The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested. In this study, using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, we explored the application of similarity information between cell lines and drugs in drug response prediction. We found that similar cell lines by gene expression profiles exhibit similar response to the same drug. Meanwhile, drugs with similar chemical structures also show similar inhibitory effects across different cell lines. Based on the above observations, we proposed a dual-layer network and local weighted model to predict drug response of a cell line using proximal information of the drug-cell line network. The only three parameters of our model are optimized by leave-one-out cross-validation for each drug. Two case studies of MAPK and ERK signal pathways on CCLE dataset proved that the predicted-to-observed correlations of our dual-layer network model is significantly better than the previous predictor using elastic net model. Interestingly, predictions based on drug similarity network (DSN) alone were much better than those based on cell line similarity network (CSN) alone for most drugs, implying that drug similarities are more informative for drug response prediction than cell line similarities. Our network model can be applied to predict the response of a new cell line to existing already tested drugs or to predict the response of an existing cell line to new drugs, thus potentially saving the cost in a drug-cell line screening. Over the past two decades, substantial improvements in high-throughput profiling technologies and systems approaches have increased expectations that personalized or precision medicine will become the paradigm of future medical science –3. In coTwo recent consortiums, the Cancer Cell Line Encyclopedia (CCLE)  and CancIn this study, motivated by the integrated model in disease genes prioritization ,21, we cWe built the dual-layer integrated cell line-drug network model using large pharmacogenomics datasets from the CCLE  and CGP Seeing that similar cells exhibit similar drug responses, we next examined whether similar drugs have similar effects on cells. We downloaded the chemical structure files from PubChem  for the A recent report showed inconsistent results between the CCLE and CGP datasets . Our anaC to a given drug D using a linear model weighting drug response from cell lines with similar gene expression profiles to C. We calculated the gene expression correlations between C with all other cell lines  and τ (hence wD) were determined, we calculated λ for each drug  a cell similarity network (CSN), based on similarities in gene expression profiles between cell lines, and 2) a drug similarity network (DSN), based on similarities in chemical structural features between drugs. This method applies a weighted algorithm to model the predictor. Using the data from the CCLE and CGP studies as benchmarks, leave-one-out cross-validation showed that our dual-layer integrated cell line-drug network model consistently outperformed the elastic net model, suggesting that our model effectively captures the interplay in the cell line-drug network. Our model also reasonably assigned much lower activity areas for BRAF mutant cell lines than for BRAF wild-type cell lines for three different MEK1/2-inhibitors, which is in good agreement with current experimental reports. Our network model can be applied to predict the response of a new cell line to existing already tested drugs or to predict the response of an existing cell line to new drugs, thus potentially saving the cost in a drug-cell line screening. Compared to the existing elastic net regression, our dual-network model is based on the entire dataset rather than a single drug, so interactions between different drugs are considered. Furthermore, our model only needs correlations between cell lines or drugs as input, thus is not seriously affected by the huge dimensionality of genome-wide gene expression and copy number variation features.d and cell line c is the largest among all training data, the linear combination of response values from their neighborhood will definitely be smaller than the truth. However, in practical clinical settings, one may care more about the relative order rather than the absolute values of drug response data due to the batch effect of different experiments. So most work in drug response prediction used correlation between true and predicted values as a measure of effectiveness [Despite these encouraging results, our model suffers from the following limitations, which we hope to address in the future. First, construction of the CSN relied solely on gene expression profile data, and future work to integrate somatic mutation information, epigenetic status ,27, and tiveness ,17, whichttp://www.broadinstitute.org/ccle). Both drug sensitivity measurements and gene expression profile data are available for 491 common cancer cell lines. The CGP dataset provided raw gene expression profiles for 789 cell lines, which we downloaded from AffyExpress under accession number E-MTAB-783. We also obtained drug sensitivity measurements (activity area) and cell line annotation data from the CGP website (http://www.cancerrxgene.org/downloads), which included data for 139 drugs and 707 cancer cell lines. The CGP study provided both drug sensitivity measurements and gene expression profile data for 653 common cancer cell lines.The cancer genomic and drug response data used in this work are available from the Cancer Cell Line Encyclopedia (CCLE)  and the http://padel.nus.edu.sg/software/padeldescriptor/) with default settings. In detail, the 1D descriptors consist of compositional or constitutional molecular properties, e.g., atom count, bond count, and molecular weight. The 2D descriptors include different quantitative properties of the topology such as Kappa shape indices [We downloaded the chemical structural information for each drug from PubChem, a database of chemical molecules and their activities in different biological assays. It contains validated chemical depiction information for 19 million unique compounds contributed from over 70 depositing organizations. We downloaded raw chemical property profiles (SDF files) for 23 drugs in the CCLE study and 124 drugs in the CGP study from the PubChem website. The 1-D and 2-D chemical structural features of each drug were retrieved using the PaDEL software program  a cell line similarity network (CSN), which connects all cell lines based on their Pearson correlation coefficients of gene expression profiles; 2) a drug similarity network (DSN), where associations between two drugs are measured by the Pearson correlation between their respective 1-D and 2-D structural properties; and 3) a drug-cell line response network, which connects the above two networks using experimentally determined drug response values (measured as activity area and IC50 in both CCLE and CGP studies). The CSNs generated using CCLE or CGP data are both complete graphs of all cell lines, where interactions between two cell lines are measured by the Pearson correlation coefficients of their respective gene expression profiles. Similarly, the DSNs are also complete graphs of all available drugs, connected with their pairwise correlation between their respective 1-D and 2-D chemical structural features. The intermediate layer between the CSN and the DSN, referred to as the drug-cell line response network, is a bipartite graph of all cell lines and drugs, labeled with the response values . Note that the intermediated drug response network is not a complete bipartite graph due to some missing values in two experiments, particularly in the CGP dataset.C to a known drug D, similar to the LWR , we take advantage of data for all neighboring cell lines to cell line C in the cell line similarity network (CSN). For drug D that is being investigated, neighboring cell lines are excluded from the analysis if drug sensitivity data is not available for them. Based on the hypothesis that cell lines with similar gene expression profiles will respond similarly to the same drug, we propose a linear weighted model to approximate the sensitivity of cell line C to drug D as follows:Sens is the sensitivity data of cell line Ci against drug D, wC is the weight parameter, and Ci is the sample that is associated with cell line C in the CSN. According to our model assumption, the most closely related cell line in terms of its gene expression profile will contribute much more to the prediction of Sens compared to other cell lines. Therefore, the weight parameter should be an increase function with their correlation to C. A fairly standard choice for the weights is:ρC is the gene expression correlation between cell line C and Ci, σ is the bandwidth parameter controlling how quickly the weight of a training example falls off with distance of its Ci from the query point C. Here, if ρC is close to 1, wC will also be close to 1, implying that this cell line has a high impact in the evaluation of Sens. On the contrary, if ρC is small , wC will be relatively small too. In this case, the corresponding cell line will have a weak contribution to the determination of the unknown cell line.In order to predict the response of a new cell line C to a new drug D based on the known sensitivities of cell line C to all neighboring drugs to drug D. We developed a similar linear weighted model to predict the response of a cell line to a new drug based on its neighboring drugs in the drug similarity network (DSN) as follows:wD is determined by the correlation between the chemical structural feature of drug D and drug Dj in the drug similarity network:τ is the bandwidth parameter.In the previous section, we developed a model to predict the response of an unknown cell line to an existing drug based on the training data. However, in some cases, we may need to predict the response of a known cell line to a new drug. Based on a model similar to that used in the previous section, we propose a linear weighted model for predicting the sensitivity of known cell line λ is the combination weight, which can be optimized through leave-one-out cross-validation. The integrated model will be dominated by DSN if λ is close to 1, and it will be dominated by CSN if λ is close to 0. Two individual models are complementary when 0 < λ < 1.In the above two sections, we developed single-layer predictive models for determining the response of a cell line to a drug based on the cell line similarity network (CSN) and the drug similarity network (DSN), depending on whether drug and cell line information are available. However, the results from either of these single-layer models may be not satisfactory when only one type of information (either cell line similarity or drug similarity) is considered. To make full use of the integrated network, we propose a linear weighted model to combine We used the leave-one-out cross-validation method to determine the parameters and validate our predictor. In detail, each possible drug-cell line pair is singled out as the test dataset to measure the consistency between predicted and observed drug response with the model trained from the remaining data. There are three free parameters to be determined in our model, i.e., σ and τ which measure the decay rate when correlations of cell line expression or drug descriptors decrease, and λ which measures the relative contribution of each single layer. In order to ensure a unified model, two decay parameters σ and τ are fixed for all drugs and cell lines in CSN and DSN respectively, but λ is optimized by each individual drug, allowing different relative contributions of two lays for different drugs.Sens is the observed sensitivity value of cell line C to drug D, and Three parameters are optimized in the following order to make sure drug response can be predicted by each individual layer and the whole integrated network. For the cell line similarity network, the decay parameter σ is optimized by minimizing sum of squared prediction errors for all possible drug-cell line combinations using Formula  as prediτ for the bottom drug similarity network was determined bySimilarly, decay parameter Finally, the best λ for each drug is obtained through minimizing the sum of squared errors after combining predictions from both CSN and DSN, i.e.,After selecting the parameters, the prediction performance of our model was evaluated using Pearson correlation coefficient, root mean squared error (RMSE) and normalized root mean squared error (NRMSE) between predicted and observed drug responses for each drug. RMSE is the square root of the mean squared error,A higher Pearson correlation coefficient and lower RMSE/NRMSE indicate a better prediction performance of a method for a given drug.S1 Fig Box plots showing cell lines with similar gene expression profiles responding similarly to the same drugs. The X-axis indicates the Pearson correlation coefficients between all possible cell line pairs based on expression profiles. The Y-axis shows the correlations of their drug response vectors as measured by IC50 in CCLE (A) and CGP (B).  Box plots showing that drugs with similar 1-D and 2-D structural features based on PaDEL exhibiting similar effects on cell lines in the CCLE (C) and CGP (D) datasets. The X-axis represents the drug similarity categories, and the Y-axis shows the correlations of drug responses across all cell lines. Statistical differences between two groups were measured by the t-test.(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(A) Bar graph showing the prediction performance of three models using experimental data from the CGP study, quantified using the Pearson correlation between the predicted and observed activity areas. (B) Pearson correlation distribution of the three different models using t-tests to determine differences between two groups. (C) Correlations between observed and predicted activity areas.(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(A) Bar graph showing the prediction performance of three models for drugs targeting the PI3K pathway using experimental data from the CGP study, which was quantified using the Pearson correlation between predicted and observed IC50 values. CSN: prediction results using only the cell line similarity network; DSN: prediction results using only the drug similarity network; Integrated: prediction results using the dual-layer integrated cell line-drug network model. (B) Pearson correlation distribution of the three different models using t-tests to determine differences between two groups. (C) Correlations between observed and predicted IC50 values for four drugs targeting the PI3K pathway using the dual-layer integrated cell line-drug network model. (D) Bar graph showing the prediction performance of three models for drugs targeting the ERK signaling pathway using experimental data from the CGP study, quantified using the Pearson correlation between the predicted and observed IC50 values. (E) Pearson correlation distribution of the three different models using t-tests to determine differences between two groups. (F) Correlations between observed and predicted IC50 values for four drugs targeting the ERK Signaling pathway.(TIF)Click here for additional data file.S6 FigSupport vector regression and random forest are used as predictors.(TIF)Click here for additional data file.S7 Fig(TIF)Click here for additional data file.S8 Fig(TIF)Click here for additional data file.S9 Fig(TIF)Click here for additional data file.S1 Table(XLS)Click here for additional data file.


