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Analysis of the influence of cyclo (L-phenylalanine-L-proline) on the proteome of Staphylococcus aureus using iTRAQ



Cyclo (L-phenylalanine-L-proline) (cFP) is an extracellular quorum sensing (QS) signal molecule that coordinates communication between Gram-negative bacteria. Some studies have also reported QS in Gram-positive bacteria. However, the effect of cFP on Gram-positive bacteria remains unknown. Therefore, an isobaric tags for relative and absolute quantitation (iTRAQ) proteomic experiment were designed to elucidate whether cFP influences protein expression in Staphylococcus aureus (S. aureus).


The iTRAQ proteomics method was used to analyze untreated (control) and S. aureus treated with cFP for 12 h. Samples were then processed by liquid-phase tandem mass spectrometry (LC-MS/MS) and analyzed using bioinformatics tools.


The results identified 1296 proteins from the S. aureus CGMCC 1.1861 proteome. Twenty-two proteins, including some two-component regulatory systems (TCRS), were associated with signal transduction. Differential expression analysis revealed that only 43 proteins were up-regulated and 41 proteins were down-regulated by cFP. The most significantly different pathways were amino acid metabolism, fatty acid degradation, and metabolism of cofactors and vitamins. Results showed that cFP down-regulated virulence factors, up-regulated lipid and amino acid metabolism, promoted acetylation and phosphorylation, and decreased alcohol dehydrogenase expression. A total of 12 significantly differentially expressed proteins (DEPs) were related to signal transduction. Among them, Rot (Q9RFJ6) and SarR (Q9F0R1), which can inhibit transcription of the Agr system, were up-regulated, whereas virulence factors such as ESAT-6 protein A (Q2G189), phenol soluble modulin (Psm, Q2FZA4), and a peptide ABC transporter permease (Q2G168) were down-regulated. AgrA (Q2FWM4) was down-regulated by cFP in S. aureus.


cFP reduced AgrA and the expression of some exotoxins but increased Rot and SarR expression.


Quorum sensing (QS) is a very important mechanism for coordinating the social behavior of bacteria to adapt to different environments according to their population densities (Banerjee and Arun 2017). QS involves the production of and response to extracellular signaling molecules called autoinducers (AIs) (Kumari et al. 2006). Some common QS phenotypes include group behaviors such as competence, colonization, motility, biofilm formation, virulence factor expression, and stress responses (Kavanaugh and Horswill 2016; Eickhoff and Bassler 2018; Bettenworth et al. 2019; Jiang et al. 2019; Zhao et al. 2019). Certain bacteria are also able to use QS to regulate bioluminescence, nitrogen fixation, and sporulation (Kavanaugh and Horswill 2016; Rutherford and Bassler 2012). Some genes are modified in response to a threshold AI concentration that depends on high cell density. This mechanism can alter local surface tension enough to create Marangoni flows, which facilitate swarming and colony motility (Daniels et al. 2006). In Gram-negative bacteria, typical QS molecules are N-acyl homoserine lactones (AHL) or other molecules whose production depends on S-adenosylmethionine as a substrate (Wei et al. 2011). In general, AHLs can bind directly to transcription factors (TFs) to up-regulate or down-regulate relative gene expression (Bassler 1999). Gram-positive bacteria typically use two-component regulatory systems (TCRS) with autoinducing peptides (AIP) such as oligopeptides for QS (Rutherford and Bassler 2012). AIP binds to a membrane-bound histidine kinase receptor (HK) to auto-phosphorylate and activate their cognate cytoplasmic response regulator. The phosphorylated response regulator activates transcription of genes in the QS regulon. Another possible mechanism is direct AIP binding to a TF that initiates or inhibits transcription (Rutherford and Bassler 2012).

