Genotoxic chemotherapy with temozolomide (TMZ) is a mainstay of treatment for glioblastoma (GBM); however, at best, TMZ provides only modest survival benefit to a subset of patients. Recent insight into the heterogeneous nature of GBM suggests a more personalized approach to treatment may be necessary to overcome cancer drug resistance and improve patient care. These include novel therapies that can be used both alone and with TMZ to selectively reactivate apoptosis within malignant cells. For this approach to work, reliable molecular signatures that can accurately predict treatment responsiveness need to be identified first. Here, we describe the first proof-of-principle study that merges quantitative protein-based analysis of apoptosis signaling networks with data- and knowledge-driven mathematical systems modeling to predict treatment responsiveness of GBM cell lines to various apoptosis-inducing stimuli. These include monotherapies with TMZ and TRAIL, which activate the intrinsic and extrinsic apoptosis pathways, respectively, as well as combination therapies of TMZ+TRAIL. We also successfully employed this approach to predict whether individual GBM cell lines could be sensitized to TMZ or TRAIL via the selective targeting of Bcl-2/Bcl-xL proteins with ABT-737. Our findings suggest that systems biology-based approaches could assist in personalizing treatment decisions in GBM to optimize cell death induction. Glioblastoma (GBM), the most common form of primary brain tumor in humans, is typically aggressive, highly infiltrative, and resistant to conventional therapy. Despite improvements in surgical technique and the addition of temozolomide (TMZ) to the armamentarium, patient median survival remains dismal at 14.6 months, with most experiencing tumor relapse within 7 months of treatment onset , 3. WhenMGMT testing is now recommended for routine clinical decision making with regards to stratification of therapy; this follows the results of two phase 3 clinical trials that clearly demonstrate ‘unmethylated’ patients benefit more from radiotherapy alone while ‘methylated’ patients benefit more from TMZ chemotherapy alone [MGMT testing has become commonplace for patient selection within clinical trials [To date, the only significant prognostic marker of GBM patient response to TMZ is promoter methylation of the gene encoding for MGMT , 6; specpy alone , 9. In tl trials –13 and il trials .Irrespective of patient responsiveness to TMZ, the dismal prognosis associated with GBM makes it clear that other therapeutic strategies are required, both as stand-alone treatment options and as sensitizing therapies that can be combined with TMZ to overcome current treatment resistance. In line with this, due to the extremely heterogeneous nature of these tumors , 16, it Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), the natural ligand for the apoptotic receptors, DR4 and DR5, is one anti-cancer therapy that has been gaining momentum in recent years , 18. Usiin vitro and in vivo findings come from the combination of TRAIL and TMZ, which evoke concomitant stimulation of the intrinsic and extrinsic apoptotic pathways [With regards to GBM, most glioma cells are resistant to TRAIL monotherapy, although several promising combination treatments to overcome this resistance have been described –28. Partpathways –31. Usedpathways , 30. Othpathways , 32.in vitro [in vivo model of GBM [Small molecules that antagonize pro-survival Bcl-2 proteins, namely BH3 mimetics, are currently under pre-clinical and clinical evaluation as single agent anti-cancer therapies and as sensitizers to apoptosis-inducing drugs , 34. Onein vitro , while il of GBM . Recentll of GBM .Despite these promising studies, molecular marker signatures that could facilitate reliable predictions on the responsiveness of individual GBM cases to TMZ and TRAIL alone or in combination currently do not exist. Likewise, tools to predict ABT-737-based sensitization of GBM cells to TMZ or TRAIL have not yet been developed. Here, we addressed these problems in a first pre-clinical proof-of-principle study in which we merged quantitative experimental studies with data- and knowledge-driven mathematical system modeling to predict treatment responsiveness of GBM cell lines.We previously demonstrated that a knowledge- and data-driven mathematical modeling approach is capable of generating reliable predictions of melanoma cell line responsiveness to both TRAIL- and genotoxic drug-induced cell death, outperforming classical statistical procedures . As partc and second mitochondria-derived activator of caspases (SMAC) into the cytosol [c interacts with apoptotic protease activating factor 1 (Apaf-1) to form the heptameric backbone of the apoptosome complex, which in turn recruits and activates caspase-9 [The apoptosis pathway activated by TRAIL is well characterized and depicted in Figure  cytosol . This mi cytosol , 44, is  cytosol –48. The aspase-9 . At the aspase-9 .We used this knowledge of pathway topology to group the above-mentioned proteins into FGs based on their relationships within the signaling network Figure . For exaMGMT promoter whilst among the patient derived cell lines, MZ18, MZ51 and MZ294 expressed a methylated promoter and MZ304, MZ256 and MZ327 expressed an unmethylated promoter. Of note, statistical analysis comparing the relative expression of apoptotic proteins, as listed in To parameterize these 11 FGs with protein data, we carried out quantitative flow cytometry to determine the cell surface expression of death receptors 4 and 5 as well as quantitative immunoblotting to determine the relative protein expression of the other 17 key players of the apoptotic pathways activated by TRAIL and TMZ . An overFollowing the parameterization of the FGs, these data were analyzed to determine if any relationship existed between FG values and the responsiveness of each cell line to TMZ or TRAIL. First, we applied a principal component analysis (PCA) to the FG data. This statistical procedure allowed us to accumulate the variance observed in the 11 FGs in the 11 cell lines in a lower number of independent dimensions, referred to as principal components (PCs) Figure . The varFor visualization purposes, the cell-line specific values of the FGs, multiplied with the FG-specific coefficients in the first three 3 PCs, allowed us to position the 11 GBM cell lines within a three dimensional PC space Figure . The posNext, we asked whether the PC space, and therefore the protein composition of the apoptosis signaling network, contains information on whether the cell lines are responsive to TMZ or TRAIL. To this end, we first experimentally measured cell viability following TMZ or TRAIL treatment in all cell lines Figure . Our datSubsequently, cell lines were defined treatment-specifically as highly responsive , low responsive  or resistant . According to the definition of response classes, the cell lines positioned in the PC space were color coded Figure . Interesth cell line into the LDA segmented PC space. If the placed cell line (test cell line) was positioned within the correct response region, the prediction was considered to be correct  in which we used the data from 10 cell lines and subsequently positioned the 11t Figure . A visuaIn situations where multiple treatment options are available, treatment decision tools are required to assist in pre-selecting the optimal treatment option. An optimal treatment in our case (TMZ vs. TRAIL) was defined as the treatment that induces higher amounts of cell death for a given cell line. If both drugs cause similar amounts of cell death, either treatment suggestion was considered acceptable. Predictions were considered to have failed when the model recommended the treatment option with a lower effect on cell viability than the alternative treatment option. The performance of the model as a treatment decision tool is shown in Figure It has previously been reported that combination therapy with TMZ/genotoxic agents and TRAIL may significantly enhance the responsiveness of highly treatment resistant cancer models, such as GBM cell lines –31. We tOverall, the responsiveness of the GBM cell line panel to TMZ/TRAIL treatment was improved when compared to the single agent treatments Figure ; this reSince the capacity of the model to predict cell line responsiveness to the dual treatment strategy of TMZ and TRAIL was limited, we next considered whether our modeling approach was better suited to study and predict response synergies. To this end, we first calculated the (CI) Value of each cell line using Webb's fractional product method , which pThe above analyses demonstrated that it is possible to predict from baseline protein expression profiles whether individual cell lines will respond to TMZ or TRAIL monotherapy. Since many of the GBM cell lines were resistant to TMZ or TRAIL or only responded poorly, we next investigated if our systems modeling approach could be used to predict whether poorly responsive cell lines could be sensitized to treatment by ABT-737. ABT-737 specifically antagonizes Bcl-2 and Bcl-xL, the major anti-apoptotic members of the Bcl-2 protein family, by binding to a hydrophobic groove of these proteins, thereby displacing bound activator BH3-only proteins and enabling the activation of pro-apoptotic Bax and Bak .To generate predictions on which cell lines could be sensitized to TMZ or TRAIL by ABT-737, we first determined how the position of such cell lines in the PC space would change upon elimination of Bcl-2 and Bcl-xL. This repositioning was determined from the PCA results and the cell lines' protein profiles. The vector for the movement direction Figure  was deteThese predictions were then validated experimentally. The selected cell lines were treated with either TMZ or TRAIL in the presence and absence of ABT-737 (1 μM) for 48 h. Cell viability measurements demonstrated that ABT-737 selectively sensitized MZ18 and U87 cells to TRAIL treatment, whereas ABT-737 did not sensitize any of the other cell lines to TMZ or TRAIL Figure . ABT-737Efficient induction of cell death is the aim of many anti-cancer therapies; thus, it follows that a cellular defect in cell death signaling pathways like apoptosis underlies one way in which these therapies can fail . In apopThe functional groups defined in our study represent low numbers of proteins logically inter-related according to their biological interplay. This approach towards systems modeling is notably simplistic when compared to alternative modeling strategies, such as deterministic mathematical models that build on the use of differential equations . The modImportantly, once our cell lines were distributed in the PC space according to their protein expression profiles, we found little evidence of cell line overlap or clustering, indicative of the widespread heterogeneity found between the FGs of each cell line. This mirrors the extensive molecular heterogeneity found between GBM patients , 16 and kB, PKB/Akt and MAPKs signaling pathways, thereby contributing to cancer cell proliferation and migration as well as resistance to TRAIL-induced apoptosis [The prediction accuracy of our model was 73% and 82% for TMZ and TRAIL treatments, respectively. The higher performance score for TRAIL was likely attributable to the fact that we included all critical TRAIL signaling components in our model. Furthermore, TRAIL-induced apoptosis is capable of proceeding independently of protein neo-synthesis, which suggests that baseline expression values for TRAIL signaling proteins is sufficient to predict the capacity of a cell to undergo TRAIL-induced apoptosis. The one obvious omission to the model, which may resolve any prediction error, is quantitative input for the TRAIL decoy receptors (DcR1 and DcR2). However, the level of decoy receptor expression in glioma cells is reportedly very low and therefore unlikely to account for TRAIL