Time-resolved quantitative proteome profiling of host–pathogen interactions: The response of...

RESEARCH ARTICLE

Time-resolved quantitative proteome profiling of

host–pathogen interactions: The response of

Staphylococcus aureus RN1HG to internalisation by

human airway epithelial cells

Frank Schmidt1�, Sandra S. Scharf1�, Petra Hildebrandt1, Marc Burian1, Jorg Bernhardt2,Vishnu Dhople1, Julia Kalinka1, Melanie Gutjahr1, Elke Hammer1 and Uwe Volker1

1 Interfaculty Institute for Genetics and Functional Genomics, Ernst-Moritz-Arndt-University Greifswald,Greifswald, Germany

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

Received: January 20, 2010

Revised: April 30, 2010

Accepted: May 10, 2010

Staphylococcus aureus is a versatile Gram-positive pathogen that gains increasing importance

due to the rapid spreading of resistances. Functional genomics technologies can provide new

insights into the adaptational network of this bacterium and its response to environmental

challenges. While functional genomics technologies, including proteomics, have been

extensively used to study these phenomena in shake flask cultures, studies of bacteria from

in vivo settings lack behind. Particularly for proteomics studies, the major bottleneck is the

lack of sufficient proteomic coverage for low numbers of cells. In this study, we introduce a

workflow that combines a pulse-chase stable isotope labelling by amino acids in cell culture

approach with high capacity cell sorting, on-membrane digestion, and high-sensitivity MS to

detect and quantitatively monitor several hundred S. aureus proteins from a few million

internalised bacteria. This workflow has been used in a proof-of-principle experiment to

reveal changes in levels of proteins with a function in protection against oxidative damage

and adaptation of cell wall synthesis in strain RN1HG upon internalisation by S9 human

bronchial epithelial cells.

Keywords:

Host–pathogen interaction / In vivo proteomics / Microbiology / Pulse-chase SILAC /

S. aureus / S9 human bronchial epithelial cells

1 Introduction

Staphylococcus aureus is a Gram-positive microorganism that

permanently colonises 20% of healthy adults and transiently

colonises up to 50% of the general population [1]. This very

versatile organism is both a major human pathogen and a

ubiquitous commensal and coloniser of the skin and

mucous membranes [2]. As a result, S. aureus can cause a

wide range of diseases from superficial skin infections to

life-threatening conditions including septicaemia, endo-

carditis, and pneumonia [1, 3]. The increasing resistance of

this pathogen to almost all antibiotics including methicillin

(methicillin-resistant S. aureus (MRSA)) severely compli-

cates therapeutic intervention. Recently, the number of

invasive infections caused by community-acquired S. aureusalso increased in healthy children and young adults [4, 5].

Therefore, the emergence of highly pathogenic MRSAAbbreviations: MEM, minimal essential medium; MRSA, methi-

cillin-resistant Staphylococcus aureus; SILAC, stable isotope

labelling by amino acids in cell culture �These authors contributed equally to this work

Correspondence: Professor Uwe Volker, Interfaculty Institute for

Genetics and Functional Genomics, Ernst-Moritz-Arndt-Univer-

sity, Friedrich-Ludwig-Jahn-Strasse 15a, D-17487 Greifswald,

Germany

E-mail: [email protected]

Fax: 149-3834-8680005

& 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

Proteomics 2010, 10, 2801–2811 2801DOI 10.1002/pmic.201000045

strains (community-acquired MRSA) causing serious infec-

tions in otherwise healthy individuals [6] represents a major

threat and underscores the need for a comprehensive

understanding of virulence mechanisms. To date, a broad

collection of virulence factors, the repertoire of which greatly

varies among different S. aureus strains, is known [7, 8].

However, a better understanding of the adaptation of

S. aureus to the host and its molecular pathogenesis will be

important for the development of new therapeutics. Despite

its capacity for survival and persistence in various tissues,

S. aureus is classically considered as an extracellular patho-

gen [9]. Nevertheless, it is able to internalise and survive for

some days within non-professional phagocytic cells such as

epithelial and endothelial cells as well as osteoblasts [10–12].

Internalisation might constitute a bacterial strategy to evade

the host’s defence reactions and the action of antibiotics that

act mainly onto extracellular pathogens. This intracellular

niche might thus constitute a reservoir for chronic or

relapsing infections, a hypothesis that is compatible with the

high relapse rate and prolonged treatment times, which are

observed for S. aureus infections [13, 14]. Contrary to their

potential importance, so far post-invasion events have been

studied only to a limited extend.

In the post-genomic era, bacterial adaptation reactions can

now be studied at genome-wide scale. Functional genomics

technologies have been used extensively to study adaptation

reactions of in vitro grown S. aureus cultures also at the

proteomic scale. Those studies have also included conditions

that mimic the behaviour in infection-related settings, such as

iron limitation and oxidative stress [15–18]. Certainly, the shift

from extracellular to intracellular life style is linked to a fast

and extensive adaptation, the analysis of which will likely

reveal new facets of S. aureus pathophysiology because shake

flask experiments mimic the conditions the pathogen

encounters inside eukaryotic host cells only to a limited extent.

However, due to the challenge of obtaining sufficient bacterial

cells for such studies, genome-wide functional genomics

analyses of the adaptation reactions of S. aureus to the host cell

environment are rare and thus far confined to expression

profiling. Voyich et al. have studied the adaptation of methi-

cillin-resistant and methicillin-susceptible S. aureus strains to

phagocytosis by human neutrophils and revealed a gene

expression program that likely contributes to evasion of innate

host defence (e.g. upregulation of capsule synthesis, oxidative

stress, and virulence) [19]. In a second example, the response

of S. aureus to internalisation by human epithelial cells has

been studied [11]. Investigations that address the proteome of

internalised S. aureus are still lacking.

In general, proteomic studies of internalised pathogens are

still rare due to the challenge of obtaining a sufficient number

of infecting bacteria. The proteome of Francisella tularensis has

been characterised after isolation from the spleen of mice viaimmunomagnetic separation by a classical 2-DE approach [20].

