Abstract
Biofilm formation is regarded as one of the major determinants in the prevalence of methicillin-resistant Staphylococcus aureus (MRSA) as pathogens of medical device-related infection. However, methicillin-susceptible S. aureus (MSSA) can also form biofilm in vitro and such biofilms are resistant to vancomycin. Hence, researching the possible mechanisms of MSSA biofilm formation is urgent and necessary. Here, we used S. aureus ATCC25923 as the model strain and studied gene expression profiles in biofilms after the treatment of ursolic acid and resveratrol using RNA-seq technology. The results showed that only ursolic acid could inhibit biofilm formation, which differed from their applied on the multiple clinical drugs resistant MRSA biofilm. RNA-seq data was validated by examining the expression of six genes involved in biofilm formation by qRT-PCR. These data analysis indicated that the mechanism of the MSSA biofilm formation was different from that of the MRSA, due to absence of accessory gene regulator (agr) function. These findings suggest that biofilms of S. aureus with agr dysfunction may be more resistant than those with agr function. Therefore, the infection from clinical MSSA may be recalcitrant once forming biofilm. Further study is necessary to uncover the mechanisms of biofilm formation in other clinical S. aureus.
Similar content being viewed by others
Introduction
Staphylococci are important nosocomial pathogens causing the common hospital and community acquired infective diseases1,2. The infection is difficult to treat due to their ability to form biofilms3. They have an array of virulence factors, including surface proteins responsible to adhesion and invasion of host tissue4. Almost all S. aureus strains are resistant to penicillin, many of which are resistant to methicillin-related drugs (hereby called Methicillin-resistant Staphylococcus aureus, abbr. MRSA strains) and have biofilm-forming capability1,5,6. Traditional antibiotic therapy could only eliminate planktonic cells, leave the sessile forms to survive within the biofilm and continue to disseminate when therapy is terminated7. There is evidence that quorum sensing is important for the construction and/or the dissolution of biofilm in some species8.
Expression of most virulence factors in S. aureus is controlled by the accessory gene regulator locus (agr), which encodes a two-component signaling pathway with an active ligand that is a bacterial density sensing peptide (autoinducing peptide [AIP]). The agr locus consists of four genes agrBDCA9. A polymorphism in the amino acid sequence of the auto-inducing peptide and of its corresponding receptor (AgrC) divides S. aureus strains into four major groups, agr-I, agr-II, agr-III and agr-IV10. All agr-II and agr-III isolates are defective in agrDCA and consequently they do not have a functional AIP as revealed by the lack of transcription of the hla gene and the late transcription of δ-toxin11. In addition, agr-III strains are able to transcribe icaR, sarA and rsbU at the late-exponential and/or the stationary phase. agr-I and agr-IV variants have functional agr-locus and sarA is always expressed, whereas rsbU and icaR were not transcribed in the mid-exponential and/or the stationary phases. Recent reports indicated that all glycopeptides intermediate S. aureus strains (GISA) so far examined belonging to agr-II group were defective on agr function. Interestingly, these strains were strong biofilm builders12,13. In this work, we confirmed that S. aureus ATCC25923, an agr-III strain which is a quality control strain in antimicrobial susceptibility test, is capable to form biofilm in vitro. These findings led to the hypothesis that S. aureus may developed its ability to form a thick biofilm due to agr-locus inactivation, which does not correlate to the antibiotic resistance. Therefore, we chose S. aureus ATCC25923 as the model strain to study the possible mechanisms of methicillin-susceptible S. aureus (MSSA) biofilm formation.
Studies on global S. aureus biofilm transcriptional profiles using microarrays suggested that planktonic cells and biofilm showed distinct patterns of gene expression14,15,16. However, few microarray studies have explored the S. aureus biofilms treated with drugs at the genetic level17, let alone using the latest RNA-seq technology. Our previous study18 showed that ursolic acid inhibited MRSA biofilm formation but had no impact on established biofilm, whereas resveratrol inhibited MRSA biofilm formation and partially removed established biofilm. Therefore, to investigate the potential mechanisms of MSSA biofilm formation at the genetic level, we used high-throughout Illumina sequencing of cDNA (Illumina RNA-seq) to study the differentially expressed genes of S. aureus ATCC25923 in biofilm that was treated with ursolic acid and resveratrol, respectively. And then a few of genes related to S. aureus biofilm formation were performed qRT-PCR assays to validate RNA-seq results. Finally, these results were compared with the genes expression of MRSA in biofilms under the same conditions18.
Results
MIC and MBC Determination
The susceptibility of the MSSA planktonic cells to ursolic acid was determined in vitro by methods recommended by the Clinical and Laboratory Standards Institute (CLSI). The results showed that the MIC of ursolic acid against S. aureus ATCC25923 was 60 μg/mL. In addition, for the MBC of ursolic acid against S. aureus ATCC25923, even when saturated in the TSB medium at the final concentration of 200 μg/mL, there were more than five colonies in the plates. So, we concluded that the MBC of ursolic acid against the MSSA was larger than 200 μg/mL.
