MCP Papers in Press. Published on May 30, 2014 as Manuscript M113.035741

Highly precise quantification of protein molecules per cell during stress and starvation responses in Bacillus subtilis

Sandra Maaß1, Gerhild Wachlin1, Jörg Bernhardt1, Christine Eymann1, Vincent Fromion2, Katharina Riedel1, Dörte Becher1*, Michael Hecker1

1

Institute for Microbiology, Ernst Moritz Arndt University Greifswald, Greifswald, Germany

2

INRA, Mathématique Informatique et Génome UR1077, 78350 Jouy-en-Josas, France

Running title: Molecular protein quantification of stress responses

Keywords: protein quantification, stress response, Bacillus subtilis, heat stress, starvation, Sigma B

*

Correspondence and requests for materials should be addressed to D.B. (Phone: +49-3834-

864230, Fax: +49-3834-864202, Email: [email protected]).

Copyright 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

ABBREVIATIONS CE

collision energy

CV

coefficient of variation

CXP

collision cell exit potential

DP

declustering potential

EP

entrance potential

PTS

phosphotransferase system

LB

Luria-Bertani medium

SRM

selected reaction monitoring

sSRM

scheduled selected reaction monitoring

S/N

signal to noise

TCC

tricarboxylic acid cycle

2-D PAGE

two-dimensional polyacrylamide gel electrophoresis

2

Molecular protein quantification of stress response

SUMMARY Systems biology based on high quality absolute quantification data, which are mandatory for the simulation of biological processes, successively becomes important for life sciences. We provide protein concentrations on the level of molecules per cell for more than 700 cytosolic proteins of the Gram-positive model bacterium Bacillus subtilis during adaptation to changing growth conditions. As glucose starvation and heat stress are typical challenges in B. subtilis’ natural environment and induce both, specific and general stress and starvation proteins, these conditions were selected as models for starvation and stress responses. Analyzing samples from numerous time points along the bacterial growth curve yielded reliable and physiologically relevant data suitable for modeling of cellular regulation under altered growth conditions. The analysis of the adaptational processes based on protein molecules per cell revealed stressspecific modulation of general adaptive responses in terms of protein amount and proteome composition. Furthermore, analysis of protein repartition during glucose starvation showed that biomass seems to be redistributed from proteins involved in amino acid biosynthesis to enzymes of the central carbon metabolism. In contrast, during heat stress most resources of the cell, namely those from amino acid synthetic pathways, are used to increase the amount of chaperones and proteases. Analysis of dynamical aspects of protein synthesis during heat stress adaptation revealed, that these proteins make up almost 30 % of the protein mass accumulated during early phases of this stress.

3

Molecular protein quantification of stress response

INTRODUCTION Recently technical approaches in systems biology have become more and more important for the life science community. Successful modeling of biological pathways as part of these approaches strongly depends on quantitative, high-quality and validated data sets (1). Proteins are an important part of these attempts to uncover the systemic properties of biological systems as they represent the central players in the complex cellular metabolic and adaptational network (2). Although relative protein quantification methods allow for comparison of protein abundances in samples and to characterize the proteome dynamics in cellular systems, these data are not sufficient for mathematical modeling in systems biology. Furthermore the availability of protein concentrations at the proteome level can provide new insights in what is going on in the cell upon stress, and thus enable us to better understand how cells adapt to changing conditions. Knowing intracellular protein concentrations is essential in order to obtain a real mass balance leading to evaluation of the costs of running an active metabolic pathway or expressing enzymes for stress responses. In order to provide suitable proteomic data for systems biology, techniques for global absolute quantification of proteins recently emerged. These approaches make use of quantitative western blotting (3), mass spectrometry (4, 5) or merge traditional 2D PAGE and mass spectrometry (6) to determine cellular protein concentrations on a global scale. Entirely mass spectrometry-based strategies have recently convincingly demonstrated the capacity to quantify about half of the predicted proteome of Leptospira interrogans (5) and therefore provide more comprehensive data for systems biology than 2-D PAGE based methods (6, 7). Although these gel-based methods are biased towards high-abundant proteins, usage of MS-calibrated 2-D gels allows distinguishing even between different protein isoforms

4

Molecular protein quantification of stress response

caused by post-translational modifications without missing values along a time course experiment. Moreover, 2-D PAGE is a well-established method for easy and convenient visualization of main metabolic pathways and the most obvious adaptational responses. Until now approaches for large-scale absolute protein quantification have had a strong technological focus. Only a few predominantly physiological applications have been reported (5, 8, 9). In this study we provide absolute protein concentrations of the bacterial model organism Bacillus subtilis during stress and starvation adaptation. As model for different stress conditions we selected the best-studied responses during heat stress and glucose starvation. A comparative analysis of different stress conditions allows to differentiate between general, nonspecific adaptive responses ensuring survival during a wide spectrum of conditions and specific stress adaptation by differential expression of particular regulons facilitating a direct interaction with the stimulus (10). The general adaptive response of B. subtilis is governed by global regulators such as the alternative RNA polymerase sigma factor Sigma B or the main stringent factor RelA. Induction of SigB-dependent genes provides cells with a multiple, non-specific and preventive stress resistance (11, 12). The SigB-dependent general stress response in B. subtilis is induced by a different set of stress and starvation stimuli. Thereby, environmental stresses, like heat shock, activate the phosphatase RsbU via a signal transduction pathway that involves additional regulatory proteins to dephosphorylate the anti-anti-sigma-factor RsbV that subsequently releases SigB (13, 14). During starvation for carbon sources, phosphorus or oxygen dephosphorylation of phosphorylated RsbV followed by the release of active SigB is catalyzed by RsbP (15).

5

Molecular protein quantification of stress response

The main (p)ppGpp synthetase RelA mediates the stringent response, which is a crucial component of the regulatory network in B. subtilis cells. The main feature of the stringent response is the down-regulation of genes whose products typically consume energy and building blocks for cell growth, particularly genes encoding components of the transcription and translation apparatus as well as genes coding for proteins involved in nucleotide biosynthesis and DNA replication (16). During glucose starvation a substantial reprogramming of protein synthesis pattern is caused by the restricted access to energy and carbon sources. The carbon starvation specific stress response is characterized by repression and degradation of glycolytic enzymes with simultaneous up-regulation of specifically gluconeogenesis and tricarboxylic acid cycle related enzymes (10, 17–19). Additionally metabolic pathways for the utilization of overflow metabolites and other secondary carbon sources such as α- or β-glucosides or amino acids are induced (10, 17). In addition to starvation B. subtilis has to adjust to various physical stresses in its natural habitat. In order to investigate this adaptational response we have chosen the model heat stress. Already described specific heat-shock induced genes of B. subtilis belong to the HrcA regulon (20), the CtsR regulon (21–23), or the HtpG operon (24). Moreover, there are heat inducible stress proteins whose regulation does not follow the already mentioned mechanisms. These proteins are for example FtsH (25), ClpX (26), SacB (27), LonA (28), AhpC, AhpF (29), NfrA, YwcH (30) and members of the SigI regulon (31). In this study we quantitatively characterize the adaptation of B. subtilis to glucose starvation and heat stress and present concentrations on the level of molecules per cell for more than 700 cytosolic proteins. Furthermore we analyzed dynamical protein repartition between main

6

Molecular protein quantification of stress response

processes of the cell during exponential growth and stress providing valuable insights in adaptation to changing conditions. Thus, this comprehensive data set may be suitable for modeling of metabolic pathways.

7

Molecular protein quantification of stress response

EXPERIMENTAL PROCEDURES Pulse-labeling with L-[35S]-methionine. Radioactive pulse-labeling experiments with

35

S-

methionine were carried out to investigate protein synthesis of B. subtilis during glucose starvation. Therefore three independent biological replicates of B. subtilis 168 trp+ (32) were grown in M9 minimal medium at 37 °C under vigorous agitation. The composition of M9 was: 0.1 % (w/v) glucose, 0.1 % (w/v) L-malate, CaCl2 * 2H2O (0.015 g L-1), MgSO4 * 7H2O (0.246 g L-1), Na2HPO4 * 2H2O (8.5 g L-1), KH2PO4 (3 g L-1), NaCl (0.5 g L-1), NH4Cl (1 g L-1) and trace elements (10 mL stock solution for 1 L medium). The trace element stock solution consisted of (per liter): ZnCl2 (0.170 g), CuCl2 * 2H2O (0.043 g), MnCl2 * 4H2O (0.100 g), CoCl2 * 6H2O (0.060 g) and Na2MoO4 * 2H2O (0.060 g). Cultures were pulse-labeled for 5 min each with 10 µCi of L-[35S]-methionine per ml at an OD600nm of 0.4 - 0.5 (for control), at maximal OD and at several time points (15, 30, 60, 120 min) after the transition to glucose starvation as described previously (10). In combination with protein amounts derived from fluorescence stained 2-D gels (see below) it can be investigated which proteins are actually synthesized at a specific time point and which protein amounts have already been accumulated. Cell culture and protein preparation. For starvation experiments B. subtilis 168 trp+ (32) was cultivated as mentioned above. Samples were harvested in exponential phase (OD600nm 0.5), in transient phase, at maximal OD and 60, 120, 180 and 240 min after entering the stationary phase triggered by glucose exhaustion (Figure 1). For heat stress experiments cells were cultivated in minimal medium (33) and stressed by a sudden temperature shift to 52 °C. Cells were harvested shortly before (control) and at 10, 30, and 60 min after continuous heat shock (Figure 1). For every experiment 3 independent biological replicates were analyzed.