One of the most described cellular defense mechanisms involved in resistance is glutathione (GSH), thus in this study, the effects of cisplatin on the total intracellular GSH level (GSHi) in some sensitive and resistant variants of human cell lines  were studied. MTT assay was performed to measure cytotoxicity of cisplatin (33.3 µM for 1 hour). Following cisplatin exposure, GSHi (per million cells) was evaluated using a photometrical assay up to 90 minutes. Our results indicate that there are significant differences between GSHi content of A2780CP and U373MGCP cells compared to other cell lines. Moreover, IC90 may be used as a factor for the prediction of cellular resistance to cisplatin.As a conclusion, it seems that resistance to cisplatin in different cell lines is more related with the diverse patterns of GSHi variations following cisplatin exposure than its original level, and/or its cellular increase or decrease. It is also suggested that GSH (mean) Standard GSH was purchased from Sigma. Tris buffer 1 N (pH = 8), TCA (Tricholoroacetic Acid) 10 % and DTNB  were purchased from Merck company. Solutions were prepared freshly for each set of experiment.Cell lines A375  and HepG2  were purchased from the Pasteur Institute of Iran. A2780S (human ovarian carcinoma-sensitive to cisplatin) and A2780CP (human ovarian carcinoma-resistant to cisplatin), U373MGS (human glioblastoma-sensitive to cisplatin) and U373MGCP (human glioblastoma-resistant to cisplatin) were obtained as a generous gift from Dr. Rakesh Goel, Ottawa Regional Cancer Center, Ottawa, Canada. Methods Cell culture2 for three passages before the start of experiments.  All cells were grown in DMEM/F12 Media supplied with 10% fetal bovine serum and 1% antibiotic. The cells were thawed and maintained in humidified 37 °C incubator with 5% COMeasuring intracellular GSH content 6 cells were centrifuged in 13500 rpm for 30 seconds, then the supernatant was removed and pellet was solved in 500 µL of distilled water. The cell lysate was treated with 10% TCA and incubated at room temperature for 30 minutes. The sample was again centrifuged in 13500 rpm for 30 seconds. 875 µL Tris buffer was added to 700 µL of supernatant before adding of DTNB (200 µL), and then the ultraviolet absorbance of samples was recorded at 412 nm. Cells were exposed to the 33.3 µM of cisplatin (as the human therapeutic serum concentration of this drug) for 15 minutes and GSH was then measured at 0, 15, 30, 45, 60 and 90 minutes, using of Ellmans method. Briefly, after exposure time, 10Cytotoxicity MTT assay6 cell per well) overnight, and were then exposed to different concentration of cisplatin. After 1 hour, MTT was added to each well in darkness, and plates were incubated for 4 hours. Then the supernatant was discarded gently and 200 uL of dimethyl sulfoxide was added to each well. The plates were then shaken for 30 minutes and the optical density was recorded in Elisa plate reader (Biotech®) at 570 nm . The results are expressed as mean ± standard error of the mean. Differences between means were evaluated by two way analysis of variance (ANOVA) by STATISTICA software . The Newman-Keuls test was used for Post Hoc analysis. The p-value < 0.05 was considered as a significance level. IC in-vitro. That is why the minimum more repeated exposure time in literatures of one hour has been selected to present statistically significant variations in these cell lines’ resistance to cisplatin. Ninety minutes is the length of observation for intracellular GSH that we could manage in the lab. Therefore, these timings are independent to each other. The selection of 15 minutes is to mimic the most relevant timing to the clinical exposure of tumor cells to cisplatin, 90 minutes observation was to follow the intracellular GSH alterations based on the general belief in clinical pharmacokinetics that the effect of a drug lasts for a minimum of 6 to 7 half lives (15 × 6 = 90). Sixty minutes exposure was the minimum time to observe the level of cellular resistance in the lab using MTT assay. To compare the intensity of resistance against cisplatin, MTT assay reveled IC50s of 0.9±0.1 µg/mL, 2±0.1 µg/mL, 2.3±0.1 µg/mL, 4.5±0.1 µg/mL, 2.8±0.1 µg/mL and 1.6±0.1 µg/mL for A2780S, A2780CP, U373MGS, U373MGCP, HepG2 and A375, respectively  are different from ovarian A2780, hepatic HepG2, skin A375 and gliblastoma U373MGS cells (P < 0.05) . These dn A2780S . HoweverThe initial (0 minutes) and the final (90 minutes) GSHi content of all understudied cell lines were statistically equal. Our results also indicate that not all resistant cell lines have high initial high level of GSHi. Although U373MGCP is a good example of a resistant cell line with high initial GSHi level compared to its sensitive strain, the initial GSHi level of A2780CP has no significant difference with its sensitive form, A2780S. Furthermore, the not-statistically significant difference of GSHi levels in sensitive cell lines during the time course of our study compared to fluctuations in GSHi content of resistant cell lines is another evidence conceding that not every cell line has a uniform, equal and alike pattern of GSHi level when it is exposed to cisplatin. Thus it can be concluded that the pattern of alterations in GSHi within time, can be a good indicator of resistance. Hence, what gives a better idea of the sensitivity or resistance ability of a cell might be the pattern of either GSHi fluctuations or its relatively steady state.50 in all cell lines and their related GSHi levels. This diversity in cells resistances might be due to the complexity of factors involved in cisplatin resistance and the intrinsic biology of tumor type and cell line, as Chain and Waxman indicated 90) for each cell line with the related IC50 (µg/mL) of cisplatin, indicates that there is a correlation between resistance to cisplatin and GSH(mean)90 for each cell line , although no correlation exists between IC50 (µg/mL) and the time that intracellular GSH reaches the highest level in the first hour (min), the highest concentration of intracellular GSH, and GSH(mean)60 in our studied cell lines. Comparison of the mean GSHi in 90 minutes (GSH50) and GSH(mean)90 were very much representing each other with a p-value of 0.028.With a comparison of As a conclusion, although it seems that considering the GSHi content in the tumor biopsy may be a great help to predict the resistance to cisplatin chemotherapy and might be a proximate guide for the selection of a more suitable chemotherapeutic choice, however, the mean GSHi content of the biopsy tissue cells exposed to cisplatin for a minimum of 90 minutes is much better predictor of resistant to cisplatin than the initial cellular content of GSHi. We are then proposing for the first time that the ability and speed of cells to respond to cisplatin with the increase in their GSHi levels is the most predictor of their resistance to cisplatin chemotherapy. Clinical investigations are needed to prove the accuracy of this finding on patients.


