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ORIGINAL PAPER

Growth phase-associated changes in the transcriptomeand proteome of Streptococcus pyogenes

Michelle A. Chaussee Alexander V. Dmitriev Eduardo A. Callegari Michael S. Chaussee

Received: 3 May 2007/ Revised: 19 June 2007/ Accepted: 4 July 2007/ Published online: 31 July 2007 Springer-Verlag 2007

Abstract Streptococcus pyogenes is responsible forapproximately 500,000 deaths each year worldwide. Manyof the associated virulence factors are expressed in agrowth phase-dependent manner. To identify growthphase-associated changes in expression on a genomescale,the exponential and stationary phase transcriptomes andproteomes of S. pyogenes strain NZ131 (serotype M49)were compared by using Affymetrix NimbleExpress genechips and two-dimensional gel electrophoresis. At thetranscript level, the expression of 689 genes, representingapproximately 40% of the chromosome, differed by two-fold or more between the two growth phases. The majorityof transcripts that were more abundant in the early-sta-tionary phase encoded proteins involved in energy con-version, transport, and metabolism. At the protein level, anaverage of 527 and 403 protein spots were detected in theexponential and stationary phases of growth, respectively.Tandem mass spectrometry was used to identify 172 pro-tein spots, 128 of which were growth phase regulated.

Enzymes involved in glycolysis and pyruvate metabolismand several stress-responsive proteins were more abundantin the stationary phase of growth. Overall, the resultsidentied growth phase-regulated genes in strain NZ131and revealed signicant post-transcriptional complexityassociated with pathogen adaptation to the stationary phaseof growth.

Keywords 2-DE Proteomics Transcriptome Growth phase Group A streptococcus Bacterialpathogenesis

AbbreviationsTHY Todd-Hewitt yeast extract broth2-DE Two-dimensional gel electrophoresis

Introduction

Streptococcus pyogenes is the cause of signicant morbidityand mortality worldwide. Colonization can result inasymptomatic carriage or uncomplicated pharyngitis, aswell as life-threatening diseases such as streptococcal toxicshock and necrotizing fasciitis. Each year, approximately1.8 million people in the United States are diagnosed withstreptococcal pharyngitis, with an associated cost of over$100 million (Neuner et al. 2003). Worldwide, nearly700,000 people acquire an invasive infection, which has amortality rate as high as 50% (Davies et al. 1996; Carapetiset al. 2005). Prompt treatment of pharyngitis is effective inpreventing post-infection autoimmune sequelae, such asrheumatic fever and acute glomerulonephritis (Cunningham2000). The importance of adequate medical care is reectedin the observation that the incidence of these sequelae is

Communicated by Erko Stackebrandt.

Electronic supplementary material The online version of thisarticle (doi: 10.1007/s00203-007-0290-1 ) contains supplementary

material, which is available to authorized users.

M. A. Chaussee A. V. Dmitriev E. A. Callegari M. S. Chaussee ( & )Division of Basic Biomedical Sciences,The Sanford School of Medicine of the University of SouthDakota, Lee Medical Building, 414 East Clark Street,Vermillion, SD 57069-2390, USAe-mail: mchausse@usd.edu

A. V. DmitrievDepartment of Molecular Microbiology,Institute of Experimental Medicine, Saint-Petersburg, Russia

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approximately 20 times higher in developing countries(Carapetis et al. 2005). Overall, S. pyogenes causesapproximately half a million deaths each year worldwide(Carapetis et al. 2005). Thus, the impact of S. pyogenes onhuman health is signicant, despite the fact that all isolatesare currently sensitive to b-lactam antibiotics.

During the initial stages of pharyngeal infection, S. py-ogenes competes with normal ora for host cell receptorsand nutrients. To maintain colonization, the pathogenpresumably must use a variety of carbon sources and adaptto the accumulation of metabolic endproducts. Suchadaptation is thought to require extensive changes in geneexpression. Many bacteria use secondary sigma factors toalter gene expression in response to changes in the envi-ronment. For example, the Bacillus subtilis genome en-codes at least 17 sigma factors, including SigB, which isinvolved in stationary-phase expression of a variety of stress responsive genes (Helmann and Moran 2002). Incontrast, the genome of S. pyogenes encodes one alterna-tive sigma factor known as SigX, which controls theexpression of competence-associated genes (Woodburyet al. 2006). Thus in contrast to many bacterial pathogens,S. pyogenes is thought to change growth phase-associatedpatterns of gene expression by interactions among tran-scriptional regulators (Kreikemeyer et al. 2003).

Batch culture is a convenient method to investigatebacterial adaptation to the depletion of preferred carbonand energy sources and the accumulation of metabolicendproducts. The relevance of the model to pathogenesis issupported by the nding that many virulence-associatedfactors of S. pyogenes are expressed in a growth phase-dependent manner (Kreikemeyer et al. 2003). When grownwith Todd-Hewitt media, S. pyogenes preferentially fer-ments glucose and produces lactic acid as the primarymetabolic endproduct (Chaussee et al. 1997; Neijssel et al.1997). The transition to the early-stationary phase of growth is associated with the depletion of glucose andacidication of the media to a pH of approximately 5.5(Chaussee et al. 2003). Therefore, examining the tran-scriptome of S. pyogenes at different growth phases mayprovide insight into the adaptive responses of S. pyogenesto nutrient limitation and the accumulation of metabolicendproducts. Moreover, because such adaptation is likelyto involve both post-transcriptional and post-translationalmechanisms of regulation, it is also of interest to comparethe exponential and stationary phase proteomes.

In this study, Affymetrix NimbleExpess DNA micro-arrays and two-dimensional gel electrophoresis (2-DE)were used to compare the transcriptomes and proteomes of S. pyogenes during the exponential and stationary phases of growth. The results indicate that entry of strain NZ131 intothe stationary phase is associated with transcriptional, post-transcriptional, and post-translational changes in the

expression of genes associated with metabolism, stressresponses, and virulence.

