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Keywords:

  • Pseudomonas putida;
  • cold adaptation;
  • proteome;
  • transcriptome

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

The cold stress response of Pseudomonas putida KT2440 was investigated by genomewide deep cDNA sequencing and gel-free MS-based protein profiling. Transcriptome and proteome profiles were assessed at 30 °C and 2 h after a downshift from 30 to 10 °C. Pseudomonas putida adapted to lower ambient temperature by the activation of ribosome-associated functional modules that facilitate translational efficiency. The outer membrane profile was reorganized, anabolic pathways and core as well as energy metabolism were repressed and the alginate regulon and sugar catabolism were activated. At the investigated early time point of cold adaptation, the transcriptome was reprogrammed in almost all functional categories, but the protein profile had still not adapted to the change of living conditions in the cold.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

Free-living bacteria are frequently exposed to temperatshifts and nonoptimal growth temperatures. In order to grow at low temperatures, the organism must overcome the growth-diminishing effects of this stress condition, such as decreased membrane fluidity, altered redox status, increased stability of RNA and DNA secondary structures and thus a reduced efficiency of replication, transcription and translation (Phadtare, 2004).

Cold shock response and adaptation have been studied extensively in bacterial model organisms such as Escherichia coli (Phadtare et al., 1999; Gualerzi et al., 2003; Inouye & Phadtare, 2004) and Bacillus subtilis (Graumann & Marahiel, 1999; Beckering et al., 2002; Weber & Marahiel, 2002; Mansilla & de Mendoza, 2005; Budde et al., 2006; El-Sharoud & Graumann, 2007). Pseudomonas putida strain KT2440 (Bagdasarian et al., 1981; Regenhardt et al., 2002) is another bacterial model organism particularly for environmental microbiology. We recently screened a transposon library for genes that are essential for the survival of P. putida KT2440 at low temperatures (Reva et al., 2006). Life at lower temperature was hampered when the transposon had inactivated key genes that are necessary for the maintenance of (1) transcription, translation and ribosomal activity, (2) membrane integrity and fluidity and (3) redox status of the cell.

Here, we report on the global genomewide response of P. putida KT2440 to a downshift of temperature from 30 to 10 °C at both the mRNA transcript and the protein level. Transcriptome and proteome analyses were accomplished using deep cDNA sequencing and a gel-free, MS-centered proteomics approach.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

Bacterial strains and growth conditions

Pseudomonas putida KT2440 (strain DSM6125) (Bagdasarian et al., 1981) was obtained from DSMZ (Braunschweig, Germany). Bacterial cultures were inoculated from a frozen stock culture and incubated at 30 °C for 8 h at 250 r.p.m. in Luria–Bertani medium. An aliquot of 0.2 mL was added to 20 mL M9 medium (Na2HPO4 33.9 g L−1, KH2PO4 15.0 g L−1, NaCl 2.5 g L−1, NH4Cl 5.0 g L−1, MgSO4 2 mM, CaCl2 0.1 mM, FeSO4·7H2O 0.01 mM, pH 6.8) supplemented with 15 mM succinate as the sole carbon source in a 100-mL flask and incubated overnight at 30 °C. Bacteria were then grown in a 1.5-L batch culture (M9+15 mM succinate) using the BioFlo 110 Fermenter (New Brunswick Scientific Co., Edison, NJ) to ensure constant pH, aeration and agitation. When cultures reached the mid-exponential phase (OD600 nm∼0.8), the temperature was decreased from 30 to 10 °C. Three samples each for RNA and protein extraction were subsequently taken immediately before temperature downshift (30 °C) and 2 h after the media had been cooled to 10 °C. Each growth condition was repeated once.

RNA isolation

Total RNA was extracted according to the protocol provided by Qiagen (RNeasy Mini Kit). For cell harvest, 2 volumes of RNAprotect Bacteria Reagent (Qiagen) were added to 1 volume bacterial culture and mixed vigorously. The solution was incubated at room temperature for 5 min and immediately centrifuged at 5000 g for 10 min. For cell lysis, the cell pellet was resuspended in a 10% aliquot of the initial sample volume containing 1 mg mL−1 lysozyme in 10 mM Tris/HCl, 1 mM EDTA, pH 8.0, and incubated at room temperature for 20 min. Then, 1.8 mL RLT buffer (Qiagen) containing 1% (v/v) β-mercaptoethanol was added and mixed intensively, followed by the addition of 1.2 mL ethanol. The RNA solution was purified using the RNeasy Mini Kit, by applying the total volume stepwise to one column. On-column DNase digestion was performed twice for 20 min to ensure the complete removal of genomic DNA. RNA integrity and purity were checked by agarose gel electrophoresis.

