Biophys Chem 1998,75(3) 249–257.CrossRef 52. Chen F-M: Acid-facilitated supramolecular assembly of G-quadruplexes in d(CGG)β4. J Biol Chem 1995,270(39) 23090–23096.CrossRef 53. Zheng L, Wang X, Zhang JL, Li W: DNA nanotechnology based on polymorphic G-quadruplex. Progress in Chemistry 2011,23(5) 974–982. Competing interests The authors declare that they

have no competing interests. Authors’ contributions MAM designed the sequences, carried out the gel electrophoresis and AFM measurements, and wrote initial drafts learn more of the manuscript. VAS conducted gel electrophoresis experiments, supervised the design and completion of the work, and wrote the final version of the manuscript. Both authors read and approved the final manuscript.”
“Background Resonance energy transfer (RET) between nanosystems is extensively researched in nanophotonics, which PR-171 solubility dmso has various important applications ranging from biological detections and chemical sensors to quantum information science [1–11]. RET may proceed in different transfer distances: the Dexter JNK inhibitor process [12] based on wave function overlap transfers within the range of about 1 nm, and the Forster process [13] caused by

the near-field resonant dipole-dipole interaction transfers usually within the range of 10 nm. The efficient transfer energy distance is still very short. It is thus important to enhance the efficiency of RET in a long distance. The RET rate by the dipole-dipole interactions can be greatly manipulated by the electromagnetic environment; many different kinds of electromagnetic environments have been used to enhance the resonant dipole-dipole interaction strength and the efficiency of the RET, such as optical cavities [2, 14–17], optical lens or fiber [18, 19], and metamaterials [20, 21]. In the last decades, it has been demonstrated that surface plasmon supported by metal nanostructures is a powerful tool to enhance

the efficiency of RET. Since Andrew et al. [5] demonstrated long-distance plasmon-mediated RET using Ag films, a great deal of from efforts have been devoted to investigate plasmon-mediated RET using nanoparticles [22–25], plasmonic waveguides [9, 11, 26], single nanowires [27–30], and nanorod or nanowire arrays [10, 19, 31]. Most of the previous works focus on the case of the donor and acceptor having parallel transition dipole moments. However, in practical devices, it is extremely difficult to satisfy the parallel condition between the dipole moments of the donor and acceptor, and when the donor and acceptor have nonparallel dipole moments, the RET rate may decrease evidently. It is thus important to design nanostructures to achieve big RET enhancement for donor and acceptor with nonparallel dipole moments. In this paper, we investigate the enhancement of the RET rate between donor and acceptor associated by surface plasmons of Ag nanorods on a SiO2 substrate.

The SOD activity of G. thermoleovorans B23 cells was also inducible upon addition of paraquat in the medium, which generates superoxide anion (figure not shown). It seemed most likely that high SOD activity was

required to detoxify superoxide anion, which was generated as a result of alkane degradation including oxidase reaction. So it is probable that a kind of oxidases catalyzes a step of alkane degradation pathway of G. thermoleovorans B23. Therefore, oxidase activity of the B23 cells was examined using tetradecane, tetradecanal, tetradecanol, or tetradecanoyl-CoA as a substrate. Increase in 500 nm (H2O2 formation) after the enzyme reaction was <0.01, 0.02, <0.01, and 0.16 for tetradecane, tetradecanal, tetradecanol, and tetradecanoyl-CoA, respectively. As far as we know, tetradecanoyl-CoA learn more oxidase activity has never been reported for bacteria. As for acyl-CoA oxidase in bacteria, the gene encoding short chain acyl-CoA oxidase has been cloned from Streptomyces fradiae, which forms a biosynthetic gene cluster of macrolide antibiotic, tylosin [19]. In both the bacterial cells and mitochondria of eukaryotic cells, the first and rate-limiting step of β-oxidation pathway is catalyzed by acyl-CoA dehydrogenase, in which acyl-CoA is transformed to enoyl-CoA.

