Burkitt lymphoma (BL) is a highly aggressive B-cell lymphoma associated with MYC translocation. Here, we describe drug response profiling of 42 blood cancer cell lines including 17 BL to 32 drugs targeting key cancer pathways and provide a systematic study of drug combinations in BL cell lines. Based on drug response, we identified cell line specific sensitivities, i.e. to venetoclax driven by BCL2 overexpression and partitioned subsets of BL driven by response to kinase inhibitors. In the combination screen, including BET, BTK and PI3K inhibitors, we identified synergistic combinations of PI3K and BTK inhibition with drugs targeting Akt, mTOR, BET and doxorubicin. A detailed comparison of PI3K and BTKi combinations identified subtle differences, in line with convergent pathway activity. Most synergistic combinations were identified for the BET inhibitor OTX015, which showed synergistic effects for 41% of combinations including inhibitors of PI3K/AKT/mTOR signalling. The strongest synergy was observed for the combination of the CDK 2/7/9 inhibitor SNS032 and OTX015. Our data provide a landscape of drug combination effects in BL and suggest that targeting CDK and BET could provide a novel vulnerability of BL. Gene mutations in BL target essential cancer pathways including e.g. p533, the SWI/SNF complex4 and the transcription factor TCF3 (E2A) or its negative regulator ID3. Pro-survival signals are elicited through phosphatidylinositol-3-OH kinase pathway (PI3K) activation by TCF3/ID3 mutations and tonic B-cell receptor signalling6.Burkitt’s lymphoma (BL) is a highly aggressive non-Hodgkin lymphoma (NHL), which is driven by the characteristic translocation of the MYC oncogene8. Current treatment of BL consists in intensive chemotherapy including combinations of cyclophosphamide, doxorubicin, methotrexate, vincristine and prednisone or combinations of methotrexate, cytarabine, etoposide, ifosfamide and dexamethasone7. Chemotherapy of BL has been successfully combined with the CD20 antibody rituximab.BL can be managed very effectively using intensive chemoimmunotherapy, especially in younger patients7, which underscores the necessity for alternative treatments. These are unlikely to emerge from further chemotherapy optimization. Relapsed or refractory BL has a dismal prognosis and is generally considered incurable. Therefore, platforms to generate rational novel combinations for BL could have immediate clinical consequences and may allow a functional dissection of genotype specific sensitivities. Cell lines provide a robust model for drug response studies and can be used to develop new treatment strategies including combinations. Recent comprehensive large-scale studies provided detailed analysis of tumour specific determinants of drug response based on molecular characterization of cell lines and their pharmacological profiles12. Pharmacological profiling studies identified synergistic drug combinations with ibrutinib in activated B-cell-like diffuse large B-cell lymphoma (ABC DLBCL)14 or NF-κB-targeted strategies in mantle cell lymphoma (MCL)15. While previous studies include a large number of cell lines, individual entities were underrepresented, i.e. the number of BL cell lines ranges from as few as 3 up to 11 in the mentioned platforms.However, the elderly and patients with immunodeficiency have an inferior outcomein vitro studies revealed synergistic drug interactions i.e. between PI3K inhibitor and chemotherapy16 as well as mTOR and histone deacetylase inhibitors17. However, currently there are no synergistic combinations of targeted drugs in clinical use, hence arises the necessity for preclinical models to provide rational drug combinations.Previous 18. While activation of MYC in mouse B cells was insufficient for lymphomagenesis, a cooperating mechanism of PI3K activation in BL was identified19.Recent studies provide evidence for dependency of BL on tonic B-cell receptor (BCR) signalling to PI3K21. BRD4 plays an important role in transcription of many genes including MYC22, which is overexpressed in BL, and hence BET inhibition potently suppresses the expression of MYC in leukaemia and lymphoma cell lines leading to induction of cell cycle arrest and apoptosis21.BET family, including BRD2, BRD3, BRD4 and BRDT, influences gene expression by recruiting transcriptional regulators to specific genomic locationsHere, we describe a detailed study of drug response and combination treatments across a panel of haematological malignancy derived cell lines focusing on BL. We identify a subgroup of BL lines responsive to PI3K and BCR pathway inhibition and delineate numerous cooperative interactions of PI3K/AKT/mTOR pathway and BET inhibition. Strong synergy between BET and cyclin dependent kinase (CDK) inhibition by SNS-032 provides a rational for clinical testing of this combination.24.To identify molecular dependencies and potential therapeutic targets in BL, we measured the effect of 32 drugs in 10 concentrations on the viability of 42 blood cancer cell lines, including 17 BL cell lines, and 6 isogenic BL lines with targeted deletion of p53, SYK, BTK, BLK or CD20 Fig. . In lineBecause of the potential bias introduced by response surrogates, we considered IC50, AUC and individual doses Figs , S1. We r = 0.93), venetoclax (median r = 0.81) or thapsigargin .To understand the relation of effects produced by different agents, we analysed drug-drug correlations across concentrations. This matrix uncovered a number of highly correlated drugs driven by both multiple concentrations of individual compounds as well as drugs targeting overlapping pathways or nodes of the same pathway. For example, strong positive correlations were shown for multiple concentrations of everolimus (median correlation coefficient 25. Dasatinib and saracatinib (Src family), the CHK inhibitor AZD7762, and the DNA-PK inhibitor NU7441, which has also been shown to inhibit PI3K and mTOR26 also showed similar response across cell lines.Idelalisib (PI3K), everolimus (mTOR) and MK-2206 (AKT) effects were highly correlated as the drugs target members of the PI3K pathway. In line with the known role of SYK, PI3K and BTK in BCR signalling, ibrutinib (BTK) and PRT062607 (SYK) Fig.  and idelIn summary, the analysis of drug-drug associations allowed us to provide evidence for the coherence of the screening platform as well as to provide insight into the pathways involved in viability effects of drugs.We clustered cell lines based on the effect on viability for all drugs and concentrations Fig.  and idenThe second response group (Cluster II) showed the strongest response to kinase inhibitors including PI3K inhibitor, as well as a number of other targeted drugs including SRC inhibitors . The dependence of those cell lines on BCR and SYK was validated by knocking out BTK, SYK and CD79A/B using CRISPR/Cas9. The effect of gene knock-out followed the pattern of drug sensitivity and showed that the cell line with highest IC50 values for BTK and SYK inhibition (Z138) was also the least affected by the knockout of CD79A, CD79B and SYK compared to BJAB and BL-60 Fig. . These r29, a subgroup of BCL-2 dependent lymphoma lines (OCI-LY-1 and OCI-LY-8)32, a BL cell line (DOGKIT) known to harbour BCL2 rearrangements33 and the B-NHL line Granta-519, which has been shown to overexpress BCL234  clustered with its parental line  analysis35. We classified combinations as synergistic for CI values below 0.85. Values between 0.85 and 1.15 indicate additive effects, and CI above 1.15 points was assigned antagonistic. The use of the CI analysis has its limitations, e.g. in the context of very toxic combinations or if the drug itself had a very small IC50 value, based on the logarithmic transformation used in the model36. Therefore, we also assessed drug interactions using dose-response curves of relative viabilities . Six of 32 idelalisib combinations were synergistic, while for none of the ibrutinib combination CI were below 0.85 Fig. . The AKT11. Bcl-2 inhibtiors, obatoclax and venetoclax, combined with ibrutinib showed median CI values off 1.12 and 0.96 respectively, which was explained by differential sensitivity to the drugs , chemotherapeutics, the AKT inhibitor (MK-2206), a downstream node of PI3K pathway, and the SYK inhibitor PRT062607 with corresponding median CIs of 0.76 (JQ1), 0.80 (doxorubicin), 0.70 (MK-2206), 0.82 (PRT062607). This suggests a positive influence of combining more than one inhibitor of the PI3K pathway, as well as combining inhibitors of BCR proximal kinase SYK and PI3K. These data also provide a rationale for combinations of idelalisib with doxorubicin, which is used in BL treatment regimens.18.Although we observed few differences between the response patterns to ibrutinib and idelalisib Fig. , we founWe observed multiple strong synergistic effects for the BET inhibitor OTX015. The lowest CI scores were found for the CDK2/7/9 inhibitor SNS-032, inhibitors of the PI3K/AKT/mTOR and BCR pathway inhibitors. Median CI scores across 18 cell lines ranged from 0.56 (SNS-032) to 0.81 (doxorubicin). Combinations of OTX015 with thapsigargin and YM155 yielded CIs of 1.24 and 1.46, suggesting that these combinations were antagonistic. We analysed the individual dose-response curves and found a subset of cell lines, which showed cooperative effects for this combination , while in 7 out of 18 lines the interaction between OTX015 and YM155 was antagonistic. Cell lines with lower OTX015 sensitivity showed higher CI values Fig. , suggest5, and OTX015 within a subgroup of BL cell lines. Specifically, the PI3K inhibitors idelalisib and IPI-145 (duvelisib), the allosteric AKT inhibitor MK-2206 and the mTOR inhibitors everolimus and deforolimus  and OTX015 showed a CI50 of 0.46 and 0.35 in BJAB and BL-60 respectively, pointing towards strong synergy. The concave gradient of viability heatmap of the 10 × 10 combination matrix also indicated these cooperative interactions  in parallel to uncovering new targets within a Burkitt lymphoma set (BTK and SYK in a subset of BL).The platform uncovered known vulnerabilities  showed strong combination activity with the highest number of compounds targeting different pathways in nearly all tested models. Synergistic combinations with idelalisib and ibrutinib were found in a subset of cell lines for a smaller set of drugs. Current clinical trials of OTX015 in non-leukaemia haematological malignancies have shown relatively good tolerance of the drug, with low-grade adverse events of anaemia, neutropenia, diarrhoea, fatigue and nausea; high-grade thrombocytopenia was also observed19. Also, it has been suggested that BET inhibition sensitizes PI3K and MYC driven cancer cells to PI3K inhibitor by blocking PI3K pathway reactivation caused by induction and activation of receptor tyrosine kinases (RTKs) through blocking the expression of RTKs45. Synergistic interactions between the PI3K pathway inhibitor and BETi have been previously described in vitro in other entities including haematological malignancies MCL46 and ABC-DLBCL14 as well as in solid malignancies including glioblastoma47, osteosarcoma48 and breast cancer45, which underscores the necessity of evaluation of this combination in clinical trials. Interestingly, dual-activity PI3K-BRD4 inhibitors have been recently developed and are tested in clinical trials for hepatocellular carcinoma49.A high number of cooperative drug interactions with OTX015 were found with multiple drugs inhibiting the PI3K pathway, including the PI3K inhibitors idelalisib and IPI-154, the AKT inhibitor MK-2206 and mTOR inhibitors everolimus and deforolimus. Those synergies