Illumina cDNA sequencing

cDNA synthesis was performed from about 10 μg total RNA with a statistically distributed mixture of hexanucleotides as primers (random priming) using SuperscriptII (Invitrogen) reverse transcriptase according to the manufacturer's protocols. An aliquot of 25 μg cDNA was sequenced using the Genome Analyzer II at GATC Biotech AG (Konstanz). For this, the cDNA was nebulized to generate fragments <800 bp long. A terminal ‘A’ was then transferred to the 3′ end and cDNA fragments were ligated to adapters, purified and bridge amplified. Thirty-six cycles of sequencing-by-synthesis were performed for each library using the Genome Analyzer GAII SR.

illumina genome analyzer pipeline software (version 0.2) was used to qualify reads (Klockgether et al., 2010). Sequence reads that passed the default signal quality filter and were not aligned by ELAND (Efficient Large-Scale Alignment of Nucleotide Databases) to a reference of the P. putida rRNA genes were used for gene expression analysis. The reads were subsequently aligned to the P. putida genome (NC_002947.3) using the bowtie software package (Langmead et al., 2009). The remaining reads mapped to rRNA were subsequently excluded with a custom PERL script. Four nucleotides were trimmed from the 3′ end of each read and a seed size of 28 bp was used, in which two mismatches were allowed. The quality mismatch sum was 100 and results were transformed into a SAM format (command line: bowtie -t putida -l 28 -e 100 –best –sam -3 4 -n 2 -p 7).

A summary table was then generated using the integrative web analysis tool galaxy (Giardine et al., 2005). The functions ‘coverage’ and ‘join’ were used, respectively, to summarize (1) the coverage of each ORF from the P. putida NCBI annotation (version NC_002947.3) in terms of the total base pairs and proportion covered by reads and (2) the number of reads mapped to each ORF. Reads mapped to ORFs had at least 1 bp overlap with the ORF. The two datasets for 30 and 10 °C differed in the absolute number of both total reads and reads that mapped to the genome. In addition, genes differ considerably in length; therefore, reads were normalized as follows: the ORF length was standardized to 1000 bp and the number of reads to one million reads per experiment (RPKM, see Mortazavi et al., 2008). Gene expression was considered to be significantly different if RPKM30 °C>RPKM10 °C+3√RPKM10 °C (or vice versa). The 99% confidence interval for the real value N of a Poisson-distributed parameter is given by N=Nexp±3√Nexp, whereby Nexp represents the experimentally determined counts. Full data are deposited in accordance with MIAME standards at GEO (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24175), accession code GSE24175.

Identification of proteins by LTQ-FT-ICR-MS

A bacterial culture volume equivalent to 40 mL of OD500 nm=1 was mixed with 0.5 volume of 20 mM Tris-HCl, 5 mM MgCl2 and 20 mM sodium azide, pH 7.5, precooled at −20 °C. After centrifugation at 5000 g for 3 min at 4 °C, the cell pellet was shock-frozen in liquid nitrogen and stored at −80 °C until further processing.

Sample preparation for gel-free tandem-MS: 10 μg protein of each sample in 8 M urea, 2 M thiourea (UT) was adjusted to a final volume of 1.3 μL. Samples were diluted 1 : 10 with 50 mM bicarbonate solution to reduce the UT concentration and to maintain a basic pH of 7.6 for optimal trypsin digestion. Trypsin solution (20 μL) (10 ng μL−1 in 20 mM bicarbonate) was added and the samples were incubated at 37 °C for 15 h. To stop digestion, 6.6 μL of 5% acetic acid (ultra pure) was added. Afterwards, peptides were purified and desalted using C18-ZipTip columns (Millipore, Bedford, MA). A commercial vacuum centrifuge was used to remove acetonitrile. The complex peptide solution was fractionated by a nanoAcquity UPLC (Waters) equipped with a C18 nanoAcquity column (100 μm × 100 mm, 1.7 μm particle sizes). The peptide separation was achieved in a nonlinear gradient within 300 min using 2% acetonitrile in 0.05% acetic acid in water (A) and 0.05% acetic acid in 90% acetonitrile (B) as eluents at a flow rate of 400 nL min−1. Three technical replicates of each sample were analyzed, each containing about 2 μg of peptides. MS data were generated using an LTQ-FT-ICR-MS equipped with a nano-electrospray ion source (PicoTip Emitter FS360-20-20-CE-20-C12, New Objective). After a first survey scan in the LTQ-FT-ICR (resolution=50 000) tandem mass spectra (MS/MS), data were recorded for the five highest mass peaks in the linear ion trap at a collision-induced energy of 35%. The exclusion time was set to 30 s and the minimal signal for triggering MS/MS was 1000.

