Global Transcriptome Changes of Biofilm-Forming Staphylococcus epidermidis Responding to Total Alkaloids of Sophorea alopecuroides

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VOLUME 67 , ISSUE 2 (June 2018) > List of articles

Global Transcriptome Changes of Biofilm-Forming Staphylococcus epidermidis Responding to Total Alkaloids of Sophorea alopecuroides

CUI-PING GUAN / HUI-XIA LUO / H.E. FANG / XUE-ZHANG ZHOU *

Keywords : Staphylococcus epidermidis, sulfur metabolism, total alkaloids of Sophorea alopecuroides, transcriptome

Citation Information : Polish Journal of Microbiology. Volume 67, Issue 2, Pages 223-226, DOI: https://doi.org/10.21307/pjm-2018-024

License : (CC-BY-NC-ND-4.0)

Received Date : 06-July-2017 / Accepted: 03-December-2017 / Published Online: 30-June-2018

ARTICLE

ABSTRACT

Transcriptome changes of biofilm-forming Staphylococcus epidermidis response to total alkaloids of Sophorea alopecuroides was observed. Bioinformatic analyses were further used to compare the differential gene expression between control and the treated samples. It was found that 282 genes were differentially expressed, with 92 up-regulated and 190 down-regulated. These involved down-regulation of the sulfur metabolism pathway. It was suggested that inhibitory effects on Staphylococcus epidermidis and its biofilm formation of the total alkaloids of S. alopecuroides was mainly due to the regulation of the sulfur metabolism pathways of S. epidermidis.

Graphical ABSTRACT

S. epidermidis is an opportunistic pathogen; however, it is capable of storing and transmitting drug-resistant genes (Bloemendaal et al., 2010; Otto, 2013). The ability of S. epidermidis to form biofilms in vivo allows it to be highly resistant to chemotherapeutics and can ultimately lead to chronic disease (Cerca et al., 2005). Some Chinese medicinal herbal products have been found to prevent biofilm formation in bacteria (Guan et al., 2013; Wang et al., 2015; Yang et al., 2016). Total alkaloids of S. alopecuroides (TASA) are an alkaloid mixture that has demonstrated a better inhibitory effect on the late stage of S. epidermidis biofilm thickening than ciprofloxacin (CIP), and erythromycin (ERY) (Li et al., 2016).

In order to investigate the inhibitory mechanism of TASA, the global transcriptome changes of biofilm-forming S. epidermidis (ATCC 35984) in response to TASA were analysed in this study. Some pre-experiments were performed to determine the transcriptome sequencing conditions. In the pre-experiments, the relative expression quantity of four main biofilm formation related genes (ica, sigB, agr and fbe) were compared after the S. epidermidis treatment by TASA in different sub-MIC (1/2 MIC and 1/4 MIC) and time periods (6 h, 12 h, and 24 h). The results showed that these four genes were down-regulated significantly when the S. epidermidis was treated by 1/2 MIC (12.5 mg/ml) of TASA for 12 h. Based on the pilot experiment results, the transcriptome sequencing was used to investigate the transcriptional differences of S. epidermidis ATCC 35984 in a blank-medium control group and the treatment group, cultured with 1/2 MIC of TASA for 12 h. The inhibitory mechanism of Chinese medicine TASA was examined through the bioinformatic analyses.

S. epidermidis ATCC 35984 was cultured overnight at 37°C, 120 rpm. The resulting suspension was diluted to 0.5 McFarland with TSB medium and 3 ml of the diluted suspension was seeded into a six-well plate. The plate was sealed with an aseptic glass cover and placed in the incubator upside down. After 12 h of incubation at 37°C, the liquid in each well was removed and washed twice with PBS. The biofilm bacteria adhered to the glass cover in the blank control samples were collected with 1 ml TSB, and centrifuged at 12000 r/min for 10 min at 4°C. For TASA-treated samples, the biofilm bacteria on the glass covers were incubated for an additional 12 h in the presence of TASA at 12.5 mg/ml (1/2 MIC) prior to the bacteria harvesting, which followed the same methodology as the blank control. Total mRNA extraction was performed with RNAprep pure Cell/Bacteria Kit (TianGen), according to the manufacturer’s instructions, then stored at –80°C, and prepared for transcriptome sequencing.

