Global Whole-Cell FTICR Mass Spectrometric Proteomics Analysis of

Apr 30, 2005 - Global Whole-Cell FTICR Mass Spectrometric Proteomics Analysis of the Heat Shock Response in the Radioresistant Bacterium Deinococcus ...
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Global Whole-Cell FTICR Mass Spectrometric Proteomics Analysis of the Heat Shock Response in the Radioresistant Bacterium Deinococcus radiodurans Amy K. Schmid,*,† Mary S. Lipton,‡ Heather Mottaz,‡ Matthew E. Monroe,‡ Richard D. Smith,‡ and Mary E. Lidstrom†,§,| Program in Molecular and Cellular Biology, University of Washington, Seattle, Washington 98195-2125, Environmental and Molecular Sciences Laboratory, Pacific Northwest National Laboratory, PO Box 999, MSIN K8-98, Richland, Washington 99352, Department of Microbiology, University of Washington 98195-2125, and Department of Chemical Engineering, University of Washington 98195-2125 Received October 18, 2004

The results of previous studies indicated that D. radiodurans mounts a regulated protective response to heat shock, and that expression of more than 130 genes, including classical chaperones such as the groESL and dnaKJ operons and proteases such as clpB are induced in response to elevated temperature. In addition, previous qualitative whole-cell mass spectrometric studies conducted under heat shock conditions indicated global changes in the D. radiodurans proteome. To enable the discovery of novel heat shock inducible proteins as well as gain greater biological insight into the classical heat shock response at the protein level, we undertook the global whole-cell FTICR mass spectrometric proteomics study reported here. We have greatly increased the power of this approach by conducting a large number of replicate experiments in addition to taking a semiquantitative approach to data analysis, finding good reproducibility between replicates. Through this analysis, we have identified with high confidence a core set of classical heat shock proteins whose expression increases dramatically and reproducibly in response to elevated temperature. In addition, we have found that the heat shock proteome includes a large number of induced proteins that have not been identified previously as heat responsive, and have therefore been designated as candidate responders. Finally, our results are consistent with the hypothesis that elevated temperature stress could lead to cross-protection against other related stresses. Keywords: heat shock • Deinococcus radiodurans • FTICR proteomics

Introduction The adaptive response to the stress of elevated temperature is extremely well conserved throughout evolution. To cope with the buildup of misfolded and aggregated proteins that occurs as a result of high temperature, cells increase the expression of chaperones and proteases collectively called heat shock proteins.1,8 In bacteria, chaperone machines GroESL and DnaKJGrpE help refold proteins back to their native state, whereas proteases such as the Clp proteins, FtsH, and Lon target proteins for degradation to prevent their aggregation into insoluble inclusions.2 These proteins are also conserved in Deinococcus radiodurans, a nonpathogenic, coccoid soil bacterium that exhibits extremely high radiation resistance.3 D. * To whom correspondence should be addressed. Institute for Systems Biology, 1441 N 34th St., Seattle, WA, 98103. Phone: (206) 732-1493. Fax: (206) 732-1299. E-mail: [email protected]. † Program in Molecular and Cellular Biology, University of Washington. ‡ Environmental and Molecular Sciences Laboratory, Pacific Northwest National Laboratory. § Department of Microbiology, University of Washington. | Department of Chemical Engineering, University of Washington. 10.1021/pr049815n CCC: $30.25

 2005 American Chemical Society

radiodurans can withstand up to 5 Mrad of gamma irradiation and 1000 J/m2 of UV radiation without mutation or loss of survival.3 Because of this extraordinary ability, the organism has been intensively studied in recent years. However, relatively little is yet known about how D. radiodurans protects itself against other environmental stressors. Our previous studies have suggested that D. radiodurans mounts a protective response to heat shock,4 and microarray, quantitative RT-PCR, primer extension, and reporter fusion experiments indicated that the corresponding increase in transcription of heat shock genes occurs on a global scale.4,5,13 In addition, a preliminary proteomics analysis using 2D gels suggested that several of the classical chaperones and proteases are induced at the protein level in response to heat shock.5 However, in the 2D gel heat shock proteomics analysis, only about 10% of the total predicted ORFs in the genome of D. radiodurans were detected. Indeed, global whole-cell proteomic technologies have been limited by their low comprehensiveness, sensitivity, and dynamic range.9 Two-dimensional gel electrophoresis and isotopecoded affinity tag (ICAT) analyses only allow detection of about Journal of Proteome Research 2005, 4, 709-718

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research articles 10% of the total cellular protein complement, with a bias toward extremely highly expressed gene products.6,10 However, recent advances in FTICR whole-cell mass spectrometric approaches have allowed up to 60% of the total cell proteins to be detected, and proteins of extremely low abundance, such as transcriptional regulators, are also detected using this technique.9,11 However, these previous FTICR methods have not been applied to discovery of novel functions, since those studies sought to achieve a “parts list” of the protein components of D. radiodurans in response to many culturing conditions, including heat shock, detailing what was present or not present under certain conditions. To discover potentially novel functions and to expand our previous FTICR and 2D gel proteomics analyses, we have conducted global whole-cell FTICR mass spectrometric proteomics on several replicate cultures of D. radiodurans cells exposed to heat shock. Subsequently, a semiquantitative approach was taken to analyze the data, employing both direct protein abundance measurements and peptide mass tag counts as complementary supportive data to gain quantitative power. Through this analysis, we have identified a core set of highly conserved classical heat shock factors exhibiting high induction by heat shock as well as many novel potentially heat-responsive factors. In addition, our data are consistent with the hypothesis that heat shock may cross-protect D. radiodurans against other types of stress.

