1) and 24(R,S),25-epiminolanosterol (EIL) (Fig. 1), Δ24(25)-sterol methyltransferase inhibitors, were synthesised, purified, and characterised as described by Urbina et al. [10]. Fluconazole (FLC) (Pfizer, São Paulo, Brazil), Itraconazole (ITC), and Amphotericin B (AMB) (both from Sigma Chemical Co., Missouri, USA) were used as reference antifungals. Drugs were diluted in dimethyl sulfoxide (DMSO) to obtain 100-times stock solutions and maintained at -70°C. Antifungal susceptibility

test The minimal inhibitory concentration (MIC) of each drug was obtained using the broth microdilution technique as described in document M27-A3 of the Clinical and Laboratory Standards Institute – CLSI [42]. Briefly, Ruboxistaurin serial two-fold dilutions of the drugs were performed MRT67307 solubility dmso in RPMI

1640 medium (Sigma Chemical Co., Missouri, USA), buffered with MOPS 0.16 M, pH 7.0, into 96-well microtitre trays to obtain concentration ranges of 0.03–16 μg.ml-1 (AZA, EIL, and ITC), 0.25–128 μg.ml-1 (FLC) and 0.007–4 μg.ml-1 (AMB). Next, the yeast inoculum was adjusted to 1–5 × 106CFU.ml-1. Dilutions of 1:50 and 1:20 in RPMI 1640 medium were performed to obtain 1–5 × 103 CFU.ml-1, and an aliquot was dispensed into each well. The microtitre trays were MM-102 order incubated at 35°C, for 48 h. MIC50 and MIC90 values (MICs that inhibit 50% and 90% of the yeast growth in relating to control, respectively) were determined using a spectrophotometer at 492 nm. MIC50 and MIC90 median values for test and standard drugs were also determined. Clinical isolates were classified according

to their MIC in three different categories: susceptible (S), susceptible dose-dependent (SDD), or resistant (R). Interpretative breakpoints proposed by the CLSI [42] for FLC and ITC were used, and concentrations above 1 μg.ml-1 were considered resistant for AMB [43]. Trailing effect for FLC and ITC was detected at visual reading after 24 h of incubation. The minimum fungicidal concentration (MFC) was determined after 48 h of treatment with the inhibitory concentrations used in the susceptibility Epothilone B (EPO906, Patupilone) test. An aliquot of each Candida isolate was transferred onto Sabouraud dextrose agar plates without the presence of drugs. The plates were incubated at 35°C for 48 h, and the minimum fungicidal concentration (MFC) was determined. MFC means the lowest concentration that showed no fungal growth [44]. Fluorescence microscopy C. albicans (isolate 77) was treated with MIC50 of AZA and EIL at 35°C for 48 h. Yeasts were washed in PBS, pH 7.2 and fixed with 4% paraformaldehyde in PBS for 30 min. Next, the yeasts were adhered to coverslips with poly-L-lysine and incubated with 5 μg.ml-1 Nile Red (Fluka, USA) for 30 min to label the lipid bodies and 1 μg.ml-1 DAPI (Sigma Chemical Co., Missouri, USA) for 10 min to label the DNA.

Missed cleavages = 2; Fixed modifications = Carbamidomethyl (C); Variable modifications = Oxidation (M); ICPL modification at both peptide N-ter and lysine side chain. Peptide tolerance ± 1.3 Da; MS/MS tolerance ± 0.5 Da; Peptide charge = 2+ and 3+; Instrument = ESI-TRAP. Only proteins identified with a protein score above the calculated Mascot ion score, defined as the 95% confidence level, were considered. Mascot distiller was also used for protein quantification with parameters as follows: integration method: simple; correlation this website threshold: 0.8; standard error threshold: 999; Xic threshold: 0.2; max Xic width: 7; fraction threshold: 0.5 and mass time matches allowed. selleck kinase inhibitor Only peptides with an ion score above 30 were considered

for quantification. The protein ratio corresponds to the average of peptide ratios. After examination that the distribution of protein ratios was almost centered on 1, a normalization based on the median of the peptide ratios