This involved construction and cloning of heavy and light chain DNAs into an expression vector and transduced into HEK 293 cells that constitutively produce the MLV gag, pro, and pol. The Pritumumab cell line was made by performing multiple rounds of transduction (multiplicity of > 1000 retrovector particles/cell) of the GPEx® parental cell line (GCHO) with retrovector made from the gene construct developed to express the Pritumumab antibody light and heavy chains. Five independent transductions were performed: two LC and three HC. Limiting dilution method was used to establish cloned cell lines. Top clones were selected based on antibody titer as determined by Protein A HPLC using a generic IgG standard. Single copies of genes were inserted efficiently in unique locations throughout the genome of the CHO cells to obtain genetically stable cell line that expressed high levels of the antibody. The selected clone represented more than a 40-fold increased production of the antibody over the original human-hybridoma. Flow cytometry binding and immunohistochemical analyses demonstrated comparability between the recombinant and the hybridoma forms of the protein. Furthermore, amino acid compositions of the two forms are identical. Thus, high level of specific productivity and titer of the CHO clone will allow the therapeutic antibody program to move forward into large and extended clinical trials of not only brain cancers but also of other types of cancers as well.Pritumumab is a natural human IgG1 kappa antibody that has been derived from a B cell isolated from regional draining lymph node of a cervical carcinoma patient. Specificity analysis of the antibody with human tissues showed the target antigen, altered tumor-associated vimentin, to be highly restricted to various epithelial cancers and not normal cells and tissues. In various Phase II clinical trials in Japan 249 patients with brain cancer were treated with the antibody. Overall response rate was between 25-30%, a 9-fold increase over standard care, with several survivors beyond 5-years post-treatment. Patients were on a low dose regimen of 1 mg given twice a week for a course of 24 weeks, suggesting Pritumumab to be safe and effective in patients with brain cancer. However, the yield from human-hybridoma was only about 1 picogram per cell per day. Therefore, there is a pressing need to produce adequate amount of GMP grade Pritumumab in a cost effective manner. The GPEx


Hsp104 disaggregase provides thermotolerance in yeast by recovering proteins from aggregates in cooperation with the Hsp70 chaperone. Protein disaggregation involves polypeptide extraction from aggregates and its translocation through the central channel of the Hsp104 hexamer. This process relies on adenosine triphosphate (ATP) hydrolysis. Considering that Hsp104 is characterized by low affinity towards ATP and is strongly inhibited by adenosine diphosphate (ADP), we asked how Hsp104 functions at the physiological levels of adenine nucleotides. We demonstrate that physiological levels of ADP highly limit Hsp104 activity. This inhibition, however, is moderated by the Hsp70 chaperone, which allows efficient disaggregation by supporting Hsp104 binding to aggregates but not to non-aggregated, disordered protein substrates. Our results point to an additional level of Hsp104 regulation by Hsp70, which restricts the potentially toxic protein unfolding activity of Hsp104 to the disaggregation process, providing the yeast protein-recovery system with substrate specificity and efficiency in ATP consumption.DOI:http://dx.doi.org/10.7554/eLife.15159.001 Under stressful conditions, such as high temperatures, many proteins lose their proper structure and clump together to form large irregular aggregates. To combat this effect, living organisms exposed to stress produce specialized proteins called chaperones, which can rescue the damaged proteins from aggregates.Studies into this “disaggregation” process often use budding yeast as a model organism. The protein-recovery machinery in this yeast is composed of a ring-shaped enzyme called Hsp104, together with a chaperone called Hsp70 and its partner Hsp40. The Hsp104 enzyme converts molecules of ATP into ADP and uses the energy released from the reaction to move, or “translocate”, damaged proteins through its central channel and release them from the aggregates.Previous studies had reported that ADP negatively affects Hsp104. Now, Kłosowska et al show that Hsp104 is almost inactive in a test-tube if the concentration of ADP is as high as that found inside a cell. This raises a question: how can Hsp104 efficiently remove proteins from aggregates in cells if the conditions are so unfavorable?Using purified proteins, Kłosowska et al. go on to show that Hsp104 is able to tolerate the level of ADP found inside cells thanks to the Hsp70 chaperone. The experiments show that ADP weakens Hsp104’s ability to bind proteins while Hsp70 supports this ability and counteracts the negative effect of ADP. Further experiments demonstrate that Hsp104 is less affected by ADP, and binds more readily to ATP, when it is translocating proteins. These findings explain how the yeast disaggregating machinery can work even at relatively high concentrations of ADP, and reveal a new control mechanism in the disaggregation process.Many important proteins have poorly organized fragments that can be recognized by Hsp104, and if Hsp104 was to bind to and translocate these proteins it could harm the cell. The findings of Kłosowska et al. suggest that Hsp70 helps Hsp104 to specifically bind to and act upon proteins in aggregates, while binding to partly unstructured proteins is limited by the high ADP concentration. Further studies are now needed to understand how the protein-recovery machinery can discriminate between aggregated and non-aggregated proteins.DOI:http://dx.doi.org/10.7554/eLife.15159.002 Saccharomyces cerevisiae this role is played by the Hsp104 chaperone, essential for development of tolerance to stress  and its homologs, Hsp104/ClpB proteins, are present in fungi, protozoans, plants and bacteria . Hsp104 S. cerevisiae) and its cochaperone Hsp40 (Ydj1 and Sis1 in S. cerevisiae)  . Hsp70 ievisiae) . Firstlyevisiae) . Secondlevisiae) . FinallyThe M-domain regulates the activity of the disaggregase. M-domains are assembled around the NBD1 ring in the hexamer. Each M-domain interacts with respective NBD1 via a network of ionic bonds and keeps Hsp104/ClpB proteins in a repressed, disaggregation-compromised state. Hsp70 binding to the M-domain causes its reposition, which transits Hsp104/ClpB into a derepressed state, manifested by highly stimulated ATPase and disaggregation activities . The latBoth Nucleotide Binding Domains of Hsp104 possess typical motifs for an AAA+ protein: Walker A, responsible for ATP binding, Walker B, essential for ATP hydrolysis and arginine fingers, necessary for the intersubunit communication . ATP hydRemarkably, Hsp104 has low affinity towards ATP and relatively high towards ADP, therefore ADP strongly inhibits ATP hydrolysis . For thiHere, we report that ADP compromises ATP hydrolysis, substrate binding and translocation by Hsp104 in the absence of the Hsp70 chaperone. We also demonstrate that protein disaggregation at physiological concentrations of ATP and ADP is only effective due to the Hsp70 assistance. Hsp70 promotes Hsp104 binding to the aggregated protein substrate but does not support processing of non-aggregated proteins. Furthermore, we show that the process of polypeptide threading by Hsp104 facilitates binding of ATP, strongly stimulating the ATPase activity of the disaggregase in the presence of ADP. Based on these findings we propose a new level of regulation of the disaggregase by its chaperone partner Hsp70.All known Hsp104 activities depend on ATP hydrolysis. In a rough comparison, Hsp104 binds ATP with at least an order of magnitude lower affinity than ADP . In the Next, we tested the influence of ADP on the Hsp104 activity at the physiological ATP concentration. Recent measurements in intact yeast cells show that ATP concentration oscillates around 2.6 mM , which iConsidering the strong effect of ADP on Hsp104 ATPase activity we asked how ATP and ADP at the physiological concentrations affect its protein translocation activity. Hsp104 in the absence of the cooperating Hsp70 chaperone is able to bind and translocate proteins which adopt disordered conformation but are not aggregated, such as reduced carboxymethylated lactalbumine (RCMLa) . We labeNext, we asked whether the strong inhibition of substrate processing by Hsp104 was associated with a defect in protein binding. We used an Hsp104 E285Q variant in the binding experiments because a complex between this mutant and the substrate is much more stable compared to the WT Hsp104 . The E28All the above results show that at the physiological concentrations of adenine nucleotides the biochemical activities of Hsp104 such as ATP hydrolysis, substrate binding and translocation through the central channel are very strongly inhibited.Hsp104 provides thermotolerance in vivo at the physiological concentrations of ATP and ADP, which implies that Hsp104 is active in disaggregation regardless of the disadvantageous nucleotide ratio . The recSince protein disaggregation is powered by ATP hydrolysis, we anticipated that the ATPase activity of Hsp104 would also be less affected by ADP during the recovery of aggregated proteins. To verify this, we measured the rate of ATP hydrolysis in the presence of the Hsp70-Hsp40 system  and aggregated GFP . We halvM apparent Kvalues: 4.2 mM for WT and 4.7 mM for D484K). Moreover, the ATPase activity of the derepressed D484K was strongly reduced under the unfavorable ATP:ADP ratio  Hsp70 acts upstream of Hsp104 (ii) the protein translocation process is associated with elevated ATPase activity of Hsp104 , implies that competition between the chaperones for their binding sites on the unfolded substrates may occur. This points to a plausible shielding role of Hsp70, protecting non-aggregated, intrinsically disordered proteins against the protein-threading activity of Hsp104.Translocation of the disordered substrates by HAP WT was at very low level at the physiological concentrations of adenine nucleotides in the presence of Hsp70 . In compDue to its remarkable ability to dissolve protein aggregates related to stress, aging, as well as to neurodegenerative diseases , Hsp104 The knowledge of ADP influence on Hsp104 is also important for interpretation of in vitro experiments. As observed during fRCMLa proteolysis by HAP or GFP disaggregation by Hsp104 D484K, accumulating ADP inhibited the reaction before the total pool of the protein substrate has been processed. This affected the renaturation yield, a parameter that should not therefore be used to evaluate Hsp104 activity. Furthermore, the kinetics of the reaction is strongly influenced by the efficiency of different ATP regeneration systems.In the light of Hsp104 dependence on adenine nucleotides, we integrate the established facts and reveal novel aspects of the complex interplay between the processes driving Hsp104-dependent disaggregation: the ATPase cycle, protein translocation trough the central channel, Hsp104 derepression trough the M-domain and the upstream action of Hsp70. The mechanism which emerges from our work implies that, in combination with the restrictive cellular ATP and ADP concentrations, Hsp70 provides an additional level of Hsp104 control, effectively limiting the toxic unspecific protein unfolding activity of Hsp104, at the same time supporting efficient recovery of aggregated proteins, the process crucial for cell survival after stress.HSP104 gene were introduced using QuickChange Site Directed Mutagenesis Kit . Published protocols were used for the purification of Hsp104 and its variants, Ydj1 . Sis1 was purified from E. coli BL21 (DE3) dnaK carrying plasmid pET11a-SIS1, a gift from Elizabeth Craig (University of Wisconsin). Purification procedure involved cell lysis with French pressure cell  and Q-sepharose FF , SP-sepharose FF  and hydroxyapatite (BIO-RAD) column chromatography. fRCMLa was prepared from α-lactalbumine  according to , containing 50 μCi/ml ATPγescribed . MeasureM apparent Kvalues for Hsp104 and Hsp104 D484K as described previously  were analyzed using reversed-phase high performance liquid chromatography (RP-HPLC) according to  on GBC 1Substrate binding assay was performed as described . ComplexfRCMLa  was incubated for 2 min with ClpP at 30 ºC and subsequently HAP was added. Reaction was carried out in the renaturation buffer, containing 2.6 mM or 10 mM ATP and, optionally, ADP or/and Ssa1 and Ydj1, as indicated. fRCMLa proteolysis by HAP-ClpP was monitored by following fluorescence anisotropy in the JASCO FP-8000 Fluorescence Spectrometer.