This acyl-CoA dehydrogenase activity is replaced by acyl-CoA oxidase in eukaryotic peroxisome [20]. SCH772984 clinical trial Peroxisome is an organella which generates and detoxifies reactive oxygen molecules like hydrogen peroxide or superoxide anions. According to the study of alkane degrading yeast Candida, peroxisome is Oxalosuccinic acid highly developed in the cells grown on alkanes or fatty acids [21]. The development of peroxisomes in the cells of C. tropicalis grown on oleic acid was accompanied by high level expression of peroxisomal proteins, including acyl-CoA oxidase [13]. Catalase is also a marker enzyme of peroxisome

and its activity in Candida cells grown on hydrocarbons was much higher than that in the cells grown on lauryl alcohol, glucose or ethanol. Although acyl-CoA oxidase is reported to increase in the Candida cells grown on fatty acids or organic acids, too, check details neither palmitic acid (hexadecanoic acid) nor oleic acid (octadecenoic acid) was an effective inducer for the production of acyl-CoA oxidase in G. thermoleovorans B23 (Fig. 7a). The acyl-CoA oxidase activity of strain B23 showed broad substrate specificity ranging from hexanoyl-CoA to octadecanoyl-CoA (Fig. 7b). Gene disruption experiments for P16, P21, P24 (SOD) and acyl-CoA oxidase have not been successful at this point to conclude that these enzymes are responsible for alkane degradation pathway of the strain.

The hypoxia-induced reduction in T-cell activity and increase in the development of Tregs may aid in preventing an uncontrolled immune response that provokes autoimmunity or pathological tissue

damage. SC79 manufacturer Manipulation of HIF by Pathogens Hypoxia-inducible factor induction is a general part of the host response to infection. HIF is induced in response to both Gram-positive and Gram-negative bacteria [11, 41], as well as by viruses [89, 90], protozoa [27], and fungi [27]. Given the centrality of HIF in the immune response, it should come as no surprise that some pathogens have developed immune evasion strategies to counteract HIF. For example, oncolytic reovirus can prevent accumulation of HIF-1α in a proteasome-dependent manner, without affecting Hif1a transcription [91]. Moloney murine leukemia virus is able to prevent HIF-1α protein accumulation in infected mice without affecting Quisinostat Hif1a gene transcription by reducing the levels of the HIF-stabilizing host protein Jab1 [92]. Chlamydia ACY-738 datasheet pneumoniae degrades HIF by secreting the chlamydial protease-like activity factor into the cytoplasm of infected cells [93]. Pseudomonas aeruginosa expresses alkyl quinolones that target the HIF-1α protein for proteasomal degradation [94]. Infections by certain other viral pathogens may increase HIF levels or activity, perhaps exerting an anti-apoptotic effect that promotes survival of the host cell they are infecting.

The carboxy terminus of HBx from hepatitis B virus was shown to enhance the transactivation of HIF-1α by enhancing its association with CREB-BP [95]. The Kaposi’s sarcoma-associated herpesvirus (KSHV)

expresses a protein known as latency-associated nuclear antigen (LANA), which targets vHL for degradation via ubiquitination, thereby increasing HIF protein levels [96], and another part of LANA promotes HIF nuclear accumulation [96]. Epstein–Barr virus (EBV) oncoprotein latent membrane protein 1 (LMP1) activates HIF-1α by upregulating Siah1 E3 ubiquitin ligase by enhancing its stability, which allows it to increase the proteasomal degradation of prolyl hydroxylases 1 and 3 that normally mark HIF-1α for degradation [97]. As a result, LMP1 prevents formation of the vHL/HIF complex, and HIF is not degraded. Other viral and parasitic GPX6 organisms are able to subvert HIF activity to their own benefit. HIF-1α stimulates the transcription of HIV-1 genes by associating with HIV-1 long terminal repeat [98], and the JCV polyomavirus genes by binding to the early promoter of the virus [99]. Other viruses may be sensing HIF as a marker of cellular stress to indicate when it is appropriate to exit the cell. Murid herpesvirus 4 [100] and EBV [101] switch from lysogenic to lytic when HIF levels are high. High levels of HIF lead to the expression of platelet-activating factor, which some pathogens then use to increase translocation across the intestinal epithelium [102]. Toxoplasma gondii survives better when HIF is elevated [103].