8

Molecular protein quantification of stress response

After harvesting and centrifuging cells were resuspended in TE-Buffer (10 mM Tris, 1 mM EDTA, pH 7.5) and mechanically disrupted using the Precellys 24 homogenizator (PeqLab, Germany; 3 x 30 s at 6.5 m s-1). Protein concentration of extracts was determined using a ninhydrin-based assay (6, 34). Preparation of 2-D gels. 2-D PAGE was performed as previously described (35) in 5 technical replicates. 100 µg protein was loaded onto 18 cm IPG strips (pH 4-7, GE-Healthcare). After 2D PAGE gels were fixed with 40 % (v/v) ethanol and 10 % (v/v) acetic acid for 1 to 2 h and subsequently stained with FlamingoTM. Stained gels were scanned (Typhoon 9400, GEHealthcare) and their images analyzed employing Delta2D 4.2 software (Decodon GmbH, Germany). For all spots detected on the gel the spot volume was assigned to proteins, exported from the software and subsequently used for calibration of 2-D gels as described earlier (6). Sample preparation for MS analysis. Protein samples were reduced, alkylated and digested as previously described (6). Samples were spiked with heavy peptides of anchor proteins used for calibration of 2-D gels to a final concentration of 5-25 fmol µl-1. A detailed list of used peptides and their optimized transition parameters has been published elsewhere (6). Targeted MS analysis and global absolute protein quantification. LC-MS analyses was performed as described previously (6). All raw files were processed using MultiQuant TM 1.1 software (Applied Biosystems). A peptide ratio of native and heavy species was based on three transitions which were weighted according to their signal to noise (S/N) ratios before being averaged. Accordingly, S/N weighted peptide ratios were combined to the final protein ratio. Based on the added amount of heavy peptides, the absolute quantity of target anchor proteins could be calculated.

9

Molecular protein quantification of stress response

Absolute amounts of targeted anchor proteins obtained by SRM were used to calibrate 2-D gels of the same sample in order to obtain absolute abundance of all proteins visible on this gel. Amounts of multiple spots of the same protein were added up. Standard deviations for proteins represented by multiple spots were calculated using error propagation. Final standard deviation was calculated using a random effect model (36). Efficiency of cell disruption and determination of cell size. Bacterial cell size and cell disruption efficiency were determined as previously published (6). Volumes of the rod-shaped cells were calculated assuming a cylinder and two hemispheres without subtracting any values for the cell envelope. At least 100 cells were dimensioned for every sample. Therefore, a standard deviation could be calculated for each analyzed population (Supplementary Table S6).

RESULTS Determination of absolute protein abundances by combining accurate targeted mass spectrometry with the resolving power of 2-D PAGE provides a global view on the concentrations of a large number of proteins. Although the derived quantification data across biological replicates can be at most a highly accurate determination of molecular counts we will use in the following the common term “absolute quantification”. In this study we present a comprehensive proteomic data set applicable for bioinformatic modeling of B. subtilis’ stress and starvation responses. As models of starvation and stress responses we chose glucose starvation and heat stress. Therefore we determined protein concentration at seven different time points along the bacterial growth curve for cells under glucose starvation and at four time points during heat stress (Figure 1). Whereas glucose

10

Molecular protein quantification of stress response

starvation causes a complete stop of cell growth, 52 °C heat stress leads to a drop of growth rate from 1.2 h-1 during exponential growth to 0.5 h-1 during heat stress. Absolute quantification of proteins. After preparation of a cell count calibrated protein sample by determination of cell titer, cell disruption efficiency and protein content according to previously published protocols (6, 34), isotopically labeled peptides of anchor proteins were spiked in the sample in known concentrations. Digestion and targeted SRM acquisition led to determination of protein concentration for all anchor proteins in the total protein sample. Hence, the absolute amount of anchor protein on fluorescently stained 2-D gels prepared from the same sample could be calculated and was used for calibration of these 2-D images by relating spot intensities to the ones of anchor proteins (6). This enabled determination of protein concentration for all proteins detectable on the 2-D gels. Detectable soluble cytosolic proteins have an isoelectric point between 4 and 7 and a molecular weight of 10-150 kDa. In order to provide reliable data, only anchor proteins with less than 15 % CV among four technical replicates of MS analysis and less than 35 % CV between five technical 2-D gel replicates were used for calibration of 2-D gels. Non-covalently binding fluorescent dyes (e.g. Flamingo, Krypton) bind proteins in an amount proportional to the protein size (6) so that the same molecular count of a small protein correlates with a lower signal compared to that of a larger protein molecule during detection. Taking this into account the detection of the same count of large protein molecules should be more sensitive compared to smaller ones, which is supported by experimental data (Figure 2). Accordingly, the molecular weight of every protein has to be considered in the calculation of the number of molecules per cell on the basis of protein concentrations.

11

Molecular protein quantification of stress response

In this study we were able to identify 783 proteins (465 proteins in starvation and 701 proteins in heat stress experiments), of which 773 proteins (465 proteins in starvation and 691 in heat stress experiments) could be reliably quantified for at least one time point (Supplementary Table S1, S2). The amount of 219 and 287 proteins changed significantly (p=0.05, one-way ANOVA) during glucose starvation and heat stress, respectively (Figure 3, Supplementary Figures S1, S2). Supplementary Figure S3 shows the distribution of these proteins according to different regulation thresholds. During exponential growth cellular concentrations of proteins differ between very few molecules per cell (11 molecules per cell for the DNA mismatch repair protein MutL) to about 250,000 molecules per cell (major cold-shock protein CspB). Considering protein abundances during stress adaptation the proteins determined to have the lowest cellular concentration are the unknown protein YobO under starvation conditions (87 kDa, 13 molecules per cell after 120 min stationary phase) and the DNA exonuclease SbcC under heat stress (128 kDa, 7 molecules per cell after 60 min heat stress). The chaperonin GroES was found to be the most abundant protein under heat stress condition (500,000 molecules per cell after 30 min heat stress), while IlvC, an enzyme involved in biosynthesis of branched-chain amino acids, and the elongation factor Tu (TufA) represent the most abundant proteins during glucose starvation (about 20,000 molecules per cell after 60 min stationary phase). Hence, as reported earlier with the method used here a range of cellular protein abundances of 3 to 4 orders of magnitude can be covered (6) (Figure 3, Supplementary Figure S4). The availability of absolute quantification data for proteins at a large scale allows calculation of stoichiometries of known oligomeric protein complexes. The already reported ratio of 1:2 for the components of the 2-oxoglutarate dehydrogenase OdhA and OdhB, was verified (6, 37).

12

Molecular protein quantification of stress response

The stoichiometric ratio for the core complex of the tricarboxylic acid cycle consisting of isocitrate dehydrogenase Icd, malate dehydrogenase Mdh and citrate synthase CitZ (38) has been determined to 4 molecules Icd : 4 molecules Mdh : 1 molecules CitZ. However, due to Mdh and Icd performing multiple interactions with other proteins of the TCC and coupled pathways, calculated stoichiometries derived from total cytoplasmic protein extracts may not necessarily reflect stoichiometries in the core complex alone. Sustainment of basal metabolic functions and cellular processes during stress adaptation. During exponential growth in defined medium the metabolic situation in a bacterial cell is comparatively simple. The bacterium uses its main resources to degrade carbohydrates in order to produce energy and building blocks necessary to build up biomass. Although in nature this situation is a rare exception there are metabolic and regulatory pathways that need to stay active even under changing conditions. Absolute quantification results obtained in this study indicate that bacterial cells sustain basal functions of metabolism and cellular processes needed during exponential growth even after exposure to stress and starvation. About 60 % of the top 100 abundant proteins (2.5*106 molecules/cell) are present in all conditions examined (Figure 4, dark blue). Although proteins functioning in the acquisition of iron and the carbon core metabolism are regulated in response to glucose starvation and heat stress, these pathways always stay active. Only few quantified proteins of these respective pathways (less than 2.5 %) were found in decreased amounts. Therefore we suggest that these enzymes are strongly needed to ensure the cell’s supply with carbon intermediates and iron during all phases of growth. Additionally, the protein amounts of enzymes involved in biosynthesis of serine, glycine and alanine do not change. Notably, also no enzyme needed for the utilization of branched amino acids was found in lowered amounts in this study, indicating that this pathway,

13

Molecular protein quantification of stress response

using the most abundant amino acid in proteins, needs to stay active during all conditions tested. Moreover, no protein with functions in DNA condensation, segregation, repair or combination was found in lowered amounts indicating that these functions are strongly needed to keep the genetic integrity and enable the cell to respond to stress. Dealing with stress is also ensured by keeping translation and transcription mechanisms active. Hence, more than 80 % of the involved proteins quantified in this study were found with stable or even increased amounts. When cells get stressed the situation described above changes rapidly. Now the focus of the cell is not anymore growth, integrity and supply with resources but survival. Analysis of protein quantification data set presented here revealed that the most pronounced regulons involved in general adaptive responses are the negative stringent response under starvation and the SigB response for cells under heat stress, indicating that quality and intensity of the general stress response differs for stress and starvation conditions. During glucose starvation the cells down-regulated glycolysis and up-regulated gluconeogenesis which is in good agreement with previously published data (10). The specific response to heat stress is mostly characterized by accumulation of cytosolic chaperones and proteases whose genes are controlled by HrcA and CtsR. However, the impact of the stress stimulus on the protein amount is surprisingly small, only 10.0 % and 9.4 % of the 100 most abundant proteins (6.1*105 molecules/cell and 2.6*105 molecules/cell) accumulated in response to heat stress and glucose starvation, respectively (Figure 4, red, yellow, orange), suggesting an exceptionally high functional efficiency during stress adaptation. The impact of the general stress proteins on the total protein amount is even

