To obtain additional parallel validations of the experimental data, some in silico modeling studies were carried out with mTOR and HDAC1 enzymes, which are very crucial drug targets, to discover novel and potent drugs for breast cancer and related brain metastases disease.Breast cancer is the second type of cancer with a high probability of brain metastasis and has always been one of the main problems of breast cancer research due to the lack of effective treatment methods. Demand for developing an effective drug against breast cancer brain metastasis and finding molecular mechanisms that play a role in effective treatment are gradually increasing. However, there is no effective anticancer therapeutic drug or treatment method specific to breast cancer, in particular, for patients with a high risk of brain metastases. It is known that mTOR and HDAC enzymes play essential roles in the development of breast cancer brain metastasis. Therefore, it is vital to develop some new drugs and conduct studies toward the inhibition of these enzymes that might be a possible solution to treat breast cancer brain metastasis. In this study, a series of 1,10-phenanthroline and Prodigiosin derivatives consisting of their copper(I) complexes have been synthesized and characterized. Their biological activities were tested   Cancer is the second leading cause of death for people, and about one in six attributed deaths is due to cancer . Breast 50 value of 133.7 nM against mTOR and 56 nM against HDAC6, presenting mediate antiproliferative activity in TNBC cells. They have also found to induce significant autophagy, apoptosis and suppress migration in MDA-MB-231 cells. via the performed immunohistochemistry on 240 breast cancer patients to assess such expression. It was found that HDAC1 and RBBP4 expression in breast cancer was significantly higher than that in normal tissues and claimed that the mechanism may be regulated transcription or translation of estrogen receptor (ER) and progesterone receptor (PR) by HDAC1. in vitro and in vivo and suppresses its transcription activity, in which their findings strongly suggest that HDAC1 affects breast cancer progression by promoting cellular proliferation in association with a reduction in both ER-α protein expression and transcriptional activity. HDAC1 may thus be a potential target for therapeutic intervention in the treatment of a subset of ER-negative breast cancers, and inhibition of HDAC1 expression or activity may provide a new strategy for breast cancer therapy. In another study (via upregulation of interleukin-8 (IL-8). Their data has shown that mRNA and protein levels of HDAC1 in 75% of the breast cancer cells are greater than that in their corresponding adjacent normal cells . They have claimed that knockdown of HDAC1 by specific siRNAs can suppress the proliferation and migration of breast cancer cells and inhibit the expression of IL-8, suggesting that HDAC1 may be a potential therapy target for breast cancer treatment.In the last decades, inhibition of histone deacetylases (HDAC) and mammalian target of rapamycin (mTOR) enzymes has emerged as a potential strategy  to reverer study , Lu and er study  that HDAAlong with the abovementioned literature studies, it can clearly be seen that there are many studies that show novel synthetic inhibitors for HDAC and mTOR enzyme inhibition that have been designed, synthesized, and examined for their antitumor and anticancer activities as well as their mechanisms of action. In one of these studies, inhibitors against HDAC for its antitumor activity and the underlying molecular mechanisms of such activity on MDA-MB-231 (231) human breast cancer cells have been examined, and the data from the study suggested that the HDAC inhibitor, namely IN-2001, is a novel promising therapeutic agent with potent antitumor effects against human breast cancers . With thin vitro and in vivo use as potential anticancer and nonsteroidal anti-inflammatory (NSAIDS) therapeutics or agents  need to be exploited. In this context, 1,10-phenanthroline and Ps derivatives including their copper(I) complexes have been selected as target drug candidates owing to their anticancer activities stated in different literatures. Including the supporting experimental evidence  for the mechanism of action of inhibitors acted against mTOR and HDAC from those abovementioned relevant literature studies, both mTORs (mTORC1 and mTORC2) and HDACs  have been aimed on purpose for computational