Materials and Methods Culture Conditions. Deinococcus radiodurans R1 wild-type cells were grown in TGY broth (0.5% tryptone, 0.1% glucose, 0.3% yeast extract) with 250 rpm shaking or TGY plates (1.5% agar added to broth (Difco)) at 30 °C. For heat shock experiments, cultures were grown to high density (OD600 ∼ 2.5) in TGY liquid overnight, and the following morning rediluted in fresh medium and grown to mid-exponential phase at 30 °C (OD600 ∼ 0.3-0.5). Cultures were split in half: one-half of the culture was continued at 30 °C as a control, the other shifted to heat shock for 1 h at 42 °C. Since heat shock expression is a very transient phenomenon, we used these conditions as determined by previous β-galactosidase time course experiments, which showed that heat shock for 1 h at 42 °C best reflects the peak expression of the proteins of selected heat shock genes, including dnaK and groES.4 Protein Preparation and Tryptic Digestion. Cells were lysed and tryptic peptides prepared for whole cell mass spectrometric proteomic analysis at the Pacific Northwest National Laboratory (PNNL, Richland, WA) as previously described.11 Briefly, cells were lysed by bead beating in a Biospec Mini bead beater (Bartelsville, OK), followed by denaturing and tryptic digestion. The insoluble fraction was then removed by ultracentrifugation and resultant fractions were snap frozen to prevent protein degradation. For proteomics experiments, three biological replicate cultures were harvested at each of 30 °C and 42 °C. Three separate protein samples were prepared from each biological replicate culture, each of which was analyzed by mass spectrometry, thus yielding nine total replicates. Proteomics Protocol. Global whole-cell mass spectrometric analysis was performed on proteins prepared from heat shocked and untreated D. radiodurans using an accurate mass and time tag strategy as described previously.11 Such a strategy entails two phases of analysis to identify and compare the tryptic peptides that serve as biomarkers for the organism.11 First, potential mass and time tags (PMTs) are identified by 710

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standard liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of complex mixtures of proteins from the organism of interest grown under a number of different culture conditions. For each condition, the proteins were digested with trypsin and analyzed by LC-MS/MS, then the fragmentation spectra of the resultant peptides were matched to the D. radiodurans genome database using the SEQUEST software. The peptide sequences identified were summarized and filtered to form a PMT tag database, listing the calculated monoisotopic mass and observed elution time for each sequence. The filtering involved applying the following rules: if a given peptide was seen once during LC-MS/MS analysis, a cross-correlation score (Xcorr) cutoff of 1.9, 2.2, or 3.5 was set for 1+, 2+, or 3+ charge states, respectively; if a given peptide was observed in more than one fragmentation spectrum, then an Xcorr cutoff of 1.9 was set. In the second phase, tryptic digests of proteins from different culture conditions were analyzed in a high-throughput manner, again using a liquid chromatography separation system, but this time coupling to a high magnetic field (11 T) Fourier transform ion cyclotron resonance mass spectrometer (LCFTICR-MS), yielding precise measurement of peptide masses with as low as 1-ppm mass measurement accuracy (MMA). The PMTs from the initial LC-MS/MS analysis were used as predetermined biomarkers to identify D. radiodurans peptides observed in the LC-FTICR-MS analysis of the nine replicate heat shocked and untreated culture conditions using replicate analyses on the FTICR-MS instrumentation. Features observed by LC-FTICR-MS were identified by comparing the mass and elution time of the feature to the mass and elution time of the PMTs in the PMT tag database. The chromatographic peak area of a given feature was used to estimate its abundance. The PMTs identified by LC-FTICR-MS were aggregated to generate protein statistics, including average protein abundances and mass tag (MT) counts, both used for subsequent data analysis. Protein abundance was computed by averaging the abundances of the PMTs detected for a given protein, and should thus correlate with protein expression level. MT counts correspond to the number of peptides detected by LC-FTICR-MS for a given protein, and are also a useful estimate of expression level. Proteins were filtered to require that at least two peptides be detected at either 30 °C or 42 °C in the LC-FTICR-MS analysis, one of which was required to be fully tryptic. Protein coverage percentages were also computed using the PMTs observed for each protein. Proteomics Data Analysis. Quality and reproducibility of the proteomics data were assessed in two phases using the TIGR_MeV (multiexperiment viewer) software package. First, ANOVA analysis t-tests were performed on the 9 replicates from the proteomics experiments in which the nonstressed datasets were grouped randomly into 6 sets. Reproducibility of datasets from heat shock experiments was assessed similarly to those of the nonstressed. Numbers of significantly different genes are reported in the Results. Two of the 42 °C datasets from the mass tag count data were deemed aberrant by ANOVA and were therefore excluded from subsequent analysis. Second, the extent of correlation between replicate datasets was assessed by randomly pairing replicates, regardless of stress condition. Pearson product moment correlation coefficients were then calculated for each pair. Coefficient ranges are reported in the Results. To determine which proteins were significantly differentially expressed in response to heat shock in the protein abundance

FTICR Proteomics Analysis of D. radiodurans Heat Shock

data, ratios of protein abundances at 42 °C compared to 30 °C for each of the 9 replicates of the proteomics data were first calculated using Microsoft Excel. The ratio datasets were then searched for proteins whose expression was induced or repressed under heat shock. Proteins exhibiting greater than 2.5fold induction and less than 0.4-fold repression in 5 out of 9 replicate experiments were considered differentially expressed. Such stringent criteria were set due to the high pervasiveness of 2- to 2.5-fold induction ratios throughout the protein abundance data (13%). To determine which proteins were differentially expressed in the mass tag (MT) count data, proteins exhibiting a greater number of mass tags at 42 °C compared to 30 °C in 5 of 7 replicate experiments and an average MT ratio of 2-fold or greater were considered induced. In contrast to the protein abundance dataset, a 2-fold cutoff could be used for the MT dataset, since 2- to 2.5-fold ratios were present at only 2.3% in the entire proteome.

Results Proteomics Experimental Setup and Data Analysis Strategy. To assess global changes in the proteome of D. radiodurans in response to heat shock, three biological replicate cultures at 30 °C mid-logarithmic growth phase (OD600 ∼ 0.3-0.5) were shifted for 1 h to 42 °C. These conditions were chosen carefully, and all data from previous studies were taken into account, which have shown that (1) the gene products detected in the proteome remain constant between OD600 of 0.3-0.5, and subsequently change during late log phase (OD600 ∼ 0.6) and again during stationary phase (OD600 ∼ 0.9-2.5),11 and (2) growth in different batches of TGY do not significantly affect the proteome (A. Schmid and M. Lipton, unpublished data, and ref 13). Proteins were extracted from three aliquots of each replicate culture of heat shocked D. radiodurans culture, yielding nine experimental replicates for each condition. Tryptic fragments were generated from the complex protein mixtures, and using the accurate mass tags developed previously for D. radiodurans,11 high throughput Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) was performed on all nine experimental replicates for each condition. The predetermined set of accurate mass tags available for D. radiodurans are biomarkers that enable comprehensive detection of over 60% of the proteome under varied culture conditions.11 In this study, using only mid-logarithmic growth conditions at 30 °C and heat shocked conditions at 42 °C, we detected expression of 30% of the proteins encoded in the genome with high confidence, with an average of 18% of the proteome detected in each of the nine biological replicate FTICR runs. These coverage values compare favorably with previous reports.9 Although protein abundance data generated by this approach is semiquantitative in nature, we were able to bolster its quantitative power using large numbers of replicate datasets for each condition. Prior to data analysis, the extent of quantitative reproducibility was assessed in two phases. First, relative protein abundances calculated for each replicate dataset for each condition were subjected to ANOVA analysis in which the nine replicates were grouped into six sets, and the p-value was set at 0.01. By this measure, 97 of the total proteins detected by direct protein abundance measurements were determined as significantly differentially expressed within all nine replicates, representing only 4% of the proteome. However, two of the mass tag count datasets at 42 °C were