However, even if this technology is still well suited for the

analysis of physiological questions particular in simple

unicellular bacteria, the number of cells required for such a

comprehensive 2-DE based proteome approach is high, often

exceeding the numbers that are available from in vivo infection

models. Therefore, a different path has been used for the study

of Salmonella enterica isolated from infected mice [21]. The

combination of flow cytometric sorting with highly sensitive

MS allowed a comprehensive analysis of the proteome of

Salmonella and indicated that its robust metabolism limits the

possibilities for the discovery of new antimicrobials [21].

However, for these experiments also large numbers of patho-

gens exceeding 108 were sorted, a number that cannot easily be

accomplished in infection models with S. aureus.In the study presented here, we now provide a first, time-

resolved in vivo proteome profile of S. aureus cells internalised

by S9 human bronchial epithelial cells. To accomplish this

analysis, we have used a pulse-chase SILAC (stable isotope

labelling by amino acids in cell culture) labelling approach in

combination with high-capacity cell sorting, direct on-

membrane digestion, and high-precision LTQ-Orbitrap-MS.

This workflow facilitated the identification of 591 and the

quantitation of 367 proteins at different time points after

invasion from only 3� 106 to 6� 106 S. aureus cells.

2 Materials and methods

2.1 Human bronchial epithelial cells

The S9 cell line (ATCCs number CRL-2778) is a human

bronchial epithelial cell line immortalised with an adeno/

SV40 hybrid virus [22]. The cystic fibrosis phenotype of its

parent cell line, 101 IB3-1, had been corrected by introduc-

tion of the gene encoding wild-type cystic fibrosis trans-

membrane conductance regulator [23]. S9 cells were grown

in minimal essential medium (MEM; PromoCell, Heidel-

berg, Germany) supplemented with 4% FBS (Biochrom,

Berlin, Germany), 1% non-essential amino acids, and 4 mM

glutamine (both PAA Laboratories, Pasching, Austria) in a

humidified incubator at 371C with 5% CO2. This medium is

referred to as eMEM.

2.2 S. aureus strain RN1HG

For SILAC, we used the S. aureus strain RN1HG [24]

carrying the plasmid pMV158GFP [25]. This strain consti-

tutively expresses GFP. Bacteria were grown in MEM

without sodium bicarbonate, arginine, and lysine (custom

formulation by PromoCell), but containing 1% of non

essential amino acids (final concentrations: 0.1 mM alanine,

asparagine, aspartic acid, glutamic acid, glycine, proline,

and serine each; PAA). Furthermore, we added to the

medium 0.6 mM heavy arginine (Arg-6, 13C6) and 0.4 mM

heavy lysine (Lys-6, 13C6; both Cambridge Isotope Labora-

tories, Andover, USA), 10 mM HEPES, 4 mM glutamine,

and 2 mM of each alanine, aspartic acid, cysteine, glutamic

acid, histidine, isoleucine, leucine, phenylalanine, proline,

2802 F. Schmidt et al. Proteomics 2010, 10, 2801–2811


serine, threonine, tryptophane, and valine (all PromoCell).

This medium is referred to as SILAC pMEM.

2.3 Internalisation experiments

For the internalisation of S. aureus by human S9 cells, S9 cells

were seeded in 24-well plates (8� 104 cells per well) and

cultivated for 3 days. One day before internalisation, S. aureusstrain RN1HG pMV158GFP was inoculated in SILAC pMEM

with 20mg/mL tetracyclin (Carl Roth, Karlsruhe, Germany) as

pre-culture. On the day of the internalisation experiment, a

main culture of S. aureus RN1HG pMV158GFP in SILAC

pMEM was inoculated from an exponentially growing pre-

culture to an optical density at 600 nm (OD600nm) of 0.05.

Bacteria were grown to OD600nm 5 0.4. At this time point,

bacteria were diluted in light eMEM to a final concentration of

1.5� 107 bacteria per mL. For stabilisation of pH during co-

cultivation of S. aureus RN1HG and S9 cells, sodium bicar-

bonate (PAN Biotech, Aidenbach, Germany) was added

to a final concentration of 2.2 g/L. The medium of the S9 cells

was replaced by 1 mL of diluted bacterial culture, which

corresponds to a multiplicity of infection of 25 and inter-

nalisation was allowed for 1 h in a humidified incubator at

371C with 5% CO2.

Extracellular bacteria were killed after 1 h by replacement

of the supernatant with 1 mL eMEM containing lysostaphin

(AMBI Products LLC, Lawrence, NY, USA) at a final

concentration of 10mg/mL. Plating assays were routinely

performed to assure that extracellular S. aureus cells were

efficiently killed (reduction of bacterial titre by at least four

orders of magnitude). After 25 min, the first aliquot of S9 cells

was harvested (referred to as time point 1 h) by aspirating the

medium and washing the cells twice with PBS containing

calcium and magnesium (PAA). S9 cells were lysed in 150mL

0.1% Triton X-100 (ESA Laboratories, MA, USA) per well by

incubation for 7 min at 371C with 5% CO2. Lysed cells were

detached by extensive pipetting and lysates of 12 wells were

pooled into a 15 mL tube on ice. Subsequently, four wells

were washed with 200mL FACSFlow buffer (BD Biosciences,

CA, USA) resulting in a total lysate volume of 200mL per well.

The pooled lysate was put on ice and subjected to flow cyto-

metric measurement and sorting. Sampling was performed

hourly for up to 6 h.

2.4 Flow cytometry

Flow cytometric measurements and sorting were performed

on a biosafety level 2 FACSAria high-speed cell sorter

(Becton Dickinson, CA, USA) with a 488 nm excitation from

a blue Coherent Saphire solid-state laser at 18 mW. Optical

filters were set to detect the emitted GFP fluorescence at

515–545 nm (FITC filter block). All data were recorded at

logarithmic scale with the FACSDiva v5.03 software (Becton

Dickinson). Prior to measurement of the bacteria containing

cell lysate, the proper function of the instrument was cali-

brated using Spherotech’s Rainbow calibration particles

(Spherotech, IL, USA).

