The size variation of the amplicons

was exact multiples o

hominis French clinical isolates. All of the VNTRs were efficiently amplified in each M. hominis isolate tested. The size variation of the amplicons

was exact multiples of the repeats (Table 2). This was confirmed by sequencing amplicons which presented an unexpected size variation using the capillary electrophoresis analysis. The marker Mho-53 was the most discriminatory VNTR, displaying six different allele sizes with repeat copy numbers ranging from 3 to 8, depending on the isolate. The markers selleck products Mho-50 and Mho-52 showed five and three different allele sizes, respectively. The other markers yielded only two different-sized PCR products. The marker Mho-116 was the most homogenous marker, as almost all of the isolates harboured one repeat (three harboured two copies). This finding PF-6463922 in vitro was reflected by the diversity index of each VNTR, estimated MK-4827 price from the HGDI, with a value of 0.784 for the most discriminatory marker (Mho-53) and a value of 0.020 for the less discriminatory one (Mho-116). The overall discriminatory index of the MLVA assay was 0.924. Table 2 Number of repeat units for the five VNTR markers MLVA type No. of repeats at the following VNTR loci

  Mho-50 Mho-52 Mho-53 Mho-114 Mho-116 1 1 8 8 1 1 2 1 8 3 1 1 3 1 8 3 2 1 4 1 8 4 1 1 5 1 8 4 2 1 6 1 8 4 2 2 7 1 8 5 1 1 8 1 8 5 2 1 9 1 8 6 1 1 10 1 8 6 2 1 11 1 8 7 1 1 12 1 8 7 2 1 13 1 8 8 2 1 14 3 8 3 1 1 15 1 9 3 2 1 16 1 9 4 1 1 17 1 9 4 2 1 18 1 9 5 2 1 19 2 8 3 1 1 20 2 8 3 2 1 21 2 8 4 1 1 22 2 8 4 2 1 23 2 8 5 2 1 24 2 9 7 1 1 25 3 8 3 2 1 26 3 8 4 1 1 27 3 8 4 2 1 28 3 8 5 2 1 29 3 8 6 2 1 30 3 8 7 2 1 31 3 9 4 2 1 32 3 9 7 2 1 33 4 8 3 2 2 34 4 8 4 2 1 35 4 8 5 2 1 36 4 8 6 2 1 37 5 8 4 2 1 38 1 10 3 2 1 39 1 10 4 2 1 40 1 10 5 2 1 A combined analysis of

the five VNTR loci in the 210 M. hominis isolates revealed 40 MLVA types (Table 2). Three MLVA types, 5, 8 and 10, were present in more than 20 isolates. In 18 cases, one unique MLVA type was observed in a single patient. Interestingly, the two ATCC strains, H34 and M132, had the identical MLVA type 10, while the PG21 ATCC strain belonged to the MLVA type 36. The 167 urogenital isolates were classified into 34 MLVA types (Additional file 1: clonidine Table S1).

77   > = 65 112 (48%) 5 (2%) 117 (50%)   Lymph node Negative 56 (

77   > = 65 112 (48%) 5 (2%) 117 (50%)   Lymph node Negative 56 (25%) 4 (2%) 60 (27%) 0.74   Positive 157 (70%) 8 (4%) 165 (73%)   Type Well/Moderately 79 (34%) 4 (2%) 83 (35%) 0.16   Poorly 144 (62%) 8 (4%) 152 (65%)   Stage I or II 126 (54%) 5 (2%) 131 (56%) 0.38   III or IV 97 (41%)

