Skip to main content

Transcriptional response of the model planctomycete Rhodopirellula baltica SH1T to changing environmental conditions



The marine model organism Rhodopirellula baltica SH1T was the first Planctomycete to have its genome completely sequenced. The genome analysis predicted a complex lifestyle and a variety of genetic opportunities to adapt to the marine environment. Its adaptation to environmental stressors was studied by transcriptional profiling using a whole genome microarray.


Stress responses to salinity and temperature shifts were monitored in time series experiments. Chemostat cultures grown in mineral medium at 28°C were compared to cultures that were shifted to either elevated (37°C) or reduced (6°C) temperatures as well as high salinity (59.5‰) and observed over 300 min. Heat shock showed the induction of several known chaperone genes. Cold shock altered the expression of genes in lipid metabolism and stress proteins. High salinity resulted in the modulation of genes coding for compatible solutes, ion transporters and morphology. In summary, over 3000 of the 7325 genes were affected by temperature and/or salinity changes.


Transcriptional profiling confirmed that R. baltica is highly responsive to its environment. The distinct responses identified here have provided new insights into the complex adaptation machinery of this environmentally relevant marine bacterium. Our transcriptome study and previous proteome data suggest a set of genes of unknown functions that are most probably involved in the global stress response. This work lays the foundation for further bioinformatic and genetic studies which will lead to a comprehensive understanding of the biology of a marine Planctomycete.


Marine ecosystems, covering approximately 71% of the Earth's surface, host the majority of biomass and contribute significantly to global cycles of matter and energy. Microorganisms are known to be the 'gatekeepers' of these processes, and insight into their lifestyle and fitness enhances our ability to monitor, model and predict the course and effect of global changes. Nevertheless, specific knowledge about their functions is still sparse. The 'genomic revolution' [1] has opened the door to investigations targeting their genetic potential and activity on the molecular level.

A particularly interesting representative of the marine picoplankton community is Rhodopirellula baltica SH1T, a free-living bacterium which was isolated from the water column of the Kiel Fjord (Baltic Sea) [2]. R. baltica belongs to the phylum Planctomycetes, a broadly distributed group of bacteria, whose members can be found in terrestrial, marine and freshwater habitats [37], but also in extreme environments like hot springs [8], marine sponges [9] and the hepatopancreas of crustaceans [10].

In terms of cell biology all Planctomycetes share several morphologically unique properties, such as a peptidoglycan-lacking proteinaceous cell wall [11, 12], intracellular compartmentalization [13] and a mode of reproduction via budding. The latter results in a cell cycle that is characterized by motile and sessile morphotypes similar to Caulobacter crescentus [1417]. A specific holdfast substance produced by sessile cells allows R. baltica to attach to macroscopic detrital aggregates (marine snow) [3, 7].

At present, four planctomycete genomes are currently available [18]. Of these, the genome of R. baltica is the only one completely closed [16]. The genome was found to be 7,145,576 bases in size and codes for 7325 open reading frames (ORFs) plus 72 RNA genes. Originally, only 45% of the ORFs were assigned particular functions [16]. Thus, over 55% of all proteins in the genome remain functionally uncharacterized. These were referred to as 'hypothetical proteins' with or without the affix 'conserved' contingent on wider phylogenetic distribution [19]. A subset of these conserved hypothetical proteins is specific for Planctomycetes [18]. It seems likely that some of these genes code for the unique planctomycetal cellular characteristics and metabolic traits.

The availability of the genome information triggered several key post-genomic studies including studies of the proteome [2024], enzyme activity [25] and protein crystallization [26].

In summary, these studies confirmed the hypothesis of Glöckner et al. that R. baltica is a polysaccharide degrader [16]. It appears R. baltica is gaining carbon and energy from the decomposition of complex heteropolysaccharides originally produced by algae in the photic zone while slowly sedimenting with the marine snow.

Marine microorganisms like R. baltica are exposed to rapidly changing environmental conditions such as varying temperature, salinity, irradiance and oxygen concentration. Typically, sudden changes of these environmental conditions induce a stress response in the exposed planktonic community characterized by a distinct change in their gene expression pattern. This stress response enables the organisms to protect vital processes and to adapt to the new condition. Such responses have been described for a set of organisms from different environments including Shewanella oneidensis [27, 28], Pseudomonas aeruginosa [29], Desulfovibrio vulgaris Hildenborough [30], Xylella fastidiosa [31], Synechocystis sp. [32] and Yeast [33].

To gain insights into the stress responses of R. baltica with respect to salinity and temperature the first whole genome array for R. baltica - also the first Planctomycete microarray - was established and applied. The reported data will serve as a resource to expand our understanding of the physiological and transcriptional response of R. baltica to the wide range of changing environmental conditions a free-living marine bacterium is exposed to.

Results and Discussion


54 distinct, total RNA samples were analyzed by whole-genome microarray hybridization. Differential expression of 2372, 922 and 1127 genes was noted during heat shock, cold shock and salt stress, respectively, at one or more of the five time points when compared to reference samples (FIGURE 1i; ii &1iii). With only 45% of the genes in R. baltica's genome functionally annotated, it is not surprising that most of the differentially expressed genes were hypothetical or conserved hypothetical proteins. The complete list of the differentially expressed genes for each shift experiment and time point is available in the ADDITIONAL FILE 1.

Figure 1

Number of regulated genes per stress experiment. Columns show the total number of up- (gray) and down- (white) regulated genes at each sampled time point compared to reference samples. i) heat shock, ii) cold shock and iii) high salinity

Only 32% of the regulated genes in the heat and cold shock experiments could be assigned with a COG function (FIGURE 2i &2ii) while 37% were assignable in the salt stress experiment (FIGURE 2iii). This is in line with the 36% (2661 genes) of COG functional class designations in R. baltica. A striking feature of the expression profiles displayed is the stereotypical response of a large fraction of the genome to all three stress conditions. In summary, 152 genes are up- or down-regulated at any time point for all stressors. Of these 152 genes, 62 are induced and 90 are repressed (TABLE 1 and TABLE 2). 49% of the induced and 61% of the repressed genes were annotated as hypothetical proteins. The Venn diagrams shown in FIGURE 3 provide an overview of the specific and common genes of the three stress-specific responses. To identify co-regulated patterns of gene expression, we classified all differentially expressed genes of all three stress expressions into 30 k-means clusters based on their expression log ratio. To determine the necessary number of clusters a figure of merit was generated. 30 clusters were considered as adequate. The cluster data are available in the ADDITIONAL FILE 2. Clusters 1, 3 and 4 show a similar response to the specific environmental changes, called environmental stress response (ESR) over all experiments. Clusters 2, 4, 5, 7, 15 and 22 describe genes reacting to a specific environmental factor.

Table 1 Shared stress response to heat, cold and high salinity: Results for induced genes are shown
Table 2 Shared stress response to heat, cold and high salinity: Results for repressed genes shown
Figure 2

Number of regulated genes with an assigned COG-category. Columns show the number of up- (gray) and down- (white) regulated genes per assigned COG-category according to the NCBI database (cut off e-value e-4). i) heat shock, ii) cold shock and iii) high salinity; Columns: [C] Energy production and conversion, [D] Cell division and chromosome partitioning, [E] Amino acid transport and metabolism, [F] Nucleotide transport and metabolism, [G] Carbohydrate transport and metabolism, [H] Coenzyme metabolism, [I] Lipid metabolism, [J] Translation, ribosomal structure and biogenesis, [K] Transcription, [L] DNA replication, recombination and repair, [M] Cell envelope biogenesis, outer membrane, [N] Cell motility and secretion, [O] Posttranslational modification, protein turnover, chaperones, [P] Inorganic ion transport and metabolism, [Q] Secondary metabolites biosynthesis, transport and catabolism, [R] General function prediction only, [S] Function unknown, [T] Signal transduction mechanisms

Figure 3

Venn diagrams of specific and common stress response. The diagram shows the distribution of stimulus-specific and common stress responses. All genes of all time points are represented in one diagram divided into repressed and induced genes.

