Dogs present a significant opportunity for studies in comparative oncology. However, the study of cancer biology phenomena in canine cells is currently limited by restricted availability of validated antibody reagents and techniques. Here, we provide an initial characterization of the expression and activity of key components of the DNA Damage Response (DDR) in a panel of hematopoietic canine cancer cell lines, with the use of commercially available antibody reagents.The techniques used for this validation analysis were western blot, qPCR, and DNA combing assay.Substantial variations in both the basal expression  and agonist-induced activation (p-Chk1) of DDR components were observed in canine cancer cell lines. The expression was stronger in the CLBL-1 (B-cell lymphoma) and CLB70 (B-cell chronic lymphocytic leukemia) cell lines than in the GL-1 (B-cell leukemia) cell line, but the biological significance of these differences requires further investigation. We also validated methodologies for quantifying DNA replication dynamics in hematopoietic canine cancer cell lines, and found that the GL-1 cell line presented a higher replication fork speed than the CLBL-1 cell line, but that both showed a tendency to replication fork asymmetry.These findings will inform future studies on cancer biology, which will facilitate progress in developing novel anticancer therapies for canine patients. They can also provide new knowledge in human oncology. Thanks to that, it can provide new insights into cancer development and risk factors that may also affect humans. According to the American Veterinary Medical Association, about half of the dogs aged over 10 years will suffer from cancer . With thSeveral fundamental regulatory cellular processes are frequently altered in cancer. Disturbances in the functioning of the DNA Damage Response (DDR) pathway are often connected with carcinogenesis –11 and rThe DDR is one of the pathways whose dysfunction can lead to cancer. ATR and Chk1 comprise the principal DDR pathway available to most cancer cells that lack functional p53, which is found altered in almost 50% of human cancers and has also been reported in a variety of canine cancers , 20. TheTo facilitate research into the significance of DDR pathway disturbances in cancer, as well as to inform studies on the development of new therapies targeting the DDR in dogs, we conducted a series of experiments on canine lymphoma/leukemia cell lines to assess (1): the expression of transcripts of DDR components by RNA sequencing in two selected canine cancer cell lines (2), the basal expression levels of key proteins involved in the DDR , together with checking the feasibility of using commercially available antibodies, and (3) the functionality of the DDR pathway in canine model cells by assessing the DDR pathway activation after DNA damage, using the DNA damaging agent etoposide (detection of γH2AX and p-Chk1). Finally, we performed DNA combing assays to assess DNA replication dynamics in canine lymphoma/leukemia cells by directly visualizing replication fork progression rates and replication origin firing.2.2.1.Ad Hoc Committee on Animal Research and were approved by the First Local Committee for Experiments with the Use of Laboratory Animals, Wroclaw, Poland .A panel of canine lymphoma/leukemia cell lines: CLBL-1 (B-cell lymphoma), CLB70 (B-cell chronic lymphocytic leukemia), and GL-1 (B-cell leukemia) was used in this study. The CLBL-1 cell line was a gift from Barbara Rütgen from the Institute of Immunology, Department of Pathobiology from the University of Vienna , the GL-2 and 95% humidified air, at 37°C in 25 cm2 cell culture flasks .The culture medium RPMI 1640  was used for the CLBL-1 and GL-1 cell lines, and Advanced RPMI  for the CLB70 cell line. The culture media were supplemented with 2 mM L-glutamine , 100 U/mL of penicillin, 100 μg/mL of streptomycin , and 10–20% heat-inactivated fetal bovine serum . The cells were cultured in an atmosphere of 5% CO2.2.RNA was obtained from cultures of the selected cell lines CLBL-1 and GL-1 during unperturbed growth and sequenced by Novogene (United Kingdom). The expected number of Fragments Per Kilobase of transcript sequence per Millions of base pairs sequenced (FPKM) was used to calculate relative gene expression . The DDR2.3.DNA damage was induced by treatment with etoposide  (a topoisomerase II inhibitor), at 20 μM for 2 h. Treatment conditions were selected based on literature  and prev2.4.5cells/mL were cultured in 10 mL of media in a 25 cm2 culture flask per condition. After 48 h of incubation, the samples were lysed in urea/ SDS buffer , 50 μL 20% SDS), and run in 8–12% bis-tris acrylamide gels prepared using a BioRad Mini-PROTEAN Tetra Vertical Electrophoresis Cell system. The samples were transferred to nitrocellulose membrane using BioRad Mini Trans-Blot® Cell for wet transfer and BioRad Trans-Blot® Turbo™ Transfer System device for semi-dry transfer.3 x 10The antibodies used in the study were selected based either on available literature data on reactivity with canine cells  or compa2.5.2.5.1.Canis lupus familiaris nucleotide accession number sequences for mRNA of the target genes (TGs): Atr, Claspin, and six housekeeping genes (HKGs): Actb, Ppia, and Rplp0 were taken from the Nucleotide Center for Biotechnology Information (NCBI) database . The sequences were transferred into the Universal Probe Library. The designed primers and their amplified sequences were additionally verified for their specificity in the Nucleotide Basic Local Alignment Search Tool - Nucleotide-BLAST . Gene names, primer sequences for TGs and HKGs, amplicon size, as well their respective gene accession numbers are summarized in The 2.5.2.7 cells cultured in 10 mL from the CLBL-1, CLB70, and GL-1 lymphoma cell lines were centrifuged at 300 g, 4° C, and resuspended in 500 μL of TRIzol reagent . The cells were immediately transferred to a low-temperature freezer and stored in Eppendorf tubes at −80° C for further analysis. Total RNA isolation was performed using Total RNA Zol-Out™ D  according to the protocol provided in the isolation kit. Briefly, the cells were removed from the low-temperature freezer and thawed on ice for 30 min. After that, 167 μL of ultra pure molecular biology water  were added, and the sample was mixed by inversion. Next, the cells were spun for 10 min at 10000 rpm. The supernatant was mixed with 1 volume of 96–100% ethanol  and gently agitated until a homogenous solution was obtained. The supernatants from each tube were transferred into new tubes with an RNA membrane binding column and were centrifuged through the column for 1 min at 10000 rpm and 4° C. The columns were rinsed with 700 μL washing A2W buffer for 2 min at 10000 rpm. DNA digestion for 15 min at 37° C in a thermoblock was performed using DNAse according to the manufacturer’s protocol. The enzymatic activity of the digestive buffer was inhibited by adding 700 μL of R81 buffer and centrifugation [1 min at 10000 rpm at room temperature (RT)]. The filtrate was collected and loaded again onto the column. The membranes were rinsed twice with 700 μL and 200 μL of A2W buffer, centrifuged as described above, and transferred into new Eppendorf tubes. Then, 40 μL of sterile water were added, and after 3-min incubation at RT the tubes with the membranes were centrifuged as above. RNA quality and quantity were estimated using Implen NanoPhotometer , and only the samples with a 260/280 nm absorbance coefficient between 1.8 and 2.1 were used for the final experiments. The TranScriba noGenome Kit  was used to perform reverse transcription, according to the manufacturer’s recommendations in the MJ Research PTC-100 thermocycler . First, 1 μg of total RNA was mixed with the noGenome master mix. After a 10-min incubation at 42° C, 7 μL of the mentioned mix were added to the RT master mix. The RT master mix included 4 μL of TranScriba buffer, 0.5 μL of RNAse inhibitor, 2 μL of dNTP, 1 μL of starter oligo (dT), 4 μL of TranScriba reverse transcriptase and 1.5 μL of sterile water for one reaction. The reverse transcription protocol was as follows: the first step of 60 min at 42°C, the second step of 5 min at 70°C, and the final step of 5 min at 4°C. The obtained cDNA was stored at −20°C.A total of 1×102.5.3.The real-time PCR gene expression analyzes were performed in triplicate from three independent cell cultures. The reaction mix (per well) included 5 μL of RT PCR Mix SYBR® , 0.5 μM of forward and reverse primers , and 1 μL of cDNA diluted with molecular biology water (16.65 ng cDNA per well). Real-time PCR was performed using the LightCycler 480 II  instrument under the following conditions: pre-incubation at 95°C for 10 min, 50 cycles of amplification: 10 s at 95°C for denaturation, 30 s at 60°C for annealing, and 15 s at 72°C for elongation. The gene detection analyzes and primer specificity were further improved by melting curve analysis. The gene expression was categorized using the following scale:“0” lack of gene expression, Ct values above 35.“1” very low gene expression, Ct values between 30 and 35.“2” low gene expression, Ct values between 28 and 30.“3” regular gene expression, Ct values between 22 and 28.“4” high gene expression, Ct values between 15 and 22.“5” very high gene expression, Ct values below 15.2.6.6 cells were cultured in 10 mL of media, then pulse-labeled with 5-iodo-2′-deoxyuridine (IdU) at 25 μM, followed by 5-chloro-2′-deoxyuridine (CldU) at 200 μM, for 15 min each. The cells were recovered by centrifugation at 300 g after each pulse, and the media were refreshed with each analog. Next, the cells were resuspended in cold PBS and warmed to 42°C, using 5×105 cells per agarose plug. The cells were gently mixed with 1% agarose in PBS and divided into the casting mold to generate the plugs. After treatment with proteinase K  at 50°C overnight, the DNA was stained with YOYO-1 (5 μM in TE solution for 2–5 min) to check the quality of the fibers. After melting the agarose, the extracted DNA was poured into a reservoir, where a coverslip was inserted, and the DNA fibers were stretched. The resulting fibers were visualized by immunofluorescence detecting the IdU and CldU analogs with red and green antibodies. The coverslips were incubated for 45 min with murine anti-BrdU , and rat anti-BrdU , as primary antibodies for the analogs, and for 30 min with goat anti-mouse IgG1 Alexa 564  and chicken anti-rat Alexa 488  as secondary antibodies for the analogs. The coverslips were incubated for 30 min with autoanti-ssDNA DSHB by Voss, E.W. (Hybridoma Product autoanti-ssDNA) autoanti-ssDNA DSHB by Voss, E.W.  to detect whole DNA fibers, and then for 30 min with a secondary antibody goat anti-mouse IgG2a Alexa 647 . Image acquisition was performed with a 40x objective using a confocal microscope . The fork velocity (FV) was calculated by multiplying the length of the green track of the fiber in micrometers by 2 to obtain Kb, and dividing it by 15 min (time of pulse). Fork asymmetry (FA) was calculated by dividing the long track by the short track. For this analysis, only the first analog incorporation tracks (green tracks) were considered.A total of 1.6×102.7.For the combing assay analysis, the Mann–Whitney test was performed to compare the cell lines and analyze potential differences. Scatterplots were prepared to visually represent the differences between the two cell lines.http://statistica.io.Statistical analysis was performed using TIBCO Software Inc. (2017) Statistica , version 13 3.3.1.The canine lymphoma cell line CLBL-1 and the canine leukemia cell line GL-1 expressed a total of 16,220 genes, from which 271 (~2%) are DDR pathway members. Specifically, the CLBL-1 cell line expressed 260 DDR genes, and the GL-1 cell line 266, with 255 genes in common . The rel3.2.Considering that ATR, Claspin, Chk1, and Rad51 are among the most important proteins of the DDR pathway, they are still quite uncharacterized in dogs. Thanks to the progress made in recent years in veterinary medicine, and in particular in veterinary oncology, currently there are some tools available for their study in dogs (for example antibodies and siRNAs).Our initial aim was to identify commercial antibodies against key components  of the DDR that would be suitable for use in canine cells. Western blot screening was performed to analyze the basal protein expression levels, and to determine whether the pathway activation in response to DNA damage occurs in canine cells  corresponded with the ~220 kDa molecular mass expected for this protein. ATR was readily detected in only two of the three cell lines analyzed, with expression of ATR being higher in CLB70 cells than CLBL-1 and undetectable in GL-1 cells . As ATR qPCR analysis confirmed that ATR mRNA is expressed in the three cell lines tested, with mean threshold cycle (Ct) values of 25.21 ± 0.27 for the CLBL-1 cell line, 28.15 ± 3.29 for the CLB70 cell line, and 25.72 ± 0.61 for the GL-1 cell line. Although the expression of ATR was substantially lower than the expression of the HKGs , Ct valu3.2.2.In the case of Claspin, BLAST alignment