are in line with the oncogenic signalling activated by tonic BCR signalling50, which consists, among others, of CDK9 and cyclin T, and plays a crucial role in activation of transcription. Another possibility is that a Brd4-independent mechanism may recruit or retain P-TEFb and lead to elevated MYC transcription22, which in turn can be down-regulated by CDK9 inhibition by dinaciblib in BL cell lines51. However, it has to be noted that dinaciclib is not as target-specific as SNS-032. SNS-032 targets not only CDK9, leading to inhibition of transcription, but also CDK7 and CDK2, whose inhibition can block cell cycle progression53.The current study uncovers a novel synergistic combination in BL, a specific CDK2,7,9 inhibitor SNS-032 with BET inhibitor OTX015. This exact mechanism of this combination activity is currently unclear. The targets of both drugs, cyclin-dependent kinase 9 (CDK9) and Brd4, act as positive regulators of p-TEFbin vitro pharmacological profiling in cell lines is an excellent tool of preclinical evaluation of drug combinations. The dataset provided can function as a training set to predict synergistic drug interactions on a larger scale54 and help plan future evaluation of drug combinations. The possibility of in silico modelling of predicting drug resistance, drug-target interaction and potential synergistic drug combinations on a large scale based on current drug target and pharmacological profiling databases57 has been extensively discussed as a relevant alternative drug discovery. Information from such models could help develope efficient in vitro experimental setups in the context of developing rational combination therapy.This study shows that Cell lines were obtained from G. M. Lenoir , A. Rickinson  or the DSMZ . p53-deficient BL line Séraphine was generated and provided by J. Hüllein . MCL lines  were kindly provided by University of Frankfurt and University of Würzburg. KS. Lee  generated isogenic BL lines. Cell lines for the screen were chosen with a focus on BL, other entities were introduced for comparison.2 with humidification. Cells with a passage number of 3–6 were used for drug screening. Cell lines were authenticated externally at Multiplexion GmbH  by Single Nucleotide Polymorphism (SNP) profiling and by comparison of obtained signatures to reference signatures58.Cells were cultured in RPMI 1640 medium supplemented with 10–20% FBS, 1% Glutamine and 1% Penicillin/Streptomycin at 37 °C, at 5% COFor the single agent drug screen we built a library of 32 drugs, 10 concentrations each using threefold dilution to reach end concentrations from 30 µM to 1.5 nM. The range of concentrations allows for a wide range of viability effects, which is needed to generate IC50 and AUC. For the combination screen we built a library of 32 drugs, 10 concentrations each using threefold dilution to reach concentration ranges 30 µM–1.5 nM, 15 µM–0.76 nM or 10 µM–0.5 nM depending on drug potency. The choice of library drugs aimed at covering a wide range of key oncogenic pathways, especially those active in haematological malignancies. For easier translation of the results into the clinical setting only drugs that are currently in clinical use or in clinical trials were used.We used a parallel approach probing three combination components: ibrutinib at 200 nM, idelalisib at 200 nM and OTX015 at 200 nM with the drug library. Single agent control was generated by combining drug library with solvent solution.59. The remaining drugs were obtained from Sigma-Aldrich , Enzo Life Sciences  and Selleck Chemicals . Compounds were dissolved in DMSO at 0.1–50 mM and stored at −20 °C.SD07 was provided by Prof. A. Mokhir, Erlangen, Germany3–7 × 103 cells per well depending on the growth rate and exposed to the drug library (10 µl) or the drug library combined with 200 nM ibrutinib, idelalisib or OTX015 (10 µl each) for 48 h; the final volume per well was 50 µl. We included DMSO and PBS controls to exclude DMSO toxicity effects. Cells were then incubated with 14 µl CTG reagent. Luminescence was measured after 20 minutes using a TECAN Microplate Reader (Tecan Group) with an integration time of 200 ms. Relative viability was determined by normalizing luminescence units for each well to DMSO control.Cell viability was determined using CellTiterGlo (CTG) ATP assay (Promega). Cells were seeded onto 384-well plates (Griner Bio One) at 1.5 × 102) between the datasets was 0.77  from 0.76 to 0.96 was shown  of two drugs; we determined optimal starting concentrations based on dose-response curves to those drugs: 1 µM for SNS-032, 10 nM for everolimus, 10 µM for MK-2206 and 10 µM for OTX015. Luminescence data from the CTG viability assay was normalized to DMSO controls and then bound between 0% and 100% by a second normalization.61. All sgRNA target sequences were extracted from Human Brunello CRISPR knockout pooled library  except sgRNA target sequences for mCherry and Luciferase (see below). Lentivirus packaging protocol was adapted from elsewhere61. In brief, it was performed in HEK293T/17 cell line  using 2ndsgRNA was cloned into pLKO5.sgRNA.EFS.GFP plasmid vector  followed by cloning protocol from elsewhereCells were infected with lentivirus particles in 6 well cell culture plate and were maintained at appropriate cell culture condition. Cells were analyzed using BD LSRII flow cytometry  to determine the percentage of GFP positive cells. Data obtained from FB LSRII was analyzed using FlowJo v.10 software .Normalisation of the data and basic data analysis were performed with Microsoft Excel. Luminescence data was normalized to a mean of DMSO controls to obtain relative viability in per cent, which in turn was used for further analyses.IC50 curves and bee swarm plot were generated using GraphPadPrism 5 software . For the IC50 curves a non-linear regression with viability data bound between 0 and 100% was used. This model:P values (two-tailed) were calculated using an unpaired parametric t-test.https://www.r-project.org/).Subsequent data analysis was performed in R statistical environment . Correlation matrices were calculated using cor function and plotted using corrplot function  from the ‘corrplot’ package.35. IC50 values for this analysis stem from the drm calculation Combination indices for 10 × 10 matrices and isobologram analyses were performed using CompuSyn software .Combination index (CI) was calculated using the formula:Supplementary informationDataset 1: Supplementary figuresDataset 2: Supplementary tables


Prediction of response to specific cancer treatments is complicated by significant heterogeneity between tumors in terms of mutational profiles, gene expression, and clinical measures. Here we focus on the response of Estrogen Receptor (ER)+ post-menopausal breast cancer tumors to aromatase inhibitors (AI). We use a network smoothing algorithm to learn novel features that integrate several types of high throughput data and new cell line experiments. These features greatly improve the ability to predict response to AI when compared to prior methods. For a subset of the patients, for which we obtained more detailed clinical information, we can further predict response to a specific AI drug. Breast cancer is the second most common type of cancer in women, with an incidence rate of over 250,000 cases per year, and breast cancer cases show significant heterogeneity in clinical and omic measures. Estrogen receptor positive (ER+) tumors typically grow in response to estrogen, and in post menopausal women, estrogen is only produced in peripheral tissues via the aromatase enzyme. Inhibition of aromatase is often an effective treatment for ER+ tumors, but aromatase inhibitor therapy is not effective for all tumors, and causes of this heterogeneity in response are largely not known. In this work, we present a feature construction and classification method to predict response to aromatase inhibitor therapy. We use network smoothing techniques to combine tumor omic data into predictive features, which we use as input to standard machine learning algorithms. We train predictive models using clinical data, including high-quality clinical data from UPMC patients, and show that our method outperforms previous approaches in predicting response to aromatase inhibitor therapy. A number of recent large efforts have focused on collecting genomic data from tumors. While these datasets led to several successful studies and insights, in many cases the clinical data available for patients enrolled in these studies is incomplete. This makes it hard to use such datasets for predicting tumor specific outcomes and tailoring treatments to individuals.To develop accurate methods for for predicting treatment responses we need both, a comprehensive genomic dataset profiling the individuals being studied and accurate complimentary clinical information. To date, methods that used the former (detailed genomic data) usually were unable to use the latter for a significant number of individuals while methods that only relied on clinical information are limited in their ability to distinguish between tumor responses .de novo pathway identification via identification of mutually exclusive mutations , we compute a function F(v) ∀v ∈ V that is both smooth over the network and accounts for the prior knowledge about each node.As has been shown in the past, protein interaction networks provide a useful way to overcome data sparsity and noise when predicting cancer responses . Here weet al.  to combiW′, computed via Laplacian normalization of the edge weight matrix W: we first construct a diagonal matrix Δ with Δ = ∑jW, and compute W′ = Δ−1/2WΔ−1/2. Given a prior knowledge vector Y, we then compute the smoothed vector F using the iterative procedure described by Zhou et al.  quantifies gene j’s proximity to both somatic mutations in tumor i and the gene set S represented by the smoothed vector T. We compute DP,T similarly, replacing MP with DP in Given one of the smoothed “target” vectors described above, denoted as i:We use these matrices to compute features for response to treatment in tumor e.g. targets of a specific drug).Row-wise mean, representing the total network proximity between a tumor’s somatic mutations  and genes in a predefined set , and the aromatase inhibitor Arimidex. Each of these two drugs were tested on a single cell line, and we create LINCS features for each tumor by combining that tumor’s continuous log-fold differential expression with the expression change induced by that drug in the appropriate cell line. We compute two features for each  pair:Correlation between that tumor’s differential expression and the cell line differential expression induced by administering that drug.Dot product between that tumor’s differential expression and the cell line differential expression induced by administering that drug.magnitude of change in expression between a tumor and the cell line in question.While the two features above are conceptually similar, we note that in addition to the direction of agreement, the dot product also represents the We use the following categorical variables from the general TCGA clinical data:Tumor pathological stageNode pathological stageMetastasis pathological stageOverall pathological stageHistological typeICD-10 typeICD-O-3 histologyHER2 immunohistochemistry level resultPost-surgery margin statusWe expand each categorical variable listed above into 0/1 indicator columns for use in classification methods. We additionally extract the estrogen receptor status of each tumor, used for selecting additional patients based on prior clinical knowledge.With the above features, we