Data-independent acquisition mass spectrometry promises higher performance in terms of quantification and reproducibility compared to data-dependent acquisition mass spectrometry methods. To enable high-accuracy quantification of Staphylococcus aureus proteins, we have developed a global ion library for data-independent acquisition approaches employing high-resolution time of flight or Orbitrap instruments for this human pathogen. We applied this ion library resource to investigate the time-resolved adaptation of S. aureus to the intracellular niche in human bronchial epithelial cells and in a murine pneumonia model. In epithelial cells, abundance changes for more than 400 S. aureus proteins were quantified, revealing, e.g., the precise temporal regulation of the SigB-dependent stress response and differential regulation of translation, fermentation, and amino acid biosynthesis. Using an in vivo murine pneumonia model, our data-independent acquisition quantification analysis revealed for the first time the in vivo proteome adaptation of S. aureus. From approximately 2.15 × 105 S. aureus cells, 578 proteins were identified. Increased abundance of proteins required for oxidative stress response, amino acid biosynthesis, and fermentation together with decreased abundance of ribosomal proteins and nucleotide reductase NrdEF was observed in post-infection samples compared to the pre-infection state.
The pathogen Staphylococcus aureus is a major health threat, both in the general population and in the hospital environment. Although the bacterium can be a harmless nasal or skin commensal in part of the population1, 2, S. aureus can cause a variety of diseases as an opportunistic pathogen3. S. aureus strains resistant to both standard antibiotics and to last-resort antibiotics such as vancomycin have been reported4. Vaccination against S. aureus infection has been successful in rabbit models5, but vaccination trials in humans have thus far failed6. S. aureus is not only an extracellular pathogen; it can also bypass the host immune system and therapeutic countermeasures by entering human cells via phagocytosis. Intracellular S. aureus is extremely durable and resistant, making it difficult to treat ultimately causing relapse of infections7.
The investigation of bacterial adaption is important for the discovery of new anti-staphylococcal strategies. During infection the interplay between host and pathogen is reflected in a changing protein composition. Until recently, only transcriptomic studies have been used to quantify gene expression of infection models. However, transcriptional profiling fails to provide information on post-transcriptional regulation, post-translational modification or protein localization. Although proteomic studies using mass spectrometry MS have partially met these requirements, there is the need to extend proteomics to provide highly reproducible, quantitative, sensitive, and comprehensive analysis with a single measurement of complex samples to enable the delineation of biological interactions relevant for infection.
Qualitative and quantitative proteomics techniques range from classical gel-based (two-dimensional gel electrophoresis (2-DE)) to more sensitive gel-free approaches, including protein or peptide labeling for relative quantitation. Modern gel-free mass spectrometry-based techniques in combination with label-free quantitation are superior to 2-DE in terms of throughput and sensitivity. The reasons for such significant improvements using gel-free MS are due to developments in bottom-up proteomics, where a protein sample is proteolytically digested into peptides typically with trypsin and analyzed by reversed-phase high-performance liquid chromatography (RP-HPLC) separation coupled to a mass spectrometer by an electrospray interface. The most common mass spectrometric method is data-dependent acquisition (DDA), where the most abundant peptide ion precursors (“TopN”) from a survey scan (MS1 spectrum) are isolated individually and fragmented resulting in an MS/MS or MS2 spectrum. DDA data are analyzed by searching the MS1 and corresponding MS2 masses using sequence database search engine tools (e.g., Comet8) against a species-specific protein sequence database. The search engine performs an in silico digestion of the protein sequence database based on the selected protease to assign putative peptide ion identifications to each MS2 spectrum. The spectral matching results are aggregated in a list of peptide identifications. The drawback of this method is the stochastic process by which ions are selected, coupled with the fact that the number of different ions that can be selected per unit time is often smaller than the number of different ions entering the instrument.
In recent years, further technical improvements of MS instruments and data analysis methods have enabled in vitro studies in which roughly 1,700 proteins of the S. aureus strain COL were identified by DDA; this corresponds to 65% of the predicted open reading frames (ORFs)9. However, these high protein identification rates were only possible by combining data from different samples, the use of fractionation techniques, and the separate analysis of different bacterial compartments. This required a large amount of instrument time, which is not compatible with the analysis of large samples series or with the low material quantities available from in vivo infection experiments.
Thus far, we have achieved the identification of ~1,450 S. aureus proteins using 12 separate analyses, each from 2 × 106 sorted bacterial cells extracted from cell culture infection samples10, whereas in a single analysis, identification rates yield on average only 1,109 proteins (+/−56 proteins).
Innovative advancements in MS methods and instruments promise to render such exhaustive and time-consuming efforts unnecessary in the future. Recently introduced data-independent acquisition mass spectrometry (DIA-MS) methods do not rely on selecting the “TopN” most abundant peptide precursor ions for fragmentation. Rather, the techniques record multiplexed fragmentation spectra of all peptide precursor ions within given sequential mass windows. DIA has the advantage of collecting periodic spectral data for all peptide ions entering the instrument. Thus, it is a parallel, unbiased, and continuous process, in contrast to the DDA method, which is a serial, biased, and discontinuous process. DIA enables quantitation with high reproducibility11 similar to RNA transcriptomics arrays. However, the prerequisite for the most common analysis workflow of DIA data is the existence of a peptide fragment ion spectral library12, 13, which enables robust identification of collisional-induced fragmentation of peptide ions based on these fragment ion signatures derived from either previously collected data from traditional DDA workflows or from the data contained within the current dataset.
Results and Discussion
Generation of an S. aureus proteome DIA ion library
We developed an S. aureus PeptideAtlas (www.peptideatlas.org) from a comprehensive set of data (Supplemental Material 2.1, Supplemental Table 1) as a prerequisite for the generation of a peptide ion fragment spectral library. The S. aureus PeptideAtlas build comprises an extensive collection of high-accuracy MS/MS data sets acquired in a uniform manner, and contains an extensive collection of detected proteotypic peptides. A special feature of bacteria is the high variation in the proteome in response to environmental cues. Therefore, sample sets covering the different environmental conditions encountered in nature and extensive proteome fractionation are required for a comprehensive coverage of the bacterial proteome in the PeptideAtlas (Supplemental Material 2.2).