For protein identification, the MS/MS data were extracted using the elucidator software package (http://www.rosettabio.com/products/elucidator/default.htm) (Rosetta Biosoftware, Seattle, WA) and searched via the sorcerer v3.5 (Sage-N Research Inc., Milpitas, CA) without charge state deconvolution and deisotoping. All MS/MS samples were analyzed using Sequest (ThermoFinnigan, San Jose, CA, version v.27, rev. 11), which was set up to search against the P. putida KT2440 database assuming full digestion with trypsin. sequest searches were performed with a precursor ion tolerance of 20 p.p.m. and a fragment ion mass tolerance of 1 Da. Oxidation of methionine was specified as variable modifications and null missed cleavages were allowed. Peptide and protein identifications were accepted if they exceeded a specific Peptide–Teller threshold of 0.8 and a Protein–Teller threshold of 0.95. Furthermore, identification of proteins by a minimum of two peptides was required. For quantitative analysis, peptide intensities were used and the following Elucidator parameters were applied: frame and feature annotation was performed using a retention time minimum cut-off of 55 min, a retention time maximum cut-off of 285 min, an m/z minimum cut-off of 300 and maximum 2000. An intensity threshold of 1000 counts, an instrument mass accuracy of 5 p.p.m. and an alignment search distance of 10 min were applied. For quantitative analysis, the data were normalized and further grouped (three technical from two biological replicates 10 and 30 °C each).

Results and discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

Bacterial growth at 10 and 30 °C

Pseudomonas putida is a mesophilic organism and typically grows within the temperature range from 8 to 35 °C. We followed the short-term adaptation of the bacterium from the optimal growth temperature of 30 °C to a low temperature (10 °C) by the parallel profiling of proteome and transcriptome. Bacteria were grown at 30 °C to a density of ∼6 × 108 CFU mL−1. After the temperature had been cooled down within 45 min to 10 °C, the bacteria continued to grow for another 4 h at a constant rate of 0.1 and then entered the stationary phase within the next 3–6 h (n=4 experiments). Samples at 10 °C were taken at the midpoint of linear growth.

RNA-seq data

The transcriptome was analyzed once by cDNA sequencing and on technical and biological replicates by hybridization of microarrarys (GEO database GSE24176). RNA-seq and microarrays consistently identified 994 mRNA transcripts to be differentially regulated, and a further 287 and 1343 mRNA transcripts were detected to be differentially expressed by either microarray (FDR<0.05; P<0.05) or RNA-seq criteria (N=Nexp±3√Nexp), respectively. Because cDNA sequencing as the less biased technique detected the differential regulation of gene expression irrespective of the absolute expression level, only the outcome of cDNA sequencing is reported.

Deep cDNA sequencing identified 859 significantly downregulated and 1478 significantly upregulated genes during cold adaptation (Supporting Information, Table S1). Thus, for 43% of all annotated ORFs, expression was significantly changed during the shift from 30 to 10 °C. The expression profile was drastically altered by the temperature shift both in qualitative and in quantitative terms as it is visualized in the differential contribution of functional categories to up- and downregulated genes (Fig. 1).

image

Figure 1.  Classification of the transcriptional cold shock response of Pseudomonas putida KT2440 by functional categories (COG prediction derived from http://www.pseudomonas.com, July 2010). The numbers indicate the percentage of assigned genes according to their physiological role. In total, 2337 genes were found to be significantly differentially expressed [RPKM 10°C>RPKM 30°C±3√(RPKM 30°C)] by at least twofold, of which (a) 1478 were upregulated and (b) 859 were downregulated at 10°C.