perform cross-validation experiments to assess our ability to predict response to aromatase inhibitor treatment. We examine all patients who were given any of the aforementioned drugs: 279 patients were given anastrozole, 51 were given exemestane, and 80 were given letrozole. An additional 180 samples were not considered for aromatase inhibitor therapy due to having ER– tumors, which are known not to respond to AI therapy. In this general aromatase inhibitor response prediction task, we assign a patient a “non-response” label if they were removed from any such drug for clinical reasons, or if the patient died during drug treatment. We also include prior clinical knowledge in this “all aromatase inhibitor” analysis; we integrate this prior knowledge by also computing the above features for the 180 patients who were not given an aromatase inhibitor, but who had estrogen receptor negative (ER–) tumors. These tumors are known not to respond to this type of treatment, so we assign these samples “non-response” labels. We use the features discussed above to learn various types of classifiers including logistic regression (with both L1 and L2 regularization), Random Forest and Probabilistic SVMs. For each of these methods and each setting we perform leave-one-out cross-validation.We performed cell line experiments to compare breast cancer cell line growth with and without the addition of estrogen. We initially grow cell cultures for 5 days, with estrogen present, simulating the initial growth of breast cancer in a patient. We then separately grow cultures with a continued supply of serum estrogen, or with replacement cell medium that lacks estrogen—the environment without estrogen simulates the introduction of an aromatase inhibitor. We measure cell line growth with and without serum estrogen after the initial growth period, and from these cell counts we compute measures of how much each cell line responded to the presence of estrogen. We performed this experiment with 12 replicates of each cell line, 6 with and 6 without estrogen after the initial growth period, and used a mixture of ER+ and ER– cell lines , using a VICTOR X4 plate reader . Bars represent the mean of six biological replicates ± SD. 17β-Estradiol (E2) was obtained from Sigma-Aldrich .MCF-7, BT474, BT483, CAMA1, Uacc812, ZR75-1 ZR75-30 and T47D breast cancer cell lines were purchased from American Type Culture Collection [ATCC], Manassas, VA, USA. SUM44PE was purchased from Asterand Bioscience, Detroit, MI, USA, and 600MPE cells were a gift by Dr. Rachel Schiff. For the estrogen removal experiments, the cells were kept for 5 days in IMEM supplemented with 10% charcoal stripped serum (CSS) with 1nM E2, and then plated into 96-well plates with or without 1 nM estradiol. An exception are Sum44PE cells that were kept in IMEM with 2% CSS. After 5 days, cell numbers were measured using Cell-titer Glo  according to the manufacturer’s instructions. Luminescence was measured with GloMaxp = γpc+(1 − γ)pp, with pc denoting predictions from cell lines and pp denoting predictions from tumors. The validation set predictions then combines the tumor and cell line predictions via the hyper-parameter γ tuned by cross-validation .We separated a random 10% of patients to use for training weights between tumor and cell line classifiers, and used the remaining 90% of patients for cross-validation analysis. In each cross-validation fold, we fit classifiers to the corresponding training set of patients, and then used those classifiers to produce non-response predictions of the 10% of patients initially set aside. We then computed predictions for those 10% of patients using classifiers trained on cell lines, and chose the optimal convex combination of tumor and cell line predictions in the training set, producing final prediction S1 TextDescriptive statistics of the UPMC patient cohort, details of comparisons with other methods, and results of additional analyses.(PDF)Click here for additional data file.S1 Data(ZIP)Click here for additional data file.


Modelling of pathological processes in cells is one of the most sought-after technologies of the 21st century. Using models of such processes may help to study the pathogenetic mechanisms of various diseases. The aim of the present study was to analyse the literature, dedicated to obtaining and investigating cybrid models. Besides, the possibility of modeling pathological processes in cells and treatment of different diseases using the models was evaluated. Methods of obtaining Rho0 cell cultures showed that, during their creation, mainly a standard technique, based on the use of mtDNA replication inhibitors (ethidium bromide), was applied. Cybrid lines were usually obtained by PEG fusion. Most frequently, platelets acted as donors of mitochondria. According to the analysis of the literature data, cybrid cell cultures can be modeled to study the dysfunction of the mitochondrial genome and molecular cellular pathological processes. Such models can be very promising for the development of therapeutic approaches to the treatment of various human diseases. Modelling of pathological processes in the cells is one of the most sought-after technologies of the 21st century. With the use of such models, it may become possible to study the pathogenetic mechanisms of various diseases. In addition, efforts to transfer genes, which do not have mutations, in human cells and tissues for the treatment of certain diseases, were made by the scientists throughout the world. Great success has been achieved in the implementation of genetic constructs, containing nuclear gene mutations associated with pathologies, into cell cultures. Afterwards, such structures were embedded into the nuclear genome. The final result of the transfer of genetically engineered constructs into the nuclear genome was the expression of mutant RNA in these cultures and synthesis of mutant proteins. Concerning the mitochondrial genome, such progress is not found. For example, experimental data of the members of our laboratory suggest that the transduction of genetic constructs or synthetic RNA in the mitochondria of monocytic origin cell line THP-1 was not possible. Scientists began to pay more and more attention to cybrid cultures that are more suitable models for the detection of pathological mechanisms and the development of approaches to the treatment of mitochondrial cytopathies and also other diseases associated with mutations in mtDNA.Cytoplasmatic hybrids (cybrids) are created by fusion of mitochondria-deficient cell lines (Rho0) and cell donors of mitochondria. Since in the mitochondria with deactivated mtDNA, any fission processes do not occur because of the dysfunction of these organelles, the cell gradually loses them. Therefore, some researchers conventionally call Rho0 cells mitochondria deficient.As a material for creating cybrid cultures, most often cell line 43B –8 is useμg/ml) partially or completely inhibit the replication of the mitochondrial genome, but they do not affect the replication of nuclear DNA [The possibility of Rho0 human cell creation is based on the use of mtDNA replication inhibitors, such as DNA intercalating dye ethidium bromide . Relatively low concentrations of ethidium bromide (0.1 to 2 lear DNA .μg/ml of sodium pyruvate).The verification of possibility of mitochondria-deficient cell obtaining. The native cell line is cultured in a growth medium supplemented with ethidium bromide and uridine in one embodiment and with only the addition of ethidium bromide in another embodiment. In this case, the growth medium should contain glucose and pyruvate in concentration of complete medium DMEM  standard, the methodology of King and Attardi is used, which includes two stages:After obtaining the cell lines, stably living in previously described conditions, a cell analysis is performed in order to confirm the absence of mitochondria in these lines. The scheme of this method is shown in μg/ml uridine [μg/ml uridine and after 120 days of cultivation, with increasing addition of ethidium bromide up to a concentration of 500 ng/ml within 3-4 months [μg/ml uridine were added to the medium. Despite the assertion that the research team used the technique of King and Attardi, ethidium bromide concentration exceeded 10 times, and uridine exceeded 2 times the concentration of these additives in the original paper [It is noteworthy that while checking the possibility of certain cell lines to receive Rho0 cell cultures, it is necessary to choose favorable conditions of cultivation, namely, the concentration of various additives, especially ethidium bromide and uridine which may considerably vary. For example, King and Attardi for obtaining Rho0 cells from the cell line 143B.TK− cultivated the cells on a growth medium supplemented with 50 ng/ml ethidium bromide and 50 4 months . Muellertrans retinoic acid and arsenic trioxide on differentiation and cell survival in human leukemic cell line HL60 and mitochondria-deficient HL60rho0. As a result, HL60rho0 cells possessed a lower differentiation ability but a higher respiratory activity than parental cell HL60. HL60rho0 cells were also significantly more resistant to apoptosis [Herst et al. analyzed the efficiency of the impact of all-poptosis .Shen et al. conducted a comparative analysis of oxygen consumption levels in cultures of human lymphoblast Molt-4 of wild type and in Rho0 cultures. The rate of oxygen consumption in mitochondria-deficient culture was significantly lower as compared to that in the native culture (7 times). The level of oxygen consumption equally changed in the native and in the mitochondria-deficient cell line Molt-4 in both the inhibition of parachloromercurobenzoate (PCMB) level and during the stimulation of sodium bisulfite by menadione (MSB). At the same time, potassium cyanide reduced and carbonyl cyanide m-chlorophenylhydrazone (CCCP) increased the oxygen consumption rate only in native culture Molt-4. The authors suggest that this can be associated with the absence of important protein subunits encoded by the mitochondrial genome, which in its turn leads to disruption of the electron transport in the respiratory chain .Brar et al. compared the ultrastructural morphology of mitochondria of human epithelial cell line A549 of wild type and Rho0 cells. The mitochondria-deficient culture contained irregularly shaped mitochondria with a translucent matrix in contrast to the native cell line. In addition, the morphology of the cristae Rho0 cells was more heterogeneous: for example, both short and circular growths of the inner membrane of the mitochondria occurred. The authors analyzed the effect of bleomycin on these cell cultures, which is an antitumor agent, and in clinical use, it causes damage to the lungs, and in 1-2% of patients, it causes severe progressive pulmonary fibrosis. It was shown that Rho0-A549 cell lines are resistant to bleomycin, which, according to the authors, may be associated with the deterioration of mitochondria-dependent apoptosis .Cheon et al. analyzed the role of mtDNA mutations in carcinogenesis. This research group investigated Rho0 cells obtained from cell line SK-Hep1 . Native cell culture SK-Hep1 was used as a control culture. It was revealed that in mitochondria-deficient cells, an expression of the gene and protein level of hypoxia-inducible factor- (HIF-) 2 alpha has increased. Korean researchers suggested that mitochondrial dysfunction caused by the inhibition of mtDNA by ethidium bromide increases the angiogenic potential of tumor cells. In addition, an increase in the expression of vascular endothelial growth factor (VEGF), a key angiogenic molecule induced by HIF, was observed. Increased expression of genes associated with invasion, such as genes of urokinase-type plasminogen activator (uPA) and metalloproteinases of matrix (MMPs) in Rho0-cells of SK-Hep1 can also, according to the authors, indicate mitochondrial dysfunction, which in its turn leads to increased invasive potential of tumor cells .Our research team found that Rho0 cells of THP-1 culture have lower levels of cellular and mitochondrial respiration, compared to cells of native lines of monocytic origin THP-1.