We have integrated our high-resolution MS/MS data sets (Supplemental Table 1) measured with standardized retention time peptides (iRT)14 into a very large fragment spectral ion library generated from data without iRT spike-in peptides15. To achieve this, the peptides of the standard ion library (12 DDA measurements with iRT-peptides, Q ExactiveTM and TripleTOF® 5600+) were used as “retention time landmarks” to align the RTs of the high-resolution MS data of 152 proteome measurements without iRT-peptides (Supplemental Material 2.3). The resulting DIA ion library consists of 2,057 proteins with 25,664 peptides with a length of 7 to 49 amino acids. This final ion library represents approximately 72% of the theoretical S. aureus proteome and is publicly available at http://www.swathatlas.org. The remaining 28% of the S. aureus proteome was not detected either because these proteins are not expressed under the conditions tested, are in too low abundance to be detected by DDA, or have only peptides with physiochemical properties, including hydrophobicity, hydrophilicity, isoelectric point or length, that cannot be readily detected by MS analysis. By using prediction tools such as CHEMscore16, CONSeQuence17 or DetectabilityPredictor18, which consider such physiochemical properties to calculate the detectability of peptides, it is found that a part of the proteome of S. aureus is not accessible by classical MS approaches15. To achieve greater proteome coverage of S. aureus, additional fractionation or enrichment methods, different enzyme combinations for protein digestion, or alternative MS methods such as top down proteomics must be applied in combination with cultivation of the pathogen under a wider array of different physiological conditions.
The S. aureus spectral library developed here is a highly valuable proteomic resource and an essential basis for several targeted MS approaches such as Selected Reaction Monitoring (SRM), Parallel Reaction Monitoring (PRM) and DIA (e.g., SWATHTM, HRM-MSTM) to perform relative and absolute quantification of proteins. Furthermore, shotgun data analyses using peptide fragment spectral libraries and spectral library search tools such as SpectraST rely on high-quality spectral data and can outperform sequence-based database searches in terms of speed, sensitivity, and specificity19. In the present study, we identified and quantified on average with DIA-MS 1,433 proteins (+/−43 proteins) in a single run, which is 29.2% more than previously by DDA-MS analyses (Fig. 1). In total, we quantified 1,750 S. aureus proteins over 19 measurements in an S9 bronchial epithelial cell infection model. SRM is currently the gold standard for high-quality peptide quantification across multiple samples due to its high sensitivity, specificity, and wide dynamic range20. It relies on MS-generated prior-knowledge such as multiple precursor and fragment masses with known relative intensities in combination with the retention time of the targeted peptides. The S. aureus PeptideAtlas is a valuable proteome repository, facilitating the design of targeted proteome analysis approaches and extensive proteome quantitation.
S. aureus proteome DIA ion library – range of applications
It is well known that the pathogen S. aureus possesses a high genomic variability, and currently there are 47 sequenced and annotated strains available in public databases. To investigate if our recently developed fragment ion library, which is based upon the S. aureus strain HG001, can also be used to analyze these additional 46 strains, we generated a phylogenetic tree based on each strain’s proteome and performed an in-silico digestion of each strain. Finally, we compared the resulting theoretical peptide sequences with the peptides in the fragment ion library. Approximately 80% of the peptides contained in our fragment ion library were observed in nearly all strains (Supplemental Fig. 1) except for S. aureus MSHR1132 with a coverage of approximately 60%, the reason for that is not completely understood.
The S. aureus fragment ion library constitutes a core set of assays which provide an excellent starting point for use with strains other than S. aureus HG001. However, for addressing variable parts of the proteome the fragment ion library would need additional proteomic data covering these strain-specific sets of protein variants.
Furthermore, we investigated the cross-device usability of DIA-MS assays generated with multiple mass spectrometer types (i.e., SCIEX TripleTOF® 5600+ and Thermo Q ExactiveTM instruments). Our data suggest that DIA-MS analysis can be performed with our S. aureus fragment ion library on both devices, and that the ion library is well-suited for correlation and cross-lab studies, underlining the benefit of an established, well-characterized fragment ion library for the scientific community (Supplemental Material 2.4). To address sensitivity afforded by different instruments, a thorough analysis of the dynamic range of the DIA-MS measurements of S. aureus protein samples revealed the necessity of applying at least 1,000 ng of digested protein or peptide sample material from approximately 1 × 106 S. aureus cells (or more) on a Q ExactiveTM mass spectrometer for DIA-MS analysis (Supplemental Material 2.5).
Human proteome interference in S. aureus infection experiments
The goal of the development of DIA-MS methods and the generation of databases such as the S. aureus PeptideAtlas described here is to provide tools and resources to analyze the adaptation reactions and pathophysiology of S. aureus as comprehensively as possible. This is especially important for infection-relevant settings, either in cell culture or in in vivo models. In contrast to classical in vitro shake flask experiments, both experimental settings present the challenge of intermixed host cell material, which can contaminate the bacterial samples even when purification strategies like cell sorting are applied (Fig. 2a). Host proteins in samples could interfere during the MS analysis and may cause problems in the quantification, because contaminating host peptides can have similar precursor and product ion mass values as the peptides targeted from the pathogen.
To test the host protein sample contamination interference, defined mixtures of S9 human cell protein and S. aureus protein extracts were prepared, measured in DDA-MS and DIA-MS mode, and analyzed with MaxQuant (DDA; FDR 0.001) and SpectronautTM (DIA; FDR 0.001), respectively (Fig. 2b). The numbers of S. aureus peptide identifications obtained in DDA-MS or DIA-MS analysis continuously followed the mixture gradient (Fig. 2b). Despite comparable numbers of peptide identifications, the coefficient of determination of the linear regression model (x = dilution; y = peptide intensity normalized to pure S. aureus sample) was significantly closer to a perfect linear model fit in the DIA-MS approach compared to the DDA-MS approach (Fig. 2c). The median of the normalized peptide intensities was appreciably closer to the expected value of the proteome dilution in the DIA-MS approach, whereas the DDA-MS approach showed a nearly 2-fold deviation from the expected value (Fig. 2d). In the DDA-MS approach, the ions were selected in an isolation window of 3 m/z, which was specified in the method. To determine if human peptide ions in the mixture were interfering inside the isolation window, we used the ions detected in the DIA-MS approach and searched for the closest human precursor m/z in an iRT window of ±1 min for every detected S. aureus ion. The S. aureus and closest human precursor m/z were used to calculate the difference in precursor m/z, and as a result, 86% of the S. aureus ions were contaminated by an interfering human precursor inside the isolation window of 3 m/z in the DDA data (Fig. 3).
In summary, only minor interference from contaminating host material was detected when using DIA-MS compared to high interferences on DDA MS1-based quantification and, therefore, DIA-MS analysis and the ion library generated in this study can be preferentially applied to investigate infection-related models or proteome mixtures, further emphasizing the value of providing publicly available resources such as the S. aureus PeptideAtlas (www.peptideatlas.org) and SWATHAtlas (www.swathatlas.org).
S9 bronchial epithelial cell infection model
An S9 bronchial epithelial cell culture infection model was used to investigate effects during the encounter with the host and internalization of bacteria into host cells. The cell culture model aims to mimic processes of human infection. To gain insight into the intracellular lifestyle of S. aureus, especially in the lung epithelial cell environment, several cell-line infection studies were carried out. While previous studies10, 21,22,23 only focused on the very first hours (up to 8 h) post-infection (p.i.), which represent the initial adaptation to the intracellular milieu and initial intracellular growth, we also included later time points (24 h and 32 h p.i.) in the S9 bronchial epithelial cell infection model. In order to visually follow the pathogen over time inside host cells, GFP-labeled S. aureus HG001 was used21, 24 for live-cell imaging (Fig. 4a, Supplemental Movie S1). During the first hours of intracellular life, the pathogen adapted to its new environment and reached a maximal intracellular growth rate (estimated generation time = 1.81 h) between 7 and 10 h p.i., which was visualized by the quantified average GFP signal over time with a peakmax at approximately 12 h p.i., leading to killing of host cells after that point in time (Fig. 4b).