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Of the most abundant mRNA species, the P. putida cell reduced its pool for transcripts that are translated into chaperonins, elongation factors EF-Tu and EF-Ts, ATP synthase and enzymes of the core metabolism. The cells shut down the transcription of operons that encode the biosynthesis of purines, pyrimidines, coenzymes and branched amino acids and those that encode transporters for amino acids, siderophores, polyamines and sulfur compounds. The most strongly downregulated genes encode heat shock proteins and enzymes of the citric acid cycle and of the pathway for the synthesis of valine and leucine. In summary, the cells constrained its mRNA repertoire for biosynthesis, nutrient uptake, core and energy metabolism.

Of the top 100 downregulated genes, the encoded function has been experimentally demonstrated for 83 genes in P. putida or in another proteobacterium (Hoshino & Kose, 1990a, b; Auerbach et al., 1993; Best & Knauf, 1993; Holtmann et al., 2004; Carruthers & Minion, 2009; Kazakov et al., 2009; Molina-Henares et al., 2010). In contrast, 67 of the >10-fold upregulated 169 genes at 10 °C were found to be conserved hypotheticals. Other over-represented categories were genes encoding transporters (20), transcriptional regulators (15) or phage proteins, integrases and transposons (11). During cold adaptation, P. putida activated a transcriptional program whose most key players have not been characterized so far in any organism. The most striking upregulation was seen for the two hypotheticals PP1690 and PP1691 that were expressed at a low level at 30 °C, but belonged to the 10 most abundant transcripts at 10 °C. Among the strongly upregulated genes of known encoded function, the majority of genes are orthologs of the alginate biosynthesis regulon in Pseudomonas aeruginosa and the affiliated catabolism of glycerol and glucose through the Entner–Douderoff pathway. Furthermore, the PhoPQ two-component system and the multienzyme complex for the degradation of valine, leucine and isoleucine were activated. Another interpretable key feature of the cold adaptation was the strong upregulation of the rbfA–nusAinfB operon. The orthologs in E. coli coordinate transcription and translation during cold stress (Bae et al., 2000; Bylund et al., 2001): the cold shock protein RbfA converts nonadapted translationally inactive into cold-adapted translationally competent ribosomes. InfB is necessary for efficient and accurate de novo initiation and re-initiation of translation. NusA is an essential, multidomain protein that functions in both termination and antitermination of transcription. The rbfA–infBnusA operon is highly conserved, and hence, we assume that its upregulation fulfills similar roles during cold stress for E. coli and P. putida cells.

Furthermore, the degradation pathway of valine to branched-chain fatty acids (bkd operon) was upregulated. Branched-chain fatty acids are important membrane compounds to ensure membrane fluidity at changing temperatures (Klein et al., 1999).

Proteome data

Deep cDNA sequencing identified 2337 genes with significantly differentially expression 2 h after the cultures had been cooled down from 30 to 10 °C. The abundance of proteins in the proteome had significantly changed for 59 proteins by >1.5-fold (Table 1), although in total over 1000 proteins could be identified by LTQ-FT-ICR-MS. For all those proteins, the quantitation data showed low SDs, high P-values and ratios of 1 : 1 between the two biological replicates of 10 and 30 °C, which indicated a high reproducibility for the two experiments. The corresponding data can be found in the Supporting Information (Tables S2 and S3). A reasonable explanation for this comparably low number of proteins would be the simple fact that the downshift by 20 °C is a strong stressor that leads to an accumulation of cold-unadapted nontranslatable ribosomes. Thus, the protein profile did not change within these first 2 h – metaphorically, the protein profile was ‘frozen’. Upon conversion into cold-adapted translatable ribosomes, translation would start again. This was furthermore reflected by the reduced growth rate at 10 °C (μ30 °C=0.9 h−1, μ10 °C=0.1 h−1, data not shown). In accordance with this interpretation, the most remarkable change of the proteome from 30 to 10 °C ambient temperature was the increased abundance of proteins that are involved in ribosome processing, assembly and maintenance (Table 1). Prominent examples were RbfA, the ribosome-binding factor mentioned above, the GTP-binding proteins EngA and BipA and the translation initiation factor IF-3. The increased level of IFs after cold shock is due to the fact that the genes were activated at the transcriptional level by rarely used promoters and synthesized de novo (Giangrossi et al., 2007; Giuliodori et al., 2007). Outer membrane proteins such as OmpA, OprQ, OprH, OprL, OprI and OprF proteins were the second class of more abundant proteins during cold adaptation (Table 1). The increased expression of cell envelope proteins most likely reflects the stress response of the bacterial cell to maintain homeostasis by transport control. The 49 upregulated proteins were grouped into functional categories, and the respective distribution is shown in Fig. 2.

