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For increased confidence, we repeated each microarray assay twice. The scatter diagrams and correlation assessment of all spots showed that the reproducibility and reliability were good (Figure 2). The supervised cluster analysis, based on differentially expressed miRNAs, generated a tree

with clear distinction between cancerous and normal tissues (Figure 3). Table 2 MicroRNAs microarray SAM results and correlation with cancer microRNA Name Fold Change Type Numerator (r) Denominator (s+s0) Correlation with cancer squamous cell carcinoma vs normal cheek pouch tissue hsa-miR-21 2.590 up 2.495 1.371 Up-regulated in glioblastomas[11], breast[8], colon[7], lung[9], pancreatic[17], thyroid[10], and ovarian cancer[15] hsa-miR-200b 2.192 up 1.645 0.964 Up-regulated in ovarian cancer[15] hsa-miR-221 2.018 up 1.561 0.988 Up-regulated in CLL[8], glioblastomas[11], thyroid[10], Alectinib solubility dmso and pancreatic cancer[17] hsa-miR-338 2.436 up 1.323 0.493   mmu-miR-762 2.379 up 1.863 1.052   hsa-miR-16 0.182 down -2.501 0.458 Down-regulated in CLL[8], and prostate cancer[12]

Birinapant supplier hsa-miR-26a 0.135 down -2.288 1.148 Down-regulated in prostate[12], and ovarian cancer[15] hsa-miR-29a 0.245 down -1.532 0.785 Down-regulated in ovarian cancer[15] hsa-miR-124a 0.216 down -1.819 0.702 Down-regulated in colon[7], breast[8] and lung cancer[9] hsa-miR-125b 0.414 down -1.282 0.418 Down-regulated in breast[8], lung[9], ovarian[15], cervical[16], and prostate cancer[12] mmu-miR-126-5p 0.424 down -1.117 0.536   hsa-miR-143 0.393 down -1.245 Bay 11-7085 0.605 Down-regulated in prostate[12], Lung[9], breast[8], hepatocellular[14], colon[7], cervical[16], and ovarian cancer[15] hsa-miR-145 0.317 down -2.130 0.899 Down-regulated in prostate[12], Lung[9], breast[8], hepatocellular[14], ovarian[15], cervical[16], and colon cancer[7] hsa-miR-148b 0.317 down -2.130 0.899 Down-regulated in pancreatic[17], and colon cancer[7] hsa-miR-155 0.376 down -1.374 0.486 Up-regulated in CLL[8], thyroid[10], lymphomas[13], lung[9], breast cancer[8] Down-regulated in pancreatic cancer[17] hsa-miR-199a 0.261 down -1.411

0.847 Down-regulated in prostate[12], and hepatocellular cancer[14] hsa-miR-203 0.175 down -1.925 0.910 Down-regulated in colon[7], and breast cancer[8] Up-regulated in ovarian cancer[15] CLL: chronic lymphocytic leukemia Figure 2 Experimental variation and reproducibility assessment from twelve microarray hybridizations in six different samples. Scatter diagram showing high reproducibility between the replicate experiments of every sample. The R-value in each microarray analysis showing that most of the average correlations are well above 0.9, indicating high reproducibility. Panel A~C: self-hybridization results obtained after probing the microarray with the same RNA sample prepared from three normal tissues and labeled separately with Cy3 dye.

The cells were resuspended in DMEM containing 1% FBS at a density of 5 × 105 cells per milliliter. The cell suspensions (100 μl) were seeded into the upper chambers, and 600 μl of DMEM medium containing 10% FBS and 10 μM VLP H1 or VLP H2 was added to the lower chambers. The cells were allowed to invade for 12 h in a CO2 incubator, fixed, stained, and

quantitated as described previously [18]. Results Expression and purification of fusion proteins RGD-IFN-α2a (300)-core, RGD- core-IFN-α2a(300), RGD-IFN-α2a-core, and RGD-core-IFN-α2a fragments were amplified using pMD-RGD-IFN-α2a (300)-core, pMD-RGD- core-IFN-α2a(300), pMD-RGD-IFN-α2a-core, and pMD-RGD-core-IFN-α2a as templates and subcloned into the pFastBacHTb-EGFP via BamH1/EcoRI sites and produced pFastBacHTb-RGD-IFN-α2a (300)-core (pH1), pFastBacHTb-RGD-core-IFN-α2a(300) (pH2), pFastBacHTb-RGD-IFN-α2a-core