Ursolic acid could inhibit MSSA biofilm formation but resveratrol not
The MSSA was able to form biofilms on 96-well plates after 18 h incubation. The SEM images showed that the MSSA formed thick, heterogenous clumps on the coverslips (Fig. 1a,d), which was thicker than MRSA biofilm in the same conditions18. Crystal violet staining assays revealed that ursolic acid could inhibit MSSA biofilm formation with an inhibitory rate of 46.50% (Supplementary Fig. S1 online). Moreover, the SEM image showed ursolic acid could inhibit the MSSA biofilm formation (Fig. 1b), while resveratrol could not inhibit the MSSA biofilm formation (Fig. 1c, Supplementary Fig. S1 online).
Scanning electron microscopic images showing the structure of the biofilm of Staphylococcus aureus ATCC25923.
Magnifications, ×3000. (a) Control without ethanol (18-h incubation) (218), (b) 30 μg/mL ursolic acid (2U30), (c) 100 μg/mL resveratrol (2R100), (d) control without ethanol (36 h) (236), (e) 150 μg/mL resveratrol (2R150), (f) 8 μg/mL vancomycin (2V) and (g) the mixture of 8 μg/mL vancomycin and 150 μg/mL resveratrol.
Resveratrol, vancomycin and their mixture could not remove established MSSA biofilm
In vitro the effects of resveratrol, vancomycin and their mixture on established MSSA biofilm were also investigated using the same methods described above with certain modification. Both the semi-quantitative assays and SEM images demonstrated that they had no effect on established MSSA biofilm (Fig. 1e–g, Supplementary Fig. S1 online), which was also different from the fact that they could remove partial established MRSA biofilm18.
Transcriptome assembly and annotation
The cDNA libraries of nine samples (Supplementary Table S1 online) were pooled and subjected to high-throughout sequencing. The generated sequencing reads were used to assemble the transcriptome of S. aureus ATCC25923. Two independent Illumina sequencing runs generated a total of 394,463,176 reads consisting of 39,446,317,600 nucleotides (nt) after trimming the adaptors and removing low-quality sequences, with an average length of 100 bp for each short read. Due to the absence of a proper reference genome, Trinity de novo assembly19,20 was applied to construct transcripts. 3,054 transcripts were constructed (Supplementary Fig. S2a online). The length of these assembled transcripts ranged from 200 bp to over 3000 bp, with an average of 850.37 bp (Table 1, Supplementary Fig. S2a online). In total, 3819 coding sequences (CDSs) were generated by TransDecoder (http://transdecoder.sourceforge.net/) and BLAST (Table 1, Supplementary Fig. S2b online). This Transcriptome Shotgun Assembly project has been deposited at DDBJ/EMBL/GenBank under the accession GBKB00000000. The version described in this paper is the first version, GBKB01000000.
Out of the obtained 3819 CDSs, 3807 (99.69%) matched to known protein sequences in the Nr database (Table 2). In addition, the statistical analysis showed that 2341 CDSs (61.5%) had an E-value < 1e-45 (Supplementary Fig. S3a online) and 94.9% of CDSs had alignment identities greater than 95% (Supplementary Fig. S3b online). At a cut-off E-value of 1e-5, 1155 CDSs (30.3%) had matched to the Staphylococcus aureus protein database, 836 (22.0%) to the Staphylococcus aureus subsp. aureus MRSA252 protein database, 298 (7.8%) to the Staphylococcus aureus subsp. aureus Mu50 protein database and 297 (7.8%) to Staphylococcus aureus subsp. aureus TCH60 protein database (Supplementary Fig. S3c online).
Gene Ontology (GO) terms were subsequently assigned to S. aureus ATCC25923 CDSs based on their sequence matched to known protein sequences in the Nr database. A total of 2876 CDSs (75.31%) and 2074 (67.91%) transcripts were assigned (Table 2). 1942 (63.59%) transcripts were in the biological process category, 1326 (43.42%) transcripts were in the cellular component category and 1715 (56.16%) transcripts were in the molecular function category (Supplementary Fig. S4 online). In addition, to further elucidate the functionality of S. aureus ATCC25923 transcriptome, the annotated CDSs were categorized into different functional groups based on the COG database (Cluster of Orthologus Groups). Out of the 3819 CDSs, 2747 (71.93%) could be classified into 25 COG categories (Table 2). Among the latter, 466 (16.96%) were assigned to the COG category of general function prediction, which was the largest functional group, followed by amino acid transport and metabolism (449, 16.35%), inorganic ion transport and metabolism (354, 12.89%), carbohydrate transport and metabolism (276, 10.05%) and unknown function (273, 9.94%) (Supplementary Fig. S5 online).
General changes in gene expression after treatment
To determine the gene expression variations between treatment groups and control groups, RNA samples from cells under each condition were sequenced. These nine sample libraries contained clean-up reads ranging from 12,524,649 to 31,001,659. The number of reads from these nine samples that could be mapped to our transcriptome data was then calculated and normalized.
In the study, 130 and 34 transcripts were categorized as up- and down-regulated in response to ursolic acid treatment, respectively. Among these genes, 69 were annotated with known functions with gene names associated with S. aureus in the Swiss-Prot database (Supplementary Table S2 online). Although resveratrol had no effect on MSSA biofilm, 407 and 403 transcripts were categorized as significantly regulated under it inhibiting biofilm formation (2R100) and removing established biofilm (2R150) conditions, respectively. In addition, among these transcripts of these two samples, 154 and 190 were annotated with gene names associated with S. aureus in the Swiss-Prot database, respectively (Supplementary Table S3 online, Supplementary Table S4 online).