The extracellular medium of the cell culture system contained lysostaphin to eliminate S. aureus released from destroyed host cells and to preserve the cell culture, which enabled monitoring of strictly intracellular S. aureus over longer periods of time. The lysostaphin-mediated killing of S. aureus cells, which were released to the culture medium after host cell lysis, resulted in a sudden decrease of mean GFP intensity as shown in the live-cell imaging movie and a corresponding decrease of the count of internalized S. aureus per host cell (Fig. 4b,c). Mainly non-growing, intracellularly surviving bacteria were observed in low amounts after 12 h p.i.
The DIA-MS analysis was carried out with 5 × 106 S. aureus cells per sample and resulted in the identification of a total of 1,719 proteins over all samples. Of these, 1,309 proteins could be quantified against the non-adherent control sample (non-internalized bacteria after 1 h of infection), and 410 proteins displayed significantly altered abundance (absolute fold-change ≥1.5) in at least one sample time point compared to the control (Fig. 5, Supplemental Table S4). The magnitude of protein abundance changes increased from the earlier to the later sample points (Fig. 6). In direct comparison to the study of Surmann and co-workers10 we quantified 305 additional proteins and missed only 36. An increased coverage of approximately 15% in mean could be observed for the regulon-annotated proteins (Fig. 7a).
DDA data10 provided stochastic measurements in identification and quantitation whereas the DIA data collected (this work) proved superior in consistency of detection and quantification when comparing replicates of intracellularly growing S. aureus cells, which contain a higher proportion of human proteins sticking to the sample (Fig. 7b). This effect was not as intense in nearly pure bacterial samples (non-adherent control).
With respect to the physiological adaptation of S. aureus after internalization, two different intracellular states could be distinguished: bacterial growth in the early phase (8 h p.i.) and survival of slow/non-growing bacteria in the late phase (24 h and 32 h p.i.), presumably reflecting the fact that two S. aureus subpopulations established inside host cells. As illustrated in Fig. 4c (bottom right graph) and the time-lapse movie (Supplemental Movie S1), the fraction of slow-/non-growing, intracellularly surviving bacteria (24 h and 32 h p.i.) constituted less than 10% of internalized staphylococci present at 8 h p.i. (measured as number of bacteria per S9 cell). Thus, changes in protein abundances observed at 8 h p.i. mainly reflect the adaptation of the intracellularly growing subpopulation, whereas the late intracellular samples (24 h and 32 h p.i.) represent reactions of slow-/non-growing, intracellularly surviving bacteria (Supplemental Fig. 2). Accordingly, proteins undergoing abundance changes during the course of infection were classified into three groups (Fig. 8): 1. proteins present in different amounts compared to the non-adherent control at all intracellular time points, i.e., proteins affected in both subpopulations; 2. proteins altered at 8 h p.i., i.e., in the intracellularly growing subpopulation, and returned to control level in the late phase (24 h and 32 h p.i.); 3. proteins exhibiting different abundance at 24 h and/or 32 h compared the 8 h sample, i.e., between intracellularly growing and slow-/non-growing staphylococci.
Group 1 included a number of proteins under the control of two global regulators modulating gene expression in response to the nutritional status of the cell, i.e., the carbon catabolite repressor CcpA and the GTP and amino acid sensing repressor CodY. Increased abundance of CcpA-regulated proteins reflects the switch from utilization of glucose to utilization of nutrients available within host cells25. Specifically, enzymes like GlpK (glycerol kinase), a putative dihydroxyacetone kinase (SAOUHSC_00655), PckA (phosphoenolpyruvate carboxykinase), RocF (arginase), RocD (ornithine transaminase), RocA (pyrroline-5-carboxylate dehydrogenase), GudB (glutamate dehydrogenase), and CitC (isocitrate dehydrogenase) showed increased abundance after internalization (8 h p.i.). The proteins encoded by the CcpA-repressed fatty acid degradation operon (FadA, FadB, FadD, FadE and FadX) were only found in intracellular samples and not detectable in the non-adherent control. Interestingly, levels of all these CcpA-regulated proteins appeared further enhanced in the slow-/non-growing population (24 h and 32 h p.i.), where additional CcpA-regulated proteins showed increased amounts (including, for example, GlpD, AldA, Ald2, Lip, AcsA, MurP, RbsK, PutA, and IpdC). The increased levels of GlpK and GlpD might reflect the utilization of glycerol, which is known as one of the preferred carbon sources during intracellular growth of other pathogens such as Listeria monocytogenes 26. For S. aureus, previous studies reported upregulation of the uhpT gene encoding a hexose phosphate transporter upon internalization23, 27, 28. Enzymes of the TCA cycle increased in level in S. aureus following internalization as already observed by Surmann et al.10. However, significantly higher levels at 8 h p.i. were only observed for CitC and FumC, whereas most of the TCA enzymes increased in level only at 24 h and 32 h p.i. (see below).
Derepression of the CodY regulon was reflected by higher amounts of amino acid biosynthetic enzymes (DapA, DapB, DapD, Asd, LysA, IlvE, IlvC, Hom, ThrC, SerA, and ArgG), subunits of the oligopeptide transporter Opp (OppA, OppC, OppD, OppF), and the acetoin reductase ButA at all time points in the intracellular niche. Increased levels of enzymes involved in arginine and lysine biosynthesis were already observed in three different host cell lines including S910. Most of the CodY-dependent proteins showed a further slight increase at the later time points which was highest (2-fold) in the case of ButA. Remarkably, we observed increasing levels of the ribosome hibernation factor SaHPF (SAOUHSC_00767) recently identified as potential CodY target28 during the whole course of infection. It was shown that highest levels of 100 S ribosomes and HPF occur in S. aureus at the transition from exponential to stationary phase29 and that preservation of ribosome integrity by HPF is critical for long-term viability of S. aureus 30. Inside host cells, S. aureus is suggested to experience microaerophilic conditions as concluded from elevated levels of fermentation enzymes and the CydAB terminal oxidase in internalized bacteria10. Interestingly, except for ButA, fermentation enzymes were upregulated only in intracellularly growing bacteria (8 h p.i.) (Group 2 proteins, see below).
In agreement with previous transcriptome studies23, 28, we observed increased amounts of proteins involved in iron acquisition. The Fur-dependent Sbn proteins, which are responsible for the production of the siderophore staphyloferrin B31, the putative siderophore biosynthesis protein SAOUHSC_02434 and the heme-degrading monooxygenase IsdI were increased at all time points post-infection.