was realized by mascot distiller on the complete dataset. Proteins with fold changes above 1.5 or below 0.66 were considered as in modified abundance. Statistical APR-246 mouse analysis All experiments were performed in triplicate, unless stated otherwise. The statistical determination of significance (α = 0.05) was calculated using a Student’s t-test on the biological replicates of each experimental condition. Acknowledgements This work was partially supported by the European Space Agency ESA/ESTEC through the PRODEX program in collaboration with the Belgian Science Policy through the BASE project. We thank Ilse Coninx, Wietse Heylen and Giuseppe Pani for excellent technical assistance. Electronic supplementary material Additional file 1: Figure S1. Morphologic analysis of a P. putida KT2440 isogenic recA mutant grown at 50 rpm and 150 rpm. Flow cytometry dot plot (forward scatter versus side scatter) of P. putida KT2440 recA mutant grown at 50

rpm (A) and 150 rpm (B). Microscopic imaging of Hoechst-stained P. putida KT2440 recA mutant grown at 50 rpm (C) and 150 rpm (D) (magnification = 1000x). ID-8 Flow cytometry histogram of P. putida KT2440 recA mutant grown at 50 rpm (grey line) and 150 rpm (black line) (E), representing the average bacterial length. (PPT 592 KB) Additional file 2: Figure S2. 3 Heat shock resistance of a P. putida KT2440 isogenic recA mutant grown at 50 and 150 rpm, as compared to wild type. Bacteria were exposed to 55°C during 30 min. (PPTX 43 KB) References 1. Wu X, Monchy S, Taghavi S, Zhu W, Ramos J, van der Lelie D: Comparative genomics and functional analysis of niche-specific adaptation in Pseudomonas putida. FEMS Microbiol Rev 2011,35(2):299–323.PubMedCrossRef 2. Dixon RA: Natural products and plant disease resistance. Nature 2001,411(6839):843–847.PubMedCrossRef 3. Manzanera M, Aranda-Olmedo I, Ramos JL, Marques S: Molecular characterization of Pseudomonas putida KT2440 rpoH gene regulation. Microbiology 2001,147(Pt 5):1323–1330.PubMed 4.

schenckii, the sscmk1 gene was targeted using

RNAi directed to knockdown the expression of this gene. S. schenckii yeast cells were first transformed with pSD2G-RNAi1 containing a segment of the 3′ end of the sscmk1 gene. The size of the sscmk1 insert used for transformation was in the range used for other fungal RNAi transformations [43, 44]. Real-time PCR (qRT-PCR) confirmed that the levels Epacadostat ic50 of sscmk1 transcript were lower for the cells transformed with the pSD2G-RNAi1 than for the cells transformed with the empty plasmid at 35°C. The pSD2G-RNAi1 transformants grew from the beginning as mycelium type colonies in the selection plates at 35°C. Later when cultivated in liquid medium with aeration at 35°C, the growth observed, if any, was scarce and had the appearance of mycelium clumps with very few yeast cells. Upon further transfers to fresh medium, some of the conidia lost the capacity to grow at 35°C but could grow as mycelia when these

same cultures were transferred to 25°C, as stated previously. The inability to grow at 35°C could be due to a gradual lowering GDC-0994 price of the intracellular SSCMK1 levels and the resulting impairment of thermotolerance in these cells, not viability. The fact that the conidia from some pSD2G-RNAi1 transformants could not grow at 35°C but if transferred to 25°C developed into mycelia and grew almost as abundantly as the wild type reinforces our previous results that suggest that SSCMK1 is MycoClean Mycoplasma Removal Kit necessary for the VRT752271 molecular weight development of the yeast form of the fungus. In order

to dismiss the possibility that the morphological effects could be due to an off-target effect, a second transformation was done using a different insert, this time from the 5′ end of the sscmk1 gene. The same abnormal morphology and growth at 35°C was observed when pSD2G-RNAi2 was used for transformation. The growth phase affected by silencing the sscmk1 gene was that of the yeast form of the fungus. In S. schenckii, the development of the yeast form of this fungus is favoured by increasing the temperature to 35°C. The capacity to tolerate temperatures between 35-37°C is essential for S. schenckii to grow in the human host. Some other species of the Ophiostomaceae that are plant pathogens, can produce yeast cells but most lack the ability to grow at 35-37°C and are non-pathogenic to humans [1]. Previous results using CaMK inhibitors pointed to the role of SSCMK1 for the proliferation of the yeast cells induced to re-enter the cell cycle and for the maintenance of the yeast morphology in S. schenckii. In this work, we observed these same results but we also observed that the actual effect could lie in the loss of thermotolerance by the fungus when sscmk1 was silenced.