resistance . Of courpoptosis . Improvipoptosis . Howeverpoptosis , 69.Cell line responsiveness to TMZ and TRAIL varied substantially, with individual cell lines frequently responding differently to TMZ and TRAIL monotherapy. This gave us the opportunity to assess whether our model could be exploited as a treatment decision tool. The prediction accuracy of our model was 82%, with treatment recommendations correct 9 times out of 11. However, it should be noted that few cell lines responded well to either treatment, highlighting once again the profound resistance of glioma cells to apoptotic stimuli and the need for more effective and rational combination therapies. In recent years, there has been an abundance of studies reporting on mechanisms to overcome apoptosis resistance in GBM for both established and novel therapies –32. For nd generation receptor ligands [Strikingly, we were also able to predict cell line sensitivity to TMZ and TRAIL upon their co-treatment with ABT-737 with 100% accuracy, highlighting that the consequences of a targeted intervention are captured by our approach; this finding was further validated by siRNA depletion of Bcl-2 and Bcl-xL. In light of the recent FDA approval of the first Bcl-2 inhibitor, Venetoclax (ABT-199), for patients with chronic lymphocytic leukemia with the 17p deletion, as well as the encouraging phase I-III clinical trials in development for Venetoclax as a monotherapy and/or combination therapy for various types of cancer , our cap ligands , 72, it With the era of personalized medicine looming, our approach, if validated translationally in subsequent studies, could help to stratify patients for TRAIL mono- or combination therapies. Such translational studies would initially require use of large-scale quantitative protein data from fresh or archived GBM tissue as well as the identification of high quality antibodies for the use of high through-put screening methods, like reverse phase protein or tissue micro arrays. For a clinical setting, our model could be refined to focus on cancer stem cell populations, believed to be the tumor initiating cells that drive the growth, progression and invasion of GBMs as well as their post-treatment recurrence –75. In tIn conclusion, we have described the first pre-clinical proof-of-principle study that merges quantitative protein-based analysis of apoptosis signaling networks with data- and knowledge-driven mathematical systems modeling to predict treatment responsiveness of GBM cell lines to various apoptosis-inducing stimuli. Furthermore, we have shown that a systems biology-based approach could assist in personalizing treatment decisions in GBM to optimize cell death induction.TMZ was purchased from Sigma-Aldrich Ireland Ltd . Human recombinant TRAIL was purchased from Enzo Life Science (UK) Ltd . ABT-737 was purchased from Biorbyt , and Bcl-2 and Bcl-xL esiRNAs were purchased from Sigma-Aldrich Ireland Ltd .2.GBM cell lines A172, U87, U251, U343 and U373 are commercially available from the ATCC. GBM cell lines MZ18, MZ51, MZ294, MZ304, MZ327, and MZ256 were kindly donated by Professor Donat Kögel of Johann Wolfgang Goethe University Hospital, Frankfurt, Germany and have previously been characterized . The celhttp://rsb.info.nih.gov/ij). For each protein, the integrated density of the signal was measured, corrected for background signals and normalized to a β-actin loading control. Standard curves from HeLa cell extracts (5-20 mg) were run concurrently with the GBM cell line extracts to ensure linearity of the signal detection range. The absolute concentration of most proteins of interest was previously determined in HeLa cells [In order to determine the basal expression level of apoptotic proteins, GBM cell lines were homogenized in lysis buffer containing 0.5 mmol/l Tris-HCl (pH6.8), 2% SDS (w/v), 10% glycerine (w/v), and protease and phosphatase inhibitor cocktails (Sigma-Aldrich). After determining the protein concentration of samples using a BCA protein assay , 20 μg samples were boiled in gel-loading buffer and separated on 10-15% SDS-PAGE gels. Proteins were transferred to nitrocellulose membranes using the iBlot gel transfer device . The membranes were blocked in 5% non-fat milk in TBST for 1 h at room temperature prior to being incubated with primary antibodies overnight at 4°C. The following primary antibodies were used: rabbit polyclonal Apaf-1 ; rabbit polyclonal Bak ; rabbit polyclonal Bax ; mouse monoclonal Bcl-2 ; mouse monoclonal Bcl-xL (Cat# sc-8392); rabbit polyclonal Bid ; rabbit monoclonal Bim ; rabbit polyclonal caspase-3 ; mouse monoclonal caspase-8 ; rabbit polyclonal caspase-9 ; mouse monoclonal cFLIP ; mouse monoclonal FADD ; mouse monoclonal Mcl-1 ; mouse monoclonal Noxa ; rabbit polyclonal PUMA ; mouse monoclonal SMAC/DIABLO ; and mouse monoclonal XIAP ; and mouse monoclonal β-actin . Membranes were then washed three times with TBST for 5 min prior to being incubated with peroxidase-conjugated secondary antibodies  for 1 h at room temperature. Protein bands were visualized using the Immobilin western chemiluminescence HRP substrate (Millipore) and images were captured using a LAS-4000 imager equipped with a cooled 12 bit digital CCD camera . To guarantee accurate quantifications, special care was taken not to over-expose the protein bands. Densitometry was carried out on 12-bit raw images using ImageJ 1.4.10 software  on ice for 20 min, cells were incubated with a mouse monoclonal antibody for DR4  or DR5  for 30 min, pelleted and washed x 3 with PBS, and then incubated with a secondary anti-mouse FITC-conjugated antibody  for 30 min in the dark. Controls were stained with secondary antibody only. FITC was excited at 488 nm and fluorescence emission was collected in the FL1 channel through a 520 nm band-pass filter. A total of 1 × 104 gated cells were acquired for each cell line. The relative expression of DR4 and DR5 was determined by comparison of specific staining intensities compared to individual cell line negative controls. For cell death measurements, flow cytometry was used to assess the number of AnnexinV+ cells following treatment with TRAIL (100 ng/ml) or TRAIL+TMZ (150 μM) in the presence or absence of zVAD-fmk  for 96 h as well as the number of PI+ cells following transfection with Bcl-2/Bcl-xL siRNA and treatment with TRAIL (100 ng/ml) for 48 h. Briefly, cells were seeded into 6-well plates and allowed to adhere overnight. Following appropriate treatment conditions, cells were pelleted and incubated in 100μl of binding buffer  containing AnnexinV- FITC conjugate (5 μl/ml)  for 10 min on ice in the dark or in DMEM containing PI (1.33 μg/ml) for 15 min at 37°C. FITC was excited at 488 nm and fluorescence emission was collected in the FL1 channel through a 520 nm band-pass filter. PI was excited at 561 nm and fluorescence emission was collected through a 605/40 nm band-pass filter and a 570 nm long pass filter. A total of 1 × 104 gated cells were acquired for each cell line.The basal surface expression of DR4 and DR5 were assessed using a BD LSR II flow cytometer (BD Biosciences). Briefly, 1 × 10To downregulate Bcl-2 and Bcl-xL protein expression, we used endoribonuclease-prepared small interfering RNAs (esiRNAs), which are comprised of a heterogeneous mixture of siRNAs that all target the same mRNA sequence . Briefly, MZ18 and MZ304 cells were transfected with 1 μg of Bcl-2 esiRNA and 1 μg of Bcl-xL esiRNA for 4 h using LipofectamineTM 2000 in antibiotic-free Opti-MEM medium . Following this incubation period, the transfection mixture was replaced with fresh DMEM and the cells were allowed to recover for 48 h, after which they were used for experiments.GBM cell lines were seeded into 96-well plates (2000 cells/well) and allowed to adhere overnight. To validate cell line responsiveness to TMZ and TRAIL monotherapy, cells were treated at clinically-informed “maximum effect” (Emax) conditions and accordingly were treated with TMZ (150 μM) or TRAIL (100 ng/ml) for 96 h. To confirm cell lines responsiveness to TRAIL and TMZ combined therapy, cells were treated with TRAIL (100 ng/ml) and TMZ (150 μM) for 96 h. To substantiate the sensitization of cell lines to TRAIL or TMZ with ABT-737, cells were either treated with TRAIL (100 ng/ml) in the presence or absence of ABT-737 (1 μM) for 48 h or with TMZ (150 μM) in the presence or absence of ABT-737 (1 μM) for 48 h. Following treatment, cells were incubated with thiazolyl blue tetrazolium bromide  for 4 h at 37°C, after which the medium was aspirated and replaced with DMSO (200 μl). The absorbance of each sample was measured at 560 nm using a microplate reader . MTT is a yellow tetrazole that is converted to dark blue/purple formazan by mitochondrial dehydrogenase of living cells. Thus, the absorbance value for each sample was considered proportional to the number of viable cells.MZ294 and U251 cells were stained with Hoechst 33258 (1 μg/ml) for 20 min. Changes in nuclear morphology were detected by epifluorescence microscopy (Nikon Eclipse TE300). Normal bright field images were taken to examine morphological changes, such as cell shrinkage and membrane blebbing.DEVDase (Caspase-3-like) activity was determined fluorometrically using carbobenzoxy-Asp-Glu-Val-Asp-7-amino-4-methyl-coumarin (DEVD-AMC) as substrate. Cleavage of DEVD-AMC to liberate free AMC was monitored by measuring fluorescence after 1 and 2 h intervals. Protein content was determined using Pierce Coomassie Plus Protein assay reagent . Caspase activity is expressed as a change in fluorescent units per hour and per microgram protein.P-values are indicated in the figure panels and legends.Student's t-test or one-way ANOVA with post hoc Bonferroni test were performed to identify statistically significant differences between treatment groups. All data processing and analysis was performed using a programming code developed for MATLAB 2007b , equipped with the statistics toolbox; a detailed description of the procedures has previously been published . In brie


Kocuria. Carotenoids from this strain were extracted by methanol. MTT assay for extracted carotenoid was carried out against seven cancer cell lines belonging to breast, lung and prostate cancer with negative control of fibroblast cell line through six concentration levels to find out IC50. Based on statistical analysis of data from MTT assay, IC50 of 1, 4 and 8 mg/ml for MCF-7, (A549 and MDA-MB-468) and MDA-MB-231 respectively. Additionally, qualitative carotenoid determination was carried out using spectrophotometric method in 300-600 nm and thin layer chromatography, respectively. According to the obtained results from mass spectrophotometry, absorption spectrum of strain QWT-12 is similar to the absorption spectrum of the carotenoid neurosporene.Halophilic prokaryotes are extremophile microorganisms that grow optimally in media containing salts and almost appeared pigmented. Many of them contain high concentrations of carotenoids. Amongst 15 strains of halophilic prokaryotes isolated from industrial tannery wastewater in Qom, a Gram-stain-positive coccoid, aerobic, non-endospore-forming, halotolerant bacterium designated as strain QWT-12 showed high capacity in the production of carotenoids in a wide range of culture medium factors. 