(pH3), and pFastBacHT Saracatinib clinical trial b-RGD-core-IFN-α2a (pH4). The expression vectors pH1, pH2, pH3, and pH4 were confirmed on an agarose gel after double digestions with BamHI and EcoRI (Figure 1B,C) and further Selleckchem Nutlin-3a confirmed by DNA sequencing. Finally, the successfully constructed expression vectors pH1, pH2, pH3, and pH4 mediated the insertion of genes into the AcBacmid by Tn7-mediated transposition to generate AcH1, AcH2, AcH3, and AcH4 bacmids, respectively (Figure 1A). These recombinant bacmids were introduced by transfection into Sf9 cells to produce the recombinant proteins His-H1, His-H2, His-H3, and His-H4. The fusion proteins were purified from the supernatants of cell lysates using Ni-NTA affinity resin under click here native conditions. Intense bands corresponding to the molecular weights of the expected proteins are shown: 59.4 kDa for His-H1 and His-H2; 71.9 kDa for His-H3 and His-H4; the concentration of His-H1 and His-H2 is higher than the His-H3 and His-H4 (Figure 1D). Identification of virus-like particles To identify the impurities in the VLP

H1 and VLP H2 preparation after crude purification with successive sucrose gradients, various analyses were performed. First, confirmation of protein identity in the VLP preparation was performed by immunoblotting using HCV core-specific monoclonal antibodies (Figure 1G,H). In addition, EM analysis of the protein was performed and revealed spherical VLPs of 30 to 40 nm in size (Figure 1E,F). RGD- core-IFN-α2a fusion protein specifically binds with cancer cell line RGD (arginine-glycine-aspartic acid) can specifically bind with αvβ3 integrin, which is highly expressed on the cancer cell surface. The recombinant RGD- core-IFN-α2a protein was expressed and purified in Sf9. As expected, the recombinant RGD- core-IFN-α2a can specifically bind breast cancer cells MDA-MB231 and colon cancer cells HCT116 (data not shown) but do not bind normal cells such as normal human embryonic kidney cell 293 T.

The conserved aspartic acid residues shown to be essential for enzymatic activity in yeast and mammalian lipins are indicated by asterisks (*). Subcellular localization of TbLpn To determine the subcellular this website localization of TbLpn, PF T. brucei cells were fractionated into cytosolic and nuclear extracts, and the presence of TbLpn within these compartments assessed by western hybridization. The efficiency of the fractionation procedure was confirmed by using antibodies directed against cytosolic Hsp70 and nuclear

RNA polymerase II. As shown in Figure 3, a band of the expected size for TbLpn (~ 83 kDa) was present exclusively in the cytoplasm of the parasite. This is in contrast to all previously characterized mammalian and yeast lipins which display cytoplasmic as well as nuclear localization [34, 39, 49–51]. In addition, SMP2, the yeast lipin homologue, has been shown to be present in the cytosol as

well as associated with the membrane [43]. We did however detect the presence of a protein band with decreased electrophoretic mobility (~120 kDa) in the nuclear extract. This strongly suggests that TbLpn is present in both cytosol and nucleus and, in the nucleus, is heavily modified by post-translational modifications such as arginine methylation and/or phosphorylation. Figure 3 Analysis of TbLpn subcellular localization. PF T. brucei were fractionated into cytosolic EPZ-6438 clinical trial (C) and nuclear (N) extracts as described under Material and Methods. The presence of TbLpn was detected by western hybridization using anti-TbLpn polyclonal antibodies (1:1,000), followed by goat anti-rabbit IgGs, and signals detected using chemiluminescence.

Efficiency of the fractionation procedure was assessed by western blot using antibodies against Hsp70 and RNA polymerase II as cytosolic and nuclear markers, respectively. TbLpn interacts with TbPRMT1 in vitro and in vivo We further confirmed the TbPRMT1/TbLpn interaction Edoxaban identified by yeast-two-hybrid first by Far Western hybridization. To this end, recombinant His-TbLpn was electrophoresed and transferred to PVDF, and the membrane was incubated with recombinant His-TbPRMT1. Detection of His-TbPRMT1 with polyclonal anti-TbPRMT1 antibodies revealed the presence of a band at 105 kDa, which is the predicted size of His-TbLpn, thereby demonstrating direct binding of His-TbPRMT1 to His-TbLpn (Figure 4A). As a negative control, His-RBP16, expressed and purified using the same protocol as for the purification of His-TbLpn, was used. Using this negative control, no band was detected. The data indicate that TbLpn and TbPRMT1 interact directly. Figure 4 TbLpn interacts with TbPRMT1. A) Far western analysis of TbPRMT1-TbLpn interaction. Purified His-TbLpn and His-RBP16 were separated on a 10% polyacrylamide gel, transferred to PVDF, and incubated with purified TbPRMT1 as described under Material and Methods.