calculations and modeling studies with the selected compounds and their relevant metal complexes. Upon synthesis, purification and characterization of such derivatives and their copper(I) complexes, they have been tested on five breast cancer cell lines MDA-MB-231 (231), MDA-MB-361 (361), HTB131, HTB22, and MDA-MB-231BR (231BR) and one brain cancer cell line (CRL-1620 (CRL)) for the novel anticancer agent screening  cell line MCF10A—a breast epithelial cell collected from patient with fibrocystic disease).Some experimental details have been presented below. All other materials, methods, additional figures and tables, and instrumentation have been described in the http://www.rcsb.org,  and ] ] . Each st 3.0 Å)]  for a mo 3.0 Å)]  graphicahttps://www.wavefun.com/) and optimized at (PM3) level and saved in pdb format. The ADT package was also used here to generate the docking input files of ligands. Autodock4.2.6  (L4 and L5) (3CN)4]PF6 was purchased from Aldrich. All other starting materials were either purchased from commercial sources and used without any further purification, or they were prepared according to the procedures reported in literature. 1H NMR and 13C NMR (decoupled mode) spectra were recorded on a JEOL ECS-400 or Varian Unity Inova 500 MHz spectrometer. DMSO-d6 (99.9% D with 0.05% v/v TMS) was used as the NMR sample solvent.Anhydrous dichloromethane and acetonitrile were distilled over CaHd L3)  and ligad L5)  were prePs) and 1,10-phenanthroline derivatives are synthesized and tested. The syntheses of the ligands (L2–L5) have been previously reported , MDA-MB-361 (361), CRL-1620 (CRL), HTB131, and HTB22 were purchased from American Type Culture Collection , and MDA-MB-231BR (231BR) was a generous gift from Dr. Paul Lockman . Cell media, penicillin-streptomycin solution, fetal bovine serum, and phosphate-buffered saline (PBS) were purchased from ATCC. Trypsin-EDTA solution (Corning), cell culture flasks, and pipettes were purchased from VWR.2 flasks and incubated at 37°C with 5% CO2 for 4–7 days. After reaching 80% of cell confluence, cells were ready for subculturing or testing.MDA-MB-231 (231) and HTB131 were grown with RPIM-1640 medium . MDA-MB-231BR (231BR), CRL-1620 (CRL), and MCF10A were grown with DMEM . HTB22 was grown with EMEM . Cell lines were taken out from liquid nitrogen and thawed in a 37°C water bath. After thawing, cell cultures were transferred to 75 cm2 for 24 h. Then, the compounds were diluted into a serial of concentrations in DMSO. Next, 0.5 µl of diluted compounds were added to 100 µl of cell medium and gently mixed, resulting in the final concentration of DMSO at 0.5%. 100 µl of the compound contained medium were then used to replace the original medium and incubated at 37°C for 48 h. After incubation with compounds, 10 µl of CCK8 reagent was added to each well and mixed by gently tapping the plate. Then, the plate was incubated at 37°C for 2–4 h until the orange color was dark enough to measure. The plate was measured using a Multiskan microplate reader (Fisher Scientific) at 450 nm. The IC50 was calculated by SPSS statistics (IBM). Three controls were used to estimate the analysis: 1) positive control, contained cells and medium (0.5%DMSO) without compounds, used to calculated IC50; 2) negative control, contained medium without compounds and cells, used to estimate the influence of medium color to the CCK8 results; 3) chemical control, contained medium and compounds without cells, used to estimate the influence of chemical color to the CCK8 results. All cytotoxicity tests were performed in six biological replicates. Each compound was performed two cycles of screening: primary screening using 10 times serial dilution to determine the approximate concentration and then secondary screening using a narrow concentration range to acquire the accurate IC50.Anticancer activity tests were performed using a Cell Counting Kit-8  following manufacturer’s instruction . BrieflyL1–L5) and complexes (C1–C5) were synthesized  helped serve as additional spectroscopic markers for identification of the ligand as well as the corresponding copper(I) complexes C1 and C4 that were synthesized in high yields as shown in C1 is a copper(I) complex of 2,9-di (4-methoxyphenyl)-1,10-phenanthroline (L2) and the green macrocycle (L1 in 6− anion as the only bis(hetero-) complex. The synthesis of 2,9-dimesityl-1,10-phenanthroline (dmesp) ligand (L6) was adapted from the literature procedures (The ligands (thesized  in