-1 creatine kinase and 20 mM creatine phosphate. The samples of reaction mixtures before and after incubation were subjected to SDS-PAGE and Coomassie Brilliant Blue staining.α-casein (20 μM) was incubated at 30ºC with HAP (1 μM), ClpP (3.6 μM). Reaction was carried out in the renaturation buffer, containing 2.6 mM or 10 mM ATP and, optionally, 1 mM ADP, 2 μM Ssa1 and 0.5 μM Ydj1 or an ATP regeneration system comprising 0.2 mg ml-1) was thermally inactivated at 85ºC for 15 min. The reactivation reaction was carried out at 25ºC in the renaturation buffer, with 10 mM or 2.6 mM ATP, and, optionally, with the indicated levels of ADP or an ATP regeneration system comprising 0.2 mg ml-1 pyruvate kinase and 40 mM phosphoenolpyruvate or 0.2 mg ml-1 creatine kinase and 120 mM creatine phosphate. Renaturation was initiated by adding the indicated chaperone proteins: Hsp104 WT (1 μM), D484K , Ssa1 (2 μM), Ydj1 (0.5 μM) or Sis1 (0.4 μM). Sis1 was used instead of Ydj1 for reactions requiring longer incubation times, due to observed low stability of Ydj1. GFP fluorescence was detected in JASCO FP-8000 Fluorescence Spectrometer or a Beckman Coulter DTX880 microplate reader.GFP renaturation assay was performed as described previously , with sl In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by John Kuriyan as the Senior Editor.Thank you for submitting your article "Inhibition by ADP restricts the protein remodeling activity of Hsp104 chaperone to Hsp70 assisted disaggregation" for consideration by The reviewers have discussed your paper and considered each other's comments and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:All three reviewers recognized the significance of your findings and the elegance of the model you propose.The main critiques to emerge from the review process concern the strength of the correlation you have established between (A) The ability of substrate – introduced by the Hsp70/40 pair – to stimulate ATP binding  and (B) the observed rate of substrate translocation through Hsp104. And the robustness of this correlation across a range of ATP/ADP concentrations. This point is crucial especially given the comments of reviewer three on the two papers cited in reference for the choice of the canonical concentration of ATP and ADP in the yeast cytosol: As the concern regarding the validity of the choice of nucleotide concentration would be mitigated if the aforementioned correlations were documented over a broad range of ATP/ADP concentrations.A specific weakness were the differences in conditions under which the ATPase assays and substrate translocation assays were performedEssential revisions:1) Expanding If possible these ADP titrations could be carried out at different ATP concentrations (e.g. 2.6 mM and 10 mM) but we leave this last point to the author's discretion.2) HAP-D484K seems less sensitive to ATP/ADP mixtures  as compaDiscretional points to take account of:1) Please provide further information validating your choice of ATP/ADP concentrations in the yeast cytosol addressing the concerns of reviewer 3 in regards to Ytting et al. 2012 and Özalp et al. 2010 as well as the potential role of adenylated kinase in converting ADP to AMP and ATP, as articulated by the comments of reviewer 1.2) Binding of RCLMa to Hsp104 is suggested to increase affinity for ATP , thereby3) Please review the figure legends with an eye to rendering them interpretable independently of the text. The sophisticated reader should be able to grasp the essence of the paper simply by visiting the figures and figure legends.Reviewer #1:This paper is concerned with the mechanism of protein de-aggregation by the yeast chaperone Hsp104. It sheds light on the dependence of de-aggregation by Hsp104 on the collaboration of an Hsp70-Hsp40 pair. The key findings concern a feature of Hsp104, namely its low affinity for ATP and its susceptibility to inhibition by ADP.not due to allosteric activation of Hsp104 by Hsp70-Hsp40 but rather by their ability to "feed" substrate proteins to the Hsp104 machine. These last observations are rendered interpretable by the finding that substrate proteins stimulate the ATPase activity of Hsp104 and overcome the inhibitory effect of ADP. A final physiological touch is added by the observation that the stimulatory effect of Hsp70-Hsp40 of the reluctant (ADP-inhibited) Hsp104 is limited to aggregated proteins and does not extend to natively disordered protein. The story hangs together well.In what appear to be a series of well done in vitro experiments, the authors confirm the relative inactivity of Hsp104 at (what they claim are) physiological ADP/ATP concentrations and the marked stimulatory activity of the Hsp70-Hsp40 pair at these physiological circumstances. Using a mutant Hsp104 that is frozen in the active allosteric state  they go on to show that this stimulatory effect is I am not in a position to judge the level of novelty and there is a concern that the central conclusion – that these mechanistic features evolved to entrain Hsp104 to selectively de-aggregate and not to interfere with natively-disordered proteins – rests on too narrow a comparison of model substrates . A mutant Hsp104 that is not inhibited by ADP would be extremely useful to test the authors' hypothesis, but I assume one does not exist.Finally, despite references to the contrary (duly provided) I am not sure about the relevance of millimolar concentrations of ADP in yeast: I thought ADP gets quickly converted to ATP and AMP by adenylate kinase, an enzyme that is also present in yeast, with the effect that ADP concentration are kept very low.Reviewer #2:Hsp104 cooperates with the Hsp70 chaperone system in the reactivation of aggregated proteins. Hsp70 recruits Hsp104 to protein aggregates by interacting with Hsp104 M-domains, thereby liberating Hsp104 ATPase activity from M-domain repression. Derepressed Hsp104 M-domain mutant variants exhibit unleashed ATPase activity and become partially independent from Hsp70. Here, the authors show that physiological ATP/ADP ratios fine-tune Hsp70-Hsp104 cooperation by making Hsp104 activity more dependent on the Hsp70 partner. It is demonstrated that accumulation of ADP strongly poisons Hsp104 ATPase and threading activity in absence of Hsp70. This can be explained by previous findings showing higher affinity of Hsp104 for ADP as compared to ATP. Remarkably, the inhibitory effect of ADP is overcome in presence of Hsp70, ensuring that Hsp104 activity is restricted to Hsp70-coated protein aggregates. The authors provide evidence that substrate binding and translocation increases Hsp104 affinity for ATP, overruling ADP inhibition. The substrate targeting function of Hsp70 is in line with the presented model.The presented findings are novel and interesting and provide valuable detailed insights into Hsp104 regulation and the mechanism of protein disaggregation. How exactly ADP inhibition is overcome upon substrate binding remains speculative. The authors suggest that ADP release from Hsp104 NBD2 is triggered upon substrate interaction. Direct evidence for this scenario is missing but is also not considered to be mandatory for acceptance of the manuscript. The following aspects should be addressed in a revised manuscript:1) The authors frequently use varying nucleotide concentrations and ATP/ADP ratios. It is advised to perform all analysis (e.g. entire 2) The authors show that the apparent affinity for ATP is reduced in presence of substrate fRCLMa. On the other hand, the processing of exactly the same substrate is strongly inhibited by ADP. Can the authors provide a rationale for these seemingly conflicting data? Is the fRCLMa concentration used in the degradation assays simply too low?3) A derepressed Hsp104 variant seems less sensitive to ADP inhibition . In contrast ADP only inhibits 50% of the Hsp104-mediated disaggregation activity in the presence of Hsp70-Hsp40 at 1 mM ADP. The relative insensitivity of the disaggregation activity to ADP inhibition is explained by increased affinity for ATP during substrate translocation. The authors propose that ADP inhibits Hsp104 in the cell, and that Hsp70-Hsp40 promotes binding of Hsp104 to aggregates resulting in translocation that activates the ATPase activity.This manuscript provides a series of elegant in vitro experiments and a simple attractive model that explains how Hsp104 activity is regulated in the cell. The manuscript is well written, easy to understand and addresses an important biological mechanism. A complementary set of in vivo experiments to test the proposed model would make the study more conclusive.1) Relevance of Hsp104-regulation by ADP inhibition depends on a specific in vitro 'physiological condition'A concern that has to be raised is that the observed regulation of Hsp104 activity does not reflect in vivo behavior of the protein. This concern becomes important in context of this study since the experimental setup contrasts the traditional biochemical approach when studying enzymes by not employing conditions under which Hsp104 displays optimal activity (For Hsp104: high ATP and low ADP). It is under such non-optimal buffer conditions  that Hsp104 displays the potentially physiologically relevant behavior of activity-assay specific ADP inhibition; lower ADP inhibition of the Hsp70-Hsp40-dependent disaggregation activity than of the substrate-free ATPase and translocation activities. Thus, a concern is that the in vitro assay conditions are not a good proxy for the in vivo conditions and suffer from problems with biological relevance.1.1) Literature problemThe authors refer to Ytting et al. 2012 and Özalp et al. 2010 to argue that the ADP concentration in yeast cells oscillates around 1 mM (row 95). However, Ytting et al. 2012 does not report on ADP levels and Özalp et al. 2010 reports on ATP measurements and calculated ADP levels _assuming_ a total adenine nucleotide pool of 3.6 mM. Thus, the notion that the used nucleotide levels mimic physiological conditions is not well supported by the cited papers.1.2) Robustness of the modelA key question is whether the ADP inhibition of Hsp104 is apparent under various buffer conditions . If the Hsp104 behavior is robust under many different buffer conditions, it is likely to represent a mechanism of physiological importance. If the window of function for their proposed mechanism is narrow, it is likely not of physiological importance. Can the authors experimentally define the window of ADP-ATP ratios and total nucleotide concentrations where their proposed ADP-inhibition mechanism would be functional?2) Targeting Hsp104 to aggregated substrates by Hsp70The authors propose that Hsp70 not only activates Hsp104 by repositioning the M-domain but also relieves ADP-inhibition by physically linking Hsp104 to translocation competent substrates (for example see paragraph starting at row 364). This important part of their model is readily testable by linking Hsp104 or its M-domain repositioned variant D484K to the aggregates using fusions to dimerization domains . Their model predicts that merely bringing Hsp104 to the vicinity of aggregates should result in acceleration of disaggregation and ATPase activity in the presence of ADP. Such as direct test of the proposition that Hsp70-independent physical targeting regulates the ADP-inhibited ATPase rates potentially strengthens their model. Essential