research articles deemed aberrant and therefore excluded from the analysis (see also Supplementary Table 2 in the Supporting Information). In the second phase of data quality assessment, replicates from each condition for each of the protein abundance and mass tag measurements were paired randomly until all permutations were met (e.g., 1 vs 2, 1 vs 3, 2 vs 3) and Pearson product moment correlation coefficients calculated. Coefficients ranged from 0.6 to 0.9, with higher proteome coverage datasets exhibiting higher correlation between replicates. In contrast, when each 42 °C dataset was paired with each 30 °C dataset in all permutations, coefficients ranged from 0.2 to 0.5, indicating that, despite a relatively high level of variation in the replicate data, it is still possible to detect differences in protein expression in response to heat shock using relative protein abundance as a quantitative estimate. Ratios of relative protein abundances at 42 °C to 30 °C were then calculated for each replicate dataset, and only proteins demonstrating ratios greater than 2.5-fold induction or less than 0.4-fold repression in five out of nine replicates were considered differentially expressed in response to heat shock. For the mass tag data, proteins were considered induced only if they demonstrated average induction ratios greater than 2-fold and a greater number of mass tags detected at 42°C compared to 30°C in 5 out of 7 replicates. Results of Proteomics Data Analysis Using Protein Abundance Measurements. As listed in Table 1, the averaged induction ratios of protein abundances from nine replicate experiments for 163 induced proteins from 12 predicted functional categories were identified by protein abundance measurements as heat shock-inducible. We observed good reproducibility across replicates, as evidenced by the standard deviations in protein abundance measurements (Table 1). As expected from previous 2-dimensional electrophoresis studies of the D. radiodurans proteome in response to heat shock, the highly conserved chaperones GroES, GroEL, DnaK, GrpE, trigger factor (Tig), and the putative small heat shock protein Hsp20 (31% homologous to Hsp18 of Streptomyces albus) were observed to be highly induced upon heat shock in this global analysis. In addition, as demonstrated by the number of peptide mass tags observed corresponding to these proteins, these heat shock proteins were highly expressed even in the absence of stress (Table 3, see below). For example, nearly 40 peptide mass tags corresponding to GroEL were detected at 30 °C, in contrast to most proteins, which exhibited on the order of about 2 (Table 3 and not shown). Interestingly, several predicted transcriptional regulators were also observed to increase their protein expression under heat shock. Regulatory proteins are known to be expressed at extremely low levels in most organisms,9 suggesting that the whole cell proteomics FTICR method employed here was extremely sensitive and able to detect low abundance proteins. The majority of proteins (45%) observed to be potentially induced under heat shock by the measure of protein abundances fell into the category of hypothetical proteins and transposases. This correlates well with the whole genome, since it encodes ∼30% hypothetical proteins (excluding transposases). Protein abundance data for all proteins in the proteome are listed in Supplementary Table 1 of the Supporting Information. Intriguingly, only 10 proteins were found to be repressed lower than 0.4-fold in response to heat shock, which included four proteins involved in central metabolism, three hypothetical proteins of unknown function, two antibiotic resistance proteins, and one ABC transporter. The protein abundance ratios Journal of Proteome Research • Vol. 4, No. 3, 2005 711

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Table 1. Proteins Induced by Heat Shock above 2.5-Fold According to Protein Abundance Calculations

ORF

DR0128 DR0129 DR0606 DR0607 DR1114 DR1117 DR1948 DR1598 DR0329 DR1374 DR1775 DR1258 DR2356 DR0508 DR0587 DRA0155 DR0219 DR1146 DR0630 DR2257 DR2609 DRB0129 DR0997 DR1935 DR2287 DRB0091 DRA0071 DR0353 DRA0050 DR0750 DR0258 DR0575 DR0788 DR1038 DR1185 DR1358 DR1568 DR1571 DR1712 DR2052 DR1955 DR2153 DR2488 DRA0135 DRA0246 DR2243 DR0808 DR0056 DRA0034 DRA0042 DRB0133 DR0085 DR0102 DR1553 DR0112 DR0980 DR1626 DR0345 DR0612 DR0966 712