7 (3%) 104 (44%)   Total   223 (95%) 12 (5%) 235 (100%)   EBV RNA expression in gastric tissue We tested 249 gastric carcinoma tissues. Of the 249 tumor specimens, 235 were fully assessable. The yield after tissue processing was 94% (235 of 249). Among the 235 tumor cases, 72 also contained selleck kinase inhibitor non-neoplastic gastric tissue (9 cases from EBV positive tumor cases and 63 from EBV negative cases). EBER1 was detected by in situ hybridization. Positive control samples revealed a distinctive diffuse nuclear stain. Sections incubated with preabsorbed or preimmune rabbit antisera showed

no immunostaining. Overall, 12 of the 235 tumors (5.1%) exhibited positive EBV expression (Figure 1). The intensity varied slightly from tumor to tumor but was consistent within the same tumor. No relationship was found between the intensity of EBER-1 expression and any clinicopathological features. EBV expression was noted in both diffuse (including lymphepithelial carcinoma) and intestinal type of GC (Table 1). Expression of EBV was not noted in nonneoplastic gastric mucosal, intestinal metaplastic, or stromal cells (endothelial cells and fibroblasts), or infiltrating inflammatory cells within the tumor sections. Twelve of 235 gastric tumor cases exhibited EBV expression, while none of the 72 samples containing non-neoplastic gastric epithelium displayed EBV expression. The difference between find more EBV positivity in carcinoma tissues and corresponding non-neoplastic

gastric tissues was statistically significant (χ2 = 9.0407; P = 0.0028). In addition, one representative positive lymph node from each metastatic case was examined. We observed that a fairly uniform expression of EBER1 in metastatic tumor cells. Among the 12 EBVaGC cases, eight patients displayed lymph node metastasis. Tumor cells in all eight positive lymph nodes revealed EBV expression (Figure 2). Ten additional metastatic cases were randomly chosen and lymph nodes with tumor cells were examined for EBER1. No Rho tumor cells in the lymph nodes of the 10 additional cases displayed EBER1 expression. Figure 1 Photomicrographs of Epstein-Barr virus (EBV) expression in gastric cancer. Epstein-Barr virus (EBV)-encoded RNA 1 (EBER1) in situ hybridization in a gastric carcinoma reveals specific EBER1 transcripts (dark) in the nuclei of the tumor cells. 1A-B: intestinal type of gastric cancer with EBV nuclear expression. Note, all tumor glands were positive for EBV, while stromal cells between the tumor glands were negative. 1C-D: diffuse type of gastric cancer with EBV nuclear expression, while scattered lymphocytes were negative. (Original magnification × 10 in Fig.

To our knowledge, there is no evidence demonstrating that antimic

To our knowledge, there is no evidence demonstrating that antimicrobial peptide or protein concentrations and/or their activities might be modified by the exposure of the hen to pathogenic and/or non-pathogenic buy OSI-906 environmental microbes, as demonstrated for yolk antibodies [3, 11]. This question is of interest since EU-directive 1999/74 became effective at the beginning of 2012. Conventional cage housing has been banned and only eggs issuing from

alternative breeding systems are marketable. This major change in the hen breeding system has modified the hen microbial environment [12, 13] and might increase egg shell contamination, as suggested by some comparisons between cage and non-cage breeding systems [14, 15]. Therefore, we explored whether the microbial environment of the hen influences innate immunity by increasing the oviduct secretion of antimicrobial proteins into eFT508 cell line the egg white, and its antibacterial GS-1101 datasheet activity. Any modification in egg antimicrobial molecules which are much less selective for specific pathogens compared to IgY and are potentially active against a wide

range of microbes including bacteria, viruses or parasites [4] might positively impact on the hygienic quality of table eggs. With this objective in mind, we studied three experimental models reflecting large differences in hen microbial environment and immunological status: Germ-free animals (GF), Specific Pathogen Free animals (SPF), and Conventional hens (C). Germ-free (GF) animals are reared in sterile conditions and show a wide range of defects in the development of their immune system and in antibody production, particularly intestine IgA. In GF mice, the

normal immune function is also impaired at the tissue, cellular and molecular levels in the absence of gut microbiota [16, 17]. SPF females are not subjected to any vaccination treatment and are bred in strictly controlled environments that are free of pathogens. In contrast, the conventional hens are vaccinated against highly virulent microorganisms PAK5 and are reared in commercial facilities where environmental microbes are diverse and might even include pathogens. In the present study, we have used these extreme breeding conditions to explore the impact of the hen microbial environment on the modulation of innate immunity in the egg, as reflected by egg white antibacterial activity. Results Maintaining germ-free, specific pathogen free and conventional hens GF hens were bred in two isolators and strict conditions were applied to keep them in a sterile environment. The absence of bacteria in the isolators was checked twice a month throughout the experimental period using the referenced method (PFIE-NT-0061) on fresh faeces directly sampled from the cloaca and inoculated into two cultivation media: thioglycolate resazurine broth and heart infusion broth.