14

Molecular protein quantification of stress response

lower. Only 1 % of the 100 most abundant proteins accumulated under both stress conditions (Figure 4, orange). General adaptive response under glucose starvation. When exponential growing cells begin to starve, the needs of the cells change dramatically within a very short time. While producing biomass has been the main purpose during growth, it is now the demand for energy and metabolic intermediates. In this study we select glucose starvation as model for starvation conditions. Hierarchical clustering of protein expression patterns during glucose starvation revealed that the cell handles the changed situation by decreasing the amounts of almost 50 % of all proteins exerting functions in protein biosynthesis or in biosynthesis and acquisition of amino acids, cofactors or nucleotides emphasizing the important role of the negative stringent response and related responses under glucose starvation (Supplementary Figure S5). The main feature of the stringent response is the down-regulation of genes typically expressed in growing cells (16). Those genes are involved in transcription and translation, nucleotide biosynthesis and DNA replication which was also reflected by reduced molecular counts in stationary phase for the ribosomal protein RpsB, the elongation factor FusA, adenylate kinase Adk, phosphoribosylpyrophosphate synthetase Prs and the single-strand DNA-binding protein SsbA. Additionally, a decreasing protein concentration was observed for cell-shape determining proteins like Mbl or proteins involved in ATP synthesis and respiration, like AtpA and AtpD (Table 1). The lower level of proteins necessary in starved cells is caused not only by repression of the corresponding genes, but also by degradation of vegetative proteins no longer active in non-growing cells (10, 18, 19). Besides the negative stringent response the alternative sigma factor SigB is expected to be a key player of the general stress response, but only 19 proteins whose regulation is controlled by

15

Molecular protein quantification of stress response

SigB could be quantified during glucose starvation. Quantitative results for 10 of these were statistically significant (p=0.05, one-way ANOVA). Seven of these changed more than 2fold in at least one of eight time points examined. Thereby, the general stress proteins YdbD, YdaD, YdaG, and YfkM showed highest fold changes (Table 1). With 14,400 to 81,500 molecules per cell the protease ClpP and the general stress proteins YvgN, YvaA were the most abundant SigB-dependent proteins. Five out of seven significantly changed proteins accumulated not until late stationary phase, namely the general stress proteins YdbD, YdaD, YdaG, YfkM, and the catalase KatE. In contrast, the general stress protein Ctc and the SigB- and CtsRdependently expressed protein arginine kinase McsB accumulated only transiently. While Ctc was present in highest amounts during transient phase, McsB accumulated most after 120 minutes stationary phase caused by glucose exhaustion. Supporting analysis by 35S-pulselabeling during glucose starvation allowed relative quantification of the protein synthesis of 37 SigB-dependent proteins. 27 of these changed significantly (p=0.05, one-way ANOVA) more than 3fold in at least one time point examined (Supplementary Table S3, Supplementary Figure S6). The induction of general stress proteins peaked 30 minutes after entry into starvation-triggered stationary phase and reached almost control levels after 120 minutes of glucose exhaustion. Despite this transient induction only a slight accumulation of a few stress proteins could be observed (Figure 5). This raises the question how long it takes until changes in protein synthesis become detectable on the level of protein amounts. Therefore, we compared absolute protein quantities during glucose starvation of this study with previously published data on protein synthesis (10). As only proteins with changed expression pattern are of interest for this kind of analysis only common proteins of both studies which show significant changes in protein synthesis (>2fold)

16

Molecular protein quantification of stress response

were compared leading to 41 induced and 100 repressed proteins. For about half of the induced proteins an increase in protein amount could be detected in the same time point indicating an immediate protein translation and accumulation (Figure 6). These proteins are involved in genetic information processing like the ribosomal protein paralogue Ctc, transcriptional elongation factor GreA and the sigma factor SigB, or are required for the utilization of alternative carbon sources (AcsA, LicH, AcoB). For other induced proteins accumulation takes at least 60 minutes (Figure 6). Functions of proteins which accumulate much later (after at least 240 min) are very diverse and do not follow an obvious direction. 80 % of the repressed proteins are stable for more than 240 min after repression of protein synthesis (Figure 6). Most of these proteins (62.5 %) function in carbon core metabolism or biosynthesis of nucleotides, amino acids and cofactors. Only three proteins were found to be very unstable as their amount decreases at the same time point where the repression was detected. These proteins are Tgt, functioning in translation, CarB, involved in biosynthesis of arginine, and Sat, an enzyme of the sulfur metabolism. Specific response to glucose starvation. Growth of B. subtilis in its natural environment, the upper layers of soil, is characterized by alternating phases of glucose supply and limitation. Adaptation to starvation for glucose and other carbon sources is the key for survival of the cells. Hence, after exhaustion of glucose the glycolytic pathway is repressed because of the need of a high glucose concentration for gapA operon expression (39–41). As repression in non-growing cells would mean non-changing protein amounts, an additional proteolytic degradation of unemployed glycolytic enzymes probably occurs. This is supported by halved protein concentrations after 180 min of starvation triggered stationary phase in non-growing cells (Table 1, Figure 3, Supplementary Figure S5). In general, numbers of protein copies per

17

Molecular protein quantification of stress response

cell differ in metabolic pathways, which is most probably caused by different enzymes efficiencies as a result of varying binding coefficients and metabolic rates (42) (Supplementary Figure S7). This is also true for glycolytic enzymes for which molecules per cell differ with an average factor of 15 (Table 1). However, average stoichiometries of constitutively expressed glycolytic enzymes catalyzing reversible reactions seem to remain stable during all time points examined (5 molecules Pgi : 19 molecules FbaA : 5 molecules Pgk : 2 molecules Pgm : 20 molecules Eno, Table 1). In contrast, measured stoichiometries between key players of glycolysis, phosphofructokinase PfkA, glyceraldehyde 3-phosphate dehydrogenases GapA and GapB change during transition into stationary phase caused by starvation (exponential growth: 20 molecules Pgi : 4 molecules PfkA: 10 molecules GapA : 1 molecule GapB; stationary phase: 20 molecules Pgi : 4 molecules PfkA : 4 molecules GapA : 2 molecules GapB, Table 1 , Supplementary Figure S7). Dealing with glucose starvation also requires new accumulation of proteins specifically needed to react to the changed supply of carbon sources. Hence, after glucose exhaustion gluconeogenesis becomes necessary because cells start to use secondary carbon sources like for example overflow metabolites produced during excess of the preferred carbon source. Hence, the protein amount of the gluconeogenic glyceraldehyde-3-phosphate dehydrogenase GapB increased more than 2fold in late stationary phase. Moreover, for phosphoenolpyruvate carboxykinase

PckA,

feeding

into

gluconeogenesis

by

converting

oxalacetate

to

phosphoenolpyruvate, an increased amount could be detected (Table 1). Additionally, amounts of enzymes for the utilization of secondary carbon sources like the subunits of acetoin dehydrogenase AcoABC increased significantly. This also applies to acetyl-CoA synthetase AcsA, 6-phospho-alpha-glucosidase MalA and IolD, necessary for the catabolism of acetate,

18

Molecular protein quantification of stress response

maltose and myo-inositol, respectively, indicating that some CcpA-dependent catabolic genes also seem to be derepressed in glucose-starved cells without any obvious external inducer (Table 1, Figure 3, Supplementary Table S4). For AcsA this can be explained by a possible internal inducer as lipid degradation during stationary phase could provide an additional source of acetyl-CoA synthesis (17). During glucose starvation downregulation of glycolysis and induction of gluconeogenesis occur simultaneously with induction of TCC enzymes (10, 17, 18). Induction of this metabolic pathway allows for utilization of organic acids and free amino acids, which might become available due to protein degradation, as energy sources. As expected protein amounts for these enzymes revealed an increased need for citrate cycle intermediates. Hence, molecules per cell for citrate synthase CitZ, 2-oxoglutarate dehydrogenase subunit OdhA, both subunits of the succinyl-CoA synthetase (SucC, SucD), and succinate dehydrogenase subunit SdhA increased at least 1.7fold when cells starve for glucose (Table 1, Figure 3, Supplementary Figure S5). Accumulation of new proteins needed for the specific reaction to starvation conditions requires a lot of energy. However, the availability of energy is restricted in a starved cell. Therefore the decreased growth rate and the reorientation of protein synthesis is necessary when energy becomes limited. Investigation of the repartition of protein amounts among the main processes in B. subtilis during adaptation to glucose starvation detected significantly lowered protein amounts in amino acid biosynthetic pathways already at entry into stationary phase. After 180 minutes stationary phase caused by glucose exhaustion this becomes only more pronounced (Supplementary Figure S5). Most drastic changes in protein amount could be detected for synthetic pathways of methionine, arginine and branched amino acids. Hence, the amount of cystathionine beta-lyase MetC and methionine synthase MetE lowered more than