In Gram-positive QS circuits, the pro-AIP, transporter, HK, and TF are typically encoded in an operon (Ji et al. 1995; Peterson et al. 2000). Expression of this operon is activated by the phosphorylated response regulator, initiating an auto-inducing feed-forward loop that synchronizes the QS response. The Staphylococcus aureus accessory gene regulator (Agr) system is a model Gram-positive QS system also known as a TCRS signal transduction pathway (Thoendel et al. 2011). When the concentration of the signal molecule reaches a threshold, the membrane-bound HK AgrC is phosphorylated, stimulating the Agr system. The AIP precursor is synthesized via AgrD, processed into the mature AIP octapeptide, and transported out of the cell by AgrB. The AIP is then detected by AgrC and the TF AgrA is phosphorylated by phosphorylated AgrC. AgrA can then activate the agr P2 (RNAII) and P3 (RNAIII) promoters. RNAIII post-transcriptionally activates expression of virulence factors and represses Rot, which is the main repressor of toxin genes encoding virulence factors. Gram-positive bacteria can initiate pathogenic processes via QS circuits that allow for detection of cell population density. The final consequence of this QS regulatory cascade is down-regulation of surface virulence factors such as protein A (spa) and up-regulation of secreted virulence factors such as α-toxin and σ-toxin (Rutherford and Bassler 2012; Kavanaugh and Horswill 2016). In S. aureus, some researchers have suggested that the Agr system inhibits biofilm formation (Vuong et al. 2000; Boles and Horswill 2008). This leads bacteria to establish biofilm communities at low cell densities but terminate biofilm production and decrease surface proteins and adhesions once they reach high cell densities. In this way, the cell is able to secrete virulence factors that facilitate its dispersal and invasion (Yarwood et al. 2004; Boles and Horswill 2008).

Cyclo (L-phenylalanine-L-proline) (cFP) is a cyclic dipeptide formed by the condensation of two amino acids also called 2, 5-diketopiperazines. It consists of a stable, six-membered ring structure, which includes one hydrogen donor and four acceptor bonds (Hu et al. 2019). This makes it an active ligand with key roles in protein binding. It is a secondary metabolite produced by numerous fungi as well as Gram-positive and Gram-negative bacteria (Prasad 1995; Huang et al. 2010; Brack et al. 2014; Mishra et al. 2017). In Gram-negative bacteria such as Pseudomonas spp., cFP was shown to be part of a novel class of AIs that uses lux-based AHL biosensors (Bellezza et al. 2014). Furthermore, cFP can reduce bioluminescence and biofilm formation (Kim et al. 2013), induce the virulence factor OmpU to influence host–pathogen interactions in Vibrio vulnificus (Park et al. 2006), up-regulate pathogenicity and toxin proteins such as Ctx in Vibrio cholera (Bina et al. 2013), decrease toxic shock syndrome toxin-1 (TSST-1) expression (Li et al. 2011), and inhibit serine/threonine protein kinase (Akt) (Hong et al. 2008). cFP is a very small molecule that can be easily obtained by fermentation or synthesis (Park et al. 2006). Its multiple uses include clinical applications as an antibiotic prophylaxis and inhibition of biofilm formation and multi-drug resistance in bacteria (Rhee 2004; Pan and Ren 2009; Kim et al. 2015). Recently, it was found to be toxic to tumor cells and can inhibit virus and human innate immunity (Brauns et al. 2004; Brauns et al. 2005; Chen et al. 2018; Kwak et al. 2018; Lee et al. 2018). However, the study of cFP has focused mainly on LuxR-mediated QS systems in bacteria, while its signal transduction mechanism, binding receptors, and in particular its effect on Gram-positive bacteria remain unknown. One transcriptomic study of V. vulnificus indicated that cFP up-regulated transport and metabolism of inorganic molecules and down-regulated glycolysis, anaerobic energy metabolism, and the pyruvate oxidase system (Kim et al. 2013). It was also found that cFP acts as a quorum quenching agent to inhibit Agr (Li et al. 2011; Tang and Zhang 2014).

Isobaric tags for relative and absolute quantitation (iTRAQ) are a proteome quantification technique that can be used to analyze up to eight samples in one experiment (Wiese et al. 2007) and can be used for high-throughput generation of abundant, repeatable, high-resolution data with high quantitative accuracy (Chai and Zhao 2017). This technology, developed by Applied Biosystems Inc., has been widely used since it was first proposed by Ross et al. (2004) at a mass spectrometry (MS) conference. In 2009, more than 150 research articles reported using this method, and it is now the most common procedure for proteomic analysis. iTRAQ labeling coupled with multidimensional liquid chromatography and MS analysis (LC-MS/MS) is a gel-free quantitative proteomics technology that uses amine-specific isobaric tags to compare the intensity of reporter ions of labeled peptides and infer quantitative values for corresponding proteins (DeSouza et al. 2005; Zhang et al. 2017). In this study, iTRAQ was conducted to elucidate the influence of cFP on protein expression in S. aureus. This work provides crucial clues about how cFP affects the QS mechanism via TCRS, especially the main QS system AgrC/A, through analysis of differential protein expression.