Experimental design and array data quality assessment

The experimental conditions used were chosen to mimic the natural environment of R. baltica; however, stress conditions were constrained by the detection limit of the microarray technology used and, hence, were required to elicit a sufficiently pronounced response from the organism. In contrast to steady-state or single-time-point studies, time series experiments can show the dynamic of gene expression.

The negative, positive and stringency controls printed on the array gave no indications for unspecific hybridizations. Co-hybridizations of two cDNA samples prepared from the same total cellular RNA (self-self hybridization) suggested that genes with an expression log ratio value greater than 1.5 and smaller than -1.0 for heat and cold shock, respectively, could be regarded as differentially expressed. Salt stress log ratio values over 1.2 and below -1.0 were considered as significant.

Effect of stress on Rhodopirellula baltica

No growth was detectable during stress conditions nor were any obvious morphological changes by microscopic investigation. Under optimal conditions R. baltica has a doubling time of 10-12 hours [21], suggesting physiological effects are not measurable during the short stress period of, at maximum 5, hours.

Specific results of the shift experiments

Heat shock

In their natural environment R. baltica cells can be regularly exposed to higher temperatures, for example, due to irradiation at the water surface. Therefore, R. baltica cells were rapidly shifted from 28°C to 37°C and observed over a period of 300 min in the first experiment. This is approximately 9°C above the optimal growth temperature reported by Schlesner et al. [2]. Employing a higher temperature is very likely to kill the cells. The time series reveals a quick response of R. baltica to sudden temperature up-shifts. In total 2372 genes are regulated out of which 1140 genes encode hypothetical proteins. 390 genes (5%) were regulated after 10 min. This number increased to 750 genes (10%) after 300 min (FIGURE 1i). The COG classes containing the translation [J] and amino acid transport and metabolism [E] were the largest down-regulated classes. Up-regulated genes were assigned to the COG classes of replication, recombination and repair [L], post-translation modification, protein turnover and chaperons [O], transcription [K], secondary metabolites biosynthesis, transport and catabolism [Q], cell envelope biogenesis, outer membrane [M] and general function prediction [R] (FIGURE 2i).

Taking a closer look at the response of R. baltica to thermal stress revealed the induction of many known heat shock proteins (Hsp): ClpB (RB6751), GroEL (RB8970), DnaJ (RB8972), GrpE (RB8974), Hsp20 (RB10279, RB10283), dnaK (RB9105), as well as the ATP-dependent protease ClpP (RB9103). Also up-regulated were the chaperonins Cpn10 (RB10627 and RB8969) and Cpn60 (RB8966) as well as the cell division protein FtsH (RB2966) (Cluster 4 in ADDITIONAL FILES 2). Previous proteomic studies found the proteins of these genes as well, except FtsH, DnaJ and Hsp20 [22, 24].

The regulation of the heat shock response in R. baltica involves many transcriptional regulators. TetR (RB838) and GntR (RB1862, RB8695) showed an up-regulation, which affirms their important role in early heat shock response [34]. A gene encoding for GntR was also found in the environment on the planctomycete fosmid 3FN from a Namibian coast metagenome study [18]. In E. coli the induction of the majority of heat shock genes results from a rapid and transient increase in the cellular level of an alternative 32-kDa sigma factor (sigma32) encoded by rpoH along with the alternative sigma factors E and 54, encoded by rpoE (RB2302) and rpoN (RB6491), respectively [27]. Although, all genes are present in the R. baltica genome, they were not observed to be regulated, suggesting a significantly different response cascade.

R. baltica also showed an extracytoplasmic stress response. The gene coding for SecA (RB11690), belonging to the Sec system, was induced. This indicated an activation of protein translocation, most probably from the riboplasma to the paryphoplasm or medium. Proton channels were induced and motility was inhibited as the flagellar motor switch protein (Fli G - RB12502) was down-regulated after 20 min. This was followed by the inhibition of the type 4 fimbrial assembly protein (pilC - RB11597) after 40 min.

Cold Shock

To investigate the response to cold shock, R. baltica cells were shifted from the optimal growth temperature 28°C [35] to 6°C and observed for a period of 300 min. 6°C was chosen for this study as this is a common temperature in the Baltic Sea. Sudden temperature chances occur naturally due to turbulences between water layers. Further, the temperature difference of 22°C is generally regarded as standard for cold shock studies with bacteria [27, 36]. Compared to heat shock only one third (922) of the regulated genes were differentially expressed. Out of these 922 regulated proteins, 391 genes (42%) encode for hypothetical proteins. The cold shock response reached its peak after 20 min with 419 differentially expressed genes (6%) and decreased thereafter (FIGURE 1ii). In contrast to the heat shock experiment, it seemed that R. baltica needed approximately one hour to adapt to cold conditions. Like other bacteria, R. baltica responded to cold conditions with the up-regulation of genes coding for stress response [COG class O], cell envelope and transport [M], transcription factors and solute uptake. Genes for amino acid biosynthesis [E] as well as protein fate and synthesis [J] were down-regulated (FIGURE 2ii) [28].

Transcriptional activity was regulated by the up-regulation of diverse RNA polymerase sigma factors, such as rpoD (RB6780) and sigK (RB1392). A homolog of rpoD (RB6780) was also found on the planctomycete fosmid 13FN [18]. 20 min after the exposure of R. baltica to cold stress conditions it started to express genes implicated in the modification of cytoplasmic membrane composition, fluidity as well as morphology. The alteration of the lipid composition in the cold has been previously reported in other microorganisms [37]. In R. baltica genes coding for cell envelope (RB6114 and RB6895), transport (RB4870), lipid metabolism (RB316) and 18 genes coding for membrane proteins were repressed after 20 min.

Furthermore, R. baltica repressed genes involved in sporulation oppB (RB12861) and O-antigen flippase (RB2503), flaA (RB4454) and pilus assembly (RB4061 and RB5478), leading to reduced motility and budding ability. Genes associated with amino acid biosynthesis, especially with synthesis and fate of glutamine (RB4269) and glutamate (RB5653) were also affected. The latter have been shown to be translated [22, 24]. A glycosyltransferase (RB12831) and glycosidases (RB2988, RB2990 and RB2991) were up-regulated at 300 min probably to aid in cell wall remodeling.

Although, incorrect protein folding at low temperature is less expected than at high temperatures, chaperons and proteases are required to deal with intracellular protein perturbations [28, 38]. Here, this was observed in the induction of GroEL (RB8970) [22, 24] and htr A-protease (RB12752). One of the most prominent responses of microorganisms to cold shock is the induction of cold shock proteins. However, the two annotated cold shock proteins of class I (CspA - RB4681 and Cspl - RB10009) [39, 40] were not observed to be regulated. One may hypothesize that the stabilization of RNA in R. baltica employs a different protein compliment than observed in E. coli.

