Woolstencroft RN, Beilharz TH, Cook MA, Preiss T,


Woolstencroft RN, Beilharz TH, Cook MA, Preiss T,

Durocher D, Tyers M: Ccr4 contributes to tolerance of replication stress through control of CRT1 mRNA poly(A) tail length. J Cell Sci 2006,119(24):5178–5192.PubMedCrossRef 41. Jorgensen P, Nishikawa JL, Breitkreutz B-J, Tyers M: Systematic identification of pathways that couple cell growth and division in yeast. Science 2002,297(5580):395–400.PubMedCrossRef 42. Perkins D: BIBF 1120 in vivo Main features of vegetative incompatibility in Neurospora. Fungal Genetics Newsletters 1988, 35:44–46. 43. Smith ML, Yang CJ, Metzenberg RL, Glass NL: Escape from het-6 incompatibility in Neurospora crassa partial diploids involves preferential deletion within the ectopic segment. Genetics 1996,144(2):523–531.PubMed 44. Chevanne D, Saupe S, Clave C, Paoletti M: WD-repeat instability and diversification of the Podospora anserina hnwd nonself recognition gene family. BMC Evol Biol 2010,10(1):134.PubMedCrossRef 45. Loubradou G, Begueret J, Turcq B: MOD-D, a Galpha subunit of the fungus Podospora anserina, is involved in both regulation of development and vegetative incompatibility.

Genetics 1999,152(2):519–528.PubMed 46. Xiang Q, Glass NL: Identification of vib-1, a locus involved in vegetative incompatibility mediated by het-c in Neurospora crassa. Genetics 2002,162(1):89–101.PubMed 47. GSK2245840 molecular weight Nelson MA, Kang S, Braun EL, Crawford ME, Dolan PL, Leonard PM, Mitchell J, Armijo AM, Bean L, Blueyes E: Expressed sequences from conidial, mycelial, and sexual stages of Neurospora crassa. Fungal Genet Biol 1997,21(3):348–363.PubMedCrossRef 48. Dolan P, Natvig D, Nelson M: Neurospora proteome 2000. Fungal Genetics Newsletters 2000, 47:7–24. 49. Kushnirov VV, Kryndushkin DS, Boguta M, Smirnov VN, Ter-Avanesyan MD: Chaperones that (-)-p-Bromotetramisole Oxalate cure yeast artificial [PSI+] and their prion-specific effects. Curr Biol 2000,10(22):1443–1446.PubMedCrossRef 50. Muchowski PJ, Schaffar

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Figure 1 shows the schematic of the TDTR experimental setup used

Figure 1 shows the schematic of the TDTR experimental setup used in this study (Manufacturer – PicoTherm, Ibaraki, Japan). The output of the Er-doped fiber laser has a repetition frequency of 20 MHz. The pump beam of wavelength 1,550 nm heats the surface of a 135-nm-thick optothermal selleck chemicals Al transducer film deposited on the sample by sputtering. The pump beam thermally excites the sample creating a temperature-dependent reflectivity change. The reflectivity change is separately monitored with a time-delayed probe laser of wavelength 775 nm. The in-phase component (V in) and the out-of-phase component

(V out) of the probe signal variations were measured using a photodiode detector and audio frequency lock-in at 150 kHz. Figure 1 Schematic of the picosecond time domain thermoreflectance setup. The violet and red lines show the optical transport path of the pump beam and probe beam, respectively. The signals were analyzed assuming a unidirectional heat flow thermal model between the Al transducer film and the material [16]. In brief, the analysis model accounts for thermal transport in layered structures from time periodic power source with a Gaussian intensity distribution [17]. In our experiments, the modulation

frequency of the pump beam is 150 kHz. The pump CP673451 mw and probe beam spot sizes (1/e2 radius) are 37 μm and 14 μm, respectively. The Al transducer film thickness was measured as 135 nm using a profilometer. Results and discussion The thermal conductivity of single crystalline buy Captisol silicon with the Al transducer film was measured using TDTR and is found to be consistent with the literature value [18] within the experimental uncertainties of ±10%. The results of thermal conductivities of the HPT-processed samples measured using TDTR are shown in Figure 2. Figure 2a,b shows the example data sets and the corresponding

numerical fitting to the thermal model. The free parameters used in the model, the thermal interface conductance of the Al/sample and thermal conductivity of the HPT sample are adjusted to fit the experimental data at different delay Amisulpride times. Figure 2 Example data set of HPT-processed sample and corresponding fitting of thermal model (a) before and (b) after annealing. Figure 3 shows the thermal conductivity results of the HPT-processed silicon before and after annealing. The thermal conductivity of the HPT-processed silicon at 24 GPa was approximately 18 Wm−1 K−1 which is an order of magnitude less than the intrinsic literature value of 142 Wm−1 K−1 for single crystalline silicon. The thermal conductivity of HPT-processed samples reduces to approximately 7.6 Wm−1 K−1 when further strained by HPT processing. Figure 3 Thermal conductivities of the HPT-processed before and after annealing. An order of magnitude reduction in the thermal conductivity of Si upon HPT processing is observed.