The reduced growth rate of S. aureus after internalization by host cells was accompanied by an approximately 2-fold decrease in ribosomal protein abundance (24 of the 56 ribosomal proteins displaying significantly altered levels) at 8 h p.i. compared to the non-adherent control, which was only slightly higher at 24 h and 32 h p.i. (Table S4). This rather small difference between 8 h p.i. and 24/32 h p.i. was unexpected when comparing intracellularly growing and slow-/non-growing bacteria.
The group of proteins found in decreased amounts in internalized staphylococci also included the ribonucleotide reductase (NrdEF), proteins involved in polyamine metabolism (PotA, PotB, PotD) and the covalently surface-anchored adhesin clumping factor B (ClfB).
Whereas most of the stress response-related proteins affected during internalization were specific for one of the two subpopulations, the universal stress protein UspA_1, the chaperone ClpL, the superoxide dismutase SodA, and the major cold-shock protein CspA were detected in higher amounts at all time points, further increasing over the infection period.
Higher expression of many virulence factor genes, in particular those regulated by the SaeRS two-component system, after internalization by S9 bronchial epithelial cells as well as human THP-1 macrophages has been recently reported at the transcriptional level28. Indeed, the SaeS, SaeR, and SaeP proteins encoded by the auto-regulated sae locus showed a significant increase in abundance compared to the non-adherent control, which was highest at the early time point. Of the SaeR targets, LukH followed the same pattern, whereas Hla and Sbi were only upregulated at 24 h and 32 h p.i. The expression of hla and sbi underlies complex regulation by the SarA transcription factor and/or regulatory RNAs32, 33. An increasing abundance of the virulence regulator AgrA was observed during the whole course of infection, indicating a higher upregulation in the non-growing population. In a long-term infection model with HUVEC cells, agrA expression in internalized S. aureus was highest in the acute phase (two days p.i.) and decreased five days later34. AgrA is the transcriptional activator of the regulatory RNAIII that inhibits translation of target genes including the virulence regulator Rot and staphylococcal protein A (Spa), which were found in decreased amounts at all time points p.i.
Group 2 comprises proteins that were specifically altered at the early time point (8 h p.i.) compared to the non-adherent control sample, thus linked to the physiological adaptation of the intracellularly growing subpopulation. Strikingly, many proteins involved in cell wall stress response including 10 VraSR-dependent proteins (e.g., PrsA, TcaA, FmtA, MurA2, RelP), the methionine sulfoxide reductases MsrA2 and MsrB, also inducible by cell wall targeting antibiotics35, and the putative regulator MsrR were increased in abundance at 8 h p.i. Their levels were largely reverted towards control levels in the 24 h and 32 h p.i. samples, so that most of them were significantly less abundant in the late samples compared to the early sample (Table S5). Methionine sulfoxide reductases are enzymes coping with oxidized methionine residues, and it was recently shown that a mutant lacking MsrA2 is sensitive to oxidative stress and exhibits reduced survival in mice36. Increased levels of the regulator VraR and the foldase PrsA, which were the most upregulated VraSR-dependent proteins in this study, were already reported for S. aureus internalized by S9 cells21.
The level of the alternative terminal oxidase CydAB was 2-fold higher at 8 h p.i. compared to the control and decreased slightly at the late time points. Several fermentation enzymes were upregulated only in early-stage intracellular bacteria including pyruvate formate lyase PflB, alcohol dehydrogenase Adh, and lactate dehydrogenase Ldh2. In contrast, Ldh1, which is highly induced under anaerobic conditions in the presence of glucose and the most abundant fermentation enzyme37, was unchanged after internalization and even decreased in abundance at 24 h and 32 h p.i. This observation is in good agreement with the fact that anaerobic induction of ldh1 through inactivation of the redox-sensitive Rex repressor requires, in addition to a low NAD+/NADH ratio, the presence of glucose38.
Proteins belonging to Group 3 were specifically altered in the slow-/non-growing population, exhibiting different abundance in the late samples (24 h and/or 32 h p.i.) compared to 8 h p.i. (Table S5). Almost all proteins of the tricarboxylic acid cycle (CitB, SucA, SucB, SucC, SucD, SdhA, SdhB, Mqo1) showed increased amounts in the late samples, where also CitC and FumC were significantly increased compared to already elevated levels at 8 h p.i. In addition, proteins involved in coping with oxidative stress were induced, e.g., Dps (DNA protection protein), AhpCF (alkyl hydroperoxide reductase), and KatA (catalase). The same pattern was observed for another PerR-controlled protein, the ferritin-like protein FtnA, and for Frp, an NAD(P)H-flavin oxidoreductase, the B. subtilis ortholog of which confers protection against hypochloric acid39. Stress proteins under the control of the heat shock regulator CtsR were also significantly more abundant in the late samples than in the 8 h sample: ClpC (Clp protease ATP-binding subunit), the chaperones ClpB and DnaK, McsB (arginine kinase involved in CtsR degradation), and CtsR. Interestingly, it was recently shown that Mycobacterium tuberculosis ClpB protects the bacterium against irreversibly oxidized and aggregating proteins by sequestering them asymmetrically inside the cell and is crucial for surviving inside the host40. On the other hand, we observed decreased levels of HslO (Hsp33-like chaperone), ClpX (Clp protease ATP-binding subunit), and the cold-shock family proteins CspB and CspC. Enzymes involved in nucleic acid biosynthesis (e.g., PurH, PurC, PurD, GuaB, Xpt) were also significantly less abundant.
The alternative sigma factor SigB controls a large regulon including genes involved, amongst others, in stress response, cell envelope homeostasis, metabolism, and virulence. SigB is important for intracellular growth22 and plays a critical role in promoting bacterial intracellular persistence34, 41. In the present study, the amounts of only 13 SigB-dependent proteins, including ClpL, SgtB and LysP were increased and 14 were decreased in level (e.g., AmaP, CsbD, SpoVG) at 8 h p.i. compared to the non-adherent control. Strikingly, in the slow-/non-growing population (24 h and 32 h p.i.), 31 proteins that are at least under partial SigB-control, such as SpoVG and Asp23, exhibited 2-3fold higher levels than at 8 h p.i., including several hypothetical proteins. Of note, as SigB-dependent gene expression in S. aureus is particularly associated with the stationary phase28, 42, this observation is indicative of a stationary-phase status of the intracellularly surviving population analyzed at 24 h and 32 h p.i.