Materials and methods

Culture conditions

Streptococcus pyogenes strain NZ131 (serotype M49) wasgrown at 37 C with 5% CO 2 in 50 ml Todd-Hewitt brothcontaining 0.2% (w/vol) yeast extract (THY; Difco Labo-ratories, Detroit, MI, USA) without agitation until eitherthe mid-exponential (OD 600 = 0.35; approximately 3.5 h of incubation), early-stationary (OD 600 = 0.7; approximately6.5 h incubation), or stationary (OD 600 = 0.7; approxi-mately 18 h of incubation) phase of growth.

DNA microarray analysis

RNA was isolated with an RNeasy Mini Kit (QIAGEN,Valencia, CA, USA) from 40 ml cultures, as previouslydescribed (Dmitriev et al. 2006). The concentration andquality of RNA was assessed with an Agilent 2100 Bio-analyzer (Agilent, Palo Alto, CA, USA) using an RNA6000 Nano LabChip Kit (Agilent). cDNA synthesis andlabeling was done as previously described (Dmitriev et al.2006). Affymetrix NimbleExpress Arrays were purchasedfrom Affymetrix (Santa Clara, CA, USA). The array designwas based on the S. pyogenes strain SF370 genome se-quence (Ferretti et al. 2001) and consisted of 2,543 quali-ers representing 1,694 predicted S. pyogenes ORFs and804 intergenic region probes. In addition, 45 control oligoswere used for spike-ins. The GeneChips were hybridizedand washed with an automated Affymetrix GeneChipFluidics Station 450 (Dmitriev et al. 2006). The arrayswere scanned at 570 nm at a resolution of 1.56 l m using aconfocal GC3000 laser scanner (Affymetrix) and geneexpression levels were determined with GeneSpring 7software and normalized with the per chip algorithm (Sil-icon Genetics, Redwood City, CA, USA). For each growthcondition, two independently isolated RNA samples wereanalyzed. The average signal intensity value of each genewas transformed to a log 2 (log base 2) value. The changebetween two experimental conditions ( n-fold) was calcu-lated by taking the ratio of the signal intensity (differenceof the log 2 value) between experimental conditions. Presentand absent calls were assessed and genes with a twofolddifference in RNA levels were considered to be differen-tially expressed (Table S1). Statistically signicant ( t test;P < 0.05) differences of vefold or greater are summarizedin Table 2.

All of the microarray data are available through theGene Expression Omnibus data repository at NCBI ( http://

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www.ncbi.nlm.nih.gov/geo/ ) via accession number GSE3989.

Quantitative reverse transcription (RT)-PCR

Oligonucleotide primers and TaqMan probes (Table 1)were designed with Primer Express 2.0 software (ABIPrism, PE Biosystems, Framingham, MA, USA) and pur-chased from Sigma-Genosys (The Woodlands, TX, USA).Amplication and detection were done with the ABI Prism7700 Sequence Detection System (PE Applied Biosystems)using TaqMan One-Step RT-PCR Master Mix reagents(Roche, Indianapolis, IN, USA), as recommended by themanufacturer. Each assay was done in triplicate with atleast two independently isolated RNA samples. Thequantity of cDNA for each gene was normalized to thequantity of gyrA cDNA in each sample, and the standarderror was determined as previously published (Chausseeet al. 2003).

Two-dimensional gel electrophoresis

Cytoplasmic proteins were isolated from S. pyogenes , aspreviously described (Chaussee et al. 2006). Briey, 40 mlcultures centrifuged and the pellets were suspended in lysisbuffer. Proteins were isolated and claried using FastPrepprotein isolation and PlusOne 2-D Clean-up kits (GEHealthcare, Piscataway, NJ, USA) to remove non-proteincontaminants. Protein determinations were done with aPlusOne 2-D Quant kit (GE Healthcare), as described bythe manufacturer. Isoelectric focusing (IEF) was doneusing 24 cm immobiline dry strips with a linear pH rangeof 4 to 7 (GE Healthcare). Following IEF, SDS-poly-acrylamide gel electrophoresis (SDS-PAGE) separationwas done with a DALT II six electrophoresis apparatus(GE Healthcare) and 10% acrylamide resolving gels. Theproteins were stained with Sypro Ruby (Molecular Probes),and digital images were acquired with a Typhoon 9410imager (GE Healthcare). Analysis of the gels, includingprotein spot detection and quantitation, was done withPDQuest software (Bio-Rad, Hercules, CA, USA). Gelswere normalized based on the sum of all protein spotsdetected in each sample. For each phase of growth, proteinswere isolated on three independent occasions and statisti-cally signicant differences were determined usingPDQuest software ( t test; P < 0.05).

Protein identication

Proteins of interest were excised from the SDS-PAGE gelswith a robotic spot cutter (Bio-Rad) and identied withtandem mass spectrometry, as previously described(Chaussee et al. 2004). Spectra were obtained in positive T

able1

PrimersandTaqManprobesusedforquantitativereversetranscription(RT)-PCR

Spy

no.a

Designation

b

Foldchangesin

gene

transcriptsc

Forward(53

)

Reverse(53)

Fluorescentprobe(53)d

165

nga(nicotineadeninedinucleotide

glycohydrolaseprecursor)

4

CACCTACACTAAAAAACCGCATCA

CAAAAGTGACCTCTG

ACAAGGCTAA

TAACAGAGGTCCATCAGGGACAAAACAAGGAT

216

None(putativeregulatoryprotein-RofA

related)

4

ACCCGCTTGTTTGGTTTGAG

GGTGCAAGGCTAAGATGGTTTT

ACTGTATCCAACAGCAGCGCGAAGATTT

901

pyrE(putativeorotate

phosphoribosyltransferase)