revisions:1) Expanding If possible these ADP titrations could be carried out at different ATP concentrations (e.g. 2.6 mM and 10 mM) but we leave this last point to the author's discretion.The suggested experiment was performed and included in the manuscript (The degree of inhibition of the ATPase activity  and of t2) HAP-D484K seems less sensitive to ATP/ADP mixtures  as compaThe experiment showing the influence of ADP on degradation of fRCMLa by HAP D484K and ClpP is now presented . At the These results are consistent with the observed effects of ADP on ATP hydrolysis and on the disaggregation activity displayed by Hsp104 D484K alone. Hsp104 D484K did not show higher affinity towards ATP (in the third paragraph of the aforementioned subsection), its relative inhibition by ADP was similar to the WT  and Hsp1At the same time, we do not exclude the possibility that stimulation of Hsp104 activity associated with the interaction between Hsp70 and the M-domain might facilitate Hsp70-dependent binding to aggregates, which has been stated in the Discussion (eighth paragraph).Discretional points to take account of:1) Please provide further information validating your choice of ATP/ADP concentrations in the yeast cytosol addressing the concerns of reviewer 3 in regards to Ytting et al. 2012 and Özalp et al. 2010 as well as the potential role of adenylated kinase in converting ADP to AMP and ATP, as articulated by the comments of reviewer 1.Several studies , including our own measurements  show thaWe agree with the reviewers’ and editors’ comments that our choice of ATP/ADP concentrations was not explained enough in the previous version of the manuscript. In the revised version we included more comprehensive justification . We also stressed that the actual “physiological ATP/ADP concentrations” vary depending on cell metabolism (subsection “ADP inhibits the ATPase activity and polypeptide processing by Hsp104”). For that reason and according to the reviewers’ and editors’ suggestions, we assessed the degree of Hsp104 inhibition at a broad range of ADP concentrations .Therefore, we believe that our experimental conditions allow to estimate the degree of Hsp104 inhibition under the range of ATP/ADP concentrations that occur in yeast cells.From the analyses of ADP level in yeast cells we conclude that the rate of ADP conversion to AMP or ATP (by Adenylate Kinase) is low enough to allow for residual ADP level to be at the millimolar range.2) Binding of RCLMa to Hsp104 is suggested to increase affinity for ATP , therebyIn the response to the reviewers’ and editors’ comments we assessed the degree of ADP inhibition of fRCMLa proteolysis by HAP-ClpP at various concentrations of fRCMLa . The resThis observation be explained by dividing fRCMLa processing by Hsp104 into two phases: protein binding and translocation. Protein binding is highly affected by ADP  and protWe speculated that with decreasing ATP:ADP ratio the frequency of occurrence of Hsp104 conformations allowing for substrate binding decreases. When binding to Hsp104 is available, increasing fRCMLa concentration increases the probability of its successful binding to Hsp104 (or HAP), resulting in higher observed rate of fRCMLa proteolysis. However, substrate concentration does not seem to affect, how often Hsp104 is in a binding-competent state, the latter being determined by ATP:ADP ratio. This could explain, why the relative effect of ADP is similar regardless of fRCMLa concentration. We have revised our Discussion in this context (sixth paragraph).3) Please review the figure legends with an eye to rendering them interpretable independently of the text. The sophisticated reader should be able to grasp the essence of the paper simply by visiting the figures and figure legends.To improve the clarity of the presentation of our results, we added experimental scheme in the Reviewer #1:This paper is concerned with the mechanism of protein de-aggregation by the yeast chaperone Hsp104. It sheds light on the dependence of de-aggregation by Hsp104 on the collaboration of an Hsp70-Hsp40 pair. The key findings concern a feature of Hsp104, namely its low affinity for ATP and its susceptibility to inhibition by ADP.In what appear to be a series of well done in vitro experiments, the authors confirm the relative inactivity of Hsp104 at (what they claim are) physiological ADP/ATP concentrations and the marked stimulatory activity of the Hsp70-Hsp40 pair at these physiological circumstances. Using a mutant Hsp104 that is frozen in the active allosteric state  they go on to show that this stimulatory effect is not due to allosteric activation of Hsp104 by Hsp70-Hsp40 but rather by their ability to "feed" substrate proteins to the Hsp104 machine. These last observations are rendered interpretable by the finding that substrate proteins stimulate the ATPase activity of Hsp104 and overcome the inhibitory effect of ADP. A final physiological touch is added by the observation that the stimulatory effect of Hsp70-Hsp40 of the reluctant (ADP-inhibited) Hsp104 is limited to aggregated proteins and does not extend to natively disordered protein. The story hangs together well.I am not in a position to judge the level of novelty and there is a concern that the central conclusion – that these mechanistic features evolved to entrain Hsp104 to selectively de-aggregate and not to interfere with natively-disordered proteins – rests on too narrow a comparison of model substrates . A mutant Hsp104 that is not inhibited by ADP would be extremely useful to test the authors' hypothesis, but I assume one does not exist.Finally, despite references to the contrary (duly provided) I am not sure about the relevance of millimolar concentrations of ADP in yeast: I thought ADP gets quickly converted to ATP and AMP by adenylate kinase, an enzyme that is also present in yeast, with the effect that ADP concentration are kept very low.Please see our response to the editors’ discretional point 1, Reviewer #2:The presented findings are novel and interesting and provide valuable detailed insights into Hsp104 regulation and the mechanism of protein disaggregation. How exactly ADP inhibition is overcome upon substrate binding remains speculative. The authors suggest that ADP release from Hsp104 NBD2 is triggered upon substrate interaction. Direct evidence for this scenario is missing but is also not considered to be mandatory for acceptance of the manuscript. The following aspects should be addressed in a revised manuscript:1) The authors frequently use varying nucleotide concentrations and ATP/ADP ratios. It is advised to perform all analysis  and ADP (1 mM) concentrations . The degWe also measured ATP hydrolysis by Hsp104 D484K at 10 mM ATP and at increasing concentrations of ADP , analogoMapparent K values shown in The disaggregation activity of Hsp104 D484K in the absence of Hsp70 was at a very low level and the use of 10 mM concentration of ATP in the reaction mixtures allowed for higher rate of GFP renaturation. The resulting better signal to noise ratio enabled us to detect linear increase in GFP fluorescence even at very low GFP concentrations and assess precisely the 2) The authors show that the apparent affinity for ATP is reduced in presence of substrate fRCLMa. On the other hand, the processing of exactly the same substrate is strongly inhibited by ADP. Can the authors provide a rationale for these seemingly conflicting data? Is the fRCLMa concentration used in the degradation assays simply too low?Please see our response to the editors’ discretional revision point 2.3) A derepressed Hsp104 variant seems less sensitive to ADP inhibition  Relevance of Hsp104-regulation by ADP inhibition depends on a specific in vitro 'physiological condition'A concern that has to be raised is that the observed regulation of Hsp104 activity does not reflect in vivo behavior of the protein. This concern becomes important in context of this study since the experimental setup contrasts the traditional biochemical approach when studying enzymes by not employing conditions under which Hsp104 displays optimal activity (For Hsp104: high ATP and low ADP). It is under such non-optimal buffer conditions  that Hsp104 displays the potentially physiologically relevant behavior of activity-assay specific ADP inhibition; lower ADP inhibition of the Hsp70-Hsp40-dependent disaggregation activity than of the substrate-free ATPase and translocation activities. Thus, a concern is that the in vitro assay conditions are not a good proxy for the in vivo conditions and suffer from problems with biological relevance.1.1) Literature problemThe authors refer to Ytting et al. 2012 and Özalp et al. 2010 to argue that the ADP concentration in yeast cells oscillates around 1 mM (row 95). However, Ytting et al. 2012 does not report on ADP levels and Özalp et al. 2010 reports on ATP measurements and calculated ADP levels _assuming_ a total adenine nucleotide pool of 3.6 mM. Thus, the notion that the used nucleotide levels mimic physiological conditions is not well supported by the cited papers.Please see our response to the editors’ discretional point 1. Reference Ytting et al. (2012) was removed from the manuscript in response to reviewer’s comment.1.2) Robustness of the modelA key question is whether the ADP inhibition of Hsp104 is apparent under various buffer conditions . If the Hsp104 behavior is robust under many different buffer conditions, it is likely to represent a mechanism of physiological importance. If the window of function for their proposed mechanism is narrow, it is likely not of physiological importance. Can the authors experimentally define the window of ADP-ATP ratios and total nucleotide concentrations where their proposed ADP-inhibition mechanism would be functional?The revised figures For comparison, ATP:ADP ratios reported in previous studies and in this study  are pres2) Targeting Hsp104 to aggregated substrates by Hsp70The authors propose that Hsp70 not only activates Hsp104 by repositioning the M-domain but also relieves ADP-inhibition by physically linking Hsp104 to translocation competent substrates (for example see paragraph starting at row 364). This important part of their model is readily testable by linking Hsp104 or its M-domain repositioned variant D484K to the aggregates using fusions to dimerization domains . Their model predicts that merely bringing Hsp104 to the vicinity of aggregates should result in acceleration of disaggregation and ATPase activity in the presence of ADP. Such as direct test of the proposition that Hsp70-independent physical targeting regulates the ADP-inhibited ATPase rates potentially strengthens their model.We agree that it would support our project if a proposed experiment turned out to be successful. However, the approach suggested by reviewer 3 poses some serious technical problems. Firstly, to introduce a dimerization domain in a vicinity of the entrance to the central channel, N-terminal fusion (or close to the N-terminus) would be required. From our experience, fusion tags introduced close to the N-terminus of Hsp104 strongly impair the protein activity and instead C-terminal tags are used . Secondly,if a dimerization domain was fused to a substrate protein prior to aggregation, its structure would be lost under the brutal aggregation conditions. One solution would be to carry out fusion with an unstable protein, which aggregates under conditions at which the dimerization domain is stable. Since the only labile model protein for studying disaggregation is Luciferase (standard aggregation temperature is 48°C), which requires Hsp70 for proper refolding, therefore a new assay system with other model substrate would have to be developed. Other solution would be to crosslink a dimerization domain to a pre-formed aggregate. Unfortunately, the latter approach would result in the dimerization domain placed only at the surface of an aggregate and after disaggregation of the outer layer by Hsp104 the interaction site would be lost. Due to the emerging challenges, testing our model by tethering Hsp104 to aggregates is a substantial project on its own.