19

Molecular protein quantification of stress response

3fold during glucose starvation. This was also the case for proteins involved in biosynthesis of arginine like N-acetyl-g-glutamyl-phosphate reductase ArgC and acetylornithine transaminase ArgD. Similar results could be obtained for enzymes which function in synthesis of branched amino acids like threonine dehydratase IlvA, aminotransferase YwaA, and 2-isopropylmalate synthase LeuA (Table 1). This supports the assumption that under starvation conditions the degradation of unemployed enzymes not protected in functional metabolic complexes can help to provide the amino acids necessary for de novo protein synthesis. However, the arrest of biomass production is most probably the major actor of this repression. An integrated view of protein repartition is provided in Figure 7. A considerable portion of protein mass dedicated to amino acid biosynthesis pathways seems to be allocated to the central carbon metabolism. The increased need of protein mass is most probably caused by induction of the TCC, gluconeogenesis and pathways for acquisition of secondary carbon sources and cannot be covered by lowered protein amounts of glycolytic enzymes alone (Supplementary Figure S5). General adaptive response under heat stress. In this work heat stress was selected as well described model for physical stresses. In contrast to glucose starvation here the availability of carbon sources is not limited and protein damage is the main challenge the cell has to face. For the growth-restricting heat stress (µexp=1.2 h-1,µheat=0.5 h-1) analyzed in this work hierarchical clustering revealed that 40 % of all proteins with increasing amounts are SigB-dependent stress proteins or mediate stress resistance. Altogether 44 members of the SigB regulon could be absolutely quantified from which 33 were found to be accumulated more than 2fold. Indeed, heat stress seems to elicit the induction of the SigB regulon, but it could be a substantial burden for the cell, as it can occupy up to 20 % of the translation capacity (43). Accordingly, only a transient transcription of genes of the SigB regulon is reported for both conditions (17, 44). As

20

Molecular protein quantification of stress response

cells starving for glucose and heat stressed cells exhibit considerably different cell volumes (differences around factor 2, Supplementary Table S6), absolute protein abundances in heat stressed cells have been corrected for the differences in cell size by normalizing to cell volumes of glucose starved cells in order to allow comparison of protein concentration per cell under both conditions. For comparison of the SigB response in both conditions tested, corrected protein abundances will be given in molecules per size-corrected cell in the following. In contrast to glucose starvation, a clear accumulation of induced SigB-dependent proteins can be measured only during heat stress (Figure 5). Thereby, the general stress proteins YhdN, YvyD, and GsiB showed highest increase in protein amount (more than 19fold in size-corrected cells, Table 2). With 97,800-151,000 molecules per cell (49,400-110,000 molecules per sizecorrected cell) most abundant SigB-dependent proteins during heat stress were the relatively small general stress protein GsiB, the anti-anti-SigmaB-protein RsbV, and the protease ClpP. These increases indicate an important functional role of the SigB-dependent proteins during heat stress which legitimates the high translation capacity needed for them. Further analysis of the distribution of protein amounts to different regulons in the cell additionally revealed that proteins whose expression is controlled by PerR (response to peroxide) and Spx (response to thiol specific oxidative stress) are also enriched (Supplementary Table S5), emphasizing the overlap between heat shock response and reaction to oxidative stress. As secondary oxidative stress is described to occur after different environmental stresses (45, 46) quantitative data of proteins with function in adaptation to oxidative and electrophile stress derived from this study were checked. Indeed, the amount of 25 out of 35 quantified oxidative stress proteins increased at least 2fold (Table 3). The molecules per cell of the general stress proteins YvyD, OhrB, SigB, and Dps increased more

21

Molecular protein quantification of stress response

than 10fold after 60 minutes of heat stress (Supplementary Table S1, Supplementary Figure S8). Protein concentrations of the nitro/ flavinreductase NfrA, the probable thiol peroxidase Tpx, the alkyl hydroperoxide reductase AhpC/AhpF, and superoxide dismutase SodA were amplified at least 4fold. Specific response to heat stress. Adaptation to growth-restricting heat stress was mainly realized by a strong accumulation of proteins belonging to the HrcA and CtsR regulons. Under this condition for 6 out of 9 proteins of the HrcA regulon, which mainly function in protein folding, increased amounts could be determined. The molecular chaperon DnaK and its activator GrpE accumulated 4-5fold (Table 2, Figure 3, Supplementary Figure S8). Amounts of chaperonins GroEL and GroES even increased more than 10fold (Table 2, Figure 3, Supplementary Figure S8). With that GroES was the most abundant protein under stress conditions constituting more than 7 % of the total molecule amount detected. Besides proteins of the HrcA regulon, products of genes controlled by CtsR were clearly enriched. On protein level 7 of 12 members of the regulon could be absolutely quantified and were found to be accumulated. A significant (p=0.05, one-way ANOVA) increase in protein concentration of the ATPases ClpE, ClpC, and protease ClpP was detected. The amount of ClpE increased about 7fold after 10 minutes of heat stress. However, after 30 minutes of stress ClpE concentration had already reached basal level of about 800 molecules per cell again indicating low protein stability. Similar observations have been made by Gerth and co-workers (47). In contrast, enrichment of ClpC and ClpP (Table 2) was determined to be 4-8fold already after 10 minutes of heat stress, but remained stable during all time points of stress examined. Although the already described induction of the HrcA and CtsR regulons represent the main response to heat stress, various other heat inducible proteins could be absolutely quantified in

22

Molecular protein quantification of stress response

this study. HtpG was found to be induced 14fold during heat stress. LonA accumulated about 3fold, but failed to reach significance level (p=0.05, one-way ANOVA). NfrA was found to be accumulated 5fold whereas protein amounts of AhpC and AhpF increased about 4fold (Table 2). Under heat stress most altered protein amounts were positively influenced, but there were also enzymes whose amount decreased. The majority of these proteins is involved in biosynthesis and acquisition of amino acids and cofactors (Supplementary Figure S8). Examples for negatively controlled enzymes involved in biosynthesis and acquisition of amino acids or cofactors are tyrosine transaminase HisH, methionine synthase MetE, and ThiF, involved in thiamine biosynthesis (Table 2). The protein amount of these proteins was lowered constantly during heat stress (Supplementary Table S1). In contrast, the concentrations of cyclase-like protein HisF, N-acetylglutamate 5-phosphotransferase ArgB, cystathione-beta-lyase PatB, and the molybdopterin biosynthesis protein MoeA were decreased until 30 minutes of heat stress to reach control levels at 60 minutes of stress again (Table 2, Supplementary Table S1), emphasizing the global coordination of various regulation in response to the growth rate adaptation during heat stress. The results of this study show that B. subtilis needs to reorientate protein synthesis during heat stress adaptation in order to accumulate general and stress-specific proteins although the main metabolic processes seem to be compromised due to the stress. The graphical representation of the repartition of protein amounts among main pathways of B. subtilis during heat stress depicts the very pronounced accumulation of chaperones and proteases during heat stress (Figure 8, left). These proteins constitute 4 % of the total protein mass during exponential growth at 37 °C, but their fraction is already increased to 13 % after 30 min heat stress at

23

Molecular protein quantification of stress response

52 °C. Furthermore, the left part of Figure 8 indicates that protein amounts dedicated to chaperones and proteases are most probably derived from resources won by turnover of enzymes responsible for amino acid synthesis (22 % of total protein amount under control conditions, 13 % after 30 min heat stress). Dynamical aspects of heat stress adaptation. The availability of large-scale absolute protein concentrations allows analysis of dynamical aspects of protein synthesis during stress adaptation. In order to calculate protein production rates during heat stress we assume that proteins synthesized during exponential growth under control conditions and still increasing during heat stress are stable. Studies on glucose starvation revealed that this is quite reasonable at least for vegetative enzymes (19). Regulatory proteins may have a much higher turnover rate since they are required for temporary reaction of the living cell to changing surroundings. However, in our representative data set regulatory proteins make up less than 1.5 % of the total protein amount in a cell and their mass will therefore not essentially influence calculation on protein production. The optical density of a bacterial culture reflects cell growth and therewith the “dilution” of protein amounts by cell division. In this study the optical density increases 1.62fold between the control sample of exponentially growing cells and the sample after 60 minutes of heat stress at 52 °C (Figure 1). Hence, we would estimate that 62 % of the total protein mass in heat stressed cells was already present in the control sample. In reverse this could mean that 38 % of the total protein amount present in the stressed cells was newly produced during the 60 minutes heat stress phase (Figure 8, circles). In order to illustrate this point the distribution of protein mass accumulated between the sample points to functional groups was analyzed. This led to a figure, which shows the repartition of protein production in a given phase (Figure 8 [left, bars],

24

Molecular protein quantification of stress response

Supplementary Figure S9). In the first phase, between 0 and 10 min heat stress, 22 % of newly accumulated proteins are chaperones. In contrast, the portion of protein amount dedicated to amino acid synthesis is strongly reduced. During the second phase, between 10 and 30 min of stress, the main portion of all protein amount accumulated is devoted only to chaperones, the portion of accumulated proteins with functions in amino acid synthesis is now smaller than 1 %. In the third phase, between 30 and 60 minutes of heat stress, the heat adaptational response seems to be finished. A significant amount of accumulated proteins is now again dedicated to amino acid synthesis.