Materials and methodsBacteria and reagents

Staphylococcus aureus subsp. aureus CGMCC1.1861 (ATCC 6538P; S. aureus 1.1861), supplied by the China General Microbiological Culture Collection Center (CGMCC, Beijing, China), is a positive control strain used in medicine and microbial hygiene tests (Holowachuk et al. 2003; Holowachuk et al. 2004). cFP (IUPAC name: (3S,8aS)-3-benzyl-2,3,6,7,8,8a-hexahydropyrrolo[1,2-a]pyrazine-1,4-dione, CAS number: 3705-26-8) was synthesized by Nanjing Peptide Biotech Ltd. (Nanjing, China). S. aureus 1.1861 was previously shown to be sensitive to cFP in growth curves (Duiyuan 2017). It was grown in basic nutrient beef extract peptone media in Erlenmeyer flasks at 37 °C with shaking for 18 h. After continuous culture for two generations, cells were diluted to an optical density at 600 nm (OD600) of 0.1 per mL and 100 mL was separated into one Erlenmeyer flask for each condition. The prepared samples were divided into two groups. Control (CK) samples of S. aureus treated with 0.1 mL dimethylsulfoxide (DMSO) were cultured at 37 °C with shaking for 12 h in basic nutrient beef extract peptone media. Treatment group (cFP) samples of S. aureus treated with cFP dissolved in 0.1 mL DMSO at a final concentration of 10 mM were cultured for 12 h in the same conditions. Each group included three independent replicates (i.e., CK-1, CK-2, and CK-3 in the control group and cFP-1, cFP-2, and cFP-3 in the treatment group). Both groups were collected by centrifugation at 1200×g for 5 min at 4 °C and frozen in liquid nitrogen until further analysis.

iTRAQ proteomic experimentThe iTRAQ workflow is illustrated in Fig. 1. Total protein was extracted from processed samples using the cold acetone method (Xiao et al. 2017). Samples were ground to a powder in liquid nitrogen, dissolved in 2-mL lysis buffer (8 M urea, 2% SDS, and 1× protease inhibitor cocktail (Roche Ltd., Basel, Switzerland)), sonicated on ice for 30 min, and centrifuged at 13,000 rpm for 30 min at 4 °C. The supernatant was transferred to a fresh tube. For each sample, proteins were precipitated with ice-cold acetone at − 20 °C overnight. The precipitates were washed with acetone three times and re-dissolved in 8 M urea with sonication on ice. Protein quality was examined by SDS-PAGE.

Fig. 1
figure 1

iTRAQ workflow. Abbreviations: SDS-PAGE, sodium dodecyl sulfate polyacrylamide gel electrophoresis; LC-MS/MS, liquid phase tandem mass spectrometry analysis; SCX, strong cation exchange chromatography separation

A Bradford protein concentration assay (BCA) kit (P0010S, Beyotime Institute of Biotechnology, Beijing, China) was used to determine the protein concentration of the supernatant. A total of 100 μg protein was transferred to a new tube and adjusted to a final volume of 100 μL with 8 M urea. A total of 11 μL of 1 M DL-dithiothreitol was added and samples were incubated at 37 °C for 1 h. Then, 120 μL of 55 mM iodoacetamide was added and the sample was incubated for 20 min at room temperature and protected from light. For each sample, proteins were precipitated with ice-cold acetone and re-dissolved in 100-μL triethylammonium bicarbonate buffer. Proteins were then tryptic digested with sequence-grade modified trypsin (Promega, Madison, WI, USA) at 37 °C overnight. The resulting peptide mixture was covalently labeled with stable isotope molecules with the following tags introduced from iTRAQ-specific reagents to the N-terminus and side chain amines of peptides according to the manufacturer’s instructions: control group CK-1 with a 113 mass isotope tag; CK-2 with a 114 tag; CK-3 with a 115 tag; and treatment group cFP-1 with a 116 tag; cFP-2 with a 117 tag; and cFP-3 with a 118 tag. The labeled samples were combined and dried in a vacuum (Zhang et al. 2017).