ρ0 with lines BALB/cJ and C57BL/6J [There are two fundamentally different fusion methods of mitochondria-deficient lines with cell donors of mitochondria. The transfer of mitochondria into Rho0 cells can only occur during the formation of pores in the cytoplasmic membranes of cells resulting from the influence of certain chemical or physical agents on them. One of the methods involves culturing Rho0 cells with enucleated cytoplasts under the influence of a flow of direct current (electrofusion) . ScientiC57BL/6J , the creC57BL/6J , in the C57BL/6J  and fibrC57BL/6J . The secC57BL/6J , 3. It sC57BL/6J , cell liC57BL/6J –25 and nC57BL/6J .Cybrids are used as cellular models for studying mitochondrial dysfunction and its association with a variety of human diseases such as Parkinson's disease, Alzheimer's disease, HIV, human herpes virus type 8, MELAS syndrome, Leber's optic atrophy, and Leigh syndrome , 27–29. Several research groups discovered an association of certain mutations and mitochondrial genome haplogroups with cell dysfunction. For example, Mueller et al. evaluated the survival index of cell line HEK293. Cybrids carrying in one case haplogroup T and haplogroup N in the other were obtained from this cell culture. During the treatment with hydrogen peroxide, cybrids with haplogroup T had a higher survival index than the other cell line [Trounce et al. found an association between mtDNA mutation m.8993T>G and dysfunction of mitochondria isolated from lymphoblasts of patients with Leigh syndrome. This association is characterized by a decrease in oxidative phosphorylation processes in the studied cells. In their investigations, a decrease in the activity to 26–50% of 3 complexes of respiratory chain and also a 30% reduction of ADP/oxygen ratio have been shown [A research about creating mitochondria-deficient cell lines from cells of patients with mitochondrial encephalopathy and MERRF syndrome, having a reduced activity of cytochrome C oxidase, is rather interesting. The obtained Rho0 cells were fused with enucleate cells, HeLaCOT. As a result, cybrids with restored oxidase activity of cytochrome C were obtained .Some researchers study the association of mitochondrial dysfunction with oncological diseases, using various cybrid lines.For example, Cruz-Bermúdez et al. created cytoplasmic hybrids based on cell line 143B. Their cybrids had mitochondrial genome mutations “with varying degrees of pathogenicity”: one line was with a severe pathogenicity (mutation m.8363G>A in gene tRNA-Lys) and 3 lines with a mild pathogenicity, namely, Leber's optic atrophy (LHON). The recent cell lines carried one mtDNA mutation in each cybrid culture . The study results showed that these cybrid lines have mild pathogenicity and have OXPHOS dysfunction. However, they were not associated with tumorigenicity and do not cause tumors when they were used with “naked” mice .Yoshii et al. determined mitochondrial dysfunction in cybrid lines based on cell line 143B carrying mutant mitochondria in patients with MELAS syndrome. In this article, radioactive Cu-diacetyl-bis (N4-methythiosemicarbazone) (Cu-ATSM) was used, which is a potential marker for the visualization of hypoxic tumors for positron emission tomography (PET). Cybrid cells with MELAS had an increased absorption of Cu-ATSM under normoxia compared to wild-type cells. It also showed that Cu-ATSM absorption correlates with the change in the level of NADH and NADPH .At the predementia stage of Alzheimer's disease (AD) may occur MCI (mild cognitive impairment). During this stage of disease, a decrease in cognitive abilities may be observed, which often represents a transition between AD dementia and normal cognition. A group of researchers transferred mitochondria from MCI, AD, and platelets of control subjects to mtDNA-depleted SHSY5Y cells to model bioenergetic dysfunction of AD and MCI. Bioenergetics-related infrastructures and bioenergetic fluxes were characterized in the obtained cybrid cell lines. Compared to cybrids of control subjects, AD and MCI cybrids had changes in respiratory coupling, consumption of oxygen, and utilization of glucose. The level of ADP/ATP in AD and MCI cybrids was higher and the ratios of NAD1/NADH were lower , 34.Trimmer and Bennett created and analyzed cybrid neuroblastoma SH-SY5Y and NT2 with mitochondria of patients with Parkinson's disease. The research of cells in cybrid cultures revealed an increase in oxidative stress and the frequency of apoptosis compared to normal cells. SH-SY5Y and NT2 in case of applying the light therapy, characteristic for the treatment of neurological diseases, normalization in the mitochondrial respiratory activity cybrid cells SH-SY5Y and NT2 was observed .In another investigation, Trimmer and colleagues studied that the possibilities of the treatment of Parkinson's disease (PD) on a cybrid model were created with the use of platelets from patients with this pathology. It should be noted that ATP, necessary for the support of axonal transport and many other cellular functions essential for the survival of neurons, is produced by mitochondrial respiratory chain. Scientists exposed cybrids to low level, near-infrared laser light and found that in these cells, mitochondrial metabolism enhanced, the level of oxidative phosphorylation increased, and redox capacity improved. The results of this investigation show the opportunity of low-level light therapy to improve defects in neuronal cells in individuals which have Parkinson's disease .The analysis of literature dedicated to the methods of obtaining Rho0 cell cultures showed that during their creation, mainly a standard technique, based on the use of mtDNA replication inhibitors (ethidium bromide), is used. Cybrid lines are usually obtained by PEG fusion. Most frequently, platelets act as donors of mitochondria.It should be emphasized that cybrid cell cultures can serve as models for studying the dysfunction of the mitochondrial genome and molecular cellular pathological processes. Such models can be very promising for the development of therapeutic approaches to the treatment of various human diseases.


Recent advances in sequencing technologies have enabled parallel assays of chromatin accessibility and gene expression for major human cell lines. Such innovation provides a great opportunity to decode phenotypic consequences of genetic variation via the construction of predictive gene regulatory network models. However, there still lacks a computational method to systematically integrate chromatin accessibility information with gene expression data to recover complicated regulatory relationships between genes in a tissue-specific manner.http://bioinfo.au.tsinghua.edu.cn/jianglab/CSNETS/.We propose a Markov random field (MRF) model for constructing tissue-specific transcriptional regulatory networks via integrative analysis of DNase-seq and RNA-seq data. Our method, named CSNets (cell-line specific regulatory networks), first infers regulatory networks for individual cell lines using chromatin accessibility information, and then fine-tunes these networks using the MRF based on pairwise similarity between cell lines derived from gene expression data. Using this method, we constructed regulatory networks specific to 110 human cell lines and 13 major tissues with the use of ENCODE data. We demonstrated the high quality of these networks via comprehensive statistical analysis based on ChIP-seq profiles, functional annotations, taxonomic analysis, and literature surveys. We further applied these networks to analyze GWAS data of Crohn’s disease and prostate cancer. Results were either consistent with the literature or provided biological insights into regulatory mechanisms of these two complex diseases. The website of CSNets is freely available at CSNets demonstrated the power of joint analysis on epigenomic and transcriptomic data towards the accurate construction of gene regulatory network. Our work provides not only a useful resource of regulatory networks to the community, but also valuable experiences in methodology development for multi-omics data integration.The online version of this article (10.1186/s12864-018-5277-6) contains supplementary material, which is available to authorized users. The complicated process of transcription in eukaryotes largely attributes to the collaboration among DNA regulatory elements, RNA polymerases, mediator and cohesion complexes, and sequence-specific transcription factors (TFs). Such collaboration is encoded in a comprehensive gene regulatory network that determines how the expression of a gene is regulated, what responses a cell would adopt to answer external stimuli, and which phenotypic consequences a genetic variation could result in . In the A gene regulatory network is often inferred based on high-throughput assays about interactions among transcription factors and their target genes. RNA-seq, as a means of capturing a snapshot of the whole transcriptome, has provided the most abundant data in such studies. For example, Hu and Chen constructed a transcriptional regulatory network in memory CD8+ T cells with gene expression profiles and predicted TF information, and then identified the core TFs . Li et aWith the promise of detecting TF binding sites at high resolution, ChIP-seq has been used with RNA-seq data to infer gene regulatory networks. For example, Roy et al. constructed a mixed regulatory network that combines transcriptional regulation by TFs from ChIP experiments and posttranscriptional regulation by miRNAs . Chen etThere are several difficulties that restrict large-scale applications of the ChIP-seq technology. Besides the restriction of suitable antibodies for TFs, the number and cost of experiments required by a large number of TFs also limit the feasibility to construct gene regulatory networks for a variety of phenotypes and species via ChIP-seq. To overcome these limitations, DNase-seq has been developed to enable the capture of chromatin accessibility in whole-genome scale , 20. TakMotivated by the above understanding, we propose in this paper a Markov random field (MRF) model, named CSNets (Cell-line Specific regulatory Networks), that integrates DNase-seq data with RNA-seq data towards large-scale inference of gene regulatory networks. In this method, we first roughly infer regulatory networks for individual cell lines using DNase-seq data alone. Then, we fine-tune these networks using an MRF model, based on pairwise similarity between cell lines derived from RNA-seq data. Focusing on data released by the ENCOODE project, we constructed regulatory networks specific to 110 cell lines and 13 major tissues for human. Using ChIP-seq experimental data as a gold standard, we showed the superior quality of our networks over that obtained by existing methods. Through functional enrichment analysis, we demonstrated that TFs and their predicted targets tend to share similar biological functions. Besides, integrative analysis of our networks with GWAS data of Crohn’s disease and prostate cancer both suggested genes and