3A) t delay increased with increasing concentration, and P max decreased. For heat (Fig. 3B), there was virtually no effect on ΔQ/Δt. As before, Q max tended to a value of 9–10 Protein Tyrosine Kinase inhibitor J independent of whether an antibiotic was present. The calorimetric data thus suggest the modes of action of amikacin and gentamycin on E. coli are essentially the same. MICs for S. aureus ATCC29213 For

S. aureus we determined the MICs of 10 of the 12 antibiotics using IMC. However, for the sake of brevity and to illustrate the main findings, we only present 6 antibiotics which have the same modes of action as those presented for E. coli. The tests were also performed in parallel in a water bath and evaluated using the standard visual turbidity method. Again, unless otherwise stated,

the results of both methods were in agreement with each other. S. aureus and cell wall synthesis inhibitors. (Fig. 4) The antibiotics evaluated were cefoxitin and vancomycin. For cefoxitin, the MIC was determined as 4 mg l-1 whereas vancomycin had a MIC of 1 mg l-1. Both values were in agreement with the reference MIC in the CLSI manual [15]. For both antibiotics, the t delay values for the heatflow curves (Fig. 4A) increased with increasing concentration, but the effect was stronger for cefoxitin. Also P max was reduced at the highest concentration not inhibiting growth. For the heat curves (Fig. 4B) there was little change in ΔQ/Δt with antibiotic concentration. However Q max declined, but as shown, the highest value observed was ~5 J. This is far below the maximum value of 9–10 J seen repeatedly Gefitinib for E. coli, independent of antibiotic concentration, and differences here can be attributed to differences

in t delay . Thus the chief difference shown by IMC was the stronger effect of RANTES cefoxitin on initial bacterial activity. S. aureus and protein synthesis inhibitors. (Fig. 5) The MICs were determined as 16 mg l-1, 0.5 mg l-1 and 0.25 mg l-1 for chloramphenicol, erythromycin and tetracycline, respectively, which are identical to the values in the CLSI manual [15]. The overall profiles of the subinhibitory heatflow curves (Fig. 5, column A) and heat curves (Fig. 5, column B) were remarkably similar for all three antibiotics. None of the three antibiotics produced a substantial increase in t delay . The only substantial difference was for the maximum heatflow rate, P max . Tetracycline had a much larger influence on P max than the other two antibiotics. All three antibiotics produced a decline in ΔQ/Δt with increasing concentration. Changes in Q max with concentration can be attributed to the differences in ΔQ/Δt. The IMC data suggest that all three antibiotics affect the rate of bacterial growth but do not delay its onset. S. aureus and an antibiotic acting on DNA. (Fig. 6) Only one antibiotic was tested which interacts with bacterial DNA, namely ciprofloxacin. The MIC was determined as 0.5 mg l-1 using IMC which corresponds to the reference value in the CLSI manual [15].

The latter complex issue has recently been reviewed by Renier

et al. [11]. Figure 4 Effect of overproduction of PBP3 on the susceptibility of L. monocytogenes to β-lactams. (A) Susceptibility of L. monocytogenes pAKB (Lm pAKB) and L. monocytogenes pAKB-lmo1438 (Lm pAKB-lmo1438) to ampicillin measured using the E-test. The extent of the zone of partial autolysis of L. monocytogenes pAKB-lmo1438 is indicated by an arrow. (B) Survival of L. monocytogenes pAKB (○) and L. monocytogenes pAKB-lmo1438 (•) in the presence of a lethal dose of penicillin G (0.6 μg/ml). Following nisin induction, penicillin G was added (at the time indicated by an arrow) to the cultures in BHI broth and incubation at 37°C was continued. Survival was measured by performing viable cell counts. Error bars represent standard deviations from the means of three independent

experiments, each performed in triplicate. Conclusions CX-5461 solubility dmso The findings of RAD001 mouse the present study have helped to elucidate the somewhat conflicting results regarding the contribution of PBP3 to the β-lactam susceptibility of L. monocytogenes. Using the NICE expression system, it has been directly shown that PBP3 is encoded by the lmo1438 gene. Despite the excellent correlation between the MICs of different β-lactams and their affinity for PBP3 [4, 5], neither the absence [8] nor an excess of this protein affects the susceptibility of L. monocytogenes to β-lactams, and so it is not the primary lethal target of these antibiotics. An interesting