Furthermore, our more recent results suggest that SigB is involve

Furthermore, our more recent results suggest that SigB is involved in the emergence of SCVs under aminoglycoside pressure [20], which Selleckchem Proteasome inhibitor suggests that the appearance of SCVs may be a regulated process influenced by environmental cues. Our current hypothesis is that SigB plays an important role in the establishment of chronic and difficult-to-treat S. aureus infections. SigB is involved

in the find more response to environmental stresses such as during stationary phase, heat exposure and change in osmotic pressure [21]. Moreover, the activity of SigB positively influences the expression of several cell-surface proteins whereas it down-regulates a variety of toxins [22], which suggest an important role for SigB in pathogenesis. The effect

of SigB on virulence gene expression can be direct or indirect, since the genes regulated by SigB also include at least another global regulator of virulence, sarA (Staphylococcal accessory regulator) [22, 23]. SarA modulates the expression of several virulence factors either by stimulating RNAIII transcription or by pathway(s) independent of the agr (accessory gene regulator) system [24]. In turn, selleck it is proposed that the quorum-sensing agr system controls the transition from colonization to dissemination by up-regulating the expression of several exotoxins and proteolytic enzymes and by repressing the expression of cell-surface proteins involved in colonization [25]. agr new [26], SigB [27, 28] and SarA [29] are known to influence the formation of biofilms by S. aureus. At least two different mechanisms of biofilm formation exist in S. aureus [26, 29–33]. The first mechanism implies the production of the polysaccharide intercellular adhesin (PIA), which requires the ica gene cluster, whereas the second mechanism is ica-independent. With opposite effects, SarA and agr are both involved in the ica-independent mechanism of biofilm formation. SarA is thought

to be indirectly required for the initial attachment step to biological matrices [29, 32, 33], while agr is controlling the dispersal process of biofilms [26]. Recently, Lauderdale et al. [30] have shown that SigB is an essential regulator of the ica-independent biofilm formation and suggested that SigB acts upstream of the agr system, allowing the formation of biofilm to be regulated as a function of environmental factors. Noteworthy, biofilms have been linked to chronic infections, especially in the case of those found in the airways of CF patients [1, 34], and an increased formation of biofilms has been associated with the SCV phenotype [20, 35]. The aim of this study was to investigate the association between the activity of SigB, the emergence of SCVs and biofilm production in S.

Appl Environ

Appl Environ

PRIMA-1MET mouse Microbiol 1993, 59:208–212.PubMed 49. Wheeler DL, Church DM, Federhen S, Lash AE, Madden TL, Pontius JU, Schuler GD, Schriml LM, Sequeira E, Tatusova TA, Wagner L: Database resources of the National Center for Biotechnology. Nucleic Acids Res 2003, 31:28–33.PubMedCrossRef 50. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997, 25:3389–3402.PubMedCrossRef 51. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG: Clustal W and Clustal X version 2.0. Bioinformatics 2007, 23:2947–2948.PubMedCrossRef 52. Grote A, Hiller K, Scheer M, Münch R, Nörtemann B, Hempel DC, Jahn D: JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res 2005, 33:W526–531.PubMedCrossRef 53. Münch R, Hiller K, Grote A, Scheer M, Klein J, Schobert M, Jahn D: Virtual Footprint and PRODORIC: an

