The percentage of differentially regulated genes was calculated by dividing number of genes up- and down-regulated in each category by the total number of up- and down-regulated genes, buy RG7112 respectively × 100. The COG functional categories are as follows: information storage and processing (includes J, translation; A, RNA processing and modification; K, transcription; L, replication, recombination, and repair; B, chromatin structure and dynamics); cellular processes and

signaling (includes D, cell cycle control, cell division, chromosome partitioning; selleck screening library Y, Nuclear structure; V, defense mechanisms; T, signal transduction mechanisms; M, cell wall, membrane, or envelope biogenesis; N, cell motility;

Z, cytoskeleton; W, extracellular structures; U, intracellular trafficking, secretion, and vesicular trans- port; O, posttranslational modification, protein turnover, chaperones); metabolism (includes C, energy production and conversion; G, carbohydrate transport and metabolism; selleck chemical E, amino acid transport and metabolism; F, nucleotide transport and metabolism; H, coenzyme transport and metabolism; I, lipid transport and metabolism; P, inorganic ion transport and metabolism; Q, secondary metabolite biosynthesis, transport, and catabolism); poorly characterized (includes R, general function prediction only; S, function unknown; and -, not in COGs). The most highly up-regulated gene (11.5-fold) Dapagliflozin was LIC13291, encoding a putative ankyrin repeat protein [Additional file 1]. Ankyrin repeat-containing proteins are ubiquitous proteins that play a role in protein-protein interactions [42–44]. LIC13291 is one of 12 predicted proteins with ankyrin

repeat domains in L. interrogans [34]. However, protein interactions and partners of ankyrin repeat proteins in L. interrogans have not yet been characterized. It is possible that up-regulation of this gene may be crucial for interactions of proteins involved in several functions such as intracellular signaling, nutrient acquisition, and transcriptional regulation to promote survival of Leptospira in response to stress conditions encountered in serum. Interestingly, 11 of 55 (20%) genes that were shown to be up-regulated in our study are unique to L. interrogans and are not present in the genome of the saprophytic L biflexa [45] [Additional file 1] which is susceptible to complement killing. These up-regulated unique L. interrogans genes may encode unique leptospiral virulence factors but their role, if any, in pathogenesis has yet to be determined. The complete lists of significantly up- and down-regulated genes are shown as [Additional files 1 and 2] respectively. Differentially regulated genes of known or predicted function in each broad COG category (Tables 2 and 3) are discussed below.

Figure 6 shows that the expression

of E5 oncogene had no effect on tyrosinase mRNA levels both in M14 and FRM cells and confirmed that in these cell lines the amelanotic phenotype is associated with a fair transcription of tyrosinase mRNA [27]. Moreover, WB analysis showed that tyrosinase protein levels were not modulated in E5 expressing cells in comparison with controls. These results, while confirming the poor connection between pigmentation genes expression and the pigmentary status of melanomas, Selinexor indicate that the amelanotic phenotype of FRM and M14 cells is indeed related to post-translational regulatory process in melanocytes that express normal amounts of tyrosinase protein. Figure 6 Expression

of HPV-16 E5 oncogene does not affect tyrosinase mRNA transcription and protein expression levels. Tyrosinase mRNA levels were evaluated by RT-PCR in FRM and M14 melanoma control cells (CTR), in cells treated with 20 nM Con-A (+ ConA) and in cell expressing the HPV-16 E5 (+ E5). Panel a) – Total mRNA (1 μg) was reverse transcribed and amplified with HuTyr-1/HuTyr-2. Four independent experiments gave similar results. All the samples showed similar levels of tyrosinase mRNA. Western Dactolisib manufacturer blot analysis Anidulafungin (LY303366) (panel b) and densitometric quantisation (panel c) of the chemo-luminescent signals of tyrosinase protein levels. No protein modulation was observed under any experimental condition. Results represent the mean ± standard deviation (SD) of four independent experiments. (A.U. = Arbitrary Unit). The tyrosinase reactivation could be exploited as a target for the