Table 1.   Proteins that were detected to be significantly differentially expressed in both biological samples with a calculated P-value≤(t-test) 0.05 and an absolute fold-change of at least 1.5
Locus tagFold- change*P-value*ProteinProtein nameFunctional category
  • *

    Fold-change and P-value were calculated as the geometric mean value derived from both biological samples.

  • Functional category according to the COG predictions derived from the Pseudomonas homepage (http://www.pseudomonas.com), last updated: July 2010.

  • Discrepancy in regulation compared with transcriptome data.

  • §

    § Proteins were only found by proteome analysis; genes were not found to be differentially expressed by Illumina cDNA sequencing.

PP_02681.60.0120Outer membrane protein OprE3OprQFunction unknown
PP_0566−1.70.0460Translation initiation factor SUI1 Translation, ribosomal structure and biogenesis
PP_0572§3.30.0010Penicillin-binding protein 1CPbpCCell envelope biogenesis, outer membrane
PP_0623§1.50.0240  Function unknown
PP_07731.50.0003OmpA family protein Cell envelope biogenesis, outer membrane
PP_0799§1.80.0006Porin, putative Function unknown
PP_08571.5<0.0001GTP-binding protein EngAEngAGeneral function prediction only
PP_08682.7<0.0001Glycine betaine/carnitine/choline ABC transporter, AT Amino acid transport and metabolism
PP_08705.2<0.0001Glycine betaine/carnitine/choline ABC transporter, pe Amino acid transport and metabolism
PP_08862.0<0.0001Conserved hypothetical protein Function unknown
PP_10241.50.03202-Dehydro-3-deoxyphosphogluconate aldolase -EdaCarbohydrate transport and metabolism
PP_1071−2.0<0.0001Amino acid ABC transporter, periplasmic amino acid-binding Amino acid transport and metabolism/signal transduction mechanisms
PP_1082−1.80.0020BacterioferritinBfrInorganic ion transport and metabolism
PP_10991.7<0.0001Cold shock domain family protein Transcription
PP_1111−1.80.0090Synthetase, putative General function prediction only
PP_1131§1.80.0001outer membrane lipoprotein, putative Cell envelope biogenesis, outer membrane
PP_1141−1.9<0.0001Branched-chain amino acid ABC transporter, periplasmicBraCAmino acid transport and metabolism
PP_1185§1.7<0.0001Outer membrane protein H1OprHCell envelope biogenesis, outer membrane
PP_11861.50.0001Transcriptional regulatory protein PhoPPhoPSignal transduction mechanisms/transcription
PP_1223§1.60.0010Peptidoglycan-associated lipoprotein OprLOprLCell envelope biogenesis, outer membrane
PP_12511.60.0008Malate : quinone oxidoreductaseMqo-2General function prediction only
PP_1428−2.4<0.0001Sigma factor algU negative regulatory protein MucAMucASignal transduction mechanisms
PP_14331.8<0.0001RNase IIIRncTranscription
PP_1463§1.7<0.000116S rRNA gene processing protein RimMRimMTranslation, ribosomal structure and biogenesis
PP_18351.80.0050Conserved hypothetical protein Function unknown
PP_18683.7<0.0001ATP-dependent RNA helicase, DEAD box family DNA replication, recombination, and repair/transcription/translation, ribosomal structure and biogenesis
PP_20891.5<0.0001Outer membrane protein OprFOprFCell envelope biogenesis, outer membrane
PP_21051.70.0320Conserved hypothetical protein Function unknown
PP_22961.7<0.0001Hypothetical protein Function unknown
PP_23222.4<0.0001Outer membrane lipoprotein OprIOprIFunction unknown
PP_2396−1.70.0220Hypothetical protein Function unknown
PP_24489.1<0.0001Conserved hypothetical protein Function unknown
PP_2466§1.5<0.0001Translation initiation factor IF-3InfCTranslation, ribosomal structure and biogenesis
PP_29361.9<0.0001ABC transporter, ATP-binding protein General function prediction only
PP_39302.4<0.0001Hypothetical protein Function unknown
PP_40041.70.0060cell division protein FtsKFtsKCell division and chromosome partitioning
PP_4378§−3.3<0.0001Flagellin FliCFliCCell motility and secretion
PP_44702.2<0.0001Alginate biosynthesis transcriptional activatorAlgZFunction unknown
PP_4496§2.0<0.0001Hypothetical protein Function unknown
PP_45481.50.0020oxidoreductase, putative Amino acid transport and metabolism
PP_45631.70.0010Conserved hypothetical protein Function unknown
PP_4591§1.50.0001RNase DRndTranslation, ribosomal structure and biogenesis
PP_46831.90.0090Penicillin-binding proteiMrcBCell envelope biogenesis, outer membrane
PP_47111.6<0.0001Ribosome-binding factor ARbfATranslation, ribosomal structure and biogenesis
PP_4787§2.4<0.0001PhoH family protein Signal transduction mechanisms
PP_47881.90.0009conserved hypothetical protein TIGR00043 Function unknown
PP_48091.7<0.0001Conserved hypothetical protein Function unknown
PP_4870−1.60.0010Azurin Energy production and conversion
PP_4879§1.7<0.0001RNA methyltransferase, TrmH family, group 3 Translation, ribosomal structure and biogenesis
PP_4880§1.6<0.0001RNase RVacBTranscription
PP_4974§1.50.0500Na+/H+ antiporter, putative Inorganic ion transport and metabolism
PP_50381.50.0370Conserved hypothetical protein Function unknown
PP_5044§1.5<0.0001GTP-binding protein TypA/BipA Signal transduction mechanisms
PP_51141.60.0090Conserved hypothetical protein TIGR00095 Function unknown
PP_5172−2.70.0001Conserved hypothetical protein Function unknown
PP_5184§1.5<0.0001Glutamine synthetase, putative Amino acid transport and metabolism
PP_5187§1.6<0.0001Conserved hypothetical protein Function unknown
PP_52781.9<0.0001Aldehyde dehydrogenase family protein Energy production and conversion
PP_5338§1.6<0.0001Aspartate ammonia-lyaseAspAAmino acid transport and metabolism
image