1

CCCTTTTACGTGGGCATCTG

CTTCTGGGAAATGAG

CCTTGAT

CACTGATAATCGTGTCACCC

TTTCTTATCCTAAGACTC

1059

None(putativephosphotransferase

system

(PTS),enzymeII,componentC

)

11

TTGGAATGTTGGCAGCCTTT

CCAAGTGCCATAGTG

GAGCAT

TTCATGAATATCTTTGCTCCAATGGTTGATAAAGC

1547

arcA(argininedeiminase)

79

GGTGGTGGTAAAGTGCCTATGGT

CAAGTTCGTCCCCACCTTCA

TATGACCGTAATGAAACCACTCGCA

1856

norA(putativeantibioticresistanceprotein

NorA)

7

GAGTGGATGAG

CACCGCTTT

AAAGGAAGCCACACCACTACCA

TCTTGATTTAGGTTTAGGTGCTGGGCCTTACC

1981

relA((p)ppGppsynthetase,GTP

pyrophosphokinase)

5

CCGAAAACCATCGCAAAATG

CGAAATGCGCTCTTGTTTGTC

TGAAATTGGCTGACCGCCTGCATA

2039

speB(pyrogenicexotoxinB)

16

CGCACTAAACCCTTCAGCTCTT

ACAGCACTTTGGTAA

CCGTTGA

TACTGGTGGCGGCGCACC

2042

rgg(transcriptionregulator)

0.6

AAGTCAACAAA

GGAAAAGAACCTTTT

AAATAAGTCCGCTCTGTCAGACAGT

TTGGTCAAGGTGTTATTAGC

AACTCTTACTGAGGAA

2043

mf(mitogenicfactor)

5

CACAGGTCTCAAATGATGTTGTTCT

CTGTCATTGAATGTCCAAGCTAATG

ATGATGGCGCAAGCAAGTACCTAAACGA

2084

None(putativeserinecycleenzyme)

3

ACCTGGTTTCTGCCGTTTTTG

TTCGACCATGAGGGT

CATCA

CCAAAAGAGACTGACGAAGACAAGGCTGCT

a

SpynumbersarebasedonannotationofSF370S.pyogenesstraincomplete

genome(Ferrettietal.2001)

b

Genedesignationsareinitalics,andthecorrespondingproteindesignations

areinparentheses

c

Foldchangesingenetranscriptsintheearly-stationaryphaseincomparison

withmid-exponentialphaseasdeterminedbyRT-PCR

d

Covalentlylinkedatthe5endto5-carboxyuoresceinandatthe3endtoN,N,N-tetramethyl-6-carboxyrhodamine

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Table 2 Growth phase-associated differences in the transcriptome greater than vefold ( P < 0.05)

SPy a Gene Description Fold change(stat/exp)

More abundant in early stationary phase

Translation552 Hypothetical protein 5

Transcription2172 Hypothetical protein 151763 hrcA Putative heat shock transcription repressor protein 111259 Transcriptional regulator 81602 Transcriptional regulator 81285 Transcriptional regulator 71817 scrR Putative sucrose operon repressor 52177 Putative transcriptional regulator (TetR AcrR

family)5

Replication, recombination and repair1846 dinP DNA-damage-inducible protein P 141510 mutT Mutator protein 8

185 polA DNA polymerase1 6550 Hypothetical protein 61314 uvrB Excinuclease (subunit B) 51369 deaD2 Putative RNA helicase 5

Defense mechanisms837-838 b /yknZ ABC transporter-hypothetical protein 891286 ABC transporter 7

Signal transduction mechanisms1780 Hypothetical protein 7584 ptsK Hpr kinase phosphatase 5

Cell wall/membrane biogenesis836 Hypothetical protein 10585 lgt Prolipoprotein diacylglycerol transferase 5716 agaS Tagatose-6-phosphate aldose ketose isomerase 52059 pbp2A Penicillin-binding protein 2a 5

Posttranslational modication, protein turnover, chaperones888 clpL ATP-dependent protease 221509 clpE ATP-dependent protease 141759-1760-1761 dnaJ/dnaK/grpE Heat shock proteins/Hsp-70 cofactor 10-9-122105 nrdG Putative anaerobic ribonucleotide reductase

activator8

2037 Peptidylprolyl isomerase 72072 groES Heat shock protein 6395 clpP ATP-dependent protease subunit 62079 ahpC Putative alkyl hydroperoxidase 5

Energy production and conversion1191 oadA Oxaloacetate decarboxylase alpha chain 211189 citF Citrate lyase, alpha subunit 191184-1186 /citD Decarboxylase, beta subunit- citrate lyase, gamma

subunit14-18

1683-1684 glpO/glpK Alpha-glycerophosphate oxidase-glycerol kinase 9-9352 Acylphosphatase 8512 NADH-avin oxidoreductase 8

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Table 2 continued

SPy a Gene Description Fold change(stat/exp)

839 Glycerophosphodiester phospohodiesterase 82049 pD Pyruvate formate lyase-2 6801 Ferredoxin 5

1029 acoC Putative dihydrolipoamide S-acetyltransferase 51192 citC Putative citrate lyase synthetase (citrate (pro-3S)-

lyase ligase)5

2047 gldA Glycerol dehydrogenase 5Carbohydrate transport and metabolism1188 citE Citrate lyase, beta subunit 151816 scrB Sucrose-6-phosphate hydrolase 91287 Hypothetical protein 81599 Beta glucosidase 81604 Alpha-mannosidase 81707-1708-1709-1710-1711 lacB.1A.1/// Galactose-6-phosphate isomerase-PTS 7-11-6-8-61976 msmK Multiple sugar metabolism transporter 7

