688–2690 (2006).
CAS PubMed Google Scholar
Yurkov, V. V., Krieger, S., Stackebrandt, E. & Beatty, J. T. Citromicrobium bathymonarium, a novel aeorobic bacterium isolated from deep sea hydrothermal vent plume waters that contains photosynthetic pigment–protein complexes. J. Bacteriol. 181, 4517–4525 (1999).
CAS PubMed PubMed Central Google Scholar
Karr, D. B., Waters, J. K. & Emerich, D. W. Analysis of poly–β–hydroxybutyrate in Rhizobium japonicum bacteroids by ion–exclusion high–pressure liquid chromatography and UV detection. Appl. Environ. Microbiol. 46, 1339–1344 (1983).
CAS PubMed PubMed Central Google Scholar
DuBois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. A. & Smith, F. Colorimetric method for determination of sugars and related substances. Anal. Chem. 28, 350–356 (1956).
CAS Google Scholar
Zougman, A., Nagaraj, N. & Mann, M. Universal sample preparation method for proteome analysis. Nat. Methods 6, 3–7 (2009).
Google Scholar
Wang, L. et al. Dynamics of chloroplast proteome in salt–stressed mangrove Kandelia candel (L.) druce. J. Proteome. Res. 12, 5124–5136 (2013).
CAS PubMed Google Scholar

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Acknowledgements

We thank Dr. Deli Wang in **amen University for providing data of concentrations of thiosulfate and sulfate. This work was supported by the National Key Basic Research Program of China (2013CB955700&2016YFA0601100), the National Natural Science Foundation of China project (41276131&91428308), the National Programme on Global Change and Air-Sea Interaction (GASI-03-01-02-05).

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State Key Laboratory for Marine Environmental Science, Institute of Marine Microbes and Ecospheres, **amen University, **amen, 361102, P.R. China
Kai Tang, Yujie Yang, Dan Lin, Shuhui Li, Wenchu Zhou, Yu Han, Keshao Liu & Nianzhi Jiao

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Kai Tang
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Yujie Yang
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Dan Lin
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Shuhui Li
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Wenchu Zhou
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Yu Han
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Keshao Liu
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Nianzhi Jiao
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K.T. and N.J. conceived and designed the experiments; Y.Y., S.L., D.L. and W.Z. carried out the expreiments; K.T., Y.Y., Y.H. and K.L. analyzed the data. All of the authors assisted in writing the manuscript, discussed the results and commented on the manuscript.

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Tang, K., Yang, Y., Lin, D. et al. Genomic, physiologic, and proteomic insights into metabolic versatility in Roseobacter clade bacteria isolated from deep-sea water. Sci Rep 6, 35528 (2016). https://doi.org/10.1038/srep35528

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Received: 12 July 2016
Accepted: 28 September 2016
Published: 20 October 2016
DOI: https://doi.org/10.1038/srep35528
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Abstract

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Introduction

Major functions shared by two deep-sea roseobacters

Carbon fixation and sulfur oxidation

Central carbon metabolism

Transporter systems

Exopolysaccharide (EPS) biosynthesis

Comparative genomics of marine roseobacters

Growth experiments and physiological characteristics of T. profunda JLT2016

Comparative proteomics of T. profunda JLT2016

Methods

Genome sequencing

Bioinformatics analysis

Culture conditions

Flow cytometry measurements

Biochemical measurements

Carbon isotope measurements

iTRAQ-based quantitative proteomic analysis

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