development of selective chemotherapeutic agents Subsequently we wondered whether the above reported endosomal alkalinisation and the reactivation of tyrosinase was associated with modifications in cell phenotype eventually resulting in an altered susceptibility to chemotherapeutic agents. Based on the notion that 3,4-DHBA, a dopamine mimetic CHIR98014 pro-drug, is a substrate for tyrosinase with consequent production of toxic intermediates [40] we evaluated its cytotoxic effect in E5 expressing cells. Fig. 7 shows that a 30 μM concentration induced a much stronger impairment of cell viability on E5 expressing melanomas than on the control cells. The same figure shows also that BSO, a well-known inhibitor of glutathione synthesis whose cytotoxic effects are correlated with the level of tyrosinase activity [40], determined a drastic reduction of cell viability in E5 expressing cells, while control cells were scarcely affected.

The kinetic data were

fitted see more to the Michaelis-Menten equation by a non-linear least square regression method. The calculations and JAK inhibitor graphic results were generated by Prism 3.03 software. The catalytic constant k cat = Vmax/[E] (μmol s-1mg-1)/(mol mg-1). The molar concentrations of α-IPMS-2CR and α-IPMS-14CR were 1.426 × 10-8 and 1.084 × 10-8 moles/mg, respectively. Acknowledgements This work was supported by the National Center for Genetic Engineering and Biotechnology, Thailand. We thank Porntip Poolsawat for technical assistance. We also thank Dr. Vittaya Meewutsom, Microbiology Department, Mahidol University, for his help with the gel filtration experiment. Electronic supplementary material Additional file 1: Gel filtration profiles of α-IPMS-2CR. Gel filtration of α-IPMS-2CR. Material, Superdex 200 HR/30. A, B, C, D, E, F, and AZD0156 G (with arrows) refer to the peak positions of blue

dextran, amylase, alcohol dehydrogenase, BSA, carbonic anhydrase, cytochrome C, and vitamin B12. The major peak fractions was dimer protein and the minor peak fractions was tetramer protein. Enzyme activity of the minor peak fractions was approx. 1/3 of the major peak fractions. (PPT 77 KB) Additional file 2: Gel filtration profiles of α-IPMS-14CR. Gel filtration of α-IPMS-14CR. Material, Superdex 200 HR/30. A, B, C, D, E, F, and G (with arrows) refer to the peak positions of blue dextran, amylase, alcohol dehydrogenase, BSA, carbonic anhydrase, cytochrome C, and vitamin B12. The major peak fractions was dimer protein and the minor peak fractions was monomer protein. Enzyme activity of the minor peak fractions was approx. 1/6 of the major peak fractions. (PPT 78 KB) References 1. Stieglitz BI, Calco JM: Distribution of the isopropylmalate this website pathway to leucine among diverse bacteria. J Bacteriol 1974, 118:935–941.PubMed 2. Kohlaw GB, Leary TR: α-Isopropylmalate synthase from Salmonella typhimurium : purification and properties. J Biol Chem 1969, 244:2218–2225. 3. Wiegel J: α-Isopropylmalate synthase as a marker for the leucine biosynthesis pathway in several Clostridia and in Bacteroides fragilis. Arch Microbiol 1981, 130:385–390.PubMedCrossRef

4. Chanchaem W, Palittapongarnpim P: A variable number of tandem repeats result in polymorphic α-isopropylmalate synthase in Mycobacterium tuberculosis. Tuberculosis (Edinb) 2002, 81:1–6.CrossRef 5. Beltzer JP, Chang L, Hinkkaneen AE, Kohlhaw GB: Structure of yeast Leu4. J Biol Chem 1986, 261:5160–5167.PubMed 6. Webster RE, Gross SR: The α-isopropylmalate synthase of Neurospora . I. The kinetics and end product control of α-isopropylmalate synthase function. Biochemistry 1965, 4:2309–2318.CrossRef 7. de Kraker JW, Luck K, Textor S, Tokuhisa JG, Gershenzon J: Two Arabidopsis genes (IPMS1 and IPMS2) encode isopropylmalate synthase, the branchpoint step in the biosynthesis of leucine. Plant Physiol 2007, 143:970–86.PubMedCrossRef 8.