demonstrated 84.47% identity between the human and canine proteins, again supporting the possibility of cross-reactivity of human antibodies with canine proteins. The expression of Claspin was detectable using the Claspin B-6  antibody. The antibody recognized a protein of the expected molecular mass of ~180 kD, thus confirming cross-reactivity with canine Claspin. Claspin expression was observed in all three cell lines, although the expression levels varied. Claspin expression levels were substantially higher in the CLBL-1 and CLB70 cell lines than in the GL-1 cell line, similar to the expression of ATR .qPCR analysis showed that the Claspin mRNA was also expressed in the three cell lines, with Ct values of 24.95 ± 0.17 for the CLBL-1 cell line, 27.63 ± 2.70 for the CLB70 cell line, and 26.19 ± 0.74 for the GL-1 cell line. Similar to ATR, the expression of the Claspin mRNA in each case was substantially lower than that of the HKGs .3.2.3.BLAST alignment for Chk1 protein sequences between humans and dogs showed a high 96.2% identity. This analysis also confirmed that canine Chk1 contains the key regulatory site, serine 345 (S345), that is phosphorylated by ATR to activate Chk1 in response to genotoxic stress. This means that with the use of the tested antibodies Chk1 G-4  and Phospho-Chk1 (SER345) 133D3  directed against human epitopes, both the basal level expression of Chk1 kinase and its activation after DNA damage can be tested in canine cells. Indeed, both antibodies detected proteins of the expected molecular mass (~56 KD) in all three cell lines . Interes3.2.4.BLAST alignment comparing Rad51 protein sequences demonstrated 99.12% identity between human and canine protein, indicating a high probability that an antibody directed at the human protein will detect the canine homolog. As expected, using Rad51 G-9  antibody, we detected a band of ~37 kDa corresponding to the expected molecular mass of Rad51. Basal Rad51 recombinase expression was detected in all three cell lines with the highest level of expression in the CLBL-1 cell line .3.3.Next, we analyzed the expression levels and possible regulatory modifications of the canine DDR proteins after inducing activation of the DDR pathway by treating the cells with a classic DNA damaging agent, etoposide. Several studies have reported DDR activation through phosphorylation of Chk1 and an increase of Rad51 expression after treatment with the DNA damaging agent etoposide , 56. Thi3.4.Advanced techniques, such as DNA combing, can provide much greater insight into DNA replication dynamics by directly visualizing replication fork progression rates and replication origin firing. Due to the important role of ATR-Chk1 in replication, we wished to evaluate the feasibility of performing DNA combing in canine cells.The selected cell lines for this study were GL-1 and CLBL-1, since both present high expression level of Rad51 , which mp = 8.86−21) faster in the GL-1 line (1.5 Kb/min) than in the CLBL-1 line , while RAD51 exhibited the lowest relative expression  and leukemia (GL-1). After sorting the genes by Gene Ontology terms (GO) related to the DDR , we founpression , in blueThus, this RNA-Seq analysis revealed that the canine lymphoma and leukemia cells shared expression of a majority of DDR genes, but that the expression of certain key components differed substantially between the tumor types.4.2.The first screening for the basal expression of DDR proteins  indicate4.2.1.ATR was detected in all the cell lines at the mRNA level . The obtATR gene is widely expressed in the canine lymphoma/leukemia cell lines. However, as ATR has a high molecular weight of 220 kDa, and high-molecular-weight proteins are difficulted to transfer . Indeed, Claspin protein expression was detected in all the cell lines of our panel as a single band with a molecular mass of 180 kDa as seen in human cell lines . Claspinell line . This coell line .Claspin has been described to be highly expressed in prostate cancer cells in comparison with non-cancerous prostate cells . Many ca4.2.3.Chk1 showed a 96.7% identity between the human and canine protein sequences in the BLAST alignment . ConsistUpregulation of Chk1 has been proposed as a target for anticancer therapies. Different studies have confirmed Chk1 inhibitors acting as apoptosis inducers in various human and canine tumor cells, indicating, for example, proliferation decrease in human neoplastic B-cells and mast tumor cell (MTC) canine cell lines , 69. Cur4.2.4.in vivo mice models , highlighting the similarity of both species in the way naturally occurring cancer and lymphoma respond to therapies , 99. Cel5.To conclude, we propose the use of canine lymphoma/leukemia cells as a model to study DDR in cancer, with ATR, Claspin, Chk1, and Rad51 as promising targets for further analysis. Our results will facilitate further investigation on DDR in canine cancer by identifying validated antibodies for ATR, Claspin, Chk1, p-Chk1, and Rad51, and primers for ATR and Chk1, and bringing numerous opportunities to develop new targeted-anticancer therapies which later may be also implemented in human medicine.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/AP: conceptualization and project administration. BH-S and AP: methodology. BH-S: software, validation, formal analysis, data curation, writing–original draft preparation, visualization, and funding acquisition. BH-S, AP, PK, and ED: investigation. BO-M: resources. AP and DG: writing–review and editing. AP, DG, and BO-M: supervision. All authors have read and agreed to the published version of the manuscript.The publication was financed by the project “UPWR 2.0:international and interdisciplinary program of development of Wrocław University of Environmental and Life Sciences,” co-financed by the European Social Fund under the Operational Program Knowledge Education Development, under contract No. POWR.03.05.00-00-Z062/18 of June 4, 2019 and by the Polish National Agency for Academic Exchange under Grant No. PPI/APM/2019/1/00044/U/00001. DG was an Agustín de Betancourt Investigador of the Universidad de La Laguna.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Metabolomics analysis showed clear separation between the parental MDA-MB-231 cell line and the drug-resistant cell lines. Pathway analysis revealed that arginine and proline metabolism, glutathione metabolism, and beta-alanine metabolism were significantly perturbed in the drug-resistant cell lines compared to the parental cell line. After matching signals to an in-house library of reference standards, significant decreases in short- and medium-chain acylcarnitines and significant increases in long-chain acylcarnitines, 5-oxoproline, and 7-ketodeoxycholic acid were observed in the resistant cell lines as compared to the parental MDA-MB-231 cell line. In addition to baseline metabolic differences, we also investigated differences in metabolic responses in resistant cell lines upon a second exposure at multiple concentrations. Results indicate that whereas the parental MDA-MB-231 cell line had many metabolites that responded to doxorubicin in a dose-dependent manner, the two resistant cell lines lost a dose-dependent response for the majority of these metabolites. The study’s findings provide insight into how metabolism is altered during the acquisition of resistance in TNBC cells and how the metabolic response to doxorubicin changes upon repeated treatment. This information can potentially identify novel targets to prevent or reverse multi-drug resistance in TNBC, and also demonstrate the usefulness of metabolomics technology in identifying new mechanisms of drug resistance in cancer and potential drug targets.This study aimed to investigate metabolic changes following the acquisition of resistance to doxorubicin in the triple-negative breast cancer (TNBC) cell line MDA-MB-231. Two drug-resistant cell lines, DOX-RES-50 and DOX-RES-100, were generated by treating MDA-MB-231 cells with doxorubicin for 24 h and allowing them to recover for six weeks. Both drug-resistant cell lines demonstrated an increase in doxorubicin IC In the United States, breast cancer is the most diagnosed cancer for women (excluding skin cancers) and is the second leading cause of cancer deaths in women . One subDoxorubicin is a commonly used chemotherapeutic drug for the treatment of TNBC . The mecAn understudied factor in the development of doxorubicin resistance is cancer cell metabolism. Metabolic reprogramming is a hallmark of cancer and has been shown to play a critical role in drug resistance . Cancer Metabolomics technologies can be used to identify new mechanisms of drug resistance in cancer by analyzing the metabolic profile of cancer cells before and after treatment with drugs . By anal50 values, demonstrating the acquisition of drug resistance  in DOX-RES-50 and DOX-RES-100 cells, respectively, compared to the parental MDA-MB-231 cell line.After the preprocessing of metabolomics data, 4063 features remained for analysis. Principal component analysis (PCA) showed clear separation between the parental MDA-MB-231 cell line and the drug-resistant cell lines. Additionally, the DOX-RES-50 and DOX-RES-100 cell lines showed noticeable overlap in the PCA plot, indicating similarities in metabolic profiles B. Pairwimparison . Signifip-values for the MDA-MB-231 versus DOX-RES-50 and the MDA-MB-231 versus DOX-RES-100 comparisons. Results revealed that arginine and proline metabolism, glutathione metabolism, and beta-alanine metabolism were significantly perturbed between the MDA-MB-231 and DOX-RES-50 cells  in DOX-RES-50 and DOX-RES-100, respectively, compared to the parental MDA-MB-231 cell line  significantly increased metabolites in both resistant cell lines  in baseline secreted metabolite profiles of doxorubicin-resistant cell lines compared to the parental MDA-MB-231 cell line . Statistell line . Volcanoll lines A,B. Metall lines . Also, ill lines . Methylt50 values in both resistant cell lines. While the fold increase in IC50 values is more modest compared to other studies, this degree of resistance is more clinically relevant—indeed, other methods of generating drug resistance can lead to fold changes of 100–1000 in IC50 levels, but cause cellular changes that are not reflective of this in cancer patients [50 levels allows for more relevant biological changes to be observed, and may even reflect more accurately the early stages of resistance development. We used an untargeted metabolomics approach to gain insights into metabolic reprogramming during this stage by assessing metabolic differences between drug-resistant and parental MDA-MB-231 cells. Our results showed clear metabolic differences in baseline intracellular and secreted metabolomes of parental and drug-resistant cells. Moreover, we demonstrated that while parental MDA-MB-231 cells had metabolite sets with clear dose-response activity following a dose range of doxorubicin treatment, drug-resistant cells lost dose-response relationships with the majority of these metabolites. Interestingly, a small number of metabolites were significantly correlated with doxorubicin treatment in the resistant cell lines but not the parental MDA-MB-231 cells, suggesting that these metabolites may play a role in suppressing the metabolic changes associated with doxorubicin exposure in resistant TNBC cells.This study created doxorubicin-resistant cell lines by exposing MDA-MB-231 cells to a single dose of doxorubicin followed by a six-week recovery period. The acquisition of drug resistance was confirmed through an increase in doxorubicin ICpatients . Our genPathway analysis (using KEGG maps) of all peaks revealed arginine and proline metabolism, glutathione metabolism, and beta-alanine metabolism as significantly altered in both resistant cell lines. The arginine and proline metabolism pathway connects arginine, proline, and glutamate, as well as their intermediates. This metabolic pathway connects key processes of cancer cell metabolism, including biosynthesis of other amino acids, nucleotide biosynthesis, TCA cycle activity, and polyamine biosynthesis. Interestingly, this pathway is being investigated as a therapeutic target of several cancers through starvation of specific amino acids in this pathway, including arginine, proline, and glutamine . This paUpon investigating metabolites that were matched to our in-house library, we identified several acylcarnitines as significant differentiators of parental MDA-MB-231 cells and the doxorubicin-resistant cell lines. In general, we observed a decrease in short- and medium-chain acylcarnitines and an increase in long-chain acylcarnitines in the doxorubicin-resistant cell lines. Acylcarnitines are intermediates in fatty acid metabolism and are generated during the process of mitochondrial and peroxisomal β-oxidation of fatty acids, making them markers of energy metabolism. Additionally, they have been identified as indicators of metabolic diseases including cardiovascular disease, diabetes, and certain cancers . More reOne of the unique contributions of this study is the analysis of metabolites