One way to promote cell death induction is to use Smac mimetics (SMs),2 which are small molecules mimicking the proapoptotic protein Smac. Smac inhibits members of the inhibitor of apoptosis protein (IAP) family of which cellular IAP 1 (cIAP1) and 2 (cIAP2) are important for apoptotic signaling involving the death receptor TNFα receptor 1 (TNFR1). SMs, thereby, re-direct the TNFR1 from NF-κB signaling towards apoptosis. This is achieved by downregulating, and thus inhibiting, cIAP1 and cIAP2 and their function as ubiquitinators of proteins recruited to the TNFR1. Without cIAP-mediated ubiquitination of proteins such as receptor interacting protein 1 (RIP1), the NF-κB signaling cannot be conveyed. Instead, a complex containing RIP1 recruits caspase 8, activating the caspase cascade and apoptosis. In addition to redirecting the TNFR1, SM-mediated downregulation of cIAPs also leads to accumulation of NF-κB inducing kinase (NIK) and the initiation of the noncanonical NF-κB signaling pathway, which can result in induction of TNFα synthesis and possible stimulation of the TNFR1 in an autocrine manner. Whether SM treatment leads to upregulation of TNFα or not has been suggested to determine sensitivity to SMs.5The evasion of cell death is an important contribution to cancer development and may also confer resistance to chemo and radiotherapy.6 However, for basal-like breast cancers, which typically are estrogen- and progesterone receptor-negative as well as lack ERBB2 amplification, there is still no targeted treatment and they are more difficult to treat.8 Therefore, strategies to treat this patient group are warranted. Some basal-like breast cancer cell lines, such as MDA-MB-231 are indeed sensitive to SMs suggesting that this pathway may be a potential target for therapy. However, most cell lines are resistant to SM as single treatment. Now, Cornmark et al.9 present a report demonstrating that basal-like breast cancer cell lines can be sensitized to SMs by activating protein kinase C (PKC)  . They repathway9 .α production. However, a majority of cancer cell lines are insensitive to SMs that seems to be due to an inability to induce TNFα expression.3 To investigate mechanisms explaining differences in Smac sensitivity, the response to SM in sensitive MDA-MB-231 and insensitive MDA-MB-468 breast cancer cell lines was compared. One striking difference was that the sensitive cell line had many more upregulated genes after SM treatment compared with the insensitive cell line where few genes were differentially expressed. However, both cell lines responded to SM with increased expression of the BIRC3 gene that encodes cIAP2. Furthermore, both cell lines responded to SM in the same way by initiating noncanonical signaling as detected by p100 processing and p52 nuclear translocations.It has previously been described that cancer cell lines sensitive to SM as a single agent are dependent on autocrine TNFκB signaling pathway has been suggested as the mediator of SM-induced TNFα production.10 Nevertheless, in MDA-MB-468 cells, SM induces the noncanonical pathway and gene expression (BIRC3 induction), but still no increase in TNFα. This raises the possibility that other pathways are critical for SM-mediated TNFα synthesis. Therefore, Cornmark et al.9 used a siRNA approach to target either NIK and the noncanonical NF-κB pathway and/or IKKβ and the canonical NF-κB pathway. They found that only downregulation of both pathways resulted in a decrease in SM-mediated TNFα production that also resulted in a suppression of SM-induced cell death. However, although the absolute TNFα levels were suppressed, the relative induction still persisted as TNFα levels were markedly suppressed by the siRNAs also in unstimulated cells. This suggests that pathways other than NF-κB may be necessary to obtain sufficient TNFα levels to induce cell death.The noncanonical NF-et al.9 have shown that the SM-sensitive breast cancer cell line MDA-MB-231 needs either the canonical or the noncanonical pathway to mediate TNFα induction and cell death. Even if both pathways are suppressed, SM can still increase TNFα synthesis to some extent, suggesting that other mechanisms are also involved.Thus, Cornmark 2 that a simultaneous induction of TNFα may be one strategy to increase SM sensitivity. In basal-like breast cancer cells, this can be achieved by PKC activation that may open up for novel ways to target this group of breast cancers.Taken together, these results further support previous studies


Increased expression of the epidermal growth factor receptor (EGFR) is observed in more than 90% of all head and neck squamous cell carcinomas (HNSCC). Therefore, EGFR has emerged as a promising therapeutic target. Nevertheless, drug resistance remains a major challenge and an important potential mechanism of drug resistance involves the hypoxic tumor microenvironment. Therefore, we investigated the cytotoxic effect of the EGFR-targeting agents cetuximab and erlotinib under normoxia versus hypoxia.2) for 24 or 72 h immediately after drug addition. Cell survival was assessed with the sulforhodamine B assay. Cetuximab and erlotinib established a dose-dependent growth inhibition under both normal and prolonged reduced oxygen conditions in all three HNSCC cell lines. However, a significantly increased sensitivity to cetuximab was observed in SC263 cells exposed to hypoxia for 72 h (p = 0.05), with IC50 values of 2.38 ± 0.59 nM, 0.64 ± 0.38 nM, and 0.10 ± 0.05 nM under normoxia, hypoxia for 24 h and hypoxia for 72 h, respectively. LICR-HN5 cells showed an increased sensitivity towards erlotinib when cells were incubated under hypoxia for 24 h (p = 0.05).Three cetuximab-sensitive HNSCC cell lines  were treated with either cetuximab or erlotinib. Cells were incubated under normal or reduced oxygen conditions (<0.1% OOur results suggest that both EGFR-inhibitors cetuximab and erlotinib maintain their growth inhibitory effect under hypoxia. These results suggest that resistance to anti-EGFR therapy in HNSCC is probably not the result of hypoxic regions within the tumor and other mechanisms are involved. Personalized medicine using targeted therapies is a promising alternative to the current conventional treatment strategies , which are only effective in 50% of head and neck squamous cell carcinoma (HNSCC) patients . IncreasTumor hypoxia, or low oxygen concentration, is a result of disordered vasculature found in almost all solid tumors, including HNSCC, and is evoked by rapid rate of tumor growth, poor tumor perfusion or transiently disrupted tumor blood flow. Cells exposed to prolonged hypoxia accumulate changes in their growth properties and DNA, leading to chemo- and radioresistance and enhanced metastatic potential , 10. In Tumor cells will adapt quickly to the hypoxic stress by regulating the expression of various genes such as hypoxia-inducible factors (HIFs) and vascular endothelial growth factor (VEGF). The HIFs are transcription factors that play an essential role in the cellular response to hypoxic stress, as they are able to activate the EGFR signaling pathway. Several studies have shown that hypoxic regions within the tumor have an increased expression of EGFR compared to normoxic regions –15. HypoGiven the link between hypoxia and EGFR signaling, we hypothesized that hypoxia and its subsequent signaling might be responsible for anti-EGFR drug resistance. Therefore, the present study investigates cell survival after treatment with the EGFR-targeting monoclonal antibody cetuximab and the EGFR tyrosine kinase inhibitor erlotinib under normoxic versus hypoxic conditions, in three cetuximab-sensitive HNSCC cell lines.2/95% air atmosphere at 37°C. Cells were tested for mycoplasma infection through regular testing .Three cetuximab-sensitive, EGFRvIII negative HNSCC cell lines SC263, LICR-HN2 and LICR-HN5 were included in this study. All cell lines were grown as monolayers in Dulbecco’s Modified Eagle Medium (DMEM), supplemented with 10% fetal calf serum, 2 mM glutamine and 1% penicillin/streptomycin. All media and supplements were obtained from Invitrogen . Cultures were maintained in exponential growth in a humidified 5% CO2, 5% CO2, 95% N2) were achieved in a Bactron IV anaerobic chamber  and we used the hypoxia model previously optimized and characterized [2. Hypoxic incubation was initiated after cells had been cultured under normoxic conditions overnight, allowing attachment to culture dishes.Hypoxic conditions  was diluted in sterile PBS and erlotinib  was diluted in DMSO.treated/ODuntreated) × 100. The IC50 value, representing the drug concentration reducing cell growth to 50%, was calculated using WinNonlin software with pharmacodynamic model 107. All experiments were performed in triplicate.Cytotoxicity studies were performed in 48 well plates and optimal seeding densities for each cell line were determined to ensure exponential growth during the assay. After an overnight recovery period, cells were treated either with 0–10 nM cetuximab for 168 h or with 0–20 μM erlotinib for 72 h. Cells were incubated under normal or reduced oxygen conditions for 24 or 72 h immediately after addition of the drug. After incubation, cell survival was evaluated by the sulphorhodamine B assay, as previously described . OpticalAll experiments were performed at least in triplicate. Results are presented as mean ± standard deviation. Possible significant differences (p ≤ 0.05) were evaluated with Mann–Whitney U test using SPSS v20.0 software .As the presence of tumor hypoxia may confer resistance to EGFR inhibitors, we performed growth inhibition experiments under normoxic and hypoxic conditions for the tyrosine kinase inhibitor erlotinib and the monoclonal antibody cetuximab on three human, cetuximab sensitive HNSCC cell lines.50 values of these three oxygen conditions were calculated for each cell line (Table The cytotoxicity profiles of the HNSCC cell lines under normoxic and hypoxic conditions (24 and 72 h) for erlotinib (0–20 µM) are shown in Figure ne Table .Figure 1Erlotinib established a dose-dependent growth inhibition under both normal and reduced oxygen conditions in all three HNSCC cell lines. Furthermore, LICR-HN5 cells were more sensitive to erlotinib when exposed to hypoxia for 24 h compared with normoxic LICR-HN5 cells (p = 0.05). However, this increased sensitivity to erlotinib was not observed when exposed to hypoxia for 72 h. As such, our results suggest that resistance to erlotinib could not be elicited by hypoxic incubation for 24 or 72 h.50 values of these three oxygen conditions were calculated for each cell line (Table The cytotoxicity profiles of the HNSCC cell lines under normoxic and hypoxic conditions (24 and 72 h) for cetuximab (0–10 nM) are shown in Figure 50 values (p = 0.05 in both cases). Overall, our results showed that no therapeutic resistance towards cetuximab could be evoked by prolonged hypoxia in these HNSCC cells.The effect of cetuximab on cell survival was not affected by the presence or absence of oxygen in LICR-HN2 and LICR-HN5 cells. However, SC263 cells were more sensitive to higher concentrations of cetuximab when cells were incubated under reduced oxygen tension for 24 or 72 h, resulting in significantly decreased ICEGFR and K-Ras mutations in HNSCC would likely preclude a major role for these mutations as predictive biomarker [As the tumor and its surrounding microenvironment can affect each other and the (extremely) low prevalence of iomarker , 25, druiomarker .In HNSCC patients, high levels of hypoxia-associated factors are associated with relapse following induction therapy that included cetuximab, and co-localization of EGFR and hypoxia markers are associated with poor outcome , 26. WitIn contrast, however, our study demonstrated that prolonged hypoxia (24 and 72 h) did not induce resistance towards cetuximab and erlotinib therapy in three HNSCC cell lines. Therefore, no predictive biomarkers with regard to drug resistance and hypoxia could be identified. In line with our observations, only few papers were able to illustrate hypoxia-induced treatment resistance , 29 and Concerning the EGFR-targeted monoclonal antibodies, it has been speculated that hypoxia enhances the sensitivity to the cytotoxic effect of these drugs. For example, cetuximab was more cytotoxic against hypoxic than well-oxygenated A431 lung cancer cells grown in vitro and it reduced the overexpression of hypoxia markers like HIF-1α, CA9 and VEGF . In addiSimilarly, considering the effect of EGFR-targeting tyrosine kinase inhibitors under reduced oxygen conditions, several studies indicated that treatment with gefitinib or erlotinib was associated with a dramatic reduction in the proportion of viable hypoxic tumor cells , 36–40. Our results suggest that both anti-EGFR therapeutics cetuximab and erlotinib maintain their efficacy both under 24 h and 72 h of reduced oxygen tension in the HNSCC cell lines included in our study. Furthermore, an increased sensitivity to cetuximab was observed in SC263 cells exposed to hypoxia for 72 h and LICR-HN5 cells exhibited an increased sensitivity to erlotinib when exposed to hypoxia for 24 h. Our results, therefore, suggest that resistance to anti-EGFR therapy in HNSCC is most likely not the result of hypoxic regions within the tumor and, hence, other mechanisms are involved. One important limitation of our in vitro study is the lack of the real microenvironment that surrounds tumors in vivo. As our research was based on cell lines in vitro, no other factors of the tumor microenvironment were taken into account. Therefore, further studies using tumor animal models would certainly be warranted.