The amplification experiments performed with both mTOR inhibitor purified genomic DNA of bacteria and with spiked clinical check details samples allowed to obtain a detection limit of 50 genome copies per PCR reaction which is acceptable for diagnostic use. Due to the lack of comparative data and, to the absence of a gold standard for the

molecular diagnosis of the three pathogens, it was difficult to compare the efficiency of this m-PCR with other PCR methods previously described. However, the data obtained in this study showed that our m-PCR was ten-fold less sensitive than the real-time multiplex-PCR assays already described for Chlamydios and Q fever [31, 33, 35, 22]. The sensitivity of this assay could be further increased by adapting the m-PCR to a real-time multiplex PCR format. Real-time quantitative PCR methods offer an attractive advantage, in the clinical diagnostic laboratory, to detect and quantify multiple pathogens simultaneously. However, the routine and the high-throughput analysis cost remains very high, especially for emerging countries. Attempts to isolate Chlamydophila and Coxiella strains DZNeP mw were performed on 20-different PCR positive samples to confirm the presence of the involved bacteria and to compare the efficacy of the two diagnostic methods as well. All attempts to pathogen isolation were not successful and, only two Cp. abortus, one Cp. pecorum and two C. burnetii strains isolates were obtained from vaginal swabs and

milk samples. Fifteen m-PCR positive samples were negative upon selective culture suggesting that the m-PCR method detected bacteria that are unable to grow in vitro. In our study, the investigated animals were already receiving antibiotic therapy at the time of sampling. When antibiotic treatment compromises the chance of bacterial isolation, PCR detection is not affected by the lack of viability of the microorganism Niclosamide and is more sensitive than culture for the detection of non-viable organisms and cellular DNA that have not been cleared. The performance

of the m-PCR in field studies with infected flocks that reported the occurrence of the two zoonotic diseases further validates its use as an optimal tool for surveillance for chlamydiosis and Q fever. Thus, our investigation showed that these two infections were widespread within the tested flocks as evidenced by the presence of the Cp. abortus and C. burnetii m-PCR products in over 25% of the tested clinical samples. Two vaginal swab samples were contaminated with both Cp. abortus and C. burnetii and the ability of the multiplex assay to detect dual infections was therefore known. Recently, an outbreak of enzootic abortion in ovine and caprine herds caused by mixed infections was reported and both Cp. abortus and C. burnetii were simultaneously detected, using a simplex PCR, in aborted female placentas and foetuses [36]. During our study, the developed m-PCR allowed the detection of Cp.

The first oligomer has a higher Caspase pathway this website energy of binding with the tube than the flexible one (325 kcal/mol vs 250 kcal/mol). After 50-ns modeling of spontaneous adsorption of r(C)25 onto the nanotube (at 343 K), 19 cytosines (from 25) were stacked with the nanotube surface. Figure 4 Snapshot of r(I) 10 and r(C) 25 adsorbed to SWNT (16,0). (a) In the initial simulation step and (b) after 50-ns simulation. Water molecules and Na+ counterions were removed for better visualization. The sugar-phosphate backbone of r(C)25 and

r(I)10 is shown by red and blue strip, respectively. After r(C)25 adsorption, the complementary oligomer r(I)10 was located near the hybrid prepared and then the system was modeled for the next 50 ns. To accelerate the hybridization process, r(I)10 was moved to r(C)25 NT from the side of one of its ends (Figure  4). The starting structure of r(I)10 was ordered in A-form.