that reacted in a dose-responsive manner to a range of doxorubicin doses in the drug-resistant cell lines and the parental MDA-MB-231 cell line. The loss of dose response in the metabolome, particularly at lower doxorubicin levels, suggests that the resistant cells rewire cellular metabolism to survive doxorubicin exposure. Our analysis revealed novel metabolic features that are perturbed during resistance acquisition and may represent novel targets that could be targeted to reverse or mitigate doxorubicin resistance in TNBC. Additionally, these metabolites may have potential to be used as markers to be monitored during therapy to guide treatment dose, treatment length, and/or number of treatments. The correlation data also provide additional insights into the mechanism of action of doxorubicin in breast cancer cells, which, despite its widespread clinical use, still remains unclear . MethyltIn conclusion, the findings of this study contribute to our understanding of metabolic reprogramming in drug-resistant breast cancer cells and suggest potential mechanisms and treatment strategies for this challenging clinical problem. Our findings point to a significant role of acylcarnitines/fatty acid oxidation, as well as specific metabolites such as MTA, 5-oxoproline, and 7-ketodeoxycholic acid in the process of acquired drug resistance. Surprisingly, metabolic investigations regarding the acquisition of drug resistance are sparse in the literature, particularly for TNBC. Because of this, our study provides many novel insights into the metabolic changes that occur during TNBC drug resistance, forming the basis for future research to further investigate this process and generate novel findings, such as metabolic targets that could be manipulated to potentially increase drug sensitivity. Further studies are needed to validate the findings of this study and to explore the functional significance of the metabolic changes observed in the drug-resistant cell lines. Additionally, in vivo model systems should also be investigated to determine if these metabolic changes are observed in this setting as well.Optima grade solvents (water with 0.1% formic acid and methanol with 0.1% formic acid) and fetal bovine serum (FBS) were purchased from Fisher Scientific . Dubelcco’s Modified Eagle Medium (DMEM) with high glucose and phosphate-buffered saline (PBS) was purchased from Gibco . Doxorubicin was purchased from SelleckChem . The MDA-MB-231 cell line was purchased from the American Type Culture Collection (ATCC) .4 cells per well and were allowed to attach overnight. Cells were then treated with an 11-point dose curve of doxorubicin with a top concentration of 10 µM and a dilution series of 1:1 (n = 3–6 per group). Cell viability was assessed using the alamarBlue assay according to manufacturer instructions (Thermo Scientific). GraphPad was used to generate viability dose curves and calculate doxorubicin IC50 values. DMSO was used as a vehicle, with a final concentration of 0.1% for all treatments.MDA-MB-231 cells were cultured according to manufacturer guidelines. Cells were cultured in DMEM supplemented with 10% FBS, 2 mM glutamine, 50 U/mL penicillin, and 50 µg/mL streptomycin. To generate doxorubicin-resistant cells, MDA-MB-231 cells were treated with 50 or 100 nM doxorubicin for 24 h and were allowed to recover in supplemented DMEM for six weeks. To verify resistance, treatment-naïve and treatment-experienced cells were plated into 96-well plates at 2 × 10g at 4 °C for 10 min, and supernatants were transferred to autosampler vials for analysis by ultra-high-pressure liquid chromatography–high-resolution mass spectrometry (UHPLC-HRMS). Quality control study pools (QCSP) were created by combining 10 µL of each sample into a single mixture. Method blanks were created by adding 500 µL of 80% methanol to empty tubes and were processed in an identical manner as the study samples.Cells were plated at approximately 80% confluency in 6-well culture plates and were treated with 500, 200, 100, 50, or 0 nM doxorubicin with a final DMSO amount of 0.1% (n = 4 per group). After 24 h of treatment, metabolites were extracted from cell samples as described previously ,32,33,34m/z. QCSP and blank injections were placed at a rate of 10% throughout the study samples. An injection volume of 5 µL was used for analysis of each sample. Raw UHPLC-HRMS data were imported into Progenesis QI  for alignment, peak picking, and deconvolution. Background signals were removed by filtering out peaks with a higher average abundance in the blank injections as compared to the QCSP injections. Data were normalized using a QCSP reference sample using the “normalize to all” function in progenesis [Metabolomics data were acquired via previously published UHPLC-HRMS methods ,35,36,37ogenesis .Peaks were matched to an in-house library of reference standards or public mass spectral databases from the National Institute of Standards and Technology (NIST) and METLIN. Peaks were matched to metabolites by retention time , exact mass , and fragmentation pattern . An ontology system was given to denote the evidence basis for each metabolite assignment. OL1 refers to a match to the in-house library for RT, MS, and MS/MS; OL2a refers to an in-house match to the in-house library for RT and MS; OL2b refers to a match to the in-house library for MS and MS/MS; PDa refers to a match to public databases for MS and MS/MS; PDb refers to a public database match for MS and theoretical MS/MS (HMDB); PDc refers to a public database match for MS and isotopic similarity; PDd refers to a public database match for MS only.p-values were calculated for each peak for each treatment as compared to the vehicle control. p-values were calculated using Student’s t-test. Correlation analyses were performed by using the Statistical Analysis (metadata table) module in MetaboAnalyst 5.0 using Pearson r as the correlation measure. p-values were not adjusted for multiple testing due to the small sample size of this study and the exploratory, rather than confirmatory, nature of this study [p-value cutoff of 0.05.Principal component analysis (PCA) and partial least squares discriminant analysis (OPLS-DA) was performed in SIMCA 16  using the normalized, filtered data. Unit Variance (UV) scaling was used for all multivariate plots. PCA plots were used to assess data quality by verifying the clustering and centering of QCSP samples by PCA, and OPLS-DA plots were used to assess the separation of metabolomes between vehicle and treated cells, as well as to calculate variable importance to projection (VIP) scores for each peak. Heatmaps were generated using MetaboAnalyst 5.0 . Fold chis study . Pathway


Acquired chemoresistance during chemotherapy, often accompanied by cross- and multi-resistance, limits therapeutic outcomes and leads to recurrence. In order to create in vitro model systems to understand acquired doxorubicin-resistance, we generated doxorubicin-resistant sublines of canine prostate adenocarcinoma and urothelial cell carcinoma cell lines. Chemoresistance to doxorubicin, cross-resistance to carboplatin, and the reversibility of the acquired resistance by the specific MDR1-inhibitor tariquidar were quantified in metabolic assays. Resistance mechanisms were characterized by expression of the efflux transporters MDR1 and RALBP1, as well as the molecular target of doxorubicin, TOP2A, with qPCR and Western blotting. Six out of nine cell lines established stable resistance to 2 µM doxorubicin. Drug efflux via massive MDR1 overexpression was identified as common, driving resistance mechanism in all sublines. MDR1 inhibition with tariquidar extensively reduced or reversed the acquired, and also partly the parental resistance. Three cell lines developed additional, non-MDR1-dependent resistance. RALBP1 was upregulated in one resistant subline at the protein level, while TOP2A expression was not altered. Combination therapies aiming to inhibit MDR1 activity can now be screened for synergistic effects using our resistant sublines. Nevertheless, detailed resistance mechanisms and maintained molecular target expression in the resistant sublines are still to be examined.  Canine prostate cancer, regardless of whether adenocarcinoma (PAC) or the frequent urothelial or transitional cell carcinoma (TCC) of the prostate, is often diagnosed at a late stage and with a poor outcome . In partDoxorubicin is an effective and thus commonly used chemotherapeutic drug in canine oncology . IntercaABCB1, belongs to the ABC family and is a well-known multidrug-resistance protein with a broad spectrum of substrates in both dogs and humans  = ln(2)/ln(r).Growth kinetics were assessed as previously described . Cells w®, Corning inc., Corning, NY, USA), coated with basement membrane . The next day, the lower chambers were filled in duplicate with medium containing 10% serum as attractant. Serum-free medium served as negative control. Following 48 h incubation time, non-invasive cells were gently removed from the upper chamber by using moistened cotton swaps. The remaining invasive cells attached to the membranes were fixed in 10% formalin for 10 min, permeabilized in methanol for 20 min, and washed twice in PBS. Finally, the membranes with attached cells were stained for 2 min with 1% crystal violet, washed again three times in PBS, and allowed to dry. Three representative regions of each membrane were photographed with a DMI600 B microscope  at 100-fold magnification using the LAS AF 2.6.0 software. The proportion of area covered by invasive cells was determined with ImageJ 1.53c. [As described for the parental cell lines , cells iJ 1.53c. . ® NP80 . For cDNA synthesis, applying the QuantiTect Reverse Transcription Kit , 2 µg RNA per 10 µL reaction was used. Primer pairs  and purchased from Microsynth AG . Specific primer binding was confirmed by sequencing the amplicons . Reactions were run in triplicates on a LightCycler®96 real-time PCR System . Cycling conditions were as follows: 95 °C for 10 min, 45 cycles , followed by melting. Data analysis was carried out using Roche LightCycler®96 software (1.1.0.1320). A two-fold dilution series of pooled cDNA served for calculating primer efficiencies. GAPDH and ACTB gene expression was more stable compared to HPRT, which is why they were chosen as normalization genes. Gene expression values relative to Adcarc0846 were calculated according to an efficiency-corrected model, taking into account both normalization genes [In three independent experiments, cells were cultured for 96 h in presence of 2 µM doxorubicin (doxo lines only) or without doxorubicin . Afterwards, cells were washed with ice-cold PBS and harvested using cell scratchers. RNA was isolated with the SV Total RNA Isolation System (Promega GmbH) and quantified photometrically using a NanoPhotometerer pairs  were deson genes . ®TGX™ Gels, Bio-Rad Laboratories Inc., Hercules, CA, USA). After blotting onto a nitrocellulose membrane , unspecific binding was blocked by EveryBlot blocking buffer (Bio-Rad Laboratories Inc.). Primary antibodies  by scraping. Protein concentrations were assessed by Pierce™ BCA Protein Assay Kit . Without previous cooking, as required by the ABCB1-antibody, proteins (30 µg per lane) were separated by SDS-PAGE . For qPCR and Western blot analysis, Log2 transformed relative expression values were used. To compare fluorescence intensities as well as gene and protein expression of the parental cell lines with the resistant clones, cultured either with or without doxorubicin, a one-way ANOVA was performed followed by Tukey’s post-hoc test in case of normal distribution (Shapiro–Wilk test). If the data were not normally distributed, the Kruskal–Wallis test was used, followed by Dunn’s multiple comparisons test. Regarding cell numbers for doubling times and area covered by cells for invasion assay, parental and resistant cell lines were compared with Student’s t-test because of normally distributed data. Acquired chemoresistance limits the therapeutic outcome in veterinary and human oncology, yet no satisfying treatment strategy for canine prostate cancer exists. In order to create in vitro models to overcome acquired chemoresistance, we generated six doxorubicin-resistant sublines from nine well characterized canine prostate cancer cell lines. Drug efflux by massive MDR1 overexpression was identified as a common driving resistance mechanism in all sublines. Moreover, Adcarc0846doxo, Adcarc1508doxo, and TCC1506doxo developed additional, non-MDR1 dependent resistance mechanisms, which still need to be characterized. MDR1 inhibitors, such as the herein efficiently used tariquidar, reduce or reverse the acquired doxorubicin-resistance. Furthermore, tyrosine kinase inhibitors show potential in reversing MDR1-dependent drug-resistance, while exhibiting antiproliferative effects themselves. Potential synergistic effects can now be investigated with our six pairs of parental and resistant cell lines. However, in advance, the expression signature of molecular targets initially observed in the parental cell lines remains to be confirmed.