It is expected that an achievement of such flexible- and nonvolatile-type memory device will be the next step toward the realization of flexible electronic systems. Recently, flexible resistive memories have been reported in various oxides ��-Nicotinamide molecular weight including graphene oxide (GO) [13], HfO2[14], NiO [15], and single-component polymer [16] thin films. However,

the huge Selleck S3I-201 dispersion in switching parameters, deprived reliability, and poor understanding of the RS behavior are some of the fundamental issues which hinder its application for high-density flexible electronics. It is well articulate that the amorphous high-κ gate dielectrics, which have already been established to be promising for semiconductor transistor technologies, JQ1 order can be good alternative for ReRAM applications as long as such these materials can perform good RS behaviors. Rare earth metal oxides as high-κ dielectrics

are considered as the replacement of hafnium-based technology [17–19], among which Lu2O3 is the promising one as it shows well-insulating property, large bandgap (5.5 eV), better hygroscopic immunity, good thermal stability, and adequate dielectric constant of approximately 11 [20]. Gao et al. reported promising unipolar RS behavior in amorphous Lu2O3 oxide [21]. In contrast, ROS1 we previously demonstrated the bipolar RS in various high-κ rare earth metal oxides, such as Tm2O3, Yb2O3, and Lu2O3, on silicon substrate [22]. The different RS behavior may be originated from their distinguished morphological changes. However, no flexible memory device has been demonstrated and detail switching dynamics is still unclear in this material. The superior experimental switching characteristics in Lu2O3 and room temperature deposition process allow it to be a possible functional material for flexible electronics. Therefore, in this study we investigate the RS behaviors of the sputter deposited lutetium sesquioxide

(Lu2O3) thin film on flexible substrate for nonvolatile flexible memory application. In addition, we demonstrate that the memory performance of ReRAM on a flexible substrate has excellent electrical and mechanical reliabilities due to the high ductility of amorphous Lu2O3 thin film and the merit of the low-temperature process. Unlike other typical flexible resistive memory, better RS characteristics were achieved for advanced flexible memory applications. Methods Flexible Ru/Lu2O3/ITO RS memory devices were fabricated on flexible polyethylene terephthalate (PET) substrates. The sputtered ITO-coated PET substrate was glued on a Si dummy wafer with polyimide tape to mechanically support the flexible substrate during fabrication process.

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40. Georgiades K, Raoult D: Defining pathogenic bacterial species in the genomic era. Front Microbiol 2010, 1:151.PubMed 41. Martinez-Murcia AJ, Benlloch S, Collins MD: Phylogenetic interrelationships of members of the genera Aeromonas and Plesiomonas as determined by 16 S ribosomal DNA sequencing: Lack of congruence with results of DNA-DNA hybridizations. Int J Syst Bacteriol BMN-673 1992, 42:412–421.PubMedCrossRef 42. Huys G, Kämpfer P, Swings J: New DNA-DNA hybridization and phenotypic data on the species Aeromonas ichthiosmia and Aeromonas allosaccharophila: A. ichthiosmia Schubert et al. 1990 is a later synonym of A. veronii Hickman-Brenner et al. 1987. Syst Appl Microbiol 2001, 24:177–182.PubMedCrossRef 43. Nhung PH, Hata H, Ohkusu K, Noda M, Shah MM, Goto K, Ezaki T: Use of the novel phylogenetic 4-Aminobutyrate aminotransferase marker dnaJ and DNA-DNA hybridization to clarify interrelationships within the genus Aeromonas. Int J Syst Evol Microbiol 2007, 57:1232–1237.PubMedCrossRef 44. Saavedra MJ, Perea V, Fontes MC, Martins C, Martínez-Murcia A: Phylogenetic identification of Aeromonas strains isolated from carcasses of