High salinity

As a marine organism, R. baltica must adjust to the haline stratification of the Baltic Sea [41, 42]. While moving through the water column R. baltica cells are exposed to variable concentrations of dissolved salts. In general, an osmotic up-shift forces bacteria to change their physiology by activating or deactivating specific enzymes or transporters, in order to maintain osmotic balance [43]. To gain an understanding of the genetic events that occur during the early stages of salt adaptation, R. baltica cells were subjected to salt up-shock from 17.5‰ salinity (Baltic Sea) to 59.5‰ (hyper saline environment). Previous experiments have shown that R. baltica is able to grow between salinities of 4.2‰ and 59.5‰ [2] and does not grow at salinities over 90‰ (Wohlrab, unpublished data).

In total, 1127 genes showed differences in gene expression over the whole time series. 656 of these genes (58%) were annotated as hypothetical proteins. The salt up-shock results indicated an increase in the number of regulated genes over time. After 10 min, 61 genes (1%) were regulated. The largest number (543 - 8%) was observed at 300 min (FIGURE 1iii). R. baltica cells seem to adapt slowly to high salt concentration. This might be a result of the cell compartmentalization and resulting ability of R. baltica to temporarily resist higher salt concentration without notable cellular responses.

The response of R. baltica to salt stress includes repression of genes associated with the following COG classes: induction of amino acid transport and metabolism [E], lipid metabolism [I], transcription [K], translation process [J]. Induced genes were involved in classes of the heat shock experiment (discussed above): [O], [M] and [L]. In addition, genes in the energy production [C] and cell division and chromosome partitioning [D] classes were induced (FIGURE 2iii). Similar to other bacteria, R. baltica accumulated glutamate and trehalose as cytoplasmic osmoprotectants in response to osmotic stress [44]. Glutamate dehydrogenase (RB6930) showed an up-regulation after 10 min and was also present in the proteome [24]. Trehalose synthetase treS (RB519) was induced after 60 min. Cysteine, as a general protective component, was only needed in the first hour in elevated salt concentrations and was repressed afterwards (RB4386).

The accumulation of compatible solutes is a widely distributed mechanism used in coping with changing salinity concentrations [44, 45]. In R. baltica 74 planctomycetes-group-specific genes are annotated as hypothetical proteins carrying a Domain of Unknown Function (DUF1559) [18]. This domain belongs to a new family of solute binding proteins (PF07596) [46] and was also found on the planctomycete fosmid 8FN [18]. Nine of these genes were up-regulated during the first hour of the cold and salt shock experiments. During the heat shock experiment, 16 of these genes were down-regulated. In vitro experiments have shown that some of these compatible solutes also possess general protein stabilization properties in addition to their osmoprotective property [47]. These homologous proteins do not play an integral role in the transport process per se, but probably serve as receptors that trigger or initiate translocation of solutes through membranes by binding external sites of the integral membrane proteins of the efflux system. In addition, some solute-binding proteins function in the initiation of sensory transduction pathways [46].

R. baltica up-regulated an efflux pump (RB7603) and a Na+/H+ antiporter (RB1433) 300 min after salt shift. Both may play a role in the active export of salt ions out of the cells. Quinone oxidoreductase-like protein (RB10967), induced after 40 min, had been implicated in respiration-coupled Na+ efflux as also shown in D. vulgaris [30]. Regulatory proteins like sigma-54 factor rpoN (RB6491), rpoA (RB12626) and rfaY (RB12251) were down-regulated. rpoN and rpoA were found to be translated [22, 24]. R. baltica inhibited the genes for cell division (soj - RB2291) and chromosome segregation (SMC - RB6065) after 60 min salt stress, as well as diverse transferases (RB12080, RB8898, RB12690, RB2498, RB8222, RB9617) involved in the cell envelope modification. Interestingly, the pilin transport apparatus and the thin-pilus basal body (pilM - RB2860 and pilT - RB12773) were induced after one hour as were principle pilus associated adhesion (pilC - RB12781) and pilB (RB12774). Genes coding for biopolymer transport proteins (exbB - RB12053 and exbD - RB12055) were also induced. A homolog to exbD was annotated on the planctomycete fosmid 3FN [18]. It is known from studies of other organisms that genes encoding the flagellar and chemotaxis systems are up-regulated to move away from the stressful cations [30]. However, none of the flagellar genes were regulated and the genome does not harbor any essential chemotaxis genes except cheY [16]. Notably, the survival protein (SurE - RB10258) and two genes coding for the mechanosensitive ion channel (MscS - RB12279 and RB10255) were induced. The latter provides protection against hypo-osmotic shock, responding both to stretching of the cell membrane and to membrane depolarization [48]. Genes in Cluster 22 (ADDITIONAL FILE 2) seemed to be significantly affected by salt stress only.

Common stress response

R. baltica showed a common stress response to all three tested environmental factors. Several known general stress genes were induced, such as genes coding for the manganese-containing catalase (RB10727), which is also present in the proteome [21, 22, 24]. Ferritin and Dps (RB4433) or pyridoxamine 5'-phosphate oxidase (RB4438) belong to a general stress cluster (RB4432-4438) and were initially described by Hieu et al. [24]. Thioredoxin (RB10378) could serve as an electron donor for the up-regulated methionine sulfoxide reductase gene (msrB - RB2268) [49, 50]. The genes could be regulated via rpoN found on the proposed upstream sigma 54-dependent promoter (RB10378) [51].

Perhaps to cope with reactive oxygen species (ROS), typically present under stressful conditions [50], the nitrogen fixation protein (nifU - RB3596) was induced. NifU is involved in the biosynthesis and repair of ROS scavenging iron-sulfur clusters. Finally, the peptidase M50 (RB6092) may have been induced to regulate stress response, sporulation, cell division, and cell differentiation [52].

Genes involved in R. baltica's fatty acid metabolism - for example, oA-acyl carrier protein transacylase (fabD - RB314), the acyl carrier protein (acpP - RB318) and the fabB (RB320) gene - were repressed under all conditions.

Interestingly, the machinery for the rearrangement and interchange of genetic material was induced under all three stressful conditions. It seems to play an important role in the organism's long-term adaptation. R. baltica harbors 81 non-randomly distributed transposases in its genome. Notably, under heat stress three times more transposase genes were up-regulated than under cold stress and twice as many as under salt stress. Shared induction shows five IS3/IS911, three ISXo8, two putative transposases (RB170, RB5888, RB11749, RB11802, RB12940, RB2186, RB9907, RB12239, RB934 and RB7389), and one integrase (RB11750). Rearranging the genome to select the most efficient gene combination has been described as a common way to adapt quickly to extreme environments [34]. Relaxed DNA may also be required to get better access to the gene regions for increased expression. Here, DNA relaxation is suggested by the repression of histone-like DNA-binding protein (RB6276).

In line with an alternative global sensing and regulation system initially proposed by Glöckner et al. [16], a common pattern concerning sensing and regulation response was detected. R. baltica contains 37 genes belonging to the extracytoplasmic function (ECF) subfamily of sigma 70 [53]. The genes RB138, RB13241 and RB10049 are up-regulated under all three stress conditions. Studholme et al. [46] suggests that ECF-factor RB10049 is the regulator for the conserved hypothetical protein RB10051. The conserved domain belongs to a new group of proteins that share novel domains referred to as planctomycete-specific (PSD) or planctomycete-specific cytochrome C (PSC). RB10051 contains the PSD1 (DUF1553 - PF07587) and PSC2 (DUF1549 - PF07583) domains, suggesting a function in redox reactions [46]. Each domain is represented 41 times in the whole genome of R. baltica [18].

Additionally, at 300 min the ECF-sigma factor RB138 was up-regulated together with serine/threonine protein kinase (RB140). Protein kinases are believed to be involved in stress response [37, 54]. The serine/threonine protein kinase (RB12942) and two histidine-kinases (RB4511 and RB10330) were up-regulated during heat shock. Whereas, under cold shock only one serine/threonine kinase (RB8505) was induced. Under salt stress a histidine-kinase (RB13122) and three two-component systems (RB5780, RB12952 and RB13118) were induced.