1592 Hypothetical protein 61682 glpF Glycerol uptake facilitator 61918-1919-1921-1922 lacFD.2C.2B.2 PTS-tagatose-PTS-system-1,6-diphosphate aldolase-

tagatose 6-phosphate kinase- galactose-6-phosphate isomerase

5-6-5-5

Amino acid transport and metabolism1544 arcB Putative ornithine transcarbamylase 1071542 Putative Xaa-His dipeptidase 951547 arcA Arginine deiminase 41511 gloA Lactoylgutathione lyase 9513 pepQ Putative XAA-PRO dipeptidase 7183-184 opuAAABC Glycine betaine proline ABC transporter-glycine-

betaine binding permease protein

7-7

1111 Alcohol dehydrogenase 61991 trpG Anthranilate synthase component II 5

Nucleotide transport and metabolism2110 nrdD Anaerobic ribonucleoside-triphospate reductase 8

Coenzyme transport and metabolism1096 folC.1 Folyl-polyglutamate synthetase 6

Lipid transport and metabolism1190 citX Hypothetical protein 201183 Decarboxylase, gamma chain 17

Inorganic ion transport and metabolism1715 copA Putative cation-transporting ATP-ase 9

158 Toxic anion resistance protein 7408 Hypothetical protein 62115 Hypothetical protein 5

General function prediction only1546 Hypothetical protein 632170 Hypothetical protein 11775, 1339 Hypothetical proteins 82106-2107 Hypothetical protein-putative oxidoreductase 7-7357, 500, 588 Hypothetical proteins 5

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Table 2 continued

SPy a Gene Description Fold change(stat/exp)

Function unknown1543 Hypothetical protein 851260-1261-1262-1263-1264-1265 Hypothetical proteins 19-15-12-19-11-12

2173 yoxJ Hypothetical protein 16238 Hypothetical protein 15470 Myosin-crossreactive antigen 121603, 1686 Hypothetical proteins 71440 Putative holin - phage associated 5

Not in COGs2040 Hypothetical protein 442043 mf Mitogenic factor 181156-1157 Hypothetical proteins 9-151436 mf3 Putative deoxyribonuclease 112039 speB Pyrogenic exotoxin B 11577, 802 Hypothetical proteins 61600 Hyaluronidase 6997 hylP2 Hyaluronidase phase associated 5159, 492, 553, 578, 914, 1405, 1437 Hypothetical proteins 5

Less abundant in early stationary phase

Translation, transcription, replication, recombination and repair870 fms Polypeptide deformylase 7307 Hypothetical protein 5847 16S rRNA processing protein 5

Defense mechanisms1205 Putative antimicrobial resistance factor 15

Posttranslational modication, protein turnover, chaperons

850 Thioredoxin reductase 6Transport and metabolism323 braB Putative branched-chain amino acid transport

protein16

1506-1507 Putative amino acid ABC transporter 11111270 Amino acid symporter 8386 Ferrichrome ABC transporter 71136-1137 xpt/- Santhine phosphoribosyltransferase-purine

permease75

1114 Hypothetical protein 72193 Hypothetical protein 5

General function prediction only, function unknown, not in COGs1139 4-oxalocrotonate tautomerase 8314 Hypothetical protein 7312, 1735 Hypothetical proteins 61206-1208 ABC transporter-hypothetical protein 65305-306 Hypothetical proteins 55421 yoaK Hypothetical protein 51203 hypothetical protein 5

a SPy numbers are based on the SF370 S. pyogenes genome annotation (Ferretti et al. 2001)b Contiguous genes likely to be co-transcribed are grouped together

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ion mode, deconvoluted, and analyzed with MassLynx 4.0software (Waters Corp., Milford, MA, USA). Protein LynxGlobal Server v. 2.1 (Waters) was used to search databasesconsisting of S. pyogenes genome sequences and the NCBIv20060512 non-redundant genomic databases. Theparameters for the search were as follows: the modicationon cysteine residue by carboxyamidomethylation was setas xed; asparagine and glutamine deamidation, andmethionine oxidation were considered as variable modi-cations. The maximum number of missed cleavages wasone. Monoisotopic masses were considered, and the pep-tide and fragment tolerances were 100 ppm and 0.25 Da,respectively. Proteins were identied by matching MS/MSspectra from at least two tryptic peptides or by de novopeptide sequence determination, when only one MS/MSmatch was identied.

Results

Growth phase-associated changes in the transcriptomeand proteome

Growth phase-associated changes in the S. pyogenesNZ131 transcriptome were identied with AffymetrixNimbleExpress whole-genome chips. RNA was isolatedduring the mid-exponential and early-stationary phases of growth. The transcript levels of 689 genes, representingapproximately 40% of the chromosome, changed by two-fold or more upon entry into the early-stationary phase of growth; 522 and 167 gene transcripts were more and lessabundant, respectively (Table S1). Quantitative RT-PCRwas used to measure transcripts of 11 genes (Table 1) inboth the mid-exponential and early-stationary phases of growth and the results correlated with the microarray data,which supports the validity of the array data (Fig. S1). Thegenes selected included several known or putative viru-lence-associated genes and regulators, which are of general

interest. In addition, metabolic genes likely to be growthphase-regulated were selected. The majority of growthphase-responsive genes encoded proteins involved inmetabolism and transport, stress responses, protein trans-lation, cell-wall metabolism, and protein synthesis (Fig. 1).Transcript changes vefold or greater are reported in Ta-ble 2. In addition to the transcriptome analysis, changes inthe proteome were identied by using 2-DE and repre-sentative gels are shown in Fig. 2. An average of 527 and403 protein spots were detected in gels containing samplesfrom the exponential and stationary phases of growth,respectively. Several hundred protein spots were excisedfrom gels containing both exponential and stationary-phaseproteins and analyzed with tandem mass spectrometry. Theresults were used to conrm protein spot matching amongthe gel set. Among the spots analyzed, 172 unique proteinswere identied, which represented 96 loci (Table S2). 128protein spots, representing 80 loci, were altered in a growthphase-specic manner. Statistically signicant changes(P < 0.05) are summarized in Table 3.