In summary, the data obtained from the analysis of internalized S. aureus in the S9 cell line infection model revealed detailed information about the stress and nutrient conditions of S. aureus during adaptation to the host cell milieu as well as long-term intracellular survival. Our data of early intracellular phase (8 h p.i.) and non-adherent control are in accordance with previous findings obtained for intracellularly growing S. aureus cells10, 23, 24, while also adding a lot of new information. As major adaptations to the intracellular niche, we detected increased amounts of a number of proteins under the control of CcpA and CodY, reflecting a nutrient limiting environment, and the induction of various stress responses (e.g., cell wall stress, oxidative stress) showing striking differences between intracellularly growing and slow-/non-growing staphylococci. In this study higher levels of SigB-dependent stress proteins were also recorded at the later time points, extending the previous observation of Pförtner et al.22 of an activation of SigB immediately after internalization and providing a link to the requirement for SigB for long-term intracellular survival and promotion of persister formation41.
In vivo proteomics in a murine pneumonia model
A broad range of S. aureus proteome analyses have been conducted to describe and characterize the dynamic behavior of the human pathogen under laboratory conditions, mainly performed in shaking flasks9, 43,44,45,46. To mimic the natural behavior of the facultative pathogen in the context of human host-cell interaction, cell culture infection experiments were carried out21, 24. To date, these studies represent the closest interrogation of S. aureus proteome dynamics upon interaction with human cells, but still under laboratory-controlled conditions in a cell culture dish. The current and future challenge is true in vivo proteomics, which means the infection of a living organism with S. aureus and investigation of the dynamics of the pathogen’s proteome within the organism. Only few proteomics data have been published from true in vivo settings. In addition to recent research which identified the proteome of S. aureus during a murine bloodstream infection after six days of infection47, we aimed to identify S. aureus proteins in an in vivo pneumonia setting after 8 h, 24 h, and 32 h of infection to complement the data of the S9 human bronchial epithelial cell infection model.
The major challenge in conducting in vivo experiments with S. aureus is the low number of bacteria extractable post-infection and, therefore, the limiting amount of proteins. Diep and colleagues47 recovered 5 × 107 cfu from mouse kidneys after blood stream infection, which was reflected in the 342 proteins identified in their surface enrichment proteome analysis. In our pneumonia setting we identified 578 proteins (570 proteins with two peptides in at least one sample time point) from approximately 2.15 × 105 S. aureus cells (Supplemental Table S6) using DIA-MS. The quantified proteins comprised proteins with a wide spectrum of cellular functions. Among them were proteins required for oxidative stress response such as KatA (catalase), amino acid biosynthesis (e.g., DapD, Asd, RocD, ProC) and fermentation, e.g., PflB (pyruvate formate lyase) and ButA (acetoin diacetyl reductase), which were found with increased abundance, whereas ribosomal proteins and the nucleotide reductase NrdEF were found with decreased abundance in comparison to pre-infection conditions.
Furthermore, the ribosomal activity modulation protein SaHPF, glycolytic enzymes GapA and Pgi, the chaperone ClpB and the general stress proteins UspA_1 and UspA_2 showed increased abundance during the in vivo infection period, reflecting a glucose containing and stressful environment for S. aureus compared to the controlled, high nutrient-supplying shaking flask conditions from the pre-infection sample.
In summary, we revealed new findings using DIA-MS and confirmed several previous results generated with the S9 cell infection model greatly expanding the knowledge of staphylococcal adaptation in the same experimental setup. Extending the experimental time window to 24 h and 32 h p.i. allowed identification of slow-/non-growing, intracellularly surviving bacteria with increased stress adaptation and a proteome profile indicating an aggravated nutrient limitation. This finding supports the fact that the pathogen adapts to a changing environment in the host with a variety of stresses including oxidative, acid, or cell wall stress and starvation for, e.g., glucose or iron. Enzymes involved in the detoxification of reactive oxygen species such as the catalase KatA and the Mn-dependent superoxide dismutase SodA were detected at elevated levels in internalized S. aureus cells.
The murine pneumonia model analysis from a very limited number of cells showed overlapping results with those known from the S9 cell infection model, as well as previously unknown, pneumonia model-specific adaptation processes. This demonstrates the power and reliability of the DIA-method for proteome analysis under infection conditions.
Preparation of samples for DDA-MS analysis, used for the generation of an S. aureus PeptideAtlas and a DIA fragment ion library
Samples for the generation of an S. aureus PeptideAtlas or a DIA library were obtained from S. aureus HG001 and from an isogenic sigB mutant derivative of this strain. The different experimental conditions for sample preparation included: different media (TSB, pMEM), samples from different growth phases (exponential growth, stationary phase of growth), iron limitation (2,2′-bipyridyl treatment), and iron excess. Furthermore, protein extracts from different cellular compartments (intracellular, extracellular, and membrane proteins) were analyzed. In general, protein extracts were prepared from the different samples, protein concentrations were determined, a tryptic digestion was performed, and tryptic peptides were purified using C18 RP-HPLC material. Peptides were analyzed by LC-MS/MS using different combinations of LC-instruments and MS devices. A description of the sample preparation methods is supplied in Supplemental Material 3.1.
Acquisition of standard proteins
To increase the information included in the fragment ion spectral library, a set of proteins including virulence factors were recombinantly expressed in E. coli (Protagen AG, Dortmund, Germany), and proteomics data were acquired by nanoLC-MS/MS. The major goal of including recombinant proteins was to produce higher quality spectra from additional peptides per specific interest protein (e.g., virulence factors). In total, 73 recombinant proteins of S. aureus HG001 and other strains were included (Supplemental Table S7). Two protein mixes each containing half of the recombinant protein set were prepared to prevent the samples from becoming too complex; 20 mM ammonium bicarbonate was added to obtain a final protein concentration of 1 pmol/µl. The total protein amount of each protein mix was calculated, and trypsin (Promega, Madison, WI, USA) was added to obtain a substrate:enzyme ratio of 25:1. After 16 h incubation at 37 °C, proteolytic digestion was stopped by adding glacial acetic acid to a final concentration of 1%. Peptides were purified using ZipTip columns (Millipore, Schwalbach, Germany), dried using a vacuum centrifuge, and reconstituted in 10 µl buffer A [2% (v/v) ACN and 0.1% (v/v) acetic acid in HPLC-grade water (Baker)] before DDA acquisition on a Q ExactiveTM instrument.
Preparation of bacterial in vitro samples for DIA-MS analysis
Growth conditions and sampling for the experiments are summarized in Table S1B. In brief, for the exponential growth and stationary phase comparison of S. aureus HG001, cells were grown in TSB and harvested via centrifugation (8,000 x g, 4 °C, 10 min) at OD540nm = 0.5 and 4 h after entry into stationary phase. Three independent biological sampling series were carried out. Cells were disrupted using a FastPrep® and subsequently pelleted by centrifugation for obtaining protein extracts. For mass spectrometric analysis 1 µg of protein extract was digested with trypsin (Promega, Madison, WI, USA) in a ratio of 1:25 over night at 37 °C. Thereafter, the digestion was stopped by adding 1% v/v purified trifluoroacetic acid (TFA) (Sigma). Peptide samples were purified using ZipTipµ-C18 columns (Millipore) and measured using a Q ExactiveTM mass spectrometer (Thermo-Fisher Scientific, Waltham, MA, USA). Just before measurement HRM-spike-in-mix (Biognosys AG) was added to the sample. For the on-column concentration test, a mixture of samples from cells of the exponential and stationary phase was digested as described above, followed by a dilution series and spiking in of the HRM-spike-in-mix (Biognosys AG). This procedure resulted in on-column concentrations of 10 ng, 50 ng, 100 ng, 500 ng, 1,000 ng, 2,000 ng, and 4,000 ng. Two technical replicates were measured.