Differentially expressed genes related to biofilm formation
Notably, the sets of genes were highly up-regulated and down-regulated in both conditions including some key known genes encoding virulence factors, surface proteins, proteases and adhesins, which are related to S. aureus biofilm formation. In the sample that ursolic acid was used to inhibit MSSA biofilm formation (2U30), 69 detected transcripts showed high similarity to known S. aureus genes, many of which were known to be associated with S. aureus biofilm formation (Supplementary Table S2 online, Fig. 2a). icaR, inhibiting ica-dependent biofilm formation in S. aureus, was up-regulated by 4.6-fold compared to control. The expressions of genes (isaA, isaB, clpP and clpX) encoding proteases IsaA, IsaB, ClpP and ClpX increased by 4.4, 2.9, 2.8 and 2.3-fold, respectively. The expression of sarX, a sarA paralog, increased by 9.5-fold. Moreover, treatment of ursolic acid increased the expression levels of genes that encode adhesins and surface proteins (fib, map, sdrC, sdrD and spa) by 2.3, 13.2, 10.7, 5.2 and 5.4-fold, respectively, when MSSA biofilm formation was inhibited. However, the expression of hld gene encoding δ-hemolysin, a surfactant and an important element of S. aureus agr system, decreased 2.6-fold, which was different from the observation in MRSA biofilm of which ursolic acid is also inhibitive18.
Heatmap of differentially expressed genes associated with S. aureus ATCC25923 biofilm formation and virulence.
(a) ursolic acid inhibiting S. aureus ATCC25923 biofilm formation condition, (b) resveratrol inhibiting S. aureus ATCC25923 biofilm formation condition, (c) resveratrol removing established S. aureus ATCC25923 biofilm condition and (d) the mixture of resveratrol and vancomycin removing established S. aureus ATCC25923 biofilm condition.
Although resveratrol did not inhibit MSSA biofilm formation, the expression levels of many genes related to S. aureus biofilm formation changed. Particularly, the expression of icaR increased 33.8-fold. In addition, the expressions of fib, isdC, isdI, map, sdrC and sdrD that encode adhesion and surface protein increased by 2.7, 9.5, 2.0, 7.9, 12.9 and 2.6-fold, respectively. However, the expressions of ebh and fnbA decreased by 28.8 and 560.3-fold, respectively. Surprisingly, the expressions of the virulence genes (clpC, hla, hlb, hld, hlgC, lip2 and sarS) reduced by 2.8, 4.4, 3.5, 6.4, 3.9, 3.3 and 2.5-fold, respectively (Supplementary Table S3 online, Fig. 2b). In spite of the expression levels of these genes changed, resveratrol had no observable impact on MSSA biofilm formation, which was different from the case of applying resveratrol to MRSA biofilm18.
When MSSA biofilm was established, both resveratrol and the mixture with vancomycin had no effect, which was different from their effects on established MRSA bioflm18. The differentially expressed genes analysis revealed that expressions of agrA and hld increased by 128.0 and 72.0-fold, respectively, when resveratrol was applied to established MSSA biofilm (2R150). In addition, when the mixture of resveratrol and vancomycin was applied on established MSSA biofilm (2VR), the expressions of agrA and hld increased by 142.0 and 136.2-fold, respectively. However, when resveratrol was used alone and in the combination with vancomycin on established MRSA biofilm samples, the expression levels of agrA and hld decreased, which were confirmed by qRT-PCR assays18. The expressions of genes (bbp, ebhA, map, sdrC, sdrD and spa) encoding adhesins and surface proteins in S. aureus biofilm formation were up-regulated. Moreover, the expressions of most cap locus encoding the capsule polysaccharide synthesis enzymes were not significantly changed, except that cap5A increased a little. In the samples 2R150 and 2VR, the expression levels of 21 and 13 genes encoding ribosomal proteins were down-regulated, respectively, which were different from their effects on established MRSA biofilm18. It is surprising that the expressions of blaZ and pbp associated with Beta-lactam-inducible penicillin-binding protein increased thousands folds in the samples 2R150 and 2VR (Supplementary Table S4 online, Supplementary Table S5 online, Fig. 2c–d).
S. aureus ATCC25923 was identified as agr-III isolate by polymerase chain reaction (PCR) assays
S. aureus ATCC25923 agr specificity was identified by the expected PCR product size according to Shopsin et al. report21. The lengths of the PCR products were estimated by comparing with the 100 bp DNA ladder (Takara, Japan). The results showed that only agr-III genotype was obtained by the PCR assay and the length of the PCR product was 406 bp, which corresponded to expected product size (Fig. 3). In addition, we also sequenced the PCR product and aligned it with Staphylococcus aureus RN8462 (GenBank accession number AF001783) using BLAST, which is agr group III strain10,22,23. The results revealed that their identity was 99% and amplified fragment corresponding to the region was right. Therefore, based on above results, we determined that S. aureus ATCC25923 was agr-III genotype strain.
PCR product agarose gel electrophoresis for the identification of S. aureus ATCC25923 agr genotype.
Whole- cell PCR was performed with each of the four agr specificity primers. Lines 1 to 4, PCR product using the same forward primer pan-agr and different reverse primers (agrI, agrII, agrIII and agrIV) with S. aureus ATCC25923 genomic DNA as templates (439 bp for agrI, 573 bp for agrII, 406 bp for agrIII and 657 bp for agrIV); Lines 5 to 8, negative controls responsible to lines 1 to 4, respectively; Lines M, 100 bp for DNA ladder.