Upon simulation, this oligomer approaches the nanotube and interacts both with the nanotube surface and with r(C)25. The dynamics of interactions between components can be observed in Figure  5 which demonstrates changes in the interaction energy between different components of the system with time. Figure 5 Changes in the interaction energy. Dependence of interaction energy between r(I)10 and PD0332991 chemical structure r(C)25 adsorbed to SWNT (black), (rI)10 and SWNT (red) on simulation time at 343 K. Arrows indicate the appearance of stacked and H-bonded dimers. At first, we consider changes in the energy of interactions between r(I)10 and SWNT surface (Figure  5). A notable energy increment takes

place after 5 ns of simulation when the oligomer approaches the nanotube and two or three bases (hypoxanthines) are adsorbed on its surface. At the same time, the binding energy of components of the complex reaches approximately 32 kcal/mol. The next energy growth (up to about 60 kcal/mol) takes place after 15 ns when the whole oligomer comes nearer to the nanotube, and this chain is placed practically transversely to the nanotube CYTH4 axis. However, the further simulation does not result in the increase of this energy value. It should be noted that r(I)10 oligomer moving along the tube is prevented by r(C)25 adsorbed earlier onto the nanotube, the conformation of which changes insignificantly with time. Now we consider how the energy of the interaction between two oligomers depends on simulation time (Figure  5). First of all, we note the wide range of fluctuations in the interaction energy. Already at the beginning of simulation, the interaction energy reaches about 30 kcal/mol for a short time (<1 ns), and then the energy varies in the range of 10 to 30 kcal/mol with time.

Plates were covered with a Breathe-Easy® sealing membrane to avoid evaporation and incubated for 24 hours at 37°C. The lowest antibiotic concentration that inhibited visible bacterial growth was defined

the MIC. The determined MIC values are listed in Additional file 1: Table S1. Test for #Ipatasertib concentration randurls[1|1|,|CHEM1|]# persister cell formation Chemically defined RPMI 1640 medium was inoculated with 1 × 107 CFU of either exponential or stationary grown cryo-conserved bacteria. Freshly prepared antimicrobial substances were added at a final concentration of 100-fold MIC, if not stated otherwise. Suspensions were incubated with end-over-end rotation at 37°C. Samples were taken after 1, 2, 4, 6, and 8 hours for determination of CFU by serial dilution and plating. For this 100 μl of bacterial suspensions were immediately harvested by centrifugation, once washed in sterile 0.85% NaCl solution and spotted as 10 μl aliquots on sheep blood Columbia agar plates in serial dilutions. Plating of the aliquots

was performed in triplicates and all antibiotic killing experiments were performed at least with two biological replicates. Bacterial colonies were counted 24 and 48 hours after incubation at 37°C to ensure detection of slow growing bacteria. The results were analyzed with the GraphPad Prism 5 software and expressed in CFU/ml on a logarithmic scale. The limit of detection was defined as 100 CFU/ml and lower bacterial numbers were considered BB-94 ic50 not detectable (n. d.). If indicated statistical significance was determined by one-sided Student t test. Heritability of persistence An overnight culture was diluted

to an OD600 of 0.02 in fresh THB medium and further incubated until the early exponential growth phase was reached. Then bacteria were harvested by centrifugation, once washed with PBS, and inoculated in fresh RPMI medium containing 100-fold MIC of the respective antibiotic to a final Cyclic nucleotide phosphodiesterase bacterial concentration of 1 × 107 CFU/ml. The suspensions were incubated at 37°C with moderate end-over-end rotation. Samples were taken hourly as indicated and the CFUs were determined after removal of remaining antibiotics by washings as described above. After 3 hours of antibiotic treatment (surviving) bacteria were collected by centrifugation, once washed in PBS, inoculated in fresh THB medium and grown overnight. This culture was then used to start a new cycle of antibiotic treatment with exponential grown bacteria. This procedure was repeated with three consecutive cycles and the experiment performed at least with two biological replicates. Colonies were counted and CFUs calculated as described above. Test for persister cell elimination To dissect whether the antibiotic tolerant persister population of S. suis strain 10 comprises type I or type II persister cells, we performed a persister cell elimination test as described by Keren et al.[14], with some modifications. Briefly, an overnight culture of S. suis strain 10 was adjusted to OD600 = 0.