Preparation of samples from infection models subjected to DIA-MS analysis
The cell infection experiment was carried out with the S9 human cell line as described previously22. Samples were taken at 8 h, 24 h, and 32 h after infection. Non-adherent cells, obtained from the supernatant of S9 cells 1 h post-infection, and exponentially growing cells (pMEM OD600nm = 0.4) were used as controls. The proteome measurements were carried out with 5 × 106 sorted S. aureus cells each by on-filter digestion24.
The animal study was approved by the local government of Mecklenburg-Western Pomerania, Germany (LALLF M-V permit no. 7221.3‐1.1‐006/09 and 7221.3‐1.1‐019/11) and performed in strict accordance with guidelines for animal care and experimentation.
Prior to infection, S. aureus HG001 pJLGFPopt (strain construction is described in the Supplemental Materials and Methods) was grown in pMEM until an OD600nm of 0.4 was reached. The bacterial cell concentration was determined using a Guava easyCyte™ Flow Cytometer (Millipore). Eight weeks old BALB/c mice were intra-nasally infected with 6 × 108 S. aureus cells. Lung lavage samples were taken 8 h, 24 h, and 32 h post-infection. Finally, the lavage samples were sorted in a FACSAria flow cytometer, pooled, tryptically digested24, and analyzed using a Q ExactiveTM mass spectrometer (Thermo-Fisher Scientific).
Live-cell imaging during the cell culture infection experiment
Internalization experiments for long-term live-cell imaging were performed as described above but in special cell culture dishes (MatTek Corporation, 35 mm Glass Bottom Culture Dishes No. 0; Part No. P35G-0-20-C) with an infection volume of 4 ml. S9 cells were infected with S. aureus HG001 pJLGFPopt at an MOI of 25. After 1 h of incubation, the supernatant was replaced by fresh eMEM containing 10 µg/µl lysostaphin. The cell culture dishes containing the infected cells were placed onto a Deltavision RT Image Restoration Workstation with an Olympus IX71 Nomarski Differential Interference Contrast microscope with a 40x objective using immersion oil. The microscopy chamber was pre-warmed to 37 °C and provided with an atmosphere of 5% CO2. After 15 min incubation time with lysostaphin (75 min p.i.), microscopy pictures were acquired every 5 min until about 34 h p.i. GFP-expressing bacteria were visualized using a filter with excitation 490/20 nm and emission 528/38 nm. The non-fluorescent S9 cells were observed in parallel by light microscopy. The pictures obtained were processed using Fiji (http://fiji.sc/Fiji). Deltavision files were first adjusted for brightness and contrast and then prepared using the “image5D z/TP Montage function”. Afterwards, a non-destructive scale bar was added to all slices. The time-stamper plug-in allowed labeling of observation time for all slices. Finally, the movies were exported as avi-files. The measurement of the mean GFP signal over the image was performed with Fiji via single picture analysis.
Mass spectrometric data acquisition using different LC instruments, MS devices, and acquisition methods
S. aureus tryptic digests were analyzed on a TripleTOF® 5600+ mass spectrometer (Sciex, Foster City, CA, USA) equipped with an Eksigent ekspert™ nanoLC 425 and cHiPLC® system in trap-elute configuration. The 10 most intense ions were acquired in DDA mode for later use in library generation. SWATH data were generated with a SWATHTM acquisition method.
Tryptic peptides of another sample set were separated on a nanoAcquity UPLC reversed phase column (BEH130, C18, 100 µm × 100 mm, Waters Corporation, Milford, USA) operated on a nanoAcquity UPLC system (Waters), and MS/MS data were recorded in data-dependent mode for precursor ions with charge 2 or 3 using an LTQ-FT instrument (Thermo-Fisher Electron, Bremen, Germany). These data were integrated into the S. aureus PeptideAtlas, as well as the MS data of analyses performed in DDA mode with an on-line coupled Dionex nLC system (Thermo-Fisher Scientific Inc., Idstein, Germany) connected to a Q Exactive Orbitrap-MS (Thermo-Fisher Scientific Inc.).
Mass spectrometric data-independent acquisition (DIA) and data-dependent acquisition (DDA) analyses using a Q ExactiveTM instrument (Thermo-Fisher Scientific) were performed in combination with LC on an UltiMate 3000 RSLC (Dionex/Thermo-Fisher Scientific, Idstein, Germany).
For further details see Supplemental Material 3.2.
Data processing for S. aureus PeptideAtlas construction, S. aureus and S9 cell ion library building, as well as DIA data analysis
The S. aureus PeptideAtlas construction was based upon a Comet database search using an S. aureus protein database comprising 2,891 proteins in a target-decoy approach. Static and variable modifications were considered according to the sample characteristics.
The search results were processed with the Trans-Proteomic Pipeline (TPP, version 4.6)48 including PeptideProphet, iProphet, and ProteinProphet49,50,51,52. Per experiment a PSM FDR threshold was applied to maintain a constant FDR. The build named S. aureus HG001 2015-12 is available at www.peptideatlas.org.
Ion libraries were similarly constructed and based on a Comet database search and spectral library. Retention time values were incorporated by using the data with iRT peptides spiked-in (Biognosys, Schlieren, Switzerland) and RT alignment. The resulting aligned single-search ion library was filtered for 6–10 transitions per assay to obtain a robust, non-redundant ion library for the DIA data analysis. The procedures were carried out independently for the S. aureus and S9 data, resulting in separate ion libraries, which were deployed for the analysis of the LC-MS/MS data recorded in DIA mode. This data analysis was performed using SpectronautTM (v7.0.8065.3.13226 academic+)53. This included the comparison of the application of TripleTOF® - and Q ExactiveTM-derived S. aureus MS-assays. Settings for SpectronautTM analysis data extraction are given in Supplemental Table S2.
After SpectronautTM data extraction only highly significant assays (Q-value < 0.001) were normalized with respect to the control. They were subsequently analyzed protein-wise with a Wilcoxon rank sum test against an absolute fold change of 1.5 using Benjamini and Hochberg’s multiple testing correction54. Proteins with a p-value below 0.05 were assumed to be significantly regulated. The protein annotation and symbols used came from Aureowiki (http://aureowiki.med.uni-greifswald.de/Main_Page).
A detailed description of PeptideAtlas construction, ion library building, and DIA data analysis is given in Supplemental Material 3.3.