genetic variants that were either consistent with the literature or provided biological insights into regulatory mechanisms of these two complex diseases.We extracted DNase-seq profiles for 110 human cell lines, representing 70 diverse cell types and 13 unique tissue lineages, from the ENCODE project . We collWe proposed to construct a transcriptional regulatory network specific to a cell line by integrating DNase-seq data, transcription factor binding motif information, and gene expression data, as illustrated in Fig. Homo sapiens (GRCh37), and position weighted matrix of motif for the 368 transcription factors. Focusing on predicted binding sites in promoter regions (TSS ± 2 kb), we linked transcription factors to their target genes, and thus obtained preliminary regulatory networks specific to the 110 human cell lines. Second, we incorporated gene expression data and adopted a rigorous Markov random field model to fine-tune these preliminary networks. The basic assumption behind this model is that similar cell lines tend to share similar regulatory patterns. With this understanding, we used gene expression data to measure the similarity between cell lines, and then connect regulatory relationships of different cell lines by using a Markov random field (MRF) model based on the similarity. Detailed explanations of components in our method are given below.We first followed a computational method called Protein Interaction Quantitation (PIQ)  to perfoq-value = 0) in the 110 cell lines. Then, we applied the singular value decomposition (SVD) to expression data of these genes to perform a dimensionality reduction. In detail, expression values of a gene across the 110 cell lines was first normalized to zero mean and standard deviation one. The resulting expression matrix regarding the 592 genes and 110 cell lines were then decomposed into USVT, where columns of U were called eigenarrays, diagonals in S singular values, and rows of VT right singular vectors. Finally, we characterized the similarity between two cell lines as the Pearson’s correlation coefficient of the first 16 dimensions of the eigenarrays in the SVD decomposition. Here, we calculated CSI values based on different numbers of dimensions and found that they were robust and slightly changed. We name such a similarity measure as the Cell-line Similarity Index (CSI).We adopted a measure, called TSI (Tissue Similarity Index) , to charA regulates target gene B), we constructed an MRF network G = {VAB, EAB}, where a node vABi ∈ VAB,  indicates the regulation of TF A on target gene B in cell line i, and an edge  ∈ EAB denotes the regulation coherence for TF A and gene B between cell line i and j. We introduced an indicator variable xi ∈ X for the node vABi, indicating whether the regulation exists (xi = 1) or not (xi = 0) in cell line i. Suppose that the higher the degree of similarity between cell line i and j, the stronger the positive correlation of variable xi and xj, we define a criterion called the TF non-specific index (TNI) as the proportion of common targets for the TF involved in cell line i and j. The larger the TNI value, the more similar of the regulatory mechanism corresponding to the concerned TF. We define the edge weight, wij ∈ W, as the average of CSI and TNI. We set a threshold c with default value 0.5, and regarded cell line i and j as connective if and only if wij > c.We proposed a Markov random field model to fine-tune the preliminary networks. Specifically, for a given regulatory relationship  or failure (xi = 0) of the given regulatory relationship in cell line i, respectively. We used the probability of TF binding inferred from PIQ as the probability of Pi) is named as pseudo-energy function. With this formulation, we transformed the problem of maximizing the joint distribution Pr(X) into that of minimizing the pseudo-energy function [With the definition of two types of potential functions, the joint distribution of all indicator variables X can be denoted asfunction , 34. We function  to furthαi(xi) = ln ϕi(xi)and βij = ln ψi, j). When the value of xi is not consistent with its probability distribution (i.e. Pi), the value of αi(xi) is 0, rather than ∣ ln ϕi(1) − ln ϕi(0)∣. When cell lines i and j are connective and xi and xj are of differential values, the value of βij is 0 rather than  + TNI)/2. It is verified that we can transform the problem of minimizing the pseudo-energy function into that of summing total losses of αi(xi)and βij when the following inequality satisfies [In detail, firstly we define atisfies .\documenThis equation suggests that the problem of minimizing the pseudo-energy function is transferred into the maximum flow problem of networks. We finally applied the loopy belief propagation algorithm  to calcuWe collected 353 ChIP-seq experiments, regarding 108 TFs in 59 cell lines, from the ENCODE project. We then evaluated the contribution of the MRF model as follows.M) of three gene sets, which include candidate target genes according to ChIP-seq data, target genes of the TF according to the network constructed by the MRF model for the given cell line, and target genes of the TF according to the preliminary network for the given cell line. Finally, we ranked candidate target genes according to their scores and used those ranked among top M as the gold standard of target genes for the TF in the given cell line.We first generated a gold standard of target genes for a TF in a cell line from the corresponding ChIP-seq experiment. To achieve this objective, we mapped binding sites identified in the experiment to promoter regions (TSS ± 2Kbps) of protein coding genes and assigned experimental scores of the binding sites to the mapped genes, which were used as candidate target genes. To further reduce false positives in these genes, we identified the median size  curve (sensitivity versus 1-specificity) and calculated the AUC score as the area under this curve. In a similar way, we obtain the AUC score of for the preliminary network. The relative change of these two AUC scores is then used to compare the performance of these two networks, for the given TF in the give cell line.M and then counting the number of genes shared in the remaining target genes. Using a similar strategy, we obtained an overlapping score for the preliminary network. The relative change of these two scores can then be used to compare the performance of these two networks, for the given TF in the give cell line.We further evaluated quality of the constructed networks by checking the overlap between target genes in the networks and those identified by ChIP-seq experiments. This was done by filtering out low confidence target genes that were ranked below the threshold We constructed regulatory networks specific to 110 human cell lines, and we further merged networks specific to cell lines belonging to the same tissues to obtain 13 tissue-specific regulatory networks, as summarized in Table We then extracted sub-networks regarding transcription factors only from the 13 networks and illustrated 6 of such networks in Fig. We evaluated contributions of the MRF model using 353 ChIP-seq experiments collected from the ENCODE project. Briefly, we first identified a gold standard of target genes for a TF in a cell line from the corresponding ChIP-seq experiment. Then, we evaluated the improvement of a network constructed by using the MRF model over the corresponding preliminary one in terms of relative changes in the AUC score and the overlapping score, as detailed in Methods.p-value = 8.4e-08). In terms of the relative change in the overlapping score, as shown in Fig. p-value< 2.2e-16).As shown in Fig. p-value = 1.1e-05). In terms of the relative change in the overlapping score, networks constructed by using the MRF model show higher consistency with ChIP-seq data for 88 out of the 108 (81.5%) TFs. Again, he positive contribution of the MRF model is supported by the one-sided binomial exact test .We further performed a TF level evaluation by aggregating ChIP-seq experiments according to TFs and averaging a criterion over corresponding cell lines. As shown in Fig. We testified the rationality of the tissue similarity measured by gene expression data and observed the consistency between regulatory networks and the human cell hierarchical taxonomy graph. After extracting the directed acyclic subgraph of the human cell hierarchical taxonomy graph from the Foundational Model of Anatomy Database  in the UFirst, we performed hierarchical clustering of tissues according to gene expression profiles. Figure Further, hierarchical clustering was performed for the 110 cell lines based on their similarity index, with results shown in Fig. Next, we inspected the consistency between regulatory networks and the human cell hierarchical taxonomy graph to evaluate the rationality of the tissue similarity measured by regulatory networks. For this objective, we merged regulatory networks specific to cell lines belonging to the same tissue, used the Jaccard coefficient of tissue-specific regulatory networks to measure the similarity between tissues, and then perform hierarchical clustering on the 13 tissues, as shown in Fig. Finally, we performed hierarchical clustering on the 110 cell lines based on their specific regulatory networks. The results, in Fig. p-values < 2.2e-16 for all these cell lines). These results suggest that the networks constructed by using our method can well characterize the regulatory relationship between transcription factors and their target genes.We evaluated whether expression of TFs exhibited positive correlation with their predicted target genes in a cell line-specific manner. For this objective, we collected gene expression data for the GM12878, K562, MCF-7, and SK-N-SH cell lines. For each of these cell lines, we calculated Pearson’s correlation coefficient between a TF and each of its target genes, and we plot the statistical significance of the correlation coefficient in Fig. With the assumption that a TF and its target genes tend to share common biological functions, we performed a functional enrichment analysis on the target genes of a TF. To achieve this objective, we collected 1454 gene sets from the MSigDB database, covering 825 biological process terms, 396 molecular function terms, and 233 cellular component terms in GO. We then derived two criteria to characterize the degree that a TF and its target genes share a common function, as follows.Given a TF, a cell line and a GO term, we identified target genes of the TF in the network specific to the cell line, and we performed a Fisher’s exact test to see whether the target genes were enriched in the function corresponding to the GO term. With results of such an analysis for every TF, in every cell line, and for every GO term collected, we were able to derive a statistic to indicate consistency between functions of TFs and their target genes. Particularly, we defined such a statistic as the proportion of significant tests (FDR ≤ 0.2) over all tests performed, and we calculated a statistic for each of the three GO categories, biological process, molecular function, and cellular component, separately. Because all the 1454 GO terms were used in the above analysis, we referred to such a statistic as the total enrichment score. By contrast, we repeated the above enrichment analysis with GO terms not relevant to a TF discarded, and we referred to this formulation as positive enrichment analysis.p-values are as small as 5.60 × 10− 7, 7.95 × 10− 3 and 7.80 × 10−4for the cellular component, biological process and molecular function, respectively.As shown in Fig. p-values