Although significant progress has been made in the treatment of myeloid malignancies, this issue remains a focus of interest for a large number of research teams. Current research is especially focused on elderly patients who are not suitable for intensive chemotherapy and bone marrow transplantation or patients who have achieved remission after such therapy but subsequently enter into relapse of the disease. Here, therapy with demethylating agents is indicated. However, we do not know of another treatment option in the case of resistance to such treatment. Therefore, knowledge of the causes and mechanisms of resistance to demethylating agents is an essential issue for improving the treatment of such patients.Three AML cell variants  were established for resistance by the same protocol using 5-azacytidine (AZA) as a selection agent. These AZA-resistant variants differ in their responses to other cytosine nucleoside analogs, including 5-aza-2′-deoxycytidine (DAC), as well as in some molecular features. Differences in global DNA methylation, protein levels of DNA methyltransferases, and phosphorylation of histone H2AX were observed in response to AZA and DAC treatment in these cell variants. This could be due to changes in the expression of uridine-cytidine kinases 1 and 2 (UCK1 and UCK2) demonstrated in our cell variants. In the M/A variant that retained sensitivity to DAC, we detected a homozygous point mutation in UCK2 resulting in an amino acid substitution (L220R) that is likely responsible for AZA resistance. Cells administered AZA treatment can switch to de novo synthesis of pyrimidine nucleotides, which could be blocked by inhibition of dihydroorotate dehydrogenase by teriflunomide (TFN). This is shown by the synergistic effect of AZA and TFN in those variants that were cross-resistant to DAC and did not have a mutation in UCK2. Myelodysplastic neoplasms  and acute myeloid leukemia (AML) share a common denominator in abnormal myeloid hematopoiesis, which leads to cytopenias and an increased presence of immature myeloid blasts in the bone marrow and peripheral blood. One of the treatment options for these diseases is the use of the hypomethylating agents (HMAs) 5-azacytidine (AZA) and 5-aza-2′-deoxycytidine (DAC) ,2.AZA, commercially available under the name Vidaza, has been approved by the European Medicines Agency (EMA) for the treatment of patients with malignancies caused by defective differentiation in the myeloid branch of hematopoiesis who are not suitable for hematopoietic stem cell transplantation. The EMA has recommended the use of Vidaza for patients with MDS, chronic myelomonocytic leukemia (CMML), AML with 20–30% blasts, and multilineage dysplasia and adult patients aged 65 years or older with AML with >30% blasts in bone marrow . MoreoveUnfortunately, approximately half of patients may not respond to AZA treatment from the start (primary resistance), and others may become unresponsive during repeated cycles of HMA therapy (secondary resistance) . For a pWe have previously described the preparation of AZA-resistant MOLM-13 and SKM-1 cell variants by sequential passaging in medium with increasing concentration of AZA. These variants presented overexpression of the multidrug efflux pump P-glycoprotein (P-gp) and exhibited resistance to P-gp relative substances in addition to resistance to AZA . SimilarStudying possible mechanisms of resistance, we mainly focused on the uptake of HMAs and their fate in sensitive and resistant cells. Both AZA and DAC are prodrugs that must be phosphorylated to the corresponding triphosphates in order to be incorporated into DNA or RNA and exert their effects . Since H2.Two cell lines were used in this study. The MOLM-13 cell line (ACC 554) and the SKM-1 cell line (ACC 547) were derived from the peripheral blood of a 20-year-old and a 76-year-old man with AML following MDS, respectively . The sensitive MOLM-13 and SKM-1 cell lines were adapted to 5-azacytidine  over a 6-month period with repeated passaging in medium containing AZA in stepwise increasing concentrations beginning at 0.1 nM up to final concentration of 1 μM. This procedure yielded AZA-resistant SKM-1/AZA and MOLM-13/AZA* cell variants. The MOLM-13/AZA cell variant was described previously . The celSensitive and resistant MOLM-13 and SKM-1 cells were incubated under standard culture conditions with various concentrations of AZA, DAC, teriflunomide , or combinations of these drugs for 72 h. The cell lines were treated with AZA/DAC/TFN every 24 h. The number and viability of cells were determined by measuring the plasma membrane integrity of individual cells through changes in electrical resistance induced by cells passing through the detector in the CASY Model TT Cell Counter  according to the manufacturer’s protocol.® AQueous One Solution cell proliferation assay  was used to determine the metabolic activity of cells according to the manufacturer’s protocol. The IC50  was computed by nonlinear regression according to Equation (1) using SigmaPlot 8.02 software :50 is the half maximal inhibitory concentration; and n represents order exponents for cytotoxic effects. The data represent computed values ± standard error with 30 degrees of freedom.Sensitive and resistant MOLM-13 and SKM-1 cells were incubated under standard culture conditions with different concentrations of cytarabine or gemcitabine for 48 h and various concentrations of AZA, DAC, TFN, or their combinations for 72 h. The cell lines were treated with AZA/DAC/TFN every 24 h, and cytarabine and gemcitabine were added only once. After cultivation, the CellTiter 96ACTB.Total RNA was isolated using TRI Reagent  according to the manufacturer’s instructions. Reverse transcription (RT) was performed with 1 μg of RNA using a RevertAid™ H Minus First-Strand cDNA synthesis kit  according to the manufacturer’s protocol. PCR was performed in a total volume of 25 μL using a DreamTaq PCR kit  according to the manufacturer’s protocol. After heating the samples at 95 °C for 5 min, samples were subjected to 30 cycles of denaturation , annealing  according to the manufacturer’s protocol. PCR thermal cycling conditions were as follows: initial denaturation ; 30 cycles of denaturation ; annealing , 1% Triton X-100, 0.5% sodium deoxycholate, 0.1% SDS, 150 mM NaCl, and protease inhibitor cocktail from Sigma-Aldrich . The protein concentrations were measured by a Pierce™ BCA Protein Assay kit . The protein samples were separated by sodium dodecyl sulfate-polyacrylamide electrophoresis (SDS–PAGE) in a 12% gel. The proteins were then transferred by electroblotting to a nitrocellulose membrane . The primary antibodies used were UCK1 (HPA050969) and UCK2 (SAB1411384) ; DCK (ab96599) from Abcam, Cambridge, UK; GAPDH (MAB374) ; and γ-H2AX (CST 9718) and α-tubulin (CST 3873) . Goat anti-rabbit (SC-2054) and mouse anti-rabbit antibodies (SC-2357)  and horse anti-mouse (CST 7076) and goat anti-rabbit antibodies (CST 7074) , all conjugated with horseradish peroxidase, served as secondary antibodies. Protein bands were visualized by ECL detection  and Amersham Imager 600 . Protein quantities were established by densitometry using ImageQuant software  and normalized to GAPDH or α-Tubulin.Cells were incubated with various concentrations of AZA, DAC, TFN, or a combination of these drugs for 24 or 72 h under standard culture conditions. The cells were treated with AZA/DAC/TFN every 24 h. After the incubation period, the proportions of apoptotic and necrotic cells were measured using an Annexin V /propidium iodide  assay. The cells were washed with PBS and gently resuspended in binding buffer containing 0.5 μg/mL FITC-labeled Annexin V. The mixtures were incubated for 15 min at room temperature in the dark. Finally, propidium iodide  was added to each sample, after which the samples were analyzed by flow cytometry using an Accuri C6 flow cytometer .Cells were incubated with or without 0.5 μM AZA or DAC for 72 h. The cells were treated with AZA/DAC every 24 h. Total DNA was isolated using TRI Reagent  according to the manufacturer’s instructions. Global DNA methylation status was determined by a Methylated DNA Quantification Kit  according to the manufacturer’s instructions. Numerical data are expressed as the mean ± SD of three independent measurements. Statistical significance was assessed by unpaired Student’s t test. Correlations were determined using Pearson correlation analysis. SigmaPlot 8.0 software  was used. 50) values of four (deoxy)cytidine analogs—AZA, DAC, gemcitabine (GEM), and cytarabine (AraC)—in our cell variants. M/A did not show any cross-resistance to other analogs. However, S/A showed a slightly lower sensitivity to the other three analogs, while M/A* showed considerably increased cross-resistance to the analogs, especially DAC (Three AZA-resistant cell sublines were prepared in our laboratory: one from the SKM-1 cell line (SKM-1/AZA—S/A) and two from the MOLM-13 cell line (MOLM-13/AZA—M/A and MOLM-13/AZA*—M/A*). The M/A subline was already described in our previous work . Using tally DAC . Even atally DAC .After incorporation into DNA, HMAs exert two main effects: DNA hypomethylation by DNA methyltransferase (DNMT) inhibition and DNA damage response induction . TherefoCDA) in M/A and M/A* cells compared to MOLM-13 cells. In M/A, we also observed slightly decreased expression of dihydroorotate dehydrogenase (DHODH) , but neither was expressed in any of our cell variants.Using RT–PCR, we measured the mRNA expression of genes involved in the metabolism and transport of AZA and DAC . For mos (DHODH) . We alsoFor both hypomethylating agents, phosphorylation to the corresponding monophosphate form is the rate limiting step in their activation . TherefoUCK1 and UCK2 protein coding regions were sequenced. We found a homozygous point mutation of UCK2 in the M/A cell variant , an inhibitor of dihydroorotate dehydrogenase (DHODH), exerted slightly different impacts on our three AZA-resistant cell sublines. In the case of M/A, TFN caused a decrease in viable cell counts compared to the untreated control, but it did not induce cell death even at the highest concentration used. In M/A*, the number of viable cells was also lower than in the control, but we also observed an increased number of apoptotic cells in the population. In S/A, we observed only a small decrease in the metabolic activity of the cells, with no change in their number. We also did not observe a significant increase in the apoptotic cell counts. When compared to sensitive parental cell lines, TFN had a similar or weaker effect on AZA-resistant cell variants . Except Next, we measured the impact of TFN and AZA combined treatment on our AZA-resistant cell variants . We cultWe also measured the impacts of DAC and TFN combined treatment in the two cell variants that showed cross-resistance to DAC. We used 5 μM TFN as in the previous experiments and DAC at four concentrations . However, in this case, we did not observe a significant synergistic effect .50 value cannot be calculated but is higher than 40 µM  than DAC (20 mg/m2 for 5 days). However, both AZA and DAC have demonstrated