integrative framework for selleck compound regulon prediction in prokaryotes. Bioinformatics 2005, 21:4187–4189.PubMedCrossRef 54. Dunn NW, Holloway BW: Pleiotrophy of p-uorophenylalanine-resistant and antibiotic hypersensitive mutants of Pseudomonas aeruginosa . Genet Res 1971, 18:185–197.PubMedCrossRef 55. Rahme LG, Stevens EJ, Wolfort SF, Shao J, Tompkins RG, Ausubel FM: Common virulence factors for bacterial selleck screening library pathogenicity Selleckchem Erastin in plants and animals. Science 1995, 268:1899–1902.PubMedCrossRef 56. Klausen M, Heydorn A, Ragas P, Lambertsen L, Aaes-Jørgensen A, Molin S, Tolker-Nielsen T: Biofilm formation by Pseudomonas

aeruginosa wild type, flagella and type IV pili mutants. Mol Microbiol 2003, 48:1511–1524.PubMedCrossRef 57. Daniels C, Griffiths C, Cowles B, Lam JS: Pseudomonas aeruginosa O-antigen chain length is determined before ligation to lipid A core. Environ Microbiol 2002, 4:883–897.PubMedCrossRef 58. Abeyrathne PD, Daniels C, Poon KKH, Matewish MJ, Lam JS: Functional characterization of WaaL, a ligase associated with linking O-antigen polysaccharide to the core of Pseudomonas aeruginosa lipopolysaccharide. J Bacteriol 2005, 187:3002–3012.PubMedCrossRef Authors’ contributions JG designed the study and performed the experiments. BB assisted with bioinformatics knowledge and reassembled the JG004 genome sequence. Electron microscopically examinations were done by MR. MS designed the study, did bioinformatic analyses and revised the manuscript. All authors read and approved the final manuscript.

095 when the Atlantic sample was included in the analysis) Blue

095 when the Atlantic sample was included in the analysis). Blue mussel Overall F ST is 0.47 (Table 2) with a strong barrier separating two southwestern samples and a second

barrier distinguishing island and mainland samples in the Baltic Proper West. High diversity at southern sampling sites contrasted with lower diversity and higher divergence in northern samples. The strikingly high F ST might reflect species mixture and introgression. M. trossulus is indigenous to the Baltic Sea but is closely related to M. edulis (common name also blue mussel), native to the North Sea. These taxa are known to hybridize and it is possible that our southern samples include very rare M. edulis specimens. The two species are difficult to distinguish even by genetic techniques, and geographic distribution and genetic characteristics of these species are continuously FDA-approved Drug Library purchase subject to revision (Riginos and Cunningham 2005; Steinert et al. 2012). Bladderwrack The three strongest barriers to gene flow occur in the northern part of the Baltic, although the high overall F ST (0.14; Table 2) indicated strong genetic structuring overall, with all sampling locations being significantly differentiated from each other (Table S2g). Discussion We conducted the first multi-species

study in the Baltic Sea where a large number of individuals and loci were collected from the same areas covering the full Baltic Sea. Surprisingly, we detected few shared genetic patterns in the seven species analyzed with respect to location of the three Selleckchem BMS345541 major genetic barriers to gene flow and diversity-divergence patterns (Fig. 2). An exception to this general lack of consistence is the genetic break between the Atlantic

and the Baltic Sea. We observe a variety of genetic patterns ranging from large and significant differences among sampling regions in both genetic variation and divergence, to very little differentiation within the Baltic Sea. The most pronounced, genetic breaks occurred almost individually for each species in different regions Erythromycin of the Baltic Sea, although STA-9090 chemical structure several species showed significant pairwise differentiation between the majority of the samples (Table S2a–g). At the northern extreme, five of six samples from the Bothnian Bay showed high diversity, but no shared major genetic barrier was present in this region (Table 3; Fig. 2). Unlike previous studies of herring and perch (Jørgensen et al. 2005; Olsson et al. 2011) we found few shared major genetic breaks associated with the different sub-basins of the Baltic Sea, e.g. around the Åland Islands. Potential causes of variability patterns The species-specific genetic patterns in the Baltic Sea, including relative amount of genetic variation, location of major genetic breaks, and isolation by distance are likely dependent on a multitude of factors including salinity tolerance, oceanographic features, life history, and population history (Table 1).