predicted functiona

protein alias

proteome average abundance ratio 42 °C/30 °C

Protein fate (heat shock responsive chaperones and proteases) cochaperone GrpE 8.94 chaperone DnaK 4.37 chaperonin GroES 2.54 chaperone GroEL 5.55 small heat shock protein Hsp20 4.66 ATP-dependent Clp protease, ATP-binding subunit ClpC ClpC 5.94 trigger factor Tig 2.74 putative protease 3.58 DNA metabolism (repair and recombination) mutT, nudix hydrolase family MutT 11.81 DNA topoisomerase I TopA 9.25 DNA helicase II UvrD 2.86 SNF2/Rad54 helicase-related protein 3.58 mutT, nudix hydrolase family 2.82 mrr restriction system protein Mrr-1 5.25 mrr restriction system protein Mrr-2 2.80 integrase/recombinase, putative 3.21 Hypothetical/ionizing radiation and desiccation responsive12 DdrF 3.45 Cellular processes (cell division, detoxification, toxin resistance, and stress response) general stress protein 26, putative 2.65 cell division protein FtsA FtsA 3.22 erythromycin esterase, putative 3.20 Brk protein, putative 3.69 hemolysin, putative 8.37 Transcription and transcriptional regulators transcriptional regulator, FNR/CRP family (radiation and DdrI 4.31 desiccation responsive12) transcriptional regulator 3.60 transcriptional regulator, AsnC family 2.55 response regulator 4.82 Transcriptional repressor SmtB 4.32 ribonuclease Rnr 2.52 photoreceptor 7.18 sensory box/GGDEF family protein 6.58 Membrane functions and transport multidrug-efflux transporter, putative 3.36 preprotein translocase, SecA subunit SecA 2.78 branched-chain amino acid ABC transporter, 3.59 periplasmic amino acid-binding protein, putative branched-chain amino acid ABC transporter, LivK 4.96 periplasmic amino acid-binding protein S-layerlike array-related protein 3.36 outer membrane protein 5.43 peptide ABC transporter, ATP-binding protein 3.55 peptide ABC transporter, periplasmic peptide-binding protein 2.73 extracellular solute-binding protein, family 5 4.13 ABC transporter, ATP-binding protein, MsbA family 3.84 extracellular solute-binding protein, family 5 3.40 sugar ABC transporter, ATP-binding protein 11.38 ABC transporter, permease protein, CysTW family 8.76 ABC transporter, periplasmic substrate-binding protein, putative 7.05 extracellular solute-binding protein, family 5 4.31 phosphate transport system regulatory protein PhoU 5.59 UDP-N-acetylglucosamine pyrophosphorylase GlmU 3.80 benzoate membrane transport protein, putative 5.98 UDP-galactose-lipid carrier transferase AmsG 6.45 glucose-1-phosphate thymidylyltransferase RfbA 2.58 Na(+)-linked D-alanine glycine permease DagA 2.82 Translation ribosomal protein L27 RmpA 3.64 ribosomal protein L9 RplI 3.40 peptide chain release factor 3 PrfC 11.59 Amino acid biosynthesis and energy metabolism glutamine cyclotransferase 2.72 glutamate dehydrogenase, putative 4.57 branched-chain amino acid aminotransferase 4.47 cytochrome c biogenesis protein, thiol:disulfide interchange protein CcmG 6.28 arginine utilization protein RocB, putative 6.02 5-methyltetrahydrofolateshomocysteine methyltransferase 5.84

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std devb

3.21 0.89 1.71 3.73 2.26 5.06 1.74 2.18 6.89 7.60 1.19 1.73 0.58 2.42 1.13 1.67 1.71 0.69 1.36 1.41 2.90 2.18 1.44 3.02 0.73 3.17 1.44 1.04 3.05 4.76 0.91 0.95 2.64 3.01 0.52 3.70 1.32 1.73 2.58 2.49 1.18 5.26 3.24 2.59 2.60 2.02 1.54 1.02 1.97 0.75 1.11 2.76 0.84 5.11 0.75 2.44 1.51 1.87 2.36 1.81

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FTICR Proteomics Analysis of D. radiodurans Heat Shock Table 1 (Continued)

ORF

DR1031 DR1122 DR1401 DR1758 DR1928 DR2033 DR2036 DRA0054 DRA0223 DR0460 DR0950 DR1701 DR1871 DR2238 DRA0130 DRA0127 DRA0231 DRA0233 DRA0319 DRA0325 DRB0080 DR1084 DR1316 DR1487 DR1692 DR1967 DR2510 DRA0005 DR0668 DR1878 DRA0201 DR0918 DR1891 DR2567 DRA0033 DRA0222 DR0116 DR0161 DR0210 DR022 DR0253 DR0268 DR0368 DR0503 DR0509 DR0554 DR0666 DR0792 DR0826 DR0877 DR0917 DR0938 DR0978 DR1048 DR1136 DR1148 DR1172 DR1241 DR1245 DR1261 DR1315 DR1381 DR138 DR1470 DR1515

predicted functiona

protein alias

proteome average abundance ratio 42 °C/30 °C

Amino acid biosynthesis and energy metabolism (S)-2-hydroxy-acid oxidase 7.05 prephenate dehydrogenase TyrA 6.51 ribulose-phosphate 3-epimerase Rpe 4.02 diaminopimelate decarboxylase LysA 3.08 glycerol kinase GlpK 3.94 Glutamine synthase GlnA 2.64 trehalose synthase, putative 7.74 succinyl-CoA:3-ketoacid-CoA transferase, putative 5.69 4-hydroxyphenylacetate-3-hydroxylase 4.21 Central intermediary metabolism (synthesis of nitrogen, sulfur, polyamine compounds) acetyl-CoA synthase Acs 9.42 NADH dehydrogenase II 3.25 2-hydroxyacid dehydrogenase, putative 3.17 chloromuconate cycloisomerase, putative 4.42 Carbonic anhydrase IcfA 4.52 putative methyltransferase 6.91 GMC oxidoreductase 3.56 oxidoreductase 7.94 oxidoreductase, iron-sulfur subunit 5.63 urease, beta/gamma subunit UreAB 4.00 N-glycosidase F, putative 4.67 3-alpha-hydroxysteroid dehydrogenase, putative 3.33 Fatty acid and phospholipid metabolism methylmalonyl-CoA mutase, beta subunit McmA 3.59 propionyl-CoA carboxylase, alpha subunit, putative 3.05 enoyl-CoA hydratase/3,2-trans-enoyl-CoA isomerase/ 3.56 3-hydroxyacyl-CoA dehydrogenase long-chain fatty acid- -CoA ligase FadD-1 7.23 enoyl-acyl carrier protein reductase FabI 5.39 enoyl-CoA hydratase, putative 10.15 alcohol dehydrogenase, zinc-containing 5.27 Purines, pyrimidines, nuclotides, and nucleoside synthesis and metabolism carbamoyl-phosphate synthase, large subunit CarB 10.95 inosine-5-monophosphate dehydrogenase GuaB 3.74 NH3-dependent NAD+ synthase NadE 3.10 Hypothetical proteins, transposases, and unknown functions MoxR-related protein 5.69 tetratricopeptide repeat family protein 3.09 N-acetylmuramoyl-L-alanine amidase-related protein 4.21 ExoP-related protein 4.12 TDP-glucose-4,6-dehydratase-related protein 6.72 HPc 4.37 CHP 4.30 CHP 3.63 CHP 4.21 HP 6.92 HP 4.51 CHP 2.82 HP 2.82 HP 2.95 HP 2.80 putative transposase 5.44 CHP 8.17 CHP 6.80 HP 3.12 HP 7.09 HP 2.76 putative transposase 5.44 HP 2.73 HP 7.18 HP 3.80 HP 3.15 HP 2.77 HP 3.43 HP 3.42 HP 7.58 putative transposase 5.44 CHP 5.87 HP 5.67 HP 3.75

std devb

2.98 3.64 2.22 0.48 1.84 0.78 4.07 2.35 2.63 5.97 2.21 1.77 2.46 2.30 2.90 2.51 4.01 1.67 2.47 1.99 1.39 2.59 2.03 2.93 2.49 2.31 6.56 2.58 4.80 2.04 1.16 2.35 1.76 2.63 2.62 2.87 2.61 2.83 1.69 2.63 2.80 2.41 1.11 1.11 0.89 3.00 2.83 3.64 11.47 0.62 1.31 0.71 2.83 1.02 3.05 1.08 1.02 0.93 1.56 1.62 4.18 2.83 1.76 4.17 2.77