efficacy in the treatment of MDS and AML that preceded the drugs’ approval [A more pronounced effect of DAC than AZA was observed in both our parental cell lines . Consistapproval ,4,30,31.Interestingly, IC50 of GEM is much lower compared to all the other drugs tested in our cell variants, even in those showing resistance to both AZA and DAC . DrenberSLC28A1 and SLC28A3 genes encoding Na+-mediated concentrative nucleoside transporters hCNT1 and hCNT3 were not expressed in any of our cell variants (not shown). Very low or no expression of these two genes was reported in leukemia and solid tumor cell lines, as well as in blasts from AML patients [SLC29A1 or SLC29A2 encoding equilibrative nucleotide transporters hENT1 and hENT2, respectively  of four nucleoside transporters capable of transporting pyrimidine nucleosides and their analogs . The SLCpatients ,34,35. Dpatients . None ofectively A. SimilaNT5C3A) that catalyzes the dephosphorylation of pyrimidine 5’-monophosphates, as well as deoxynucleoside triphosphate triphosphohydrolase 1 (SAMHD1). Oellerich et al. (2019) observed increased SAMHD1 expression at both the mRNA and protein levels in AZA-resistant cell variants developed from the HL-60 cell line, which did not express this gene. Degradation of SAMHD1 in the AZA-resistant cell subline sensitized it to DAC [HMAs, as well as GEM and AraC, are prodrugs, and to be incorporated into nucleic acids, they must first be enzymatically activated by sequential phosphorylation to mono-, di-, and triphosphate forms. We therefore measured the mRNA expression of UCK1 and UCK2, which are capable of cytidine (AZA) and uridine phosphorylation, as well as DCK, which is responsible for deoxycytidine  phosphorylation to the corresponding monophosphate forms . While Dh et al. 19 observThe only change in the gene expression of enzymes involved in the metabolism of HMAs that we observed at the mRNA level when comparing our resistant and sensitive cell variants was the expression of CDA, which was upregulated in M/A and M/A* A. SimilaDespite the lack of changes in the expression of genes at the mRNA level, we observed many changes at the protein level in the expression of UCK1, UCK2, and DCK . We deciConsistent with the absence of cross-resistance of the M/A cell variant to DAC , DCK proUnlike DAC, AZA does not induce global DNA demethylation in M/A cells; however, the slight upregulation of γ-H2AX  may indiIn M/A*, UCK1, UCK2, and DCK were all downregulated compared to the parental cell line at the protein level B,C. NeitHowever, in the case of S/A, despite a partially reduced sensitivity to DAC, DCK protein was not downregulated B,C, and In other studies, UCK2 was downregulated in AZA-resistant AML cell variants at the mRNA level  or at thConsidering the changes in AZA-resistant cell sublines related to the phosphorylation of AZA catalyzed by enzymes important in the pyrimidine salvage pathway, we were interested in the question of whether inhibition of the de novo pyrimidine synthesis pathway would have different effects on AZA-resistant cell variants compared to the parental cell lines. We expected the AZA-resistant cells to be more sensitive to the inhibition of DHODH by TFN , as It is also interesting that TFN seems to have slightly different effects on each of our AZA-resistant cell variants. In M/A*, similar to the parental cell lines, the effect of TFN on the number of viable cells correlated with the proportions of viable and apoptotic cells. However, the decrease in viable cell number in the M/A group was not reflected in the proportions of viable and apoptotic cells. It seems that in this case, TFN treatment caused cytostatic rather than cytotoxic effects on the cells. Finally, in S/A, TFN does not seem to have an effect on viable cell counts or on a significant increase in the proportion of apoptotic cells. In this cell variant, we observed an effect only on metabolic activity . We also studied the effect of AZA and TFN combination treatment on AZA-resistant cells. In S/A and M/A* cell variants, a synergistic effect of the drugs was observed. The presence of TFN potentiated AZA-induced apoptosis . This efThe differences in the response of our AZA-resistant sublines to the combination of TFN and AZA could be related to the expression and activity of the UCK2 protein. Little knowledge exists regarding UCK1 and UCK2 substrate specificity and affinity to AZA compared to naturally occurring cytidine and uridine. However, a genome-wide CRISPR/Cas9 knockout screen identified only UCK2 (and not UCK1) as a nonredundant and rate-limiting enzyme for AZA activation . Some cyTaken together, we hypothesize that UCK1 is much less effective in AZA phosphorylation than UCK2. In the cell variants with functional UCK2 (M/A* and S/A), inhibition of de novo pyrimidine synthesis , green bThe variability of the response of our AZA-resistant cell variants to HMAs, specifically highlighting differences between them, is summarized in Since there is still no treatment approved for MDS and AML patients after HMA treatment failure , the posIn our laboratory, three AZA-resistant cell variants were prepared using the same induction protocol, but with different observed outcomes. The variability among our cells is in accordance with the diverse results of other research groups. However, despite the different molecular characteristics of our models, specific mutations or changes in the expression of genes involved in the activation of HMAs were observed in all of them. The possible involvement of different pyrimidine nucleotide synthesis pathways in cells seems to play an important role in the response to AZA treatment. In AZA-resistant cells showing cross-resistance to DAC, we observed a different response to AZA and TFN combined treatment compared to the cells in which cross-resistance had not developed. After confirmation of these findings in clinical samples, the incorporation of teriflunomide or other substances inhibiting de novo pyrimidine synthesis into the MDS/AML treatment protocol should be considered for a certain group of patients with acquired AZA resistance.


Pancreatic cancer remains a disease that is very difficult to treat. S100 proteins are small calcium binding proteins with diverse intra- and extracellular functions that modulate different aspects of tumorigenesis, including tumor growth and metastasis. High mobility group box 1 (HMGB1) protein is a multifaceted protein that also actively influences the development and progression of tumors. In this study, we investigate the possible correlations, at the transcript level, between S100s and HMGB1 in pancreatic cancer. For this purpose, we calculated Pearson’s correlations between the transcript levels of 13 cancer-related S100 genes and HMGB1 in a cDNA array containing 19 pancreatic cancer tumor samples, and in 8 human pancreatic cancer cell lines. Statistically significant positive correlations were found in 5.5% (5 out of 91) and 37.4% (34 of 91) of the possible S100/S100 or S100/HMGB1 pairs in cells and tumors, respectively. Our data suggest that many S100 proteins crosstalk in pancreatic tumors either with other members of the S100 family, or with HMGB1. These newly observed interdependencies may be used to further the characterization of pancreatic tumors based on S100 and HMGB1 transcription profiles. Pancreatic cancer is a devastating disease with a current 5-year survival rate of 12% ,4. HMGB1The 8 pancreatic cancer cell lines were purchased from ATCC  and grown in their recommended media containing 10% FBS and supplemented with penicillin (100 U/mL) and streptomycin (100 μg/mL). The respective media for each cell line was as follows: AsPC-1 (RPMI), BxPC-3 (RPMI), Capan-2 (McCoy’s 5a), CFPAC-1 (IMDM), HPAF-II (EMEM), Hs 766T (EMEM), MIA PaCa-2 (DMEM), and PANC-1 (DMEM). In addition, MIA PaCa-2 cell culture media was supplemented with 2% horse serum, as recommended by ATCC. These cell lines were established from tumors or ascites isolated from pancreatic cancer patients. Four cell lines were established from primary tumors , two from ascites , and two from metastatic tissues (CFPAC-1 and Hs766T). These cell lines have been characterized at the molecular level and histologically, and showed variations in histologic differentiation, as well as mutations in at least one of the following four carcinogenesis-driving genes: K-Ras, P53, P16, and SMAD4  stably transfected with human telomerase reverse transcriptase (hTERT) were from ATCC and used as non-cancerous control cells. These cells were grown in DMEM high glucose , supplemented with 10% fetal bovine serum, 10 ng/mL human EGF  and 750 ng/mL puromycin .The pancreatic tumor cDNA PNRT101 arrays were purchased from OriGene Technologies . The arrays consisted of 23 samples including 4 normal, 2 stage IB, 2 stage IIB, 2-III, and 13 samples where the stage of the tumor was not reported. As described in the product documentation, the array cDNAs were synthesized from high quality total RNAs of pathologist-verified tissues, and normalized with b-actin in two sequential qPCR analyses. For this reason, the Ct values obtained from each of the 19 tumor samples were used, without normalization with actin, for the correlation studies.For the preparation of RNAs from the cultured cell lines, cells were grown to 70–80% confluency and lysed with a lysis solution provided with the PARIS kit . RNAs were extracted and purified using the same kit and RNA concentrations were determined by UV absorption at 260 nm. The quality of the RNAs was assessed by gel electrophoresis on 1% agarose gels. RNAs (1 μg/20 μL sample) were reverse-transcribed into cDNA using the M-MuLV reverse transcriptase .