pig as new members of the species Aeromonas allosaccharophila. Antonie Van Leeuwenhoek 2007, 91:159–167.PubMedCrossRef 45. Miñana-Galbis D, Urbizu-Serrano A, Farfán M, Fusté MC, Lorén JG: Phylogenetic analysis and identification of Aeromonas species based on sequencing of the cpn60 universal target. Int J Syst Evol Microbiol 2009, 59:1976–1983.PubMedCrossRef 46. Vial L, Chapalain A, Groleau M, Déziel E: The various lifestyles of the Burkholderia cepacia complex species: a tribute to adaptation. Env Microbiol 2011, 13:1–12.CrossRef 47. Monfort P, Baleux B: Dynamics of Aeromonas hydrophila, Aeromonas sobria and Aeromonas caviae in a sewage treatment pond. Appl Env Microbiol 1990, 56:1999–2006. 48. Goñi-Urriza M, Capdepuy M, Arpin C, Raymond N, Caumette P, Quentin C: Impact of an urban effluent on antibiotic resistance of riverine Enterobacteriaceae and Aeromonas spp. Appl Env Microbiol 2000, 66:125–132.CrossRef 49.

Microbiol Rev 1993,57(2):383–401.PubMed 21. Fabrizio P, Longo VD: The chronological life span of Saccharomyces cerevisiae. Aging Cell 2003,2(2):73–81.PubMedCrossRef 22. Roux AE, Quissac A, Chartrand P, Ferbeyre G, Rokeach LA: Regulation of chronological aging in Schizosaccharomyces pombe by the protein kinases Pka1 and Sck2. Aging Cell 2006,5(4):345–357.PubMedCrossRef 23. Zuin A, Carmona M, Morales-Ivorra I, Gabrielli N, Vivancos AP, Ayte J,

Hidalgo E: Lifespan extension by calorie restriction relies on the Sty1 MAP kinase stress pathway. EMBO J 2010,29(5):981–991.PubMedCrossRef 24. Miki R, Saiki R, Ozoe Y, Kawamukai M: Comparison of a coq7 deletion BIX 1294 mw mutant with other respiration-defective mutants in fission yeast. FEBS J 2008,275(21):5309–5324.PubMedCrossRef 25. Zuin A, Gabrielli Hedgehog inhibitor N, Calvo IA, Garcia-Santamarina S, Hoe KL, Kim DU, Park HO, Hayles J, Ayte J, Hidalgo E: Mitochondrial dysfunction increases oxidative stress and decreases chronological life span in fission yeast. PLoS One 2008,3(7):e2842.PubMedCrossRef CX-5461 mw 26. Jakubowski W, Bilinski T, Bartosz G: Oxidative stress during aging of stationary cultures

of the yeast Saccharomyces cerevisiae. Free Radic Biol Med 2000,28(5):659–664.PubMedCrossRef 27. Drakulic T, Temple MD, Guido R, Jarolim S, Breitenbach M, Attfield PV, Dawes IW: Involvement of oxidative stress response genes in redox homeostasis, the level of reactive oxygen species, and ageing in Saccharomyces cerevisiae. FEMS Yeast Res 2005,5(12):1215–1228.PubMedCrossRef 28. Mata J, Lyne R, Burns G, Bahler J: The transcriptional program of meiosis and sporulation in fission yeast. Nat Genet 2002,32(1):143–147.PubMedCrossRef 29. Mata J, Wilbrey A, Bahler J: Transcriptional regulatory network for sexual differentiation in fission yeast. Genome Biol 2007,8(10):R217.PubMedCrossRef 30. Jeong J-H: Role of manganese superoxide dismutase and its gene expression in Schizosaccharomyces pombe . In Seoul National University. Department of Microbiology; 1999. 31. Grimm C, Kohli J, Murray J, Maundrell K: Protein kinase N1 Genetic engineering of Schizosaccharomyces pombe: a system for gene disruption and replacement using