2.5 Separation of bacteria by cell sorting

Prior to sorting, the drop delay was adjusted to 499%

sorting with ACCUDROP beads (Becton Dickinson). To

limit artefacts during the sorting process, cells to be sorted

were kept on ice and the filter device on which sorted cells

were collected was cooled to 41C. Sorting was performed

from the gate set in SSc versus FITC dot plot at a sort rate of

up to 3000 cells per second with the sort mode ‘‘purity’’

resulting in a sorted sample that was highly pure, at the

expense of recovery and yield. Bacteria were directly sorted

into wells of a 96-well filtration plate with a hydrophilic low

protein binding Durapore membrane (MSGVS2210, Milli-

pore, Schwalbach, Germany). Using a Millivac Maxi

diaphragm pump (Millipore) and a custom-made manifold

base (University of Greifswald, Germany) vacuum was

applied to the filtration plate during the sorting procedure

(pressure approximately 500 mbar) to accommodate three to

six million bacteria per well and remove the fluid. After

sorting, filter wells with attached bacteria were rinsed once

with 200 mL FACSFlow buffer. The membranes were then

cut from the bottom of the wells right after filtration and

stored at �201C until digestion with trypsin.

2.6 Identification of proteins by LC-LTQ-Orbitrap-

MS/MS

Membranes were cut into four pieces, dissolved in 16mL of

25 mM ammonium bicarbonate (pH 7.8) buffer, and

proteolytically digested with 4 mL of trypsin (0.25 mg/mL

Promega, Madison, WI, USA) overnight in a water bath at

371C [26]. Digestion was stopped by addition of TFA (Merck,

Darmstadt, Germany) to a final concentration of 0.1%.

Debris was removed by a centrifugation step at 16 000� gfor 10 min at room temperature. The supernatant was

transferred into a new 1.5 mL tube and peptides were

purified and desalted using C18-ZipTip columns (Millipore).

A commercial vacuum centrifuge concentrator was used to

remove ACN. MS was performed on a Proxeon nano-LC

system (Proxeon, Odense, Denmark) connected to a LTQ-

Orbitrap-MS (ThermoElectron, Bremen, Germany) equip-

ped with a nano-ESI source. For LC separation, we used a

Acelaim PepMap 100 column (C18, 3 mm, 100 A) (Dionex,

Sunnyvale CA, USA) capillary of 15-cm bed length. The flow

rate used was 300 nL/min for the nano column, and the

solvent gradient used was from 0% B (15 min) to 60% B

(290 min). Solvent A was 0.1% acetic acid, whereas aqueous

90% ACN in 0.1% acetic acid was used as solvent B. The MS

was operated in data-dependent mode to automatically

switch between Orbitrap-MS and LTQ-MS/MS acquisition.

Proteomics 2010, 10, 2801–2811 2803


Survey full scan MS spectra (from m/z 300 to 2000) were

acquired in the Orbitrap with resolution R 5 60 000 at m/z400 (after accumulation to a target of 1 000 000 ions in the

LTQ). The method used allowed sequential isolation of up to

five of the most intense ions, depending on signal intensity,

for fragmentation on the linear ion trap using collision-

induced dissociation at a target value of 100 000 ions. Target

ions already selected for MS/MS were dynamically excluded

for 60 s. General MS conditions were electrospray voltage,

1.5 kV; no sheath and auxiliary gas flow. Ion selection

threshold was 500 counts for MS/MS, and an activation

Q-value of 0.25 and activation time of 30 ms were also

applied for MS/MS.

2.7 Analysis of MS data

Differential analysis of SILAC MS data was performed with

Rosetta Elucidator version 3.3.0.1.39 (http://www.rosettabio.

com/products/elucidator; Rosetta Biosoftware, MA, USA).

The frame and feature annotation was done using the

following parameters: retention time minimum cut-off

41 min, retention time maximum cut-off 284 min, m/zminimum cut-off 366 and maximum 1264 m/z. An intensity

threshold of 1000 counts, an instrument mass accuracy of

10.0 ppm, and an alignment search distance of 10.0 min

were applied for binning process. For quantitative analysis,

the data pairs were built using a binning tolerance of

10 ppm, an RT location tolerance of 0.8 min, and a mass

label shift of 6.0202 Da for both arginine and lysine. For

identification, the S. aureus ssp. aureus NCTC 8325 FASTA

sequence (downloaded from NCBI repository, 2009) in

combination with SEQUEST/Sorcerer (Sorcerer version 3.5,

Sage-N Research, CA, USA) was used. MS/MS spectra were

searched with precursor ion tolerance of 20 ppm and a

fragment ion mass tolerance of 1.00 Da. Oxidation of

methionine and SILAC of arginine and lysine (6.0202 Da)

were specified as variable modification. Peptide/protein

identifications were accepted if they exceeded the Peptide-

Teller score of 0.8 and a ProteinTeller score of 0.95 in at

least one experiment [27, 28]. Proteins were considered for

further investigation when at least two peptides in at least

one experiment were identified. For quantitation, only

labelled pairs were considered reaching a Labelled Pair

Status of ‘‘good’’ (at least one of the isotope groups in the

labelled pair was annotated by peptide identification) and for

further quantitative analyses, only proteins having at least

two ‘‘good’’ pairs were taken into account. For protein

classification such as GO, TMD, and signal peptides, the

ProteinCenter (Proxeon Bioinformatics, Version 3.1.2,

Odense, Denmark) was used. Identified proteins of S. aureusssp. aureus NCTC 8325 have been further mapped by using

NCBI to S. aureus COL SACOL locus tags. According to

their function, KEGG [29, 30] orthology classifies genes/

proteins in an acyclic multihierarchical tree-graph. For a

graphical planar representation of this tree-like structure, we

have developed Voronoi Treemaps [31] according to Balzer

and Deussen [32]. Our Voronoi treemap-based layout of

KEGG’s S. aureus COL gene orthology intuitively visualises

the coverage of general cell functions (e.g. metabolism –

blue, cellular processes – yellow, environmental information

processing – red, and genetic information processing –

green) by our gel-free experimental approach.

3 Results and discussion

3.1 Experimental set-up for quantitative analysis of

the bacterial proteome

Even with optimisation of cell sorting for high purity, we

were still expecting significant amounts of contaminating

proteins from human airway epithelial cells. Such contam-

inating human proteins would probably compromise the

quantitation of bacterial proteins present in low quantities.