Labeled samples were combined and subjected to strong cation exchange fractionation on a column connected to a high-performance liquid chromatography system. The peptide mixture was dissolved in buffer A (20 mM ammonium formate in water, pH adjusted to 10.0 with ammonium hydroxide) and fractionated by high pH separation using an Ultimate 3000 system (Thermo Fisher Scientific, Waltham, MA, USA) connected to a reverse-phase column. Peptide fractions were resuspended in 30-μL solvent C (0.1% formic acid in water) or D (0.1% formic acid in acetonitrile), separated by nanoLC, and analyzed by on-line electrospray tandem mass spectrometry. Experiments were performed on an Easy-nLC 1000 system (Thermo Fisher Scientific) connected to an Orbitrap Fusion Tribrid mass spectrometer (Thermo Fisher Scientific) equipped with an online nano-electrospray ion source. Survey full-scan mass spectra (m/z 350–1550) were acquired with a mass resolution of 120 K, followed by sequential high energy collisional dissociation tandem mass spectrometry scans with a resolution of 30 K. The isolation window was set to 1.6 Da. The AGC target was set to 400,000. The fixed first mass was set to 110. In all cases, one microscan was recorded using a dynamic exclusion of 45 s (Chai and Zhao 2017).

Database analysisMass spectrometry data were transformed into Mascot generic format (MGF) files using Proteome Discovery 1.2 (Thermo, Pittsburgh, PA, USA) and analyzed using the Mascot search engine version 2.3.2 (Matrix Science, London, UK) (Fig. 2). The Mascot database was set up for protein identification using the reference transcriptome or NCBInr/SwissProt/Uniprot/IPI database. Mascot was searched with a fragment ion mass tolerance of 0.050 Da and a parent ion tolerance of 10.0 ppm. NCTC 8325 is the only reference proteome in Uniprot (UP000008816, strain NCTC 8325, protein count: 2889, and is well studied and annotated (Gillaspy et al. 2006). Therefore, it was selected as the main reference proteome for our data search results. The Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa et al. 2008) pathway enrichment analysis was used to identify differentially expressed proteins (DEPs) significantly enriched in metabolic or signal transduction pathways when compared with the entire proteome. The following formula was used to calculate P values:

Fig. 2
figure 2

Proteomic analysis workflow. The raw data were transformed and analyzed using Mascot software. These data were then assessed for protein identification using the NCBInr/SwissProt/Uniprot/IPI database. Afterwards, identified proteins were quantified to identify significantly differentially expressed proteins (DEPs). Finally, DEPs were analyzed for KEGG pathway enrichment, Gene Ontology (GO) enrichment, and protein interactions to identify mechanisms at the overall proteome level

$$ P=1-\sum \limits_{i=0}^{m-1}\frac{\left(\begin{array}{c}M\\ {}i\end{array}\right)\left(\begin{array}{c}N-M\\ {}n-i\end{array}\right)}{\begin{array}{c}N\\ {}n\end{array}} $$

where N is the number of all genes with a KEGG annotation, n is the number of DEPs in N, M is the number of genes annotated to specific pathways, and m is the number of DEPs in M. The calculated P values were adjusted using the false discovery rate (FDR) correction with FDR ≤ 0.05 set as the threshold. Pathways meeting this condition were defined as pathways significantly enriched in DEPs.

ResultsProtein identification and quantificationProteomics experiments referring to the NCBInr/SwissProt/Uniprot/IPI database identified 1296 S. aureus proteins in both control and treatment groups (Table S1). Mascot search results were averaged using medians and quantified. Proteins with a fold change > 1.2 or < 0.83 and an unadjusted significance level of P < 0.05 were considered differentially expressed. In total, 1153 proteins were quantitative statistics and annotated by KEGG (Table S2). Of these, 43 proteins were significantly up-regulated and 41 proteins were down-regulated in cFP treatment groups compared with controls (P < 0.05; Table 1, Table S3). These results revealed clues about target proteins influenced by cFP. The adenosylmethionine-8-amino-7-oxononanoate aminotransferase protein BioA (Q2FVJ6) was the most up-regulated protein, followed by the biotin synthase protein BioB and four ribonuclease M5 proteins. A total of 12 proteins were directly related to transcription and signal transduction among these 84 DEPs, including the up-regulated proteins Rot (Q9RFJ6), acetyl-CoA acetyltransferase (Q2G0K2), SarR (Q9F0R1), MalR (A0A0Y9ARI8), MalR (Q2FY63), transcription factor (TF) (Q2FWG9), and PhoP (Q2FXN6) and the down-regulated proteins ATP-binding Cassette (ABC, Q2G168), RpiR TF (Q2FVV2), EsxA (Q2G189), TF (Q2G2H0), and phenol soluble modulin beta 1 (Psm, Q2FZA4) (Table 2).