additional observation was that PBP3 overexpression SPTLC1 is accompanied by increased expression of PBP4. This finding indicates that the composition of the L. monocytogenes cell wall is subject to tight regulation, but it also makes it difficult to analyze the physiological role of PBP3 on the basis of overexpression studies, since the observed changes in growth rate and antibiotic sensitivity cannot be attributed to PBP3 overexpression alone. The overexpression of PBP3 induced the formation of short cells in the stationary phase of growth, which strongly suggests the involvement of PBP3 in cell division at this growth stage. It is possible that the changes in cell morphology produced by overexpression of PBP3 may be due to a putative FtsI activity, whereas the parallel increase in the expression of PBP4 (a cell wall synthetic enzyme not specific for cell division) could play an auxiliary role in this process. Finally, in the course of clarifying the contribution of PBP3 to the β-lactam susceptibility of L. monocytogenes, a new vector was constructed that is suitable for the overexpression of genes of interest in L. monocytogenes. The placement of components of the NICE system on a single plasmid provides an easy to use tool for expressing any protein of choice in L. monocytogenes. The construction of the plasmid pAKB and its successful application in L.

EMBO J 1993, 12:3779–3787.PubMed 94. Shea JE, Hensel M, Gleeson C, Holden DW: Identification of a virulence locus encoding a second type III secretion system in Salmonella typhimurium . Proc Natl Acad Sci USA 1996, 93:2593–2597.PubMedCrossRef 95. Collazo CM, Galan JE: The invasion-associated type III system of Salmonella typhimurium directs the translocation of Sip proteins into the host check details cell. Mol Microbiol 1997, 24:747–756.PubMedCrossRef 96. Ochman H, Groisman EA: Distribution of pathogenicity

islands in Salmonella spp. Infect Immun 1996, 64:5410–5412.PubMed 97. Deiwick J, Nikolaus T, Erdogan S, Hensel M: Environmental regulation of Salmonella pathogenicity island 2 gene expression. Mol Microbiol 1999, 31:1759–1773.PubMedCrossRef

98. Chan K, Kim CC, Falkow S: Galunisertib ic50 Microarray-based detection of Salmonella enterica serovar Typhimurium transposon mutants that cannot survive in macrophages and mice. Infect Immun 2005, 73:5438–5449.PubMedCrossRef 99. Lawley TD, Chan K, Thompson LJ, Kim CC, Govoni GR, Monack DM: Genome-wide screen for Salmonella genes required for long-term systemic infection of the mouse. PLoS Pathog 2006, 2:e11.PubMedCrossRef 100. Yoon H, Lim S, Heu S, Choi S, Ryu S: Proteome analysis of Salmonella enterica serovar Typhimurium fis mutant. FEMS Microbiol Lett 2003, 226:391–396.PubMedCrossRef 101. Wilson RL, Libby SJ, Freet AM, Boddicker JD,

Fahlen TF, Jones BD: Fis, a DNA nucleoid-associated protein, is involved in Salmonella typhimurium SPI-1 invasion gene expression. Mol Microbiol 2001, 39:79–88.PubMedCrossRef 102. Sheikh J, Hicks S, Dall’Agnol M, Phillips AD, Nataro JP: Roles for Fis and YafK in biofilm formation by enteroaggregative Escherichia coli . Mol Microbiol 2001, 41:983–997.PubMedCrossRef IKBKE 103. Schmitt CK, Ikeda JS, Darnell SC, Watson PR, Bispham J, Wallis TS, et al.: Absence of all components of the flagellar export and synthesis machinery differentially alters virulence of Salmonella enterica serovar Typhimurium in models of typhoid fever, survival in macrophages, tissue culture invasiveness, and calf enterocolitis. Infect Immun 2001, 69:5619–5625.PubMedCrossRef 104. Schechter LM, Jain S, Akbar S, Lee CA: The small nucleoid-binding proteins H-NS, HU, and Fis affect hilA expression in Salmonella enterica serovar Typhimurium. Infect Immun 2003, 71:5432–5435.PubMedCrossRef 105. Goldberg MD, Johnson M, Hinton JCD, Williams PH: Role of the nucleoid-associated protein Fis in the regulation of virulence properties of enteropathogenic Escherichia coli . Mol Microbiol 2001, 41:549–559.PubMedCrossRef 106. Falconi M, Prosseda G, Giangrossi M, Beghetto E, Colonna B: Involvement of Fis in the H-NS-mediated regulation of virF gene of Shigella and enteroinvasive Escherichia coli . Mol Microbiol 2001, 42:439–452.