Tukey’s test P ≤ 0.05, R2 = Coefficient of determination, **Significant at 1% level. Morphological abnormalities The inhibitory effect of extract was further manifested in the form of deformed Barasertib supplier adults

which emerged from the larvae fed on S. hydrogenans extract ITF2357 manufacturer supplemented diet. The deformed adults had crumpled and underdeveloped wings as well as were half emerged from pupa. These deformities in adults were recorded only at 400 and 800 μg/ml concentrations (Figure 2). Figure 2 Developmental stages of S.litura reared on control diet (a,c,f) and abnormalities in different stages fed on diet supplemented with different concentrations of ethyl acetate extract of S. hydrogenans (b,d,e,g,h). Food utilization assay The diet utilization experiments indicated significant effect of S. hydrogenans solvent extract on S. litura. As is apparent from Table 5, there was significant decrease in relative growth and consumption rate of S. litura as well as efficiency of conversion

Caspase phosphorylation of ingested and digested food. Diet supplemented with extract resulted in 13–49% reduction in RGR over the control (P ≤ 0.01). Food consumption rate reduced to half of that in control at highest concentration (P ≤ 0.01). Table 5 Effect of ethyl acetate extract of S. hydrogenans and azadirachtin on food utilization and feeding of S.litura Treatments Concentrations (μg/ml) RGR (mg/mg/day) (Mean ± S.E.) RCR (mg/mg/day) (Mean ± S.E.) AD (%) (Mean ± S.E.)   Control 2.17 ± 0.07a 6.97 ± 0.39a 28.35 ± 1.05a

Streptomyces ethyl acetate extract 400 1.88 ± 0.03ab 7.29 ± 0.26a 30.00 ± 0.29a 800 1.66 ± 0.10b 6.99 ± 0.38a 51.96 ± 0.44b 1600 1.10 ± 0.11c 3.53 ± 0.29b 66.00 ± 1.33c f- value 26.45** 27.53** C1GALT1 416.91** R2 0.95 0.59 0.92 Azadirachtin 400 1.54 ± 0.20d 3.92 ± 0.80c 43.56 ± 9.37d 800 – - – 1600 – - – f- value – - – R2 – - – Mean ± SE followed by different letters with in a column are significantly different. Tukey’s test P ≤ 0.05, R2 = Coefficient of determination, *Significant at 5% level, **Significant at 1% level. A concentration dependent decrease in ECI and ECD was observed in the larvae of S. litura (Figures 3 and 4). The diet amended with extract caused 18–67% decline in ECI and 17–72% decline in ECD over the control. Approximate digestibility increased by 43% at 1600 μg/ml in comparison to control as shown in Table 5 (P ≤ 0.01). The reduction in diet utilization suggests that reduced growth and development might have resulted from both behavioral and physiological effects. It is likely that this decrease in consumption rate (RCR) could be due to the antifeedant nature of the extract and accounts for the majority of the decrease in growth rate (RGR). The Streptomyces extract also altered food utilization indices in S. litura and revealed less conversion of ingested (ECI) and digested (ECD) food to body biomass.

PubMedCrossRef 8. Kingsley RA, Msefula CL, Thomson NR, Kariuki S, Holt KE, Gordon MA, Harris D, Clarke L, Whitehead S, Sangal V, Marsh K, Achtman M, Molyneux ME, Cormican M, Parkhill J, Maclennan CA, Heyderman RS, Dougan G: Epidemic multiple drug resistant Salmonella Typhimurium causing invasive disease

in sub-Saharan Africa have a distinct genotype. Genome Res 2009,19(12):2279–2287.PubMedCrossRef 9. Grimont PAD: Antigenic formulae of the Salmonella serovars. www.selleckchem.com/products/qnz-evp4593.html F.X.Weil. [9th ed.]. Paris, France: WHO Collaborating Center for Reference and Research on Salmonella, Institut Pasteur; 2007. 10. Agron PG, Walker RL, Kinde H, Sawyer SJ, Hayes DC, Wollard J, Andersen GL: Identification by subtractive hybridization of sequences specific for Salmonella enterica serovar enteritidis. Appl Environ Microbiol 2001,67(11):4984–4991.PubMedCrossRef 11. Clinical and Laboratory Standards Institute: Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for bacteria Isolated