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Table 1 (Continued)

ORF

DR1535 DR1593 DR1602 DR1651 DR1654 DR1678 DR1715 DR1726 DR1739 DR1830 DR1852 DR1873 DR1933 DR1934 DR1953 DR1962 DR2090 DR2147 DR2184 DR2234 DR2251 DR2324 DR2360 DR2392 DR2582 DR2586 DR2624 DRA0023 DRA0112 DRA0129 DRA0182 DRA0213 DRA0230 DRA0369 DRB0006 DRB0051 DRB0060 DRB0130

predicted functiona

proteome average abundance ratio 42 °C/30 °C

protein alias

std devb

Hypothetical proteins, transposases, and unknown functions CHP 3.28 putative transposase 5.44 HP 7.69 putative transposase 5.44 HP 2.61 CHP 4.72 CHP 7.65 HP 7.88 CHP 2.82 HP 6.26 HP 3.09 HP 3.12 putative transposase 5.44 HP 4.21 HP 3.51 HP 2.65 HP 2.98 HP 8.69 HP 6.92 CHP 10.78 HP 8.17 putative transposase 5.44 HP 6.92 HP 4.68 CHP 7.03 HP 3.13 HP 5.69 CHP 4.21 HP 2.76 CHP 6.10 HP 11.63 CHP 6.20 HP 5.23 HP 4.00 HP 6.28 HP 3.41 HP 3.17 HP 9.67

1.20 2.83 3.18 2.83 0.99 1.19 6.24 2.43 1.11 3.15 1.09 2.38 2.83 2.63 1.70 1.56 1.14 5.41 2.80 8.29 1.86 2.83 2.80 3.44 3.36 2.17 2.35 2.63 0.78 3.79 6.71 1.49 1.89 1.01 1.87 2.04 0.96 9.27

a Predicted functions and DR open reading frame (ORF) numbers are derived from the D. radiodurans genome database (www.tigr.org). b Std dev, standard deviations of average protein abundance ratios caluclated across all nine proteome replicates. c (C)HP, (conserved) hypothetical protein.

Table 2. Proteins Repressed by Heat Shock below 0.4-Fold by Protein Abundance Measurements in D. radiodurans

ORF

DR2155 DR1371 DR0125 DR1498 DR1318 DRA0012 DRA0241 DR1524 DR2428 DRA0358

predicted functiona

amino acid ABC transporter, permease protein HPd acetyltransferase, putative NADH dehydrogenase I, H subunit acyl-CoA dehydrogenase HP beta lactamase-related protein cephalosporin acylase CHP aldehyde dehydrogenase

protein alias

NuoH AcdA-3 acyI

average protein abundance ratio 42 °C/30 °C

std devb

average proteome MT count ratio 42 °C/30 °Cc

0.48 0.465 0.45

0.26 0.153 0.128

1 1 1

0.4017 0.401 0.382 0.376 0.262 0.255 0.245

0.0197 0.259 0.129 0.297 0.081 0.0674 0.06

1 1.63 1.3 0.835 1 1.05 1.25

a Predicted functions and DR open reading frame (ORF) numbers are derived from the D. radiodurans genome database (www.tigr.org). b Std dev, standard deviations of average protein abundance ratios calculated across all nine proteome replicates. c MT, mass tag counts. See also Table 3. d (C)HP, (conserved) hypothetical protein.

and mass tag counts calculated for these proteins in response to heat shock are listed in Table 2. Results of Proteomics Data Analysis Using Mass Tag Counts. Surprisingly, several heat shock factors that had previously been shown to be important in heat shock protection of D. radiodurans by several methods, including 2D gel 714

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analysis, promoter mapping, quantitative real time RT-PCR, and gel shift analysis,4,5,13 were not detected as heat shock induced in the proteomics analysis when only protein abundance ratios were considered. Such factors include the cochaperone DnaJ (DR0126) and proteases Lon2 (DR0349) and ClpB (DR1046). Therefore, peptide mass tag (MT) counts were

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FTICR Proteomics Analysis of D. radiodurans Heat Shock Table 3. Proteins Induced by Heat Shock above 2-Fold According to Mass Tag Counts

ORF

DR0126 DR0128 DR0129 DR0349 DR0583 DR0606 DR1020 DR1046 DR1114 DR1974 DR1459 DR1849 DR0001 DR1913 DR0326 DR0906 DR2340 DRA0346 DR1620 DRA0353 DR1384 DR1110 DR0163 DR0383 DR0561 DR1115 DR1365 DR1648 DRB0042 DR0463 DR1655 DR1649 DRA0040 DR0845 DR2611 DR2087 DR2394 DR0551 DR0496 DR1614 DR2095 DR0028 DR1467 DR0930 DRA0005 DRA0185 DRA0203 DRA0216 DRA0237 DRA0364