high thesized . The synyntheses  of both L1 in  with theocedures . The dmeC1–C6), as PF6− salts, were accomplished by addition of tetrakis (acetonitrile)copper(I) hexafluorophosphate to the solutions of ligands in a dry solvent mixture of acetonitrile:dichloromethane (50:50). The adopted synthetic procedures  and complexes (C7–C15) were synthesized (L7–L15) were synthesized by using different commercially available boronic acid precursors and characterized by spectroscopic analysis. Similar to the abovementioned complex synthesis (C7–C15), as PF6− salts, were prepared, purified and characterized by spectroscopic analysis (L1–L15) and their corresponding complexes (C1–C15) are air-stable and capable of being dissolved in most common organic solvents.The ligands . Compounds C1 and L14 showed excellent binding affinity against mTOR (0.808 μM) and HDAC1 (0.220 μM) enzymes, supporting the experimental data , MDA-MB-361 (361), HTB131, HTB22, and MDA-MB-231BR (231BR), and one brain cancer cell line, CRL-1620 (CRL). The cell lines have been treated with these synthetic molecules to estimate their IC50 anticancer capacities (Section E in the 50 was estimated in the primary screening step and used to design the concentration range of the secondary cytotoxicity tests (50 less than 10−5 M in primary screening were selected for the secondary cytotoxicity tests that had a molar concentration range within two orders of magnitude to acquire the accurate IC50. Anticancer drug taxol (paclitaxel) was used as the reference to estimate the efficacy of each compound. Among these cell lines, 231BR was used in the primary screening because this cell grows the fastest and most sensitive to all compounds. The anticancer activity against 231BR is of highest interest. Meanwhile, the same treatments were performed against the normal breast cell line, MCF10A, to test their cytotoxicity. After the primary screening, five compounds, L7, C1, Ps, L13, and C10 exhibited relatively promising anticancer activities against 231BR with an IC50 < 10−5 M (50 against different cancer cell lines (50 was applied to represent the corresponding anticancer activity. Two of the abovementioned compounds (C1 and L7) have demonstrated high anticancer activities and relatively low (Ps) derivative L7 exhibited lowest IC50 (highest activities) against brain-seeking cell line 231BR (4.63E-08 ± 6.50E-10 M) and 231 (7.58E-08 ± 7.20E-09 M) that are considered as most invasion breast cancer cell lines while showed more than 80 times lower cytotoxity on normal cell line (3.39E-06 ± 5.57E-07 M). Additionally, L7 also exhibited an overwhelming anticancer activity when compared with the precursor prodigiosin (Ps), the natural drug. The anti-breast cancer activity of the compound L7 is more than 20 times better than the precursor drug, Ps.The newly synthesized molecules in which their binding affinities were predicted ty tests . Any com< 10−5 M . Then, all lines . Figure vely low  cytotoxiC1, on the other hand, is a novel compound. C1 that we have thus far synthesized has the different selectivity among cancer cell lines from compound L7. The copper(I) complex, C1, has better anticancer activities on breast cancer cell line HTB131 (2.28E-07 ± 1.11E-08 M) and brain cancer cell line CRL (3.15E-07 ± 9.77E-09 M). This new compound may provide us a novel anticancer mechanism and target, with some possible structural modifications and changes to decrease cytotoxicity on normal cells. However, all compounds showed lower anticancer activity against 361, demonstrating the selectivities need to be considered cautiously. Moreover, as a reference, commercialized anticancer reagent Taxol (paclitaxel) achieved more than 700 times lower cytotoxicity on normal cell line (IC50 of 3.00E-05 M) relative to 231BR (IC50 3.90E-08 M), making it an efficient anticancer drug. When compared to commercialized anticancer reagent paclitaxel, the relatively lower anticancer activity on cancer cell lines and higher cytotoxicity on normal cell line of all compounds demand future efforts to enhance the anticancer activity while decreasing the cytotoxicity.Compound C14 and C9 are copper(I) complex of compounds L14 and L9, respectively. IC50 of complexes C14 and C9 significantly decreased which meant the increasing of anticancer activity when compared with their ligands L14 and L9. When compared C14 and L14, the anticancer activity increased 5.9 times on 231BR from IC50 7.08E-05 ± 3.89E-06 M to 1.20E-05 ± 1.75E-06 M  