®® EvaGreen®®qPCR Mix  and 250 nM of each forward and reverse primer. For each RT-PCR, 40 cycles were run. Each cycle consisted of a 15 s dissociation step at 95 °C, followed by a 20 s annealing step at 60 °C, and a 20 s extension step at 72 °C. Dissociation plots for each sample were analyzed to ensure that specific products, with melting temperature (Tm) above 85 °C, were generated during the PCR. In addition, the presence of a PCR amplicon of the expected molecular size was assessed by agarose gel electrophoresis. The primers used to detect the transcripts of actin and of the various S100s are listed in RT-PCR experiments were performed using 10 ng cDNA in 20 mL reaction volume using the HOT FIREPolgene − Ctactin). For each gene, at least three RT-PCRs were performed, using independent RNA preparations. The Cts were then used for statistical analysis. For the RT-PCR performed with the pancreatic tumor cDNA array, the Ct values for each gene and each array sample were also obtained from the amplification plots and used for the statistical analysis. RT-PCRs were determined on average two times for each set of primers with the tumor array. As described by Origene, the cDNA arrays contained 2–3 ng cDNA per well. Ct values obtained from the tumor array were used for statistical analysis.For the RT-PCR performed with the cDNAs from cultured cells, the Ct values for each gene were obtained from the amplification plots, and the Cts were calculated using -actin as a housekeeping gene (Ct = Ctgene − Ctactin), relative to the housekeeping gene β-actin, and their standard deviations were calculated. Low Cts corresponded to high levels of transcripts, and conversely, high Cts corresponded to low levels of transcripts relative to actin  were calculated between S100s and HMGB1 transcripts using the Cts from the 8 cell lines   . The Ct ft Excel . CorrelaTranscript levels for 13 S100 proteins and HMGB1, determined by qPCR, are summarized in In cancer cell lines, S100A2 presented the lowest transcript level among the 13 selected S100 genes. Indeed, the Ct values for S100A2 were above 16 for 6 cell lines. Hs766T and BxPc3 showed the highest transcript levels, with Ct of 11.45 +/− 0.65 and 9.17 +/− 1.78, respectively. Interestingly, these two cell lines are the only cell lines in our panel that have wild-type KRAS genotypes. Overall, the lowest level of S100A2 transcripts was observed in the non-cancerous human pancreatic epithelial HPNE cell line with Ct of 19.80 +/− 0.12 .Correlation analysis identified a very strong positive correlation (R = 0.82) between S100A2 and S100A13 in cancerous cell lines , but thaIn the tumor array, S100A2 transcription levels were consistently very low. However, correlations were observed between S100A2 and S100A7 (R = 0.48), S100A11 (R = 0.54), S100A14 (R = 0.50), S10016 (R = 0.61) and S100P (R = 0.85), when comparing transcript levels from the tumor array .In cancer cell lines, the levels of S100A4 transcript were on average higher (mean Ct = 5.31 +/− 2.99) than those of other S100s, with large variations being observed between cell lines. AsPC-1 showed the highest level (Ct = 1.86 +/− 0.26), and CFPAC-1 showed the lowest transcript level (Ct = 10.13 +/− 0.73). Large variations in S100A4 levels in different pancreatic cancer cell lines have previously been reported ,19,20. ANo correlation with statistical significance was observed between S100A4 and other S100s or HMGB1 in the cell lines . HoweverThe transcript levels of S100A6 were overall very low in all cell lines (mean Ct = 12.1 +/− 0.83), with very little variation between cell lines. AsPC1 showed the highest level (Ct = 11.04 +/− 1.75) and CFPAC-1 the lowest level (Ct = 13.09 +/− 0.21). Interestingly, the immortalized HPNE cells show higher levels of S100A6 transcript (Ct = 7.93 +/− 0.31) than any of the cancer cell lines .No correlation was observed between the transcript levels of S100A6 and other S100s or HMGB1 in cells. However, in the pancreatic tumor array, correlations were observed between S100A6 and S100A11 (R = 0.49) and S100A14 (R = 0.45).Overall, all cell lines showed low levels (mean Ct = 14.19 +/− 1.53) of S100A7 transcript with little variations among the eight cell lines. CFPAC-1 presented the highest level (Ct = 12.12 +/− 0.21) and AsPC-1 the lowest (Ct = 15.9 +/− 0.53). The highest levels of S100A7 transcript were observed with the immortalized HPNE control cells (Ct = 5.93 +/− 1.64). Our results agree with those of Liu et al., who reported lower levels of S100A7 in a cohort of 45 pancreatic ductal adenocarcinomas when compared to adjacent non-tumor tissues [No statistically significant correlation was observed between S100A7 and the other S100s, or with HMGB1, in the eight cancer cell lines. However, in tumor tissues, correlations were observed between S100A7 and S100A14 (R = 0.47), and S100P (R = 0.56), suggesting crosstalk between these different S100s in tumors.All cancer cell lines showed low levels of S100A8 transcript (mean Ct = 12.79 +/− 1.16). The lowest level was observed in MiaPaCa-2 cells (Ct = 13.87 +/− 0.36) and the highest level in HPAF-2 cells (Ct = 10.85 +/− 1.63). No transcript was detected in the immortalized control HPNE cells.In cells, correlations were observed between S100A8 and S100A9 (R = 0.82), and S100A8 and S100P (R = 0.91). In the tumor array, correlations were observed between S100A8 and S100A4 (R = 0.66), S100A9 (R = 0.62) and S100A10 (R = 0.46).All cell lines showed overall low levels of S100A9 transcript (mean Ct = 11.11 +/− 1.99), with the lowest level in MiaPaCa-2 (Ct = 13.20 +/− 0.36) and the highest in BxPC3 (Ct = 7.17 +/− 1.01). As observed with S100A8, no S100A9 transcript was detected in immortalized HPNE control cells.In cells, correlations were only observed between S100A9 and S100A8 (R = 0.82). In the tumor array, correlations were observed between S100A9 and S100A8 (R = 0.62), S100A4 (R = 0.78), S100A10 (R = 0.51), S100A12 (R = 0.76), and HMGB1 (R = 0.80).In the cancer cell lines, the transcript levels of S100A10 were among the highest of all S100s, with a mean Ct = 4.15 +/− 0.88. The highest level was observed in AsPC-1 (Ct = 3.34 +/− 0.90) and the lowest in MiaPaCa-2 (Ct = 5.97 +/− 0.33). Overall, the lowest level was observed with the immortalized HPNE control cell line (Ct = 6.93 +/− 1.05) . Our resIn cells, no statistically significant correlation was found between S100A10 and the other S100s or HMGB1. However, in tumors, correlations were observed between S100A10 and S100A4 (R = 0.78), S100A8 (R = 0.46), S100A9 (R = 0.51), S100A12 (R = 0.51), S100A16 (R = 0.48), and HMGB1 (R = 0.70).The transcript levels of S100A11 were very similar across all eight cell lines (mean Ct = 6.71 +/− 0.65), the lowest level was observed in MiaPaCa2 (Ct = 7.49 +/− 0.13) and the highest in BxPC3 (Ct = 5.38 +/− 1.29). The lowest level was noted in the control HPNE pancreatic cell line (Ct = 7.70 +/− 0.87).A statistically significant correlation was found between S100A11 and S100P (R = 0.73), both in cell lines  and in tOverall, the levels of S100A12 transcript were lower than those of S100A11 in all cell lines (mean Ct = 9.01 +/− 1.22). The lowest level was found in Panc-1 cells (Ct = 10.37 +/− 0.05) and the highest in CFPAC-1 (Ct = 6.54 +/− 0.2). S100A12 was detected at a similar level in the control HPNE cells (Ct = 8.12 +/− 0.67) and in the cancer cell lines , in agreNo statistically significant correlation was found between S100A12 and the other S100s or HMGB1 in cells; however, correlations were observed between S100A12 and S100A4 (R = 0.64), S100A9 (R = 0.76), S100A10 (R = 0.51) and HMGB1 (R = 0.62) in the tumor array.The transcript levels of S100A13 were relatively high in all cell lines with the highest level observed in Hs766T cells (Ct = 5.27 +/− 0.01) and the lowest in AsPC1 (Ct = 8.33 +/− 0.11). The levels of S100A13 transcripts were higher in the control HPNE cell line (Ct = 4.78 +/− 1.33) than in the cancer cell lines .A statistically significant correlation was found between S100A13 and S100A2 (R = 0.82), in cell lines, but no correlation between S100A13 and S100A2 transcript levels was observed in tumors. However, a correlation was found between the transcript levels of S100A13 and S100A11 (R = 0.67), in the tumor array.Very low levels of S100A14 transcript were found in all cell lines examined, with the lowest being observed in MIAPaCa-2 (Ct = 21.26 +/− 0.53) and the highest in BxPC3 (Ct = 8.96 +/− 1.14). No S100A14 transcript could be detected in the HPNE cell line .In the cell lines, no statistically significant correlation was found between S100A14 transcripts and the other S100s, or HMGB1. However, in the tumor array, positive correlations were observed between S100A14 and S100A2 (R = 0.50), S100A4 (R = 0.55), S100A6 (R = 0.45), S100A7 (R = 0.47), S100A11 (R = 0.65), S100A16 (R = 0.57) and S100P (R = 0.66).S100A16 transcript levels were relatively low in all cell lines, with the lowest level observed in Panc-1 cells (Ct = 9.27 +/− 0.23), and the highest ion AsPC1 cells (Ct = 6.25 +/− 1.02). The lowest levels were observed in the control HPNE cell line (Ct = 10.12 +/− 0.33) .As observed with S100A14, no statistically significant correlation was found between S100A16 and other S100s or HMGB1 in cells. However, in tumors, positive correlations were found between S100A16 and S100A2 (R = 0.61), S100A4 (R = 0.60), S100A10 (R = 0.48), S100A11 (R = 0.78), S100A14 (R = 0.57) and S100P (R = 0.54).Very large variations in S100P transcript levels were observed between cell lines, with the lowest level observed in MIAPaCa-2 (Ct = 12.22 +/− 1.48) and the highest in BxPC3 (Ct = 4.18 +/− 0.18). S100P transcripts were not detected in HPNE control cells .In the cancer cell lines, statistically significant correlations were found between S100P and S100A8 (R = 0.91), and S100A11 (R = 0.73). A correlation between S100P and S100A11 was also observed in tumors (R = 0.62). In addition, positive correlations were found in tumors between S100P and S100A2 (R = 0.85), S100A7 (R = 0.56), S100A14 (R = 0.66) and S100A16 (R = 0.54).HMGB1 levels were very similar in all cell lines, with the lowest level in CFPAC-1 (Ct = 8.67 +/− 0.32) and the highest in Capan-2 (Ct = 7.10 +/− 0.77). The lowest level of transcript was observed in the control HPNE cell line (Ct = 9.63 +/− 0.40) .A statistically significant positive correlation was observed between HMGB1 and S100P in cells (R = 0.71), and this correlation was also observed in tumors (R = 0.52). Additionally in tumors, correlations were found between HMGB1 and S100A4 (R = 0.50), S100A9 (R = 0.80), S100A10 (R = 0.70) and S100A12 (R = 0.62).For S100A2, S100A8, S100A9, S100A10, S100A11, S100A14, S100A16, S100P, and HMGB1, we observed higher transcript levels in the cancer cell lines than in the HPNE control cell lines. The higher levels of S100A2, S100A11, and S100A14 transcript in the eight cancer cell lines than in the HPNE control cell line suggest that these three S100s might modulate the behavior of the cancer cells. S100A2, S100A11, and S100A14 are three S100s with complex roles in cancer, as they have been described as either tumor suppressors or tumor promoters. For instance, S100A2 functions either as a tumor promoter or tumor suppressor, depending on the type of cancer ,9,29. EvLike S100A2, S100A11 is described to function as either a tumor suppressor, such as in bladder cancer, or a tumor promoter, such as in pancreatic cancer ,29,33,34Similarly to S100A2 and S100A11, S100A14 appears to have a complex role in cancer, showing overexpression in breast, liver and cervical cancer, but downregulation in other cancers such as rectal, kidney, colon and esophageal cancers . Chen etThe absence of detectable transcripts for S100A8 and S100A9 in HPNE cells was anticipated because these two S100s are mainly expressed in immune cells, such as monocytes and neutrophils . For thiFor S100A10, our observations agree with the current literature describing the role of this S100 in promoting the development of pancreatic tumors. S100A10 is upregulated in pancreatic cancer and contributes to cell proliferation, migration, invasion, and tumor growth ,26,27,46S100A16 is one of the latest S100 family members identified and characterized . AlthougOur observations with S100P, where transcripts could only be detected in the cancer cell lines and not in the HPNE control cell line, are also in agreement with many studies that demonstrated the involvement of this S100 in the progression of pancreatic cancer ,54. BecaHMGB1 was also found at lower levels in the HPNE control cells than