It is possible that senescence-associated modifications of the le

It is possible that senescence-associated modifications of the leaf tissue enabled the penetration of the mycelium inside the host cells and the saprotrophic development of these strains. It should be noted that some mycelium development could be detected by real-time RT-PCR prior to any visible necrotic

symptom, as early as 1 dpi in case of E139, E70 and CCP. We suspect that these isolates may have a phase of epiphytic development before the mycelium penetrates through the cells upon toxin action (necrotrophy) or Selleck SB431542 senescence-induced alteration of the tissues (saprotrophy). In the case of the isolate E78, which remained avirulent even at 9 dpi, learn more we cannot rule out all saprobic activity but the very low amount of mycelium detected at 5 and 9 dpi demonstrated that it is clearly less competitive than the other isolates in senescing tissue. Discovery of new cassiicolin gene homologues New cassiicolin gene homologues potentially encoding two new cassiicolin precursor protein isoforms (Cas3 and Cas4) were found in the endophytic C. cassiicola isolates. Their predicted amino acid sequence is similar to that of the Cas1 reference isoform. In particular, the

Go6983 mature cassiicolin domain is highly conserved, with only one amino acid substitution (S instead of T) at position 2. This amino acid is especially important because it carries the sugar moiety (0-methyl-mannose) of the active cassiicolin (Barthe et al. 2007; de Lamotte et al. 2007). of Although the role played by this sugar in toxicity is still unknown, it should be noted that Serine (S), like Threonine (T), can be 0-glycosylated. Therefore, the glycosylation of the toxin is not jeopardized by the T to S substitution. The cassiicolin gene may be under purifying selection pressure, as indicated by the low (<1) d N /d S ratios. This suggests that this gene is playing and important functional role in C. cassiicola. However, this will have to be confirmed when a higher number of Cas gene sequences reflecting C. cassiicola

evolution history will be available. Although the genes encoding Cas3 and Cas4 appear structurally functional, no Cas3 and Cas4 transcripts could be detected post-inoculation. Therefore, if Cas3 and Cas4 genes are functional, it seems that their transcription is negatively controlled under the conditions used in this experiment. We have previously shown (Déon et al. 2012) that Cas1 is transiently expressed, with a sharp peak of expression at 1 or 2 dpi depending on the cultivar. This was confirmed in this work for RRIM 600 inoculated with CCP. In the cultivar FDR 5788 inoculated with CCP, Cas1 was expressed, but no peak of expression was observed. We suggest that the peak may have occurred at a different time-point not tested in this experiment. Whether Cas3 and 4 can be switched on and under which conditions is unknown.

Figure 3 Probability density (B) The probability density with sq

Figure 3 Probability density (B). The probability density with squeezing parameters r 1 = r 2 = 0.7 and ϕ 1 = ϕ 2 = 1.5 is shown here as a function of q 1 and t. Various values we have taken are q 2 = 0, n 1 = n 2 = 2, , R 0 = R 1 = R 2 = 0.1, L 0 = L 1 = L 2 = 1, C 1 = 1, C 2 = 1.2, p 1c (0) = p 2c (0) = 0, and δ = 0. The values of are (0,0,0,0) (a), (0.5,0.5,10,4) (b), and (0.5,0.5,0.5,0.53)(c). You can see the check details effects of squeezing from Figure 3. The probability densities in the DSN are more significantly distorted than

those of the DN. We can see from Figure 3b,c that the time behavior of probability densities is highly affected by external power source. If there is no power source in the circuit, the displacement of charge, specified with an initial condition, may gradually disappear according to its dissipation induced by resistances in the circuit. This is the same as that interpreted from the DN and exactly coincides with