complexes of the ligands were tested on the same cell lines as well. 75E-06 M . The cop86E-06 M . AlthougAmong all the breast cancer cell lines, MDA-MB-231BR (231BR) is unique. This cell line has a 100% tendency to brain metastasis and cannot migrate to any other organs . This br6− salts, have been isolated and characterized. Their biological activities were tested in vitro on six different cell lines, and several compounds with good activity were preliminarily verified. Of these compounds, L7, C1, Ps, L13, and C10 exhibited relatively promising anticancer activities against our highest interest cell line, 231BR, with an IC50 < 10−5 M after the primary screenings. C1 and L7 have also demonstrated higher anticancer activities and relatively lower cytotoxicities on normal cell line MCF10A in the performance of the secondary cytotoxicity test using a narrower concentration range against different cancer cell lines. L7, in particular, has exhibited lowest IC50 (highest activity) against brain-seeking cell line 231BR and 231 that are considered as most invasion breast cancer cell lines while also showing more than 80 times lower cytotoxity on MCF10A. In addition, L7 has an overwhelming anticancer activity (more than 20 times better) than Ps, the natural drug. C1, on the other hand, has better anticancer activities on HTB131 and CRL compared to L7. The IC50 of C14 and C9 also showed increased anticancer activities compared to their relevant ligands L14 and L9, respectively. When compared C14 and L14, the anticancer activity of C14 increased by 5.9 times on 231BR, whilst the anticancer activity of another copper(I) complex C9 on CRL also rised by 9.8 times. These increases showed that the copper(I) complexes of the ligands were even more significant role on anticancer activities of such complexes. To obtain additional validations of the experimental data, some in silico modeling studies were carried out with mTOR and HDAC1 enzymes to predict their binding affinities and binding modes. Such studies also supported the experimental data that C1 and L14 showed excellent binding affinity against mTOR and HDAC1 enzymes. Through anticancer agent screening, we can acquire the compounds which have very high activity on 231BR compared to other cell lines and may provide great therapeutical potential for the breast cancer patients who are at a large brain metastasis risk. Further syntheses of some of the above listed compounds as well as the structural optimization and modification of the synthesized molecules are still under study in our laboratories.In this study, a series of 1,10-phenanthroline and Prodigiosin (Ps) derivatives consisting of their 2:1 ligand-to-metal copper(I) complexes, as PF


Inactivation of the Hippo pathway promotes Yap nuclear translocation, enabling execution of a transcriptional program that induces tissue growth. Genetic lesions of Hippo intermediates only identify a minority of cancers with illegitimate YAP activation. Yap has been implicated in resistance to targeted therapies, but the mechanisms by which YAP may impact adaptive resistance to MAPK inhibitors are unknown.S127A, alone or in combination with endogenous expression of either HrasG12V or BrafV600E. We also generated cell lines expressing dox-inducible sh-miR-E-YAP and/or YAPS127A. We used cell viability, invasion assays, immunofluorescence, Western blotting, qRT-PCRs, flow cytometry and cell sorting, high-throughput bulk RNA sequencing and in vivo tumorigenesis to investigate YAP dependency and response of BRAF-mutant cells to vemurafenib.We screened 52 thyroid cancer cell lines for illegitimate nuclear YAP localization by immunofluorescence and fractionation of cell lysates. We engineered a doxycycline (dox)-inducible thyroid-specific mouse model expressing constitutively nuclear YAPWe found that 27/52 thyroid cancer cell lines had constitutively aberrant YAP nuclear localization when cultured at high density (NU-YAP), which rendered them dependent on YAP for viability, invasiveness and sensitivity to the YAP-TEAD complex inhibitor verteporfin, whereas cells with confluency-driven nuclear exclusion of YAP (CYT-YAP) were not. Treatment of BRAF-mutant thyroid cancer cells with RAF kinase inhibitors resulted in YAP nuclear translocation and activation of its transcriptional output. Resistance to vemurafenib in BRAF-mutant thyroid cells was driven by YAP-dependent NRG1, HER2 and HER3 activation across all isogenic human and mouse thyroid cell lines