Differential induction of certain AvBDs by the wild type SE and

Differential induction of certain AvBDs by the wild type SE and

the pipB mutant was also observed at these times. Among the constitutively and highly VX-809 mouse expressed AvBD genes, infection of COEC with ZM100 (wt) or ZM103 (sipA) resulted in a temporary repression of AvBD4, and AvBD9-11 (≤ 1.5-fold), but not AvBD5 and AvBD12 (Figure 3). Infection of COEC with ZM106 (pipB) had reduced or no suppressive effect on the transcription of AvBD9-11, compared to infections with strains ZM100 and ZM103 (Figure 3). With the moderately expressed genes, infection of COEC with ZM100 (wt) or ZM103 (sipA) had minimal effect (< 1.5-fold) on the expression of AvBD1 and AvBD13-14, whereas ZM106 (pipB) temporarily induced the expressions of these genes at 1 hpi (Figure 4). The expression of another moderately expressed gene, namely AvBD3, was initially suppressed by ZM100, but not ZM106, and then induced by all three SE strains at 4 hpi and 24 hpi (Figure 4). With the minimally expressed genes, AvBD2 and AvBD6 were induced by all SE strains examined. However, the expression levels of AvBD2 and AvBD6 in COEC infected with ZM106 were significantly higher than

that in COEC infected with ZM100 or ZM103 (Figure 5). The expression of AvBD7 and AvBD8 in COEC was minimally check details affected by exposures to ZM100 and ZM103. Compared to the wild type strain and the sipA mutant, ZM106 also induced elevated expression of AvBD7 (Figure 5). Figure 3 Transcriptional changes of constitutively and highly expressed

AvBDs in COEC following infections with SE. Data shown (fold change) are geometric means of three independent experiments ± standard deviation. Open bar, ZM100 (wt); solid bar, ZM103 (sipA); JQEZ5 mw hatched bar, ZM106 (pipB). * indicates that the difference between the transcriptional changes induced by the wild type SE and the mutant is significant (p < 0.05). Figure 4 Transcriptional changes of Dichloromethane dehalogenase moderately expressed AvBDs in COEC following infections with SE. Data shown (fold change) are geometric means of three independent experiments ± standard deviation. Open bar, ZM100 (wt); solid bar, ZM103 (sipA); hatched bar, ZM106 (pipB). * indicates that the difference between the transcriptional changes induced by the wild type SE and the mutant is significant (p < 0.05). Figure 5 Transcriptional changes of minimally expressed AvBDs in COEC following infections with SE. Data shown (fold change) are geometric means of three independent experiments ± standard deviation. Open bar, ZM100 (wt); solid bar, ZM103 (sipA); hatched bar, ZM106 (pipB). * indicates that the difference between the amounts of AvBD transcripts in ZM100-infected COEC and ZM106-infected COEC is significant (p < 0.05).

The reported frequency #

The reported frequency PF-04929113 of infection by astrovirus was 8% during the winter season (from December 2000 to March 2001) in Beijing [3]. Astroviruses are among the most resistant viruses; they show resistance against different physical and chemical agents, they are able to maintain their infectivity at 60°C for 10 min, and they are resistant to treatment at pH 3 [4]. Astroviruses spread via

the fecal–oral route, through direct personal contact, or via contaminated food and water, and they have been reported to affect otherwise healthy people exposed to astrovirus-contaminated food or water [1]. However, the number of reports on astrovirus detection is relatively low. Several detection methods have been developed to detect the presence of astrovirus in clinical isolates, raw sewage samples, groundwater and surface water, including cell culture [1], enzyme immunoassay and nucleotide sequencing [5], and PCR-based assays [4]. All of these methods are effective and accurate in detecting the virus infection in the laboratory. However, these methods have

some intrinsic disadvantages such as the requirement for expensive equipment and reagents, and being laborious and time consuming, and are thus unfavorable for use on a wide scale. A detection method that is not only rapid and sensitive, but also simple and economical to handle, is needed for practical application. To meet these requirements, a reverse transcription loop-mediated ever isothermal amplification (RT-LAMP) reaction was developed as an alternative method. The LAMP assay is a rapid, accurate and cost-effective