Generation of a proteome-based phylogenetic tree over 46 sequenced S. aureus strains and its comparison to the ion library
The proteome sequences of 46 S. aureus strains were obtained from the NCBI sequence repository. The proteome-based unrooted and unscaled phylogenetic tree was generated by using the micropan package (v 1.0)55 in R 3.2.356. Therefore, the single strain protein FASTA files were used for a protein blast all vs. all approach resulting in a phylogenetic similarity matrix. For comparison of the strains’ proteomes to those in the ion library an in silico tryptic digestion (missed cleavages [MC] = 2) was performed, and the resulting theoretical peptides were compared to the ion library.
Visualization of proteomics results in Voronoi-like treemaps
Voronoi-like treemaps57 were generated using the Paver 2.0 software (Decodon GmbH, Greifswald, Germany). The application uses functional annotations on several levels and displays them in one interactive Voronoi-like treemap image. The functional annotation data was extracted from the latest “theSeed.org” build58, 59 (extraction date: 9th of December 2015) for S. aureus NCTC8325 and S. aureus COL. The template treemap calculation of each strain was performed using the free swarm algorithm. The length of each protein (amino acid count) was normalized to the amino acid count of the largest protein to calculate Voronoi cell-size data based on protein length. These normalized data were deployed for visualization of the PeptideAtlas peptide data (Supplemental Fig. 3).
Wertheim, H. F. L. et al. The role of nasal carriage in Staphylococcus aureus infections. The Lancet infectious diseases 5, 751–762 (2005).
Kluytmans, J., Van Belkum, A. & Verbrugh, H. Nasal carriage of Staphylococcus aureus: epidemiology, underlying mechanisms, and associated risks. Clinical Microbiology Reviews 10, 505–520 (1997).
Foster, T. J. The Staphylococcus aureus “superbug”. J Clin Invest 114, 1693–1696 (2004).
Chambers, H. F. & DeLeo, F. R. Waves of resistance: Staphylococcus aureus in the antibiotic era. 7, 629–641 (2009).
Spaulding, A. R. et al. Vaccination Against Staphylococcus aureus Pneumonia. 209, 1955–1962 (2014).
Jansen, K. U., Girgenti, D. Q., Scully, I. L. & Anderson, A. S. Vaccine review: “Staphyloccocus aureus vaccines: problems and prospects”. Vaccine 31, 2723–2730 (2013).
Tuchscherr, L. et al. Staphylococcus aureus phenotype switching: an effective bacterial strategy to escape host immune response and establish a chronic infection. EMBO Mol Med 3, 129–141 (2011).
Eng, J. K., Jahan, T. A. & Hoopmann, M. R. Comet: an open-source MS/MS sequence database search tool. PROTEOMICS 13, 22–24 (2013).
Becher, D. et al. A proteomic view of an important human pathogen–towards the quantification of the entire Staphylococcus aureus proteome. PLoS ONE 4, e8176 (2009).
Surmann, K. et al. Comparative proteome analysis reveals conserved and specific adaptation patterns of Staphylococcus aureus after internalization by different types of human non-professional phagocytic host cells. Front Microbiol 5, 392 (2014).
Vowinckel, J. et al. The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics. F1000Res. doi:10.12688/f1000research.2-272.v1 (2013).
Gillet, L. C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Molecular & Cellular Proteomics 11(O111), 016717 (2012).
Tsou, C.-C. et al. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat Methods 12, 258–64–7 p following 264 (2015).
Escher, C. et al. Using iRT, a normalized retention time for more targeted measurement of peptides. PROTEOMICS 12, 1111–1121 (2012).
Depke, M. et al. A peptide resource for the analysis of Staphylococcus aureus in host-pathogen interaction studies. PROTEOMICS 15, 3648–3661 (2015).
Parker, K. C. Scoring methods in MALDI peptide mass fingerprinting: ChemScore, and the ChemApplex program. J Am Soc Mass Spectrom 13, 22–39 (2002).
Eyers, C. E. et al. CONSeQuence: Prediction of Reference Peptides for Absolute Quantitative Proteomics Using Consensus Machine Learning Approaches. Molecular & Cellular Proteomics 10, M110.003384–M110.003384 (2011).
Tang, H. et al. A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics 22, e481–8 (2006).
Lam, H. et al. Development and validation of a spectral library searching method for peptide identification from MS/MS. PROTEOMICS 7, 655–667 (2007).
Picotti, P., Bodenmiller, B., Mueller, L. N., Domon, B. & Aebersold, R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics 138, 795–806 (2009).
Schmidt, F. et al. Time-resolved quantitative proteome profiling of host-pathogen interactions: the response of Staphylococcus aureus RN1HG to internalisation by human airway epithelial cells. Proteomics 10, 2801–2811 (2010).
Pförtner, H. et al. Activation of the alternative sigma factor SigB of Staphylococcus aureus following internalization by epithelial cells - an in vivo proteomics perspective. Int J Med Microbiol 304, 177–187 (2014).
Garzoni, C. et al. A global view of Staphylococcus aureus whole genome expression upon internalization in human epithelial cells. BMC Genomics 8, 171 (2007).
Pförtner, H. et al. A proteomics workflow for quantitative and time-resolved analysis of adaptation reactions of internalized bacteria. Methods 61, 244–250 (2013).
Eisenreich, W., Heesemann, J., Rudel, T. & Goebel, W. Metabolic Adaptations of Intracellullar Bacterial Pathogens and their Mammalian Host Cells during Infection (‘Pathometabolism’). Microbiol Spectr 3, (2015).
Eylert, E. et al. Carbon metabolism of Listeria monocytogenes growing inside macrophages. Mol Microbiol 69, 1008–1017 (2008).
Park, J. Y. et al. Characterization of a novel two-component regulatory system, HptRS, the regulator for the hexose phosphate transport system in Staphylococcus aureus. Infect Immun 83, 1620–1628 (2015).
Mäder, U. et al. Staphylococcus aureus Transcriptome Architecture: From Laboratory to Infection-Mimicking Conditions. PLoS Genet 12, e1005962 (2016).
Ueta, M., Wada, C. & Wada, A. Formation of 100 S ribosomes in Staphylococcus aureus by the hibernation promoting factor homolog SaHPF. 15, 43–58 (2010).
Basu, A. & Yap, M. Ribosome hibernation factor promotes Staphylococcal survival and differentially represses translation. Nucleic Acids Research gkw180 doi:10.1093/nar/gkw180 (2016).
Cheung, J., Beasley, F. C., Liu, S., Lajoie, G. A. & Heinrichs, D. E. Molecular characterization of staphyloferrin B biosynthesis in Staphylococcus aureus. Mol Microbiol 74, 594–608 (2009).
Goerke, C., Fluckiger, U., Steinhuber, A., Zimmerli, W. & Wolz, C. Impact of the regulatory loci agr, sarA and sae of Staphylococcus aureus on the induction of alpha-toxin during device-related infection resolved by direct quantitative transcript analysis. Mol Microbiol 40, 1439–1447 (2001).