Ehrlichia canis, a small gram-negative coccoid bacterium that infects circulating monocytes. The disease is transmitted by the brown dog tick Rhipicephalus sanguineus s.l. and is acknowledged as an important infectious disease of dogs and other members of the family Canidae worldwide. E. canis is routinely cultured in vitro in the canine monocyte-macrophage cell line DH82 and in non-vector Ixodes scapularis tick cell lines, but not in cells derived from its natural vector. Here we report infection and limited propagation of E. canis in the tick cell line RSE8 derived from the vector R. sanguineus s.l., and successful propagation through six passages in a cell line derived from the experimental vector Dermacentor variabilis. In addition, using bacteria semi-purified from I. scapularis cells we attempted to infect a panel of cell lines derived from non-vector species of the tick genera Amblyomma, Dermacentor, Hyalomma, Ixodes and Rhipicephalus with E. canis and, for comparison, the closely-related Ehrlichia ruminantium, causative agent of heartwater in ruminants. Amblyomma and non-vector Dermacentor spp. cell lines appeared refractory to infection with E. canis but supported growth of E. ruminantium, while some, but not all, cell lines derived from Hyalomma, Ixodes and Rhipicephalus spp. ticks supported growth of both pathogens. We also illustrated and compared the ultrastructural morphology of E. canis in DH82, RSE8 and I. scapularis IDE8 cells. This study confirms that E. canis, like E. ruminantium, is able to grow not only in cell lines derived from natural and experimental tick vectors but also in a wide range of other cell lines derived from tick species not known to transmit this pathogen.Canine monocytic ehrlichiosis is caused by  Ehrlichia canis   is a serious and sometimes fatal tick-borne disease of members of the family Canidae, predominantly dogs . The aettsiales)  that inveus s.l. ; experimreported , while tpathogen .E. canis in monocyte cell cultures derived from dogs in the acute phase of the disease was reported over 40 years ago  was isolated from blood of a naturally-infected dog into the I. scapularis cell line IDE8, and subsequently transferred into canine DH82 cells , 0.1% NaHCO3 and 10 mM HEPES but without antibiotics (ECM)   with weeE. canis growth was monitored at 1–3 week intervals by microscopic examination of Giemsa-stained cytocentrifuge smears. Briefly, cells were resuspended and 50 μl aliquots of cell suspension were centrifuged for 5 min at 1000 x g (Shandon Cytospin 2) and air-dried. The resultant smears were fixed in technical methanol for 3 min and stained in 10% Giemsa for 20 min    was maineviously . Infecte6 and 1 × 107 IDE8 or ISE6 cells infected with E. canis or E. ruminantium at a rate >50% were harvested by pipetting, 2–5 ml cell suspension was centrifuged at room temperature for 5 min at 200 x g, the supernatant was discarded and the cell pellet was resuspended in 500 μl of trypsin (500 μg/ml in PBS) and incubated for 20 min at 37 °C. The original volume was restored by adding ECM (E. canis) or L-15B (E. ruminantium) medium and the cell suspension was passed 10 times through a bent 26G needle to mechanically rupture the cells and release the intracellular bacteria. The resultant suspension was centrifuged at room temperature for 5 min at 1500 x g. Supernatant containing cell-free bacteria was collected and 200–500 μl aliquots were added to uninfected cell cultures growing in flat-sided tubes in ECM (E. canis) or complete L-15B (E. ruminantium). Uninfected cells were also inoculated on some occasions with 200–500 μl aliquots of supernatant from Ehrlichia-infected cell cultures centrifuged for 5 min at 1500 x g without prior digestion and disruption. Following inoculation, cultures were maintained in ECM (E. canis) or complete L-15B (E. ruminantium) and monitored for bacterial infection as above for up to 10 weeks, at which point if no infection was detected, the cultures were discarded. When E. canis cultures became heavily infected and began to destroy the host cells, subcultures were carried out if required onto fresh cells of the same line in ECM by transfer of 0.3–0.5 ml supernatant.Between 5 × 10E. canis-infected RSE8 cell cultures using the DNeasy® Blood and Tissue Kit (Qiagen) according to the protocol for purification of total DNA from animal blood or cells (Spin-Column protocol). A PCR was conducted using species-specific primers ECAN5 and HE3 (2 (Promega), 1 μl of each primer at 10 μM, 1 μl of 10 mM dNTP Mix, 0.4 μl of Taq, 1 μl of DNA and nuclease-free water to make up to the final volume. The PCR was carried out with a thermal cycling profile of 95 °C for 1 min, and 35 cycles of 95 °C for 15 s, 55 °C for 15 s and 72 °C for 30 s, followed by a 72 °C extension for 7 min and a 4 °C hold . The PCR products were visualised by agarose gel electrophoresis.DNA was extracted from uninfected and  and HE3 ; a 20 μlE. canis-infected tick and DH82 cells were harvested as above, centrifuged for 5 min at 200 x g, washed once in PBS and resuspended in cold 2% glutaraldehyde in phosphate buffer. The cell suspensions were transferred to 1.5 ml Eppendorf tubes and immediately centrifuged to form a pellet. After 60 min the fixative was carefully removed, replaced with 1% aqueous osmium tetroxide and left at room temperature for a further 60 min. After dehydration in a graded series of ethanols  the pellet was washed for 10 min in propylene oxide before infiltration with epoxy resin. Cell pellets were washed in a 1:1 mixture of propylene oxide and Agar 100 hard epoxy resin  for 1 h before being washed in 100% Agar 100 resin for 2 h on a rotator. This resin was replaced with fresh resin before being polymerised at 60 °C for 18 h, after which the Eppendorf tube was cut away from the polymerised resin block. Ultra-thin sections (70 nm) were cut using a Leica UC6 ultramicrotome, stained with uranyl acetate and lead citrate using a Leica AC20 staining machine and imaged at 100 kV in a FEI T12 transmission electron microscopy using a Tietz F214 CCD camera.Uninfected and R. sanguineus s.l., was successfully infected with E. canis semi-purified from IDE8 cells on 3/6 occasions  microplus, with infection rates ranging from <1% to >50%  microplus were all successfully infected with the Ball3 strain of E. ruminantium  achieved in IDE8 cells , suggesting the possibility that not all R. sanguineus s.l. populations are competent vectors of this pathogen as recently postulated  used for E. mineirensis. Small vesicles seen in the intra-morular matrix of E. canis-infected cells were also reported for tick cells infected with E. ruminantium (E. chaffeensis (E. mineirensis (E. chaffeensis and E. mineirensis but not E. ruminantium.The ultrastructure of ell line , althougE8 cells  revealedinantium , E. chafffeensis  and E. meirensis , while rEhrlichia spp. that can be propagated in vitro in tick cells, E. canis, E. ruminantium, E. chaffeensis and E. mineirensis, all grow well in one or more cell lines derived from the non-vector tick I. scapularis. Three of the four also grow in cell lines derived from their natural vectors (E. mineirensis in cell lines derived from R. microplus, the tick species from which it was originally isolated (E. canis and E. ruminantium; it would be interesting to test the same cell lines for ability to support growth of E. chaffeensis and E. mineirensis. Such studies might help to elucidate the factors governing in vivo vector competence of different ixodid tick species for these closely-related bacteria.Of the four  vectors , while tisolated . In the


MLN0128 inhibited both mTORC1 and mTORC2 signaling, with more robust inhibition of downstream 4E-BP1 phosphorylation and cap-dependent translation compared to rapamycin in vitro. Rapamycin-sensitive BT474 cell line acquired rapamycin resistance (BT474 RR) with prolonged rapamycin treatment in vitro. This cell line acquired an mTOR mutation (S2035F) in the FKBP12-rapamycin binding domain; mTORC1 signaling was not inhibited by rapalogs but was inhibited by MLN0128. In BT474 RR cells, MLN0128 had significantly higher growth inhibition compared to rapamycin in vitro and in vivo. Our results demonstrate that MLN0128 may be effective in tumors with intrinsic as well as acquired rapalog resistance. mTOR mutations are a mechanism of acquired resistance in vitro; the clinical relevance of this observation needs to be further evaluated.We tested the antitumor efficacy of mTOR catalytic site inhibitor MLN0128 in models with intrinsic or acquired rapamycin-resistance. Cell lines that were intrinsically rapamycin-resistant as well as those that were intrinsically rapamycinsensitive were sensitive to MLN0128  The Phosphatidylinositol 3-kinase (PI3K)/Akt/ mammalian target of rapamycin (mTOR) pathway plays a central role in cell metabolism, growth, proliferation and, survival , 2. ActiRapamycin and its analogs are allosteric mTOR inhibitors that bind FKBP12 and mTOR, and predominantly inhibit mTORC1. The rapamycin analog temsirolimus is approved by the Food and Drug Administration for the treatment of advanced renal cell carcinoma and the rapamycin analog everolimus is FDA approved for the treatment of pancreatic neuroendocrine tumors, renal cell carcinoma, sub-ependymal giant cell astrocytoma associated with tuberous sclerosis, as single agent therapy, and for the treatment of hormone-receptor positive breast cancer as combination therapy with exemestane. Rapalogs have also shown promise in clinical trials in other tumor types, such as mesothelioma and endometrial cancer . HoweverThese therapeutic failures have been attributed, in part, to rapamycin-induced Akt activation, as a result of inhibition of the S6K/IRS-1 feedback loop. Rapamycin not only inhibits S6K phosphorylation but also induces Akt S473 phosphorylation, hence activating Akt , 21. Altin vitro and in vivo. We demonstrate that MLN0128 caused greater inhibition of mTORC1 signaling, mTORC2 signaling, cell cycle progression and translation in most cell lines compared to rapamycin. Likewise, MLN0128 sensitivity was significantly greater in cell lines that have either intrinsic resistance to rapamycin or have acquired rapamycin resistance highlighting the efficacy of this compound in resistant settings compared to rapamycin. Furthermore, MLN0128 caused growth inhibitory effect in several in vivo models, and had a greater growth-inhibitory effect in in vivo models of acquired rapamycin resistance. Further, we report that prolonged rapamycin treatment in vitro was associated with acquisition of an mTOR kinase mutation, with