Table 1 Significantly differentially expressed proteins between control and cFP-treated S. aureus
Table 2 Significantly differentially expressed signal transduction proteins between control and cFP-treated S. aureus

Pathway annotation and enrichmentIn this study, 522 proteins from the treated and control groups were found to be associated with known pathways (Table S4). The most enriched pathway was carbon metabolism, which included 77 proteins, followed by biosynthesis of amino acids with 61 proteins, ribosome with 54 proteins, purine with 38 proteins, pyruvate with 37 proteins, pyrimidine with 34 proteins, and glycolysis/gluconeogenesis with 33 proteins. In addition, 22 proteins were related to signal transduction (Table S4, Fig. S7). Nineteen ABC transporters and 11 proteins related to S. aureus infection were identified.

Furthermore, we identified, compared, and predicted the possible functions of identified proteins and determined their functional classification using the Clusters of Orthologous Groups of proteins (COG) database (Fig. 3). In total, 1038 proteins were annotated (Table S5) and categorized into 20 groups (C-V). Abundant clusters included J: translation, ribosomal structure, and biogenesis with 123 proteins; C: energy production and conversion with 87 proteins; E: amino acid transport and metabolism with 87 proteins; K: transcription with 86 proteins; and carbohydrate transport and metabolism with 78 proteins. A total of 29 proteins were associated with signal transduction mechanisms.

Fig. 3
figure 3

Clusters of orthologous groups (COG) of protein-based classification of S. aureus proteins. Using the COG database, 1038 proteins were classified into 20 groups (C-V). Abundant clusters included R: general function with 146 proteins; J: translation with 123 proteins; S: function unknown with 90 proteins; C: energy production and conversion with 87 proteins; and E: amino acid transport and metabolism with 87 proteins

Pathway enrichment analysis was based on KEGG pathways and we applied a hypergeometric test to find pathways that were significantly enriched in DEPs between control and treatment groups compared to all identified background proteins. Significantly enriched pathways were used to determine the most important biochemical, metabolic, and signal transduction pathways associated with DEPs (Fig. 4, Fig. S8, Table S6). As shown in Fig. 4, based on Q-values, the significantly enriched KEGG class B pathways were amino acid metabolism, lipid metabolism, and metabolism of cofactors and vitamins. Glycine, serine, and threonine metabolism (P = 0.0108); riboflavin metabolism (P = 0.0155); fatty acid degradation (P = 0.0299); and valine, leucine, and isoleucine degradation (P = 0.0496) were classified as class C pathways.

Fig. 4
figure 4

Top 20 enriched pathways of S. aureus proteins influenced by cFP. The Y-axis represents pathways and the X-axis represents their corresponding RichFactor. A larger area indicates a greater number of DEPs, and the color (from white to red; QValue) indicates the greatest difference in protein expression between the control and treatment groups. The most significantly differentially expressed pathways were glycine, serine, and threonine metabolism (P = 0.0108), riboflavin metabolism (P = 0.0155), fatty acid degradation (P = 0.0299), and valine, leucine, and isoleucine degradation (P = 0.0496)