Therefore, we wanted to apply metabolic labelling using the

well-established SILAC workflow. However, in bacteria, usage

of amino acids labelled with stable isotopes is usually

hampered by the ability of the bacteria to synthesise amino

acids de novo, requiring the availability of mutants auxo-

trophic for arginine and/or lysine. S. aureus strains usually

need a set of amino acids for growth in synthetic medium

albeit having the full complement of enzymes required for

synthesis of amino acids. Therefore, we tested labelling of

S. aureus strain RN1HG in the adapted cell culture medium

pMEM with a mixture of 13C labelled arginine and lysine and

discovered to our surprise saturation labelling in the absence

of a true mutation causing amino acid auxotrophy. This

observation allowed application of SILAC in a pulse-chase

approach, which is an established method for quantitation of

turnover rates and PTMs [33]. Using the workflow depicted in

Fig. 1, S. aureus was initially labelled to completion during

growth in pMEM and then exponentially growing 13C labelled

bacteria were exposed to S9 human bronchial epithelial cells

in eMEM just containing the light forms of arginine and

lysine. Since only light amino acids can be incorporated into

bacterial proteins during contact and internalisation, changes

in the bacterial proteome could be monitored due to the

increase in 12C counterparts compared to 13C amino acids.

After 1 h, non-internalised bacteria were killed by treatment

with lysostaphin, which cannot enter eukaryotic cells.

Subsequently, at each of the six time points, the invasive

bacteria were subjected to flow cytometry and sorted directly

onto a filter device. This set-up allowed the efficient collection

of very dilute bacterial samples in a very small volume in a

time range of 40–50 min. After on-membrane digestion with

trypsin, bacterial proteins/peptides were identified by routine

LTQ-Orbitrap-MS, quantified and classified according to

physiological function. Using the workflow just described, we

performed two independent internalisation experiments in

which we followed the fate of bacteria up to 6 h after inter-

nalisation in hourly intervals.



3.2 Flow cytometric separation of S. aureus RN1HG

for proteome profiling

With the sorting approach applied, we were able to collect

up to 6.4� 106 GFP-positive bacterial events in a standar-

dised sorting time of around 50 min per sampling point.

GFP-positive bacteria were gated in a range of 104�105

intensity units in the FITC/GFP channel and 102�103

intensity units in Side or Orthogonal Scatter (SSc) (Fig. 2).

This range was used to exclude most of the remaining

debris of S9 cells. The number of sorted GFP-positive

bacterial events varied from 3.1 million after 1 h of

internalisation to 6.4 million after 6 h. Both independent

internalisation experiments showed the same trend

of a clear increase in number of events over the entire

period of 6 h, probably indicating that S. aureus RN1HG

continues to multiply after internalisation (see also general

increase of light versus heavy peptide/protein signals

described below).

3.3 Identification of proteins of internalised

S. aureus RN1HG and assignment of proteins

to cellular compartments

Direct sorting of GFP-positive bacteria onto the low protein

binding Durapore membrane facilitated easy concentration,

washing, and on-membrane digestion and concomitant

minimisation of sample loss. The sorted GFP-positive

bacteria were tryptically digested and analysed by LC-LTQ-

Orbitrap-MS using a long gradient of 290 min. Thus, we

were able to obtain from only 3� 106 to 6� 106 sorted

S. aureus cells 491 proteins in experimental series 1 and 526

proteins in experimental series 2, with an overlap of 426

proteins identified in both internalisation experiments with

at least two peptides. Sixty-five proteins were only seen in

experiment 1 and 100 only in experiment 2, which is likely

due to the fact that these proteins were present at threshold

levels sometimes not allowing detection of two peptides.

However, the quantification was not compromised by these

differences because heavy and light peptides were present in

the same sample and we used stringent selection criteria

only looking for quantitative information for proteins

detected in both experiments with at least two peptide ratios.

S. aureus RN1HG pMV158GFP culture

pMEM + C labeled arginine/lysinepMEM + labeled arginine/lysine

for internalization experiment

Level of stable isotope incorporation

h 61h 0

100 % C

Transfer to S9 monolayer

eMEM + C arginine/lysine

start of internalization and

pulse-chase

Replacement of

Lysostaphin

h 6h 0

100 % C

Replacement of

C arginine/lysine by C

cell harvest every hour

0 % C

Flow cytometry and sorting

removal of cell debris from S9

cells by sorting GFP-positive

bacteria on filter device+ -

Tryptic digestDigestion on membrane

purification and measurement

by LTQ-Orbitrap followed by

identification and

quantitation with Elucidator

Pathway mapping

Figure 1. Workflow for quantitative proteome mapping of inter-

nalised S. aureus. S. aureus RN1HG pMV158GFP grown in heavy

medium was transferred to S9 monolayers and internalisation

was allowed for 1 h. After killing of extracellular bacteria with

lysostaphin, eukaryotic cells were lysed at defined time points to

release intracellular S. aureus cells. GFP-positive bacteria were

separated via flow cytometry and collected on a filter device.

Sorted bacteria were subjected to on-membrane tryptic diges-

tion and purified peptides were measured by LTQ-Orbitrap-MS.

Data analysis was performed with Rosetta Elucidator and iden-

tified proteins were mapped onto metabolic pathways.

Figure 2. Summary of sorted bacterial events. Representative

FITC (GFP) versus SSc dot plot of internalised S. aureus RN1HG

pMV158GFP. GFP-positive bacterial events in the highlighted

area were sorted onto a filter device and subjected to analysis by

LC-ESI-MS/MS.