Finally, the ECF-sigma factor RB1790 was up-regulated, but only under high salinity conditions. In summary, the results confirmed that ECF sigma factors, as well as two-component systems, are heavily involved in stress sensing and regulation of R. baltica. The importance of these genes in the natural environment is asserted by the presence of a homolog to RB12952 on the planctomycete fosmid 6N14 [18].

The down-regulation of genes associated with the ribosomal machinery (55%) was observed. During heat shock and high salinity these genes were permanently repressed, whereas under cold shock they were only repressed within the first hour. Of the 51 ribosomal proteins in the whole genome, 18 genes encoding proteins of the small- and large subunit (RB1233, RB12821, RB12824, RB12839, RB7117, RB7837 - RB7841, RB7849, RB7850, RB7852, RB7854, RB7856, RB7857, RB7859 and RB7899) were repressed. Additionally, a set of genes involved in RNA metabolism, protein synthesis, as well as R. baltica's only translation elongation factor (EF-Tu - RB7894) were repressed. The genes for the conserved hypothetical protein RB12818 and the hypothetical protein RB12837 were co-regulated which suggests an association with the translation machinery. The repression of the ribosomal genes, along with a large set of genes involved in RNA metabolism, protein synthesis, cell growth (Cluster 1 ADDITIONAL FILE 2), has been reported as a general feature of the environmental stress responses (ESR) [33]. It has been assumed that they are acting as stress sensors [55]. This coincides nicely with the induction of the ribosomal proteins at 300 min under cold shock conditions. Recovery and ongoing adaptation of R. baltica was further supported by the up-regulation of the ribosomal-binding factor rbfA (RB5503), which is, aside from csdA, required for optimal growth at low temperatures [56].

Hypothetical proteins

Approximately 50% of the regulated genes observed have no known function in each of the three environmental stress experiments. Some of these share a similar expression profile (ADDITIONAL FILE 2). We propose that some of these genes are involved in cell morphology changes, stress sensing and regulation. The low number of known transcriptional regulators (2.4%) in the genome of R. baltica [53], coupled with the fact that most of the essential pathways encoded are not organized in operon structures [16] support the hypothesis of novel global regulation mechanisms. Hypothetical proteins that carry regulatory domains, like the FHA domain in RB1789 or a putative transcriptional regulatory domain in RB9999 are strong candidates. RB11766 might regulate the gene next to it, which is a so called giant gene (RB11769) [57]. This giant gene encodes a novel peptide motif that is most likely involved in cell morphology changes [46]. The importance of the hypothetical proteins RB11505, RB10954, RB10956 and RB10958 was further supported by their presence on the proteome gels of Hieu et al. as well as Gade et al. [21, 22, 24]. The latter three of these genes were claimed to be among the most abundant proteins in R. baltica cultures grown on mineral medium.

Planctomycete special feature: Genes encoding sulfatases

The genome of R. baltica contains no less than 110 sulfatases. It is assumed that they are involved in the recycling of carbon from complex sulfated heteropolysaccharides. Although the mineral medium does not contain any sulfated polysaccharides, we found 11 sulphatase genes were up- or down-regulated (TABLE 3) during the different stress experiments. These included one choline sulphatase (RB1205), seven arylsulfatases (RB13148, RB1477, RB3403, RB406, RB5146, RB684 and RB9498), two sulphatase genes without specificity (RB3956, RB5294), and one alkylsulfatase (RB11502). Furthermore, during life cycle experiments (unpublished data) we found evidence that certain sulfatases are only regulated in specific growth stages, which could indicate their involvement in the remodeling of the distinct morphological features of R. baltica. Sulfatase genes RB1477, RB5294, RB9498 and RB11502 were induced. We propose that RB9498 and RB11502 have an extracellular function and may be involved in the formation of an extrapolymeric substance.

Table 3 Differentially expressed sulfatase genes of R. baltica are shown

Six sulfatase genes (RB406, RB684, RB1205, RB3403, RB5146 and RB13145) were repressed after 300 min of heat shock. They may have been involved in the rearrangement of the cell wall formation, which comprises a protein sacculus with disulfide bonds [12]. In summary, these results show the diverse roles that sulfatases may have and, furthermore, that only a variety of different experimental approaches will increase our knowledge of these roles.


This work presents the first transcriptome study of the environmental stress response of a marine, free-living Planctomycete. Although R. baltica is an unusual organism in many aspects, its stress responses to heat and cold shock as well to changing salinity were in line with earlier results reported for other model organisms. Heat shock induced a set of chaperons, likely to protect cellular proteins from denaturation and breakdown. Growth in the cold may be followed by the induction of genes altering lipid metabolism. Salinity shifts resulted in the activation of planctomycete-specific groups of genes including genes involved in morphological change and an extracytoplasmic stress response. All stressors triggered the down-regulation of the ribosomal machinery, the up-regulation of transposases and the induction of several ECF-sigma factors and two-component systems. This supports the hypothesis that R. baltica is regulating its gene activity on a global rather than operon level. Aside from well characterized stress response genes, about 2000 genes of unknown function, constituting 30% of the genes predicted in the genome, were affected. This, combined with proteome studies and the presence of some of the genes in fosmid libraries, provides a strong indication that the vast number of genes with unknown function play a vital role in the organism's environmental response. The regulation of 11 sulfatases during stressful conditions suggests that these genes are heavily involved in the core cellular function of R. baltica. The data presented lead to the conclusion that R. baltica's rich repertoire of genes is combined with a fine tuned regulation mechanism to best respond to the changing conditions of its habitat. Nevertheless, data analysis has just started and further investigations concerning the genes involved in the life-cycle, the stress response pathways, promoter regions and network analysis are already ongoing or planned for the near future.


Bacterial growth conditions

For all experiments Rhodopirellula baltica SH1T cells were grown as chemostat cultures in a mineral medium containing 10 mM glucose as the sole carbon source and 1 mM ammonium chloride as a nitrogen source at 28°C [20]. Chemostat (Ø 13.5 cm × 25 cm, 1 l, Schott, modified by Ochs, Bovenden) parameters used were: pH 7.4, average dilution rate 0.75 ml/min and pO2 around 100%. The cultures had an OD600 nm of 0.5 - 0.6 (corresponding to log phase). The cells were harvested after 5 dwell times.

Sample collection, cell lyses, RNA Isolation and cDNA synthesis

After harvesting the R. baltica cultures, an aliquot was collected to serve as the time-zero reference. The culture broth was collected in 500 ml tubes and swirled briefly in an ethanol-dry ice bath to rapidly cool the cultures and prevent shifts in the RNA profile. Subsequently, the broth was centrifuged at 6000 rpm for 20 min at 4°C (Beckman Coulter™ AvantiTM626 J-20XP, JA10 Rotor). The pellets were re-suspended in 0.1 M Tris-HCL and then re-centrifuged. Cell pellets were shock-frozen in liquid nitrogen and stored at -80°C. Total RNA was isolated using the protocol of the TRI Reagent® Kit by Ambion (Austin, USA). The purity and quality of the extracted total RNA was checked with an Agilent 2100 Bioanalyzer (Agilent Technologies, Palto Alto, USA) and gel electrophoresis. cDNA synthesis was performed using the SuperScript direct cDNA labeling kit by Invitrogen (Karlsruhe, Germany) according to the manufacturer's instructions with random hexamers and unlabeled dCTP/dUTP, followed by a three hour reverse transcription incubation step at 46°C. The RT reaction was halted by incubation for 3 min at 95°C. To hydrolyze the RNA, 0.1 M NaOH was added, incubated at 65°C for 15 min and neutralized with 0.1 M HCL. The remaining cDNA was precipitated overnight at -20°C and the pellet washed with 70% Ethanol.

cDNA was directly labeled using the Platinum Bright™ nucleic acid labeling kit based on KREATECH's patented Universal Linkage System (ULS) (Biocat, Heidelberg, Germany) according to the manufacture's protocol.