Standard sequencing procedure and data analysis were performed by using Illumina HiSeq 2500 high-throughput sequencing platform from Biomarker Technologies Co., Ltd (China). Separate sequence read dataset was used as inputs into DESeq2 package (Love et al., 2014) to analysis the unigenes expression based on RPKM (reads per kilobase transcriptome per million mapped reads). A fold change of ≥ 2 and a minimum false discovery rate (FDR) of < 0.01 were accepted as indicators of the differentially expressed genes (DEGs) after Benjamini-Hochberg post hoc correction. The DEGs were BLASTX against the Kyoto Encyclopedia of Genes and Genomes (KEGG), Clusters of Orthologous Group (COG) and Gene Ontology (GO) to identify their predicted biological function. In order to verify the RNA-Seq results, the expression levels of selected DEGs were quantified by RT-qPCR. First, Nanodrop 2000 (Thermo Scientific, Wilmington, USA) was used to measure the concentration of total mRNA and 500 ng of RNA was reverse-transcribed into cDNA with PrimeScriptTM RT Master Mix (TaKaRa). RT-qPCR was performed with an iQ5 light cycler (Bio-Rad) by using SYBR® Premix EX TaqTM II (TaKaRa) in a 20 μl reaction volume, which consisted of 0.25 mM of each primer, 10 μl of SYBR Premix Ex Taq II and 1 μL of template cDNA. PCR conditions were as follows: 30 s at 95°C followed by 40 cycles of 5 s at 95°C and 30 s at 58°C. The reference gene was 16S rRNA, data were acquired through Bio-Rad and analyzed by using the 2–ΔΔCT method (Livak and Schmittgen, 2001). All experiments were performed in triplicate.

282 unigenes were differentially expressed under the screening criteria (fold change ≥ 2 and FDR < 0.01). Within these DEGs, 92 unigenes were significantly up-regulated and 190 unigenes were significantly down-regulated. The DEGs were searched against the KEGG database to identify their biological pathways. The 20 greatest enriched pathways were listed in Fig. 1. Pathways (represented by symbols) in the upper left quadrant of Fig. 1 contain DEGs with more significant and reliable enrichment levels, therefore the sulfur metabolism pathway ranked first. In sulfur metabolism pathway, there are nine DEGs which were all down-regulated significantly, corresponding to the encoded enzymes included serine acetyltransferase (CysE, EC 2.3.1.30), phosphoadenosine phosphosulfate reductase (CysH, EC 1.8.1.2), sulfite reductase (CysI and CysJ, EC 1.8.4.8), cystathionine beta-lyase (Cbl, EC 2.5.1.48), cysteine synthase (CysK and CysM, EC 2.5.1.47), sulfate adenylyl transferase (Sat, EC 2.7.7.4) and adenylylsulfate kinase (CysC, EC 2.7.1.25) (Fig. 2). These enzymes directly affect the content of sulfur metabolites such as cysteine (Cys), methionine (Met), glutathione (GSH), etc. RT-qPCR results further demonstrated that all nine genes showed similar expression patterns to those of RNA-seq, confirming the RNA-seq based transcriptome datasets were accurate and robust (Fig. 3).

Fig. 1.

KEGG pathway scatter diagram of differentially expressed genes. Each pattern represents a KEGG pathway with its name listed in the right. X-coordinate represents the enrichment factor showing the ratio of all genes annotated in the pathway, in contrast to the differentially expressed genes annotated in the same pathway. The smaller the enrichment factor, the enrichment level of the differentially expressed genes was more significant in this pathway. Y-coordinate represents log10(Q_value). The higher the absolute Q_value, the enrichment level of the differentially expressed genes was more significant and reliable in this pathway.