the ura4 gene as a selectable marker. Mol Gen Genet 1988,215(1):81–86.PubMedCrossRef 32. Arndt GM, Atkins D: pH sensitivity of Schizosaccharomyces pombe: effect on the cellular phenotype associated with lacZ gene expression . Curr Genet 1996,29(5):457–461.PubMed 33. Basi G, Schmid E, Maundrell K: TATA box mutations in the Schizosaccharomyces pombe nmt1 promoter affect transcription efficiency but not the transcription start point or thiamine repressibility . Gene 1993,123(1):131–136.PubMedCrossRef 34. Wright A, Maundrell K, Heyer WD, Beach D, Nurse P: Vectors for the construction of gene banks and the integration of cloned genes in Schizosaccharomyces pombe and Saccharomyces cerevisiae . Plasmid 1986,15(2):156–158.PubMedCrossRef 35.

Conversely, we expected genes whose expression was associated with good prognosis to generally be highly expressed

in Selleck PRN1371 patients who survived and to be expressed at low levels in those patients who succumbed. Therefore, the ranking of the genes was performed as follows for genes predictive of poor or good prognosis. Genes predictive of poor prognosis i) A predictive score for each gene was computed for each gene across all patients, and was initially set at 0.   ii) 1. The score for each gene was increased by 1 when the patient had both high gene expression and died, or had both low gene expression and survived.   2. The score was decreased by 1 when the patient had both low GSK126 gene expression and died, or had both high gene expression and survived.   3. Average gene expression levels did not lead to any changes in the predictive score.     Genes predictive of good prognosis i) A predictive score for each gene is computed for each gene across all patients, and was initially set at 0.   ii) 1. The score was increased by 1 when the patient had both high gene expression and survived, or had both low gene expression

and died.   2. The score is decreased by 1 when the patient had both low gene expression and survived, or had both high gene Seliciclib expression and died.   3. Average gene expression levels did not lead to any changes in the predictive score.     We then combined the top ranked genes from both the Fluorometholone Acetate poor-prognosis and good-prognosis gene lists to generate a predictor gene signature. We completed all of the steps described above using Microsoft Excel™ 2007. Template file available upon request. Measuring the predictive ability of the gene signature In order to separate the training data set into good prognosis and poor prognosis groups we summed the expression of both poor-prognosis genes (poor-prognosis gene score) and good-prognosis genes (good-prognosis gene score) for all the patients in our training set. To give each patient a single overall-prognosis score we subtracted the good-prognosis gene score from the poor-prognosis

gene score, and ranked the patients according to this new total. This led patients with the highest and lowest expression of poor-prognosis and good-prognosis genes, respectively, to receive the highest scores, and patients with the lowest and highest expression of poor-prognosis and good-prognosis genes, respectively, to receive the lowest scores. In this fashion, high scores were indicative of poor prognosis and low scores were indicative of good prognosis. In order to determine a optimal cut-off score which would yield prognosis predictions with the highest possible specificity and sensitivity, we used receiver-operator characteristic curves (ROC) [6]. This generated a list of possible cut-off scores, as well as each score’s associated specificity and sensitivity.

The fragment was cloned into a pET21a vector at the NdeI/EcoRI sites. The second fragment (bp 377-753) was amplified with forward primer 5′-CCGCCGGgaattcAGTATAAAAGTGAGGGCTTA-3′, containing an EcoRI site, and reverse primer 5′-CCaagcttTTAAAACACTTCTTTCACAATCAATCTCTC-3′, Tariquidar containing a HindIII site. The second fragment was cloned in tandem with the first fragment, thus generating the full-length phage P954 lysin gene with an internal EcoRI site. The cat gene was isolated along with its constitutive promoter from the S. aureus – E. coli shuttle plasmid pSK236 by ClaI Selleck Liproxstatin-1 digestion. Cohesive ends were filled with the Klenow