16S rRNA gene analysis showed that this strain belongs to the genus  Animalal., 2009). Some oal., 2009). In thial., 2009). Additial., 2009) and amoal., 2012). Many oal., 2012). With tal., 2012), in thiAll the materials and reagents for bacterial cultures and TLC, were purchased from Merck .Water samples were obtained from tannery wastewater of a company in Qom, Iran. Approximately 1 ml of each sample was poured in 20 ml of nutrient broth medium with 20 % solar salt. The pH was adjusted to 7.2. The flasks were incubated on a shaker at 37 °C until turbidity appeared. The turbid cultures were streaked on nutrient agar 20 % with solar salt and incubated at 37 °C. After 14 day of incubation, yellow colonies were selected and purified  NaCl. Gram reaction, shape and color of colony, catalase, indole formation and oxidase activities, nitrate reduction and tween 80 hydrolyses were checked as recommended by Smibert and Krieg (1994). CarbonGenomic DNA from strain QWT-12 was prepared using the method described by Marmur (1961). The 163, Na2SO4 and KCl in TSB medium were also evaluated to optimize cell growth and pigmentation. Growth curve of strain QWT-12 was drawn in rational to OD and cell pigmentation to find proper time point for pigment extraction.Five culture media including TSB, NB, HM, SW and MH mediums were used to find the proper medium for pigment production. The cells were grown at 37 ºC for 24 hours in each medium. After evaluation of optical density and optical pigmentation, TSB medium culture was selected as proper medium to optimize carotenoid production. Additionally, gradient of NaCl, NaNOIn order to pigment extraction, the yellow pigmented bacterial isolates were grown in TSB with 3 % (w/v) of salt, in a rotary shaker at 180 rpm in 37 °C for three days. After three days, cells were harvested by centrifugation at 8,000 g for 15 min. Then the pellet was washed with sterile distilled water and spin for 15 min at 4,000 g. Five ml of methanol was added to the pellet and suspended it. Then it was incubated in a water bath at 60 °C for 15 min until all visible pigments were extracted and centrifuged at 4,000 g for 15 min. The colored supernatant was separated, then it was filtered through Whatman no. 1 filter paper. The carotenoid extracts were analyzed by scanning the absorbance in the wavelength region of 450 nm using the spectrophotometer. The total carotenoid content in the methanol extract was estimated by measuring the absorbance at λmax (450 nm) . For MT2. After 24 h of growth, the cells were transferred into a 96-well plate (2000 cells/well) and treated with the carotenoid extracts for 48 h. The carotenoid extracts were dissolved in RPMI medium and served as a stock solution that was later diluted to have a final solvent concentration.Human cancer cell lines that were used in this project were DU145, PC3, LNCaP for prostate cancer and MDA-MB-468, MDA-MB-231, MCF-7 for breast cancer and A549 for lung cancer. As control cell line we used human skin fibroblast cell line Hu02. All cell lines were purchased from IBRC Cell bank. The cells were grown as a monolayer in an RPMI 1640 medium (Gibco) supplemented with 10 % (v/v) fetal bovine serum (FBS) (Gibco), 1.0 mM sodium pyruvate (Sigma) and 2 mM L-glutamine (Sigma) at 37 °C in a humidified atmosphere of 95 % air and 5 % COThe MTT-assay was used to evaluate the carotenoid extract effect on mentioned cell line's cell viability, and the results were expressed as viable cell percentages with respect to the control. Approximately 2000 cells/well were seeded onto a 96-well plate and allowed to adhere for 24 h. Three replicates of each plate were incubated with different concentrations of carotenoid extracts , and viability was recorded at 48 h. After treatment, the medium was removed, and 20 μl of 5 mg/ml solution of MTT (Sigma) in PBS were added to each well. The plate was then incubated for 3 h at 37 °C. Finally, the medium was removed, and 200 μl of the DMSO (Merck) were added in each well to solubilize the blue formazan. Dye absorbance was measured at 560 nm ; Comparing solutions: regarding to online pigment libraries, stock solution of Neoxanthin, Violaxanthin and Neurosporene were considered to molecular structure alignment .A TLC plate, on which the pigment was developed, was sprayed with an absolute chloroform solution of 20 % antimony chloride (III). Mass spectrophotometry test has been carried out in order to align results with online library of similar carotenoids and finally determining the exact structure .Kocuria with more than 99.8 % similarity to Kocuria marina  NaCl. Lag phase of the bacterial growth was 15 h and after 23 h, growth reached stationary phase. Pigment production activity was undetectable during the early- and mid-exponential growth phase but started from the end of the exponential phase and continued in the stationary phase Figure 3. To dete3, Na2SO4 and KCl in TSB medium on cell pigmentation and OD were shown in Figure 53, Na2SO4 and KCl and 0. 5 M for NaCl.In Figure 4Kocuria carotenoid against seven cancer cell lines have been shown. Abbreviations located beside histogram are due to cell lines while Hu02 has been considered negative control. After calculation for the chi-square test significant IC50s of 1, 4 and 8 mg ml of extracted pigments against MCF-7, (A-549 and MDA-MB-468) and MDA-MB-231 cancer cell lines were obtained respectively. The extracted pigments have no effect on viability of fibroblast cell line and only 20- 30 percent decrease the viability of prostate cancer cell lines.In Figure 6Extracted pigments were separated into several spots by TLC. The orange colored pigment which had the highest affinity for the mobile (solvent) phase migrated towards the top of the TLC, and had Rf equal to the obtained Rf from stock solutions. Two other extracted pigments were also present, including the yellow pigments which have the higher affinity for the stationary (silica) phase. The carr-price test for all spots confirming that these pigments are carotenoids. The UV/light absorption spectra of two yellow pigments were identical to those violaxanthin and neurosporene Figure 7. The absIn ethanol:For both two pigments, it was found that N=9 and according to chemical structure of neoxanthin (N=8), violaxanthin (N=9) and neurosporene(N=9) we could estimate that these extracted pigments are violaxanthin or neurosporene.Kocuria sp. strain QWT-12 is neurosporene. To more structure elucidation of the extracted pigments, mass spectrophotometry was done for the yellow pigment, as it seems to be the major part of the pigment extraction. Figure 8Dunaliella, producer of beta-carotene, Salinibacter ruber, rich in salinixanthin . There al., 2012). Nazirial., 2012) exhibitKocuria sp. strain QWT-12 has pigment likewise neurosporene pigments as major carotenoid pigment. Although neurosporene is an intermediate in carotenoid synthesis, its accumulation exhibited in a mutant strain of Rhodobacter capsulatus . This cal., 1988). in vivo studies confirm chemoprevention effects of some carotenoids but also in vitro studies support it by many tissue modeling anticancer activity . Based al., 2010). Caroteal., 2003). Abbes Although fermentation of carotenoids through microbial cell factory has not reached yet to yield due to plant natural carotenoids but a great spectrum of microbial carotenoids is identified and extracted increasingly and our research established cutting edge ideas toward both preclinical studies and screening for anticancer activity of natural products.


We observed that the degree of relative sensitivity to chemotherapeutic agents was associated with the status of neurofibromin in these cell line models. Using a combination of agents that target MEK and mTORC1/2, we effectively silenced RAS/PI3K/MEK/mTOR signaling in vitro. Moreover, we employed RNAi against NF1 to establish that MPNST drug sensitivity is directly proportional to relative level of intracellular neurofibromin. Thus, two-drug combinations that target MEK and mTORC1/2 are most effective in halting the RAS signaling cascade, and the relative success of this and related small molecule interventions in MPNSTs may be predicated upon the molecular status of neurofibromin.Malignant peripheral nerve sheath tumor (MPNST) is a type of soft-tissue sarcoma strongly associated with dysfunction in neurofibromin; an inhibitor of the RAS pathway. We performed high-throughput screening of an array of FDA approved and promising agents in clinical development both alone and in combination at physiologically achievable concentrations against a panel of established MPNST cell line models. We found that drugs targeting a variety of factors in the RAS pathway can effectively lead to cell death  NF1 gene cause neurofibromatosis type 1 (NF1), an autosomal dominant condition characterized by formation of benign tumors and an increased risk of developing Malignant Peripheral Nerve Sheath Tumors (MPNST) [Inactivating mutations in a copy of the  (MPNST) . MPNST i (MPNST) , 5. Unre (MPNST) .NF1 gene encodes the protein neurofibromin that functions as a negative regulator of RAS signaling through its GTPase activating protein (GAP) activity, converting active RAS-GTP to inactive RAS-GDP, which is essential for the regulation of cell proliferation and differentiation [NF1 lead to elevated levels of RAS-GTP and thus increased RAS signaling [NF1 allele by somatic mutation, rendering the cell deficient in neurofibromin activity. Because such cells have increased RAS activity, most approaches to developing treatments for MPNSTs have been focused on inhibiting targets downstream of RAS such as pathways associated with mTOR or MEK/MAPK [The ntiation –9. Conseignaling , 11. BotMEK/MAPK –15. EffeMEK/MAPK , 17. ThiMEK/MAPK , 18–21.Utilizing an array of FDA approved drugs or agents in clinical development that included current standard of care for MPNST and those that target factors downstream of RAS, we sought to evaluate if these small molecules, alone or in combination, could not only reduce downstream RAS signaling in cell line models of MPNST, but also whether that blockade would lead to cell death to support further translation towards clinical trials. Moreover, we sought to examine the association of residual neurofibromin with the phosphorylation cascade downstream of RAS and determine the role played in the sensitivity of MPNST cells to chemotherapeutic agents. This study is novel in its approach to interpreting drug screening results in the context of genomic findings revealing a neurofibromin level contextual mechanism for RAS pathway drug sensitivity in MPNSTs.in vitro and animal studies have identified several of these agents – bortezomib [p < 0.00001, Mann–Whitney–Wilcoxon test).We screened 59 agents with diverse mechanisms of action at multiple clinically achievable concentrations with the use of high throughput assays of cell viability with CT-Glo, which provides a luminescence signal in proportion to ATP concentration . Fractiortezomib , gemcitartezomib , romideprtezomib , and trartezomib  – as proFrom amongst the active single agents, we focused on those that either demonstrated high efficacy or acted upon targets of the RAS pathway. Seventy-two two-drug combinations were tested at 25 concentrations across the 4 MPNST cell lines. Figure . All druThe efficacy and synergy for two-drug combinations against MPNST cell lines fell into a few distinct groups. The most highly synergistic combinations contained HDAC, mTOR, MEK, PI3K, inhibitors. Of particular interest clinically are combinations that demonstrate strong synergism and efficacy at lower concentrations relative to Cmax. Translating such agents to the clinic could minimize toxicity and maximize the combination opportunities. The strongest synergism observed in this study involved combinations that included the dual pan-PI3K/mTORC1/2 inhibitor GSK2126458  and the HDAC inhibitor romidepsin. Though certainly active, we chose not to follow-up with GSK2126458 due to clinical evidence that the drug cannot be tolerated at effective dosages. Romidepsin, like other potent HDAC inhibitors, is frequently active in cell line models, and also because of this class's pleotropic effects we did not chose to further explore this compound. However, because romidepsin continues to demonstrate activity and synergistic potentiation of INK128 at concentrations that are <0.1% of Cmax, we believe that further work should be done to investigate this potential relationship.in vitro and in animal models, particularly with regard to observed synergism in combinations