Proteomics 2010, 10, 2801–2811 2805


The ProteinCenter program package was used to calculate

the number of TMDs and signal peptides. As a result, 69% of

the proteins identified were detected with null, 20% with one,

6% with two, and 5% with more than two TMD, indicating

that the majority of the proteins belongs to the cytoplasmic

fraction. This is not surprising because due to the low

amount of material available enrichment was not feasible and

no efforts were made to increase the solubility of proteins

with extreme hydrophobicity such as integral membrane

proteins. Similarly, only 42 out of 591 proteins were predicted

to have a specific signal sequence because true extracellular

proteins were likely lost during cell sorting. To benchmark

the workflow developed, we compared the data of the in vivoproteomics approach with the most comprehensive proteome

analysis reported for S. aureus so far (Fig. 3) [34]. In that

study, complementing subproteomes were analysed for

exponentially growing and glucose-starved S. aureus cultures

to derive by combination of gel-based and gel-free technolo-

gies a coverage of close to 80% of the expressed proteome of

S. aureus COL. First of all, the number of proteins covered in

internalised S. aureus RN1HG was of course with 591 distinct

proteins much smaller than the 1703 proteins reported by

Becher et al. [34]. Second, particularly integral membrane

proteins and extracellular proteins were barely covered due to

the reasons described above. However, we also detected a

number of cell wall-associated proteins and lipoproteins

probably owing to the direct digestion of S. aureus cells

without prior cell disruption. The majority of proteins iden-

tified from S. aureus RN1HG after sorting and on-membrane

digestion were cytoplasmic (83%).

3.4 Mapping the proteome of internalised S. aureus

RN1HG onto pathways – the value of in vivo

proteomics

To investigate the coverage of known pathways in S. aureusstrain RN1HG, a Voronoi treemap-based layout of KEGG’s

S. aureus gene orthology was used to intuitively visualise

coverage of detected proteins compared to the theoretical

ones (Fig. 4). The 591 identified proteins were mainly

assigned to metabolism and genetic information processing.

Thus, particularly good coverage was accomplished for the

ribosome, amino acyl t-RNA synthetases, RNA-polymerase,

and other proteins involved in transcription as well as many

metabolic pathways including among others purine and

pyrimidine, peptidoglycan and fatty acid synthesis, and

central carbon metabolism with gylcolysis/glyconeogenesis,

pentose phosphate pathway, pyruvate metabolism, and

citrate cycle. Thus, the time-resolved proteomics snapshots

provide insights into many important branches of metabo-

lism likely because these proteins are present in rather high

levels. On the other side, information on environmental

information processing is mostly missing because integral

membrane proteins and low-abundance proteins were not

covered (Fig. 4).

Figure 3. Allocation of identified proteins to bacterial compartments and coverage of the different sub-proteomic fractions. The data of the

in vivo proteomics approach presented here are benchmarked with the most extensive proteomic investigation of S. aureus reported thus

far [34]. Starting from DNA array-based expression studies, Becher et al. defined the expressed proteome and then used extensive

proteome analysis of in vitro grown S. aureus COL to provide a proteomic inventory of 80% of the expressed proteome [34]. Six different

protein classes (cytosolic proteins, integral membrane proteins, extracellular proteins, cell wall associated proteins, lipoproteins, and

sortase substrates) are displayed. The figure displays the proportions of the different protein classes as percent of the total proteins

covered and the total number of proteins covered in the respective study. These numbers are presented for the proteins whose genes are

predicted to be expressed in vitro, the proteins identified by Becher et al. and the proteins covered in this study of internalised S. aureus

cells.



3.5 Quantitation of proteins via pulse-chase SILAC

labelling approach

For the quantitative analysis of the time course experiment,

S. aureus strain RN1HG was grown in pMEM containing

heavy 13C arginine and lysine and subsequently, labelled

bacteria were transferred to S9 cells growing in eMEM

containing only light amino acids. This time point was set as

null (internalisation and start of chase phase). After 1 h,

extracellular bacteria were killed and only internalised

bacteria were investigated further. Because light amino

acids accumulate in addition to their remaining heavy

counterparts, for each peptide a labelled and unlabelled

form could be observed. These pairs were further used to

quantify the proteins over a period of 6 h. Figure 5 illustrates

the rationale of such a pulse-chase experiment. At the pulse-

chase starting point, only heavy peptides are present. During

time, the intensities of light peptides continuously increase

until heavy peptides are not detected against the vast

majority of light peptides. By plotting all detected ratios, the

majority of proteins showed slightly increasing intensities of

the light peptides compared to the heavy ones (shown as

grey thick zone). This overall increase of ratios likely reflects

growth of the S. aureus population, but might in part also be

due to turnover of old proteins. Ratios significantly higher

than the median (shown as thin grey line) can be assumed

to either reflect increased levels due to upregulation of

synthesis rates or increased degradation, which would

continuously dilute heavy signal because in contrast to the

light form it would not be refilled by new synthesis. On the

other side, a significantly slower slope of the ratios than the

median (shown as thin grey line) can be interpreted as a

decreased synthesis compared to the phase prior to inter-

nalisation.

Figure 4. Overview of S. aureus pathways

plotted as Voronoi treemap. The map is

based on KEGG’s S. aureus gene orthology

and visualises the coverage of the protein

inventory. Each small field represents an

independent protein. Proteins covered in

KEGG’s S. aureus gene orthology but not

found in this study are displayed in grey and

those identified from internalised S. aureus

are displayed by the colour of the respective

branch of physiology (blue – metabolism;

red – genetic information processing; green

–environmental information processing; and

yellow – cellular processes). The figure

displays the 591 proteins identified in the

study, which are all listed in Supporting

Information Table 1.

Figure 5. Idealised principle of quantitation after pulse-chase

SILAC. S. aureus was initially grown in heavy labelled medium

for saturation labelling of all proteins with heavy amino acids.

For proteome analysis, the bacterial cells were transferred to

light medium allowing incorporation exclusively of light amino

acids and simultaneously exposed to human bronchial epithelial

S9 cells. Thus, the log-ratio of light versus heavy amino acids

allows analysis of changes in the proteome due to the exposure

to S9 cells and internalisation of S. aureus RN1HG by S9 cells.

The idealised grey zone covers the changes observed for the

majority of proteins and represents intracellular growth. If

changes for ratios of proteins in time deviate significantly (more

than twofold) from the median of all proteins (outside of the

zone flanked by thin grey lines), these candidates were consid-

ered as differentially regulated (either up- or downregulated).