fragment of DNA polymerase I and ligated into the blunted EcoRI site of the full-length phage P954 endolysin gene, thereby disrupting it. The S. aureus-specific temperature-sensitive origin of replication from the shuttle vector pCL52.2 was introduced PF-573228 chemical structure at the XhoI restriction site of this construct to generate pGMB390. Mitomycin C induction of phage P954 lysogens The S. aureus RN4220 lysogen of phage P954 was inoculated in LB medium and incubated at 37°C with shaking at 200 rpm for 16 hr. The cells were then subcultured in LB medium at 2% inoculum and incubated at 37°C with shaking at 200 rpm until the culture attained an absorbance of 1.0 at 600 nm. Mitomycin C was then added to a final concentration

of 1 μg/ml, and the culture was incubated at 37°C with shaking at 200 rpm for 4 hr for prophage induction. Recombination and screening for recombinants S. aureus RN4220 cells were transformed with pGMB390 by electroporation according to the protocol described by Schenk and Laddaga [30] with a BioRad Gene Pulser, plated on LB

agar containing chloramphenicol (10 μg/ml), and incubated at 37°C for 16 hr. Chloramphenicol-resistant colonies were selected and grown in LB at 37°C until the cultures reached an absorbance of 1.0 at 600 nm. Recombination was then initiated by infecting these cells with phage P954 (MOI = 3) for 30 min. Progeny phage were harvested from the lysate as described previously, lysogenized in S. aureus RN4220, and plated on LB agar containing chloramphenicol (10 μg/ml) Thiamet G (round I). Ninety-six chloramphenicol-resistant colonies were picked up, grown, and induced with Mitomycin C. Cultures that did not lyse after the 16-hr Mitomycin C induction were treated with 1% chloroform and lysed with glass beads; the released phages were again lysogenized in S. aureus RN4220 (round II). Chloramphenicol-resistant colonies of round II lysogens were similarly grown and subjected to Mitomycin C induction. The chloramphenicol-resistant lysogens that did not release phages upon Mitomycin C induction were selected for PCR analysis. Genomic DNA of the selected lysogens was purified, and PCR was performed with different sets of primers to confirm disruption of the phage P954 endolysin gene.

Table 4 Identification of observed TRF combinations AluI a RsaI a Clone libraryb RDP databasec 93 74 – Unclassified Euryarchaota 142 Out of ranged – Methanosarcina 176 74 Methanosaeta Methanosaeta 176

238/239 – Methanomicrobia 176 Out of range – Unclassified Euryarchaota 184/185 74/77 Methanosaeta Methanosaeta       Unclassified Euryarchaota       Thermoplasmatales       Methanomicrobiales       Methanosarcinales 184/185 142 – Unclassified Euryarchaota 184/185 238/239 Methanosaeta Methanosaeta       Unclassified Euryarchaota       Methanomicrobia       Methanosarcinales 184/185 259 ARC I Unclassified Euryarchaota       Thermoplasmatales       Methanomicrobiales 184/185 Out of range – Unclassified Euryarchaota       Unclassified Archaea       Methanosarcinales Out of range 74/77 – Unclassified Euryarchaota       Unclassified Archaea Out of range 238/239 – Unclassified this website Euryarchaota     GSK1904529A concentration   Methanosarcinales

Out of range 259 – Unclassified Euryarchaota a Observed TRF combinations that are not included in the table were not found in the database nor in the clone library. b Identification by BKM120 comparison with predicted TRF lengths of clone library sequences. c Identification by comparison with predicted TRF lengths of RDP database sequences. d Out of range: The TRF in the database or the clone library was either shorter than 50 bases or longer than 1020 bases and would therefore not have been detected. Correlation analysis Several TRFs showed a significant correlation with process parameters (Table 5). The parameters that correlated with most TRFs were water temperature and nitrogen concentration. There were also significant correlations between several TRFs and the sludge and effluent water properties (Table 6). The parameter effluent non-settleable solids (NSS) and the concentration of carbohydrates in extracted extracellular polymeric substances (EPS) correlated with most