​mit.​edu/​primer3/​). All quantifications were normalized to the this website P. gingivalis 16S rRNA gene. The transcriptional ratio from qRT-PCR analysis was logarithm-transformed and then plotted against the average log2 ratio values obtained by microarray analysis [48]. Table 6 Real-time quantitative RT-PCR confirmation of selected genes Locus no. a Primer sequence (5′-3′)

b Product size (bp) 16S rRNA F: TGTTACAATGGGAGGGACAAAGGG 118 R: TTACTAGCGAATCCAGCTTCACGG PG0090 F: CAGAAGTGAAGGAAGAGCACGAAC 197 R: GTAGGCAGACAGCATCCAAACG PG0195 F: TCCACGGCTGAGAACTTGCG 149 R: TGCTCGGCTTCCACCTTTGC PG1545 F: CCAAACCCTCAACCACAATC 142 R: GGTACCGGCTGTGTTGAACT PG0593 F: CGTGTGGGAGAGTGGGTATTGG 175 R: CGCCGCTGTTGCCTGAATTG PG1089 F: CCATCGCGATCGATGATCAGGTAA 104 R: GGCATAGTTGCGTTCAAGGGTTTC PG1019 F: TTCGCAGTATCCCATCCAAC 126 R: TCCGGCTCATAGACTTCCAA PG1180 F: CAGTCTGCCACAGTTCACCA 124 R: CCCTACACGGACACTACCGA PG1983 F: GCTCTGTGGTGTGGGCTATC 146 R: GGATAACAGGCAAACCCGAT PG0885 F: CAGATCCAAATCGGGACTGA 156 R: GTAGAGCAAGCCATGCAAGC PG1181 F: GATGAATTCGGGCGGATAAT

184 R: GM6001 datasheet CCTTGAAGTGCTCCAACGAC aBased on the genome annotation provided by TIGR (http://​cmr.​jcvi.​org/​cgi-bin/​CMR/​GenomePage.​cgi?​org=​gpg). bPrimers were designed using Primer3 program for the study except for the primers of P. gingivalis 16S rRNA and PG1089 [49], which were prepared based on the primer sequences published previously. The 16S rRNA gene was used as the reference gene for normalization. F, forward; R, reverse. Gene ontology (GO) enrichment analysis The Adenosine triphosphate GO term annotations for P. gingivalis were downloaded from the Gene Ontology website (http://​www.​geneontology.​org/​GO.​downloads.​annotations.​shtml, UniProt [multispecies] GO Annotations @ EBI, Apr. 2013). To test the GO category enrichment, we calculated the fraction of gene in the test set (F test ) associated with each GO category. Then, we generated the random control

gene set that has the same number gene of test set. In this process, the random control gene was selected by matching the length of the test gene. The fraction of genes in this randomly selected control set (F control ) associated with the current GO category was calculated. This random sampling process was repeated 10,000 times. Finally, the P-value for the enriched GO category in a test gene set was calculated as the fraction of times that F test was lower than or equal to F control . Protein-protein interaction network analysis The protein-protein interaction network data including score were obtained from the STRING 9.1 (http://​string-db.​org) [50], for P. gingivalis W83. We used Cytoscape software [51] for network drawing, in which nodes and edges represented DEGs and Selleck CBL0137 interactions among DEGs, respectively. DEGs with no direct interaction were discarded, and the final dataset consisting of 611 DEGs and 1,641 interactions were used for the network construction. In order to find significant interaction between DEGs, we applied the confidence cutoff as 0.400 (medium confidence).

His research interests are wide-gap semiconductor materials, novel semiconductor devices, and semiconductor quantum structures. Acknowledgements This work was supported by the Natural Science Foundation of China under Contract Nos. 11104271 and 1117904 and the Natural Science Foundation of Anhui Province under Contract No. 1308085MA10. References 1. Günes S, Neugebauer