DISCUSSION In the study presented here we compared for the first time the differential adaptation of B. subtilis to heat stress and glucose starvation on the basis of absolute protein concentrations at a large scale. Using 2-D PAGE with a pH range of 4-7 we were able to determine protein amounts on the single cell level for 773 cytosolic proteins at seven time points of glucose starvation and four time points in a heat stress experiment including controls. Hence, only 26.7 % of all cytosolic proteins (locateP (48)) could be identified by this gel-based approach. However, assuming that only 80 % of all proteins are expressed at the same time (49), the protein coverage of the presented approach increases to 33.4 %, covering most main metabolic pathways and processes in B. subtilis (Supplementary Figure S7, Supplementary Figure S10). 427 proteins quantified during glucose starvation in this study have been relatively quantified elsewhere (18). Whereas the study presented here is limited to cytosolic proteins which can be detected by 2-D PAGE Otto and coworkers (18) could provide quantitative data for additional 890 cytosolic proteins using mass spectrometry-based techniques. However, they were only

25

Molecular protein quantification of stress response

able to provide relative quantification data whereas the study presented here for the first time reports physiologically relevant absolute protein concentrations on B. subtilis under glucose starvation. For the proteomic analysis of the heat stress adaptation this is even more pronounced. Until now relative protein quantification data for 246 cytosolic proteins have been available (50). This could be extended by additional 455 proteins. For all 701 proteins identified during heat stress in this study absolute quantification data are available. The quantification of protein molecules per cell renders the possibility to compute the redeployment of resources in the bacterium during stress and to accurately estimate the associated energy costs. However, such applications will require the accurate estimation of the costs associated to the mRNAs redeployment. This could be achieved if access to the absolute quantification of mRNAs during the stress period and their half-life during the same stress period was provided. Additionally the integration of non protein parameters, such as known polysaccharides or lipids would be of great interest in order to gain new insight on, for example, the membrane composition during different stress conditions. Unfortunately, until now the absolute quantification of membrane proteins is still a challenge due to the need for a complex sample preparation (51). Furthermore, absolute quantification of non-proteinogenic components would be inevitable in this context. Adaptation of B. subtilis cells to various stresses is the key to survival in the natural habitat. Thereby it is crucial to balance saving of energy and resources and accumulating of inevitably needed stress proteins. This study illustrates different characteristics of general and specific stress responses to distinct stresses as exemplarily shown for glucose starvation and heat stress. While general, non-specific adaptive responses ensure survival during various conditions, the specific stress adaptation allows a direct interaction with the stimulus (10).

26

Molecular protein quantification of stress response

The general response to starvation is mainly marked by the negative stringent response whereas non-specific response to heat stress is dominated by activation of promoters controlled by the alternative sigma-factor SigB. Although the SigB regulon is activated under stress and starvation, proteins of SigB-dependent genes accumulate only after heat stress. During glucose starvation these proteins are transiently synthesized, but only 5 accumulated significantly more than 2fold. On average SigB-dependent proteins accumulated 1.8fold after 240 minutes of glucose starvation, but 2.6fold after 60 minutes of heat stress. Concurrently, gelfree relative quantification data on salt stress in B. subtilis (52) revealed an average accumulation of 3.2fold. Maximal accumulation ratios were 5.4 for salt stress, 8.0 for glucose starvation, and 26.5 for heat stress indicating pronounced differences in the SigB-dependent regulation in response to various stresses. Secondary oxidative stress is a phenomenon recently described to occur after ethanol treatment, hyperosmotic and cold stress (45, 46). For four proteins recently described to be induced after heat and oxidative stress (45), namely SigB, OhrB, YsnF and YvyD, a significantly increased amount after heat stress could be detected in this study. Furthermore, 21 additional proteins with functions in the resistance against oxidative and electrophile stress were found to be accumulated (Table 3). The molecules per cell for 4 proteins (YvyD, OhrB, SigB, Dps) increased more than 10fold. As these proteins are controlled by SigmaB this high accumulation is most probably caused by additive effects of the general stress response and the response to oxidative stress. This is supported by the observation that concentrations for proteins without additional regulation by the general stress response increased not more than 5fold. It has been shown that oxidative stress also induces genes otherwise repressed by MgsR controlling a subregulon within the general stress response (53). In this study, 23 target genes of MgsR could

27

Molecular protein quantification of stress response

be absolutely quantified after heat stress. 13 of these proteins show an expression pattern similar to that after ethanol treatment suggesting a regulatory function of MgsR after heat stress. In contrast, secondary oxidative stress does not seem to play an important role in adaptation to glucose starvation. In this study the amount of only two proteins with functions in the resistance against oxidative and electrophile stress were increased more than 2fold (KatE, YbdD). Both proteins are also under control of SigB and their accumulation might rather be an effect of this regulatory mechanism. Additionally, 16 proteins with functions in adaptation to oxidative stress were quantified, but not found in higher copy numbers during stationary phase. This is also supported by recent data from Otto et al. (18). They quantified 24 SigBindependent cytosolic proteins involved in oxidative stress resistance. Only 3 of these proteins were found to be significantly accumulated. However, this observation might have been caused rather by other direct or indirect regulatory effects than by secondary oxidative stress. In addition to the general responses the specific stress response to various conditions ensures a direct interaction with the stimulus. After exhaustion of the preferred carbon source, the substantial reprogramming of cellular metabolism is characterized by down-regulation of glycolysis and simultaneous mostly CcpA-dependent induction of metabolic pathways for the utilization of overflow metabolites and other secondary carbon sources (10, 17–19). However, accumulation of TCC enzymes, proteins catalyzing gluconeogenetic reactions as well as of enzymes for the metabolism of secondary carbon sources was less significant in this study when compared to recently published relative quantification data of Otto et al. (18). Relative quantification data from Otto et al. (18) compare protein abundances to calculate changes in protein accumulation without considering protein concentration in the cell. In contrast, absolute

28

Molecular protein quantification of stress response

quantification data derived from this study consider the cell size and cell count of a sample in order to calculate copy numbers per cell. Hence, if cell size is reduced, like it is the case during starvation (Supplementary Table S6), a stable protein concentration in a sample may result in negative changes of copy numbers per cell compared to the control sample leading to enhanced quantitative effect of negative regulations and reduced quantitative effects of positive regulations (Figure 2). This fact may explain the lowered protein accumulation of TCC enzymes, gluconeogenetic proteins and proteins for the utilization of secondary carbon sources when considering copy numbers per cell. Specific heat stress response is marked by induction of class I and class III heat stress genes controlled by HrcA and CtsR, respectively. On transcriptional level it is described that after moderate heat stress dnaK, grpE, and hrcA are strongly induced whereas downstream genes (dnaJ, yqeT, yqeU, yqeV), groEL, and groES are not induced more than 2fold (44). Under growth-restricting heat stress condition as described here amounts of all detectable proteins of the regulon increase at least 5fold. In 1997 Schulz et al. found htpG to be induced 10fold after transition from 37 °C to 48 °C both at the level of transcription and translation (54). The even higher accumulation rates of HtpG in this study are therefore most probably caused by the higher temperature of 52 °C. Although it is described to be heat inducible, no member of the CssRS regulon could be detected in this study. This is most probably because these proteins are, with exception of CssR, membrane-anchored and therefore not accessible with 2-D PAGE. Comparisons of our data with recent relative protein quantification (50) revealed a good overlap of results. Of 46 heat-induced proteins 23 were also found to be induced by Wolff and coworkers (50). A broad overlap also applies to proteins with reduced amounts which function

29

Molecular protein quantification of stress response

in biosynthesis and acquisition of amino acids and cofactors or in transcription and translation. Of 38 proteins negatively influenced in either of the studies 27 show a similar regulation pattern in both studies. The availability of a comprehensive data set of absolute protein concentrations allows calculation of accumulated proteins during a given phase of stress adaptation without “classical” quantitative synthesis data at hand. However this calculation makes use of the assumption that examined proteins remain stable during the stress phase analyzed. As this may be a reasonable assumption for the comparable short heat stress study presented here (up to 60 min stress) this may not be transferred to the glucose starvation data which became available within this work (up to 240 min starvation). The amount of a protein is a balance between production and degradation. In exponential phase, almost all reduced protein amounts are a direct consequence of the so-called dilution effect caused by cell growth. That means that the protein production in balanced systems can be easily estimated since it represents the protein amount needed to compensate the dilution effect. Consequently, protein production for each protein is equal to its steady-state amount times the growth rate. In a non-balanced system, e.g. in transient phase during a stress, it is also possible to deduce the protein production program during a given period, but only under the assumption that the protein degradation is restricted to the dilution. In this case possible proteolysis of some proteins will lead to underestimation of their production. Hence, the presented results can only be an estimation of the distribution of produced and accumulated proteins to functional categories. However, our data can give valuable insights in regulatory aspects during adaptation of B. subtilis to changing growth conditions.

30

Molecular protein quantification of stress response

The high amount of proteins with decreased amount in this study (especially after glucose starvation) leads to the question how the proteins are selected to be degraded. The assignment of proteins with lowered amounts to functional categories (Supplementary Table S7) revealed that most detected enzymes involved in biosynthesis of cofactors are degraded and not newly accumulated under both, glucose starvation and heat stress. Notably, the enzymes catalyzing the first committed steps in biosynthesis of branched amino acids, pyrimidines and purines, namely IlvB, CarA, CarB and Prs) have been found in decreased amounts only during response to glucose starvation pointing to a energy saving mechanism behind this regulatory effect. Hence, we suggest that starving cells need to save energy due to the limited availability of resources and therefore restrict metabolism to minimal activity to guarantee survival. Consequently, in a first instance, cells degrade biosynthetic enzymes, in many cases after regulation of their expression by the stringent response. While proteins that are active and integrated into functional complexes are protected against a proteolytic attack, these enzymes may be damaged or structurally pertubated in the absence of their (co-) substrates leading to the recognition of these proteins by the degradation machinery. After the introduction of quantitative western blotting (3), flow cytometry (55) and MS-based or MS-coupled strategies (4–6, 56) for a global determination of absolute protein abundance, we herewith present first physiologically relevant data for stress adaptation in the model bacterium B. subtilis. Due to a large number of examined time points with high coverage of reliably quantified proteins we are confident to provide suitable data for modeling of cellular regulation under altered growth conditions not only for the systems biology community.