DiscussionAnalysis of cFP-treated and control groups revealed that up-regulated proteins included two TCRS receptors, Rot (Q9RFJ6) and SarR (Q9F0R1), and an acetyl-CoA acetyltransferase (Q2G0K2). In addition, some DNA-binding TFs such as MalR (Q2FY63), SAOUHSC_02322 (Q2FWG9), and PhoP (Q2FXN6) were also up-regulated, whereas two TFs (Q2FVV2, Q2G2H0), a peptide ABC (Q2G168), and the virulence factors EsxA (Q2G189) and Psm (Q2FZA4) were down-regulated. Rot, a global TF, negatively regulates the transcription of several known virulence factors, such as lipase (geh), hemolysins (hla and hlb), and proteases (splA-splF, sspB, and sspC) (McNamara et al. 2000; Said-Salim et al. 2003), and positively regulates the expression of sarS and other genes, including those encoding cell surface adhesins (clfB, sdrC, and spa). Rot and Agr have opposing effects on several genes. Another helix-turn-helix (HTH) type global TF, SarR, negatively regulates sarA transcription (Manna and Cheung 2001, 2006). Additionally, it negatively regulates expression of RNAII and RNAIII primary transcripts in the Agr locus. EsxA (Q2G189) plays a role as a partial virulence factor belonging to the ESAT-6 secretion system (Sundaramoorthy et al. 2008). Psm (Q2FZA4) is a phenol soluble modulin produced by the Psm-alpha gene cluster that enhances virulence and destruction of white blood cells and increases infectivity (Wang et al. 2007). Psm is promoted by phosphorylated AgrA.

Twenty-two other TCRS proteins were annotated by KEGG pathway analysis, including the histidine kinases NreB (Q2FVM6) and KapB (Q2FZV0), the alkaline phosphatase PhoA (Q2FUY6), and the TF AgrA (Q2FWM4), VraR (Q2FX09), and SrrA (Q2FY79). NreB belongs to the NreB/NreC family and is involved in the control of dissimilatory nitrate/nitrite reduction in response to low oxygen. PhoA acts in phosphate assimilation in response to phosphate limitation. AgrA is an Agr response protein (Wang and Muir 2016). VraR (Q2FX09) functions similarly to a LuxR-type HTH TF by stimulating peptidoglycan synthesis to promote cell wall formation. SrrA (Q2FY79), a global regulator of S. aureus virulence factors in response to environmental oxygen levels, is involved in the TCRS SrrA/SrrB system. SrrA binds to agr, spa, and tst promoters and represses their transcription under low oxygen conditions (Yarwood et al. 2001; Pragman et al. 2004). However, in this study SSrA was not differentially expressed between the two groups. In addition, we identified a bacitracin ABC transporter (Q2G0D8) and two acetyl-CoA acetyltransferases (Q2G0K2 and Q2G124) in S. aureus 1.1861.

Agr is the main QS pathway associated with S. aureus virulence and consists of AgrC/A TCRS (Wang and Muir 2016). In this study, the HK AgrC was not identified. However, AgrA (Q2FWM4), a crucial regulator in the QS circuit of S. aureus, was identified. Two oligopeptide ABC transporters, OppA (A0A1D4HL93) and OppD (Q2FYQ7), were also found in the S. aureus 1.1861 proteome (Canovas et al. 2016). AIP can bind to AgrC and phosphorylate AgrA to stimulate the expression of RNAIII (Wang et al. 2014). At high cell densities, the Agr system provides positive feed-back to AIP and increases the secretion of virulence factors (Pollitt et al. 2014). Therefore, we hypothesize that, in S. aureus, cFP is transported by membrane transporters such as OppA and OppD and binds directly to AgrA without the need for AgrC, then activates TF signal transducers and regulators at a large scale. Due to sequence divergence among the four Agr groups (Canovas et al. 2016), it is also possible that another unidentified AgrC receptor recognizes cFP in S. aureus 1.1861. In fact, cFP did not increase Agr system positive feed-back but instead significantly down-regulated AgrA (Q2FWM4, cFP/CK log2 fold change = − 0.5507). However, cFP did up-regulate Rot (Q9RFJ6; cFP/CK log2 fold change = 0.7715), which is a major repressor of virulence that has opposing effects on certain genes modified by Agr. SarR (Q9F0R1), which competes with AgrA to bind the P2 and P3 promoters and inhibit agrB, agrD, agrC, and agrA transcription, was also up-regulated (cFP/CK log2 fold change = 0.4325). However, EsxA (Q2G189; cFP/CK log2 fold change = − 0.6866) and the important virulence factor Psm (Q2FZA4; cFP/CK log2 fold change = − 1.1924) that is activated by AgrA, were both down-regulated. These results suggest that cFP did not activate the Agr system but seemed to play the opposite role to that of the octapeptide AIP in the Agr QS system of S. aureus. cFP acted similarly to a competitive inhibitor or a QS quenching agent that binds to AgrC/AgrA to inhibit phosphorylation and suppress cognate accessory gene expression due to its similar conformation to the octapeptide but not the endogenous AIP. Because four S. aureus subgroups can block QS via their AIPs (AIP1, AIP2, AIP3, and AIP4) (Otto et al. 2001; Tal-Gan et al. 2016), they are cross-inhibitory to the S. aureus Agr system. This indicates that each AgrC can only recognize its endogenous AIP and is repressed by similarly structured molecules. Li et al. (2011) demonstrated a similar phenomenon. These observations suggest that cFP may interfere with the Agr system, decrease toxin and enzyme expression, increase lipid and amino acid metabolism by promoting acetylation and phosphorylation, and reduce alcohol dehydrogenase expression.