Selleckchem E7080 H, Sariciftci NS: Conjugated polymer-based organic solar cells. Chem Rev 2007, 107:1324–1338.CrossRef 2. Chen LM, Hong Z, Li G, Yang Y: Recent progress in polymer solar cells: manipulation of polymer: fullerene morphology and the formation of efficient inverted polymer solar cells. Adv Mater 2009, 21:1434–1449.CrossRef 3. Benanti TL, Venkataraman D: Organic solar cells: an overview focusing on active layer morphology. Photosynth Res 2006, 87:73–81.CrossRef 4. Liao SH, Li YL, Jen TH, Cheng YS, Chen SA: Multiple functionalities of polyfluorene grafted with metal CP673451 molecular weight ion-intercalated crown ether as an electron transport layer for bulk-heterojunction polymer solar cells: optical interference, hole blocking, interfacial dipole, and electron conduction. J Am Chem Soc 2012, 134:14271–14274.CrossRef 5. Huang JS, Hsiao CY, Syu SJ,

Chao JJ, Lin CF: Well-aligned single-crystalline silicon nanowire hybrid solar cells on glass. Sol Energy Mater Sol Cells 2009, 93:621–624.CrossRef 6. Hu L, Chen G: Analysis of optical absorption in silicon nanowire arrays for photovoltaic applications. Nano Lett 2007, 7:3249–3252.CrossRef 7. Sivakov Ketotifen V, Andrä G, Gawlik A, Berger A, Plentz J, Falk F, Christiansen SH: Silicon nanowire-based solar cells on glass: synthesis, selleck chemical optical properties, and

cell parameters. Nano Lett 2009, 9:1549–1554.CrossRef 8. Muskens OL, Rivas JG, Algra RE, Bakkers EPAM, Lagendijk A: Design of light scattering in nanowire materials for photovoltaic applications. Nano Lett 2008, 8:2638–2642.CrossRef 9. Muskens OL, Diedenhofen SL, Kaas BC, Algra RE, Bakkers EPAM, Rivas JG, Lagendijk A: Large photonic strength of highly tunable resonant nanowire materials. Nano Lett 2009, 9:930–934.CrossRef 10. Garnett E, Yang P: Light trapping in silicon nanowire solar cells. Nano Lett 2010, 10:1082–1087.CrossRef 11. Tsai SH, Chang HC, Wang HH, Chen SY, Lin CA, Chen SA, Chueh YL, He JH: Significant efficiency enhancement of hybrid solar cells using core-shell nanowire geometry for energy harvesting. ACS Nano 2011, 5:9501–9510.CrossRef 12. Zhang F, Sun B, Song T, Zhu X, Lee S: Air stable efficient hybrid photovoltaic devices based on poly(3-hexylthiophene) and silicon nanostructures. Chem Mater 2011, 23:2084–2090.CrossRef 13. Li J, Yu HY, Wong SM, Li X, Zhang G, Lo PGQ, Kwong DL: Design guidelines of periodic Si nanowire arrays for solar cell application. Appl Phys Lett 2009,95(243113):1–3. 14. Li J, HY Y, Wong SM, Zhang G, Sun X, Lo PGQ, Kwong DL: Si nanopillar array optimization on Si thin films for solar energy harvesting. Appl Phys Lett 2009,95(033102):1–3. 15.

pseudotuberculosis exoproteome. Non-classically secreted proteins Intriguingly, a much higher proportion (29.0%) of the exoproteome of the 1002 strain of C. pseudotuberculosis was composed by proteins predicted by SurfG+ as not having an extracytoplasmic location, when compared to only 4.5% in the exoproteome

of the strain C231 (Figure 2). The possibility of these proteins being non-classically secreted has been evaluated using the SecretomeP algorithm Proteasome inhibition assay [29]. We have also reviewed the literature for evidence of other bacterial exoproteomes that could support the extracellular localization found for these proteins in our study. High SecP scores (above 0.5) could be predicted for 5 of the 19 proteins in the exoproteome of the 1002 strain considered by SurfG+ as having a cytoplasmic location (additional files 2 and 3); this could be an indicative that they are actually being secreted by non-classical mechanisms this website [29]. Nonetheless, 2 of these 5 proteins ([GenBank:ADL09626] and [GenBank:ADL20555]) were also detected in the exoproteome of the C231 strain, in which they were predicted by SurfG+ as possessing an extracytoplasmic location (additional file 2). A comparative analysis of the sequences encoding these proteins