On the other hand, accumulated photon echo (APE) experiments on the same system (Lampoura et al. 2000) yielded values of Γhom at 4.2 K that were about five times smaller than those by Wu et al. (1997b). Lampoura et al. (2000) suggested that the discrepancy between the results from the APE experiments and HB experiments was due to spectral diffusion, since the experimental time scales in APE are much smaller than those in HB (picosecond vs. minutes, respectively). However, our HB results at 4.2 K coincide with those of the APE experiments, from which we conclude that the APE–HB discrepancy does not arise from spectral diffusion, but

is caused by the see more much too high burning fluences used in the HB experiments of Wu et al. (1997b). This shows that Γhom values extracted from HB experiments are reliable only selleck compound when obtained from an extrapolation of the hole width to Pt/A → 0, as shown in Fig. 6a and b. Spectral diffusion: hole widths as a function of delay time The dependence of spectral diffusion on the size of photosynthetic complexes Proteins are materials that display both crystalline and glassy properties. On the one hand, they have rather well-defined tertiary structures reflected in their crystalline properties. On the other hand, and in contrast to crystals, the structures of proteins are not static: they

may undergo conformational changes between a large number of somewhat different intermediates called conformational sub-states (CSs; Frauenfelder et al. 1991, 2001; Friedrich et al. 1994; Hofmann et al. 2003; Rutkauskas et al. 2004, 2006). These CSs are separated by a wide distribution Tau-protein kinase of energy barriers with multiple minima on a potential energy landscape, reminiscent of TLSs in glasses. TLSs, however, are randomly distributed, whereas CSs are assumed to be hierarchically organized, possessing a large degree of complexity. Whether conformational changes in proteins have a continuous distribution of relaxation rates as observed in glasses (Koedijk et al. 1996; Littau et al. 1992; Meijers and Wiersma 1994; Silbey et al. 1996; Wannemacher et al. 1993), or are characterized by discrete and sharp rates (Thorn-Leeson

and Wiersma 1995; Thorn-Leeson et al. 1997), is still a controversial issue (Baier et al. 2007, 2008; Schlichter and Friedrich 2001; for reviews, see Berlin et al. 2006, 2007). One way to study the conformational dynamics of proteins is by following their time evolution through spectral diffusion (SD; Berlin et al. 2006; Creemers and Völker 2000; Den Hartog et al. 1999b; Richter et al. 2008; Schlichter and Friedrich 2001; Störkel et al. 1998). Here, we show that the size of the protein influences the amount of SD in photosynthetic pigment–protein complexes. We have investigated three sub-core complexes of photosystem II (PSII) of green plants (spinach) at low temperature by time-resolved spectral hole burning, covering 10 orders of magnitude in time (Den Hartog et al. 1999b; Groot et al.

3 ± 9.2), for a total of 90 participants. Three participants’ scans were lost due to corrupted scan files. A total of 87 women’s scan results were included in this report. The local find more human research committee for each facility approved the study, and participants signed an approved informed consent prior to participating. There were no participant restrictions for ethnicity or body mass. Bone densitometry All women were scanned twice on both Hologic Delphi (Hologic, Inc., Waltham, MA, USA) and GE-Lunar Prodigy (Madison, WI, USA) DXA systems using each manufacturer’s standard scan and positioning protocols. Spine phantom quality control scans were

acquired on each of the six systems on a continual basis during the study, but no cross-calibration was performed for any of the systems. Each patient was positioned for the lumbar spine scan and then the left and right proximal femur scans. The subjects were asked to stand between each scan and then repositioned. The 30-s scan mode was

used on both systems and for all positions. The legs were elevated using the Hologic positioning cushion for spine scans on the Hologic systems; legs were flat on the table for the femur Mitomycin C mouse scans. The Onescan™ method was used to scan the participants on the GE-Lunar system, except one study facility (UCSF), where the single femur mode was used to scan each hip separately. The positioning and scan modes were picked to mimic current clinical practice and manufacturer 5-Fluoracil order recommendations. Scan analysis Using the methods recommended by each manufacturer for the ROI placement, one technologist analyzed all the images using either Hologic Apex 3.0 (prerelease) or GE-Lunar EnCore 10.10. The “compare” (Apex) or “copy” (Prodigy) methods were used to analyze the repeat measurements, thereby facilitating consistent size and placement of analysis regions for each participant. The test–retest precision of the scans was previously reported [6]. In short, the pooled precision from duplicate scans on this population for Apex and Prodigy was statistically the same for L1-L4 (1%) and