tested, which was abrogated by silencing YAP and relieved by pan-HER kinase inhibitors. YAP activation induced analogous changes in BRAF melanoma, but not colorectal cells.V600E-mutant cells encompassing effectors in the NRG1 signaling pathway, which play a central role in the insensitivity to MAPK inhibitors in a lineage-dependent manner. HIPPO pathway inactivation serves as a lineage-dependent rheostat controlling the magnitude of the adaptive relief of feedback responses to MAPK inhibitors in BRAF-V600E cancers.YAP activation in thyroid cancer generates a dependency on this transcription factor. YAP governs adaptive resistance to RAF kinase inhibitors and induces a gene expression program in BRAFThe online version contains supplementary material available at 10.1186/s12943-022-01676-9. V600E-driven cancers, cell autonomous intrinsic resistance to RAF kinase inhibitors in melanomas, colorectal or thyroid tumors is commonly due to distinct lineage-dependent mechanisms that ultimately converge on reactivation of ERK [Complete responses to targeted therapies for most cancer types remain elusive due to development of primary or acquired resistance. In the context of BRAFn of ERK –6.V600E-mutant lung cancer cell line. YAP knockdown also overcame resistance to RAF and MEK inhibitors in other cell types driven by mutant BRAF or RAS [Illegitimate activation of YAP, a transcriptional coactivator that upon translocation to the nucleus executes the gene expression program of the Hippo pathway, has been identified as an underlying factor driving resistance to genetic or small molecule MAPK pathway inhibitors. A genome-wide CRISPR-Cas9 screen identified NF2, a canonical upstream regulator of the Hippo pathway, as a top hit driving resistance to vemurafenib in melanoma cells . SimilarF or RAS . Two indF or RAS . MoreoveF or RAS –13.The Hippo pathway is an evolutionarily conserved kinase cascade that controls multiple biological processes during development and tissue regeneration, most prominently cell growth and organ size . The canPhysiological control of cytoplasmic-nuclear shuffling of YAP is subject to multiple inputs . Cell coOverexpression or inappropriate activation of YAP or TAZ promote tumorigenesis in mouse models and likely play a pivotal role in many human cancers. However, mutations of canonical effectors in the HIPPO pathway are rare in cancer , 24. DesV600E melanoma cell lines induces actin cytoskeletal remodeling, which increases YAP/TAZ nuclear localization and promotes drug resistance and cancer cell viability [BrafV600E context, constitutively nuclear YAPS127A expression induces larger tumors and decreases survival. Intrinsic resistance to vemurafenib in BRAF-mutant thyroid cancer cell lines has been shown to be due to attenuation of the negative feedback by vemurafenib on the NRG1-HER3/HER2 pathway, with the consequent reactivation of ERK [The mechanisms accounting for YAP-induced resistance to small molecule inhibitors of the RAS-MAPK signaling pathway remain unclear. Prolonged vemurafenib treatment in BRAFiability . YAP caniability , 34. HowIntriguingly, vemurafenib treatment of BRAF-mutant thyroid cancer cell lines induced translocation of Yap to the nucleus and activation of its transcriptional output, which included key effectors in the NRG1 signaling pathway. Intrinsic resistance to vemurafenib was dampened by genetic or pharmacological targeting of Yap and by HER kinase inhibitors. These data implicate Yap as a central orchestrator of primary resistance to RAF kinase inhibition in BRAF mutant thyroid cancer. The Yap transcriptional output encompasses other MAPK effectors, including the three RAS genes , suggestThe origin of the mice and the generation of the animal models is described in 2. Thyroid tumors were imaged using Vevo-770 High-Resolution In Vivo Micro-Imaging System . Volume was calculated by manually tracing the margin of the tumor every 250 μm using the instrument software.Mice were anesthetized with isoflurane with 1%OResected mouse thyroid and metastases tissues were fixed in 4% paraformaldehyde (PFA), paraffin-embedded and sectioned into 4 μm sections. H&E-stained sections were evaluated by thyroid pathologists blinded to the mouse genotype. The sections were deparaffinized and immunostained with specific antibodies  as described previously .Tumors were collected and disaggregated as previously described . KATE-poCells were cultured in 150 mm plates and upon reaching confluency, washed with PBS