TRFs. No TRF showed a significant correlation with the sludge volume or shear sensitivity. Table 5 Correlations between TRF abundances and WWTP process parameters a AluI Identityb, c Observationsd Temp.e SRTf F/Mg CODh NO23-Ni AluI Selleckchem Lenvatinib 142 Methanosarcina b 2 **         AluI 176 Methanosaeta c 24     ** *   AluI 184 Methanosaeta c 33 *   *     RsaI               RsaI 142 Euryarchaeota b 3 ***       * RsaI 238 Methanosaeta c 31 * *     * RsaI 259 ARC I c 4 ***       * a The correlations are marked with asterisks corresponding to the level of statistical significance: 95% (*), 99% (**) and 99.9% (***). TRFs that are not included did not show any statistically significant correlation with any parameter. The WWTP process parameter data was taken from [22]. b Identification by comparison with the RDP database. c Identification by comparison with the clone library. d The number of times the TRF was observed. e Water temperature (°C). f Solids retention time (days). g Food to mass ratio ( g/kg*s).

The reaction was initiated by addition of the enzyme, and at 0, 5, 10, and 15 min intervals, 10 μl reaction mixture was withdrawn and spotted onto the DE81 filter paper and dried. The unreacted substrate was washed and the products were eluted and counted in a liquid scintillation counter. With [3H]-Gua Doramapimod as substrate

the reaction (in a total of 25 μl) was initiated by addition of the enzyme (10 μl), incubated at 37°C for 2 min, stopped by addition of 1 M HCl (10 μl), and placed immediately on ice. After neutralization, 15 μl of the mixture was spotted onto the DE81-filter paper. The filters were then washed, and the products were eluted and counted by liquid scintillation. IC50 values for purine analogs were determined for both Mpn HPRT and human HPRT using fixed concentrations of [3H]-Hx (10 μM) or [3H]-Gua (10 μM) and variable concentrations of the inhibitors.

Thymidine kinase assay was performed using tritium labelled thymidine ([3H]-dT) as substrate and various concentrations of the inhibitors essentially as previously described [40] to determine the IC50 values of TFT and 5FdU. Kinetic parameters for TFT were determined by using the phosphoryl transfer assays as previously described [52]. Briefly, each reaction was performed in a total volume of 20 μl containing 50 mM Tris/HCl, pH 7.5, 0.5 mg/ml BSA, 5 mM DTT, 2 mM MgCl2, 15 mM NaF, variable concentrations of TFT, 0.1 mM [γ-32P]-ATP, and 50 ng purified enzyme at 37°C for 20 min, and stopped by heating at 70°C for 2 min. After brief centrifugation, 1 μl supernatant was spotted onto a TLC plate KPT 330 (PEI-cellulose, Merck) and dried. The TLC plates were developed in isobutyric acid/ammonia/H2O (66:1:33). The reaction products were visualized and quantified by phosphoimaging analysis (Quantity One, Bio-Rad). Statistical analysis The data were analysed by unpaired student’s t-test (two tailed) using GraphPad Prism 5 software. P < 0.05 is considered as significant. Acknowledgements This work was supported by a grant from the Swedish Research Council for Environment, Agricultural Sciences, and

Spatial Planning. We thank Professor Pär Nordlund, Karolinska Institute, Stockholm, for providing the nucleoside and nucleobase analogs. References 1. Razin Phospholipase D1 S, Yogev D, Naot Y: Molecular biology and pathogenicity of Mycoplasmas . AZD8186 in vitro Microbiol Mol Biol Rev 1998, 62:1094–1156.PubMed 2. Waites KB, Talkington DF: Mycoplasma pneumoniae and its role as a human pathogen. Clin Microbiol Rev 2004, 17:697–728.PubMedCrossRef 3. Narita M: Pathogenesis of extrapulmonary manifestations of Mycoplasma penumoniae infection with special reference to pneumonia. J infec Chemother 2010, 16:162–169.CrossRef 4. Lenglet A, Herrado Z, Magiorakos A, Leitmeyer K, Coulombier D: Surveillance status and recent data for Mycoplasam pneumoniae infection in the European Union and European Economic area, January 2012. Euro Surveill 2012, 17:2–7. 5.