Proteomics 2010, 10, 2801–2811 2807


Taking all time points together, we were able to identify

591 proteins from aliquots of only 3�6� 106 sorted

S. aureus cells. Of those, 511 could also be quantified with at

least two ‘‘good’’ labelled pairs; 430 proteins in experiment 1

and 458 proteins in experiment 2. When comparing the

independent experimental series, 367 proteins (72%) could

be quantified in both and those were used for the assess-

ment of the impact of internalisation on the proteome

pattern of S. aureus RN1HG. In Fig. 6, the overall ratios of

the six time points from experiment two (Fig. 6A) and the

Log10 intensities of light versus heavy peptides (Fig. 6B)

were plotted. The calculated median, shown as green line,

continuously increased from 1 h (0.9) to 6 h (4.4) indicating

approximately two doublings within 6 h. Applying a fold

change cut-off of factor two (Fig. 6A, red lines), approxi-

mately 10–15% of the proteins were up- or downregulated

over time (Supporting Information Table 2). An example of

the intensity calculation for a non-regulated protein (EF-Tu)

is given in panel 6D (Fig. 6) and Fig. 7A. After 1 h, the

intensity of the light peptide (red) was almost similar to

the intensity of corresponding heavy peptide. During the

experimental time window, this ratio continuously increased

Figure 6. Display of the time-

resolved analysis of changes in

the proteome pattern of inter-

nalised S. aureus cells. Ratio

data of stable isotope labelling

by amino acids in cell culture

(SILAC) after pulse-chase are

displayed for the time period

from 1 to 6 h after internalisa-

tion into S9 cells. (A) Protein

ratios of experiment two are

plotted. In the x-axis, all quan-

tified proteins are ranked from

the smallest to the largest ratio

to display the overall distribu-

tion of the ratios. The y-axis

displays the protein ratios (L/H)

of experiment two. (B) Dot-Plot

display of intensity values.

Dashed lines represent the

median calculated for each

time point. Dotted lines indi-

cate a twofold change. (C)

Mean intensity values of heavy

and light forms of the protein

PrsA as an example of a

protein displaying internalisa-

tion-related increase in level;

(D) mean intensity values of

heavy and light forms of the

protein EF-Tu, the level of

which only increases in the

light from due to growth of

S. aureus RN1HG in S9 cells.



very similar to the median. In contrast to EF-Tu, the

intensity pattern of the protein PrsA is exemplarily shown in

panel 6C. After 2 h, the intensity of the heavy peptide

compared to the light one is much lower than for EF-Tu,

indicating rapid new synthesis or degradation of PrsA post-

invasion.

3.6 Proteins of S. aureus RN1HG displaying

internalisation-associated changes in level

Considering both replicates and the six time points, it was

obvious that the level changed significantly for a number of

proteins after internalisation. Figure 7 summarises the

time-resolved changes for a number of typical examples

against the background of the majority of proteins, which

displayed only a fourfold increase in intensity during the 6-h

period monitored. Besides EF-Tu, almost all ribosomal

proteins displayed this slow increase just mentioned.

However, the ribsosomal proteins S9 (30S subunit compo-

nent) and the 50S ribosomal protein L9 (see Fig. 7A) showed

a much stronger increase in the light signal intensity which

was distinctively different from those of the other 23 50S

ribosomal proteins observed. However, in both cases, the

strong increase in the light to heavy signal very likely does

not indicate more pronounced synthesis but rather

decreased stability. L9 has been shown to display much

greater exchange in vivo by isotope-transfer experiments

already a long time ago [35, 36].

Other prime examples of regulated proteins are the foldase

protein PrsA [37] (Figs. 6C and 7B), which plays a major

role in protein secretion by assisting in the post-transloca-

tional extracellular folding of several secreted proteins

and the glucose-specific enzyme II A of the PTS system

[SAOUHSC_01430] (Fig. 7B). However, the list of proteins

displaying internalisation-associated increases in the

ratios of light to heavy signals can be extended to proteins

such as ornithine cyclodeaminase, threonine synthase,

aspartate-semialdehyde dehydrogenase, and the iron

compound ABC transporter (Supporting Information Table

2). Besides these proteins, some hypothetical proteins such as

SAOUHSC_00717 showed a very strong post-invasive

response to the host (fold change 5 21). The latter is a 146

amino acid protein consisting of a signal peptide and an

electron transfer domain 13. This domain is a component of a

novel electron-transfer system potentially involved in oxida-

tive modification of animal cell-surface proteins [38]. From a

functional point of view, the observation of fast increases in

the ratios of the peptide methionine sulphoxide reductases

MsrA2 and MsrB was particularly interesting. Peptide

methionine sulphoxide reductases have an important func-

tion as repair enzymes for proteins that have been inactivated

Figure 7. Quantitative analysis of changes in the protein level of internalised S. aureus RN1HG. (A) Total amount of quantified proteins per

experiment (grey lines). Dashed lines represent the median calculated for each time point. Dotted lines indicate a twofold change from the

median. Elongation factor Tu as an example of a non-regulated protein over time and 50S ribosomal protein L9 as the only regulated

protein of all identified 50S ribosomal proteins (see Supplorting Information Tables 1 and 2). (B) Ratio data of the response regulator VraR

and two proteins affected by this two-component regulatory system (PrsA, foldase protein; PTS Enzyme IIA, phosphotransferase system

enzyme IIA). Dashed lines represent the median calculated for each time point.

Proteomics 2010, 10, 2801–2811 2809


due to oxidation [39] and thus this observation might be

interpreted as a hint for the exposure to oxidative stress, a

probable defence reaction of the epithelial cells.

3.7 Regulatory adaptation of S. aureus RN1HG after

internalisation by human S9 cells

Particularly interesting was the observation of a strong

increase in the ratio of light to heavy (post to pre-inter-

nalisation) ratio of the response regulator VraR (Fig. 7B).