classical analysis of the system. While various means and technologies to generate squeezed and/or displaced light are developed in the context of quantum optics after the seminal work of Slusher et selleckchem al. [31] for observing squeezed light in the mid 1980s, (displaced) squeezed number state with sufficient degree of squeezing for charges and Blasticidin S mw currents in a circuit quantum electrodynamics is first realized not long ago by Marthaler et al. [32] as far as Glutamate dehydrogenase we know. The circuit they designed not only undergoes sufficiently low dissipation but its potential energy also contains a positive quartic term that leads to achieving strong squeezing. Another method to squeeze quantum states of mechanical oscillation of charge carriers in a circuit is to use the technique of back-action evasion [33, 34] that is originally devised in order to measure one of two arbitrary conjugate quadratures with high precision beyond

the standard quantum limit. Though it is out of the scope of this work, the superpositions of any two DSNs may also be interesting topics to study, thanks to their nonclassical features that have no classical analogues. The quantum properties such as quadrature squeezing, quantum number distribution, purity, and the Mandel Q parameter for the superposition of two DSNs out of phase with respect to each other are studied in the literatures (see, for example, [35]). Quantum fluctuations Now let us see the quantum fluctuations and uncertainty relations for charges and currents in the DSN for the original system. It is well known that quantum energy and any physical observables are temporarily changed due to their quantum fluctuations. The theoretical study for the origin and background physics of quantum fluctuations have been performed in [36] by introducing stochastic and microcanonical quantizations.

coli[36] Disruption of disulfide bond formation affects this sys

coli[36]. Disruption of disulfide bond formation affects this system largely via an additional small protein component, MgrB, and its conserved cysteine residues. Currently, we cannot exclude the possibility that the interaction between CacA and TrxA is an artifact CacA protein overexpression because TrxA interacts with many proteins, including the RR RcsB [37]. Because we were unable to detect the 63-amino

acid CacA protein at native levels, we employed a larger tag or carrier protein in several biochemical experiments, including the pull-down assay. Protein instability likely precludes thorough analysis of small Torin 1 proteins of less than 50 amino acids or so [38]. Notably, deletion of trxA did not impact cpxP transcription levels in normal growth conditions (e.g., LB medium). More strict conditions LOXO-101 need to be tested, as some MLN2238 purchase small proteins accumulated within bacterial cells upon exposure to sodium dodecyl sulfate (SDS) and ethylenediaminetetraacetic acid (EDTA) [38]. The specificity that TCS connectors exhibit for their targets is likely a key contributing factor in the fidelity of the integration of TCS signals at a post-translational level. In fact, the PmrD connector protein can inhibit the dephosphorylation of phospho-PmrA

but not of its closest homolog, the response regulator YgiX [6]. Although recognizing

novel connectors in genomic sequences based on their uniqueness is far from trivial, genetic approaches will continue to help elucidate links amongst TCSs. Conclusions others In this study, we identified the CacA protein as an activator of the CpxR/CpxA system. This factor may be another example of an emerging class of small proteins [39] that function as nodes in the TCS network and function to integrate their signaling pathways in Salmonella. Methods Bacterial strains, plasmids, primers, and growth conditions Bacterial strains and plasmids used in this study are listed in Table 1. Primers used in this study are listed in Table 2. All S. enterica serovar Typhimurium strains are derived from wild-type 14028s and were constructed by phage P22-mediated transduction as previously described [40]. Bacteria were grown at 37°C in N-minimal media [41] buffered with 50 mM Bis-Tris, pH 7.7, and supplemented with 0.1% casamino acids, 38 mM glycerol and 10 μM or 10 mM MgCl2. E. coli DH5 α was used for preparing plasmid DNA. Ampicillin and kanamycin were used at 50 μg/ml, chloramphenicol at 20 μg/ml and tetracycline at 10 μg/ml. Table 1 Bacterial Strains and Plasmids Used in This Study Strain or plasmid Description Reference or source S.

Figure 6 shows the evolution of the two Gaussian fitting curves a

Figure 6 shows the evolution of the two Gaussian fitting curves as function of P in. At low incident power, the separation between their peak energies ΔE keeps constant, together with the ratio of their amplitude I