RNA-seq results were verified by qRT-PCR
On the basis of these six genes (agrA, hld, icaR, spa, cna and bbp) were significantly differential transcribed (P < 0.05, FDR < 0.001) under inhibiting biofilm formation condition or removing established biofilm condition and their expression was related to S. aureus biofilm formation, they were selected for qRT-PCR to investigate gene expression difference between treatment samples and controls. The results of gene expression ratios between treatment groups and controls are shown in Fig. 4. Although the trend of hld expression ratio under removing established biofilm condition was inconsistent between RNA-seq and qRT-PCR, a liner regression analysis showed an overall correlation of r = 0.842 for these six genes under inhibiting biofilm formation condition or removing established biofilm condition, which indicates a high correlation between transcript abundance assayed by qRT-PCR and the transcription profile revealed by RNA-seq data24. In general, although the exact fold difference for each gene by qRT-PCR was different from the RNA-seq, the comparison pairs had the similar trends with RNA-seq, which suggested that there would be the relative high consistency between RNA-seq and qRT-PCR.
Expression ratio (log2) obtained by qRT-PCR and RNA-seq of these six selected genes associated with S. aureus biofilm formation.
pyk was used as a reference gene for normalization of qRT-PCR data. All gene expression ratios from qRT-PCR between treatment groups and controls under different conditions were significantly different (P < 0.05). Bars represent the error standard (n = 3). The x-axis indicates the comparison between treatment groups and controls under different conditions. The y-axis shows the gene expression ratios.
Discussion
S. aureus ATCC25923, a quality control strain in antimicrobial susceptibility test, is a clinical isolate and able to form biofilm in vitro25. Vancomycin had no effect on its biofilm formation (Supplementary Fig. S1 online), indicating that the formation of bacterial biofilm may be the first and probably an important barrier of multiple drugs resistance. Therefore, we employed S. aureus ATCC25923 as the model strain to study the mechanisms of MSSA biofilm formation. Qin et al.18 reported that ursolic acid could inhibit MRSA biofilm formation and resveratrol could not only inhibit MRSA biofilm formation but also remove partial established MRSA biofilm. However, only ursolic acid could inhibit S. aureus ATCC25923 biofilm formation. Given above differences, the processes of MRSA and MSSA biofilm formation may be different. So, we studied the gene expression profiles of S. aureus ATCC25923 in bioflms after the treatment of ursolic acid and resveratrol using RNA-seq technology.
In this work, with the RNA-seq technology a total of 3054 transcripts were obtained, representing a comprehensive transcriptome of S. aureus. Functional annotation showed that these transcripts covered every basic biological process. Moreover, we used Trinity de novo assembly to connect the short reads, which is believed to be more suitable for constructing a de novo transcriptome without a reference genome than other de novo assemblers of RNA-seq19,20. In addition, the assembled transcripts were matched with genome sequences from Staphylococcus aureus subsp. aureus NCTC 8325: 47.70% of matched sequences had an E-value lower than 1e-20. We also aligned reads to evaluate the integrity and reliability of the assembly. The results showed that 99.4% of reads mapped the assembled transcripts, which confirmed that the de novo assembly we performed was accurate.
Intercellular signaling, often referred to quorum sensing, has been shown to involve in biofilm development in several bacteria8. The S. aureus quorum sensing system is encoded by the agr locus, which consists of four genes agrBDCA9. Although structurally conserved, agrB, D and C have diverged widely among Staphylococci, giving rise to different specificity groups11,21. S. aureus ATCC25923 was classified as agr-III strain as mentioned by others26,27. In this research, agrD was not found in the global transcriptome, but agrA, icaR, hld, sarA and spa were detected. In addition, we validated the results from RNA-seq analysis by PCR and qRT-PCR assays. Our results confirmed that S. aureus ATCC25923 is indeed an agr-III strain, namely the agr system of this strain is nonfunctional. Due to the differential expression of icaR found in transcriptome, we speculated that the biofilm formation may be ica-dependent, which was validated by qRT-PCR assay.
In this study, we also performed comparative analysis between different control samples (218 & 236). Both crystal violet staining assays and SEM images revealed that S. aureus ATCC25923 biofilm formed at 18 h was similar to the biofilm formed at 36 h (Fig. 1, Supplementary Fig. S1 online). Moreover, gene expression analysis showed that fib, spa, sarA and sarX were down-regulated in the sample 236 compared to the sample 218. However, the expression levels clpB, clpC, hlb, hlgA, hlgB, lip1, lip2 and sbi that encode virulence factors increased by 7.4, 24.8, 3.2, 4.0, 4.7, 2.4, 2.5 and 2.1-folds, respectively. These three aspects showed that S. aureus ATCC25923 could form stable and mature biofilm after 18 h incubation. Meanwhile, S. aureus ATCC25923 is agr-III strain, which formed thicker biofilm than the MRSA strain. The MRSA biofilm previously described by our group was thicker after 36 h than that formed after 18 h18. RNA-seq data analysis showed that in the sample M36, splA, splB, splC and sspB2 genes that encode extracellular proteases and are controlled by the agr system27 were significantly down-regulated compared to the sample M18, indicating that the agr is active in the MRSA strain. Through comparative analysis, we confirmed that i) the MSSA biofilm developed at a quicker rate than MRSA, ii) the MSSA biofilm was thicker than MRSA biofilm due to the absence of agr function and iii) the ability of S. aureus to form biofilm is independent on antibiotic resistance. Therefore, it is not difficult to understand that i) resveratrol did not inhibit MSSA biofilm formation and ii) resveratrol, vancomycin and their mixture had no effect on established MSSA biofilm. Meanwhile, Qin et al.18 proposed that resveratrol inhibited MRSA biofilm formation by disturbing the QS system. Based on the comparative analysis of resveratrol effecting on S. aureus biofilm in different agr genotypes, we suggest that resveratrol would be used to block S. aureus biofilm formation in which the agr system is active.