10.21307_pjm-2018-024_f001.jpg
Fig. 2.

Distribution of the differentially expressed genes in a sulfur metabolism pathway. Compared to the control group, the enzyme marked by a green frame is related to a drown-regulated gene product. The number in the frame represents the enzyme code.

10.21307_pjm-2018-024_f002.jpg
Fig. 3.

Comparison of folds changes detected by RNA-seq (RPKM) and q-PCR.

Bars represent the log2 value of the fold changes of the gene expressions between the control and TASA treatment groups. On the top of the bars are the abbreviations of enzymes encoded by the nine genes.

10.21307_pjm-2018-024_f003.jpg

In this research, according to the analysis of transcriptome data, the sulfur metabolism pathway in S. epidermidis was greatly influenced by TASA. Sulfur metabolism is an important metabolic pathway in bacteria and its metabolites are involved in many physiological and biochemical processes in cells (Zeng et al., 2013). Cysteine (Cys), methionine (Met) and glutathione (GSH) are associated with bacterial activity and biofilm formation (Murillo et al., 2005; Gales et al., 2008; Soutourina et al., 2009; Li et al., 2013; Solis et al., 2016). A link between sulfate assimilation, Met biosynthesis and biofilm formation have been found in Candida albicans (Murillo et al., 2005). Deletion of the ecm17 gene encoding the sulfite reductase beta subunit resulted in reduced adhesion and poor biofilm formation in C. albicans (Li et al., 2013). Additionally, deletion of the master regulator of Cys metabolism cymR in S. aureus also results in diminished biofilm formation (Soutourina et al., 2009). A recent research has provided further evidence of a role for sulfate assimilation and Cys/Met biosynthesis in S. epidermidis ATCC 35984 (RP62A) biofilm formation (Solis et al., 2016). According to the comparison of non-biofilm forming S. epidermidis ATCC 12228 and biofilm-forming ATCC 35984, Solis et al. (2016) showed that the sulfate assimilation and cysteine/methionine biosynthesis pathways in ATCC 35984 contained elevated levels (~25% increase) of methionine that were likely linked to biofilm formation. GSH, another important sulfur metabolite, which is associated with intracellular reactive oxygen species (ROS) and hydrogen sulfide (H2S) also played an important role in biofilm formation (Gales et al., 2008; Klare et al., 2016; Ooi and Tan, 2016). In this study, TASA significantly influenced the sulfur metabolism by down-regulation of nine important genes in this pathway. The concentration of Cys, Met and GSH were directly affected, and most probably finally disrupted the biofilm formation of S. epidermidis. This result also validated the above-mentioned relationship between sulfur metabolism and biofilms.

In the pre-experiments, the relative expression of biofilm-related genes including ica, sigB, agr and fbe in S. epidermidis was measured. Results showed that these four genes were differentially expressed after treatment with TASA at 1/2 MIC for 12 h. However, these four genes did not appear in the 282 DEGs obtained by transcriptome sequencing due to the DEGs screening criteria (fold change ≥ 2 and FDR < 0.01). It was suggested that genes related to biofilm formation did have differential expression, but they may not be directly regulated by TASA. Instead, their expression may be indirectly affected by the changes of other metabolic pathways (i.e. sulfur metabolism).

For a long time, the formation of biofilm has been widely recognized as a dynamic process and regulated by some genes including ica, sigB, agr and fbe etc. Now, it was shown that there is a relationship between sulfur metabolism and biofilm formation in S. epidermidis. Based on the results of transcriptome analysis, it was suggested that TASA’s inhibitory effects on S. epidermidis and its biofilm formation is mainly due to the regulation of the sulfur metabolism pathway.