Growth phase-associated changes in regulatory genes

In the absence of a stationary phase-specic secondarysigma factor, genomewide changes in expression arethought to result from interactions among regulons. In theearly-stationary phase of growth, changes in the transcrip-tome correlated with increases in transcripts encoding glo-bal regulatory proteins (Table S1). These included: (a) codY (threefold), which controls the cellular response to nitrogenstarvation (Fisher 1999). (b) relA (twofold), which mediatesthe stringent response to amino acid starvation (Steiner andMalke 2000). and (c) luxS (twofold), which synthesizes anautoinducing quorum sensing molecule (Lyon et al. 2001).The increase in codY expression was associated withchanges in the CodY regulon (Malke et al. 2006). Forexample, genes involved in peptide and amino acid trans-port (oppBCD and dppBCDE , and braB ) were less abundant

0 10 20 30 40 50 60

Translation (152)

Transcription (133)

Replication, recombination and repair (128)

Signal transduction mechanisms (70)

Cell wall and membrane biogenesis (76)

Posttranslational modification, protein turnover, chaperones (56)

Energy production and conversion (62)

Transport and metabolism (513)

Fig. 1 Growth phase-associated changes in functionally categorizedgene transcripts in NZ131. The percentages of more-abundant ( openbars ) and less-abundant ( closed bars ) gene transcripts in eachfunctional category during the early-stationary phase of growth

compared to those during the mid-exponential phase of growth areshown. The number of genes within each category is indicated inparentheses

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in the early-stationary phase of growth compared to theexponential phase. Similarly, the increase in relA transcriptswas associated with increased expression of murE , rplL , andfus, which are part of the stringent response. The expressionof several other regulatory gene transcripts was also alteredcoincident with entry into the early-stationary phase of growth (Table S1). The only change in regulatory proteinsdetected with 2-DE gels was PyrR (SPy 830), which wasdetected only in protein samples from stationary phasecultures (Table 3). The absence of other regulatory proteinsin the 2-DE gels is not surprisingly given that they aretypically expressed at relatively low levels.

Growth phase-associated changes in metabolic genes

In THY broth, S. pyogenes obtains energy primarily fromthe fermentation of glucose. All of the glycolytic enzymes,

except glucose kinase, were identied in 2-DE gels andwere more abundant in the stationary phase of growth(Fig. 3). The increase correlated with transcriptome results(Table S1). Proteins involved in pyruvate metabolism werealso elevated in the stationary phase of growth includingpyruvate dehydrogenase (AcoB, SPy 1028; AcoL, SPy1031), which converts pyruvate to acetyl CoA and CO 2 ,and L-lactate dehydrogenase (LDH) (Fig. 3, Table 3).Presumably, the increase in central metabolic enzymesenhances the ability of the pathogen to scavenge carbo-hydrates present at very low concentrations (Harder andDijkhuizen 1983). In this regard, Escherichia coli adapts toglucose-limiting conditions by increasing the expression of genes involved in central metabolism (Hua et al. 2004) andthe abundance of proteins involved in glucose uptake(Wick et al. 2001). Such adaptation may be particularlyimportant during infection of tissues in which the con-centration of carbohydrates is very low.

Transcripts associated with the transport and metabo-lism of lactose, sucrose, mannose, and amylase were alsomore abundant during the stationary phase of growth(Table S1). Similarly, transcripts encoding arginine dei-minase ( arcA ) and serine dehydratase ( sdh ), which areinvolved in the catabolism of arginine and serine,respectively, were elevated in the stationary phase of growth (Table S1). The changes in the expression of amino acid catabolic enzymes were associated with in-creased expression of several genes encoding putativepeptidases, such as pepA, pepC, pepD, pepP, pepQ,pepXP . Several of the changes were cognate with thoseobtained with 2-DE (Table 2). Finally, transcripts encod-ing the citrate lyase complex ( citDEFX ) and oxaloacetatedecarboxylase ( oadA ) were more abundant in the early-stationary phase of growth (Table S1). Together, thechanges in genome expression are consistent with thede-repression of genes involved in the metabolism of non-glucose carbon sources.

Growth phase-associated changes in stress responsivegenes

Nutrient limitation and decreasing pH trigger a variety of bacterial stress responses (Len et al. 2004). Not surpris-ingly, the abundance of several stress responsive proteinswas elevated in the stationary phase of growth (Table 3)including DnaK (SPy 1760), ClpE (SPy 1509), and Nox1(SPy 2080); the cognate transcripts were elevated by 9, 14,and 6-fold, respectively (Table 2).

In some instances, growth phase-associated changes instress responsive proteins were detected at either the tran-script or the protein level. For example, transcript levels of clpL, groEL, groES were more abundant upon entry intothe stationary phase of growth (Table 2); however, no

Fig. 2 Representative 2-DE gels of S. pyogenes proteins isolatedduring the a exponential and b stationary phases of growth. The SPynumbers (based on the SF370 genome annotation) of proteinsidentied with tandem mass spectrometry are indicated

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difference was detected at the protein level. In contrast, thepeptidyl-prolyl cis -trans isomerase RopA (SPy 1896),which is required for the secretion of the cysteine proteaseSpeB (Neely et al. 2003), was more abundant during thestationary phase; however, changes in ropA transcriptswere not detected. Similarly, the abundance of cysteinesynthase CysM (SPy 1618) increased sevenfold in the

stationary phase and a proton translocating ATPase AtpA(SPy 0758) was only detected among stationary phaseproteins (Table 3); however, no changes in the corre-sponding transcripts were observed (Table S1). Thus, post-transcriptional mechanisms of regulation are involved inthe adaptation of S. pyogenes to the culture conditionsassociated with the stationary phase of growth.