in the cancer cell lines. The role of HMGB1 is complex with different cellular functions depending on the cellular localization . In the Although for most S100s, and for HMGB1, the transcript levels were lower in the control HPNE cells than in the cancer cell lines, this was not the case for S100A4, S100A6, S100A7, S100A12, and S100A13. For S100A7, the lower transcript levels observed in the cancer cell lines than in the control cell line are in agreement with the study of Liu et al., who reported lower levels of S100A7 in a cohort of 45 pancreatic ductal adenocarcinomas when compared to adjacent non-tumor tissues . These oFor S100A13, based on the current literature, we anticipated observing lower levels of S100A13 in HPNE cells than in cancer cells. Indeed, a recent metadata analysis using Oncomine and GEPIA found significantly higher levels of S100A13 transcripts in pancreatic tumors than in control tissues . Higher In our study, the transcripts of both S100A4 and S100A6 were found at a high level in the HPNE cells when compared to the pancreatic cancer cells . As descThe higher transcript levels of S100A12 observed in HPNE cells than in the cancer cells could also be indirectly caused by changes in the senescence abilities of these cells. Indeed, S100A12 was recently identified as 1 of the 20 upregulated genes of a senescence associated secretory phenotype (SASP) cluster . TherefoThe difference in transcript levels among the eight cancer cell lines was the smallest for HMGB1, with a Ct of 1.59, corresponding to a 3-fold difference among cell lines. S100A6, S100A10 and S100A11 showed between 4-fold and 8-fold differences in transcript levels among the cell lines. S100A7, S100A8, S100A12 and S100A16 showed larger differences with 8-to-16-fold differences among cell lines. Finally, S100s that showed the largest difference among cell lines were S100A9 (65-fold), S100A4 (308-fold), S100A2 (867-fold), S100P (324-fold) and S100A14 (5000-fold). It is reasonable to hypothesize that the observed variations in the transcript levels among the cell lines would also be observed at the protein levels; however, additional experiments that are beyond the scope of this study would be necessary to support this hypothesis. It is also important to note that our study only gives a snapshot of the levels of S100 and HMGB1 transcripts in the cancer cells in standard cell culture growth conditions and that cancer cells’ growth conditions are very different in the tumor environment. In our standard cell culture growth conditions, we only observed the following few correlations in transcript levels between S100s and HMGB1: correlations were observed between S100A2 and S100A13, S100A8 and S100A9, S100A8 and S100P, S100A11 and S100P and HMGB1 and S100P. On the other hand, in the context of the tumor, we observed 33 distinct positive correlations.S100s have the following complex functions in cells: they can act intracellularly by activating different target proteins, mostly in a calcium-dependent manner, resulting in multiple cellular activities . For insOur observation that a larger number of correlations was observed between the transcript levels of S100s and HMGB1 in the tumor array than in cells suggests that the tumor microenvironment provides complex transcriptional regulation mechanisms for S100s and HMGB1. It also suggests that many S100s and HMGB1 act in concert in pancreatic tumors. The upregulation of multiple S100s and HMGB1 in tumors could be a means for tumors to upregulate multiple S100-dependent pathways simultaneously.As many of the S100s and HMGB1 are ligands of RAGE, it is reasonable to consider RAGE as one of the receptors responsible for the concerted upregulation of S100s and HMGB1 in tumors ,77,78. IOverall, we observed only the following five statistically significant correlations between S100s and S100/HMGB1 in cells: correlations were observed between S100A2 and S100A13, S100A8 and S100A9, S100P and S100A8 and S100A11, and S100P and HMGB1. However, when looking at the data from the tumor array, we observed 33 statistically significant correlations between S100s and S100/HMGB1. These positive correlations suggest similar mechanisms of transcriptional regulation between these genes and that many S100 family members act in concert in pancreatic tumors. This difference in data between cells and tumors probably reflects the complexity of tumors. Indeed, tumors consist not only of cancer cells but also non-cancerous cells, such as fibroblasts, immune cells, and endothelial cells. All these cells communicate with each other, and many of them express several S100 proteins. The transcription of S100s and HMGB1 in tumors is a very well-orchestrated process that integrates multiple signals from all cells present in the tumor.


Additionally, the observation of downregulation of phosphomevalonate kinase is significant as the brain tumor initiating cells (BTIC) are maintained by the metabolism occurring via the mevalonate pathway. Results from the current study, if translated in vivo, could provide additional efficacious treatment options for glioblastoma with minimal side effects.Glioblastoma multiforme (GBM) is a major aggressive primary brain tumor with dismal survival outcome and few therapeutic options. Although Temozolomide (TMZ) is a part of the standard therapy, over time, it can cause DNA damage leading to deleterious effects, necessitating the discovery of drugs with minimal side effects. To this end, we investigated the effect of cinnamaldehyde (CA), a highly purified, single ingredient from cinnamon, on the GBM cell lines U87 and U251 and the neuroglioma cell line H4. On observing similar impact on the viability in all the three cell lines, detailed studies were conducted with CA and its isomer/analog, trans-CA (TCA), and methoxy-CA (MCA) on U87 cells. The compounds exhibited equal potency when assessed at the cellular level in inhibiting U87 cells as well as at the molecular level, resulting in an increase in reactive oxygen species (ROS) and an increase in the apoptotic and multicaspase cell populations. To further characterize the key entities, protein profiling was performed with CA. The studies revealed differential regulation of entities that could be key to glioblastoma cell circuits such as downregulation of pyruvate kinase-PKM2, the key enzyme of the glycolytic pathway that is central to the Warburg effect. This allows for monitoring the levels of PKM2 after therapy using recently developed noninvasive technology employing PET [ The majority of malignant tumors of the brain are gliomas. Glioblastoma is an aggressive, major primary brain cancer with poor survival outcome . GlioblaThe chemotherapeutic drug currently in the clinic for treating GBM is temozolomide (TMZ), which has improved patient survival and is a DNA methylating agent with slow disease progression when compared to other drugs ,7. Howev2. U87 cells expressing enhanced green fluorescent protein (U87eGFP) was derived as reported previously DASA-23, which comprises 1-((2-fluoro-6-[18F] fluorophenyl)sulfonyl)-4((methoxyphenyl)sulfonyl)piperazine, visualization of aberrant expression of PKM2 in cell culture, mouse models of glioblastoma multiforme (GBM), healthy human volunteers and patients with GBM have been reported [The drug TMZ, which is part of the current standard of care for glioblastomas, causes changes in cell metabolism and, in particular, decreased expression and activity of pyruvate kinase-PKM2. The significant decrease in the conversion of pyruvate to lactate observed in TMZ-treated glioblastoma cells is monitored by administering hyperpolarized (1–13 C) pyruvate and tracked by employing magnetic resonance spectroscopic imaging (MRSI). This method has been shown to be more reliable, more rapid and forms a biosensor for assessing the therapeutic effect of TMZ over measuring the tumor size, which takes days . In the reported . Therefo


Ferula species to treat cancer. Today, some folkloric recipes used for cancer treatment also contain the resin of Ferula species. The dichloromethane extract of the roots of Ferula huber-morathii exhibited cytotoxic activities against COLO 205 (colon), K-562 (lymphoblast), and MCF-7 (breast) cancer cell lines . Fifteen sesquiterpene coumarin ethers with cytotoxic activity were isolated from the dichloromethane extract of the roots of F. huber-morathii using bioactivity-directed isolation studies. Extensive spectroscopic analyses and chemical transformations have elucidated the structures of these sesquiterpene coumarin ethers as conferone (1), conferol (2), feselol (3), badrakemone (4), mogoltadone (5), farnesiferol A (6), farnesiferol A acetate (7), gummosin (8), ferukrin (9), ferukrin acetate (10), deacetylkellerin (11), kellerin (12), samarcandone (13), samarcandin (14), and samarcandin acetate (15). The absolute configuration of samarcandin (14) was unequivocally determined by the X-ray crystallographic analysis of the semi-synthetic (R)-MTPA ester of samarcandin (24). Conferol (2) and mogoltadone (5) were found to be the most potent cytotoxic compounds against all three cancer cell lines; furthermore, these compounds exhibit low cytotoxic activity against the non-cancerous human umbilical vein epithelial cells (HUVEC) cell line. Investigation of the biological activity mechanisms of mogoltadone (5) revealed that while suppressing the levels of Bcl-XL and procaspase-3 in the COLO 205 cancer cell line, it did not have a significant effect on the Bcl-XL, caspase-3, and β-catenin protein levels of the HUVEC cell line, which may explain the cytotoxic selectivity of mogoltadone (5) on cancer cell lines.Ancient physicians frequently used the resin of  Cancer is a complex set of diseases originating from the uncontrolled division of abnormal cells. Cancer is a major health problem worldwide and is the second leading cause of death in the United States. According to the most recent statistical estimations, breast, colon, and lymphoma cancers are the major types of new cases and causes of death in the United States . NaturalFerula drugs in his “De Materia Medica” and mentioned their use for the treatment of tumors about 2000 years ago  .F. huber-morathii were tested against the COLO 205, K-562, and MCF-7 cancer cell lines, as well as the non-cancerous human umbilical vein epithelial cells (HUVEC) cell line. The results are shown in 4, 6, 8, 9, 10, 11, 13, 14, and 15 exhibited little or no cytotoxicity. On the contrary, compounds 1, 2, 3, 5, and 7 showed moderate or strong cytotoxicity. Moreover, compound 12 showed a moderate cytotoxic effect only for MCF-7 cell lines.The cytotoxic activities of individual sesquiterpene coumarin ethers isolated from the roots of 50 values, conferol (2) and mogoltadone  are the most potent sesquiterpene coumarins against cancer cell lines. However, the selectivity indexes of mogoltadone (5) for the COLO 205, K-562, and MCF-7 cells lines  were higher than those of conferol (2) . Therefore, the caspase-3, 8, and 9 activity analyses for the COLO 205, K-562, and MCF-7 cell lines were performed by mogoltadone (5). The data showed that the caspase-3, 