The regulatory mechanism of TASA has not been analyzed due to its multi-channeled and multi-targeted actions. In the study, the effect of TASA on S. epidermidis was comprehensively analyzed by using RNA-seq. The enrichment of DEGs showed that metabolism, genetic information processing and environmental information processing were greatly influenced by TASA. The sulfur metabolism pathway was the most significant with all of the key enzymes in this pathway being down-regulated. Given the important physiological role of sulfur metabolism in bacteria and its effect on biofilm formation, it is concluded that the inhibitory effect of TASA on S. epidermidis and its biofilm formation are mainly due to its actions on this pathway.

In conclusion, according to the analysis of differential expression and metabolic pathway enrichment, the molecular mechanism of TASA regulation of pathogenicity, virulence and metabolism of S. epidermidis was further characterized. The sulfur metabolism pathway was identified and results could provide valuable information for follow-up studies to examine the regulatory mechanism of TASA on S. epidermidis, and could serve as the basis for exploring potential drug targets.

Acknowledgements

This study was supported by the National Natural Science Foundation of China (No. 31460676) and Major Innovation Projects for Building First-class Universities in China’s Western Region (ZKZD2017001). Thanks to Laura Beamish at the University of British Columbia for the review of the English writing style and grammatical editing of the manuscript.

References


  1. Bloemendaal A.L., E.C. Brouwer and A.C. Fluit. 2010. Methicillin resistance transfer from Staphylocccus epidermidis to methicillin-susceptible Staphylococcus aureus in a patient during antibiotic therapy. PloS One 5(7): e11841.
    [CROSSREF]
  2. Cerca N., S. Martins, F. Cerca, K.K. Jefferson, G.B. Pier, R. Oliveira and J. Azeredo. 2005. Comparative assessment of antibiotic susceptibility of coagulase-negative staphylococci in biofilm versus planktonic culture as assessed by bacterial enumeration or rapid XTT colorimetry. J. Antimicrob. Chemother. 56(2): 331–336.
    [CROSSREF]
  3. Gales G., M. Penninckx, J.C. Block and P. Leroy. 2008. Role of glutathione metabolism status in the definition of some cellular parameters and oxidative stress tolerance of Saccharomyces cerevisiae cells growing as biofilms. FEMS Yeast Res. 8(5): 667–675.
    [CROSSREF]
  4. Guan Y., C. Li, J.J. Shi, H.N. Zhou, L. Liu, Y. Wang and Y.P. Pu. 2013. Effect of combination of sub-MIC sodium houttuyfonate and erythromycin on biofilm of Staphylococcus epidermidis. Zhongguo Zhong Yao Za Zhi 38(5): 731–735.