StationaryExponential

Glucose-6-P isomerase(SPy0215)

Phosphofructokinase(SPy1283)

Fructose bisphosphate aldol-ase (SPy1889)

Phosphoglycerate kinase(SPy1881)

Phosphoglyceratemutase (SPy1429)

Enolase(SPy0731)

Glyceraldehyde 3-Pdehydrogenase(SPy0274)

Pyruvate kinase(SPy1282)

Glucose-6-Phosphate

Fructose-6-Phosphate

Fructose-1,3-Bisphosphate

Glyceraldehyde-3-Phosphate

1,3-bisphosphoglycerate

3-phosphoglycerate

2-phosphoglycerate

phosphoenolpyruvate

pyruvate

GlucoseGlucose kinase(SPy1529) (notdetected in 2-DEgels)

lactate acetyl-CoAL-lactate

dehydrogenase (SPy1151)

Pyruvate dehydrogenase (SPy1028)

Dihydrolipoamide dehydrogenase (SPy1031)

2

3

3

3

nd

2

4

3

Fold change (stat/exp)

mRNA Protein

6

3

2

22

4

3

2

12

SPy1031

SPy1028 4 2

4 uc

StationaryExponential

StationaryExponential

3 2

Fig. 3 Growth phase-associated differences in the abundance of proteins involved in glycolysis and pyruvate metabolism. Selectedenzymes are circled and proteins indicated in the pathway diagram.

The fold change in protein and transcript levels is indicated. The

transcript levels were determined by DNA microarray analysis. ucindicates that the ratio cannot be calculated and ND indicates thevalue was not determined

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Table 3 Growth phase-associated changes in the proteome ( P < 0.05)

SPy a Gene Description Exponentialphase b

Stationaryphase b

Foldchange c

(stat/exp)

Translation0273 fus EF-G 630 112 6

0273 fus EF-G NDd

476 uce

0273 fus f EF-G 102 1006 100611 tufA EF-Tu 6626 1222 -51688 glyS Glycyl-tRNA synthetase (beta subunit) ND 725 uc2093 tsf EF-Ts 45 1,950 43

Cell wall/membrane biogenesis0763 UDP N acetylglucosamine 1 carboxyvinyltransferase ND 423 uc0784 rmlD dTDP 4 keto L rhamnose reductase 467 2686 61280 glmS L glutamine D fructose 6 phosphate amidotransferase ND 777 uc1525 murD UDP- N -acetylmuramoylalanine- D-glutamate ligase ND 114 uc

Posttranslational modication, protein turnover, chaperones1509 clpE ATP dependent protease ND 197 uc

1509 clpE ATP dependent protease ND 103 uc1760 dnaK Heat shock protein 70 2263 6355 31896 ropA Trigger factor (prolyl isomerase) 1305 7530 6

Energy production and conversion0226 gpsA NAD(P)H-dependent glycerol-3-phosphate

dehyrodgenase70 552 8

0758 atpA Proton translocating ATPase alpha subunit ND 1047 uc0759 atpG Proton-translocating ATPase, gamma subunit ND 972 uc1028 acoB Pyruvate dehydrogenase (beta chain) 2372 4562 21028 acoB Pyruvate dehydrogenase (beta chain) ND 166 uc1031 acoL Pyruvate dehydrogenase, component E3 31 921 291069 NADH dehydrogenase ND 657 uc

1128 pta Phosphotransacetylase 975 4011 41151 ldh L-lactate dehydrogenase 1109 238 -51151 ldh L-lactate dehydrogenase 4210 9286 21371 gapN NADP-dependent glyceraldehyde-3-phosphate

dehydrogenase2232 5602 3

1371 gapN NADP-dependent glyceraldehyde-3-phosphatedehydrogenase

41 367 9

Carbohydrate transport and metabolism0215 pgi Glucose-6-phosphate isomerase 252 1427 60613 tpi Triosephosphate isomerase 4938 1360 -40731 eno Enolase 385 ND uc0731 eno Enolase 21888 41572 2

1282 pyk Pyruvate kinase 605 6499 121283 pfk 6-Phosphofructokinase 2748 7889 31372 pstI Phosphoenolpyruvate:sugar phosphotransferase system

enzyme I1930 5763 3

1429 gpmA Phosphoglycerate mutase 2728 7412 31676 tkt Transketolase 1255 4467 41881 pgk Phosphoglycerate kinase 1933 288 71881 pgk Phosphoglycerate kinase 2152 405 51881 pgk Phosphoglycerate kinase 1133 297 31881 pgk Phosphoglycerate kinase 296 4463 15

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Growth phase-associated regulation of virulence genes

Several virulence factors of S. pyogenes are expressed in agrowth phase-dependent manner (Kreikemeyer et al.2003). Surprisingly, a difference in the expression of the

Mga-regulated operon, which is known to be expressedprimarily in the exponential phase of growth, was notdetected in strain NZ131. The RNA was isolated fromcultures in the early-stationary phase of growth. Thus isseems likely that Mga-regulated transcripts decrease atlater times in the stationary phase. Nonetheless, theexpression of several genes encoding secreted proteins,such as mf-1 , mf-3 , hyluronidase, speB, cfa , and sagAincreased in the stationary phase of growth (Table S1), asexpected.