from Animals. M31-A3. 3rd Edition[Approved Standard]. Wayne, PA, USA: Clinical and Laboratory Standards Institute; 2008. 12. Clinical and Laboratory Standards Institute: Performance Standards for Antimicrobial Susceptibility Testing. M100-S16. 18th Informational Supplement. Wayne, PA, USA: Clinical and Laboratory Standards click here Institute; 2008. 13. Clinical and Laboratory Standards Institute: Methods for Dilution Antimicrobial Susceptibility Tests for Bacteria That Grow Aerobically. M07-A7. 7th Edition[Approved Standard]. Wayne, PA, USA: Clinical and Laboratory Standards Institute; 2006. 14. Ward LR, de Sa JD, Rowe B: A phage-typing scheme for Salmonella PRKACG enteritidis. Epidemiol Infect 1987,99(2):291–294.PubMedCrossRef 15. Ribot EM, Fair MA, Gautom R, Cameron DN, Hunter SB, Swaminathan B, Barrett TJ: Standardization of pulsed-field gel electrophoresis Selleckchem LY2606368 protocols for the subtyping of Escherichia coli O157:H7, Salmonella, and Shigella for PulseNet. Foodborne Pathog Dis 2006,3(1):59–67.PubMedCrossRef 16. Gerner-Smidt P, Hise K, Kincaid J, Hunter S, Rolando

S, Hyytia-Trees E, Ribot EM, Swaminathan B: PulseNet USA: a five-year update. Foodborne Pathog Dis 2006, 3:9–19.PubMedCrossRef 17. Boonmar S, Bangtrakulnonth A, Pornrunangwong S, Marnrim N, Kaneko K, Ogawa M: Predominant serovars of Salmonella in humans and foods from Thailand. J Vet Med Sci 1998,60(7):877–880.PubMedCrossRef 18. Sirichote P, Bangtrakulnonth A, Tianmanee K, Unahalekhaka A, Oulai A, Chittaphithakchai P, Kheowrod W, Hendriksen RS: Serotypes and antimicrobial resistance of Salmonella enterica ssp in central Thailand, 2001–2006. SE Asian J Trop Med Publ Health 2010,41(6):1405–1415. 19. Zheng J, Keys CE, Zhao S, Ahmed R, Meng J, Brown EW: Simultaneous analysis of multiple enzymes increases accuracy of pulsed-field gel electrophoresis in assigning genetic relationships among homogeneous Salmonella strains. J Clin Microbiol 2011,49(1):85–94.PubMedCrossRef 20.

0001   P2 21 (6) 1 (0.3) -20 (-95)     P3

277 (75) 167 (46) -110 (-40)     P4 69 (19) 197 (54) +128 (+185)   Number of cases exceeding wait-time targets, n (%)       <0.0001   P2 13 (62) 0 (0) -13 (-100)     P3 92 (33) 41 (25) -51 (-55) #ATM Kinase Inhibitor randurls[1|1|,|CHEM1|]#     P4 2 (3) 2 (1) 0 (0)   Median wait-times by priority, days (range)       0.94   P2 15 (2–29) 9 (N/A) -6 (-40)     P3 21 (0–90) 15 (0–90) -6 (-29)     P4 33 (6–92) 22 (0–90) -11 (-33)   Type of cancer, n (%)       0.027   Breast 104 (28) 79 (22) -25 (-24)     Colorectal 119 (32) 151 (41) +32 (+27)     Hepatopancreatobiliary 8 (2) 18 (5) +10 (+125)     Gastric 10 (3) 5 (1) -5 (-50)     Endocrine 100 (27) 94 (26) -6 (-6)     Lymph 1 (0) 0 (0) -1 (-100)     Soft-tissue sarcoma 6 (2) 8 (2) +2 (+33)     Skin carcinoma1 4 (1) 2 (1) -2 (-50)     Skin melanoma 15 (4) 7 (2) -8 (-53)   1Includes basal and squamous cell carcinoma. The distribution of general surgery cancer cases by priority level was significantly different (p < 0.0001) between the eras: in the post-ACCESS period, P2 and P3 cases declined by 95% and 40%, respectively, while P4 cases rose by 185%. There was no significant change in wait-times for elective general surgery cancer cases pre- and post-ACCESS, according to priority status. However, the proportion of cases that exceeded