DR1692 DRA0143

predicted function

protein alias

average MT count 30°Ca

average MT count 42°C

Protein fate (heat shock responsive chaperones and proteases) Co-chaperone DnaJ 1.90 Co-chaperone GrpE 1.50 Chaperone DnaK 44.90 ATP-dependent protease LA Lon2 5.40 Cell division protein FtsH-1 3.20 Chaperonin GroES 8.80 Cell division protein FtsH-2 2.70 ATP-dependent Clp protease, ATP-binding subunit ClpB 7.50 small heat shock protein Hsp20 1.80 ATP-dependent protease LA Lon1 5.00 serine protease, subtilase family 2.88 peptide methionine sulfoxide reductase MsrA 1.00 DNA metabolism (repair and recombination) DNA polymerase III, β subunit DnaN 1.30 DNA gyrase, subunit A GyrA 5.60 Hypothetical protein/desiccation and DdrD 1.00 radiation responsive12 DNA gyrase, subunit B GyrB 4.20 recombinase RecA 2.88 DNA damage repair protein PprA 1.50 Cellular processes (cell division, detoxification, toxin resistance, and stress response) ketoacyl reductase, putative 1.50 methyl-accepting chemotaxis-related protein 1.40 Transcriptional regulators transcriptional regulator, TetR family 1.00 pyrimidine operon regulatory protein PyrR 1.25 Membrane functions and transport ABC transporter, ATP-binding protein, 1.29 MsbA family S-layerlike array-related protein 9.20 maltose ABC transporter, periplasmic MalE 8.50 maltose-binding protein S-layerlike array-related protein 1.50 aspartate kinase LysC 2.50 amino acid ABC transporter, ATP-binding protein 3.00 oligopeptide transport periplasmic 1.00 protein, putative maltooligosyltrehalose synthase TreY 2.25 ABC transporter, periplasmic 7.90 substrate-binding protein, putative immunogenic protein Bcsp31 3.60 mannosyl transferase 1.17 Translation glutamyl-tRNA synthetase GltX 2.67 glutaminyl-tRNA synthetase 1.67 initiation factor 3 InfC 4.70 N-acetylmuramoyl-L-alanine amidase CwlD 1.70 Amino acid and energy metabolism glutaryl-CoA dehydrogenase, putative 1.67 ferredoxin/ferredoxin- -NADP reductase, putative 1.25 3-isopropylmalate dehydratase, small LeuD-1 1.00 subunit c-type cytochrome, putative 1.20 putative aminotransferase 1.56 oxidoreductase, short-chain dehydrogenase/reductase family 1.00 phosphotriesterase, putative 1.40 alcohol dehydrogenase, zinc-containing 3.90 exopolyphosphatase Ppx 1.00 putative oxidoreductase 1.80 putative esterase 1.00 oxidoreductase 1.00 oxidoreductase, short-chain 4.40 dehydrogenase/reductase family Fatty acid and phospholipid metabolism long-chain fatty acid- -CoA ligase FadD-1 1.60 3-hydroxyacyl-CoA dehydrogenase, 4.30 putative

average MT count ratio 42°C/30°C

8.83 10.67 77.67 10.67 7.67 19.50 7.33 35.33 8.83 19.83 8.17 2.00

4.65 7.11 1.73 1.98 2.40 2.22 2.72 4.71 4.91 3.97 2.84 2.00

3.33 11.33 2.67

2.56 2.02 2.67

8.50 6.33 3.00

2.02 2.20 2.00

4.00 3.33

2.67 2.38

2.17 2.67

2.17 2.13

2.83

2.20

18.83 26.33

2.05 3.10

3.17 5.00 6.17 2.83

2.11 2.00 2.06 2.83

4.50 16.50

2.00 2.09

8.00 2.67

2.22 2.29

5.50 4.50 9.50 3.67

2.06 2.70 2.02 2.16

4.17 4.17 2.00

2.50 3.33 2.00

2.67 4.67 2.17 3.50 8.67 2.00 3.67 2.17 3.67 11.00

2.22 3.00 2.17 2.50 2.22 2.00 2.04 2.17 3.67 2.50

4.17 11.83

2.60 2.75

Journal of Proteome Research • Vol. 4, No. 3, 2005 715

research articles

Schmid et al.

Table 3 (Continued)

ORF

DR0049 DR0110 DR0115 DR0404 DR0491 DR0556 DR0894 DR0904 DR1398 DR1406 DR1623 DR1669 DR1708 DR1740 DR1796 DR2229 DR2247 DR2308 DR2319 DR2374

DR2407 DRA0006 DRA0023 DRA0194 DRA0300 DRA0331 DRB0103

predicted function

protein alias

average MT count 30°Ca

Hypothetical proteins, transposases, and unknown functions CHP 1.00 CHP 2.20 HP 2.50 HP 1.14 CHP 1.50 CHP 1.89 HP 2.40 HP 1.00 HP 1.00 HP 1.00 HP 2.20 CHP 1.00 HP 2.20 HP 1.00 CHP 1.33 HP 2.30 HP 1.00 CHP 5.60 HP 3.10 ribonucleoside-diphosphate 2.60 reductase-related protein, intein-containing HP 2.30 CHP 2.00 CHP 1.33 CHP 1.40 HP 1.00 CHP 1.50 putative transposase 1.90

average MT count 42°C

average MT count ratio 42°C/30°C

2.33 4.50 5.83 2.50 3.67 4.67 6.33 2.00 2.00 3.33 10.00 2.17 4.83 2.00 2.83 4.67 2.00 12.50 6.50 6.50

2.33 2.05 2.33 2.19 2.44 2.47 2.64 2.00 2.00 3.33 4.55 2.17 2.20 2.00 2.13 2.03 2.00 2.23 2.10 2.50

4.67 8.67 2.67 2.83 2.00 3.67 4.00

2.03 4.33 2.00 2.02 2.00 2.44 2.11

a Average MT count, mass tag counts (the number of unique peptides detected in the AMT proteomics analysis that correspond to a given protein) averaged across all 9 experimental replicates (30 °C) or 7 replicates (42 °C).

used as a complementary semiquantitative measure of protein expression to increase analytical power. Each mass tag represents a tryptic peptide detected in the FTICR analysis that showed a confident match to a given protein in the D. radiodurans genome database (www.tigr.org). Further, mass tag counts indicate the number of unique peptides detected for a given protein under a given condition. Therefore, mass tag counts enable approximate measurements of absolute protein expression levels, since the number of mass tags counted for a given protein is proportional to protein expression level. For example, proteins with mass tag counts between zero and two are considered to exhibit low expression under the conditions tested, whereas those proteins with mass tag counts above 5 are considered to show high expression. Moreover, the mass tag counts for several gene products were observed to increase at 42 °C compared to treatment at 30 °C, therefore allowing us to observe the heat shock induction of the important heat shock proteins listed above as well as other key high temperature protective factors (Table 3) (co-chaperone DnaJ, and proteases ClpB, ClpC, FtsH1, FtsH2, Lon1, and Lon2). Two additional putatively heat shock-responsive proteins with predicted protein repair and maintenance functions that were missed by protein abundance measurements were also identified by MT counts: a serine protease (DR1459) and MsrA (DR1849), a peptide methionine sulfoxide reductase. Furthermore, 70 additional proteins that were missing from the heat shock induced protein abundance dataset were able to be detected by mass tag counts. In total, we observed the high-temperature induction of 83 gene products by MT counts, which we grouped into 12 categories based on predicted function (Table 3). Mass tag 716

Journal of Proteome Research • Vol. 4, No. 3, 2005

data for the entire proteome is shown in Supplementary Table 2 of the Supporting Information.