  5. Klare W., T. Das, A. Ibugo, E. Buckle, M. Manefield and J. Manos. 2016. Glutathione-disrupted biofilms of clinical pseudomonas aeruginosa strains exhibit an enhanced antibiotic effect and a novel biofilm transcriptome. Antimicrob. Agents Ch. 60(8): 4539–4551.
    [CROSSREF]
  6. Livak K.J. and T.D. Schmittgen. 2001. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25(4): 402–408.
    [CROSSREF]
  7. Li D., Y. Wang, B. Dai, X. Li, L. Zhao, Y. Cao, L. Yan and Y. Jiang. 2013. ECM17-dependent methionine/cysteine biosynthesis contributes to biofilm formation in Candida albicans. Fungal Genet. Biol. 51: 50–59.
    [CROSSREF]
  8. Li X., C.P. Guan, Y.L. He, Y.J. Wang, X.M. Liu and X.Z. Zhou. 2016. Effects of total alkaloids of Sophorea Alopecuroides on biofilm formation in Staphylococcus epidermidis. Biomed. Res. Int. 2016: 4020715.
  9. Love M.I., W. Huber and S. Anders. 2014. Moderate destimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15: 550.
    [CROSSREF]
  10. Murillo L.A., G. Newport, C. Lan, S. Habelitz, J. Dungan and N.M. Agabian. 2005. Genome-wide transcription profiling of the early phase of biofilm formation by Candida albicans. Eukaryot. Cell 4(9): 1562–1573.
    [CROSSREF]
  11. Ooi X.J. and K.S. Tan. 2016. Reduced glutathione mediates resistance to H2S toxicity in oral Streptococci. Appl. Environ. Microb. 82(7): 2078–2085.
    [CROSSREF]
  12. Otto M. 2013. Coagulase-negative staphylococci as reservoirs of genes facilitating MRSA infection: Staphylococcal commensal species such as Staphylococcus epidermidis are being recognized as important sources of genes promoting MRSA colonization and virulence. Bioessays 35(1): 4–11.
    [CROSSREF]
  13. Solis N., J.A. Cain and S.J. Cordwell. 2016. Comparative analysis of Staphylococcus epidermidis strains utilizing quantitative and cell surface shaving proteomics. J. Proteomics 130: 190–199.
    [CROSSREF]
  14. Soutourina O., O. Poupel, J.Y. Coppée, A. Danchin, T. Msadek and I. Martin-Verstraete. 2009. CymR, the master regulator of cysteine metabolism in Staphylococcus aureus, controls host sulphur source utilization and plays a role in biofilm formation. Mol. Microbiol. 73(2): 194–211.
    [CROSSREF]
  15. Wang T., G. Shi, J. Shao, D. Wu, Y. Yan, M. Zhang, Y. Cui and C. Wang. 2015. In vitro antifungal activity of baicalin against Candida albicans biofilms via apoptotic induction. Microb. Pathog. 87: 21–29.
    [CROSSREF]
  16. Yang Y., B.I. Park, E.H. Hwang and Y.O. You. 2016. Composition analysis and inhibitory effect of Sterculia lychnophora against biofilm formation by Streptococcus mutans. Evid. Based Complement Alternat. Med. 2016: 8163150.
  17. Zeng L.R., T.Y. Shi, Q.J. Zhao and J.P. Xie. 2013. Mycobacterium sulfur metabolism and implications for novel drug targets. Cell Biochem. Biophys. 65(2): 77–83.
    [CROSSREF]
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FIGURES & TABLES