assigned wait-time targets in the post-ACCESS EPZ6438 era declined

by 100% and 55% for P2 and P3 cases, respectively (p < 0.0001), while the proportion of P4 cases that exceeded wait-time targets did not change (Table 2). There was also a significant change in the type of cancer operated by general surgeons post-ACCESS: breast cancer, skin carcinoma, and skin melanoma cases declined by 24%, 50%, and 53%, respectively, whereas colorectal and hepatobiliary cases increased by 27% and 125%, respectively (p = 0.027). There were 3309 cancer surgeries performed by non-general surgeon specialists at VH during the study periods (Table 3). There was a 4% reduction in the total number of cancer surgeries performed in the post-ACCESS era. The distribution of cancer cases by priority level was also significantly different post-ACCESS Cobimetinib in vivo (p < 0.0001): P2 and P3 cases declined by 49% and 25%, respectively, while P4 cases rose by 62%. Furthermore, the number of cases that exceeded wait-time targets based on their designated priority levels declined by 100% and 55% for P2 and P3 cases, respectively, post-ACCESS (p < 0.0001). There was no significant change in the length of wait-times for elective cancer cases pre- and post-ACCESS. Additionally, the proportions by type of cancer treated at VH was significantly different post-ACCESS (p < 0.

2 times higher concentration of IL-1β and 1.6 times higher concentration of TNF-α for the Sterne strain than the Ames strain of B. anthracis. These differences were statistically significant (pairwise t-test p value = 0.0039 for IL-1β and 0.022 for TNF-α). To discriminate Y. pestis exposure from near neighbors, IL-10 levels can be used, showing cytokine concentrations following Y. enterocolitica exposure and Y. pseudotuberculosis GF120918 supplier exposure that are on average 5-fold higher and 2-fold higher, respectively, than after Y. pestis exposure (Figure 2). IL-10 differential expression was specific to the Yersinia spp. because exposure to B. anthracis strains showed comparable IL-10 levels to that in unexposed

control. The HOPACH algorithm estimated the number of clusters as five, and grouped the samples based on their host cytokine expression profiles as follows: 1) Y. pestis (KIM5 D27, India/P, and NYC), 2)

Y. pseudotuberculosis, 3) Y. enterocolitica, 4) B. anthracis (Ames and Sterne), and 5) Control (Figure 3). The closer the pathogen-exposed samples are within the tree on the left, the more similar they are. Height of the branches indicates the distance between the successive nodes in the clustering. The method separated the B. anthracis and Yersinia infected blood samples. In addition, the cytokine profile of the mock-exposed GSK2118436 research buy control was more similar to the pattern produced by B. anthracis exposure than to the profile elicited by Yersinia. Chloroambucil Figure 3 Clustering result with HOPACH using the average linkage distance between clusters is shown. The eight pathogen-exposed samples are clustered according to the dendrogram on the left and cluster into five groups, 1) Y. pestis (KIM5, NYC, and India), 2) Y. pseudotuberculosis, 3) Y. enterocolitica, 4) B. anthracis (Ames and Sterne), and 5) Control. Sixteen cytokines (Eotaxin, IL-10, IL-12(p40), IL-15, IL-1α, IL-1β, IL-6, IL-8, IP-10, MCP-1, MIG, TNFα, TRAIL, sCD23, sCD95, and sICAM-1) are also reordered based on their correlations according to the dendrogram on the top. Clusters go from root at top to leaf node

for each cytokine. Clusters in between are based on their agglomerative . The branch shows the similarity, the short the branch, the more similar. In addition, the eight rightmost proteins form a cluster that may involve inflammation-related cascades initiated by an innate immune response to these pathogen. Colors represent units of log10 [pg/ml], in ten equally spaced intervals increasing from white to dark red. A key showing the specific log10 values for each interval is shown in the figure. Results of the hierarchical clustering when using the Euclidean distance between samples depended on the distance metric between clusters. The three methods for determining the distance between clusters (complete linkage, single linkage, and average linkage, see Materials and Methods) all 4SC-202 established three major clusters: 1) Y. pestis and near neighbors, 2) B.