Discussion Here we describe the use of a sensitive, comprehensive, and high-throughput accurate mass tag whole-cell FTICR mass spectrometric technique in 9-fold replicate to detect 30% of the D. radiodurans proteome with high confidence in response to perturbation by heat shock. Upon integration of the data from both protein abundance and mass tag count measurements, we have identified 239 proteins whose expression increases and 10 whose expression decreases response to temperature upshift. Among the induced proteins, we have identified with high confidence a core set of seven heat shock specific proteins that exhibit high expression at both 30 °C and 42 °C, and are highly induced upon temperature upshift in both the mass tag count and protein abundance data (Figure 1). In addition, five members this suite of proteins have been shown by previous studies in D. radiodurans and many other bacteria to be crucial for protection against the detrimental effects of heat shock, which includes the misfolding and aggregation of proteins.1,4 Two members, DR0023 and DR1692, have not been previously identified as heat shock responsive, and therefore may be novel heat shock protective factors. A caveat to the proteomics experiments conducted here is that the use of direct protein abundance data is semiquantitative. The most commonly employed fully quantitative method of proteomic analysis is isotope-coded affinity tagging (ICAT)10 of cysteine residues followed by tandem mass spectrometry. Although this method is highly quantitative, it is limited by its

FTICR Proteomics Analysis of D. radiodurans Heat Shock

research articles expressed under all conditions and highly induced upon heat shock at the protein level.

Figure 1. Summary of proteins identified in the proteomics analysis by protein abundance and mass tag count measurements. Numbers on the circles (i.e., 163 and 83) indicate total number of proteins found for each data analysis type. Numbers within the circles indicate the number of proteins identified by each data analysis type alone. Proteins listed in the intersection of the two circles indicate the proteins identified by both types of data analysis.

comprehensiveness, with a maximum of about 10% of the proteome detected7 and a bias toward more highly expressed proteins.14 Therefore, to attain a more global picture of the proteome, we have chosen to use the more comprehensive accurate mass tag whole-cell MS approach, and have been able to circumvent the obvious limitations of the semiquantitative nature of the data using extensive biological and experimental replicates, thus bolstering the quantitative nature of the data. In addition, we have also analyzed peptide mass tag counts to increase the quantitative power of the data. We observed that the higher the number of mass tags observed for a given protein, the higher the absolute expression level. Furthermore, the core set of heat shock proteins identified in this analysis have also been categorized as predicted highly expressed (PHX) components of the genome based on codon usage calculations.15 The study reported here represents the first experimental verification of such predictions for D. radiodurans. Moreover, several other PHX proteins were also verified, but with lower confidence, including predicted proteases Lon2, FtsH1, and FtsH2 (Table 3). Surprisingly, MT counts and protein abundance measurements were extremely complementary datasets (compare Tables 1 and 3, Figure 1), since only 7 proteins were members of both lists (GrpE, DnaK, GroES, GroEL, Hsp20, Fad-1, and DRA0023, a conserved hypothetical protein), yielding a total of 239 proteins identified as heat shock inducible in our proteomics analysis. In addition, some of these proteins were also considered highly expressed relative to all other proteins detected in the proteome under both conditions tested, since more than 5 peptides were counted for each (Table 3). Several of the proteins detected in the 2D gel analysis were also corroborated here, including the highly conserved chaperones Tig (which assists DnaK to help fold nascent peptides as they emerge from the ribosome), DnaK, GroES, and GroEL.5 Together with the large overrepresentation of classical heat shock factors in the 7-protein overlap between the MT and protein abundance lists, these data suggest that a core set of heat shock protective genes, which includes the most highly conserved chaperone machines GroESL and DnaKJGrpE, are highly

Interestingly, the expression of a large number of genes with predicted functions in central metabolism of D. radiodurans were also induced by heat shock in the microarray and proteomics analyses. Such induction could reflect the general increase in metabolic rate under heat shock, since growth rates of D. radiodurans cells have been shown to increase dramatically for the first 2-3 h after a mid-exponential phase shift to either 42 °C or 48 °C, followed by growth arrest (data not shown). The increased production of central metabolic factors during the shift to heat shock could therefore cross-protect the cell against the impending nutritional stress. Such crossinduction between heat shock and nutritional factors has been observed in E. coli, where glycolytic and other carbon utilization genes are induced in response to heat shock.16 The converse has also been observed in E. coli, with dnaK and rpoH mutant strains showing survival defects under nutritional deprivation conditions.17,18 The hypothesis of cross-protection is bolstered by the surprising finding that 16 of the heat shock inducible proteins fell into the category of DNA metabolism in both the protein abundance and MT count datasets (Tables 1,3). These proteins included the DNA gyrases GyrA and GyrB, the DNA repair proteins RecA and PprA, all of which are required for DNA repair and are induced at the transcriptional level in response to both gamma irradiation and desiccation in D. radiodurans.12 Since several proteases and chaperones, including Lon2, GroES, GroEL, Hsp20, and ClpB were also induced in response to gamma irradiation and/or desiccation,12 these results suggest that heat shock may cross-protect the cell against DNA damage and vice versa. From these results in combination with those of the central metabolic proteins, we hypothesize that heat shock may therefore induce a more general stress response in D. radiodurans than was originally appreciated. Interestingly, the majority of proteins (100) induced by heat shock in the AMT proteomics analysis were of unknown function, including putative transposases and hypothetical proteins. Since many of these proteins have not been identified as heat responsive by previous experiments,4,5,13 and they were identified by only one of the quantitation methods, they are candidates for novel functions in the heat shock and/or general stress response. It is important to note that several known heat shock factors that previously have been experimentally validated to be crucial for heat shock protection in D. radiodurans as well as several other bacterial systems, including ClpB, ClpC, FtsH, and Lon1 proteases, were identified here by only one of the quantitation methods. Such an observation validates these quantitation methods as appropriate for the detection of biologically relevant heat shock responsive factors. Furthermore, this observation emphasizes the importance of considering the union of both the protein abundance and mass tag datasets to understand the full complexity of the bacterial heat shock response at the proteome level. We therefore propose that many of the new potentially heat-responsive proteins that we have identified here; including the hypothetical proteins, the metabolic proteins, and the DNA-damage response proteins; are important candidates for further experimentation within the context of the high-temperature response. These results, together with the stress cross-protection hypothesis discussed above, underscore the power of semiquantitative data Journal of Proteome Research • Vol. 4, No. 3, 2005 717

research articles analysis of whole-cell global proteomics data combined with high numbers of replicates to enable the discovery of novel biology. In summary, using a global semiquantitative proteomics approach to study the response of the D. radiodurans to heat shock, we have identified a core set of heat shock factors that are highly induced at the protein level. In addition, we have identified many novel potentially heat-responsive candidate proteins that were not able to be identified by previous experimental methods. Furthermore, our data are consistent with the hypothesis that the heat shock response in D. radiodurans may cross protect the organism from the onslaught of future stress.