in the genomes of the two C. pseudotuberculosis strains showed that the disparate results were generated due to the existence of nonsense mutations in the genome sequence of the 1002 strain, which impaired the identification of signal peptides for the two proteins at the time of SurfG+ analysis (data not shown). We believe that it is unlikely that these differences represent true polymorphisms, as the proteins were identified in the extracellular

proteome, indicating the real existence of exportation signals. This indeed demonstrates the obvious vulnerability of the prediction tools to the proper annotation of the bacterial genomes. On the other hand, the assignment of high SecP scores to these two proteins, even though they are not believed to be secreted by non-classical mechanisms, would be totally expected, as the SecretomeP is a predictor much based on a VX-689 price neural network trained to identify general features of extracellular proteins; this means the prediction tool will attribute SecP scores higher than 0.5 to most of the secreted proteins, regardless the route of export [29]. We have found reports in the literature that strongly support the extracellular localization observed for 8 of the 14 remaining proteins considered as non-secretory by SurfG+ and SecretomeP in the exoproteome of the 1002 strain, and without any detectable signal peptide (additional files 2 and 3, Figure 2).

In addition, the MICs of As (III), Cu (II) and Cd (II) in wild type C. testosteroni

S44 were 20 mM, 4 mM and 0.5 mM, respectively. In contrast, the MICs of As (III), Cu (II) selleck and Cd (II) in mutants iscR-280 and iscR-327 decreased to 10 mM, 2 mM and 0.1 mM, respectively. Those results indicated that IscR was involved in conferring resistance to a number of transition, heavy metals and metalloids in C. testosteroni S44. Figure 8 Resistance of C. testosteroni S44 and iscR mutants to As(III), Cu(II) and Cd(II). All strains were inoculated into 5 ml liquid LB medium supplemented with different concentrations of (A) As(III), (B) Cu(II) and (C) Cd(II), respectively. The OD value was determined after 24 h incubation. Different letters above bars at each metal CRT0066101 price concentration indicate significant differences between wild type S44, mutants iscR-280, iscR-327 and iscR-513 (P < 0.05). Discussion C. testosteroni S44 reduced soluble Se(IV) into insoluble and thus non-toxic SeNPs outside of cells under aerobic condition as indicated by SEM/TEM-EDX and EDS Mapping analyses. It should thus be possible to synthesize SeNPs by imitating the biological process in industrial nanomaterial manufacturing [30]. Diseases caused by high

content of Se in soils have been confirmed for the Chinese provinces Hubei and Shaanxi and Indian Punjab [1,4]. In general, the variation of Se level in humans and animals are correlated to both Se excess and deficiency through the food chain [20]. Plants took up less water-soluble Se oxyanions from soil when bacteria reduced Se(IV) to organic Se and element selenium [31]. High levels of Se are commonly associated with concurrent contamination by other heavy and/or transition metals. Therefore, C. testosteroni S44 could be very useful for bioremediation of heavy metal(loid) polluted soils because it has adapted to a metal(loid)-contaminated Phosphatidylethanolamine N-methyltransferase environment. Considering the fact that only a partial reduction of Se(IV) to Se(0) could be achieved (Figure 2), it would be better in Se bioremediation if C. testosteroni S44 was applied to the contaminated site together with other more efficient

Se(IV)-reducing bacteria. In some bacterial strains, elemental SeNPs were PI3K inhibitor observed both inside and outside of cells [12,21,32,33] whereas in other bacteria nanoparticles were only observed outside of cells [20]. We did not detect Se(IV) by HPLC-HG-AFS in cellular fractions (data not shown) although elemental Se less than 0.1 μM meets the demand of bacteria for synthesis of selenocysteine [34]. We could not observe SeNPs produced inside of cells at log phase and stationary phase by TEM, EDX and EDS Elemental Mapping (Figures 3, 4 and Additional file 1: Figure S1) although SeNPs were easily observed by TEM in many bacterial cells [12,21,32]. In contrast, we only observed a large number of SeNPs appearing outside of cells (Figure 1).