in Additional file We further analyzed whether target genes of a TF exhibit different functions in different cell lines, especially in normal and cancer cell lines. To achieve this objective, we identified 65 normal cell lines and 25 cancer cell lines. For each TF, we collected its target genes, and we performed functional enrichment analysis to see whether functions of the TF were enriched in its target genes, for the normal and cancer cell lines, separately. We showed TFs and GO terms with most significant enrichment For example, EP300 plays an important role in regulating cell growth and blocking the promotion of cancerous tumors. The targets of EP300 are enriched in normal cells rather than cancer cells in two GO terms, corresponding to apoptotic process and programmed cell death respectively, indicating the function of EP300 is altered in cancer cells Fig. . As for We applied the constructed regulatory networks to analyze a GWAS data set of Crohn’s disease, demonstrating the potential of these networks in identifying disease-related TFs and their regulatory mechanisms. We first select a regulatory network that is specific to Crohn’s disease from the 110 networks. On one hand, it is generally thought that the inflammatory reaction in Crohn’s disease is driven by the activated type 1 helper T cells (Th1) . On the p-value that indicates the strength of association between the gene and this disease, using the tool Pascal [p-values, we obtained a gene list, in which top ranked genes can be treated as candidate disease genes. To avoid determining true disease genes based on a hard cut-off of the p-value, we resorted to GSEA [We then collected GWAS data for this disease from the literature  and calcl Pascal . By rank to GSEA  to assig to GSEA –50 have We further explored the importance of the cell line specificity of a regulatory network in identifying the NFκB family in the above analysis. Briefly, we extracted target genes of the NFκB family for each of the 110 networks and used Fisher’s exact test to measure the degree of enrichment of these target sets in the candidate genes identified above for Crohn’s disease. Results show that the target gene set of the NFκB family in Th1 cell line is ranked 2nd, and specifically, the target gene set of NFKB1 in Th1 cell line is ranked 1st, among the 110 cell lines


Oxaliplatin (OHP) treatment of colorectal cancer (CRC) frequently leads to resistance. OHP resistance was induced in CRC cell lines LoVo-92 and LoVo-Li and a platinum-sensitive ovarian cancer cell line, A2780, and related to cellular platinum accumulation, platinum-DNA adducts, transporter expression, DNA repair genes, gene expression arrays, and array-CGH profiling. Pulse  and continuous exposure  resulted in 4.0 to 7.9-fold and 5.0 to 11.8-fold drug resistance, respectively. Cellular oxaliplatin accumulation and DNA-adduct formation were decreased and related to OCT1-3 and ATP7A expression. Gene expression profiling and pathway analysis showed significantly altered p53 signaling, xenobiotic metabolism, role of BRCA1 in DNA damage response, and aryl hydrocarbon receptor signaling pathways, were related to decreased ALDH1L2, Bax, and BBC3 (PUMA) and increased aldo-keto reductases C1 and C3. The array-CGH profiles showed focal aberrations. In conclusion, OHP resistance was correlated with total platinum accumulation and OCT1-3 expression, decreased proapoptotic, and increased anti-apoptosis and homologous repair genes. Oxaliplatin is a third generation analogue of cisplatin and has shown remarkable clinical activity in colon cancer with intrinsic resistance to cisplatin. Oxaliplatin is used in combination with other drugs including 5-fluorouracil with leucovorin (FOLFOX), resulting in response rates of up to ~60%  and for Clearly, subtle differences in determinants of cisplatin and oxaliplatin resistance exist which may be tissue specific and possibly influenced by p53 expression. To further dissect out mechanisms of oxaliplatin resistance we induced resistance in colon cancer cells with different p53 functional status and compared this to ovarian cancer cells. Resistance towards oxaliplatin was studied in relation to common mechanisms of drug resistance including cellular drug accumulation; expression status of relevant uptake and efflux transporters; and formation, retention, and repair of platinum DNA-adducts. Since the underlying pathways that mediate resistance towards oxaliplatin are poorly described, we also investigated alterations in gene expression in the drug-resistant cell lines and explored whether or not these changes are a result of aberrations at the genomic level.The results of the growth inhibition experiments aimed at determining the degree of drug resistance induced by exposure to gradually increasing concentrations of oxaliplatin over seven months . Since o50 value of 0.21 μM. Induction of resistance after four hours of oxaliplatin pulses resulted in a 7.9-fold resistance and after continuous drug exposure resulted in a 11.8-fold resistance. The LoVo-Li cells that express inactive p53 were 3.5-fold less sensitive to oxaliplatin and the resistant variants showed a similar pattern of resistance towards oxaliplatin as the wt p53 parental cell line LoVo-92 with resistance factors of 4.0 and 5.6 for LoVo-Li/4OHP and LoVo-Li/cOHP, respectively. Remarkably, these LoVo-92 and LoVo-Li variants retained parental cell sensitivity to cisplatin. Moreover, the oxaliplatin-resistant sublines of the ovarian cancer cell line A2780 displayed higher resistance levels after continuous exposure to oxaliplatin rather than after four hours of pulses, hence being 11.1- and 5.3-fold, respectively. In contrast to the colon cancer cell lines, these oxaliplatin-resistant ovarian cancer cells were also cross-resistant to cisplatin; 7.0- and 9.3-fold for A2780/4OHP and A2780/cOHP, respectively. However, resistance to l-OHP was lower than that of CDDP in a CDDP-resistant variant, ADDP, which was included for comparison. In order to determine the stability of the resistance, we also cultured the cells for several months without l-OHP and determined the sensitivity to l-OHP, but no loss of resistance was found. Although we did not clone our cells, this result indicates that induction of resistance led to a homogenous, resistant cell population.The colon cancer cell line, LoVo-92, was most sensitive to oxaliplatin with an ICIn order to determine whether or not oxaliplatin resistance is mediated by reduced drug uptake or increased efflux, which might result in decreased formation of platinum DNA adducts, cellular drug accumulation and DNA adduct formation were determined . EarlierConsistent with the decreased oxaliplatin accumulation, formation of platinum-DNA adducts was also decreased in LoVo-92/4OHP and LoVo-92/cOHP B. RemovaThe net cellular accumulation of oxaliplatin and consequent formation of platinum DNA adducts might be affected by influx and efflux transporters as well as DNA repair. Therefore, mRNA expression of the copper transporter, hCTR1; the organic cation transporters OCT1, 2, and 3 (SLC22A1-3); as well as the efflux transporters ATP7A and ATP7B were determined by Q-PCR. The expression of nucleotide excision repair gene ERCC1 was also evaluated . The resLoVo-Li cells displayed lower gene expression levels of influx transporters corresponding with lower drug accumulation and formation of oxaliplatin DNA adducts in LoVo-Li cells as compared to LoVo-92. The LoVo-Li/4OHP cells showed decreased expression of all influx and efflux transporters, as well as the DNA repair gene ERCC1. The hCTR1, OCT1, OCT2, and ERCC1 were decreased by 40%, whereas, OCT3 showed a decreased expression of 80%. ATP7A and ATP7B were decreased by 60% and 40%, respectively. The LoVo-Li/cOHP OCT1 and ERCC1 genes were unchanged, whereas, the other genes showed a decreased expression. Expression of the influx transporters hCTR, OCT2, and OCT3 were decreased by 15%, 40%, and 95%, respectively. The efflux transporters, ATP7A and ATP7B, showed a decreased expression of 45% and 30%, respectively. The drug-resistant ovarian cancer cell lines exhibited decreased expression of all transporter genes except for OCT2 and OCT3 which were not detectable in the parental cells, as well in the resistant cells. Expression of OCT1 in A2780/4OHP and A2780/cOHP was decreased by 20% and 60%, respectively.ATP7A was decreased by 25% in A2780/4OHP but unchanged in A2780/cOHP. ATP7B was decreased by 35% in both A2780/4OHP and A2780/cOHP. The nucleotide excision repair gene ERCC1 showed an increased expression in A2780/4OHP but was decreased by 80% in A2780/cOHP. The Pearson correlation analysis  of the QAlthough in LoVo-92 variants, total platinum accumulation, DNA adducts formation, and transporter gene expression appear to correlate and provide adequate explanation for drug resistance, however, the picture for LoVo-Li and A2780 variants is less clear. We, hence, performed gene expression arrays to gain better insight into the mechanisms underlying oxaliplatin resistance. The gene expression array data were significantly correlated with the quantitative gene expression data obtained.The Q-PCR data for the genes are described above. Ingenuity pathway analysis (IPA) of normalized log2 ratios of the resistant cell lines and corresponding parental cell line (gene expression omnibus: GSE19992) was used to pinpoint the canonical pathways that were most significantly enriched. The most frequently and significantly enriched pathways are shown in The axonal guidance signaling and aryl hydrocarbon receptor signaling pathways were significantly enriched for four out of six resistant cell lines. The expression of genes of the ALDH family was most frequently altered in the aryl hydrocarbon receptor signaling pathway. ALDH1L2 was decreased in all cell lines with log2 ratios from −2.26 for A2780/4OHP to −3.45 for LoVo-Li/4OHP. The ALDH1A2 gene was increased in all cell lines except for the LoVo-Li/cOHP. Unexpectedly, resistant ovarian cancer cell lines showed a decreased expression of the glutathione-S-transferase family of genes. However, microsomal glutathione-S-transferase 1 showed an increased expression in these resistant variants and to some extent also in drug-resistant colorectal cancer cell line variants.Various signaling pathways showed major changes in the expression of their pertaining genes, which may, in general, point to an altered, mostly reduced capacity to induce cell death, as summarized in Less frequently observed yet highly significant for the drug-resistant variants of LoVo-Li is the role of BCRA1 in DNA damage response, metabolism of xenobiotics by cytochrome p450, xenobiotic metabolism signaling pathways, and G1/S checkpoint regulation. The role of BRCA1 in DNA damage response was most prominent in LoVo-92/cOHP and LoVo-Li/4OHP, but the ovarian cancer cell lines also showed altered expression of genes in this pathway. Excluding E2F5, all genes in this pathway were increased in LoVo-Li/4OHP, whereas, in LoVo-92/cOHP increased expression of BLM, CHEK1, E2F1, Rad51, and RFC2 was observed. In the ovarian cancer cell lines A2780/4OHP and A2780/cOHP the highest increase was observed for FANCA.Both xenobiotics metabolism pathways were only significantly enriched for LoVo-Li/4OHP but included genes that were found to be increased in other resistant variants as well. Most prominent was the above described ALDH family of genes. Other frequently increased genes were aldo-keto reductase family members 1C1 and 1C3 which were increased in four out of six and all drug-resistant variants, respectively. The expression of the