insensitivity to rapamycin-mediated inhibition of mTOR signaling and cell growth, but with sensitivity to MLN0128.MLN0128, also known as INK128, is a novel ATP-competitive mTOR kinase inhibitor, currently in phase I clinical trials for advanced solid malignancies. We sought to determine the effect of MLN0128 on rapamycin sensitive cell lines as well as in cell lines with intrinsic and acquired rapamycin-resistance both PIK3CA and PTEN. These cell lines were selected as they represented cell lines with a range of sensitivities to rapamycin based on our previous study of a larger 43 cell line screen for rapamycin sensitivity  and rapamycin (P<0.01) caused significant inhibition of cell cycle progression from G1 to S phase in T47D. In MDA-MB-231 and HT29, MLN0128, but not rapamycin, significantly increased the percentage of cells in G1 phase (P<0.001). In ZR75-1 cell lines neither treatment increased cells in G1 phase; however, MLN0128 caused a significant increase in the subG1 population of cells (P<0.001).The effect of MLN0128 on cell cycle progression was analyzed by flow cytometry Fig. . Cancer P<0.0001) in ZR75-1 cells only, corresponding with an increase in the subG1 population of cells observed in cell cycle analysis. We did not see significant apoptosis in other cell lines. Immunoblotting also showed no increase in expression of cleaved caspase 3 or cleaved PARP with MLN0128 treatment  dependent translation through firefly luciferase activity. T47D  and MDA-MB-231 cells , were transfected with reporter plasmid and a dual luciferase assay kit was used to measure renilla and firefly activity Fig. . In bothin vivo, we determined the activity of MLN0128 in five different xenograft models. We analyzed xenografts from ZR75-1, MCF7 cell lines that are rapamycin-sensitive in vitro and xenografts from ACHN, MDA-MB-231 and HT29 cell lines that are rapamycin-resistant in vitro. The statistical analyses were done by comparing tumor volumes in treatment arms with tumor volumes in the vehicle arm at the termination of experiment. Mice bearing ACHN, ZR75-1 or MDA-MB-231 xenografts did not show statistically significant growth inhibition with everolimus treatment  . In MCF7 xenografts, both MLN0128 and everolimus caused significant growth inhibition . In contrast, HT29 xenografts responded to everolimus resulting in significantly lower tumor volume (P=0.0072) but with MLN0128 treatment, a decrease in tumor volume was observed but was not statistically significant (P=0.0827).To expand our findings in vitro.Although rapalogs often have antitumor efficacy of limited duration in the clinic, currently little is known about mechanisms of acquired resistance to rapalogs. To get insight into mechanisms of acquired rapamycin resistance and approaches to overcome them, we created BT474 rapamycin resistant (BT474 RR) cell lines through culturing rapamycin-sensitive BT474 parental cells (BT474 Par) in progressively higher concentrations of rapamycin. We then tested the activity of MLN0128 in BT474 Par and RR cell lines in vitro  and downstream pS6, with activation of pAkt S473, while MLN0128 treatment inhibited pAkt S473 and inhibited p4E-BP1 more robustly Fig. . StrikinP<0.001) and cap-independent  translational activity while rapamycin decreased cap-dependent (P<0.001) translational activity in BT474 Par cell lines only  compared to vehicle  in comparison with vehicle.Next, we determined the cle Fig.  and 6B. Akt/mTOR signaling plays key roles in controlling major cellular processes, including cell growth, protein translation, autophagy, metabolism, and cell survival , 2. ActiRapamycin analogs have been FDA-approved for treatment of several tumor types, but single agent treatment has resulted in modest objective response rates. Where there is activity observed with allosteric mTOR inhibitors, they appear to be cytostatic, primarily stabilizing clinical disease, rather than resulting in tumor regression . mTORC1 in vitro. In vivo, MLN0128 resulted in significant growth inhibition in five of the xenograft models tested representing cell lines with a variety of genomic backgrounds  but not by MLN0128 (P=0.0827). It is feasible that a larger study may have indeed demonstrated a statistically significant difference with MLN0128 as well. The greater antitumor efficacy in vivo than in vitro suggests that xenograft models capture additional antitumor mechanisms of action. Indeed a recently study but Mercier et al. demonstrated mTOR inhibition with rapamycin can have multiple effects on the microenvironment including decreases in the levels of angiogenesis, collagen deposition, and the total number of fibroblasts in the tumor stroma [It has been proposed that this may be due to upregulation of feedback loops in which Akt phosphorylation and activity are increased by relieving S6K–driven suppression of IGF-1R signaling . Some str stroma .Most patients who derive clinical benefit from rapalogs may have disease stabilization for several months but ultimately progress. -43The meIn summary, MLN0128 is an effective inhibitor of mTORC1 and mTORC2 activity. Cancer cell lines with intrinsic as well as acquired resistance to rapamycin are responsive to mTOR kinase inhibitor MLN0128. Ongoing clinical trials will test the efficacy of MLN0128 in patients with intrinsic as well as acquired resistance. We also report the novel finding of an acquired mTOR mutation associated with acquired rapamycin resistance. Further studies are needed to clinically validate this finding.Cancer cell lines obtained from American Tissue Culture Collection were: A498, ACHN (kidney), BT474, HCC70, HCC1428, HCC1806, MCF7, MDA-MB-231, MDA-MB-361, MDA-MB-435, MDA-MB-453, MDA-MB-468, T47D, ZR75-1 (breast), HeLa (cervix), HT29 (colon), and M14 (melanoma). NCI/ADR-RES ovarian cancer cells were obtained from the National Cancer Institute. All cell lines were maintained in DMEM/F12 media supplemented with 10% FBS and were passaged for less than 6 months after resuscitation and were authenticated by vendors.The breast cancer cell line BT474 was cultured in progressively increasing doses of rapamycin until sustained growth at supra-therapeutic (10 μM) doses was achieved (approximately 18 months). Pool populations  of the parental cell lines (BT474 Par) and the cell lines with acquired rapamycin resistance (BT474 rapamycin resistant (RR)) were generated. Their identities were confirmed by short tandem repeat (STR) profiling.in vitro studies was kindly provided by Intellikine Inc. . Additional MLN0128 was purchased from ChemieTek . For in vivo experiments methyl cellulose, polyvinylpyrrolidone and N-methyl-2-pyrrolidone (Sigma) were used as vehicles.Rapamycin was purchased from LC Laboratories., Inc. DMSO was bought from Sigma-Aldrich. MLN0128 for Antiproliferative activity was tested by SRB assay . The hal3 cells depending on the cell lines/60 mm plates in triplicate for each treatment group. Approximately 2-3 weeks later when the controls had colonies approaching confluency, plates were fixed, stained with crystal violet and scanned. The colonies were counted using NIH ImageJ v.1.46 software.Colony formation assay was performed as previously described . BrieflyFor cell cycle assay, floating cells, as well those that were attached to the culture dish, were collected. Samples were analyzed by flow cytometry and ModFit LT software (Verity Software House). Apoptosis was identified by using the Annexin V apoptosis kit (Roche) according to the manufacturer's protocol, and cells were analyzed by flow cytometry and FlowJo v.10 (Tree Star) .Immunoblotting was performed as described previously  with theThe reporter plasmid pCDNA3-rLuc-polIRES-fLUC was a gift from Nahum Sonenberg. T47D, MDA-MB-231, BT474 Par and BT474 RR cell lines were transfected with reporter plasmid using DharmaFECT transfection reagent by Thermo Fisher Scientific, Inc. . Dual luciferase assays were performed with the dual luciferase reporter assay system  according to the manufacturer's protocol .BT474 Par and RR cells were treated with DMSO, rapamycin (100 nM) and MLN1028 (100 nM) for 72 hours. Approximately 500 μg of total protein for each condition was incubated with 7-methyl GTP Sepharose 4B beads  for two hours at 4°C. Pelleted beads were washed twice with lysis buffer and suspended in 1X SDS-PAGE sample buffer (containing 12.5% v/v β-mercaptoethanol) and eIF4E, eIF4G and 4E-BP1 levels contained in the elutes were analyzed by western blotting .Genomic DNA was extracted from BT474 Par and BT474 RR cell lines using Qiagen's Qiaprep Kit according to the manufacturer's protocol. DNA was quantified by Qubit (Invitrogen) and quality was assessed using Genomic DNA Tape for the 2200 TapeStation (Agilent). DNA was sheared by sonication and to ensure the proper fragment size, samples were checked on TapeStation using the DNA High Sensitivity kit (Agilent). The sheared DNA proceeded to library prep using KAPA library prep kit (KAPA) following the “with beads” manufacturer protocol. Samples were quantified using KAPA qPCR quantification kit. Equimolar amounts of DNA were pooled (8-12 samples per pool) for capture of 202 genes that are clinically relevant in cancer  protocol, 3.5 ng DNA was loaded on to a 12.75 Digital Array IFC chip and processed on the Fluidigm BioMark HD instrument. Data analysis was performed using Fluidigm's Digital PCR Analysis software.6), MCF7 (5×106), MDA-MB-231 (2×106), and ZR75-1 (1×107) cells were injected in the mammary fat pads of female nu/nu mice , whereas HT29 (2×106) and ACHN (1×107) cells were injected subcutaneously. BT474 cell suspensions were mixed with Matrigel (BD Biosciences). Mice bearing ZR75-1, MCF7, BT474 Par, and BT474 RR xenografts were implanted with 17β-estradiol pellets (Innovative Research of America) subcutaneously. In a single agent treatment with rapamycin BT474 Par and BT474 RR xenografts mice were randomized into 4 groups . ACHN, HT29, MDA-MB-231, MCF7, and ZR75-1 xenograft mice were randomized into 4 groups (vehicle for everolimus (RAD001), vehicle for MLN0128, everolimus 10 mg/kg × 5 weekly , MLN0128 1 mg/kg × 5 weekly , n=8-10).Tumor volumes were calculated as previously described [All animal experiments were approved by the MD Anderson Animal Care and Use Committee. BT474 Par and BT474 RR (5×10escribed . Mice wein vitro studies, comparisons between two groups were performed using the Student's t-test. All in vitro experiments were performed at least three times. For in vivo studies, comparisons between control and treatment groups were performed by using Mann-Whitney U. Data were presented as means ± SE.For