that target mTOR or MEK, though prior literature is limited regarding mechanistic effects of this dual inhibition [With regard to combinations that inhibit the RAS pathway, we focused in on pan-PI3K inhibition with BKM120, MEK inhibition with cobimetinib or trametinib, and dual mTORC1/2 inhibition with INK128. We initially hypothesized that the RAS pathway in MPNSTs was balanced between the flux of PI3K and MEK. We were surprised to see that combinations of PI3K+MEK inhibitors demonstrated, at best, additive effects Figure . Indeed,hibition , 27–29.As suggested by the relative response differences between cell lines, for many of the two-drug combinations, there were striking sensitivity differences between the cell lines. Indeed, with few exceptions, we were able to reach FAs between 0.8 and 1.0 in 2 out of 4 cell lines at concentrations well below Cmax. However, the FA values for the other two cell lines reached values of at most 0.6, with relatively minimal treatment effects observed with some combinations . The relNF1 in all cell lines except STS26T, confirming those already reported for SNF94.3 and SNF96.2 , but again no additional NF1 mutation was found; SNF10.1 has a R1276X nonsense mutation and hemizygous deletion of the other allele; and SNF96.2 has two null alleles (1-bp frameshift mutation plus loss of heterozygosity). Heterozygous copy number loss of SUZ12 was identified in SNF10.1, SNF94.3, and STS26T. A missense variant in SUZ12 with loss of heterozygosity was identified in SNF96.2. Homozygous deletion of CDKN2A was detected in SNF10.1 and SNF96.2. Copy neutral LOH was present in much of the genome in SNF96.2 including chromosome 17 which contains the TP53 transcript. Overall, these mutations are analogous to the lesions also observed in sequencing data obtained from MPNST patient tumor samples [NF1 between cell lines, is delineated along the two groups defined by the observed phenotypic differences. The lines with homozygous loss of NF1 (SNF10.1 and SNF96.2), were significantly more sensitive to chemotherapeutic and RAS targeted treatments than those without complete NF1 loss, SNF02.2 and SNF94.3.With the goal of identifying a biomarker for the observed drug sensitivity differences within our cell lines, we performed whole exome sequencing and single nucleotide polymorphism (SNP) arrays of each of the tested cell lines. We additionally performed genomic characterization of the sporadic MPNST cell line STS26T which was utilized as a comparator in later functional experiments. We found mutation of 2 Figure . Specifi samples , 31. ImpNF1 genotypes at the protein level, we performed Western blots with lysates from the MPNST cell lines. At approximately 240 kD, the predicted MW of neurofibromin, we observed two distinct bands for SNF02.2, SNF94.3 and STS26T and no bands for SNF10.1 and SNF96.2. This is in accordance with the latter cell lines having homozygous loss of the NF1 gene and is consistent with that previously observed in other labs . The in vitro Raf1-RBD binding affinity for the active RAS-GTP is 20 nM. Thus, an Anti-RAS blot of the resulting pull-down allows active RAS to be visualized with high specificity. Upon treatment of STS26T lysates with saturating amounts of GTP, we observed a remarkable increase in active RAS-GTP compared to GDP treated or untreated STS26T lysates. Bands similar to the positive control appear in SNF10.1 and SNF96.2, while considerably lower signal is observed in SNF02.2 and SNF94.3 demonstrating that the residual NF1 levels lead to an increase in RAS activation in these cell line models. This is consistent with the dogma that neurofibromin has a large role in meditating RAS activity in Schwann lineage cells  and p70 S6 Kinase(Thr421/Ser424) (S6K) which were higher in SNF96.2 than SNF94.3 with y Figure . As expey Figure . HoweverWe achieved effective suppression of pathway signaling relative to control following the treatment with the use of two-drug combinations. A striking difference between the two cell lines revealed itself under those conditions. While the signaling cascades in SNF96.2 could be equally reduced when combining BKM-120+INK128, BKM-120+trametinib, or INK128+trametinib, SNF94.3 resisted profound changes to pathway flux in all cases aside from INK128+trametinib Figure . Interesp = 0.0164 and 0.0026, respectively). Similarly, S6K (Thr421/Ser424) had significantly greater phosphorylation signal at baseline in SNF96.2 (p = 0.0037). S6K in SNF96.2 was far more sensitive to trametinib than in SNF94.3 (p < 0.0001). Phosphorylation of Akt (Ser473) was considerably higher in SNF94.3 at baseline compared to SNF96.2 (p = 0.0001). Interestingly, in response to trametinib treatment, phosphorylation of AKT was increased relative to DMSO in SNF94.3; this response was significant compared to SNF96.2 where no increase in AKT was observed (p < 0.0001). This apparent compensatory response to reduction in MEK signaling may play a role in the overall tolerance to perturbation observed in SNF94.3. Indeed, even though phosphorylation of Erk1/2 (Thr202/Tyr204) was reduced in response to trametinib for both SNF94.3 and SNF96.2, the other factors in SNF94.3 were essentially unchanged compared to control. This is not the case for SNF96.2 where a decrease in phosphorylation was detected in the downstream factors. Phosphorylation signal of mTOR (Ser2481) was significantly higher in SNF94.3 overall regardless of treatment (p < .0001). INK128 was effective in reducing mTOR phosphorylation in both cell lines relative to control. It's notable that AKT and ERK were also substantially impacted by inhibition of mTOR in both cell lines, though ERK was impacted to a slightly greater degree in SNF96.2 when compared to SNF94.3. The pathway is essentially shut down with treatment of both INK128 and trametinib when comparing phosphorylation signals to baseline. While the absolute difference seems relatively minor, the percentage difference between INK128 and INK128+trametinib is considerable, particularly when considering the phosphorylation levels at baseline.Overall, we found that the mean phosphorylation signals for SNF 94.3 were higher than those for SNF 96.2 with some exceptions Figure . As alluNF1. Following transfection of SNF02.2, SNF94.3, and STS26T with an siRNA construct mapped to the NF1 gene, we were able to observe a notable reduction in the neurofibromin band relative to siRNA control construct as detected on Western blot. This indicates that NF1 expression is indeed reduced in response to siRNA transfection.Having established an association between the relative level of residual full-length neurofibromin and the difference in S6K signaling downstream of RAS we wanted to determine the degree of causality via siRNA targeting of NF1 alleles, further knockdown of this gene would not be expected to cause further impact on the cell line's phenotype. As anticipated, depletion of NF1 in SNF94.3 and STS26T resulted in a considerable decrease in IC50s relative to control siRNA treatment ; the PI3K inhibitor BKM120; the microtubule inhibitor docetaxel; the HDAC inhibitor panobinostat; and the CDK inhibitor palbociclib there is little deviation . Their pNF1-associated tumors may prove a promising avenue for investigation.We found that two-drug combinations can significantly dampen phosphorylation downstream of RAS and effectively reduce cell viability, but single agents were not nearly as effective in diminishing downstream pathway phosphorylation or cell proliferation to any considerable degree. Amongst the active agents are INK128 (Sapanisertib), a dual mTORC1/2 inhibitor which is currently being explored in a phase II trial (NCT02601209) with an MPNST cohort and with additional preclinical data . INK128 trans retinoic acid (ATRA) [The drug synergism we have observed when combining MEK inhibitors with other drugs in addition to INK128  lends fud (ATRA) , BMP-2 id (ATRA) , the BRDd (ATRA) , inhibitd (ATRA) , and othd (ATRA) . AltogetIn addition to MEK inhibition, combinations of agents that contained the dual mTORC1/2 inhibitor INK128 demonstrated considerable synergism. The likely importance of mTOR in our MPNST models is bolstered by the remarkably strong signal we observed for the mTOR repressor PRAS40 when assaying the phosphorylation status of an array of factors in the RAS/AKT/MEK/mTOR axis. Recent advances in understanding PRAS40 suggest this factor plays a significant role in regulation of mTOR in normal cellular homeostasis as well as disease contexts. A newly resolved crystal structure of Arabidopsis mTORC1 shows direct binding of PRAS40 to mTOR at multiple residues including the rapamycin-binding pocket . AbnormaNF1 and PRC2, respectively. Of course, due to the multitude of targets affected by HDAC inhibitors, more specific studies are required to realize this mechanism [Given additional oncogenic mutations outside of the RAS/MEK/mTOR axis, it may still prove necessary to combine agents that target pathways parallel or perpendicular to the RAS signaling cascade for truly effective clinical outcomes. Thus, this relatively homogenous malignancy may need 3+ agents to address its pathophysiology. As the importance of PRC2 subunits SUZ12 and EED are further revealed in the context of MPNST, treatment strategies that include targeting of epigenetic regulatory factors may be of interest. We included the Class I/II HDAC inhibitor romidepsin in our screens and observed fairly strong effects, particularly with regard to synergism when combined with INK128. This provides some credence to one model of MPNST oncogenesis, which primarily involves a combination of RAS deregulation and epigenetic dysregulation, due to lesions in echanism .NF1 status, in terms of levels of function, appears to mediate the optimal downstream pathway targets with MEK inhibition being notably important in the case of NF1 heterozygous loss. Relatedly, we expected that due to the putative parallelism of MEK and PI3K in the canonical RAS pathway, PI3K inhibition and MEK inhibition might be interchangeable as long as a related factor, such as mTOR, was being sufficiently targeted. However, at both the level of drug synergism and RAS pathway modulation, PI3K inhibition was insufficient in providing notable effects in cells with heterozygous NF1 loss. Throughout this study, we observed a series of correlations between response and the observed state of NF1 and the proportional impact on RAS activation. We did not directly rule out the possibility that CDKN2A, which also differed in status between cell lines, may also play a role in these sensitivity differences. However, it has been previously reported that mutations in CDKN2A (among others) plays a role in resistance to paclitaxel [NF1 in cell lines either wildtype for or with het-loss of this gene resulted in a shift in the sensitivity phenotype toward that of the tumor cells with complete ablation. However, due to the incomplete knockdown of NF1 with RNAi, more complex molecular tools may be essential for further confirmation of this causal relationship. Nonetheless, we hypothesize that residual NF1 plays a more significant role in MPNST drug resistance than other identified factors based on these findings.We extensively investigated mechanisms underlying drug sensitivity at both the genetic and the protein level. Importantly, clitaxel . We do nNF1 mutation in SNF02.2 and SNF94.3 is unusual given that both were derived from metastatic sites from high-grade MPNSTs; this challenges the paradigm that NF1 MPNSTs have both NF1 alleles mutated. However, it could be that the residual neurofibromin has somewhat reduced GAP activity due to its inclusion of the alternative exon within the GAP domain, failing to meet a high enough level of RAS control [NF1 heterozygosity, to allow MPNST development. Such a model may prove useful for the evaluation of novel treatment regimens and provide a glimpse into how systemic