multidrug efflux transporter, ABCC2 (MRP2), was increased in oxaliplatin-resistant ovarian cancer cell lines, however, decreased gene expression was observed in resistant LoVo-Li variants. However, resistant LoVo-Li variants showed increased expression of another MDR efflux transporter, ABCB1 (Pgp). The G1/S checkpoint regulation was only significant for LoVo-Li/4OHP and showed increased expression of cyclins D1 and D3; cyclin dependent kinases 2 and 4; E2F family members 1, 3, and 4 and also RBL1; and the transcription factor TFDP1.To determine whether alterations in gene expression could be the result of gene copy number variations, we performed aCGH. The chromosomal profiles  are shown in Analysis of aCGH data for common aberrations revealed that eight out of 10 observed common or overlapping aberrations are found in drug-resistant cell lines derived from the same parental cell line. Focal gains observed in Chr 15 and Chr 16 showed that similar aberrations are also found in both resistant colon and ovarian cancer drug-resistant cell lines. Remarkably, parental LoVo-92 cells displayed a gain at Chr15 which was completely lost in LoVo-92/4OHP and partly lost in LoVo-92/cOHP cells.Oxaliplatin resistance is a major clinical hurdle for curative chemotherapy since it is a crucial component of combination regimens for colorectal cancer. Although much knowledge has been gained regarding mechanisms underlying resistance to the first-generation platinum compound, cisplatin, those relating to oxaliplatin resistance are still not well characterized.Since the pharmacological activity or resistance to oxaliplatin might be affected by tissue type and treatment schedule as well as by other factors including p53 status, we established oxaliplatin-resistant cell lines representing these parameters. The colorectal cancer cell lines LoVo-92 and its variant LoVo-Li, as well as the ovarian cancer cell line, A2780, were made resistant via short-term drug pulses to reflect the clinical bolus administration and compared the latter to cell lines which acquired resistance after continuous exposure to oxaliplatin. The data show moderate levels of resistance  ranging from four- to 12-fold as compared with parental cells. Remarkably, low level cross-resistance towards cisplatin was observed in the colorectal cell lines, whereas, oxaliplatin-resistant ovarian cancer cell lines showed similar resistance levels for both cisplatin and oxaliplatin. This observation may be explained by the intrinsic cisplatin resistance of both parental colorectal cancer cell lines which are deficient in mismatch repair and an established mechanism of resistance to cisplatin but not oxaliplatin ,18,19,29Although both cisplatin and oxaliplatin are believed to exert their cytotoxic activity via formation of DNA adducts, involvement of mismatch DNA repair is different for each drug, although for both cisplatin and l-OHP a decreased accumulation was found in resistant cells . MoreoveThe lack of correlation between sensitivity to oxaliplatin and the formation of platinum DNA adducts may possibly be the result of cellular sequestration of the drug away from its target. Previously, it has been shown that the ATPases ATP7A and ATP7B play a role in intracellular trafficking and efflux of the drug . The effTo gain insight into the alternative mechanisms of oxaliplatin resistance, we also performed gene expression arrays and subjected the data to ingenuity pathway analysis (IPA) to search for canonical pathways that were significantly affected. Several pathways were frequently and significantly affected and might add to the resistance mechanisms described above. The most frequently altered gene in the aryl hydrocarbon receptor (AHR) signaling pathway was ALDH1L2; this gene was decreased in all oxaliplatin resistant cell lines and is the mitochondrial homolog of ALDH1L1 which has been implicated in cell proliferation and apoptosis ,38. AnotIn the p53 signaling pathway, the most frequently decreased genes were the proapoptotic genes, BAX and BBC3 (PUMA), which were reported previously to play a role in oxaliplatin resistance ,23 and iSince the expression of ERCC1 is unaltered or even decreased in the drug-resistant cell lines, it is unlikely that nucleotide excision repair is involved in the resistance towards oxaliplatin in these cell lines. Decreased ERCC1 mRNA and protein expression in oxaliplatin resistant cells has been previously demonstrated  and was The xenobiotic metabolism pathways, highly significant in LoVo-Li/cOHP, suggested a possible contribution to oxaliplatin resistance of aldo-keto reductase family members, 1C1 and 1C3. Aldo-keto reductase family members have been implicated previously in poor prognosis and resistance towards platinum and other drugs ,48,49. CChanges in gene expression observed in the drug-resistant cell lines as discussed above could be mediated by altered gene expression but might also be a result of gains and losses at the genomic level. Primarily, focal gains and losses were observed in the oxaliplatin resistant variants. The parental cell line LoVo-92 has been characterized by trisomy of chromosome (Chr) 5, 7 and 15 . The majOne of the main findings of the present study is that sensitivity to oxaliplatin is highly correlated with total platinum accumulation which was associated with expression status of the organic cation transporters OCT1-3, hence highlighting the role of these influx transporters in acquisition of oxaliplatin resistance. Since formation of DNA adducts, which was highly correlated to ATP7A, was not related to oxaliplatin sensitivity, it can be concluded that mechanisms, other than DNA damage signaling, may contribute to the pharmacological activity of oxaliplatin. Decreased expression of proapoptotic genes such as Bax and BBC3(PUMA) and increased expression of inhibitors of apoptosis, as well as genes involved in homologous repair, warrants combination studies with drugs that target these proteins, to overcome resistance to oxaliplatin and other platinum drugs.®) was obtained from Bristol-Myers Squib B.V.  in a concentration of 0.5 mg/mL. The RPMI and DMEM culture medium were obtained from BioWhittaker  and fetal bovine serum (FBS) was obtained from GIBCO . All other chemicals were of analytical grade. Solutions were made in water purified by a Millipore Reagent Q system .Oxaliplatin (l-OHP) was kindly provided by Sanofi-Synthelabo . Cisplatin . Periodic assays were performed to check for mycoplasma contamination.The colon carcinoma cell lines, LoVo-92 and LoVo-Li, were kindly provided by Dr. Poupon . LoVo-92Resistance to oxaliplatin was induced by exposing LoVo-92, LoVo-Li, and A2780 cells to increasing concentrations of oxaliplatin using two different schedules for a period of 7 months. In the first schedule, cells were exposed to 4 h pulses every passage to mimic the clinical bolus treatment. In parallel, cells were exposed every passage for 72 h to mimic a continuous infusion. Platinum-resistant cell lines were termed after the parental cell line with 4OHP and cOHP extensions reflecting the resistance induction scheme. To maintain the resistance phenotype, cells were regularly exposed to oxaliplatin during routine culturing. For all mechanistic experiments as described below , cells were cultured without drug for at least one week.50) was determined from the growth curves.Growth inhibition experiments were performed at least three times using the sulforhodamine B (SRB) assay as previously described . BrieflyTo determine cellular oxaliplatin accumulation and formation of DNA adducts, cells were exposed to either 20 μM l-OHP for 4 h or to 200 µM l-OHP for 24 h. To determine retention of the accumulated l-OHP and the formed DNA adducts, indicative for alterations in efflux or repair, cells were subsequently incubated for 3 h in a drug-free medium as described previously . Since 2To determine gene expression, RNA was isolated from cell pellets using Trizol  according to the manufacturer’s protocol. After RNA quantification (nanodrop ND-1000), 500 ng to 1500 ng of RNA were used for cDNA synthesis, as described previously . SubsequParental cell lines and their oxaliplatin-resistant sublines were subjected to microarray analysis. Both array comparative genomic hybridization (aCGH) and mRNA expression arrays were performed. The isolation of genomic DNA (gDNA) for aCGH analysis was performed using the Wizard Genomic DNA Purification kit  according to the manufacturer’s protocol. Total RNA was isolated using Trizol extraction, as described above. The gDNA and RNA quality was checked (OD 260/280 > 1.8). Subsequently, aCGH was performed using the Agilent Human Genome CGH 44 K array platform . Samples and human universal reference were hybridized using a cross array hybridization as described previously [After hybridization and scanning of aCGH slides the raw data were extracted from the images using Agilent Feature Extraction (version 9.5.1.1). Subsequently, data were normalized and smoothened with the R-script “nowave” to increase accurate detection of breakpoints . Genomicp < 0.05 were analyzed using ingenuity pathway analysis .After hybridization and scanning of slides for genome wide gene expression, the raw data were extracted from the images using Agilent feature extraction and subsequently processed using the LIMMA package in Bioconductor to determine Lowess normalized Log2 ratios. For determination of enrichment of genes in canonical pathways, normalized Log2 ratios >1 or <−1 and t-test for paired and unpaired samples, provided by the statistical option of Excel. p-Values below 0.05 were considered as statistically significant. Correlation studies, both the parametric Pearson correlation coefficient (r) for linear correlation and the nonparametric Spearman’s rank correlation coefficient (rho), were performed using SPSS 16.0. Data analysis of the microarray data were performed with specific programs as described in the microarray sections.Differences between cell lines regarding sensitivity, drug accumulation, adduct formation, and gene expression were evaluated using the Student