Dramatic changes in the lifestyle as, for example, the shift

from extracellular to intracellular life, have to be linked to

fast regulatory adaptation of the pathogen to the new

environment. In bacteria, environmental stimuli can be

sensed and transduced by two-component systems [40]. Of

the total of 17 potential two-component systems that have

been identified in S. aureus [41], in this study only the

response regulator VraR (vancomycin resistance-associated

sensor/regulator) probably increased in level over time after

internalisation into S9 cells. At least a twofold upregulation

compared to the bulk proteins could be observed 3 h after

internalisation, which constantly remained high even after

6 h (Fig. 7B). For this example, we believe in a correlation of

increased signal, synthesis, level, and activity because two of

the thirteen target genes reported in a transcriptional study

of the VraSR system [42], namely PrsA and the glucose-

specific enzyme II A of the phosphotransferase system

[SAOUHSC_01430], were shown to display strongly

increased light to heavy ratios as well (Fig. 7B). These data

are plausible from a physiological point of view because

VraSR positively modulates cell wall biosynthesis by upre-

gulation of genes associated with peptidoglycan biosynthesis

[42]. In this study, we did not observe upregulation of MurZ

and PBP2 above the threshold of two (SgtB could not be

quantified). Although the VraSR system was active during

internalisation and survival of S. aureus within S9 cells, cell

wall biosynthesis was probably not the reason for activation.

Since it is also known that VraSR responds to damage of cell

wall structure, i.e. by antibiotics that target cell wall pepti-

doglycan biosynthesis (b-lactams and vancomycin) one may

speculate that within S9 cells, cell wall damage occurs,

which is then sensed by VraSR. Because no antibiotic was

added during cultivation this might be due to host mole-

cules such as components of the complement system.

Another reason of the activity of VraSR might be a certain

rearrangement of the cell envelope.

These hypotheses can now be followed because first hints

have been provided by our study of the in vivo proteome of

S. aureus RN1HG.

4 Concluding remarks

With this study, we provide a workflow allowing time-

resolved analysis of as little as a few million internalised

S. aureus cells, thus opening the possibility of comparative

profiling of the response of wild-type strains and isogenic

mutants lacking important regulators or virulence factors.

This technology thus provides an important tool for the

deciphering of the adaptational network of S. aureus.The changes in the proteome observed for the wild-type

strain S. aureus RN1HG upon internalisation indicate a

response to oxidative stress and adjustments in cell wall

synthesis.

The authors are grateful to Ulrike Lissner for excellent technicalassistance and Leonard Menschner for construction of RN1HGpMV158GFP. Work in the Interfaculty Institute of Genetics andFunctional Genomics (U. V., F. S.) was supported within theframework of the collaborative research project SFBTRR34 by theDeutsche Forschungsgemeinschaft. The authors are grateful forthe contribution of Juliane Siebourg from the ETH Zurich for thecreation of the Voronoi treemaps and Susann M.uller, Nico Jehmlich,and Dirk Benndorf for help in establishing the cell sorting protocol.

The authors have declared no conflict of interest.

5 References

[1] Lowy, F. D., Staphylococcus aureus infections. N. Engl.

J. Med. 1998, 339, 520–532.

[2] Foster, T. J., The Staphylococcus aureus ‘‘superbug’’.

J. Clin. Invest. 2004, 114, 1693–1696.

[3] Kristinsson, K. G., Adherence of staphylococci to intravas-

cular catheters. J. Med. Microbiol. 1989, 28, 249–257.

[4] Maltezou, H. C., Giamarellou, H., Community-acquired

methicillin-resistant Staphylococcus aureus infections. Int.

J. Antimicrob. Agents 2006, 27, 87–96.

[5] Weber, J. T., Community-associated methicillin-resistant

Staphylococcus aureus. Clin. Infect. Dis. 2005, 41, S269–S272.

[6] Furuya, E. Y., Lowy, F. D., Antimicrobial-resistant bacteria in

the community setting. Nat. Rev. Microbiol. 2006, 4, 36–45.

[7] Sibbald, M. J., Ziebandt, A. K., Engelmann, S., Hecker, M.

et al., Mapping the pathways to staphylococcal pathogen-

esis by comparative secretomics. Microbiol. Mol. Bio.l Rev.

2006, 70, 755–788.

[8] Ziebandt, A. K., Kusch, H., Degner, M., Jaglitz, S. et al.,

Proteomics uncovers extreme heterogeneity in the Staphy-

lococcus aureus exoproteome due to genomic plasticity and

variant gene regulation. Proteomics 2010, 10, 1634–1644.

[9] Lowy, F. D., Is Staphylococcus aureus an intracellular

pathogen? Trends Microbiol. 2000, 8, 341–343.

[10] Sinha, B., Francois, P. P., Nusse, O., Foti, M. et al., Fibro-

nectin-binding protein acts as Staphylococcus aureus

invasin via fibronectin bridging to integrin alpha5beta1.

Cell. Microbiol. 1999, 1, 101–117.

[11] Garzoni, C., Francois, P., Huyghe, A., Couzinet, S. et al.,

A global view of Staphylococcus aureus whole genome

expression upon internalization in human epithelial cells.

BMC Genomics 2007, 8, 171.



[12] Jevon, M., Guo, C., Ma, B., Mordan, N. et al., Mechanisms of

internalization of Staphylococcus aureus by cultured

human osteoblasts. Infect. Immun. 1999, 67, 2677–2681.

[13] Plouin-Gaudon, I., Clement, S., Huggler, E., Chaponnier, C.

et al., Intracellular residency is frequently associated with

recurrent Staphylococcus aureus rhinosinusitis. Rhinology

2006, 44, 249–254.

[14] Clement, S., Vaudaux, P., Francois, P., Schrenzel, J. et al.,

Evidence of an intracellular reservoir in the nasal mucosa of

patients with recurrent Staphylococcus aureus rhinosinu-

sitis. J. Infect. Dis. 2005, 192, 1023–1028.

[15] Scherl, A., Francois, P., Charbonnier, Y., Deshusses, J. M.

et al., Exploring glycopeptide-resistance in Staphylococcus

aureus: a combined proteomics and transcriptomics

approach for the identification of resistance-related

markers. BMC Genomics 2006, 7, 296.

[16] Weber, H., Engelmann, S., Becher, D., Hecker, M., Oxidative

stress triggers thiol oxidation in the glyceraldehyde-3-

phosphate dehydrogenase of Staphylococcus aureus. Mol.

Microbiol. 2004, 52, 133–140.