Concentrations of RNA and cDNA were measured, and incorporation of the dyes Alexa 546 and Alexa 647 were checked using a Nanodrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, USA).

Experimental design and sample preparation

In three independent hybridizations conducted for each experiment and time point, the expression profiles of cells that had undergone stress were compared with those of cells at time zero. That is, the array analysis of each Alexa 647 labeled sample was compared with those of Alexa 546 labeled time-zero samples. The data shown are based on the analysis of all three replicates performed for each of the conditions.

Samples for expression profiling and microscopic analysis were collected at 10, 20, 40, 60 and 300 min in all three stress experiments.

Heat shock from 28°C to 37°C

Cells grown continuously at 28°C were collected by centrifugation. An aliquot was removed for RNA extraction and taken as the time zero reference for the heat, cold and salt stress experiments. Aliquots were re-suspended in an equal volume of 37°C medium and returned to 37°C for cultivation.

Cold shock from 28°C to 6°C

Cells grown continuously at 28°C were collected by centrifugation, re-suspended in an equal volume of 6°C medium and returned to 6°C for cultivation.

Salt stress from 17.5‰ to 59.5‰ salinity

Similar to the heat and cold shock experiments, an R. baltica culture was grown in mineral media with 17.5‰ salinity. Cells were harvested and aliquots were transferred to a mineral media with a salinity of 59.5‰.

Whole Genome Array construction, hybridization and image analysis

The whole-genome oligonucleotides for R. baltica SH1T (Pirellula AROS 630 Version 1.0) were purchased from Operon (Cologne, Germany) and diluted to 20 μM concentration in Micro Spotting Solution Plus spotting buffer (Telechem, Sunnyvale, USA). Spotting was done with three replicates per gene, per slide onto GAPS II aminosilane slides (Corning, Schiphol-Rijk, Netherlands) using a SpotArray 24 spotting device (Perkin Elmer, Wellesley, USA) together with 48 Telechem Stealth Pins (Telechem, Sunnyvale, USA). The arrays were subsequently exposed at 245 nm and 360 mJ in the GS Gene Linker (Bio-Rad, München, Germany), followed by incubation at 80°C for at least 3 h. Slides were stored at room temperature in the dark until use.

Blocking, denaturing, hybridization, washing and N2 drying procedures were carried out in an automated hybridization station HS400 (Tecan, Crailsheim, Germany). The spotted arrays were blocked in prehybridization solution containing 250 mM NaCl, 5 mM Tris/HCl at pH 8.0, 50% formamide, 0.5 × SSC, 0.05% BSA, and 1% blocking reagent from Roche Diagnostics, Mannheim, Germany for 45 min at 52°C. For hybridization at least 2 μg of Alexa 546 dye-labeled and 2 μg of Alexa 647 dye-labeled total cDNA were combined and taken up in a final volume of 100 μl DIG Easy Hyb hybridization solution (Roche Diagnostics, Mannheim, Germany). After the blocking step, the sample solution was applied to the arrays, denatured at 95°C for 3 min and hybridized under stringent conditions at 52°C for over 12 hours. After hybridization slides were washed at room temperature in ULTRArray Low Stringency Wash Buffer (Ambion, Austin, USA) and dried by N2.

Signal detection and data analysis

Slides were scanned at a resolution of 5 μm using a ScanArray Express Microarray scanner (Perkin Elmer, Wellesley, USA) with varied laser power and photomultiplier tube (PMT sensitivity) for each slide. The accompanying image analysis software, ScanArray Express Version 4.0, was used for automatic spot detection and signal quantification of both fluorophores. Raw data were automatically processed using the microarray data analysis software tool MADA, developed in-house. Firstly, the spot intensities were corrected for local background (mean spot intensity minus mean spot background intensity). Signals were only assessed as positive if mean spot pixel intensity was higher than the mean local background intensity plus twice the standard deviation of the mean local background pixel intensity. Each gene is spotted in three replicates. Spot replicates with poor quality were removed from the data set according to MADA's outlier test results. This test first computes the standard deviation of all replicates. Secondly, one replicate is omitted and the standard deviation is recalculated; if the deviation differs more than 50% from the previous deviation, the omitted replicate is regarded as an outlier. This procedure is repeated for all replicates

Expression is described through the ratio and intensity, where R is the fluorescence log ratio of the experiment time point relative to the control condition (e.g. R = log2 (result of channel 10 min/result of channel control/reference)) and I is the log mean fluorescence intensity (e.g. I = log10 (result of channel 10 min × result of channel control/reference)).

Each data point represents a regulation factor (ratio) in a logarithmic scale for one gene calculated from the positive replicates for a particular probe coming from two RNA pools (reference and sample). Normalization was carried out by LOWESS fitting on an R-versus-I plot with a smoothing factor of 0.5. Each time point of the time-series experiment was hybridized independently three times. The expression data (ratio) of the three hybridizations were combined to one expression data point (ratio) by averaging and the standard deviation of the average value was calculated. Only ratios with a standard deviation less than 25% were regarded as genes that are regulated. Differentially expressed genes were detected by a fixed threshold cut off method (i.e. a two-fold increase or decrease) based on the results of self-self hybridization. Using the same biological sample, the reference (untreated sample) is labeled twice, once with Alexa 546 and once with Alexa 647, and the variability between the two sets of measurements is calculated to estimate the experimental noise. Ideally, there should not be any variability and all expression points should have a ratio close to zero. In reality, however, this is never the case and thresholds based on the distribution of these data along the y-axis were defined for the further experiments.

Consequently, R. baltica genes detected with intensities resulting in ratios above or below these thresholds can beregarded as up- or down-regulated.

Cluster analysis

Differentially expressed genes present in the complete time course profile (10, 20, 40, 60 und 300 min) for all three experiments were clustered using the k-means clustering approach (Euclidean distance metric, k = 30 clusters and 49 (max. 500) iterations) [58] with the software tool Multiexperiment Viewer MeV Version 4.0.2 from the TM4 microarray software suite [59]. Briefly, the clustering algorithm arranges genes into a given number of clusters, k, according to similarity in their expression profiles across the entire array experiments, such that genes with similar expression patterns are clustered together. The data are displayed in tabular format where each row of colored boxes stores the variation in transcript abundance for each given gene and each column stores the variation in transcript levels of every gene in a given mRNA sample, as detected on one array. The variations in transcript abundance for each gene are depicted by means of a color scale, in which shades of red represents increases and shades of green represent decrease in mRNA levels, relative to the unstressed culture, and the saturation of the color corresponds to the magnitude of the differences. Black coloration indicates no change in transcript level while grey represents missing data.

Genome tools

The genome of Rhodopirellula baltica was automatically re-annotated based on updated homology searches (June 2005 - MicHanThi [60]). The updated annotation including all tool results is publicly available at[61]. JCoast [62] was used as a tool for the visualization, interpretation, COG-assignment statistics and comparison of genomic data stored in GenDB V2.2 [63]. The Venn diagrams were generated by BioVenn [64].