D/I L; this indicates that carriers are well localized, and delocalized excitons play a minor role. With increasing P in, excitons begin to delocalize and dominate in amplitude I D, and the hot carrier population fills the density of selleck inhibitor states moving the two Gaussians apart. The FWHM, plotted in the inset of Figure 6, shows that the localized contribution has a flatter broadening over power compared to the delocalized excitons, but both Gaussians are always present and mixed all along the investigated power range. We are indeed aware that the exciton delocalization,

even at higher P in, is not complete but dominates over the localized contribution. selleck chemical This result confirms the strong exciton localization and alloy inhomogeneity present in GaAsBi alloys [17, 18]. Figure 6 Evolution of the two Gaussian fitting curves vs. P in , in terms of ΔE separation and intensity ratio. The inset shows the P in dependence of the fits’ FWHM. Another way to distinguish the localized and delocalized excitons is to check their time evolution after laser pulse excitation. An example of the power dependence of the time-resolved photoluminescence (TRPL) curve sampled at the PL peak is shown in Figure 7. While at low P in, Teicoplanin the carriers are frozen in the localized states (extremely long decay time); at the highest P see more in, the PL decay times become shorter, confirming the saturation of these states and the increase

of the oscillator strength involved in the delocalized exciton recombination. Figure 7 Power dependence of the TRPL curve measured at the PL peak for sample 5. Curves are shifted for clarity. Again, the different exciton contributions can be spectrally separated, and this is evident when showing the streak camera image, together with the acquisition energy dependence of the PL decay curve taken at fixed excitation power, as represented in Figure 8. In Figure 8a, the GaAs TRPL transition is also visible above 1.5 eV and shows the fast decay time caused by the high defect density in the non-optimal grown LT-GaAs layer [15]. In Figure 8b, the GaAsBi PL decay is reported for different detection energies. As expected, the PL decay time increases when the detection energy decreases, due to carrier thermalization toward localized states, which are characterized by lower oscillator strength and hence longer recombination times. This observation is in good agreement with previously reported results on a similar GaAsBi sample [18]. For what concerns the GaAsBi transition, as expected, the population of hot carriers is established in the higher energy area, and correspondingly, the PL signal decays on a short time scale.

5 ± 0 2 (1 7 – 4 8) 82 ± 9 (20 – 153) 2 4 ± 0 3 (1 2 – 5 0) 77 ±

5 ± 0.2 (1.7 – 4.8) 82 ± 9 (20 – 153) 2.4 ± 0.3 (1.2 – 5.0) 77 ± 12 (16 – 173) 2.2 ± 0.2 (1.3 – 4.7) 83 ± 12 (27 – 156) Men (n = 7) 2.4 ± 0.4 (1.2 – 4.2) 92 ± 5 (78 – 109) 2.2 ± 0.4 (1.0 – 3.8) 82 ± 11 (60 – 135) 2.3 ± 0.5 (1.0 – 3.8) 74 ± 10 (45 – 106) check details Entire Group (n = 19) 2.5 ± 0.2 (1.2 – 4.8) 85 ± 8 (20 – 153) 2.4 ± 0.3 (1.0 – 5.0) 78 ± 8 (16 – 173) 2.2 ± 0.3 (1.0 – 4.7) 80 ± 8 (27 – 156) Experimental     PLX-4720 cell line         Women (n = 13) 2.0 ± 0.2 (1.0 – 4.1) 74 ± 9 (12 – 128) 1.9 ± 0.2 (1.0 – 4.0) 58 ± 6 (29 – 93) 1.7 ± 0.2 (1.0 – 3.0)

74 ± 10 (40 – 166) Men (n = 6) 3.1 ± 0.2 (2.1 – 4.0) 105 ± 15 (41 – 170) 2.8 ± 0.5 (1.1 – 5.8) 91 ± 15 (15 – 127) 3.4 ± 0.4 (2.0 – 5.8) 92 ± 16 (47 – 145) Entire Group (n = 19) 2.4 ± 0.2 (1.0 – 4.1) 85 ± 6 (12 – 170) 2.2 ± 0.2 (1.0 – 5.8) 70 ± 8 (15 – 127) 2.3 ± 0.2 (1.0 – 5.8) 81 ± 8 (40 – 166) † SRWC = self-reported water consumption as recorded within food diaries. ‡ Daily PA = daily physical activity as determined with wrist-worn physical activity monitors. Results from the diet diaries were also evaluated for changes in total caloric intake, macronutrient intake (protein, fat, and carbohydrate), mineral content (phosphorus, potassium, calcium, magnesium, sodium), as well as the number of food exchange equivalents for the consumption of fruits, vegetables, meat, starches, fat, and milk products. There were no significant changes for any these variables

for either Control or Experimental groups RGFP966 across the three test periods (P > 0.10). In addition, the