Protein isoforms

Post-translational protein modications such as phosphor-ylation, glycosylation, acetylation, and proteolysis can alterthe migration of proteins in 2-DE gels. In this study, 33 loci

were identied that encoded proteins with multiple iso-forms. Together, the isoforms were among the most abun-dant proteins and accounted for 30% of the total amount of protein detected. Among the isoforms, the migration of 25proteins deviated from the predicted M r by more than10,000 Da; the migration of ve proteins deviated from thepredicted pI by more than 1.0 (Table S2). Proteins with themost isoforms included enolase (Eno; SPy 0731), elonga-tion factor Tu (EF-Tu; SPy 0611), elongation factor G (EF-G; SPy 0273), phosphoglycerate kinase (Pgk; SPy 1881),

Table 3 continued

SPy a Gene Description Exponentialphase b

Stationaryphase b

Foldchange c

(stat/exp)

Amino acid transport and metabolism0911 bcaT Branched-chain-amino-acid transferase 2212 656 -3

1070 Dipeptidase 1149 4053 41316 ABC transporter 2179 4373 21541 arcC Carbamate kinase 1081 3585 31544 arcB Ornithine transcarbamylase ND 115 uc1618 cysM O-acetylserine lyase 1380 10213 7

Nucleotide transport and metabolism0160 purA Adenylosuccinate synthetase 84 404 50830 pyrR Pyrimidine regulatory protein ND 346 uc0892 punA Purine nucleoside phosphorylase 2279 4621 22206 guaB Inosine monophosphate dehydrogenase ND 139 uc2206 guaB Inosine monophosphate dehydrogenase 38 1160 31

Lipid transport and metabolism

1637 atoB Acetyl CoA acetyltransferase 18 233 131748 fabF Beta-ketoacyl-ACP synthase II 796 2,010 3

Secondary metabolites biosynthesis, transport, and catabolism0647 Acetoin reductase 990 4423 5

General function prediction only0044 adhA Alcohol dehydrogenase I 3166 6250 21150 nox NADH oxidase 536 5180 10

a SPy numbers are based on the SF370 S. pyogenes genome annotation (Ferretti et al. 2001). More than one isoform of each protein may havebeen detected and the corresponding quantity is shown on a separate lineb Mean quantity of protein was determined with PDQuest 7.3.0 2-D analysis softwarec The fold change in protein abundance in the exponential compared to the stationary phase. Negative values indicate the protein was moreabundant in the exponential phased Protein spot not detectede Unable to calculate ratio because one spot was not detectedf Protein spot quantities are reported for each isoform identied

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triosephosphate isomerase (SPy 0613), cell division initia-tion protein (DivIVAS; SPy 1514), heat-shock protein 70(DnaK; SPy 1760), O-acetylserine lyase (CysM; SPy 1618),and elongation factor Ts (EF-Ts; SPy 2093).

The abundance of several isoforms changed in a growthphase-associated manner. For example, one LDH isoformwas more abundant during the exponential phase of growthwhile a different isoform was more abundant during thestationary phase of growth (Table 3); the corresponding ldhtranscripts were threefold more abundant upon entry in thestationary phase of growth (Table S1). Similarly, theabundance of one DivIVAS isoform (Table S2) was greaterduring the exponential phase of growth, while the abun-dance of three other isoforms increased in the stationaryphase of growth. Growth phase-associated changes indivIVAS transcripts were not detected, suggesting thatDivIVAS changes are mediated post-transcriptionally.Further analysis of tryptic peptides revealed that oneDivIVAS isoform was phosphorylated, at the threonineresidue (position 205). This modication was similar to aprevious report of growth phase-associated phosphorylationof DivIVAS in Mycobacterium tuberculosis (Kang et al.2005). The nature of the other modications and the func-tional signicance of the changes remain to be determined.

Discussion

Growth phase-associated changes in gene expression havebeen used as a convenient, albeit imperfect, method tostudy pathogen adaptation to changing environmentalconditions. Many of the established virulence factors of S.pyogenes are regulated in a growth phase dependent fash-ion, which suggests that the model is relevant to the studyof pathogenesis. In this study, differences in gene expres-sion between the exponential and early-stationary phases of growth were identied at both the transcript and proteinlevel in strain NZ131 (serotype M49). The results showedde-repression of operons involved in non-glucose basedcatabolism, increased expression of stress responsivegenes, and increased expression of a variety of virulence-associated genes in the stationary phase of growth. Resultsobtained with DNA microarrays provided much informa-tion on the expression of genes likely to be present at lowconcentrations in the cell, such as regulatory proteins. Thecomplementary proteomic approach revealed the formationof growth phase-associated isoforms and identied changesin protein abundance not evident at the transcript level. Inaddition, several open reading frames previously annotatedas hypothetical proteins were identied in the 2-DE gels.Together, the results help to dene growth phase specicpatterns of gene expression in a serotype M49 strain of S. pyogenes .

Growth phase-associated transcriptional changesin vitro, in vivo and during growth with human saliva

DNA microarrays were recently used to characterize S.pyogenes gene expression during soft-tissue infections of mice (Graham et al. 2006). Over 80% of the transcripts thatwere most abundant during soft-tissue infections are alsogrowth-phase regulated in strain NZ131. Moreover, genesthat are highly expressed in mice were typically expressedin the early-stationary phase of growth in the present study.These include genes involved in maltodextrin utilization(SPy 1299, 1302, and 1304), a variety of stress responserelated genes including dnaK , grpE , hrcA (SPy 1760-1763), and a cluster of hypothetical protein genes (SPy1260-1265). One of the genes annotated as a hypotheticalprotein (SPy 1260) is similar to Gls24 of Enterococcusfaecalis , which is expressed in response to nutritional stressand contributes to virulence (Giard et al. 2000; Hew et al.2006). Similarly, several genes expressed at a low level insoft tissue infections were also less abundant in the sta-tionary phase of growth in strain NZ131. Exceptions to thistrend included the hypothetical proteins SPy 308-314 and2191-2193. Both soft-tissue infection and the stationaryphase of growth are associated with low concentrations of glucose, which is consistent with the shared patterns of gene expression.

The transcriptome of S. pyogenes MGAS5005 has alsobeen characterized during growth with human saliva(Shelburne et al. 2005). Among the 20 most abundant genetranscripts during the stationary phase of growth in saliva,80% were more abundant during the early-stationary phaseof growth of NZ131 with THY. Among the differences,SPy 0084 (annotated as a hypothetical protein) was themost highly expressed transcript in saliva but was lessabundant during batch culture of NZ131 (Table S1). Othernoteworthy differences were among the malAC and prtS genes, which were more abundant in the stationary phase of growth with saliva but not when grown with THY, prob-ably because THY does not contain a signicant amount of maltodextrins.