Microarray Datasets

Each microarray used in this study contained 7325 known or predicted R. baltica genes according to Glöckner et al. [16]. A detailed description of the array can be found at the NCBI's Gene Expression Omnibus (GEO) database under accession number GPL7654. The complete microarray datasets covering the expression of R. baltica cultures exposed to heat, cold and high salinity, are public available in the GEO repository under accession numbers GSE13769, G SE13856 and GSE14075 [65].



Cluster of Orthologous Group of Genes


Domain of Unknown Function


Extra Cytoplasmic Function


Environmental Stress Response




Gene Expression Omnibus




Rhodopirellula baltica


Reactive Oxygen Species


Open Reading Frame.


  1. 1.

    Field D, Kyrpides N: The positive role of the ecological community in the genomic revolution. Microb Ecology. 2007, 53 (3): 507-511. 10.1007/s00248-007-9206-5.

    Article  Google Scholar 

  2. 2.

    Schlesner H, Rensmann C, Tindall BJ, Gade D, Rabus R, Pfeiffer S, Hirsch P: Taxonomic heterogeneity within the Planctomycetales as derived by DNA-DNA hybridization, description of Rhodopirellula baltica gen. nov., sp. nov., transfer of Pirellula marina to the genus Blastopirellula gen. nov. as Blastopirellula marina comb. nov. and emended description of the genus Pirellula. Int J Syst Evol Microbiol. 2004, 54 (Pt 5): 1567-1580. 10.1099/ijs.0.63113-0.

    Article  CAS  PubMed  Google Scholar 

  3. 3.

    DeLong E, Franks D: Phylogenetic diversity of aggregate-attached vs. free-living marine bacterial assemblages. Limnol Oceanogr. 1993, 38: 924-934.

    Article  Google Scholar 

  4. 4.

    Chatzinotas A, Sandaa R, W S, Amann R, FL D, Torsvik V, Zeyer J, Hahn D: Analysis of broad-scale differences in microbial community composition of two pristine forest soils. System Appl Microbiol. 1998, 21 (4): 579-587.

    Article  CAS  Google Scholar 

  5. 5.

    Neef A, Amann R, Schlesner H, Schleifer K: Monitoring a widespread bacterial group: in situ detection of Planctomycetes with 16S rRNA-targeted probes. Microbiology. 1998, 144 (12): 3257-3266. 10.1099/00221287-144-12-3257.

    Article  CAS  PubMed  Google Scholar 

  6. 6.

    Vergin KL, Urbach E, Stein JL, DeLong EF, Lanoil BD, Giovannoni SJ: Screening of a fosmid library of marine environmental genomic DNA fragments reveals four clones related to members of the order Planctomycetales. Appl Environ Microbiol. 1998, 64: 3075-3078.

    PubMed Central  CAS  PubMed  Google Scholar 

  7. 7.

    Crump BC, Armbrust EV, Baross JA: Phylogenetic analysis of particle-attached and free-living bacterial communities in the Columbia River, its estuary, and the adjacent coastal ocean. Appl Environ Microbiol. 1999, 65 (7): 3192-3204.

    PubMed Central  CAS  PubMed  Google Scholar 

  8. 8.

    Giovannoni SJ, Scabtach E, Castenholz RW: Isosphaera pallida, gen. and comb. nov., a gliding, budding eubacterium from hot springs. Arch Microbiol. 1987, 147: 276-284. 10.1007/BF00463488.

    Article  CAS  Google Scholar 

  9. 9.

    Pimentel-Elardo S, Wehrl M, Friederich AB, Jensen PR, Henschel U: Isolation of Planctomycetes from aplysina sponges. Aqua Micro Ecol. 2003, 33: 239-245. 10.3354/ame033239.

    Article  Google Scholar 

  10. 10.

    Fuerst JA, Gwilliam HG, Lindsay M, Lichanska A, Belcher C, Vickers JE, Hugenholtz P: Isolation and molecular identification of planctomycete bacteria from postlarvae of the giant tiger prawn, Penaeus monodon. Appl Environ Microbiol. 1997, 63 (1): 254-262.

    PubMed Central  CAS  PubMed  Google Scholar 

  11. 11.

    König E, Schlesner H, Hirsch P: Cell wall studies on budding bacteria of the Planctomyces/Pasteuria group and on a Prosthecomicrobium sp. Arch Microbiol. 1984, 138: 200-205. 10.1007/BF00402120.

    Article  Google Scholar 

  12. 12.

    Liesack W, König H, Schlesner H, Hirsch P: Chemical composition of the peptidoglycan-free cell envelopes of budding bacteria of the Pirella/Planctomyces group. Arch Microbiol. 1986, 145: 361-366. 10.1007/BF00470872.

    Article  CAS  Google Scholar 

  13. 13.

    Fuerst AJ: Intracellular compartmentation in Planctomycetes. Annu Rev Microbiol. 2005, 59: 299-328. 10.1146/annurev.micro.59.030804.121258.

    Article  CAS  PubMed  Google Scholar 

  14. 14.

    Tekniepe BL, Schmidt JM, Starr MP: Life cycle of a budding and appendaged bacterium belonging to morphotype IV of the Blastocaulis-Planctomyces group. Curr Microbiol. 1981, 5: 1-6. 10.1007/BF01566588.

    Article  Google Scholar 

  15. 15.

    Fuerst JA: The Planctomycetes: emerging models for microbial ecology, evolution and cell biology. Microbiology. 1995, 141 (Pt 7): 1493-1506. 10.1099/13500872-141-7-1493.

    Article  CAS  PubMed  Google Scholar 

  16. 16.

    Glöckner FO, Kube M, Bauer M, Teeling H, Lombardot T, Ludwig W, Gade D, Beck A, Borzym K, Heitmann K, et al: Complete genome sequence of the marine planctomycete Pirellula sp. strain 1. Proc Natl Acad Sci USA. 2003, 100 (14): 8298-8303. 10.1073/pnas.1431443100.

    PubMed Central  Article  PubMed  Google Scholar 

  17. 17.

    Jacobs-Wagner C: Regulatory proteins with a sense of direction: cell cycle signalling network in Caulobacter. Mol Microbiol. 2004, 51 (1): 7-13. 10.1046/j.1365-2958.2003.03828.x.

    Article  CAS  PubMed  Google Scholar 

  18. 18.

    Woebken D, Teeling H, Wecker P, Dumitriu A, Kostadinov I, DeLong EF, Amann R, Glöckner FO: Fosmids of novel marine Planctomycetes from the Namibian and Oregon coast upwelling systems and their cross-comparison with planctomycete genomes. ISME Journal. 2007, 1 (5): 419-435. 10.1038/ismej.2007.63.

    Article  CAS  PubMed  Google Scholar 

  19. 19.

    Galperin MY, Koonin EV: 'Conserved hypothetical' proteins: prioritization of targets for experiemental study. Nucl Acids Res. 2004, 32 (18): 5452-5463. 10.1093/nar/gkh885.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  20. 20.

    Rabus R, Gade D, Helbig R, Bauer M, Glöckner FO, Kube M, Schlesner H, Reinhardt R, Amann R: Analysis of N-acetylglucosamine metabolism in the marine bacterium Pirellula sp. strain 1 by a proteomic approach. Proteomics. 2002, 2 (6): 649-655. 10.1002/1615-9861(200206)2:6<649::AID-PROT649>3.0.CO;2-R.

    Article  CAS  PubMed  Google Scholar 

  21. 21.