ConclusionsA total of 1296 proteins were identified in the S. aureus 1.1861 proteome. KEGG pathway annotation indicated that the greatest number of proteins (77) were associated with carbon metabolism, whereas 22 proteins belonged to signal transduction, 19 proteins were ABC transporters, and 11 proteins were associated with infectious diseases. Additionally, 1038 genes were annotated by COG, including 29 signal transduction proteins, 11 intracellular trafficking, secretion and vesicular transport proteins, and 13 defense mechanism proteins. In total, 43 proteins were up-regulated and 41 proteins were down-regulated between control and treatment groups. A total of 12 proteins related to transcription and signal transduction were differentially expressed. Among the DEPs, the most significant were those which are known to be involved in amino acid metabolism, lipid metabolism, and metabolism of cofactors and vitamins. The results also showed that AgrA (Q2FWM4) exists in S. aureus 1.1861. Furthermore, cFP down-regulated AgrA and the virulence factors EsxA (Q2G189) and Psm (Q2FZA4) and up-regulated Rot (Q9RFJ6) and SarR (Q9F0R1), which inhibits Agr system transcription. These findings suggest that cFP can reduce Agr system expression.


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We thank Guangzhou Sagene Biotech Co., Ltd. for help implementing the iTRAQ proteomic experiment.


Research reported in this publication was supported by the National Natural Science Foundation of China (NSFC grant numbers 31460425, 31560442, 31760466, and 31360405).

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Table S1

Protein identification information. A total of 1296 Staphylococcus aureus CGMCC 1.1861 proteins were identified by Mascot searches and matched peptide information. (XLS) (XLS 885 kb)

Table S2

Protein quantification information. A total of 1153 Staphylococcus aureus CGMCC 1.1861 proteins were quantified and annotated by KEGG and GO. (XLS) (XLS 207 kb)

Table S3

Significantly differentially expressed proteins. A total of 84 DEPs of Staphylococcus aureus CGMCC 1.1861 were quantified. Proteins with a fold change >1.2 or < 0.83 and an unadjusted significance level of P < 0.05 were considered differentially expressed between control and treatment groups. (XLS) (XLS 20 kb)

Table S4

Pathway annotation. A total of 522 Staphylococcus aureus CGMCC 1.1861 proteins were classified into 98 categories based on KEGG metabolic pathways. (XLS) (XLS 24 kb)

Table S5

COG classification. A total of 1038 Staphylococcus aureus CGMCC 1.1861 proteins were classified into 20 groups based on COG categories. (XLS) (XLS 120 kb)

Table S6

Enriched pathways ofS. aureusproteins influenced by cFP. A total of 48 Staphylococcus aureus CGMCC 1.1861 pathways were analyzed using the pathway enrichment method. Proteins with P < 0.05 were considered differentially expressed between control and treatment groups. (XLS) (XLS 5 kb)

Figure S7

Proteins identified as signal transduction TCRS by KEGG analysis. The red rectangular box represents identified Staphylococcus aureus CGMCC 1.1861 proteins that were classified as two-component regulatory systems (TCRS). (TIF) (PNG 6330 kb)

High resolution image (TIF 559 kb)

Figure S8

All pathways enriched forS. aureusproteins influenced by cFP. The first line “class” indicates the type of pathway and the second line “CK-VS-cFP” indicates their significance level. (TIF) (PNG 408 kb)

High resolution image (TIF 1102 kb)

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Ai, D., Zhang, W., Yun, J. et al. Analysis of the influence of cyclo (L-phenylalanine-L-proline) on the proteome of Staphylococcus aureus using iTRAQ. Ann Microbiol 69, 1247–1257 (2019).

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