Virology 2004,330(1):304–312.PubMedCrossRef 49. Chambers TJ, Halevy N, Nestorowicz A, Rice CM, Lustig S: West Nile virus envelope proteins: nucleotide sequence analysis of strains differing in mouse neuroinvasiveness. J Gen Virol 1998,79(10):2375–2380.PubMed 50. Halevy M, Akov Y, Ben-Nathan D, Kobiler D, Lachmi B, Lustig S: Loss of active neuroinvasiveness in attenuated strains of West Nile virus: pathogenicity in immunocompetent and SCID mice. Arch Virol 1994,137(34):355–70.PubMedCrossRef CH5424802 solubility dmso 51. Nybakken GE, Nelson CA, Chen BR,

Diamond MS, Fremont DH: Crystal structure of the West Nile virus envelope glycoprotein. J Virol 2006,80(23):11467–11474.PubMedCrossRef 52. Davis CW, Nguyen HY, Hanna SL, Sanchez MD, Doms RW, Pierson TC: West Nile virus discriminates between DC-SIGN and DC-SIGNR for cellular attachment and infection. J Virol 2006,80(3):1290–1301.PubMedCrossRef 53. Shi PY, Tilgner M, Lo MK: Construction and characterization of subgenomic replicons of New York strain of West Nile virus. Virology 2002,296(2):219–233.PubMedCrossRef Authors’ contributions Conception and design: RH; Acquisition of data: RH, TS, SY; Analysis and Interpretation of data: RH, TS, YM, MI, AM, MH, HS, TK; Drafting the paper: RH All authors read and approved the final

manuscript.”
“Background Brucella spp. are Gram-negative, non-motile, facultative intracellular KU55933 chemical structure bacterial pathogens that are the etiologic agents of brucellosis, causing abortion and sterility in a broad range of domestic and wild animals. Furthermore, brucellosis is a chronic 4��8C zoonotic disease characterized in humans by undulant fever, arthritic pain and neurological disorders. Brucella virulence relies upon the ability to enter phagocytic and non-phagocytic cells, control the host’s intracellular trafficking to avoid lysosomal degradation, and replicate in a Brucella-containing vacuole (brucellosome) without restricting host cell functions or inducing

programmed death [1–3]. Although a few genes are directly attributed to the survival and intracellular trafficking of Brucella in the host cell (e.g., cyclic β-(1,2) click here glucan, lipopolysaccharide and the type IV secretion system (T4SS)), many aspects of the intracellular lifestyle remain unresolved [4–6]. Quorum sensing (QS), a communication system of bacteria, has been shown to coordinate group behavior in a density dependent manner by regulating gene expression; including secretion systems, biofilm formation, AI production, and cell division [7–10]. QS typically follows production of a diffusible signaling molecule or autoinducer (AI) acyl-homoserine lactone (AHL).

Selection The following selection criteria were used for inclusion of studies in the analysis: (I) prospective randomized or non-randomized controlled clinical trial, or prospective single-arm cohort study (e.g. phase II trial) or pharmaco-epidemiological cohort study; (II) study population with breast or gynaecological cancer, i.e. ovary, uterus, cervix, genital cancer, or cervical intraepithelial neoplasm

(CIN); (III) intervention group treated with VAE preparation; (IV) clinically relevant outcome (i.e. survival, Epigenetics inhibitor disease-free interval, remission, relapse, QoL, or reduction of side effects or immune suppression during cytoreductive therapy); (V) completion of study; (VI) published or unpublished. Studies were excluded if they: only measured toxicity or tolerability (phase I trial), only measured stimulation of immunological parameters, were not conducted on cancer patients, or had a retrospective design (except pharmaco-epidemiological cohort studies). There were no restrictions on language. For in vitro and selleck animal experiments the criteria were adapted accordingly; unpublished material was not included