    Gade D, Stührmann T, Reinhardt R, Rabus R: Growth phase dependent regulation of protein composition in Rhodopirellula baltica. Environ Microbiol. 2005, 7 (8): 1074-1084. 10.1111/j.1462-2920.2005.00784.x.

    Article  CAS  PubMed  Google Scholar 

  22. 22.

    Gade D, Gobom J, Rabus R: Proteomic analysis of carbohydrate catabolism and regulation in the marine bacterium Rhodopirellula baltica. Proteomics. 2005, 5 (14): 3672-3683. 10.1002/pmic.200401200.

    Article  CAS  PubMed  Google Scholar 

  23. 23.

    Gade D, Thiermann J, Markowsky D, Rabus R: Evaluation of two-dimensional difference gel electrophoresis for protein profiling. Soluble proteins of the marine bacterium Pirellula sp. strain 1. J Mol Microbiol Biotechnol. 2003, 5 (4): 240-251. 10.1159/000071076.

    Article  CAS  PubMed  Google Scholar 

  24. 24.

    Hieu CX, Voigt B, Albrecht D, Becher D, Lombardot T, Glöckner FO, Amann R, Hecker M, Schweder T: Detailed proteome analysis of growing cells of the planctomycete Rhodopirellula baltica SH1. Proteomics. 2008, 8 (8): 1608-1623. 10.1002/pmic.200701017.

    Article  CAS  PubMed  Google Scholar 

  25. 25.

    Wallner S, Bauer M, Würdemann C, Wecker P, Glöckner FO, Faber K: Highly enantioselective sec-alkyl sulfatase activity of the marine planctomycete Rhodopirellula baltica shows retention of configuration. Angew Chem Intern Edition English. 2004, 44 (39): 6381-6384. 10.1002/anie.200501955.

    Article  Google Scholar 

  26. 26.

    Dabin J, Jam M, Czjzek M, Michel G: Expression, purification, crystallization and preliminary X-ray analysis of the polysaccharide lyase RB5312 from the marine planctomycete Rhodopirellula baltica. Acta Crystallogr Sect F Struct Biol Cryst Commun. 2008, 1 (64): 224-227. 10.1107/S1744309108004387.

    Article  Google Scholar 

  27. 27.

    Gao H, Wang Y, Liu X, Yan T, Wu L, Alm E, Arkin A, Thompson DK, Zhou J: Global transcriptome analysis of the heat shock response of Shewanella oneidensis. J Bacteriol. 2004, 186 (22): 7796-7803. 10.1128/JB.186.22.7796-7803.2004.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  28. 28.

    Gao H, Yang ZK, Wu L, Thompson DK, Zhou J: Global transcriptome analysis of the cold shock response of Shewanella oneidensis MR-1 and mutational analysis of its classical cold shock proteins. J Bacteriol. 2006, 188 (12): 4560-4569. 10.1128/JB.01908-05.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  29. 29.

    Aspedon A, Palmer K, Whiteley M: Microarray analysis of the osmotic stress response in Pseudomonas aeruginosa. J Bacteriol. 2006, 188 (7): 2721-2725. 10.1128/JB.188.7.2721-2725.2006.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  30. 30.

    Mukhopadhyay A, He Z, Alm EJ, Arkin AP, Baidoo EE, Borglin SC, Chen W, Hazen TC, He Q, Holman H-Y, et al: Salt stress in Desulfovibrio vulgaris Hildenborough: an integrated genomics approach. J Bacteriol. 2006, 188 (11): 4068-4078. 10.1128/JB.01921-05.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  31. 31.

    Koide T, Vencio RZN, Gomes SL: Global gene expression analysis of the heat shock response in the phytopathogen Xylella fastidiosa. J Bacteriol. 2006, 188 (16): 5821-5830. 10.1128/JB.00182-06.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  32. 32.

    Kanesaki Y, Suzuki I, Allakhverdiev SI, Mikami K, Murata N: Salt stress and hyperosmotic stress regulate the expression of different sets of genes in Synechocystis sp. PCC 6803. Biochem and Biophys Res Comm. 2002, 290 (1): 339-348. 10.1006/bbrc.2001.6201.

    Article  CAS  Google Scholar 

  33. 33.

    Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, Storz G, Botstein D, Brown PO: Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell. 2000, 11 (12): 4241-4257.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  34. 34.

    Tachdjian S, Kelly RM: Dynamic metabolic adjustments and genome plasticity are implicated in the heat shock response of the extremely thermoacidophilic Archaeon Sulfolobus solfataricus. J Bacteriol. 2006, 188 (12): 4553-4559. 10.1128/JB.00080-06.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  35. 35.

    Schlesner H: The development of media suitable for the microorganisms morphologically resembling Planctomyces spp., Pirellula spp., and other Planctomycetales from various aquatic habitats using dilute media. System Appl Microbiol. 1994, 17: 135-145.

    Article  Google Scholar 

  36. 36.

    Jones PG, Cashel M, Glaser G, Neidhardt FC: Function of a relaxed-like state following temperature downshifts in Escherichia coli. J Bacteriol. 1992, 174: 3903-3914.

    PubMed Central  CAS  PubMed  Google Scholar 

  37. 37.

    Coker J, DasDarma P, Kumar J, Muller JA, DasSarma S: Transcriptional profiling of the model Archaeon Halobacterium sp. NRC-1: Responses to changes in salinity and temperature. Sal Systems. 2007, 3 (6):

  38. 38.

    Sowell SM, Norberck AD, Lipton MS, Nicora CD, Callister SJ, Smith RD, Barofsky DF, Giovannoni SJ: Proteomic Analysis of stationary phase in the marine Bacterium "Candidatus Pelagibacter ubique". Appl Environ Microbiol. 2008, 74 (13): 4091-4100. 10.1128/AEM.00599-08.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  39. 39.

    Derzelle S, Hallet B, Ferain T, Delcour J, Hols P: Improved adaptation to cold-shock, stationary phase, and freezing stresses in Lactobacillus plantarum overproducing cold-shock proteins. Appl Environ Microbiol. 2003, 69 (7):

  40. 40.

    Phadtare S: Recent developments in bacterial cold-shock response. Curr Issues Mol Biol. 2004, 6: 125-136.

    CAS  PubMed  Google Scholar 

  41. 41.

    Ott J: Meereskunde. 1996, Stuttgart: Ulmer Verlag

    Google Scholar 

  42. 42.

    Rheinheimer G: Meereskunde der Ostsee. 1996, Berlin: Springer Verlag

    Google Scholar 

  43. 43.

    Kempf B, Bremer E: Uptake and synthesis of compatible solutes as microbial stress response to high-osmolarity environments. Arch Microbiol. 1998, 170: 319-330. 10.1007/s002030050649.

    Article  CAS  PubMed  Google Scholar 

  44. 44.

    Roeßler M, Müller V: Osmoadaptation in bacteria and archaea: common principles and differences. Environ Microbiol. 2001, 3 (12): 743-754. 10.1046/j.1462-2920.2001.00252.x.

    Article  Google Scholar 

  45. 45.

    Kunte HJ: Osmoregulation in Bacteria: Compatible solute accumulation and osmosensing. Environ Chem. 2006, 3: 94-99. 10.1071/EN06016.

    Article  CAS  Google Scholar 

  46. 46.

    Studholme DJ, Fuerst JA, Bateman A: Novel protein domains and motifs in the marine planctomycete Rhodopirellula baltica. FEMS Microbiol Lett. 2004, 236: 333-340. 10.1111/j.1574-6968.2004.tb09666.x.

    Article  CAS  PubMed  Google Scholar 

  47. 47.