chemotherapy may impact non-tumor cells. Relatedly, the importance of MEK targeting observed in this study is particularly timely with the recent identification of single agent MEK inhibition being the most active therapy for plexiform neurofibromas, precursor lesions for MPNST [The experimental conditions of this study potentially provide the foundation of a model system for assessing the impact of small molecule treatment regimens on two different cell types that may simultaneously exist in MPNST and/or neurofibromatosis patients. The lack of a second  control . Or, addor MPNST . SelumetAgents used included both cytotoxic and targeted agents, most obtained directly from Selleck Chemicals , Sequoia Research Products , and Sigma-Aldrich . Agents were assessed at clinically achievable concentrations, which were extrapolated from published human pharmacokinetic data, with a focus on Phase I trials, ideally containing pediatric patients - see The MPNST cell lines SNF02.2, SNF94.3 and SNF96.2 have been previously published by M. Wallace and are available at ATCC , 21; SNFScreening methodology developed previously , 54 was Individual agents were screened across at least 4 cell lines. Candidates were characterized at a minimum of 3 concentrations – one near physiological maximum (Cmax), one at 20% this level, and another at 4%. For drugs of interest, full dose response curves were generated, and IC50 values were determined using a suitable sigmoidal equilibrium model regression fit using the XLfit Excel add-in version 5.5 .A/FU= (D/Dm)m, where FA and FU = fraction affected and fraction unaffected, respectively (FU = 1–FA), D = dose of the drug, Dm = dose required for 50% effect , and m = exponent signifying each dose response curve's sigmoidicity. CIs were determined by the isobologram equation for mutually nonexclusive drugs with different modes of action: For x% inhibition, CI = (D)1/(Dx)1 + (D)2/(Dx)2 + (D)1(D)2/(Dx)1(Dx)2, where (Dx) and (D) represent the single agent concentration and the two-drug combination concentration, respectively, at which Drug1 and Drug2 are isoeffective, inhibiting by x%. All FA and CI calculations were performed in a custom built Microsoft Excel template using the XLfit (IDBS) add-in for fitting linear regressions to dose response data and for calculating Dm and m.Two-drug combinations were screened in 384-well plates and evaluated at 25 discrete concentration ratios in a 5 × 5 matrix to assess efficacy and synergy, as described previously . Drug sySequencing libraries were generated from 1 μg of DNA using the Kapa Library Preparation Kit . Library DNA size and quality parameters were evaluated with the Agilent BioAnalzyer , and equimolar amounts were used for whole-exome enrichment using the Roche NimbleGen SeqCap EZ Exome Library v3.0 kit, which targets 64 Mb of genomic DNA, covering over 20,000 genes . Each library was quantitated with the Kapa Library Quantification Kit and each enriched DNA library was sequenced on an Illumina NextSeq 500 v2 sequencer to generate approximately 100 million 75-base paired-end reads for a final average target coverage depth of 100X. The raw sequence data were demultiplexed using the Illumina bcl2fastq2 software .http://www.ncbi.nlm.nih.gov/clinvar/) and the Collection Of Somatic Mutations In Cancer (COSMIC) [Sequence reads were aligned to the reference human genome (hs37d5) with the Burrows-Wheeler Alignment Tool (BWA), and duplicate identification, insertion/deletion realignment, quality score recalibration, and variant identification were performed with the Picard toolkit and Genome Analysis ToolKit . Sequence variants were annotated to determine genic context  using ANNOVAR. Additional contextual information was incorporated, including allele frequency in other studies such as 1000 Genomes, the NHLBI Exome Sequence Project, in silico functional impact predictions, and observed impacts from databases like ClinVar ((COSMIC) –57.Copy number variation (CNV) and loss-of-heterozygosity (LOH) status were obtained with the CytoScan HD Assay , which was performed on each cell line starting with 250 ng of DNA. The CytoScan HD assay uses 750,000 SNP probes and 1.9 million non-polymorphic probes to report genome-wide copy number aberrations at a resolution of 25-50 Kb. In addition, the assay reports genome-wide LOH, including copy-neutral LOH when applicable. The data generated from the assay were normalized, copy number status calculated, and the data reviewed for quality using the Chromosome Analysis Suite (ChAS) v3.0 (Affymetrix).® Blocking Buffer (TBS) .Western blots were performed using Novex and NuPAGE SDS-PAGE gel systems . NuPAGE 3-8% Tris-Acetate gels were used to resolve the 240kD neurofibromin. RAS was visualized on Novex 4-12% Tris-Glycine gels. Proteins were transferred from gels to nitrocellulose membranes using a wet transfer system. Membranes were blocked and antibodies were diluted using Odyssey® 647  (Santa Cruz Biotechnology) - were used as loading controls. Secondary antibodies – to rabbit IgG  , and mouse IgG  (Rockland) allowed fluorescent visualization with the Odyssey® Imaging System (LI-COR). Western blot bands were quantified using Image Studio Lite Ver 5.2 (LI-COR).Primary antibodies – Neurofibromin Antibody (D) , Neurofibromin Antibody (H-12) , and Neurofibromin Antibody (McNFn27a)  - were used to detect neurofibromin at a 1:200 dilution . Primary antibodies - β-Actin D6A8   and α-actinin Antibody (H-2) Alexa Fluor® Akt Signaling Antibody Array Kit  . Array slides were visualized using the Odyssey® Imaging System (LI-COR) and signals were quantified using Image Studio Lite Ver 5.2 (LI-COR). Subsequent data normalization was conducted in a custom-built Microsoft Excel template. Pathway diagrams were generated using Cytoscape v 3.2.1 .Antibody array experiments were performed using the PathScanFor each phosphosite, relative fluorescent signal was normalized by division of the average of three positive control values. A further normalization was conducted between the two antibody array experiments by subtracting the mean and dividing by the standard deviation of all expression values of each antibody within each experiment in order to make the antibody expression values consistent and comparable between the two experiments for subsequent analysis. We next fitted an ANOVA model for each antibody expression with three covariates: drug treatment, cell line, and experiment. Backward elimination was used to obtain the most parsimonious ANOVA model. Statistical comparison between cell lines SNF94.3 and SNF96.2 was made by examining significance of each contrast test of the two cell lines based on the final ANOVA model of each antibody target. Bonferroni correction was used to correct for multiple comparisons from the combinations among four different treatments and five antibodies we closely examined for our study. That is, statistical significance level ≤0.0025 was used for inferring the cases with a significant expression difference between the two cell lines for each combination of drug treatment and antibody. Statistical analysis was conducted with SAS 9.4 .p-value determined, which allowed inference of significant sensitivity differences between cell lines.Relative response was determined by normalizing fraction affected (FA) values against LOG(concentration). Statistical comparison between drug relative response values for cell lines SNF02.2, SNF94.3, SNF10.1, and SNF96.2 was made through application of the Mann–Whitney–Wilcoxon test (two-tailed). The z-statistic was obtained and Cells were grown under serum starved conditions in modified EMEM with minimal supplements , and siRNA constructs were introduced using a polycationic lipid-based transfection reagent  which were both obtained from Santa Cruz Biotechnology, Dallas, Texas, USA. Neurofibromin was targeted with neurofibromin siRNA (h) . As a control, cells were treated with Control siRNA (FITC Conjugate)-A . All reagents were obtained from Santa Cruz Biotechnology.


Graft-versus-host disease (GVHD) and infection are major complications after allogeneic hematopoietic stem cell transplantation  and the leading causes of morbidity and mortality in HSCT patients. Recent work has demonstrated that the two complications are interdependent. GVHD occurs when allo-reactive donor T lymphocytes are activated by major histocompatibility antigens or minor histocompatibility antigens on host antigen-presenting cells (APCs), with the eventual attack of recipient tissues or organs. Activation of APCs is important for the priming of GVHD and is mediated by innate immune signaling pathways. Current evidence indicates that intestinal microbes and innate pattern-recognition receptors (PRRs) on host APCs, including both Toll-like receptors (TLRs) and nucleotide oligomerization domain (NOD)-like receptors (NLRs), are involved in the pathogenesis of GVHD. Patients undergoing chemotherapy and/or total body irradiation before allo-HSCT are susceptible to aggravated gastrointestinal epithelial cell damage and the subsequent translocation of bacterial components, followed by the release of endogenous dangerous molecules, termed pathogen-associated molecular patterns (PAMPs), which then activate the PRRs on host APCs to trigger local or systemic inflammatory responses that modulate T cell allo-reactivity against host tissues, which is equivalent to GVHD. In other words, infection can, to some extent, accelerate the progression of GVHD. Therefore, the intestinal flora’s PAMPs can interact with TLRs to activate and mature APCs, subsequently activate donor T cells with the release of pro-inflammatory cytokines, and eventually, induce GVHD. In the present article, we summarize the current perspectives on the understanding of different TLR signaling pathways and their involvement in the occurrence of GVHD. Allogeneic hematopoietic stem cell transplantation  is currently the only radical therapy for a wide range of benign and aggressive hematopoietic malignancies. However, the approach has limited efficacy due to lethal complications such as graft-versus-host disease (GVHD) and infection, the leading causes of morbidity and mortality in HSCT patients . GVHD isToll, which could activate the secretion of antimicrobial peptides in Drosophila melanogaster [Toxoplasma gondii) are cell surface receptors that primarily recognize bacterial structures. In contrast, TLR3 (receptor for double-stranded RNA and synthetic polysaccharides), TLR7 (receptor for single-stranded RNA), TLR8  and TLR9 (receptor for unmethylated cytosine-phosphorothioate-guanine oligodeoxynucleotides (CpG-ODN)) are mainly localized intracellularly and recognize nucleic acids of microbial or viral origin. All TLRs can activate the nuclear factor-κB (NF-κB) signaling pathway, subsequently upregulating the expression of adhesion molecules and cytokines, and eventually leading to inflammatory responses [TLRs constitute a family of transmembrane innate PRRs that are broadly expressed by both non-hematopoietic and hematopoietic cells . In mammnogaster . Thirteenogaster . For exanogaster . TLRs aresponses ,14,15.The inflammatory cascades initiated by innate immune responses contribute to the occurrence of GVHD . BesidesTrypanosoma), heat shock proteins and fibrinogen [LPS, also known as lipoglycan and endotoxin, is microbial breakdown products during pre-transplant conditioning and has been considered as a driver of GVHD in experimental models ,17. TLR4brinogen . Since hbrinogen .TLR4 gene mutations carried by both recipients and donors compared with recipients carrying the wild-type gene. However, the gene mutations are shown to have no effect on the transplant-related mortality, overall survival, and incidence of infectious complications. Due to the routine performance of bacterial gut decontamination in clinical HSCT patients, it may contribute, at least in part, to the observation of a strong effect of