Genetic Fingerprinting of Staphylococcal Enterotoxin B

Staphylococcal enterotoxin B (SEB), an exotoxin produced by staphylococcal aureus, is commonly associated with food poisoning and capable of triggering flu-like symptoms resembling those of endotoxins – such as lipopolysaccharide (LPS) released from gram-negative bacterial cell walls. By comparing the host response to these distinct toxins on a genetic level, a set of genes can be identified to facilitate the rapid diagnosis and confirmation of SEB: potentially preventing fatal septic shock resulting from prolonged toxin exposure. Genetic expression can vary with time post-exposure, thus it was necessary to acquire SEB-exposed and LPS-exposed samples of RNA spanning a number of time points, from 2 to 72 hours. A complete gene expression pattern for each toxin was obtained through microarray analysis and 30 genes of interest were identified using GeneSpring data analysis software. From these 30, ten genes were selected for initial analysis based on their function, such as apoptosis, cytokine induction, DNA binding, phosphorylation or transporter activity. After designing primers for the selected genes, real time PCR and RT-PCR were carried out to confirm the microarray expression pattern. The expression patterns for three genes of interest were confirmed and do indicate a correlation between differences in mechanism and gene regulation. Introduction Staphylococcal enterotoxins (SEs) constitute a family of proteins that are causative agents for food poisoning, skin disorders, toxic shock and autoimmune disorders 1, . SEs bind to T-cell receptors and major histocompatibility antigen complexes resulting in massive activation of Tcells in the host. LPS, a small unit of endotoxin from the cell wall of gram-negative organisms, causes the classic induction of septic shock, binding to regulatory receptors and CD14 3, . Although SEB and LPS result in similar outcomes, especially related to vascular collapse, each proceeds with individual initiation steps and exhibits differing mechanisms during the course of infection. As physical function is dictated at a genetic level, based on the DNA RNA Protein Function relationship, symptom similarities between LPS and SEB toxins would imply a potential similarity in genetic regulation – as both toxins physically alter the frequency of a given gene sequence. Any differences in genetic expression between the two toxins may allow the identification of genetic regulation specific to SEB. Background To analyze the effects of SEB and LPS at a genetic level RNA was isolated from human peripheral blood mononuclear cells (PBMCs) exposed individually to LPS and SEB at varying time points post-exposure; RNA was also isolated from untreated (control) cells. This RNA constituted the foundation of treated and control samples for time points ranging 2 to 72 hours to be used for the entirety of this study. Initial microarray data was collected by comparing experimental samples to a universal reference human RNA sample (Mendis et al., unpublished data). This data was then standardized, statistically analyzed and sorted using GeneSpring data analysis software. The 30 genes identified by this process (Table 1) were then further investigated by multiple analysis methods. Accession Number SEB Regulation LPS Regulation Gene Abbreviation AA043537 Down Up MAP4K3 AA045179 Down Up CRSP6 AA406036 Down Up KCNK4 AA421953 Down Up TRIM26 AA427927 Down Up HPRP3P AA443969 Down Up SEC13L AA457137 Down Up MGC2555 AA598926 Down Up TRIAD3 AA676874 Up Down SNRPD3 AA774034 Down Up COPG2 AA775454 Down Up ST6GALNAC6 AA994689 Down Up NPR2 AI018501 Down Up CRNKL1 AI220577 Down Up TNP2 AI355999 Down Up CENTA2 AI362134 Down Up SRPUL AI369867 Down Up DRD2 AI457797 Down Up CCL22 AI632018 Down Up TINAG AI636094 Down Up KCNJ1 AI695198 Down Up SDCCAG1 AI703487 Down Up PPL AI972634 Down Up P3 H24395 Down Up X123 N57766 Down Up BTK N64387 Down Up HDCMA18P N94424 Down Up RARRES1 T56013 Down Up HMGCS1 W47156 Down Up SEC22L2 Table 1: Microarray Expression Data For the 30 genes in Table 1, “down regulation” indicates smaller physical frequencies of the target gene sequence in toxin-exposed (either SEB or LPS) RNA samples than in control samples; similarly, “up regulation” indicates a higher frequency of the corresponding gene sequence when compared to a control. All accession numbers come from the NIH GenBank DNA sequence database. Methods Custom cDNA glass microarray analysis; Custom microarray experiments were performed to analyze the gene expression pattern of the toxin of interest. Genes of interest were amplified using AmpliTaq polymerase (Applied Biosystems, Branchburg, NJ), sub-cloned using TOPOTA cloning kits (Invitrogen, Carlsbad, CA) and purified through a high throughput Montage Plasmid purification kits (Millipore Bedford, MA). PCR products containing the inserts (GOI) were resuspended in 3X SSC at a concentration of 100-150 ng/μl and deposited at 200 μM center-to-center spacing at 60 % humidity on optically flat 25 x 76 mm glass slides coated with covalently attached linear primary amines (TeleChem International Inc., Sunnyvale, CA) using a SDDC-2 microarrayer (Engineering Sciences, Inc, Toronto, Canada) equipped with a surface contact print head SPH 48 (TeleChem Sunnyvale, CA) and 90-100 μM diameter quill SMP3 Stealth Micro Spotting Pins from TeleChem International Inc. Total RNA from Control and SEB or LPS treated human PBMCs were reverse transcribed and the cDNAs were labeled with Cy3 (Control) or Cy5 (Treated) using the NEN Micromax TSA labeling and detection kit (Perkin Elmer, Boston, MA). The resulting cDNAs were hybridized at 65°C to custom microarrays, scanned using Gene Pix 4000B Microarray Scanner (Axon Instruments Inc., Foster City, CA) and the data analyzed using Gene Pix 3.0 software package. Clustering Analysis; Average linkage hierarchical clustering of an uncentered Pearson correlation similarity matrix was carried out using the program Cluster, and the results were visualized with the program TreeView. The data was analyzed using GeneSpring TM version 4.1 (Silicon Genetics, San Carlos, CA) to identify patterns of gene regulation in PBMCs exposed to SEB and LPS from multiple (six) donors, analyzed in duplicates on multiple days. To normalize for staining intensity variations among arrays, the average difference values for all genes on a given array were divided by the sum of all measurements on that array. In addition, the average difference value for each individual gene was then normalized to itself by dividing all measurements for that gene by the mean of the gene's expression values over all the samples. Normalized values below the background levels in both the control and treated were excluded. To identify genes that showed significant variations in expression between SEB compared to control cells and LPS compared to control cells, an ANOVA test was performed by using P < 0.005 as a threshold. Real time PCR; Real time PCR was performed in triplicate using a uniprimer containing a 3’ oligonucleotide tail (Z sequence) specific for TRIAD3 and a housekeeping gene (S15) in an iCycler (BioRad, Hercules, CA). Total RNA extracted from toxin-exposed or control PBMCs were subjected to real time PCR using Amplifluor universal amplification kit (Intergen, Purchase, NY). Threshold cycle (cT) of each reaction was calculated, and normalized using the cT values of S15 (house keeping gene). Figure 1: Real Time PCR of TRIAD3 Figure 1 is an example of real time analysis for TRIAD3. Standard RT-PCR is an end point analysis in which the reaction runs to completion and PCR product is visualized via gel electrophoresis, which would be equivalent to analyzing what can be seen at Figure 1 (b). By instead using the cycle number when a product is first visible, when it crosses the baseline at Figure 1 (a), corresponding starting amounts for each gene (and thus gene frequency in vivo) are more precisely distinguished. RT-PCR; RT-PCR reactions were performed using Superscript amplification kit (Life Technologies, Gaithersburg, MD). Housekeeping gene primers (18S) were obtained and all experimental primers were designed using various primer-design software. These primers were utilized to amplify cDNA in a thermocycler (PerkinElmer model) together with a PCR master mix kit (Roche Diagnostics, Indianapolis, IN) at annealing temperatures specific for each primer, with cDNA products being labeled using a SYBR Green stain. Quantification of Gene Expression; Samples were analyzed on 1% agarose gels comparing experimental PCR product to proper controls. An in-house imaging apparatus was used to visualize the DNA samples. This system involved an Oriel 68805 Universal Power Supply emitting light which was refined by a CM Laser Corp. CM110 1/2m monochrometer to a wavelength specific to the SYBR Green stain. This allowed for clear imaging of gels using a mounted Nikon E995 Digital Camera. The resulting images were quantified using ImageJ quantification software. Figure 2: ImageJ Analysis Figure 2 illustrates the results of complete analysis, in this case of the CCL22 gene, as interpreted by ImageJ. The initial image of a gel (a) is opened in ImageJ. Lanes, each corresponding to a different time point – with a “short” time point of 4-6 hours compared to a “long” time point of 16-24 hours – and/or toxin, are selected for analysis (b) and peaks are isolated corresponding to the band in each lane (c). Found through integration, the area of these peaks should directly correlate to each gene’s frequency in vivo. All data was normalized within each gel, thus within each image, by comparing the experimental (toxin-exposed) peak area to that of an experimental control sample run on the same gel. To determine actual regulation, this normalized value was compared with a similarly normalized peak area of a housekeeping gene, a gene of known constant expression, 18S. Finally, these values were averaged between duplicate experiments. Results can be seen in Table 2. Gene Time Point Regulation TRIAD3 SEB Short Down TRIAD3 SEB Long Down TRIAD3 LPS Short Up TRIM26 SEB Short Down TRIM26 SEB Long Down TRIM26 LPS Short Up CCL22 SEB Short Down CCL22 SEB Long Down CCL22 LPS Short Up Table 2: PCR results for three genes, TRIAD3, TRIM26 and CCL22 The TRIAD3 gene codes for a cytoplasmic protein which specifically interacts with a serine/threonine protein kinase, receptor-interacting protein (RIP). RIP has been shown to inhibit transport of ADP into mitochondria, resulting in reduced ATP and cell death. TRIM26 codes for a protein of the tripartite motif (TRIM) family which may have DNA-binding activity. The gene itself localizes to the major histocompatibility complex (MHC) class I region on chromosome 6 and is thus likely involved in T-cell binding aspects of immune responses. The proteins encoded for by CCL22 are of the Cys-Cys (CC) cytokines. Cytokines, as a family, are involved in inflammatory and immunoregulatory processes – a chain of cytokine release constitutes an immune response. The corresponding protein for CCL22, specifically, is involved in the attraction of monocytes, natural killer cells and T lymphocytes. LPS is a protypical endotoxin: as it binds to the CD14/TLR4/MD2 receptor complex, one would expect an exposed cell to respond as per a typical antigen. SEB is a known superantigen: a toxin that indiscriminately activates T-cells, thus leading to a wide-spread inflammatory response. Conclusions All PCR data confirmed prior results obtained through microarray investigations. PCR analysis showed SEB to down-regulate all three investigated genes, TRIAD3, TRIM26 and CCL22. LPS was observed to up-regulate the same three genes. This is exactly the same regulation pattern that was demonstrated by microarray analysis. As expected, LPS up-regulated genes associated with pro-inflammatory cytokine release and the attraction T-cells, typical for a standard immune response. The reduction in affected cell function, via increased RIP activity, is similarly a logical defense mechanism. SEB was shown to minimize immune response against infected cells a number of ways, including reduced production of cytokines associated with T-cell attraction, and reducing the host cell’s production of antigen-presenting proteins. Furthermore, SEB actually serves to keep its host cell alive by inhibiting RIP production. While no significant variation in gene expression was observed in long vs. short time points for SEB-exposed samples, the difference in pathways and mechanisms of SEB and LPS indicated differing genetic regulation warranting continued study. Acknowledgements This research would not have been possible without the microarray investigation at WRAIR completed by Dr. Marti Jett and Nabarun Chakraboorthy. Also, Dr. James Hamilton of the University of Wisconsin-Platteville played a critical role in the development of a cost-effective imaging system. The volume of RT-PCR analysis completed would not have been possible without the help of Rachael Lehr of UW-P. Finally, the authors would like to thank the Office ofSponsored Programs at UW-P for the funding that aided in this project. References1. M. Jett, B. Ionin, R. Das, R. Neill, The Staphylococcal Enterotoxins, in: Molecular MedicalMicrobiology, M. Sussman (Ed.), Academic Press, London, 2001, pp. 1089-1116.2. M. Dinges, M. Orwin, P. Schlievert, Exotoxins of Staphylococcus aureus, Clin MicrobiolRev, 13 (2000) 16-64. 3. H. Heine, E.T. Rietschel, A.J. Ulmer, The biology of endotoxin. Mol Biotechnol, 19 (2001)279-96.4. A.J. Ulmer, Induction of proliferation and cytokine production in human T lymphocytes bylipopolysaccharide (LPS). Toxicology, 152 (2000) 37-45.5. V. Temkin, Q. Huang, H. Liu, H. Osada, and R.M.Pope, Inhibition of ADP/ATP Exchange inReceptor-Interacting Protein-Mediated Necrosis. Molecular and Cellular Biology, March 2006,p. 2215-2225, Vol. 26, No. 6.