31

Molecular protein quantification of stress response

REFERENCES 1.

Aebersold, R. (2005) Molecular Systems Biology: a new journal for a new biology? Mol. Syst. Biol. 1, 2005.0005

2.

Souchelnytskyi, S. (2005) Bridging proteomics and systems biology: What are the roads to be traveled? Proteomics 5, 4123–4137

3.

Ghaemmaghami, S., Huh, W. K., Bower, K., Howson, R. W., Belle, A., Dephoure, N., O’Shea, E. K., and Weissman, J. S. (2003) Global analysis of protein expression in yeast. Annu. Rev. Plant Physiol. Plant Mol. Biol. 41, 55–75

4.

Ishihama, Y., Schmidt, T., Rappsilber, J., Mann, M., Hartl, F. U., Kerner, M. J., and Frishman, D. (2008) Protein abundance profiling of the Escherichia coli cytosol. BMC genomics 9, 102

5.

Malmström, J., Beck, M., Schmidt, A., Lange, V., Deutsch, E. W., and Aebersold, R. (2009) Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature 460, 762–765

6.

Maass, S., Sievers, S., Zühlke, D., Kuzinski, J., Sappa, P. K., Muntel, J., Hessling, B., Bernhardt, J., Sietmann, R., Völker, U., Hecker, M., and Becher, D. (2011) Efficient, global-scale quantification of absolute protein amounts by integration of targeted mass spectrometry and two-dimensional gel-based proteomics. Anal. Chem. 83, 2677–2684

7.

Baudouin-Cornu, P., Lagniel, G., Chédin, S., and Labarre, J. (2009) Development of a new method for absolute protein quantification on 2-D gels. Proteomics 9, 4606–4615

32

Molecular protein quantification of stress response

8.

Lu, P., Vogel, C., Wang, R., Yao, X., and Marcotte, E. M. (2007) Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat. Biotechnol. 25, 117–124

9.

Schmidt, A., Beck, M., Malmström, J., Lam, H., Claassen, M., Campbell, D., and Aebersold, R. (2011) Absolute quantification of microbial proteomes at different states by directed mass spectrometry. Mol. Syst. Biol. 7, 510

10. Bernhardt, J., Weibezahn, J., Scharf, C., and Hecker, M. (2003) Bacillus subtilis during feast and famine: visualization of the overall regulation of protein synthesis during glucose starvation by proteome analysis. Genome Res. 13, 224–37 11. Hecker, M., and Völker, U. (2001) General stress response of Bacillus subtilis and other bacteria. Adv. Microb. Physiol. 44, 35–91 12. Price, C. W., Fawcett, P., Cérémonie, H., Su, N., Murphy, C. K., and Youngman, P. (2001) Genome-wide analysis of the general stress response in Bacillus subtilis. Mol. Microbiol. 41, 757–774 13. Yang, X., Kang, C. M., Brody, M. S., and Price, C. W. (1996) Opposing pairs of serine protein kinases and phosphatases transmit signals of environmental stress to activate a bacterial transcription factor. Genes Dev. 10, 2265–2275 14. Voelker, U., Voelker, A., Maul, B., Hecker, M., Dufour, A., and Haldenwang, W. G. (1995) Separate mechanisms activate sigma B of Bacillus subtilis in response to environmental and metabolic stresses. J. Bacteriol. 177, 3771–3780

33

Molecular protein quantification of stress response

15. Vijay, K., Brody, M. S., Fredlund, E., and Price, C. W. (2000) A PP2C phosphatase containing a PAS domain is required to convey signals of energy stress to the sigmaB transcription factor of Bacillus subtilis. Mol. Microbiol. 35, 180–188 16. Eymann, C., Homuth, G., Scharf, C., and Hecker, M. (2002) Bacillus subtilis functional genomics: global characterization of the stringent response by proteome and transcriptome analysis. J. Bacteriol. 184, 2500–2520 17. Koburger, T., Weibezahn, J., Bernhardt, J., Homuth, G., and Hecker, M. (2005) Genomewide mRNA profiling in glucose starved Bacillus subtilis cells. Mol. Genet. Genomics 274, 1–12 18. Otto, A., Bernhardt, J., Meyer, H., Schaffer, M., Herbst, F.-A., Siebourg, J., Mäder, U., Lalk, M., Hecker, M., and Becher, D. (2010) Systems-wide temporal proteomic profiling in glucose-starved Bacillus subtilis. Nat. Commun. 1, 137 19. Gerth, U., Kock, H., Kusters, I., Michalik, S., Switzer, R. L., and Hecker, M. (2008) Clpdependent proteolysis down-regulates central metabolic pathways in glucose-starved Bacillus subtilis. J. Bacteriol. 190, 321–31 20. Schulz, A., and Schumann, W. (1996) hrcA, the first gene of the Bacillus subtilis dnaK operon encodes a negative regulator of class I heat shock genes. J. Bacteriol. 178, 1088– 1093 21. Derré, I., Rapoport, G., and Msadek, T. (1999) CtsR, a novel regulator of stress and heat shock response, controls clp and molecular chaperone gene expression in gram-positive bacteria. Mol. Microbiol. 31, 117–131 34

Molecular protein quantification of stress response

22. Derré, I., Rapoport, G., Devine, K., Rose, M., and Msadek, T. (1999) ClpE, a novel type of HSP100 ATPase, is part of the CtsR heat shock regulon of Bacillus subtilis. Mol. Microbiol. 32, 581–593 23. Krüger, E., and Hecker, M. (1998) The first gene of the Bacillus subtilis clpC operon, ctsR, encodes a negative regulator of its own operon and other class III heat shock genes. J. Bacteriol. 180, 6681–6688 24. Schumann, W. (2003) The Bacillus subtilis heat shock stimulon. Cell Stress Chaperones 8, 207–217 25. Deuerling, E., Mogk, A., Richter, C., Purucker, M., and Schumann, W. (1997) The ftsH gene of Bacillus subtilis is involved in major cellular processes such as sporulation, stress adaptation and secretion. Mol. Microbiol. 23, 921–933 26. Gerth, U., Krüger, E., Derré, I., Msadek, T., and Hecker, M. (1998) Stress induction of the Bacillus subtilis clpP gene encoding a homologue of the proteolytic component of the Clp protease and the involvement of ClpP and ClpX in stress tolerance. Mol. Microbiol. 28, 787–802 27. Schumann, W., Hecker, M., and Msadek, T. (2002) in Bacillus subtilis and its Closest Relatives: From Genes to Cells, eds Sonenshein AL, Losick R, Hoch JA (ASM Press, Washington), pp 359–368. 28. Riethdorf, S., Völker, U., Gerth, U., Winkler, A., Engelmann, S., and Hecker, M. (1994) Cloning, nucleotide sequence, and expression of the Bacillus subtilis lon gene. J. Bacteriol. 176, 6518–6527 35

Molecular protein quantification of stress response

29. Antelmann, H., Engelmann, S., Schmid, R., and Hecker, M. (1996) General and oxidative stress responses in Bacillus subtilis: cloning, expression, and mutation of the alkyl hydroperoxide reductase operon. J. Bacteriol. 178, 6571–6578 30. Moch, C., Schrögel, O., and Allmansberger, R. (2000) Transcription of the nfrA-ywcH operon from Bacillus subtilis is specifically induced in response to heat. J. Bacteriol. 182, 4384–4393 31. Zuber, U., Drzewiecki, K., and Hecker, M. (2001) Putative sigma factor SigI (YkoZ) of Bacillus subtilis is induced by heat shock. J. Bacteriol. 183, 1472–1475 32. Jules, M., Le Chat, L., Aymerich, S., and Le Coq, D. (2009) The Bacillus subtilis ywjI (glpX) Gene Encodes a Class II Fructose-1,6-Bisphosphatase, Functionally Equivalent to the Class III Fbp Enzyme. J. Bacteriol. 191, 3168–3171 33. Stülke, J., Hanschke, R., and Hecker, M. (1993) Temporal activation of beta-glucanase synthesis in Bacillus subtilis is mediated by the GTP pool. J. Gen. Microbiol. 139, 2041– 2045 34. Starcher, B. (2001) A ninhydrin-based assay to quantitate the total protein content of tissue samples. Anal. Biochem. 292, 125–129 35. Büttner, K., Bernhardt, J., Scharf, C., Schmid, R., Mäder, U., Eymann, C., Antelmann, H., Völker, A., Völker, U., and Hecker, M. (2001) A comprehensive two-dimensional map of cytosolic proteins of Bacillus subtilis. Electrophoresis 22, 2908–2935