In conclusion, RNA-seq data analysis in this study suggested that the mechanism of biofilm formation in clinical MSSA could be different from that of the clinical MRSA. S. aureus may have an enhanced ability to form a thick biofilm if the agr-locus is inactivated and their resistance to antibiotics does not depend on the functionality of the agr system. In addition, we speculated that biofilm formed by MSSA strains may be more resistant than the biofilm of MRSA strains, which was proved by Bauer et al. using other approaches28. Therefore, the infection from clinical MSSA may be more recalcitrant once forming biofilm. Further study is necessary to uncover the mechanisms of biofilm formation in other clinical S. aureus isolates.
Methods
Strain and growth conditions
S. aureus ATCC25923 was provided by the Clinical Laboratory Department of the First Affiliated Hospital, Nanjing Medical University, Nanjing, China. The strain was susceptible to many antibiotics determined by broth dilution method as recommended by the Clinical and Laboratory Standards Institute (CLSI, M100-S22) and their MICs were shown in Supplementary Table S6. In addition, S. aureus ATCC25923 was identified as methicillin-susceptible strain using cefoxitin disk diffusion and oxacillin broth dilution methods as recommended by CLSI (M100-S22). Blood agar medium was used to culture the bacterium. Nutrient broth (NB) medium was used for routine cultivation, while trypticase soy broth (TSB) medium was used to study the effects of natural compounds on biofilms in 96-well flat-bottom polystyrene plates (Costar 3599; Corning; USA). Resveratrol and ursolic acid used in this study were isolated from natural products by our group and their purities were confirmed to be >98% using high performance liquid chromatography methods. They were dissolved in ethanol at 30 mg/mL and 5 mg/mL, respectively. The vancomycin was purchased from Sigma and dissolved in water at 5 mg/mL. All compounds were filtered with a 0.22-μm filter in sterile conditions and then stored at 4 °C.
Determination of Minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC)
MIC and MBC were determined by a microtitre broth dilution method as recommended by the Clinical and Laboratory Standards Institute (CLSI) with a little modification. Briefly, the test medium was TSB and the density of bacteria was 5 × 105 colony forming units (CFU)/mL. Cell suspensions (200 μL) were inoculated into the wells supplemented with ursolic acid at different concentrations (30, 40, 50, 60, 70, 80 and 90 μg/mL). These concentrations were selected based on the fact that the solubility of ursolic acid in TSB medium decreases as its concentration increases. We did not measure the MIC and MBC of resveratrol because it did not inhibit MSSA biofilm formation. The inoculated microplates were incubated at 37 °C for 18 h and examined. MIC was defined as the lowest concentration of the drug that inhibited >90% of the growth of the test microorganism29.
The MBC was obtained by subculturing 100 μL from each well from the MIC assay onto TSA plates. The plates were incubated at 37 °C for 24`h and MBC was defined as the lowest concentration of substance when the subcultures developed no more than five colonies on each plate7. Three replicates were carried out in the experiment.
Inhibition of resveratrol and ursolic acid on MSSA biofilm formation
This assay was performed as previously described by our group with some modification18. Briefly, MSSA culture was supplemented with resveratrol or ursolic acid at final concentrations of 100 μg/mL and 30 μg/mL, respectively, because both of concentrations were previously applied on MRSA biofilm by our group18. The plates were incubated at 37 oC for 18 h with shaking at 150 rpm. The absorbance at OD570 nm was measured with a microplate reader (BioTek, USA). All assays were performed with independent triplicates. The inhibition rates were calculated using previously described formula18.
Treatment of MSSA biofilm with resveratrol, vancomycin, or their mixture
These assays were performed as previously described by our group with a slight modification18. In brief, a total of 200 μL of fresh TSB with resveratrol (final concentration, 150 μg/mL), vancomycin (final concentration, 8 μg/mL), or their mixture (resveratrol 150 μg/mL + vancomycin 8 μg/mL) was added to the wells with established MSSA biofilm. The plates were incubated at 37 oC for 18 h with shaking. All assays were performed with independent triplicates.
Scanning electron microscope (SEM) measurement assays
SEM was performed with a scanning electron microscope (FEI Quanta 200; USA) on biofilm formed on glass coverslips according to methods described by Qin et al. with a little modification18. The observations were usually performed.