total hip (1.1%), and different for the femur neck (2.3% versus 1.8%, respectively (p = 0.03)). Data conversion and statistical analysis Demographics and other characteristics of the study population were expressed as means and standard deviation. The relationship between Apex and Prodigy software was defined using linear regression. The BMD values from both systems were converted into sBMD using the Hui et al. formulas for spinal BMD [3]: $$ \beginarray*20c \textsBM\textD_\textspine = 1.0550 \times \left( \textSPTOTBM\textD_\textHologic – 0.972 \right) + 1.0436 \hfill \\ \textsBM\textD_\textspine = 0.9683 \times \left( \textSPTOTBM\textD_\textLunar – 1.100 \right) + 1.0436 \hfill \\ \endarray $$and the Lu et al.

Specificity of the PCR reaction was verified by SYBR safe staining on a 2% (w/v) agarose gel. The internal standard curve using the unirradiated AZD1208 nmr RNA sample to estimate the change in target RNA quantity consisted of: undiluted RNA, a 1 in 2 dilution, a 1 in 4 dilution and a 1 in 10 dilution of unirradiated RNA. A no template negative control was also included. In addition, qRT-PCR was also carried out on the known endogenous housekeeping gene proC as an internal control to quantify the relative change in transcription of the gene of interest

[22]. Site-directed mutagenesis of pBAD33-orf43 Site-directed mutagenesis of pBAD33-orf43[8] was performed using specifically designed complementary mutagenic primers to linearly amplify pBAD33-orf43 to generate a mutated nicked DNA product. Non-mutated methylated template DNA was eliminated by incubation with the DpnI restriction enzyme. Mutated DNA products were then transformed into TOP10 and plated on appropriate media containing chloramphenicol, 25 μg ml-1. Resulting TOP10 colonies were cultured, had plasmid content extracted using the QIAprep

Spin Miniprep Plasmid extraction kit from QIAGEN Chlormezanone (West Sussex, RH10, 9NQ, UK) according to the manufacturer’s protocol and screened

for EGFR cancer the presence of pBAD33-orf43 by restriction enzyme digestion. Mutated pBAD33-orf43 was verified by DNA sequencing to contain the desired mutation without additional mutations. Mutated pBAD33-orf43 was confirmed to still transcribe orf43 specific mRNA by RT-PCR as described. Determination of the effect of induction of mutated pBAD33-orf43 on host cell growth rate was carried out as described [8]. Acknowledgements This work was funded by the Irish Research Council for Science, Engineering and Technology (IRSCET) to PA. The authors would like to thank Dr. P. Latour-Lambert for providing the pKOBEG plasmids and Drs. John O’Halloran and Michael P. Ryan for helpful discussion. References 1. Taviani E, Grim CJ, Chun J, Huq A, Colwell RR: Genomic analysis of a novel integrative conjugative element in Vibrio cholerae. FEBS Lett 2009,583(22):3630–3636.PubMedCrossRef 2. Michael GB, Kadlec K, Sweeney MT, Brzuszkiewicz E, Liesegang H, Daniel R, Murray RW, Watts JL, Schwarz S: ICEPmu1, an integrative conjugative element (ICE) of Pasteurella multocida: structure and transfer. J Antimicrob Chemoth 2012,67(1):91–100.CrossRef 3.

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R, Zhou T, Yu H, Poppe C, Johnson R, Du Z: Antimicrobial activity of essential oils and structurally related synthetic food selleck screening library additives towards selected pathogenic and beneficial gut bacteria. J Appl Microbiol 2006,100(2):296–305.PubMedCrossRef 45. Mytilinaios I, Salih M, Schofield HK, Lambert RJW: Growth curve prediction from optical density data. Int J Food Microbiol 2011,154(3):169–176.CrossRef 46. Osserman EF, Lawlor DP: Serum and urinary lysozyme (Muramidase) in monocytic and monomyelocytic leukemia. J Exp Med 1966,124(5):921–952.PubMedCrossRef Competing interests The authors declare that they have no competing interests. Authors’ contributions LB, EH contributed to the strategy, the experimental design, and planning of the study.