Chabelskaya, S., Bordeau, V. & Felden, B. Dual RNA regulatory control of a Staphylococcus aureus virulence factor. Nucleic Acids Res doi:10.1093/nar/gku119 (2014).
Tuchscherr, L. & Löffler, B. Staphylococcus aureus dynamically adapts global regulators and virulence factor expression in the course from acute to chronic infection. Curr. Genet. 62, 15–17 (2015).
Singh, V. K., Jayaswal, R. K. & Wilkinson, B. J. Cell wall-active antibiotic induced proteins of Staphylococcus aureus identified using a proteomic approach. FEMS Microbiol Lett 199, 79–84 (2001).
Singh, V. K. et al. Significance of four methionine sulfoxide reductases in Staphylococcus aureus. PLoS ONE 10, e0117594 (2015).
Zühlke, D. et al. Costs of life - Dynamics of the protein inventory of Staphylococcus aureus during anaerobiosis. Sci Rep 6, 28172–28172 (2015).
Crooke, A. K. et al. CcpA-Independent Glucose Regulation of Lactate Dehydrogenase 1 in Staphylococcus aureus. PLoS ONE 8, e54293 (2013).
Palm, G. J. et al. Structural insights into the redox-switch mechanism of the MarR/DUF24-type regulator HypR. Nucleic Acids Res 40, 4178–4192 (2012).
Vaubourgeix, J. et al. Stressed Mycobacteria Use the Chaperone ClpB to Sequester Irreversibly Oxidized Proteins Asymmetrically Within and Between Cells. Cell Host Microbe 17, 178–190 (2015).
Tuchscherr, L. et al. Sigma Factor SigB Is Crucial to Mediate Staphylococcus aureus Adaptation during Chronic Infections. PLoS Pathog 11, e1004870 (2015).
Pane-Farre, J., Jonas, B., Förstner, K., Engelmann, S. & Hecker, M. The σB regulon in Staphylococcus aureus and its regulation. International Journal of Medical Microbiology 296, 237–258 (2006).
Fuchs, S. et al. Aureolib - a proteome signature library: towards an understanding of Staphylococcus aureus pathophysiology. PLoS ONE 8, e70669 (2013).
Michalik, S. et al. Life and death of proteins: a case study of glucose-starved Staphylococcus aureus. Mol Cell Proteomics 11, 558–570 (2012).
Cordwell, S., Larsen, M., Cole, R. & Walsh, B. Comparative proteomics of Staphylococcus aureus and the response of methicillin-resistant and methicillin-sensitive strains to Triton X-100. Microbiology 148, 2765–2781 (2002).
Resch, A. et al. Comparative proteome analysis of Staphylococcus aureus biofilm and planktonic cells and correlation with transcriptome profiling. PROTEOMICS 6, 1867–1877 (2006).
Diep, B. A. et al. Identifying potential therapeutic targets of methicillin-resistant Staphylococcus aureus through in vivo proteomic analysis. doi:10.1093/infdis/jit662 (2013).
Deutsch, E. W. et al. A guided tour of the Trans-Proteomic Pipeline. PROTEOMICS 10, 1150–1159 (2010).
Keller, A., Nesvizhskii, A. I., Kolker, E. & Aebersold, R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383–5392 (2002).
Nesvizhskii, A. I., Keller, A., Kolker, E. & Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646–4658 (2003).
Keller, A., Eng, J., Zhang, N., Li, X.-J. & Aebersold, R. A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol Syst Biol 1(2005), 0017 (2005).
Shteynberg, D. et al. iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates. Molecular & Cellular Proteomics 10(M111), 007690 (2011).
Bernhardt, O. M., Selevsek, N. & Gillet, L. C. Spectronaut: A fast and efficient algorithm for MRM-like processing of data independent acquisition (SWATH-MS) data. Proceedings 60th ASMS Conference on Mass Spectrometry (2012).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological) 57, 289–300 (1995).
Snipen, L. & Liland, K. H. micropan: Microbial Pan-genome Analysis. 1–55 (2014).
Team, R. D. C. R: A Language and Environment for Statistical Computing. GNU General Public License (GPL) – http://www.gnu.org/licenses/old–licenses/gpl–2.0.html (2014).
Bernhardt, J., Funke, S., Hecker, M. & Siebourg, J. Visualizing Gene Expression Data via Voronoi Treemaps. 2009 Sixth International Symposium on Voronoi Diagrams 233–241 (2009).
Overbeek, R. et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res 42, D206–14 (2014).
Overbeek, R. et al. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res 33, 5691–5702 (2005).
Surmann, K. et al. A proteomic perspective of the interplay of Staphylococcus aureus and human alveolar epithelial cells during infection. Journal of Proteomics 128, 203–217 (2015).
Shteynberg, D. et al. reSpect: Software for Identification of High and Low Abundance Ion Species in Chimeric Tandem Mass Spectra. J Am Soc Mass Spectrom doi:10.1007/s13361-015-1252-5 (2015).
The authors would like to acknowledge the help of Karel Novy and Ferdinando Cerciello for QTOF measurements and Andreas Frei for Orbitrap measurements of selected sample sets as well as Martin Fraunholz for helpful suggestions concerning live cell imaging. We extend special thanks to Yulia Butscheid and Lukas Reiter (Biognosys AG) for their help and information about Spectronaut and iRT, Christian Panse and Jonas Grossmann for their support with the SpecL package, and Susanne Sievert for her help with the membrane fraction preparation. The work was financially supported by the BMBF/“Unternehmen Region” as part of ZIK-FunGene (MH, UV, FS) and the GRK1870 (UV, SH, LMPM), as well as within the framework of the SFB Transregio 34 (UV, UM, SH). This work was also funded in part by National Institutes of Health Grant RC2 HG005805 from the National Human Genome Research Institute (NHGRI) through the American Recovery and Reinvestment Act, by the National Institute of General Medical Sciences (NIGMS) grants R01GM087221, S10RR027584, and 2P50GM076547 to the Center for Systems Biology, and by the National Science Foundation for MRI Grant No. 0923536 and MCB Grant No. 1330912.
The authors declare that they have no competing interests.
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Michalik, S., Depke, M., Murr, A. et al. A global Staphylococcus aureus proteome resource applied to the in vivo characterization of host-pathogen interactions. Sci Rep 7, 9718 (2017). https://doi.org/10.1038/s41598-017-10059-w
Use of Hybrid Data-Dependent and -Independent Acquisition Spectral Libraries Empowers Dual-Proteome Profiling
Journal of Proteome Research (2021)
Molecular Omics (2021)
Canadian Journal of Microbiology (2021)
FEBS Letters (2020)
Importance of superoxide dismutases A and M for protection of Staphylococcus aureus in the oxidative stressful environment of cystic fibrosis airways
Cellular Microbiology (2020)