diagnostic method that amplifies the target nucleic acid under isothermal conditions, usually between 60°C and 65°C [6]. Hence, only simple equipment such as a heating block or a water bath is required. The final products of the RT-LAMP reaction are DNA molecules with a cauliflower-like structure and LY2874455 order multiple loops consisting of several repeats of the target sequence [7]. LAMP has been applied for the specific detection of aquatic animal viruses such as foot-and-mouth disease virus [8], Singapore grouper iridovirus [9] and H1N1 2009 virus [10, 11]. The LAMP reaction results in large amounts of pyrophosphate ion byproduct. These ions react with Mg2+ ions to form the insoluble product, magnesium pyrophosphate. Because the Mg2+ ion concentration decreases as the LAMP reaction progresses, the LAMP reaction can be quantified by measuring the Mg2+ ion concentration in the reaction solution [12]. Hydroxynaphthol blue (HNB) is used for colorimetric analysis of the LAMP reaction. The HNB dye-based assay has a remarkable advantage compared with other color-based assays [11, 12] in that HNB is mixed prior to amplification. The need to open the assay samples to add the dye is thereby omitted, thus reducing the risk of cross-contamination.

Wooden shelves were first changed after one week and every three

Wooden shelves were first changed after one week and every three weeks thereafter. The pH of the cheese surface was periodically measured in situ using a flat membrane electrode (InLab® Surface, Mettler-Toledo, Greifensee, Switzerland). Microbial analyses of cheese surface SB273005 research buy during ripening experiments Approximately 25 cm2 of cheese surface were scraped off using sterile cotton rolls (IVF Hartmann, Neuhausen,

Switzerland) and aseptically transferred into a stomacher bag. Each sample was suspended in 25 ml pre-heated (45°C) peptone water, composed of 1% (w/v) casein peptone, 0.5% (w/v) NaCl and 2% (w/v) tri-sodium citrate dehydrate, all from Merck (Dietikon, Switzerland), and homogenized for 4 min using a Stomacher (Silver Masticator; IUL Instruments GmbH, Königswinter, Germany). BKM120 solubility dmso LEE011 mouse 1 ml of this solution was submitted to total DNA extraction for TTGE as described above. Serial dilutions in 0.9% (w/v) NaCl were prepared and plated on TGYA, PY agar and Palcam agar. At the end of ripening, 10 g of smear were harvested and tested for the presence of Listeria

using an enrichment procedure as described above. Acknowledgements This work was supported by the Research Station Agroscope Liebefeld-Posieux ALP, Bern, Switzerland. We thank Daniel Goy for sharing expertise in cheese ripening. We also thank Hélène Berthoud and Monika Haueter for excellent assistance with sequencing and DNA extraction protocols. References 1. Bockelmann W, Hoppe-Seyler T: The surface flora of bacterial smear-ripened cheeses from cow’s and goat’s milk. Int Dairy J 2001, 11:307–314.CrossRef 2. Mounier J, Gelsomino R, Goerges S, Vancanneyt M, Vandemeulebroecke K, Hoste B, Scherer S, Swings J, Fitzgerald GF, Cogan TM: Surface microflora of four smear-ripened cheeses. Appl Environ Microbiol 2005, 71:6489–6500.PubMedCrossRef 3. Wenning M, Theilmann V, Scherer S: Rapid analysis of two food-borne microbial communities at the species level by Fourier-transform infrared microspectroscopy.