Phyllomedusa tarsius by the combination of molecular cloning and LC-MS analysis. Chemically synthesised DPT9 was broadly effective against the tested microorganisms through the disruption of cell membranes and showed weak haemolytic activity towards horse erythrocytes. It also exhibited anti-proliferative effect against various human cancer cells. Moreover, an analogue with enhanced cationicity, K8, 23-DPT9, in which Asp8 and Glu23 were substituted by lysine residues, had a markedly increased antimicrobial effect against all tested microorganisms and disrupted microbial cell membranes. This analogue also showed no haemolysis at its effective antimicrobial concentrations. In addition, K8, 23-DPT9 displayed an enhanced anti-proliferative effect against cancer cells, while displayed weak activity against the normal human cell line, HMEC-1.A novel dermaseptin peptide, dermaseptin-PT9 (DPT9), was isolated and identified from  Antimicrobial peptides have gained much attention as novel antimicrobial agents. Most of them are cationic that they are able to kill the microbes through the electrostatic interaction with negatively charged lipid membrane of microorganisms, causing irreversible and potent membrane disruption and cellular dysfunction ,2,3. TheDermaseptins are a group of antimicrobial peptides that have been predominantly isolated from the frogs belonging to Phyllomedusinae subfamily, and they exhibit antimicrobial activity against an array of pathogens such as bacteria, yeasts, protozoa and filamentous fungi ,6. ThesePhyllomedusa tarsius by the combination of cloning and mass spectrometry, namely dermaseptin-PT9 (DPT9). It demonstrates a high degree of similarity to the dermaseptin-5.1TR/5.2TR, while the biological functions have not been studied yet [Herein, a novel dermaseptin peptide was identified from the skin secretion of died yet . TherefoPhyllomedusa tarsius (n = 3) were commercially purchased  and maintained in our purpose-designed amphibian facility. The skin secretion was obtained by mild transdermal electrical stimulation and hand massaging [Specimens of the assaging . The proassaging . The stuThe precursor-encoding cDNA of the peptide from was isolated from a skin secretion-derived cDNA library as in a previous study . The 3′-The process of isolating the mature peptide from crude skin secretion by reverse-phase (RP)-HPLC  and the primary structure analysis of the novel peptide by tandem mass spectrometric (MS/MS) fragmentation sequencing used an LCQ-fleet electrospray ion-trap mass spectrometer  as previously described .8, 23-DTP9 were chemically synthesized via solid-phase Fmoc chemistry using a Tribute automated peptide synthesizer  with Rink amide resin. The synthesised peptides cleaved from resin by a cleavage cocktail containing trifluoroacetic acid (TFA), ethanedithiol (EDT), thioanisole and water (v/v/v/v = 94:2:2:2). The peptides were precipitated and washed by ice-cold diethyl ether, and further dissolved for lyophilisation. The final synthetic peptides were purified and identified by using RP-HPLC and matrix-assisted laser desorption/ionization, time of flight (MALDI-TOF) MS , and then lyophilised as TFA salts.DPT9 and its analogue, KPhysico-chemical properties of the peptides were predicted by Heliquest and the helical wheel plots of the secondary structures were obtained from the helical wheel projections . The secStaphylococcus aureus (S. aureus) (NCTC 10788), methicillin-resistant Staphylococcus aureus (MRSA) (NCTC12493), and Enterococcus faecalis  (NCTC 12697); Gram-negative bacteria Escherichia coli (E. coli) (NCTC 10418), Pseudomonas aeruginosa (P. aeruginosa) (ATCC27853), and Klebsiella pneumoniae (K. pneumoniae) (ATCC 43816), and the yeast Candida albicans  (NCYC 1467). The determination of MICs and MBCs of the peptides was described previously [The minimal inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the synthetic peptides were assessed using different microorganisms including Gram-positive bacterium RSA) NCTC493, and S. aureus, MRSA and E. coli as previously performed [Anti-biofilm activity of the peptides was assessed using the minimum biofilm inhibitory concentration (MBIC) and the minimum biofilm eradication concentration (MBEC) assays against erformed .C. albicans, respectively.Gentamicin (5 µg/mL), vancomycin (5 µg/mL) and amphotericin B (10 µg/mL) were employed as positive controls for Gram-negative bacteria, Gram-positive bacteria and v/v) erythrocytes were incubated with an equal volume of peptide solution in a range of final concentration from 1 to 512 μM in a 96 well plate as performed previously [Defibrinated horse erythrocytes  were used to evaluate the haemolytic activity of the peptides. A total of 4%  as performed in previously . The micThe human neuronal glioblastoma cell lines, U251MG (ECACC-09063001), the human breast cancer cell lines MCF-7 (ATCC-HTB-22), the human pancreatic cancer cell lines, PANC-1, the non-small cell lung cancer cell line NCl-H157 (ATCC-CRL-5802) and the human prostate carcinoma cell line PC-3 (ATCC-CRL-1435) were selected to test the cytotoxicity of the peptides. The human microvascular endothelial cell line, HMEC-1 (ATCC-CRL-3243) was utilized to evaluate the cytotoxicity of the peptides on normal human cells. The cell lines were cultured as described previously .The cell proliferation inhibitory rate was assessed by MTT assay as previously performed  with minThe cell lines were cultured as previous section. After the cells were seeded in the 96-well plate for 12 h, different peptide doses were subjected to the cells for 6 h. The LDH released in the cell supernatant was detected using Pierce LDH cytotoxicity assay  .Phyllomedusa tarsius. The translated open reading frame consists of 71 residues , exhibited 4-32-fold potency against microbes [5, 17-DPS3, K8, 23-DPT9 was also designed by replacing two Lys and it exhibited similar fold change of antimicrobial activity. Furthermore, from the antimicrobial potency of the other dermaseptins in Charge is one of main parameters for optimizing the bioactivity of antimicrobial peptides . In our microbes . In addi8, 23-DPT9 processes more helical content than DPT-9 in a membrane-mimetic environment. Although, the algorisms for calculating the helical contents of peptides via experimental CD spectra might not be precise enough, the helical contents of dermaseptins are not closely correlated with their activities as observed form Following initial membrane binding, the key event is the process of conformational transformation of the peptides . When atAlthough it is proposed that membrane lysis can result via various mechanisms including membrane perforation, destruction, or solubilization, the microbicidal potential is thought to result from the capacity of the antimicrobial peptides to permeate the target cell membrane ,29. The 8, 23-DPT9 displayed significant membrane permeability at their two-fold MICs and four-fold MICs, which indicated that the peptides kill microorganisms by cell membrane disruption. However, in fact, K8, 23-DPT9 induced the same membrane permeability as DPT9 at lower concentration that is consistent with the consideration of enhanced binding affinity by net positive charges.In our previous study, increasing cationicity and hydrophobicity can enhance membranolytic activity, while hydrophibicity makes dermaseptin rather nonspecifically towards both bacterial and mammalian cell membrane . It coul8, 23-DPT9 also displayed anti-proliferative effects against tested human cancer cells. According to the results of LDH, both peptides could induce significantly release of LDH in 6 h against cancer cells, suggesting that the anticancer action of the peptides might be related to necrosis by cell membrane damage at the high concentration. Previous research has demonstrated that cancer cell membranes are generally negatively-charged, containing more phosphatidyserine and other anionic molecules including heparin sulphates and O-glycosylated mucins than normal cells [8, 23-DPT9 is more effective against cancer cell membranes and thus exert increased anticancer activity, comparing to DPT9. Whilst, both peptides could induce further cell death after 6 h when comparing the MTT and LDH results, indicating more mechanisms could be involved along with cell necrosis. Dermaseptin B2 can bind to the plasma membrane of human PC3 cancer cells, aggregate on and penetrate the cells, resulting in a necrotic-like pathway which ruptures the cancer cell membrane due to alteration in the mitochondrial membrane potential and stimulation of caspase-3 [DPT9 and Kal cells ,33. Elecaspase-3 . Furtheraspase-3 . Therefo


Steroid-refractory graft-versus-host disease (SR-GvHD) is a leading cause for late non-relapse mortality and contributes to high morbidity rates and poor quality of life post allogeneic stem cell transplantation . Although numerous treatment approaches have been proposed and studied in clinical trials , SR-GvHD management still remains suboptimal , 2. ReceHerein, we present the clinical course and outcome in a patient who developed SR-cGvHD, which demonstrated refractoriness when bortezomib or ruxolitinib were administered as single agents but experienced a meaningful response to the bortezomib plus ruxolitinib combination, suggesting a potential synergistic effect of these two agents. Patient had previously signed an informed consent for the anonymous use of the medical data, including photos, for scientific purposes.2  every 35 days plus MMF plus steroids were administered but unfortunately after 2 cycles, the patient showed stable GvHD findings, while maintaining steroid dependency. Because of their different mechanism of action, we next decided to combine bortezomib (in the previous administered dose) plus ruxolitinib at the dose of 5 mg bid along with MMF and steroids (dose 1.5 mg/kg daily). After 3 months of treatment with the above combination, marked skin improvement was noticed; lichenoid lesions disappeared, sclerodermatosis was improved (mRSS:12), and liver enzymes became almost normal  methotrexate was administered as GvHD prophylaxis. The early course post transplant was uneventful; however, 2 months post allograft, the immunosuppression was tapered rigorously because of molecularly detectable residual disease. Two months later, he achieved complete molecular remission with 100% of donor origin hematopoietic cells, but at the cost of induced-GvHD with severe extensive skin involvement : 25), buccal mucosal, and liver involvement. Chronic-GVHD was treated with CNIs  plus steroids. No improvement was noticed, so MMF was added, however, only minimal response was achieved. Since ibrutinib, which has been approved by the FDA for the treatment of SR-GvHD [mal Fig. . NoticeaDespite the improvements in its prophylaxis and treatment, SR-cGvHD, continues to pose a major hurdle to ultimately improve outcome of alloSCT . EspeciaJAK signaling has been proven to be a key regulator of inflammation and tissue damage in GvHD, transducing inflammatory signaling downstream of cytokines and regulating development and function of immune cells including antigen-presenting cells (APC) and T cells. Recently JAK-pathway inhibition was proposed as a potential target for the treatment of SR-GvHD . Zeiser The proteasome-inhibitor bortezomib, through inhibition of nuclear factor kappa-B (NF-kB), results in depletion of proliferating alloreactive T cells, decrease of type-1 helper T-cell cytokine production, and inhibition of APC function, while demonstrating limited influence on the number and function of regulatory T cells, the important mediators for GvHD control . In a liInterestingly, the combination of ruxolitinib plus bortezomib resulted in almost normalization of liver function tests and a 50% improvement in the skin sclerotic lesions. Moreover, it was feasible to discontinue MMF while steroids dosing was reduced approximately by 75%.So far, there are no published data regarding the combination of ruxolitinib plus bortezomib as treatment for SR-GvHD. However, the above combination has already been tested in multiple myeloma cell lines and exhibited significant anti-myeloma activity .It is well known that ruxolitinib affects directly the JAK/STAT pathway and indirectly the PI3K/Akt and Ras/Raf/MAPK pathways, while bortezomib inhibits the NF-kB pathway. We assume that the inhibition of these four major signaling pathways by this drug combination finally resulted in the effective activity against this refractory and steroid-depended GvHD.In this heavily pretreated patient, the combination of ruxolitinib plus bortezomib proved to be highly effective, also demonstrating no toxicity. Undoubtedly, this is a single case and also other immunosuppressive agents additionally to bortezomib and ruxolitinib were used and therefore, it is  inaccurate to draw definite conclusions regarding the real impact of these agents on the treatment response. However, these promising results indicate that the combination of bortezomib plus ruxolitinib could be an alternative approach for patients with SR-cGvHD and merits further investigation in future prospective clinical trials.