computation of average daily PRAL for the Control group did not change significantly between pre-treatment (20.5 ± 4.0 mEq/day), treatment (26.6 ± 6.4 mEq/day), and post-treatment (21.6 ± 5.0 mEq/day) phases (P = 0.29). Similarly, PRAL computations for the Experimental group did not change significantly across the same test periods (22.3 ± 5.6, 20.0 ± 5.0, and 32.2 ± 15.0 mEq/day, respectively) (P = 0.66). Blood and Urine Variables Daily urine output during the pre-treatment period averaged (Mean ± SE) 2.16 ± 0.24 and 2.67 ± 0.29 L/day for the Control and Experimental groups, respectively. Each subject’s 24-hour urine output values were adjusted to change scores (i.e., 24-hour urine output minus output for first measurement) and where plotted in Figure 1. DOK2 While urine output for the Control group did not change significantly over the course of the study, output for the Experimental group began decreasing by the sixth and seventh measurements (i.e., end of the first treatment week) with the last two treatment period collections being significantly lower (-0.44 to -0.46 L/day) than the reference value of zero L/day (P < 0.05).

e , the number of taxa); P i = the relative abundance of each tax

e., the number of taxa); P i = the relative abundance of each taxon, calculated as the proportional contribution of the number of individuals of that taxon to the total number of individuals within the dataset; E = evenness. The environmental variables flooding duration, median grain size (d50) and average herb height showed right-skewed distributions and were log-transformed before further analyses.

The relations between the arthropod assemblages and the different environmental variables selleck compound (Table 1) were assessed with variance partitioning (Borcard et al. 1992; Peeters et al. 2000). Prior to the variance partitioning, the total amount of variation in each arthropod dataset was assessed by determining the sum of all canonical eigenvalues with detrended correspondence analyses (DCA; CANOCO 4.0; Ter Braak and Šmilauer 1998). DCA was also used to assess whether the arthropod assemblages followed linear or unimodal response models. The DCA was based

on logarithmically transformed arthropod numbers (log (N + 1)) and revealed short to moderate gradients for each of the four arthropod datasets LGK 974 (PXD101 mouse gradient length <3 SD). Hence, the variance partitioning was based on the linear method of redundancy analysis (RDA; CANOCO 4.0; Ter Braak and Šmilauer 1998). For each environmental variable in a canonical analysis, a so-called variance inflation factor (VIF) is calculated which expresses the (partial) multiple

correlation with other environmental variables. A VIF >20 indicates that a variable is almost perfectly correlated with other variables, which results in an unstable canonical coefficient for this variable (Ter Braak and Šmilauer 1998). Initial analyses revealed high VIFs for Racecadotril the grain size distribution parameters, i.e. clay fraction, silt fraction, sand fraction and median grain size. Of these, the median grain size was selected as representative grain size distribution parameter and the others were excluded from further analysis. Similarly, the total soil concentrations of As, Cd, Cr, Cu, Ni, Pb, and Zn were characterized by high VIFs in the initial ordinations. A principal component analysis (PCA; SPSS 16.0) was executed on the soil metal concentrations in order to reduce the amount of variables while preserving the main part of the variation. As the first principal component accounted for over 92% of the variation in the soil metal concentrations, the remaining components were discarded and for each sampling site the soil metal concentrations were replaced by the site score on the first component (Schipper et al. 2008b).

Removal of RbaY should result in an increase in

Removal of RbaY should result in an increase in selleck inhibitor RbaV-P and therefore allow unregulated inhibition of the cognate σ factor activity by RbaW; our data support this prediction but also

cannot distinguish this from the possibility that RbaV is the controller of output from the pathway, as discussed further below. The absence of RbaW results in the opposite phenotype compared with loss of RbaV or RbaY, supporting the hypothesis that it might act as a negative regulator of a σ factor that initiates transcription of the RcGTA gene cluster. The ~3-fold increase in RcGTA production in the rbaW mutant did not cause a measurable decrease in the viable cell numbers, suggesting the increase is mostly coming from the ~3% subset of the population normally activated for RcGTA production [61] even though this strain showed a population-wide

increase in RcGTA gene expression (Figure 6A). selleck chemicals llc The rbaVW and rbaW mutant phenotypes were not the same, suggesting a dominant effect of the rbaV mutation. Removal of the predicted anti-σ factor, RbaW, led to increased RcGTA gene expression and production only in the presence of a wild type copy of rbaV. The rbaW mutant had no observable differences in stationary phase cell viability or colony morphology, indicating these effects in the rbaVW strain were caused by loss of RbaV. It is not clear why rbaW (pW) maintained elevated RcGTA levels relative to SB1003, but the results with pVW demonstrate a requirement for upstream expression of rbaV for complementing the loss of