TLR4 signaling in mouse models and a weaker effect in human clinical studies.It has been demonstrated that intestinal permeability increases significantly in allo-HSCT patients who have received pre-transplant regimens such as chemotherapy and/or TBI . DisruptTLR4 (Thr399Ile) gene on both the patient and donor sides [TLR4, NF-κB, IL-6 and intercellular adhesion molecules 1 were significantly increased in patients with cutaneous cGVHD. The possible mechanism is the involvement of inflammation-mediated fibrosis in cutaneous cGVHD, which is mediated by TLR4-mediated NF-κB signaling [There is growing evidence on the role of TLR4 in the pathogenesis of GVHD, ranging from gene polymorphisms to expression levels. Emerging data show that polymorphisms in the gene encoding TLR4 are relevant to the susceptibility of aGVHD; interestingly, two single-nucleotide polymorphisms (SNPs) of TLR4, Asp299Gly and Thr399Ile, are relevant to the enhanced immune responses and increased genetic risk for aGVHD, but the association between the severity of aGVHD and the SNPs was not statistically significant ,28. A muor sides . Howeveror sides . BesidesOn the other hand, the disruption of LPS-induced TLR4 signaling can ameliorate GVHD. The depletion of certain bacteria by treatment with an LPS inhibitor or an anti-endotoxin-neutralizing antibody, such as metronidazole and ciprofloxacin, can remarkably attenuate the detrimental influence on GVHD . This caAs described above, TLR9 localizes within the endosomal compartment of immune cells and intestinal epithelial cells, and is activated by non-methylated CpG-ODN, which is derived from bacterial and viral DNAs such as malarial pigment hemozoin and herpes simplex virus DNA. The TLR9 signaling pathway has long been known to play a critical role in antitumor activity and immune responses; however, the underlying mechanism of the role of TLR9 in GVHD remains poorly understood. Since microbial breakdown products produced by conditioning regimens could include the TLR9 ligand CpG-ODN, the TLR9-mediated signaling pathway may play a critical role in the process of aGVHD . In factTLR9 gene levels in the occurrence of GVHD has gained attention for many years. Emerging data showed that two SNPs in the human TLR9 gene, T1486C and T1237C, have been confirmed to downregulate the expression of TLR9 mRNA [TLR9 gene variant is significantly lower than that in patients with the wild-type TLR9 gene. Therefore, it is reasonable to conceive that the TLR9 gene variant can protect patients from CpG-ODN-induced lethal GVHD. However, based on an analysis of TLR9 gene variants in 413 allogenic transplant patients and donors, Elmaagacli et al. [TLR9 gene variant at position 1486C had desirable outcomes, which was attributed to decreased transplant-associated mortality associated with GVHD [TLR9 gene, t1174 A/G (rs352139) and t1635 C/T (rs352140), influenced the risk of developing aGVHD and cytomegalovirus reactivation [TLR9 gene polymorphism can affect the occurrence or severity of GVHD.The role of LR9 mRNA . DuramadLR9 mRNA , suggestith GVHD . AnotherIn addition, increasing studies are performed to interpret the roles of key molecules involved in TLR9 signaling. It has been demonstrated that a single injection of the TLR9 agonist CpG-ODN greatly upregulated the production of TNF-α, IL-6 and IFN-γ, which was lethal in GVHD mice. In contrast, the blocking of TNF-α, but not IL-6 or IFN-γ, rescued GVHD mice from CpG-induced mortality . FurtherTLR gene polymorphisms. Based on the analysis of 305 HSCT patients, the genetic SNPs rs4833079 in TLR1, rs4837656 and rs17582214 in TLR4, rs10737416 in TLR5, rs6531656 in TLR6, and rs337629 in TLR10 were associated with the occurrence of aGVHD. Interestingly, two SNPs in the TLR5 gene, rs2800230 and rs2800237, were associated with cGVHD [+CD16+ cells and CD14+CD16− monocytes [Studies on the understanding of other TLR signaling pathways remain scarce, let alone those on their roles in the pathogenesis of GVHD. Initially, researchers focused on the role of th cGVHD . In concth cGVHD . Anotherth cGVHD . Interesth cGVHD . Thus, Tonocytes . In concThe depletion of donor T lymphocytes or the inhibition of proliferative T lymphocytes can decrease the occurrence and severity of GVHD; however, there is a great risk of graft failure, a reduced graft-versus-leukemia (GVL) effect and an increased incidence of leukemic relapse, as well as an increased risk of severe infection. Therefore, an effective strategy for reducing GVHD without impairing the GVL effects or infection propensity is important. Emerging data showed that donor TLR4 and MyD88 deficiencies are protective against aGVHD , while aThere is growing research in discovering new approaches relevant to TLR signaling pathways to intervene in the process of GVHD. LPS induces the release of massive inflammatory cytokines into the serum through the TLR4 signaling pathway with macrophage priming, thus leading to GVHD . ConversHeparan sulfate (HS), a ubiquitous component of the extracellular matrix, can activate TLR4 on DCs, leading to the enhancement of DC maturation and allo-reactive T cell responses . Elevate+ T cells are critical effectors of GVHD, while the GVL effect is dependent on the CD8+ T cells that can mediate cytotoxicity [+ T cells results in defective cytotoxic activity against tumor tissue and a reduced secretion of pro-inflammatory cytokines to host antigens. In conclusion, the cause of maintaining the severity of GVHD with a reduced GVL effect is associated with functional dissociation of the two T cell subsets according to MyD88 deficiency in T cells. The dissociation of the GVL effect from GVHD may be promising in designing new effective therapeutic approaches against hematologic malignancies, which can harness the benefit of the GVL effect and reduce the toxicity of GVHD [Interestingly, a study demonstrated that in experimental allo-HSCT models, the absence of MyD88 in donor T cells diminishes the GVL effect without attenuating the severity of GVHD , which mtoxicity ,56. In t of GVHD ,35,57.+ T cells is proven to be critical for the preservation of GVL activity, regardless of the occurrence of GVHD, which may be a promising therapeutic target. In addition, protective factors in recipients such as COX2, PEG2 and IDO also contribute to reduced GVHD. Indeed, the eradication of intestinal bacteria with antibiotics such as metronidazole and ciprofloxacin has been clinically applied in HSCT patients. Further experiments and clinical trials are needed to interpret the role of TLRs in the induction of GVHD, a prerequisite to exploiting the use of specific TLR agonists or antagonists for treating GVHD.Innate immune responses which can be initiated by TLR signaling pathways significantly contribute to inflammatory cascades which lead to the recruitment of allo-reactive donor T cells to target organs, which is equivalent to GVHD. In detail, pre-transplant regimens including radiotherapy and/or chemotherapy lead to aggravated intestinal epithelial damage, with the subsequent dislocation of intestinal bacteria and their breakdown components including LPS, CpG-ODN and other ligands, resulting in the activation of the MyD88-dependent signaling pathway by ligation with TLR4, TLR9 or other TLRs. The consequent activated NF-κB signaling upregulates the expression of target genes to trigger the activation of donor T cells with the production of massive cytokines including IFN-γ, IL-1β, IL-6, IL-12 and TNF-α, leading to the recruitment of allo-reactive donor T cells to target organs. The blocking of any key molecules in TLR signaling pathways can inhibit the occurrence of GVHD, however, often accompanied by some undesirable complication such as reduced GVL and bacteremia. MyD88 in donor CD8


IG) heavy chain hypermutation analysis. Revealing that subclones are not rare in B-cell lymphoma cell lines, 6/49 IG hypermutated cell lines (12%) consisted of subclones with individual IG mutations. Subclones were also identified in 2/284 leukemia/lymphoma cell lines exhibiting bimodal CD marker expression. We successfully isolated 10 subclones from four cell lines . Whole exome sequencing was performed to molecularly characterize these subclones. We describe in detail the clonal structure of cell line HG3, derived from chronic lymphocytic leukemia. HG3 consists of three subclones each bearing clone-specific aberrations, gene expression and DNA methylation patterns. While donor patient leukemic cells were CD5+, two of three HG3 subclones had independently lost this marker. CD5 on HG3 cells was regulated by epigenetic/transcriptional mechanisms rather than by alternative splicing as reported hitherto. In conclusion, we show that the presence of subclones in cell lines carrying individual mutations and characterized by sets of differentially expressed genes is not uncommon. We show also that these subclones can be useful isogenic models for regulatory and functional studies.Genetic heterogeneity though common in tumors has been rarely documented in cell lines. To examine how often B-lymphoma cell lines are comprised of subclones, we performed immunoglobulin ( Tumors evolve under selective pressures, including the inhibitory influences of anti-cancer drugs. Clonal evolution is an important topic in cancer research because it underlies development of heterogeneous, molecularly related tumors in one patient and may explain why relapsed samples are often genetically distinct from primary diagnostic clones –3.BCL6 and MYC [BCL6 can drive expression of germinal center markers in DLBCL [Permitting functional analysis of oncogenes against a syngenic background, homologous cell lines would appear ideal model systems. However, little is known about the clonal composition of cell lines, notably whether they represent true pathologic subclones of the primary tumor rather than mere culture artifacts. The diffuse large B cell lymphoma (DLBCL) cell line U-2932 is an example of a cell line comprising subclones, with differential expression of > 100 genes, including the germinal center oncogenes  and MYC . The bimin DLBCL .IG) hypermutation analysis revealed that 6/49 (12%) B-lymphoma cell lines comprised subclones. We show furthermore, that bimodal cell surface marker expression can also be indicative of subclones. Thus, the chronic lymphocytic leukemia (CLL) cell line HG3 includes CD5+ and CD5− subclones. We describe the clonal structure of this cell line in detail. The usefulness of isogenic subclones for the study of gene regulation was established by showing that CD5 expression may be regulated at the level of transcription rather than by the alternative splicing mechanism reported hitherto [Here, we set out to examine how often cell lines consist of subclones. Immunoglobulin  hypermutation analysis to detect subclones using B-lymphoma cell lines as material.The gain of IG rearrangements were determined in 59 cell lines by PCR analysis with primers specifically recognizing the different VH-JH rearrangements [IG heavy chain hypermutations (IG PCR products) exhibited subclone-specific mutations, confirming the presence of two or more clones in these cell lines (ngements . The PCRutations . Among hll lines .IG hypermutations 25/49 (51%) were DLBCL-derived. The remaining 24 (49%) represented Burkitt lymphoma (n = 9), mantle cell lymphoma (n = 1), multiple myeloma (n = 8), primary effusion lymphoma (n = 3) and Hodgkin disease (n = 3). Five cell lines showing interclonal IGHV variation  were DLBCL-derived. The only non-DLBCL cell line with subclones was the Burkitt lymphoma cell line RAJI  cross-contamination with a second line expressing discordant cell surface markers, or iii) presence of cell line subclones.The vast majority of the 284 leukemia and lymphoma cell lines immunophenotyped by us showed rather uniform CD cell surface marker expression patterns, as to be expected from monoclonal cells. However, 12/284 (4.2%) cell