8, and 9 activities were the highest in the COLO 205 cell line in comparison with the other cancer cell lines. Concerning the results for the COLO 205 cell line, mogoltadone (5) dose-dependently increased the caspase-3, 8, and 9 activities. Furthermore, mogoltadone (5) showed an increase in caspase-3 activity by 4.72-fold, in caspase-8 activity by 2-fold, and in caspase-9 activity by 1.97-fold when compared with the control at the highest concentration (131.58 µM) (Based on the IC1.58 µM) . Cisplat1.58 µM) .5) suppressed the level of Bcl-XL, an antiapoptotic protein, and decreased the procaspase-3 level in the COLO 205 cell line. The statistically significant effect for procaspase-3 was observed at a concentration of 131.58 µM. These data supported the results of the caspase-3 activity. Furthermore, the effect of mogoltadone (5) on the β-catenin levels was also investigated and the results showed that mogoltadone decreased the β-catenin levels in the COLO 205 cell line. In contrast, mogoltadone (5) did not have a statistically significant effect on the Bcl-XL, caspase-3, and β-catenin protein levels of the HUVEC cell line, which may explain the cytotoxic specificity of mogoltadone (5) on cancer cell lines , conferol (2), feselol (3), mogoltadone (5), and farnesiferol A acetate (7) show a remarkable cytotoxic effect on some cancer cell lines.The cytotoxicity evaluation of 15 cytotoxic sesquiterpene coumarins isolated from the dichloromethane extract of the roots of 1) showed that the highest cytotoxic activity of this compound was observed on the ovarian carcinoma cells (CH1) with an IC50 of 7.8 µM , 3-dimethylaminopropylamine, and (S)-MTPA Chloride ((S)-(+)-α-methoxy-α-trifluoromethylphenylacetyl chloride) were purchased from Sigma-Aldrich . The hexane (Hxn), methanol (MeOH), ethanol (EtOH), acetonitrile (ACN), benzene, diethylether (Et2O), chloroform, cyclohexane, acetic anhydride (Ac2O), pyridine, and sulfuric acid (H2SO4) were purchased from Merck . The Milli Q ultrapure water (W) was obtained from Millipore . Sephadex LH-20 was purchased from GE Healthcare . Silica gel 60 (0.063–0.200 mm) for the column chromatography was purchased from Merck, as was the LiChroprep RP-18 (40–63 µm).Sodium dichromate dihydrate, sodium borohydride .Optical rotations were recorded using an Autopol V Plus polarimeter . Infrared spectra were acquired using an Alpha FT-IR Spectrometer . The absorbance spectra were obtained on a UV-Vis Spectrophotometer UV-1700 PharmaSpec . NMR spectra were acquired on a Bruker BioSpin spectrometer  operating at 500 MHz for Ferula huber-morathii with reference compounds ferutinin (17) and elaeochytrin A (18) (isolated from Ferula elaeochytris Korovin) and cytotoxic sesquiterpene coumarin ethers of F. huber-morathii   with a pump (LC-10AD), a diode-array detector (DAD) (SPD-M10A), and an autosampler (SIL-10AD). A Luna C18 column  was used for the comparison of the dichloromethane extract of ds 1–15) .A Gilson PLC 2050  preparative HPLC system with a Luna C18 column  was used for the preparative HPLC purifications.254 PTLC plates  were used for the preparative separations. Silica gel 60 F254 TLC plates  were used for the analytical separations. A UV lamp  with 254 and 366 nm wavelength detection capabilities was used for the visualization of plates. Compounds without chromophore groups were detected by spraying plates with freshly prepared 10% p-anisaldehyde in 10% ethanolic sulfuric acid, followed by heating.Supelco Silica gel 60 FFerula huber-morathii were collected twice. The first batch was collected near the Zorabat village between Muş to Varto (37 km) in June 1983, identified by Prof. Ömer Saya and an herbarium sample was deposited in the Dicle University Herbarium (DUF 3704). The second batch was collected between Varto and Erzurum in July 2017, identified by Prof. Emine Akalın, and an herbarium sample was deposited in the herbarium of Faculty of Pharmacy, İstanbul University (ISTE 115970). Roots of Ferula huber-morathii (707.7 g) were successively extracted with dichloromethane and methanol. However, the plant material was initially subjected to a maceration (2 × 1 L for 1 h) at room temperature with the respective extraction solvent  in a Soxhlet extractor before the initiation of continuous Soxhlet extraction. The extracts obtained by maceration and continuous Soxhlet extraction were concentrated separately in vacuo in a rotary evaporator. Due to the close similarities between the TLC analyses of the extracts obtained by maceration vs. continuous Soxhlet extraction, they were combined and subjected to fractionation by column chromatography. The dichloromethane (DCM) extract was 50.5 g (yield % 7.1), and the methanol (MeOH) extract was 52.3 g (yield % 7.4) D20: +5.88 ; UV (MeOH) λmax (log ε) nm: 202 (1.20), 221 (sh) (1.79), 294 (sh) (0.94), 324 (1.20) nm (max (NaCl) cm−1: 3067, 2947, 2876, 1737, 1709, 1613, 1554, 1509, 1452, 1397, 1352, 1278, 1232,1183, 1166, 1123, 1011, 997, 836, 756, 720 cm−1   nm ; IR υmax720 cm−1 ; 1H-NMR, spectra , (+)-HRE17.2726) .deposit@ccdc.cam.ac.uk). For the crystal data and details of the structure determination of 24, see Single crystal X-ray diffraction experiments were carried out with a RIGAKU Rotating Anode Diffractometer equipped with confocal monochromator Mo Kα radiation. The structure was solved by direct methods (SHELXS97)  and refi2.Human colorectal cancer , breast cancer , chronic myelogenous leukemia , and umbilical vein/vascular endothelium  cell lines were obtained from the American Type Culture Collection. The COLO 205, MCF-7, K-562, and HUVEC cell lines were maintained in Roswell Park Memorial Institute , Eagle′s Minimum Essential Medium , Iscove’s Modified Dulbecco’s Medium , and Dulbecco′s Modified Eagle Medium  supplemented with 10% fetal bovine serum , 100 U/mL penicillin, and 100 µg/mL streptomycin , respectively. All of the cell lines were cultured at 37 °C in a humidified atmosphere containing 5% COFerula huber-morathii. HUVEC (1 × 104), COLO 205 (3 × 104), K-562 (3 × 104), and MCF7 (5 × 104) were plated in 96-well plates. Then, 24 h later, the tested compounds were dissolved in DMSO, and the cells were exposed to the different concentrations (131.58–1.028 µM) of the compounds for 72 h. Cisplatin and doxorubicin were used as a positive control. Next, an MTS/phenazine methosulfate (PMS)  mixture was added to each well and incubated for 1–4 h at 37 °C. The absorbance was read on a microplate reader  at 490 nm. The cytotoxic activities of the compounds were expressed as an IC50 value. The % inhibition of cell proliferation was calculated using the formula: % Inhibition = [1-(A490test/A490cont)] × 100, where A490test = absorbance of the test sample and A490cont = absorbance of the control sample -5-[3-carboxymethoxyphenyl]-2-[4-sulfophenyl]-2H-tetrazolium, inner salt (MTS) cell viability assay was performed to evaluate the cytotoxic activities of the sesquiterpene coumarin ethers isolated from l sample . The val5) for 24 h were harvested and lysed with a RIPA  cell lysis buffer. The lysed cells were centrifuged at 22,000 g for 30 min and the supernatant was collected in a tube. The samples (200 µg protein) were incubated with caspase substrates  at 37° C for 2 h and read on a microplate reader at 405 nm. The fold-increase in enzyme activity was detected by comparison with the untreated control.The caspase-3, -8, and -9 activities were detected by following the protocol of the caspase-3, -8, -9 colorimetric assay kits . The cells treated with mogoltadone (5) on the caspase-3, Bcl-XL and β-catenin protein levels, the COLO 205 cell line was treated with mogoltadone (5) and incubated for 24 h. At the end of the incubation period, the treated cells were harvested. After that, the harvested cells were washed with cold phosphate buffered saline and lysed in a RIPA buffer. The samples were centrifuged at 24,000 g for 45 min at 4° C, and the supernatants were used to determine the procaspase-3, Bcl-XL, β-catenin, and β-actin protein levels by western blot analysis. The cellular proteins were separated on sodium dodecyl sulfate-polyacrylamide gels . The separated proteins were transferred to the polyvinylidene difluoride (PVDF) membrane using the Trans-Blot Turbo transfer system (Bio-Rad). The membrane was then blocked with a 5% non-fat milk blocking buffer and consecutively incubated with rabbit monoclonal Bcl-XL antibody , rabbit monoclonal caspase-3 antibody , rabbit monoclonal beta-actin antibody , and rabbit polyclonal β-catenin antibody  overnight at 4° C. Furthermore, the membrane was washed with Tris-Buffered Saline with Tween (TBST) solution and incubated with goat anti-rabbit Ig-G (H+L) HRP-conjugated antibody  for 1 h at room temperature. Finally, the membrane was incubated with a chemiluminescent substrate for 5 min and the protein bands were visualized by the imaging system .To evaluate the effect of mogoltadone  to identify interactions between the ligands and the binding pocket of the proteins. Two target proteins were used in this study: compound 6-encoded ligand-bound beta-catenin protein  [Protein structures were obtained from the Protein Data Bank   and Bcl- 1.81 Å) . Missing 1.81 Å) . Then, t 1.81 Å)  was utilQikProp  modules 1–15) were isolated from the dichloromethane extract of the roots of Ferula huber-morathii using bioactivity-directed isolation studies. Their structures were determined by extensive spectroscopic techniques and chemical transformations. The absolute configuration of samarcandin (14) was unequivocally determined by the X-ray crystallographic analysis of its (R)-MTPA ester (24). The cytotoxic activities of the individual sesquiterpene coumarin ethers (1–15) were tested against the COLO 205, K-562, and MCF-7 cancer cell lines, as well as the non-cancerous HUVEC cell line. Based on the IC50 values, conferol (2) and mogoltadone (5) were the most potent sesquiterpene coumarins against the cancer cell lines. However, the cytotoxic selectivity of mogoltadone (5) against the cancer cell lines was higher than those of conferol (2) and induced higher caspase-3, 8, and 9 activations, specifically in the COLO 205 cell line. The Western Blot analysis showed that mogoltadone (5) suppressed the level of Bcl-XL and decreased the procaspase-3 level in the COLO 205 cell line. Furthermore, the effect of mogoltadone (5) on the β-catenin was investigated and it was found that the β-catenin levels were decreased in the COLO 205 cell line. In contrast, mogoltadone (5) did not significantly affect the Bcl-XL, caspase-3, or β-catenin protein levels of the non-cancerous HUVEC cell line, which may explain the cytotoxic specificity of mogoltadone (5) on the cancer cell lines. The in silico docking studies also showed that the interaction of mogoltadone (5) with the active sites of β-catenin and Bcl-XL was similar to the established ligands and reference compound .Fifteen cytotoxic sesquiterpene coumarin ethers (5) isolated from F. huber-morathii and its semi-synthetically prepared analogs could be used as a potent anticancer compound for colorectal cancer (CRC).These data indicate that both natural mogoltadone (