36

Molecular protein quantification of stress response

36. Sahai, H., and Ojeda, M. M. (2004) in Analysis of Variance for Random Models, Volume 2: Unbalanced Data: Theory, Methods, Applications, and Data Analysis (Springer), pp 331–342. 37. Pettit, F. H., Hamilton, L., Munk, P., Namihira, G., Eley, M. H., Willms, C. R., and Reed, L. J. (1973) Alpha-keto acid dehydrogenase complexes. XIX. Subunit structure of the Escherichia coli alpha-ketoglutarate dehydrogenase complex. J. Biol. Chem. 248, 5282– 5290 38. Meyer, F. M., Gerwig, J., Hammer, E., Herzberg, C., Commichau, F. M., Völker, U., and Stülke, J. (2011) Physical interactions between tricarboxylic acid cycle enzymes in Bacillus subtilis: evidence for a metabolon. Metab. Eng. 13, 18–27 39. Ludwig, H., Homuth, G., Schmalisch, M., Dyka, F. M., Hecker, M., and Stülke, J. (2001) Transcription of glycolytic genes and operons in Bacillus subtilis: evidence for the presence of multiple levels of control of the gapA operon. Mol. Microbiol. 41, 409–22 40. Tobisch, S., Zühlke, D., Bernhardt, J., Stülke, J., and Hecker, M. (1999) Role of CcpA in regulation of the central pathways of carbon catabolism in Bacillus subtilis. J. Bacteriol. 181, 6996–7004 41. Fillinger, S., Boschi-Muller, S., Azza, S., Dervyn, E., Branlant, G., and Aymerich, S. (2000) Two glyceraldehyde-3-phosphate dehydrogenases with opposite physiological roles in a nonphotosynthetic bacterium. J. Biol. Chem. 275, 14031–14037

37

Molecular protein quantification of stress response

42. Bar-Even, A., Noor, E., Savir, Y., Liebermeister, W., Davidi, D., Tawfik, D. S., and Milo, R. (2011) The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry 50, 4402–4410 43. Bernhardt, J., Völker, U., Völker, A., Antelmann, H., Schmid, R., Mach, H., and Hecker, M. (1997) Specific and general stress proteins in Bacillus subtilis--a two-dimensional protein electrophoresis study. Microbiology (Reading, U. K.) 143, 999–1017 44. Helmann, J. D., Wu, M. F., Kobel, P. A., Gamo, F. J., Wilson, M., Morshedi, M. M., Navre, M., and Paddon, C. (2001) Global transcriptional response of Bacillus subtilis to heat shock. J. Bacteriol. 183, 7318–7328 45. Reder, A., Höper, D., Gerth, U., and Hecker, M. (2012) Contributions of individual σBdependent general stress genes to oxidative stress resistance of Bacillus subtilis. J. Bacteriol. 194, 3601–3610 46. Höper, D., Völker, U., and Hecker, M. (2005) Comprehensive characterization of the contribution of individual SigB-dependent general stress genes to stress resistance of Bacillus subtilis. J. Bacteriol. 187, 2810–2826 47. Gerth, U., Kirstein, J., Mostertz, J., Waldminghaus, T., Miethke, M., Kock, H., and Hecker, M. (2004) Fine-Tuning in Regulation of Clp Protein Content in Bacillus subtilis. J. Bacteriol. 186, 179–191 48. Zhou, M., Boekhorst, J., Francke, C., and Siezen, R. J. (2008) LocateP: genome-scale subcellular-location predictor for bacterial proteins. BMC Bioinf. 9, 173

38

Molecular protein quantification of stress response

49. Becher, D., Hempel, K., Sievers, S., Zühlke, D., Pané-Farré, J., Otto, A., Fuchs, S., Albrecht, D., Bernhardt, J., Engelmann, S., Völker, U., van Dijl, J. M., and Hecker, M. (2009) A proteomic view of an important human pathogen – towards the quantification of the entire Staphylococcus aureus proteome. PLoS ONE 4, e8176 50. Wolff, S., Otto, A., Albrecht, D., Zeng, J. S., Büttner, K., Glückmann, M., Hecker, M., and Becher, D. (2006) Gel-free and gel-based proteomics in Bacillus subtilis: a comparative study. Mol. Cell. Proteomics 5, 1183–92 51. Muntel, J., Fromion, V., Goelzer, A., Maaβ, S., Mäder, U., Büttner, K., Hecker, M., and Becher, D. (2014) Comprehensive absolute quantification of the cytosolic proteome of Bacillus

subtilis

by

data

independent,

parallel

fragmentation

in

Liquid

Chromatography/Mass Spectrometry (LC/MSE). Mol. Cell. Proteomics 13, 1008–1019 52. Hahne, H., Mäder, U., Otto, A., Bonn, F., Steil, L., Bremer, E., Hecker, M., and Becher, D. (2010) A comprehensive proteomics and transcriptomics analysis of Bacillus subtilis salt stress adaptation. J. Bacteriol. 192, 870–882 53. Reder, A., Höper, D., Weinberg, C., Gerth, U., Fraunholz, M., and Hecker, M. (2008) The Spx paralogue MgsR (YqgZ) controls a subregulon within the general stress response of Bacillus subtilis. Mol. Microbiol. 69, 1104–1120 54. Schulz, A., Schwab, S., Homuth, G., Versteeg, S., and Schumann, W. (1997) The htpG gene of Bacillus subtilis belongs to class III heat shock genes and is under negative control. J. Bacteriol. 179, 3103–3109

39

Molecular protein quantification of stress response

55. Newman, J. R. S., Ghaemmaghami, S., Ihmels, J., Breslow, D. K., Noble, M., DeRisi, J. L., and Weissman, J. S. (2006) Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441, 840–846 56. Masuda, T., Saito, N., Tomita, M., and Ishihama, Y. (2009) Unbiased quantitation of Escherichia coli membrane proteome using phase-transfer surfactants. Mol. Cell. Proteomics 8, 2770-2777

40

Molecular protein quantification of stress response

DATA ACCESS Protein abundance data for heat stress and glucose starvation are available as Supplementary Information (Supplementary Tables S1 and S2).

ACKNOWLEDGMENTS We are indebted to E. Klotz, R. Jahnke, D. Ulbrich and S. Grund for excellent technical assistance. Furthermore, we are grateful to V. Liebscher for help with mathematical and statistical data analysis. We also thank Decodon GmbH for providing Delta2D software. D. Zühlke and D. Albrecht are acknowledged for support in protein digestion and identification. This work was supported by grants from the Bundesministerium für Bildung und Forschung (0315784A, 0315592B) and the EU (LSHG-CT-2006-037469, FP7-244093).

41

Molecular protein quantification of stress response

FIGURES LEGENDS Figure 1. Representative growth curves of B. subtilis during glucose starvation and heat stress. Growth of bacterial cells was followed by measuring optical density (OD) at 600 nm. Sampling points are indicated by dots. Sampling occurred as following: glucose starvation: exponential growth, transient, maximal OD, 60, 120, 180, 240 minutes stationary phase, heat stress: control (OD600nm=0.5), 10, 30, 60 minutes 52 °C heat. Phases of starvation or heat stress are indicated by the grey area.

Figure 2. Limit of detection is a function of molecular weight. Due to sequence-unspecific non-covalent binding of fluorescent dyes the same molecular count of a small protein generates a smaller signal compared to that of a larger protein. Molecules per cell for every protein quantified in this study were plotted against their molecular weight to calculate the limit of detection. The equation as well as the coefficient of determination are indicated.

Figure 3. Voronoi-treemaps of B. subtilis during glucose starvation and heat stress. Protein abundances (copies/cell * molecular weight) during control (left) and stress conditions (right). Each cell in the graph displays a protein that belongs to other functionally related elements in parent convex-shaped categories. These are again summarized in higher-level categories (see legend on the right side). Functionally related elements are depicted in close neighborhood to each other and colored similarly. Gene functional data are based on KEGGorthology. Area size in the graph encodes protein abundance.

Figure 4. 100 most abundant proteins at exponential growth, heat stress, and glucose starvation. Protein amounts (molecules/cell) of the 100 most abundant proteins during 42

Molecular protein quantification of stress response

exponential growth (first bar), after 60 min heat stress (second bar) and after 120 min glucose starvation (third bar) are used to calculate the relative amount of stable and newly accumulated proteins within the 100 most abundant proteins in the cell.

Figure 5. Changes in protein abundance for selected SigB-dependent proteins in growing and stressed cells. Protein patterns of exponential growing (green) and stressed cells (red) of selected SigB-dependent proteins during different growth stages (columns correspond to sampling points mentioned in Material and Methods). On the bottom of spot tiles the protein amount is given (molecules per cell). Bar charts show log2 ratios of protein amounts compared to control sample (exponential growth) for starvation (blue) and heat stress (orange). For the starvation experiment ratios for accumulated proteins (light blue) and synthesized proteins (dark blue) are given.

Figure 6. Distribution of duration of protein accumulation and depletion after induction and repression of synthesis after glucose starvation. Common proteins of this study and a work on protein synthesis (10), which show significant regulation in protein synthesis (at least 2fold) in one of the time points (maximal OD, 30 min, 60 min, 240 min glucose starvation normalized to exponential growth) were compared. The time point of first regulation on the level of synthesis and the first detection of changes in protein amount were compared and the period of time needed was calculated. Calculated durations were color-encoded in a VoronoiTreemap. Induced/accumulated proteins are shown in orange, repressed/depleted proteins are colored blue. The faster the change in protein abundance occurred, the darker the color appears.

43

Molecular protein quantification of stress response

Figure 7. Integrated view of functional class assigned protein abundances of B. subtilis during glucose starvation. The relative distribution of protein amounts among the main processes of B. subtilis during adaptation to glucose starvation are shown in different bars (from left to right) for exponentially growing cells (exp), cells during transient phase (trans) and at maximal optical density (max. OD) as well as for cells after 60 min (D), 180 min (E), and 240 min (F) stationary phase caused by glucose depletion.