Figure 2.  Classification of the cold shock response of Pseudomonas putida KT2440 into functional categories (COG prediction derived from http://www.pseudomonas.com, July 2010) at the protein level. The graph shows the number of proteins that were found to be significantly upregulated upon cold shock [P-value ≤0.05 (t-test) and fold-change ≥1.5] according to their physiological role in the cell. In total, 48 proteins were upregulated, of which three were assigned to two functional categories. No chart is shown for the proteins that are downregulated in expression because of the low number of 11 proteins.

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Conclusions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

The functional genomics of cold adaptation has been investigated in depth in the two bacterial model organisms B. subtilis and E. coli. This study exploited the recent developments in transcriptome sequencing and proteome peptide profiling to unravel the cold adaptation of a further major model organism of environmental microbiology, the biological safety strain P. putida KT2440. According to the RNA-seq and proteome data, P. putida adapts to lower ambient temperatures by the activation of ribosome-associated functional modules that facilitate translational efficiency. The outer membrane profile is reorganized, anabolic pathways and core as well as energy metabolism are repressed and the alginate regulon and sugar catabolism are activated. At the investigated early time point of cold adaptation, the transcriptome is reprogrammed in almost all functional categories, but the protein profile has still not adapted to the change of living conditions in the cold.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

S.F. received a predoctoral stipend from the DFG-supported IRTG 653 ‘Pseudomonas: Pathogenicity and Biotechnology’. Financial support was provided by BMBF within the framework of the SysMO consortium, part PSYSMO ‘Towards a quantum increase in the performance of P. putida as the cell factory of choice for white biotechnology,’ project 3: Key determinants of abiotic stress response of P. putida KT2440'.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

Table S1. Genes that were detected to be differentially expressed upon cold shock according to Illumina cDNA sequencing.

Table S2. Calculated SD of biological replicates at 10 °C.

Table S3. Calculated SD of biological replicates at 30 °C.

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FML_2237_sm_tables1.xls1117KSupporting info item
FML_2237_sm_tables2.xls257KSupporting info item
FML_2237_sm_tables3.xls257KSupporting info item

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