[17] Wolf, C., Hochgrafe, F., Kusch, H., Albrecht, D. et al.,

Proteomic analysis of antioxidant strategies of Staphylo-

coccus aureus: diverse responses to different oxidants.

Proteomics 2008, 8, 3139–3153.

[18] Hochgr .afe, F., Wolf, C., Fuchs, S., Liebeke, M. et al., Nitric

oxide stress induces different responses but mediates

comparable protein thiol protection in Bacillus subtilis and

Staphylococcus aureus. J. Bacteriol. 2008, 190, 4997–5008.

[19] Voyich, J. M., Braughton, K. R., Sturdevant, D. E., Whitney,

A. R. et al., Insights into mechanisms used by Staphylo-

coccus aureus to avoid destruction by human neutrophils.

J. Immunol. 2005, 175, 3907–3919.

[20] Twine, S. M., Mykytczuk, N. C., Petit, M. D., Shen, H. et al., In

vivo proteomic analysis of the intracellular bacterial patho-

gen, Francisella tularensis, isolated from mouse spleen.

Biochem. Biophys. Res. Commun. 2006, 345, 1621–1633.

[21] Becker, D., Selbach, M., Rollenhagen, C., Ballmaier, M.

et al., Robust Salmonella metabolism limits possibilities for

new antimicrobials. Nature 2006, 440, 303–307.

[22] Zeitlin, P. L., Lu, L., Rhim, J., Cutting, G. et al., A cystic

fibrosis bronchial epithelial cell line: immortalization by

adeno-12-SV40 infection. Am. J. Respir. Cell. Mol. Biol.

1991, 4, 313–319.

[23] Flotte, T. R., Afione, S. A., Solow, R., Drumm, M. L. et al.,

Expression of the cystic fibrosis transmembrane conduc-

tance regulator from a novel adeno-associated virus

promoter. J. Biol. Chem. 1993, 268, 3781–3790.

[24] Pohl, K., Francois, P., Stenz, L., Schlink, F. et al., CodY in

Staphylococcus aureus: a regulatory link between metabo-

lism and virulence gene expression. J. Bacteriol. 2009, 191,

2953–2963.

[25] Nieto, C., Espinosa, M., Construction of the mobilizable

plasmid pMV158GFP, a derivative of pMV158 that carries

the gene encoding the green fluorescent protein. Plasmid

2003, 49, 281–285.

[26] Schmidt, F., Fiege, T., Hustoft, H. K., Kneist, S., Thiede, B.,

Shotgun mass mapping of Lactobacillus species and

subspecies from caries related isolates by MALDI-MS.

Proteomics 2009, 9, 1994–2003.

[27] Keller, A., Purvine, S., Nesvizhskii, A. I., Stolyar, S. et al.,

Experimental protein mixture for validating tandem mass

spectral analysis. OMICS 2002, 6, 207–212.

[28] Nesvizhskii, A. I., Keller, A., Kolker, E., Aebersold, R.,

A statistical model for identifying proteins by tandem mass

spectrometry. Anal. Chem. 2003, 75, 4646–4658.

[29] Kanehisa, M., A database for post-genome analysis. Trends

Genet. 1997, 13, 375–376.

[30] Kanehisa, M., Goto, S., KEGG: kyoto encyclopedia of genes

and genomes. Nucleic Acids Res. 2000, 28, 27–30.

[31] Bernhardt, J., Funke, S., Hecker, M., Siebourg, J., Sixth

International Symposium on Voronoi Diagrams 2009, pp.

233–241.

[32] Balzer, M., Deussen, O., IEEE Symposium on Information

Visualization, InfoVis 2005 pp. 49–56.

[33] Gustavsson, N., Greber, B., Kreitler, T., Himmelbauer, H.

et al., A proteomic method for the analysis of changes in

protein concentrations in response to systemic perturba-

tions using metabolic incorporation of stable isotopes and

mass spectrometry. Proteomics 2005, 5, 3563–3570.

[34] Becher, D., Hempel, K., Sievers, S., Z .uhlke, D. et al., A

Proteomic view of an important human patho-

gen-Towards the quantification of the entire Staphylo-

coccus aureus proteome. PLoS One 2009, 4, e8176.

[35] Robertson, W. R., Dowsett, S. J., Hardy, S. J., Exchange of

ribosomal proteins among the ribosomes of Escherichia

coli. Mol. Gen. Genet. 1977, 157, 205–214.

[36] Subramanian, A. R., van Duin, J., Exchange of individual

ribosomal proteins between ribosomes as studied by heavy

isotope-transfer experiments. Mol. Gen. Genet. 1977, 158, 1–9.

[37] Sarvas, M., Harwood, C. R., Bron, S., van Dijl, J. M., Post-

translocational folding of secretory proteins in Gram-posi-

tive bacteria. Biochim. Biophys. Acta 2004, 1694, 311–327.

[38] Iyer, L. M., Anantharaman, V., Aravind, L., The DOMON

domains are involved in heme and sugar recognition.

Bioinformatics 2007, 23, 2660–2664.

[39] Kryukov, G. V., Kumar, R. A., Koc, A., Sun, Z., Gladyshev,

V. N., Selenoprotein R is a zinc-containing stereo-specific

methionine sulfoxide reductase. Proc. Natl. Acad. Sci. USA

2002, 99, 4245–4250.

[40] Novick, R. P., Autoinduction and signal transduction in the

regulation of staphylococcal virulence. Mol. Microbiol.

2003, 48, 1429–1449.

[41] Benton, B. M., Zhang, J. P., Bond, S., Pope, C. et al., Large-

scale identification of genes required for full virulence of

Staphylococcus aureus. J. Bacteriol. 2004, 186, 8478–8489.

[42] Kuroda, M., Kuroda, H., Oshima, T., Takeuchi, F. et al., Two-

component system VraSR positively modulates the regu-

lation of cell-wall biosynthesis pathway in Staphylococcus

aureus. Mol. Microbiol. 2003, 49, 807–821.

Proteomics 2010, 10, 2801–2811 2811


Time-resolved quantitative proteome profiling of host–pathogen interactions: The response of...

Documents

Transcript of Time-resolved quantitative proteome profiling of host–pathogen interactions: The response of...