Fig. 1.

KEGG pathway scatter diagram of differentially expressed genes. Each pattern represents a KEGG pathway with its name listed in the right. X-coordinate represents the enrichment factor showing the ratio of all genes annotated in the pathway, in contrast to the differentially expressed genes annotated in the same pathway. The smaller the enrichment factor, the enrichment level of the differentially expressed genes was more significant in this pathway. Y-coordinate represents log10(Q_value). The higher the absolute Q_value, the enrichment level of the differentially expressed genes was more significant and reliable in this pathway.

Full Size   |   Slide (.pptx)

Fig. 2.

Distribution of the differentially expressed genes in a sulfur metabolism pathway. Compared to the control group, the enzyme marked by a green frame is related to a drown-regulated gene product. The number in the frame represents the enzyme code.

Full Size   |   Slide (.pptx)

Fig. 3.

Comparison of folds changes detected by RNA-seq (RPKM) and q-PCR.

Bars represent the log2 value of the fold changes of the gene expressions between the control and TASA treatment groups. On the top of the bars are the abbreviations of enzymes encoded by the nine genes.

Full Size   |   Slide (.pptx)

REFERENCES

  1. Bloemendaal A.L., E.C. Brouwer and A.C. Fluit. 2010. Methicillin resistance transfer from Staphylocccus epidermidis to methicillin-susceptible Staphylococcus aureus in a patient during antibiotic therapy. PloS One 5(7): e11841.
    [CROSSREF]
  2. Cerca N., S. Martins, F. Cerca, K.K. Jefferson, G.B. Pier, R. Oliveira and J. Azeredo. 2005. Comparative assessment of antibiotic susceptibility of coagulase-negative staphylococci in biofilm versus planktonic culture as assessed by bacterial enumeration or rapid XTT colorimetry. J. Antimicrob. Chemother. 56(2): 331–336.
    [CROSSREF]
  3. Gales G., M. Penninckx, J.C. Block and P. Leroy. 2008. Role of glutathione metabolism status in the definition of some cellular parameters and oxidative stress tolerance of Saccharomyces cerevisiae cells growing as biofilms. FEMS Yeast Res. 8(5): 667–675.
    [CROSSREF]
  4. Guan Y., C. Li, J.J. Shi, H.N. Zhou, L. Liu, Y. Wang and Y.P. Pu. 2013. Effect of combination of sub-MIC sodium houttuyfonate and erythromycin on biofilm of Staphylococcus epidermidis. Zhongguo Zhong Yao Za Zhi 38(5): 731–735.
  5. Klare W., T. Das, A. Ibugo, E. Buckle, M. Manefield and J. Manos. 2016. Glutathione-disrupted biofilms of clinical pseudomonas aeruginosa strains exhibit an enhanced antibiotic effect and a novel biofilm transcriptome. Antimicrob. Agents Ch. 60(8): 4539–4551.
    [CROSSREF]
  6. Livak K.J. and T.D. Schmittgen. 2001. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25(4): 402–408.
    [CROSSREF]
  7. Li D., Y. Wang, B. Dai, X. Li, L. Zhao, Y. Cao, L. Yan and Y. Jiang. 2013. ECM17-dependent methionine/cysteine biosynthesis contributes to biofilm formation in Candida albicans. Fungal Genet. Biol. 51: 50–59.
    [CROSSREF]
  8. Li X., C.P. Guan, Y.L. He, Y.J. Wang, X.M. Liu and X.Z. Zhou. 2016. Effects of total alkaloids of Sophorea Alopecuroides on biofilm formation in Staphylococcus epidermidis. Biomed. Res. Int. 2016: 4020715.
  9. Love M.I., W. Huber and S. Anders. 2014. Moderate destimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15: 550.
    [CROSSREF]
  10. Murillo L.A., G. Newport, C. Lan, S. Habelitz, J. Dungan and N.M. Agabian. 2005. Genome-wide transcription profiling of the early phase of biofilm formation by Candida albicans. Eukaryot. Cell 4(9): 1562–1573.
    [CROSSREF]
  11. Ooi X.J. and K.S. Tan. 2016. Reduced glutathione mediates resistance to H2S toxicity in oral Streptococci. Appl. Environ. Microb. 82(7): 2078–2085.
    [CROSSREF]
  12. Otto M. 2013. Coagulase-negative staphylococci as reservoirs of genes facilitating MRSA infection: Staphylococcal commensal species such as Staphylococcus epidermidis are being recognized as important sources of genes promoting MRSA colonization and virulence. Bioessays 35(1): 4–11.
    [CROSSREF]
  13. Solis N., J.A. Cain and S.J. Cordwell. 2016. Comparative analysis of Staphylococcus epidermidis strains utilizing quantitative and cell surface shaving proteomics. J. Proteomics 130: 190–199.
    [CROSSREF]
  14. Soutourina O., O. Poupel, J.Y. Coppée, A. Danchin, T. Msadek and I. Martin-Verstraete. 2009. CymR, the master regulator of cysteine metabolism in Staphylococcus aureus, controls host sulphur source utilization and plays a role in biofilm formation. Mol. Microbiol. 73(2): 194–211.
    [CROSSREF]
  15. Wang T., G. Shi, J. Shao, D. Wu, Y. Yan, M. Zhang, Y. Cui and C. Wang. 2015. In vitro antifungal activity of baicalin against Candida albicans biofilms via apoptotic induction. Microb. Pathog. 87: 21–29.
    [CROSSREF]
  16. Yang Y., B.I. Park, E.H. Hwang and Y.O. You. 2016. Composition analysis and inhibitory effect of Sterculia lychnophora against biofilm formation by Streptococcus mutans. Evid. Based Complement Alternat. Med. 2016: 8163150.
  17. Zeng L.R., T.Y. Shi, Q.J. Zhao and J.P. Xie. 2013. Mycobacterium sulfur metabolism and implications for novel drug targets. Cell Biochem. Biophys. 65(2): 77–83.
    [CROSSREF]

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