lines exhibited bimodal expression of one or several markers Figure . PossiblTo test these competing explanations, we flow-sorted the 12 cell lines with double peaks using antibodies recognizing the corresponding markers . DNA proCell surface markers remained stable in the sorted subpopulations of the DLBCL cell line U-2932 and the CLL cell line HG3, which were accordingly classified “candidate-biclonal” .IG hypermutation analysis revealed that 6/49 B-lymphoma cell lines with IG rearrangement comprised subclones. The stable and differential expression of surface markers suggested that 2/284 cell lines screened might consist of more than one clone. Cell line U-2932 was identified by both techniques. Therefore, we set out to test seven cell lines for mono- or multiclonality.To verify and molecularly characterize the individual subclones, we single-cell sorted the candidate cell lines. Expression of cell surface markers was used to sort cell lines HG3 (CD5) and U-2932 . The other cell lines were single-cell sorted without specific sorting criteria. Clonal growth after sorting was observed in 4/7 cell lines .We performed whole exome sequencing (WES) of the single-cell cloned cell lines to identify clone-specific mutations as shown in Genetic and epigenetic mechanisms led to the differential expression of more than 100 genes in the U-2932 subclones . Here, w+, 2 CD5− clones) showing both shared and clone-specific mutations . To test the validity of the conclusion that cell line HG3 comprised three – and not more - clonal lineages, we performed PCR-based amplification-refractory mutation system (ARMS) assays for nine mutated sites and 60 single cell clones. According to WES, these genes were mutant in all or in subsets of the clones. We postulated that the detection of mutations in a large number of clones would inform the clonal structure of the cell line.CAV1 T46I was found in all 60 clones and thus indicated the putative mother clone  detecting clone-specific mutations to determine the subclonal composition of cell line HG3 . Roughly+/CD226−, green CD38−/CD226−, and yellow CD38−/CD226+  was CD5+, and the third, green lineage consisted of CD5+ and CD5− clones  led to a 100-fold induction of CD5 mRNA  of B lymphoma cell lines comprised subclones. Additional biclonal cell lines were detected by subclone-specific expression of cell surface markers. These results showed that subclones in cell lines are not uncommon. We isolated individual subclones to find out whether they can be used as isogenic models for regulatory and functional studies.As evidenced by the presence of specific sets of + and CD5− populations of the cell line might also represent subclones of the patient [+ and CD5− populations represented distinct subclones, we flow-sorted this cell line and performed whole exome sequencing (WES). The sequencing results suggested that CD5+ and CD5− populations were themselves polyclonal. Copy number analysis, WES and ARMS analysis of 60 single-cell clones revealed that cell line HG3 comprised three clonal lineages. One subclone was CD5+, another was CD5−, while the third included “early” CD5+ cells with few mutations and “late” CD5− cells with a higher number of mutations.Four “candidate multiclonal” cell lines  were successfully single-cell cloned. WES confirmed that cell lines SU-DHL-5 and U-2932 comprised two subclones and cell lines HG3 and TMD-8 three apiece. Due to the lack of archival patient DNA from the other cell lines, subclones could only be traced back to the patient for cell line U-2932. However, the original description of cell line HG3 showed a bimodal curve for the surface expression of CD5, suggesting that the CD5 patient . To findCD5, CD9, CD38, GPM6A, SEPT10 and TNFRSF21 [CD5 as model.Unsupervised clustering of gene expression and methylation data both confirmed the clonal structure obtained by mutational analysis. The list of differentially expressed genes included markers or functional mediators in CLL like TNFRSF21 . To showCD5 is also expressed on a subset of ‘innate’ (B1) B cells and on CLL cells where, together with CD19+, CD20low, CD23+, CD79low, and either kappa or lambda IG light chains, they constitute diagnostic criteria [CD5 physiologically constrains B cell receptor (BCR) signaling in healthy B cells [CD5 regulates selected genes and signaling pathways, thus contributing to CLL development [CD5 in B cells, whereas in T cells recent data show that CD5 is developmentally regulated and induced by T cell receptor (TCR) engagement, and furthermore that CD5 surface expression reflects TCR signal intensity and its affinity for positively selecting self-ligands [bright to CD5dim depending on its location in the proliferative compartment (lymph node or bone marrow) or in the resting compartment (blood) [CD5 mRNA independently of the alternative splice variants. Suggesting that CD5 was regulated at the transcriptional level, CD5 mRNA was high in CD5+ HG3 cells and low in CD5− cells. Supporting the notion that epigenetic/transcriptional events play a major role for CD5 regulation, CD5 showed the inverse methylation/expression pattern typical for genes regulated by DNA methylation. These results suggest that epigenetic/transcriptional mechanisms play a role in CD5 expression independent of the alternative splice variants described so far. Future studies will address whether DNA-demethylating agent Aza (100× increase of CD5 mRNA) directly affected CD5 or indirectly, e.g. by transcription factor stimulation.Originally described as a T-cell marker, elopment –25. Litt-ligands . CD5 exp (blood) . Alterna (blood) , 29. Los (blood) , 7. We uIG hypermutated B-lymphoma cell lines comprise subclones . We have shown that bimodal expression of cell surface markers may be used to detect the presence of subclones, independent of IG hypermutations. Both screening methods require additional assays like WES to verify subclones. Archival patient DNA is obligatory to determine whether cell line subclones truly represent tumor clones.In conclusion, a substantial number of CD5 expression in 2/3 subclones. We were able to show that CD5 can be regulated at the epigenetic/transcriptional level rather than by the alternative splicing mechanism described hitherto – further demonstrating the benefit of isogenic subclones for studying gene regulation.We describe the detailed clonal structure of the CLL cell line HG3 and elucidate loss of We trust that our findings will encourage other scientists to test “early” batches of their cell lines for subclones, as these may prove useful isogenic models for regulatory and functional studies, notably the potential impact of clonal heterogeneity on therapeutic response. Moreover, we urge cryopreservation of early cell line passages together with uncultured patient material and their reposition with facilities, such as cell banks, to enable investigation of clonal structure of different cancers using whole genome sequencing methods when these become routinely applicable.Cell lines were taken from the stock of the cell lines bank (DSMZ – German Collection of Microorganisms and Cell Cultures). Others were obtained “for research purpose only”. Cell lines were authenticated by DNA profiling and cytogenetics. Detailed references and cultivation protocols have been described previously .IG heavy chains in B cell lines were determined by PCR using VH- and JH primers described by van Dongen [Rearrangements of n Dongen  . APC-labeled CD5 antibody (Ab) clone UCHT2 (Becton Dickinson) was used to sort cell line HG3. PerCP-conjugated CD20 and APC-conjugated CD38 (Becton Dickinson) were used to sort U-2932 populations. For phenotypic analysis of HG3 subclones, we applied PE-conjugated CD38 and FITC-conjugated CD226 Abs (Becton Dickinson). Growth of sorted cell clones was observed over a four-week period by microscopy. Clones were harvested and subjected to expression array analysis and WES.CytoScan HD Array  hybridization analysis was performed to identify numerical aberrations. DNA was prepared using the Qiagen Gentra Puregene Kit . Data were analyzed using the Chromosome Analysis Suite software version 2.0.1.2 (Affymetrix).The concentration and quality of the purified genomic DNA (gDNA) was determined by an Agilent Technologies 2100 Bioanalyzer . Fragmentation of 100 ng gDNA in 55 μl Tris-EDTA buffer in a microtube (Covaris) was performed on a Covaris S2  to obtain fragments with an average length of 120–200 base pairs (bp). Fragment size was checked with an Agilent Technologies 2100 Bioanalyzer. The DNA sequencing library was generated from 100 ng of fragmented gDNA using Agilent SureSelectXT Reagent Kits v5 (50 Mb) and SureSelectXT Reagent Kits v5_UTR (75 Mb) according to the manufacturer's protocols. The final DNA sequencing library was purified, size controlled by Agilent Technologies 2100 Bioanalyzer (High Sensitivity DNA Chip) and prepared for sequencing according to the manufacturer's protocol (Illumina). The libraries were sequenced on Illumina HiSeq2500 using TruSeq SBS Kit v3-HS  with 25.6-44.4 million reads per sample resulting in > 50× mean coverage. Reads were trimmed for poor quality and adapter/primer sequences (ea-utils 1.1.2-686), mapped to the hg19/GRCh37 genome annotation (STAR 2.4.0b), sorted and converted (samtools 0.1.19), sequence duplicates removed (picard 1.121) and subsequently variants were identified via GATK (3.3-0) and VarScan (1.1.2-686) tools and mutation effects revealed via Ensembl VEP (release 77) , 32. OveABL1 as internal control. Relative expression levels were calculated using the 2^-DDCt-method.ARMS assay, a PCR-based mutational analysis assay, was performed for nine mutations detected by WES to determine the clonal structure of cell line HG3. Primers are listed in 500 ng total RNA were used for biotin labelling according to the 3′ IVT Express Kit (Affymetrix). 7.5 μg of biotinylated cRNA were fragmented and placed in a hybridization cocktail containing four biotinylated hybridization controls . Samples were hybridized to an identical lot of Affymetrix GeneChip HG-U133 Plus 2.0 for 16 h at 45°C. Steps for washing and SA-PE staining were processed on the fluidics station 450 using the recommended FS450 protocol (Affymetrix). Image analysis was performed on GCS3000 Scanner and GCOS1.2 Software Suite (Affymetrix). Analysis of data was performed using GeneSpring 11.5.1 . After RMA-background correction and quantile normalization of spot intensities, data were further processed by division to the sample mean and logarithmic transformation for centering the values around zero. Data processing was done via R/Bioconductor using limma and affy packages . ExpressCD5 (Hs 00204397_m1), CD38 (Hs 01120071_m1), CD226 (Hs 00170832_m1) expression levels with TBP as endogenous control. Relative expression levels were calculated using the DDCt method.RNA was prepared using the RNeasy Mini kit (Qiagen). For mRNA quantification, reverse transcription was performed using the SuperScript II reverse transcriptase kit . TaqMan probes (Applied Biosystems) were used to quantify human p-value was set to < 0.01 per CpG site.For DNA-methylation analysis the Infinium HumanMethylation450 BeaChip (Illumina) was used according to the manufacturer's instruction. This platform allows the interrogation of 485,577 assays  in parallel at a single-nucleotide resolution per sample . Arrays CD5. Cells were seeded at a density of 5 × 105 cells/ml, Aza was added at a final concentration of 5 μM. Control cells were treated with 0.03% DMSO. After 2 d, half of the medium was replenished with medium with/without Aza (5 μM). After 3 d, cells were harvested to prepare RNA.Aza (Sigma) dissolved in DMSO was used to verify the effect of methylation on expression of