Total RNA isolation
All samples used for the RNA sequencing were prepared in the 24-well flat bottomed polystyrene plates. After the biofilms were rinsed with sterile water, the biofilm cells were scraped with pipette and suspended in the RNAprotect Bacteria Reagent (Qiagen, Germany). The cell suspension was then transferred to a microcentrifuge tube and incubated for 5 min at room temperature to stabilize the mRNA. Next, the cell suspensions were centrifuged at 8,000 × g for 5 min to pellet the cells. Total RNA was purified from the pellet using RNeasy Mini Kit (Qiagen) according to the manufacturer’s protocol with modification described by Qin et al.18. Each RNA sample was suspended in 30 μL of RNA storage solution and the quality of total RNA obtained was determined using Agilent 2100 Bioanalyzer.
Enrichment and sequencing of mRNA
A total of 10 μg of each RNA sample was subjected to further purification using a MICROBExpress Kit (Ambion, USA) according to the manufacturer’s protocol. The mRNA sample was suspended in 25 μL RNA storage solution and the quality of mRNA was determined using Agilent 2100 Bioanalyzer. Bacterial mRNA was fragmented using a RNA fragmentation kit (Ambion) and the fragments were achieved in the size range of 200–250 bp. Double-stranded cDNA was generated using the SuperScript Double-Stranded cDNA Synthesis Kit (Invitrogen, Carlsbad, CA). An Illumina Paired End Sample Prep kit was used to prepare RNA-seq library. All of the samples were sequenced using the Hiseq2000 (Illumina, CA) sequencer at Beijing Genomics Institute at Shenzhen.
Transcriptome assembly and annotation
Due to lack of the reference genome of S. aureus ATCC25923, we used Trinity de novo assembler (version 20140413)19,20 to assemble these RNA-seq sequences. The raw reads were filtered using NGS QC ToolKit software30. The low-quality reads (which contained adaptor contamination, the pair-end reads satisfied N > 10% and low quality reads (quality value < 20)>50%) were removed.
Coding sequences (CDSs) were identified using TransDecoder (http://transdecoder.sourceforge.net/). These CDSs were used for BLAST searches against the NCBI Nr protein database (NCBI non-redundant sequence database) (version 2014-5) with an E-Value cut-off of 1e-5. CDSs were further aligned by BLASTX to protein database including Swiss-Prot (version 2014-5)31, KEGG (version 2013-10)32,33,34, COG (version 20090331)35,36 and GO (version 2014-5)37 to retrieve their functional annotations. If results of different database conflicted, a priority order of Nr, Swiss-Prot, KEGG, COG and GO was followed. For transcripts that were not identified by TransDecoder software but were annotated by above protein databases, they were still regarded as CDSs.
The Blast2GO program38 was used to obtain GO annotation for CDSs, as well as for KEGG and COG analyses. The WEGO software39 was used to perform GO functional classification. This analysis mapped all of the annotated CDSs to the GO terms in the database and calculated the number of CDSs associated with every GO term. COG and KEGG pathway annotations were performed using Blastall software (version 2.2.25) against the COG and KEGG databases.
Identification of differentially expressed genes
The RNA-seq reads were mapped to our transcriptome reference database and transcript abundances were quantified by RSEM40. Genes that differentially expressed among these nine samples were identified using the numbers of mapped reads as EdgeR inputs41. Genes with an adjusted P value ≤ 0.05, FDR ≤ 0.001 and fold change ≥ 2 were identified as being differentially expressed. Differentially expressed genes were regarded as up-regulated if their expression levels in treated samples were significantly higher than those in the control samples. Similarly, they were down-regulated if their expression levels in treated samples were significantly lower than those in the control samples.
Determined S. aureus ATCC 25923 agr class by PCR assays
S. aureus ATCC25923 genomic DNA was isolated using Ezup Column Bacteria Genomic DNA Purification kit (Sangon Biotech, China) according to the manufacturer’s recommended protocol. The quality of genomic DNA obtained was determined using Agilent 2100 Bioanalyzer and 0.7% agarose gel, which was stained with ethidium bromide (0.5 μg/mL).
Based on analysis of RNA-seq data, we speculated that S. aureus ATCC25923 would be agr-III isolate. Therefore, to verify the results from RNA-seq analysis, agr class-specific primer pairs were designed for S. aureus ATCC25923 agr genotype measurement according to Shopsin et al. report21 (Supplementary Table S7 online).
Amplification of agr was carried out in a 25 μL volume using 2 × Taq Master Mix (Vazyme, China) according to the manufacturer’s recommended protocol. The reaction was performed using Authorized Thermal Cycler (Eppendorf, Germany) with the following cycle parameters: an initial 5 min denaturation step at 94 oC, followed by 26 cycles of 30 sec at 94 oC, 30 sec at 55 oC, 1 min at 72 oC and extension for 10 min at 72 oC. After PCR, the product was separated on 2.0% agarose gel, which was stained with ethidium bromide (0.5 μg/mL). All measurements were independently conducted 3 times. The PCR product was recycled using an AxyPrep DNA Gel Extraction kit (Axygen, USA) and then ligated into the pMD19-T vector using pMDTM19-T Vector Cloning kit (TakaRa, Japan) according to manufacturer’s instructions. Next, they were transferred into E. coli DH5α competent cell and then cultured on LB agar plate supplemented with 100 μg/mL ampicillin overnight at 37 oC. White colonies were grown in LB broth supplemented with 100 μg/mL ampicillin overnight incubation. The plasmid DNA containing the PCR fragment insert was isolated from each culture using AxyPrep Plasmid Miniprep kit (Axygen, USA) according to manufacturer’s instructions. All DNA samples with A260/A280 above 1.8 were sequenced using 3730XL automated sequencer (Applied Biosystems, USA) at Shanghai Sunny Biotechnology Company, China. All measurements were independently conducted five times. BLAST (http://blast.ncbi.nlm.nih.gov/Blast.cgi) was used to obtain a multiple sequence alignment of sequences from five DNA samples and to identify the region of agr locus that was amplified through alignment with Staphylococcus aureus strain RN8462 (GenBank accession number AF001783), which is agr group III strain10,22,23.