Growth phase regulation in different strainsof S. pyogenes

Previously, growth phase-associated changes in the tran-scriptome of S. pyogenes strain 591, which is also a sero-type M49 strain, were analyzed using DNA microarrays(Beyer-Sehlmeyer et al. 2005). Among the growth phase-regulated genes identied in strain 591, 145 genes werealso regulated in growth phase-specic manner in strainNZ131. For 64 loci, a growth phase-associated change inexpression was detected in strain 591 but not in strainNZ131. For 16 genes, opposing results were noted. The

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discrepancies between the two studies typically involvedrelatively minor changes in transcript levels. For example,in NZ131 deoB transcripts, encoding phosphopentomutase(SPy 0890), were threefold more abundant in the early-stationary phase compared to the exponential phase. Incontrast, the transcripts were twofold more abundant dur-ing the exponential phase in strain 591. Minor differencesin experimental reagents (such as media composition) andtechniques may account for some of the differences. Incontrast to results obtained with serotype M49 strains, onlyseven transcripts ( sagA , sagI , arcT , arcA , sda and atransposase gene designated SPyM3_0221), were elevatedin strain MGAS315 (serotype M3) upon entry into thestationary phase of growth (Barnett et al. 2007). Interest-ingly, in strain MGAS315, transcript levels of the arc op-eron varied among the genes in the operon and only arcT and arcA were elevated 2.3 and 4.8-fold, respectively, inthe stationary phase of growth. In contrast, in strain NZ131,transcript levels of all six genes in the presumptive poly-cistronic operon were elevated over 40-fold in the early-stationary phase of growth (Table 1). In strain MGAS315gene-specic degradation of mRNA is an important featureof growth phase-associated gene regulation (Barnett et al.2007). In this regard, the extent to which the rate of mRNAdecay in strain NZ131 contributes to regulation remains tobe determined. The differences in growth phase regulationamong these strains, suggests that the clinical and pheno-typic diversity characteristic of S. pyogenes may result, atleast in part, from differences in the regulation of geneexpression.

Responses to changing culture conditions

The abundance of two LDH (SPy 1151) isoforms changedin a growth phase-associated manner. One isoform wasmore abundant during the exponential phase of growthwhile another was more abundant in the stationary phase of growth (Table 3). Similar results were obtained with S.pneumoniae (Lee et al. 2006). In addition, LDH levels in-crease nearly twofold when S. mutans is exposed to acidicconditions (Wilkins et al. 2002). Moreover, changes in themigration of LDH in S. pyogenes were similar to those in S.mutans . For example, one LDH isoform from S. pyogenesmigrated to a position 1.8 kDa and 0.2 pI units greater thanthe predicted values (Table S2), which is identical to thechanges in the migration of an LDH isoform in S. mutansunder acidic conditions (Wilkins et al. 2002). Thus, at leastsome of the growth phase-associated changes in the abun-dance of protein isoforms identied in S. pyogenes , arelikely to result from acidication of the media.

AtpA, CysM, and Nox1 were more abundant in thestationary phase of growth (Table 3). In E. faecalis , AtpAcontrols cytoplasmic pH under acidic conditions (Voyich

et al. 2003) and thus the increase correlates with decreasingculture pH. CysM is essential for the survival of Pseudo-monas putida during cold shock (Reva et al. 2006), andcontributes to the resistance of Staphylococcus aureus totellurite, hydrogen peroxide, and acidic conditions (Lith-gow et al. 2004). In addition, CysM was more abundantwhen S. pyogenes was grown in the presence of humanplasma (Johansson et al. 2005). Nox1 may help S. pyogenessurvive oxidative stress and its activity is decreased in thepresence of glucose (Gibson et al. 2000). Therefore, it isnot surprising to see an increase in Nox1 during the sta-tionary phase of growth when glucose is depleted. Thus inaddition to well-characterized stress responsive proteins,additional growth phase-regulated proteins were identiedwhich are likely to contribute to adaptation to the changingenvironmental conditions.

Comparison of array and proteomic-derived results

The results obtained with DNA microarrays were signi-cantly more comprehensive compared to those obtainedwith 2-DE, due to the relatively low sensitivity of proteindetection. The total number of unique proteins identied inthis study corresponded to 9% of the predicted proteinswithin the pI range of analysis (47), which is similar toresults obtained in related studies (Jungblut et al. 2000;Guillot et al. 2003; Folio et al. 2004). In general, changes inprotein abundance correlated with changes in transcriptlevels. Nonetheless, the proteomic-derived dataset pro-vided the following information. First, several proteins arepost-translationally modied in a growth phase-dependentmanner. The nature and functional signicance of themodications remain to be determined. Second, severalgrowth phase-associated changes in protein abundancewere identied that were not associated with changes in thecorresponding transcript levels. Of particular note, proteinsinvolved in cell wall and membrane biogenesis, includingRmlD, GlmS, MurD were more abundant in the stationaryphase of growth, although a change in transcript level wasnot detected. Third, several hypothetical open readingframes were found to encode expressed proteins, some of which are also expressed in a growth phase dependentmanner.

In summary, genomewide differences in expressionwere identied between the exponential and early-station-ary phase of growth in a serotype M49 S. pyogenes strain.The changes are mediated by mechanisms that do not ap-pear to rely on secondary sigma factors, which may ac-count for the diversity in growth phase-associated generegulation among various strains of S. pyogenes . Clearly,identifying the mechanisms of growth phase-mediatedcontrol of gene expression is necessary to understandingthe control of virulence factor expression in S. pyogenes .

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Acknowledgments We thank Emily McDowell for completing thequantitative RT-PCR assays. This work was supported by NIAID/ NIH grant RO1 AIO52147 to M.S.C and NIH Grant Number 2 P20RR016479 from the INBRE Program of the National Center forResearch Resources.

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