Environ Microbiol 2006, 8:848–857.PubMedCrossRef 4. Ogier JC, Lafarge V, Girard V, Rault A, Maladen V, Gruss A, Leveau JY, Delacroix-Buchet A: Molecular fingerprinting of dairy microbial ecosystems by use Glutamate dehydrogenase of temporal temperature and denaturing gradient gel electrophoresis. Appl Environ Microbiol 2004, 70:5628–5643.PubMedCrossRef 5. Feurer C, Irlinger F, Spinnler HE, Glaser P, Vallaeys T: Assessment of the rind microbial diversity in a farmhouse-produced vs a pasteurized industrially produced soft red-smear cheese using both cultivation and rDNA-based methods. J Appl Microbiol 2004, 97:546–556.PubMedCrossRef 6. Rademaker JLW, Peinhopf M, Rijnen L, Bockelmann W, Noordman WH: The surface microflora dynamics of bacterial smear-ripened Tilsit cheese determined by T-RFLP DNA population fingerprint analysis. Int Dairy J 2005, 15:785–794.CrossRef 7. Bockelmann W: Development of defined surface starter cultures for the ripening of smear cheeses. Int Dairy J 2002, 12:123–131.

: Association of Epstein-Barr virus with undifferentiated gastric

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carcinomas and gastric stump carcinomas: a late event in gastric carcinogenesis. J Clin Pathol 2004, 57: 487–491.CrossRefPubMed 5. Takada K: Epstein-Barr virus and gastric carcinoma. Mol Pathol 2000, 53: 255–261.CrossRefPubMed 6. Herrmann K, Niedobitek G: Epstein-Barr virus-associated carcinomas: facts and fiction. J Pathol 2003, 199: 140–145.CrossRefPubMed 7. zur Hausen A, Brink AA, Craanen ME, et al.: Unique transcription

pattern of Epstein-Barr virus (EBV) in EBV-carrying gastric adenocarcinomas: expression of the transforming BARF1 gene. Cancer Res 2000, 60: 2745–2748.PubMed 8. zur Hausen A, van Grieken NC, Meijer GA, et al.: Distinct chromosomal aberrations in Epstein-Barr virus-carrying gastric carcinomas tested by comparative genomic hybridization. Gastroenterology 2001, 121: 612–618.CrossRefPubMed 9. Uozaki H, Fukayama M: Epstein-Barr Virus and Gastric Carcinoma – Viral GDC-973 Carcinogenesis through Epigenetic Mechanisms. Int J Clin Exp Pathol 2008, 1 (3) : 198–216.PubMed 10. Ott G, Kirchner T, Muller-Hermelink HK: Monoclonal Epstein-Barr virus genomes but lack of EBV-related protein expression Nabilone in different types of gastric carcinoma. HistoCHIR-99021 order Pathology 1994, 25: 323–329.CrossRefPubMed 11. Imai S, Koizumi S, Sugiura M, et al.: Gastric carcinoma: monoclonal epithelial malignant cells expressing Epstein-Barr virus latent infection protein. Proc Natl Acad Sci USA 1994, 91: 9131–9135.CrossRefPubMed 12. Yamamoto N, Tokunaga M, Uemura Y, et al.: Epstein-Barr virus and gastric remnant cancer. Cancer 1994, 74: 805–809.CrossRefPubMed 13. Gulley ML, Pulitzer DR, Eagan PA, et al.: Epstein-Barr virus infection is an early event in gastric carcinogenesis and is independent of bcl-2 expression and p53 accumulation.

Hum Pathol 1996, 27: 20–27.CrossRefPubMed 14. Yanai H, Takada K, Shimizu N, et al.: Epstein-Barr virus infection in non-carcinomatous gastric epithelium. J Pathol 1997, 183: 293–298.CrossRefPubMed 15. Yanai H, Murakami T, Yoshiyama H, et al.: Epstein-Barr virus-associated gastric carcinoma and atrophic gastritis. J Clin Gastroenterol 1999, 29: 39–43.CrossRefPubMed 16. Middeldorp JM, Brink AA, Brule AJ, et al.: Pathogenic roles for Epstein-Barr virus (EBV) gene products in EBV-associated proliferative disorders. Crit Rev Oncol Hematol 2003, 45: 1–36.CrossRefPubMed 17. World Health Organisation: Classification of tumours. Pathology and genetics, tumours of the digestive system. Lyon: IARC; 2000. 18.