Acknowledgment. A part of this research was performed in the Environmental Molecular Sciences Laboratory (a national scientific user facility sponsored by the U.S. DOE Office of Biological and Environmental Research) located at the Pacific Northwest National Laboratory, operated by Battelle for the DOE. We would like to thank Dhileep Sivam at the University of Washington for his invaluable help with data analysis. We are also indebted to Beth Traxler and Franc¸ ois Baneyx for their critical reading of the manuscript. Supporting Information Available: Direct protein abundance data for the entire D. radiodurans proteome (Table S1) and mass tag count data for the entire D. radiodurans proteome (Table S2). This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Wickner, S.; Maurizi, M. R.; Gottesman, S. Posttranslational quality control: folding, refolding, and degrading proteins. Science 1999, 28, 1888-1893. (2) Gross, C. A. Function and regulation of the heat shock response. In Escherichia coli and Salmonella: Cellular and Molecular Biology; Neidhardt, F. C., Curtiss, R., III, Ingraham, J. L., Lin, E. C. C., Low, K. B., Magasanik, B., Eds.; American Society for Microbiology Press: Washington, D. C., 1996 pp. 1382-1399. (3) Battista, J. R. Against all odds: the survival strategies of Deinococcus radiodurans. Annu. Rev. Microbiol. 1997, 5, 203-224. (4) Schmid, A. K.; Lidstrom, M. E. Involvement of two putative alternative sigma factors in stress response of the radioresistant bacterium Deinococcus radiodurans. J. Bacteriol. 2002, 184, 61826189. (5) Schmid, A. K.; Howell, H. A.; Battista, J. R.; Peterson, S. N.; Lidstrom, M. E. Global transcriptional and proteomic analysis of the Sig1 heat shock regulon of Deinococcus radiodurans. J. Bacteriol. 2005, 187 (10).

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Schmid et al. (6) Gygi, S. P.; Rochon, Y.; Franza, B. R.; Aebersold, R. Correlation between protein and mRNA abundance in Yeast. Mol. Cell Biol. 1999, 19, 1720-1730. (7) Baliga, N.; Pan, M., Goo, Y. A.; Yi, E. C.; Goodlet, D. R.; Dimitrov, K.; Shannon, P.; Aebersold, R.; Ng, W. V.; Hood, L. Coordinate regulation of energy transduction modules in Halobacterium sp. analyzed by a global systems approach. Proc. Natl. Acad. Sci. U. S. A. 2002, 99, 14913-14918. (8) Yura, T.; Kanemori, M.; Morita, M. T. The heat shock response: regulation and function. In Bacterial stress responses. (G. Storz and R. Hengge-Aronis, ed.) 2000, Washington, DC: American Society for Microbiology, pp 3-18. (9) Pasˇa-Tolic´, L.; Lipton, M. S.; Masselon, C. D.; Anderson, G. A.; Shen, Y.; Tolic´, N.; Smith, R. D. Gene expression profiling using advanced mass spectrometric approaches. J. Mass Spec. 2002, 37, 1185-1198. (10) Gygi, S. P.; Rist, B.; Gerber, S. A.; Turecek, F.; Gelb, M. H.; Aebersold, R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 1999, 17, 994999. (11) Lipton, M. S.; Pasˇa-Tolic´, L.; Anderson, G. A.; Anderson, D. J.; Auberry, D. L.; Battista, J. R.; Daly, M. J., et al. Global analysis of the Deinococcus radiodurans proteome using accurate mass tags. Proc. Natl. Acad. Sci. U. S. A. 2002, 99, 11049-11054. (12) Tanaka, M.; Earl, A. E.; Howell, H. A.; Park, M.-J.; Eisen, J. A.; Peterson, S. A.; Battista, J. R. Analysis of Deinococcus radiodurans’ response to ionizing radiation and desiccation reveals novel proteins that contribute to extreme radioresistance. Genetics 2004, 168, 21-33. (13) Schmid, A. K.; Howell, H. A.; Battista, J. R.; Peterson, S. N.; Lidstrom, M. E. HspR is a global negative regulator of heat shock in Deinococcus radiodurans. Mol. Microbiol. 2005, 55, 1579-1590. (14) Griffin, T. J.; Gygi, S. P.; Ideker, T.; Rist, B.; Eng, J.; Hood, L.; Aebersold, R. Complementary profiling of gene expression at the transcriptome and proteome levels in Saccharomyces cerevisiae. Mol. Cell Proteomics 2002, 1, 323-333. (15) Karlin, S.; Mra´zek, J. Predicted highly expressed and putative alien genes of Deinococcus radiodurans and implications for resistance to ionizing radiation damage. Proc. Natl. Acad. Sci. U. S. A. 2001, 98, 5240-5245. (16) Shin, D.; Lim, S.; Seok, Y. J.; Ryu, S. Heat shock RNA polymerase (E sigma(32)) is involved in the transcription of mlc and crucial for induction of the Mlc regulon by glucose in Escherichia coli. J. Biol. Chem. 2001, 276, 25871-25875. (17) Spence, J.; Cegielska, A.; Georgopoulos C. Role of Escherichia coli heat shock proteins DnaK and HtpG (C62.5) in response to nutritional deprivation. J. Bacteriol. 1990, 172, 7157-7166. (18) Jenkins, D. E.; Auger, E. A.; Matin, A. Role of RpoH, a heat shock regulator protein, in Escherichia coli carbon starvation protein synthesis and survival. J. Bacteriol. 1991, 173, 1992-1996.

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