Figure 8. Integrated view of protein abundance and protein production assigned to functional processes of B. subtilis during heat stress. In the left part of the Figure the relative distribution of protein amounts among the main processes of B. subtilis during adaptation to heat stress are shown in different bars for (from left to right): for exponentially growing cells (control) and cells after 10 min, 30 min, and 60 min (D) heat stress. The right part of the Figure illustrates dynamical aspects of protein repartition (protein production) in B. subtilis during heat stress. The bar chart shows the repartition of proteins accumulated between different sample points during heat stress. The relative distribution of accumulated protein amounts among the main processes during adaptation to heat stress is shown in different bars for (from left to right): exponentially growing cells (exp.) and accumulated proteins between 0 and 10 min, 10 min and 30 min, 30 min and 60 min heat stress. The size of the circles represents the relative amount of proteins accumulated between single sample points (values are given in the respective circles).

44

Molecular protein quantification of stress response

Table 1. Determined protein amounts (in molecules per cell) of selected proteins after 240 min stationary phase due to glucose starvation. Absolute amounts for all quantified proteins can be found in Supplementary Table S2. *provided data at time point of maximal induction

protein

function

stringent response RpsB ribosomal protein FusA elongation factor Adk adenylate kinase Prs biosynthesis of histidine SsbA DNA replication Mbl cell shape determination AtpA ATP synthase subunit AtpD ATP synthase subunit B σ -regulon* YdbD general stress protein YdaD general stress protein YdaG general stress protein YfkM general stress protein ClpP Clp protease proteolytic subunit YvgN general stress protein YvaA general stress protein KatE general stress protein Ctc general stress protein McsB modulator of CtsR-dependent repression glycolysis Pgi PfkA FbaA GapA Pgk Pgm Eno

glucose 6-P isomerase phosphofructokinase fructose 1,6-P aldolase glyceraldehyde 3-P dehydrogenase phosphoglycerate kinase phosphoglycerate mutase enolase

molecules/cell exponential growth

stress

16,000 8,600 67,000 37,000 11,000 4,700 3,500 1,300 9,000 4,200 3,100 960 31,000 16,000 30,000 14,000 190 150 7,600 1,600 24,200 81,500 15,900 1,300 9,200 4,300

4,000 1,200 37,600 4,400 24,500 67,400 14,400 2,700 20,000 12,300

35,600 8,100 120,000 19,300 28,900 12,300 140,000

14,400 4,300 69,900 13,600 21,100 7,800 71,000

45

Molecular protein quantification of stress response

Table 1. continued. protein

function

gluconeogenesis and acquisition of secondary carbon sources GapB glyceraldehyde 3-P dehydrogenase PckA phosphoenolpyruvate carboxykinase AcoA acetoin dehydrogenase subunit AcoB acetoin dehydrogenase subunit AcoC acetoin dehydrogenase subunit AcsA acetyl-CoA synthetase MalA 6-P-alpha-glucosidase IolD myo-inositol catabolism TCA CitZ OdhA SucC SucD SdhA

citrate synthase 2-oxoglutarate dehydrogenase subunit succinyl-CoA synthetase subunit succinyl-CoA synthetase subunit succinate dehydrogenase subunit

amino acid synthesis MetC cystathionine beta-lyase MetE methionine synthase ArgC N-acetyl-g-glutamyl-P reductase ArgD acetylornithine transaminase IlvA threonine dehydratase branched-chain amino acid YwaA aminotransferase LeuA 2-isopropylmalate synthase

molecules/cell exponential growth

stress

1,700 4,400 3,700 5,200 6,300 24,000 5,900 13,800 850 2,100 4,100 8,400 1,200 3,100 160 410 21,300 34,300 4,000 7,900 17,300 28,700 4,900 11,500 4,500 7,600 4,900 1,600 67,400 21,700 2,800 800 4,300 1,400 2,400 180 15,300 7,500

5,800 3,000

46

Molecular protein quantification of stress response

Table 2. Determined protein amounts (in molecules per cell) of selected proteins after 60 min heat stress. Absolute amounts for all quantified proteins can be found in Supplementary Table S1.

protein function B σ -regulon YhdN general stress protein YvyD general stress protein GsiB general stress protein RsbV control of SigB activity chaperones and proteases DnaK molecular chaperone GrpE activation of DnaK GroEL chaperonin GroES chaperonin HtpG molecular chaperone LonA protease ClpC ATPase subunit of the ClpC-ClpP protease ClpE ATP-dependent Clp protease-like ClpP ATP-dependent Clp protease proteolytic subunit heat inducible proteins NfrA stress protein AhpC alkyl hydroperoxide reductase subunit AhpF alkyl hydroperoxide reductase subunit negatively altered proteins HisH tyrosine transaminase MetE methionine synthase ThiF biosynthesis of thiamine HisF biosynthesis of histidine ArgB N-acetylglutamate 5-phosphotransferase PatB cystathione-beta-lyase MoeA molybdopterin biosynthesis protein

molecules/cell exponential growth stress 100 5,800 200 9,500 3,900 151,000 47,400 135,000 11,000 51,000 2,000 8,400 20,000 190,000 37,000 500,000 1,200 17,000 250 680 1,100 11,200 850 900 12,500 97,800 400 2,000 56,000 250,000 7,800 33,000 1,000 70,800 370 4,200 470 3,800 730

600 42,600 200 2,700 150 2,600 290

47

Table 3. Proteins with function in adaptation to oxidative and electrophile stress and their amounts after heat stress. For all proteins listed SigB-dependent regulation or already known induction by heat and oxidative stress (45) are indicated by x. For each protein molecules per cell and the relative quantitative change is given. Protein names in boldface are subject to quantitative changes of a factor of 4 or higher. molecules per cell induced after heat BSU SigBand oxidative stress protein number dependent (45) x x YvyD BSU35310 x x OhrB BSU13160 BSU04730 x x SigB x x YsnF BSU28340 BSU30650 x Dps x SodA BSU25020 TrxA BSU28500 x YraA BSU27020 x KatE BSU39050 x YvgN BSU33400 x YdbD BSU04430 x KatX BSU38630 x NfrA BSU38110 BSU29490 Tpx AhpC BSU40090 AhpF BSU40100 OhrA BSU13140 -

48

control 10 min 207 681 1,457 12,252 53 227 115 986 1,657 11,213 50,924 110,342 9,490 23,432 1,373 4,051 570 1,028 22,012 38,419 2,088 3,282 414 577 394 1,102 7,703 16,365 56,404 122,829 7,753 14,286 502 877

30 min 60 min 3,262 9,540 26,988 20,433 459 676 509 256 14,860 17,867 179,084 224,860 36,874 36,691 5,061 4,852 1,269 1,855 44,612 54,906 3,989 3,890 361 242 1,623 2,015 26,057 35,678 179,490 253,482 21,777 33,379 1,239 1,958

relative foldchange 10 min 30 min 60 min 3.29 15.76 46.09 8.41 18.52 14.02 4.28 8.66 12.75 8.57 4.43 2.23 6.77 8.97 10.78 2.17 3.52 4.42 2.47 3.89 3.87 2.95 3.69 3.53 1.80 2.23 3.25 1.75 2.03 2.49 1.57 1.91 1.86 1.39 0.87 0.58 2.80 4.12 5.11 2.12 3.38 4.63 2.18 3.18 4.49 1.84 2.81 4.31 1.75 2.47 3.90

Table 3. continued. molecules per cell protein AzoR1 AzoR2 MsrA MhqA YgaF HypO YwbC MrgA CatR YodC YqjM MhqD BshA MsrB BshB1 BshC KatA HxlA

49

BSU SigBinduced after heat number dependent and oxidative stress BSU19230 BSU33540 BSU21690 BSU12870 BSU08720 BSU07830 BSU38370 BSU32990 BSU33680 BSU19550 BSU23820 BSU19560 BSU22460 BSU21680 BSU22470 BSU15120 BSU08820 BSU03460 -

control 3,013 4,564 164 519 1,278 979 3,854 808 105 3,942 957 2,156 312 775 406 894 2,359 180

10 min 5,211 7,377 249 986 2,110 1,402 6,778 1,210 287 5,772 1,532 2,889 444 1,264 416 1,093 2,485 267

30 min 11,391 8,379 481 1,398 2,346 1,631 8,080 1,279 316 6,104 1,805 2,322 678 1,520 349 973 1,720 87

relative foldchange 60 min 11,585 15,738 562 1,658 4,000 3,062 11,618 2,321 300 10,996 2,209 4,354 612 1,154 557 1,193 2,330 36

10 min 30 min 60 min 1.73 3.78 3.85 1.62 1.84 3.45 1.52 2.93 3.43 1.90 2.69 3.19 1.65 1.84 3.13 1.43 1.67 3.13 1.76 2.10 3.01 1.50 1.58 2.87 2.73 3.01 2.86 1.46 1.55 2.79 1.60 1.89 2.31 1.34 1.08 2.02 1.42 2.17 1.96 1.63 1.96 1.49 1.02 0.86 1.37 1.22 1.09 1.33 1.05 0.73 0.99 1.48 0.48 0.20

Molecular protein quantification of stress response

FIGURES Figure 1.

50

Molecular protein quantification of stress response

Figure 2.

51

Molecular protein quantification of stress response

Figure 3.

52

Molecular protein quantification of stress response

Figure 4.

53

Figure 5.

54

Molecular protein quantification of stress response

Figure 6.

55

Molecular protein quantification of stress response

Figure 7.

Figure 8.

56

Highly precise quantification of protein molecules per cell during stress and starvation responses in Bacillus subtilis.

Systems biology based on high quality absolute quantification data, which are mandatory for the simulation of biological processes, successively becom...
2MB Sizes 0 Downloads 3 Views