qRT-PCR assays
Cafiso et al.11 reported that strains with agr-III have an inactive agr-system, but were able to transcribe icaR, hld and sarA at late- and post-exponential phases, which are related to biofilm production. Herein, S. aureus ATCC25923 may be agr-III isolate and four genes (agrA, hld, icaR and spa) were significantly differential transcribed (P < 0.05, FDR < 0.001) under inhibiting biofilm formation condition or removing established biofilm condition. Therefore, these four genes were selected for qRT-PCR to validate RNA-seq data. Although sarA was transcribed in all samples, it was not significantly differential transcribed between all treatment samples and controls. So, sarA gene was not selected for qRT-PCR. In addition, under removing established biofilm condition, these two genes (bbp and cna) also were significantly differential transcribed and their expression was related to S. aureus biofilm formation. So, they were also selected for qRT-PCR. In general, these six genes (agrA, hld, icaR, spa, cna and bbp) were selected for qRT-PCR assays. Total RNA was reverse transcribed into cDNA using the HiScript® Q RT SuperMix for qPCR (+gDNA wiper) (Vazyme, China) according to the manufacturer’s protocol. The resulting cDNAs were stored at −20 oC until they were required. The qRT-PCR was carried out in a 20 μL volume using AceQTM qPCR SYBR® Green Master Mix (Vazyme, China) as recommended by the manufacturer. These reactions were performed using the Applied Biosystems 7300 Real-time PCR System and all cycle parameters were shown in Supplementary Table S7. All measurements were done in three independent replicates and additionally four replications within each qRT-PCR run. For data normalization, housekeeping gene pyk was used as an internal reference to obtain basis of normalization. Fold change between treatment samples and controls was calculated using 2–ΔΔCt method. All primers and sequences are listed in Supplementary Table S7.
Statistical analysis
At least three independent replicates of each 96-well plate experiment were performed. The biofilm inhibition results and qRT-PCR results were statistically analyzed using SPSS software version 18.0 (SPSS, Chicago, IL, USA). P values ≤ 0.05 were considered significant. In addition, a liner regression analysis was done to obtain the correlation between transcript abundance assayed by qRT-PCR and transcription profile revealed by RNA-seq data. The correlation coefficient (r) was obtained by Pearson’s analysis.
Additional Information
How to cite this article: Tan, X. et al. Transcriptome analysis of the biofilm formed by methicillin-susceptible Staphylococcus aureus. Sci. Rep. 5, 11997; doi: 10.1038/srep11997 (2015).
References
Nostro, A. et al. Effects of oregano, carvacrol and thymol on Staphylococcus aureus and Staphylococcus epidermidis biofilms. J Med Microbiol 56, 519–523 (2007).
Kiran, M. D. et al. Discovery of a quorum-sensing inhibitor of drug-resistant staphylococcal infections by structure-based virtual screening. Mol Pharmacol 73, 1578–1586 (2008).
Lowy, F. D. Staphylococcus aureus infections. N Engl J Med 339, 520–532 (1998).
Wyatt, M. A. et al. Staphylococcus aureus nonribosomal peptide secondary metabolites regulate virulence. Science 329, 294–296 (2010).
Cassat, J. E., Lee, C. Y. & Smeltzer, M. S. Investigation of biofilm formation in clinical isolates of Staphylococcus aureus. Methods Mol Biol 391, 127–144 (2007).
Jennings, J. A., Courtney, H. S. & Haggard, W. O. Cis-2-decenoic acid inhibits S. aureus growth and biofilm in vitro: a pilot study. Clin Orthop Relat Res 470, 2663–2670 (2012).
Wang, X. et al. Effect of berberine on Staphylococcus epidermidis biofilm formation. Int J Antimicrob Agents 34, 60–66 (2009).
Parsek, M. R. & Greenberg, E. P. Sociomicrobiology: the connections between quorum sensing and biofilms. Trends Microbiol 13, 27–33 (2005).
Yarwood, J. M., Bartels, D. J., Volper, E. M. & Greenberg, E. P. Quorum sensing in Staphylococcus aureus biofilms. J Bacteriol 186, 1838–1850 (2004).
Jarraud, S. et al. Relationships between Staphylococcus aureus genetic background, virulence factors, agr groups (alleles) and human disease. Infect Immun 70, 631–641 (2002).
Cafiso, V. et al. agr-Genotyping and transcriptional analysis of biofilm-producing Staphylococcus aureus. FEMS Immunol Med Microbiol 51, 220–227 (2007).
Sakoulas, G. et al. Accessory gene regulator (agr) locus in geographically diverse Staphylococcus aureus isolates with reduced susceptibility to vancomycin. Antimicrob Agents Chemother 46, 1492–1502 (2002).
Sakoulas, G. et al. Staphylococcus aureus accessory gene regulator (agr) group II: is there a relationship to the development of intermediate-level glycopeptide resistance? J Infect Dis 187, 929–938 (2003).