rbaW for this phenotype. These data suggest that RbaV is Isotretinoin the determinant positive regulator of RcGTA in this pathway (Figure 8). The in vitro interaction and two-hybrid experiments showed that RbaV does indeed interact with RbaW. Figure 8 Possible models for Rba effects on RcGTA gene expression. Transcript levels of the genes encoding RbaY, RbaV and RbaW are >2-fold lower in the absence of the response regulator CtrA (grey arrow) [8]. The predicted phosphatase RbaY is proposed to activate the STAS domain-containing RbaV (black arrow) by dephosphorylation in response to signal(s) from an unknown sensor kinase(s) (SKs) (grey arrow). There are then two possible scenarios that result in increased RcGTA gene expression. 1. Dephosphorylation of RbaV allows it to activate selleck undetermined intermediaries (X; black arrow) to increase RcGTA gene expression (grey arrow). In this scenario, the predicted kinase RbaW would serve as an inhibitor of RbaV. 2. Dephosphorylation of RbaV allows it to interact with RbaW to relieve inhibition of an unidentified σ factor that promotes transcription of the RcGTA gene cluster (black arrow). Our data support model 1. Studies of RsbV orthologs in Pseudomonas and Vibrio species have demonstrated that the unphosphorylated version of the STAS domain-containing protein was the key regulator of output in those systems [30, 32]. In V.

6% and -12 6%; 50 U/ml: -14 7% and -34 3% for F344 and Lewis, res

6% and -12.6%; 50 U/ml: -14.7% and -34.3% for F344 and Lewis, respectively; p < 0.05; Figure 3). The decrease in total cell number was concentration-dependent for cells from both rat strains (50 U/ml > 5 U/ml; p < 0.05). Figure 3 α-Amylase effects on cell growth in F344 and Lewis cells after treatment for 2 days with 5 and 50 U/ml. The mean α-amylase effect is shown as change in total cell number compared to the water-treated control cells (percent change; mean and SEM).

Results from four to five different experiments were summarized and evaluated together for F344 and Lewis cells (n = 29-35 wells per group). Numbers of cells were significantly decreased after α-amylase treatment (50 U/ml) indicating antiproliferative effects. Lewis cells were significantly more sensitive towards α-amylase than F344 following incubation with both 5 U/ml and 50 U/ml. Statistics: One-way-ANOVA and Bonferroni for selected Selonsertib solubility dmso pairs: significant differences

between controls and α-amylase are indicated by asterisk (p < 0.05); Two-way-ANOVA and Bonferroni: significant differences between F344 vs. Lewis and 5 U/ml vs. 50 U/ml are indicated by rhomb (p < 0.05). α-Amylase effects in mammary tumor cells of human origin Mammary cells from human breast tumors were also treated with α-amylase for two days. Similar to differences between F344 and Lewis cells, sensitivity towards salivary α-amylase differed depending on the origin (or source) of the cells. Cells from two different human breast tumor patients were treated with four different concentrations of α-amylase (0.125, 1.25, 12.5, and 125 U/ml). Statistical Vactosertib analysis revealed that cells cultured from one tumor (mammary carcinoma (MaCa) 700 II P2; Figure 4a) showed

significant decreases in cell number after 1.25 and 125 U/ml (-76% and -94.6%). Cells from the other tumor (MaCa 699 II P3; Figure 4b) only significantly responded to the lowest concentration (0.125 U/ml: -90.5%). Figure 4 Determinations of α-amylase effects in different cells of human origin. For two HBCEC cultures, a significantly reduced cell number after α-amylase treatment was demonstrated (n = 2-6; mean and SEM). MaCa 700 responded in a dose-dependent manner (a). Additionally, the SA-β-gal assay was performed in MaCa 700 cells, and the HAS1 proportion of SA-β-gal-positive cells was significantly Protein Tyrosine Kinase inhibitor increased by 125 U/ml α-amylase. The latter parameter showed a tendency for concentration-dependency (Pearson´s correlation coefficient 0.9002; not significant). In MaCa 699 cells, only the lowest concentration caused a significantly decreased cell number (b). Asteriks indicate significant differences vs. control cells (One-way-ANOVA and Bonferroni for selected pairs, p < 0.05). Primary cells from another human breast tumor that had been cultured for 296 days did not respond with a change in cell number.