Scientific Data 4:170033 doi: 10.1038/sdata.2017.33 (2017); Published 28 March 2017; Updated: 5 December 2017.The growth media for the cell lines CHP-212 and IMR-32 listed in Table 1 of this Data Descriptor were incorrectly stated.For the cell line CHP-212, the correct growth medium is: EMEM  and Ham′s F12 (1:1 mixture), 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine.For the cell line IMR-32, the correct growth medium is: EMEM , 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine.The COG cell lines were grown in antibiotic-free medium.www.atcc.org and www.cogcell.org, and are labelled correctly.The cell line labelled as ‘NB-16’ in Fig. 2, Table 1, and Table 2 was incorrectly recorded. The cell line should be listed as the rhabdomyosarcoma cell line, RD. GEO Series GSE89969 has been updated to reflect this error. We subsequently re-performed every analysis in the paper following exclusion of NB-16 and none of the conclusions of the manuscript change. As expected, all cell line—patient correlations are slightly higher . All other cell lines have been properly STR-verified using


Tamoxifen is still the most commonly used endocrine therapy drug for estrogen receptor (ER)-positive breast cancer patients and has an excellent outcome, but tamoxifen resistance remains a great impediment to successful treatment. Recent studies have prompted an anti-tumor effect of aspirin. Here, we demonstrated that aspirin not only inhibits the growth of ER-positive breast cancer cell line MCF-7, especially when combined with tamoxifen, but also has a potential function to overcome tamoxifen resistance in MCF-7/TAM. Aspirin combined with tamoxifen can down regulate cyclinD1 and block cell cycle in G0/G1 phase. Besides, tamoxifen alone represses c-myc, progesterone receptor (PR) and cyclinD1 in MCF-7 cell line but not in MCF-7/TAM, while aspirin combined with tamoxifen can inhibit the expression of these proteins in the resistant cell line. When knocking down c-myc in MCF-7/TAM, cells become more sensitive to tamoxifen, cell cycle is blocked as well, indicating that aspirin can regulate c-myc and cyclinD1 proteins to overcome tamoxifen resistance. Our study discovered a novel role of aspirin based on its anti-tumor effect, and put forward some kinds of possible mechanisms of tamoxifen resistance in ER-positive breast cancer cells, providing a new strategy for the treatment of ER-positive breast carcinoma. Approximately 50%–70% of breast cancers are considered estrogen receptor (ER) positive . EstrogeResearches show that tamoxifen can block cell cycle and inhibit cell proliferation in sensitive breast cancer cells –11. But et al [A considerable body of evidence strongly suggests that non-steroidal anti-inflammatory drugs (NSAIDs) such as aspirin can reduce the risk of cancers associated with colon, breast, gastric, prostate, lung, and skin –25. But et al  have shoMYC mRNA and c-myc protein can be inhibited by salicylates such as aspirin. This in turn reveals the anti-tumor effect of salicylates on colon cancer cell lines [MYC gene and the effect of aspirin occurred. Our studies have discovered a novel role based on anti-tumor effect of aspirin, and have put forward a few possible mechanisms of tamoxifen resistance in ER-positive breast cancer cells, providing a new strategy for the treatment of ER-positive breast cancer.In our study, we used the ER-positive breast cancer cell line MCF-7 as the research model, which was sensitive to the anti-estrogen treatment. At the same time, tamoxifen resistant cell line MCF-7/TAM was used as the model to investigate the possible underlying molecular mechanism of tamoxifen resistance. According to our studies, there were some genes which may contribute to the tamoxifen resistance. A few studies have reported that ll lines , 28. So ll lines . AspirinThrough comparison with cell databases and identification of professional institutions, there was no deterioration caused by other human cells and there was no cytometaplasias observed in MCF-7 and MCF-7/TAM cell lines. Cell lines intended to be MCF-7 whose DNA was typing well.α was expressed in every cell of both tamoxifen sensitive cell line MCF-7 and tamoxifen resistant cell line MCF-7/TAM, but the fluorescence intensity of MCF-7/TAM cells was much weaker than that of MCF-7 cells. Besides compared with MCF-7 cells, the morphology of MCF-7/TAM cells also changed, cells were smaller and more compact , the target of tamoxifen, is one of the most important biomarkers in MCF-7 cell line. Results from the immunofluorescent assay showed that ERt Figure .To examine whether MCF-7 cells were sensitive to 4-OHT while MCF-7/TAM cells were resistant, we treated two cell lines with several concentrations of 4-OHT (0–6 μM) for 7 days and measured the cell survival rate compared with negative control. As shown in Figure Previous studies suggest that tamoxifen can block cell cycle and inhibit cell proliferation in sensitive breast cancer cells –11. ThenWe used 4-OHT alone and combination with ASA to investigate the effect on two cell lines. As shown in Figure We found that the survival rate of MCF-7/TAM cells decreased with the increasing concentrations of 4-OHT when combined with 2 mM ASA, while no such phenomenon was observed when using 4-OHT alone Figure . Also weAs shown above, 4-OHT could block cell cycle in MCF-7 cells but not in MCF-7/TAM cells. We wondered whether there also existed changes of cell cycle in two cell lines when combining 4-OHT with 2 mM ASA. Then we tested by flow cytometry. As shown in Figure In MCF-7/TAM cell line, there was nearly no change of cell cycle among 4-OHT group, ASA group and negative control, but the percentage of G0/G1 phase increased evidently in the combination group Figure , which sPGR is dependent on the ERα activating estrogen response element (ERE) and also code PR protein, while its level can reflect the function of ERα. Cyclin D1 protein can be regulated by ERα, and coded by CCND1 gene, which is one of the key factors in cyclinD-CDK4/CDK6 axis and have effect on cell cycle [MYC gene, performs multiple cellular functions such as cell proliferation, metabolism, apoptosis, growth, and differentiation [By the gene microarray, we found that there were lots of differences of the expression of genes between MCF-7 and MCF-7/TAM cell lines. Through screening and analyzing relevant websites and database online, and with the help of previous literature , 27, 28,ll cycle . C-myc, ntiation , 32.CCND1 gene showed no significant difference between MCF-7 and MCF-7/TAM cell lines. The expression of oncogene MYC was much higher in tamoxifen resistant cell line, and it might contribute to the unlimited proliferation of cancer cells. When treated with 4-OHT alone, there were differences among CCND1, MYC and PGR gene expression in MCF-7 and MCF-7/TAM cell lines. In MCF-7 cells, 4-OHT could down-regulate CCND1, PGR and MYC significantly, while there was no such function observed in MCF-7/TAM cell line. However, when combined 4-OHT with ASA, these genes could be down-regulated in MCF-7/TAM cells as well  and were cultured in Dulbecco's modified Eagle medium  supplemented with 10% fetal bovine serum (FBS), 100 U/ml penicillin and 100 μg/ml streptomycin  at 37°C in a 5% CO2 humidified incubator. Cell lines were identified by Hua Ke Jian Lian cell identification company  in genetic analyzer to test the Amelogenin gene STR loci and gene.Human breast cancer cell lines MCF-7  and MCF-7/TAM  were kindly presented by Dr. 4-hydroxy-tamoxifen , aspirin (ASA) were purchased from Sigma-Aldrich , and dissolved in Dimethyl Sulphoxide . Stock solutions were 100 mM and 1 M respectively; final DMSO concentration was under 3%. Final concentrations of 4-OHT were 1–6 μM, and final concentration of ASA was 2 mM.3 cell/well, followed by an overnight incubation. Cells were then treated with increasing concentrations of 4-OHT (0–6 μM) individually or by combining with 2 mM ASA. After 7-day treatments, viable cells were quantified with MTS substrate according to the manufacturer's instructions. 450nm wave length OD values were examined by an enzyme-labeled instrument . Cell viability rates were expressed as percentage of corresponding control.The growth inhibitory effects were measured by using a colorimetric MTS assay kit (Sigma-Aldrich). Briefly, MCF-7 and MCF-7/TAM cells were plated in 96-well plates at a density of 1 × 105 cells /well, followed by overnight incubation. Cells were then treated with 4-OHT at a concentration of 5 μM individually or by combining with 2 mM ASA. After 72 hours’ treatment, nucleus was stained by Propidium Iodide (PI) and cell cycle was examined by flow cytometry  according to the kit's manufacturer's instructions. Data was analyzed by Flow Jo 7.6 software.The experiment was performed by using a cell cycle analysis kit . Briefly, cells were plated in 12-well plates at a density of 2 × 10Cells were plated on the glass slides. After 48 hours, cells were washed with PBS and fixed in 4% paraformaldehyde, then treated with 0.1% Triton X-100. Unspecific antigens were blocked by goat serum at room temperature and primary antibodies were incubated overnight at 4°C. The CY3-secondary antibodies  were incubated and FITC-phalloidin solution was used bind F-actin. Cell nucleus was stained by DAPI working solution. Finally, cells were photographed using LSM 510 META confocal microscope .−ΔΔCt. The sequences of primer for qPCR were as follows: ESR1-forward, TCCAAACCCATCGTCAGTGT. ESR1-reverse, TGAATGCAAAGGGGTCTGTGT. CCND1-forward, AAT GACCCCGCACGATTTCA. CCND1-reverse, TGAGGCG GTAGTAGGACAGG. MYC-forward, CTTGTTGCGGAA ACGACGAG. MYC-reverse, ACTCAGCCAAGGTTGT GAGG. PGR-forward, AGGTCTACCCGCCCTATCTC. PGR-reverse, AGTAGTTGTGCTGCCCTTCC. GAPDH-forward, CCCACTCCTCCACCTTTGAC. GAPDH-reverse, TGTTGCTGTAGCCAAATTCGTT.Total RNA was extracted by using TRIzol reagent (Sigma-Aldrich), and cDNA was synthesized from total RNA using a Reverse Transcript Kit . Relative quantitation of mRNA expression was achieved using real-time PCR . The SYBR Green Master Mix (Promega) was used according to the manufacturer's instructions and the level of gene expression was measured by calculating 2ImageJ software. Antibodies for Western blot were as follows: rabbit c-myc and rabbit PR primary antibodies were purchased from Abcam (USA) at 1:1000 dilution in 5% BSA. Rabbit cyclinD1, rabbit GAPDH primary antibodies and mouse anti-rabbit HRP antibodies were purchased from Cell Signaling Technology (USA) at 1:2000 dilution in 5% BSA.After treatment with drugs, whole-cell lysates (20μg) were separated by 10% SDS-PAGE, and then proteins were transferred to PVDF membranes . Membranes were blocked by 3% BSA and probed with antibodies. The quantification of protein level was performed by densitometric scanning  and then was normalized to intensity with GAPDH. The results of Western blotting were analyzed by MYC directed shRNA sequence 5′-GATCTGGAGATGATGACCGAGTTACCTCGAGGT AACTCGGTCATCATCTCCATTTTTG-3′ were cloned into the BamHI / MluI site of pLent-U6-GFP-Puro vector following the manufacturer's cloning protocol . Plasmid was isolated and purified using Plasmid Mini Kits I , then plasmid was transformed into DH5α E. coli competent cells. Lentivirus was packaged using 293T cells, and then transfected into MCF-7/TAM cells following the relevant protocols. Empty plasmid was used as negative control group at the same time. Cell lines MCF-7/TAM/shMYC and MCF-7/TAM/U6 were established from MCF-7/TAM by the selection of puromycin (Sigma-Aldrich). The transfer efficiency was detected by the percentage of green fluorescent and the gene knocking down efficiency was detected by RT-qPCR assay.Complementary oligos  containing an t test was used to compare two group-design experiments. One-way ANOVA was used to compare the effects of multiple group-design experiments, and LSD, SNK were used as post hoc test. P < 0.05 was considered to be statistically significant.Data was presented as mean ± SEM. Each experiment was performed independently for at least 3 times. SPSS 19.0 statistical software package  was applied to analyze data. Student's