Resch, A., Rosenstein, R., Nerz, C. & Gotz, F. Differential gene expression profiling of Staphylococcus aureus cultivated under biofilm and planktonic conditions. Appl Environ Microbiol 71, 2663–2676 (2005).
Resch, A. et al. Comparative proteome analysis of Staphylococcus aureus biofilm and planktonic cells and correlation with transcriptome profiling. Proteomics 6, 1867–1877 (2006).
Baker, J. et al. Copper stress induces a global stress response in Staphylococcus aureus and represses sae and agr expression and biofilm formation. Appl Environ Microbiol 76, 150–160 (2010).
Smith, K. et al. Influence of tigecycline on expression of virulence factors in biofilm-associated cells of methicillin-resistant Staphylococcus aureus. Antimicrob Agents Chemother 54, 380–387 (2010).
Qin, N. et al. RNA-Seq-based transcriptome analysis of methicillin-resistant Staphylococcus aureus biofilm inhibition by ursolic acid and resveratrol. Sci Rep 4, 5467 (2014).
Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc 8, 1494–1512 (2013).
Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 29, 644–652 (2011).
Shopsin, B. et al. Prevalence of agr specificity groups among Staphylococcus aureus strains colonizing children and their guardians. J Clin Microbiol 41, 456–459 (2003).
Lina, G. et al. Bacterial competition for human nasal cavity colonization: role of Staphylococcal agr alleles. Appl Environ Microbiol 69, 18–23 (2003).
Gilot, P., Lina, G., Cochard, T. & Poutrel, B. Analysis of the genetic variability of genes encoding the RNAIII-activating components Agr and TRAP in a population of Staphylococcus aureus strains isolated from cows with mastitis. J Clin Microbiol 40, 4060–4067 (2002).
Hu, Y. et al. De novo assembly and transcriptome characterization: novel insights into the natural resistance mechanisms of Microtus fortis against Schistosoma japonicum. BMC Genomics 15, 417 (2014).
Harriott, M. M. & Noverr, M. C. Candida albicans and Staphylococcus aureus form polymicrobial biofilms: effects on antimicrobial resistance. Antimicrob Agents Chemother 53, 3914–3922 (2009).
Malone, C. L., Boles, B. R. & Horswill, A. R. Biosynthesis of Staphylococcus aureus autoinducing peptides by using the synechocystis DnaB mini-intein. Appl Environ Microbiol 73, 6036–6044 (2007).
Boles, B. R. & Horswill, A. R. Agr-mediated dispersal of Staphylococcus aureus biofilms. PLoS Pathog 4, e1000052 (2008).
Bauer, J., Siala, W., Tulkens, P. M. & Van Bambeke, F. A combined pharmacodynamic quantitative and qualitative model reveals the potent activity of daptomycin and delafloxacin against Staphylococcus aureus biofilms. Antimicrob Agents Chemother 57, 2726–2737 (2013).
Oo, T. Z., Cole, N., Garthwaite, L., Willcox, M. D. & Zhu, H. Evaluation of synergistic activity of bovine lactoferricin with antibiotics in corneal infection. J Antimicrob Chemother 65, 1243–1251 (2010).
Patel, R. K. & Jain, M. NGS QC Toolkit: a toolkit for quality control of next generation sequencing data. PLoS One 7, e30619 (2012).
Magrane, M. & Consortium, U. UniProt Knowledgebase: a hub of integrated protein data. Database (Oxford) 2011, bar009 (2011).
Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y. & Hattori, M. The KEGG resource for deciphering the genome. Nucleic Acids Res 32, 277–280 (2004).
Kanehisa, M. A database for post-genome analysis. Trends Genet 13, 375–376 (1997).
Kanehisa, M. et al. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 34, 354–357 (2006).
Tatusov, R. L., Koonin, E. V. & Lipman, D. J. A genomic perspective on protein families. Science 278, 631–637 (1997).
Tatusov, R. L. et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 4, 41 (2003).
Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25, 25–29 (2000).
Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676 (2005).
Ye, J. et al. WEGO: a web tool for plotting GO annotations. Nucleic Acids Res 34, 293–297 (2006).
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Acknowledgements
The study was supported by the National Natural Science Foundation of China (31170131, 31070312, 81301475) and Jiangsu Qinglan Project. We thank Dr. Liyan Ping from Harvard University, USA and Dr. Heiko Maischak from Max Planck Institute for Chemical Ecology, Germany for polishing the paper and thank Dr. Jing Chen from Zhengjiang University, China for guiding qPCR experiments.
Author information
Authors and Affiliations
Contributions
X.T. and N.Q. contributed equally to this work. A.J., X.P. and N.Q. conceived and designed the study. X.T., J.S., R.Y. and B.Z. performed experiments. N.Q. and L.L. performed sequencing. C.W. designed the analysis and performed Trinity de novo assembly. X.T. and C.W. analyzed the differentially expressed genes. Z.M. checked these data. X.T., N.Q. and A.J. wrote and revised the paper.
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Electronic supplementary material
Rights and permissions
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
About this article
Cite this article
Tan, X., Qin, N., Wu, C. et al. Transcriptome analysis of the biofilm formed by methicillin-susceptible Staphylococcus aureus. Sci Rep 5, 11997 (2015). https://doi.org/10.1038/srep11997
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/srep11997
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