    Vargas C, Argandoña M, Reina-Bueno M, Rodriguez-Moya J, Fernández-Aunión C, Nieto JJ: Unravelling the adaptation responses to osmotic and temperature stress in Chromohalobacter salexigens, a bacterium with broad salinity tolerance. Saline Systems. 2008, 4 (14):

  48. 48.

    Pivetti CD, Yen M-R, Miller S, Busch W, Tseng Y-H, Booth IR, Saier MH: Two families of mechanosensitive channel proteins. MMBR. 2003, 67 (1): 66-85.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  49. 49.

    Zeller T, Klug G: Thioredoxins in bacteria: functions in oxidative stress response and regulation of thioredoxin genes. Naturwissenschaften. 2006, 93 (6): 259-266. 10.1007/s00114-006-0106-1.

    Article  CAS  PubMed  Google Scholar 

  50. 50.

    Pereira PM, He Q, Xavier AV, Zhou J, Pereira IAC, Louro RO: Transcriptional response of Desulfovibrio vulgaris Hildenborough to oxidative stress mimicking environmental conditons. Arch Microbiol. 2008, 189: 451-461. 10.1007/s00203-007-0335-5.

    Article  CAS  PubMed  Google Scholar 

  51. 51.

    Studholme DJ, Dixon R: In silico analysis of the [sigma] 54-dependent enhancer-binding proteins in Pirellula species strain 1. FEMS Microbiol Lett. 2004, 230 (2): 215-225. 10.1016/S0378-1097(03)00897-8.

    Article  CAS  PubMed  Google Scholar 

  52. 52.

    Marchler-Bauer A, Anderson J, Derbyshire M, DeWeese-Scott C, Gonzales N, Gwadz M, Hao L, He S, Hurwitz D, Jackson J, et al: CDD: a conserved domain database for interactive domain family analysis. Nucl Acids Res. 2007, 35: D237-240. 10.1093/nar/gkl951.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  53. 53.

    Lombardot T, Bauer M, Teeling H, Amann R, Glöckner FO: The transcriptional regulator pool of the marine bacterium Rhodopirellula baltica SH 1T as revealed by whole genome comparisons. FEMS Microbiol Lett. 2005, 242 (1): 137-145. 10.1016/j.femsle.2004.10.049.

    Article  CAS  PubMed  Google Scholar 

  54. 54.

    Mikami K, Kanesaki Y, Suzuki I, Murata N: The histidine kinase Hik33 perceives osmotic stress and cold stress in Synechocystis sp. PCC 6803. Mol Microbiol. 2002, 46 (4): 905-915. 10.1046/j.1365-2958.2002.03202.x.

    Article  CAS  PubMed  Google Scholar 

  55. 55.

    VanBogelen RA, Neidhardt FC: Ribosomes as sensors of heat and cold shock in Escherichia coli. Proc Natl Acad Sci USA. 1990, 87: 5589-5593. 10.1073/pnas.87.15.5589.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  56. 56.

    Thieringer HA, Jones PG, Inouye M: Cold shock and adaptation. Bioessays. 1998, 20 (1): 49-57. 10.1002/(SICI)1521-1878(199801)20:1<49::AID-BIES8>3.0.CO;2-N.

    Article  CAS  PubMed  Google Scholar 

  57. 57.

    Reva O, Tümmler B: Think big -- giant genes in bacteria. Environ Microbiol. 2008, 10 (3): 768-777. 10.1111/j.1462-2920.2007.01500.x.

    Article  CAS  PubMed  Google Scholar 

  58. 58.

    Soukas A, Cohen P, Socci ND, Friedman JM: Leptin-specific patterns of gene expression in white adipose tissue. Genes Dev. 2000, 14 (8): 963-980.

    PubMed Central  CAS  PubMed  Google Scholar 

  59. 59.

    Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, et al: TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003, 34 (2): 374-378.

    CAS  PubMed  Google Scholar 

  60. 60.

    Quast C: MicHanThi - Design and Implementation of a System for the Prediction of Gene Functions in Genome Annotation Projects. Technical Report. Edited by: Informatik T-Z. 2007, Bremen: TZI-Bremen

    Google Scholar 

  61. 61.

    The reannotated Rhodopirellula baltica genome. []

  62. 62.

    Richter M, Lombardot T, Kostadinov I, Kottmann R, Duhaime M, Peplies J, Glöckner F: JCoast - A biologist-centric software tool for data mining and comparison of prokaryotic (meta)genomes. BMC Bioinformatics. 2008, 9 (1): 177-10.1186/1471-2105-9-177.

    PubMed Central  Article  PubMed  Google Scholar 

  63. 63.

    Meyer F, Goesmann A, McHardy AC, Bartels D, Bekel T, Clausen J, Kalinowski J, Linke B, Rupp O, Giegerich R, et al: GenDB - an open source genome annotation system for prokaryote genomes. Nucl Acids Res. 2003, 31 (8): 2187-2195. 10.1093/nar/gkg312.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  64. 64.

    Hulsen T, de Vlieg J, Alkema W: BioVenn - a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genomics. 2008, 9 (1): 488-10.1186/1471-2164-9-488.

    PubMed Central  Article  PubMed  Google Scholar 

  65. 65.

    Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Edgar R: NCBI GEO: mining tens of millions of expression profiles - database and tools update. Nucl Acids Res. 2006, 35:

    Google Scholar 

  66. 66.

    Gade D, Theiss D, Lange D, Mirgorodskaya E, Lombardot T, Glöckner FO, Kube M, Reinhardt R, Amann R, Lehrach H, et al: Towards the proteome of the marine bacterium Rhodopirellula baltica: Mapping the soluble proteins. Proteomics. 2005, 5 (14): 3654-3671. 10.1002/pmic.200401201.

    Article  CAS  PubMed  Google Scholar 

Download references


We thank Sylke Wohlrab for the preliminary work leading to this study, Matthias Gottschall for excellent technical assistance in the chemostat cultivation, and Pier Luigi Buttigieg for language revision.

The setup of the microarray and transcriptomic analysis of Rhodopirellula baltica was supported by the Federal Ministry of Education and Research contract 03F0364A and the European Commission contract GOCE-CT-2004-505403.

Author information



Corresponding author

Correspondence to Frank Oliver Glöckner.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

PW conceived the study, initiated, conducted the experimental analysis, validated microarray and optimized experimental steps, wrote the manuscript, did the statistical analysis and analyzed the data.

CK was involved in the chemostat cultivation of R. baltica and statistical analysis and in the biological interpretation of the data

AE wrote MADA the microarray analysis tool and designed the microarrays.

CQ was responsible for the automatic reannotation of the genome and set up the web access

PL and JH established the chemostat cultivation of R. baltica. JH supervised the chemostat cultivation.

FOG contributed background information and was involved in writing and finishing the manuscript.

All authors read and approved the final manuscript.

Electronic supplementary material

List of differentially expressed genes for all three stress experiments

Additional file 1: . The data provided represents the differentially expressed genes of Rhodopirellula baltica exposed to three stress conditions (heat shock, cold shock and high salinity). (XLS 901 KB)

Additional file 2: List of selected clusters used in k-means clustering. The data provided represents detailed information about the gens and clusters used in the k-means clustering of the stress experiments. (XLS 262 KB)

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Authors’ original file for figure 3

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Wecker, P., Klockow, C., Ellrott, A. et al. Transcriptional response of the model planctomycete Rhodopirellula baltica SH1T to changing environmental conditions. BMC Genomics 10, 410 (2009).

Download citation


  • Salt Stress
  • Hypothetical Protein
  • Cold Shock
  • Environmental Stress Response
  • Heat Shock Experiment