however. In vitro experiments were restricted to cancer cells originating from human tumours. Validity assessment and data abstraction Criteria-based analysis was performed on the selected clinical studies to assess their methodological quality. Analyses were performed independently by two reviewers (GK, HK). There were no major click here differences in study assessment; disagreements were resolved by discussion. Criteria for assessing strength

of evidence in controlled trials were adapted from the National Health Service Centre for Reviews and Dissemination [40] and from criteria for good methodology as already applied in earlier reviews on VAE trials [34, 36, 41]. Quality criteria were adjusted for cohort studies [36]. Data were abstracted by one reviewer (GK) and checked by a second reviewer (AG). When necessary, primary authors of the trials were of contacted for additional information. Regarding animal experiments we extracted data on study size, animal model, tumour type, tumour transfer, intervention, treatment schedule, outcome, physiological monitoring, side effects, dose-response, randomization, control treatment, blinding of outcome assessment, publication in a peer-reviewed journal, and funding source. Results Result of literature search The literature search identified 306 references describing potential clinical studies (after deletion of duplicates).

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Adriamycin clinical trial adherence to fibronectin and invasion of endothelial cells. Cell Microbiol 2001, 3:839–851. 42. Lowy FD: Is Staphylococcus aureus an intracellular pathogen? Trends Microbiol 2000, 8:341–343. 43. Sachse F, Becker K, von Eiff C, Metze D, Rudack C: Staphylococcus aureus invades the epithelium in nasal polyposis

and induces IL-6 in nasal epithelial cells in vitro . Allergy 2010, 65(11):1430–1437. 44. Clement S, Vaudaux P, Francois P, Schrenzel J, Huggler E, Kampf S, Chaponnier C, Lew D, Lacroix JS: Evidence of an intracellular reservoir in the nasal mucosa of patients with recurrent Staphylococcus aureus rhinosinositis. J Infect Dis 2005, PU-H71 ic50 192:1023–1028. 45. Sinha B, Francois PP, Nusse O, Foti M, Hartford OM, Vaudaux F, Foster TJ, Lew DF, Herrmann M, Krause KH: Fibronectin‐binding protein acts as Staphylococcus aureus invasin via fibronectin bridging to integrin alpha5beta1. Cell Microbiol 1999, 1:101–118. 46. Fowler T, Wann ER, Joh D, Johansson S, Foster TJ, Hook M: Cellular acetylcholine invasion by Staphylococcus aureus involves a fibronectin bridge between the bacterial fibronectin-binding

MSCRAMMs and host cell beta1 integrins. Eur J Cell Biol 2000, 79:672–679.PubMedCrossRef 47. Agerer F, Michel A, Ohlsen K, Hauck CR: Integrin‐mediated invasion of Staphylococcus aureus into human cells requires Src family protein‐tyrosine kinases. J Biol Chem 2003, 278:42524–42531. 48. Fowler T, Johansson S, Wary KK, Hook M: Src kinase has a central role in in vitro cellular internalization of Staphylococcus aureus . Cell Microbiol 2003, 5:417–426. 49. Clem: Bacteriophage for the elimination of methicillin-resistant Staphylococcus aureus (MRSA) colonization and infection. ᅟ: Graduate School Theses and Dissertations; ᅟ. http://​scholarcommons.​usf.​edu/​etd/​2485. 50. Partridge SR: Analysis of antibiotic resistance regions in Gram-negative bacteria. FEMS Microbiol Reviews 2011, 35:820–855.CrossRef 51. Fenton M, Casey PG, Hill C, Gahan CG, Ross RP, McAuliffe O, O’Mahony J, Maher F, Coffey A: The truncated phage lysine CHAP k eliminates Staphylococcus aureus in the nares of mice. Bioengineered Bugs 2010, 1:404–407. 52. Paul VD, Rajagopalan SS, Sundarrajan S, George SE, Asrani JY, Pillai R, Chikkamadaiah R, Durgaiah M, Sriram B, Padmanabhan S: A novel bacteriophage Tail-Associated Muralytic Enzyme (TAME) from Phage K and its development into a potent anti-staphylococcal protein. BMC Microbiol 2011, 11:226.PubMedCentralPubMedCrossRef 53. Carlton RM: Phage therapy: past history and future prospects.