and incubated with 4%PFA for 15 minutes with gentle agitation. Adherent cells were scraped, collected in a 15 ml tube and centrifuged for 5 minutes at 1000 rpm at 4 °C. The cell pellet was resuspended in 4% PFA, transferred to a 15 ml tube with 2% agarose mold, centrifuged for 2 minutes at 1000 rpm, and kept at 4 °C overnight. The cell pellet was washed with 70% methanol, paraffin-embedded and processed at the Molecular Cytology Facility at MSKCC. The CMA consisted of two cores for each cell line. TMAs consisting of 41 human PTCs, 67 PDTCs and 16 ATCs were obtained from the Pathology Department at MSKCC. A subset of the PTCs and all PDTCs and ATCs were previously genotyped by MSK-IMPACT NGS.Cells were plated onto 24-well plates containing 12 mm glass coverslips. To screen for YAP subcellular localization cells were incubated with 10%FBS at high and low cell density or in 1%FBS for 2 days. For expression of YAP mutants or YAP shRNAs cells were incubated for 3 days in medium with/without dox in 1%FBS. Cells were then washed once with ice-cold PBS and fixed in cold methanol for 15 minutes followed by 15 minutes with 1.6%PFA. Cells were permeabilizated with 0.1%Triton X-100 for 5 minutes, blocked with 5%BSA for 15 minutes and incubated with YAP antibody (1:100) overnight at 4C. The cells were then incubated for 1 h with secondary antibody (1:500) and DAPI (1:100) (ThermoFisher#62248) and imaged using a confocal microscope.S127A and YAPS94A cDNAs were subcloned from pQCXIH-Flag-YAP-S127A (Addgene#33092) and pQCXIH-Myc-YAP-S94A (Addgene#33094), respectively, into the pLVX-Tight-Puro vector (Clontech) with NotI-EcoRI. Cancer cell lines were transduced with lentiviral particles containing pLVX-Tight-Puro-vector with YAPS127A or YAPS94A and with pLVX-Tet-On Advanced vector (Clontech). For YAP knockdown, miR-E-shRNA sequences were cloned into lentiviral Tet-ON all-in-one LT3GEPIR as described [YAPescribed . All conFor growth assays 20,000 cells/well were plated into 24-well plates for 24 h. Cells were treated with/without doxycycline in media containing 1%FBS for the indicated times, collected by trypsinization, and viable cells counted using Vi-CELL-XR (Beckman Coulter). For crystal violet assays the cells were pre-treated with doxycycline for 2 days and then plated onto 48-well plates (3000cells/well). After 24 h, cells were treated with vehicle or the indicated concentration of drugs in media containing 1%FBS with/ without dox for 72 h. Cells were washed 1xPBS, fixed with 4%PFA for 10 minutes, stained with 0.1% crystal violet and washed thrice by immersing in water. After drying 10% acetic acid was added and incubated for 10 minutes in a shaker. Absorbance was measured in a 96-well plate at 590 nm.Cells were seeded onto 6-well plates and after confluency wounds were generated using a sterile 1 mL micropipette tip in triplicate for each condition, PBS-washed and incubated with 10%FBS or 1%FBS media containing vehicle (DMSO) or dox. The gap was imaged at 0 and 24 h using an inverted microscope. Gap areas were calculated by ImageJ.Total RNA was isolated using TRIzol (Invitrogen#15596018). Equal amounts of RNA 2 μg) were subjected to DNA digestion  and subsequently reverse transcribed into cDNA using SuperScript® III First-Strand Synthesis SuperMix (Invitrogen#18080400). Quantitative PCR was then performed using gene-specific primer sets  supplemented with proteinase/phosphatase inhibitors (Roche). Subcellular fractions were obtained using NE-PER Nuclear and Cytoplasmic Extraction Reagents (ThermoFisher Scientific#78835). Protein concentration was determined using the Pierce-BCA kit (ThermoFisher Scientific#23225) on a microplate reader (SpectraMax M5). For western blotting, 20 μg proteins were size-separated in 4-12% Bis-Tris SDS-PAGE gels (Invitrogen#NP0336BOX), transferred to PVDF membranes and immunoblotted after blocking with 5% skim-milk with corresponding antibodies in 5%BSA (Sigma#A7906) or 5% nonfat milk. Bound antibodies were detected by chemiluminescence using the ECL detection system . Antibodies are listed in +Kate using BD FACS Aria. Approximately 20,000 +Kate cells were collected into TRIzolLS (Invitrogen#10296010) and RNA isolated. One thyroid tumor per mouse was used for each biological replicate. After RiboGreen quantification and quality control by Agilent BioAnalyzer, 1.4-2 ng total RNA with RNA integrity numbers ranging from 8.2 to 9.8 underwent amplification using the SMART-Seq v4 Ultra Low Input RNA Kit (Clonetech#63488), with 12 cycles of amplification. Subsequently, 10 ng of amplified cDNA was used to prepare libraries with the KAPA Hyper Prep Kit (Kapa Biosystems KK8504) using 8 cycles of PCR. Samples were barcoded and run on a HiSeq-4000 in a PE50 run, using the HiSeq 3000/4000 SBS Kit (Illumina). An average of 39 million paired reads were generated per sample and the percent of mRNA bases per sample ranged from 77 to 82%.Tumors were dissected and placed in ice-cold digestion media  and minced. The disaggregated tissue was collected in digestion media and incubated at 37C for 1 h, vortexing every 15 min. Cells were passed through a 70 μM filter to remove tissue debris and the single cell suspension washed with PBS. Cells were resuspended in FACS buffer  and sorted for Frozen cells were lysed in 1 mL TRIzol, and RNA extracted using the miRNeasy Micro Kit (Qiagen#217084) on the QIAcube Connect (Qiagen) with 350 μL input. Samples were eluted in 15 μL RNase-free water. After RiboGreen quantification and quality control by Agilent BioAnalyzer, 500 ng of total RNA with RIN values of 9.5-10 underwent polyA selection and TruSeq library preparation (TruSeq Stranded mRNA LT Kit#RS-122-2102), with 8 cycles of PCR. Samples were barcoded and run on a NovaSeq-6000 in a PE50 run, using the NovaSeq 6000-SP Reagent Kit (100 Cycles) (Illumina). An average of 27 million paired reads was generated per sample. Ribosomal reads represented 1.1-2.4% of the total and the percent of mRNA bases averaged 72%.www.partek.com). In brief, raw reads were aligned to mouse assembly mm10 or human assembly hg38 with STAR aligner using default parameters. The aligned reads were quantified to annotation model (Partek E/M) using mm10 Ensembl release 95 or hg38 Ensembl Release 91.v2 and normalized using Transcripts Per Million and addition of 0.0001 to generate a normalized non-zero expression count matrix.RNA-seq analysis was performed using Partek_Flow  derived from mouse thyroid cancers were cultured in the presence of dox to induce YAPS127A, resuspended in 50% Matrigel (Corning) and implanted (5 × 106 cells/mice) into the flank of 8 to 10-week-old female nude mice (TACONIC Biosciences). Mice were fed dox-impregnated chow to maintain YAPS127A expression in the implanted cells. Treatments were initiated after tumors were established, with a tumor volume of ~200mm3 as estimated by caliper measurements (width2×length× 0.52). Mice were weighed at the start and twice a week during the treatment period, tumor volume was measured every 2–3 days. Mice were humanely euthanized and the dissected tumors flash-frozen in liquid N2 or fixed in 4%PFA. For Fig. S127A) were implanted 1 week apart from each other and all mice fed with dox-impregnated chow for 2 weeks to allow tumors to engraft. The dox-chow was substituted with normal chow in group 1 (OFF-DOX) for 1 week. At this point, both groups (OFF-DOX and ON-DOX) had a similar tumor volume (200mm3), at which time each group was randomly assigned for treatment with vehicle or PLX4720  was used. Graphs represent Ẋ ± SD or SEM. Similar variance between groups was tested by F-Test; if different, Welch’s correction was applied. NF2 are the most prevalent , leading to an increase in the MAPK signaling output [HrasG12V/Nf2flox2 mice. For this we crossed the TPO-Cre/FR-HrasG12V/Nf2flox2 mice with mice harboring homozygous alleles with flox sites bracketing Yap exons 1 and 2 (TPO-Cre/FR-HrasG12V/Nf2flox2/Yapflox2) , 4.3% of poorly differentiated (PDTC) and anaplastic cancers (ATC) and 18% of thyroid cancer cell lines , 43. Amog output . As a coS127A, alone or in combination with endogenous expression of either HrasG12V or BrafV600E in thyroid cells   than cells with other or unknown driver mutations (36%)  Fig. B right. YAP Fig. C. In sumS127A mutant (YAPS127A-FLAG). Three-day exposure to dox in sh-mirE-YAP-transduced lines silenced YAP expression, whereas dox-induced expression of FLAG-tagged YAP for 3 days in CYT-YAP cell lines promoted its nuclear localization . Cells were cultured with dox to maintain expression of YAPS127A. Silencing of YAPS127A by 6 days of dox withdrawal reduced cell viability in all the mouse cell lines  or inhibiting YAP transactivation with verteporfin for 4 weeks decreased tumor size in YAPS127A mice   Fig. H. In sumYAP activation has been implicated in resistance to MAPK pathway inhibitors in other cancer lineages. We investigated whether treatment with selective RAF or MEK inhibitors altered YAP cytoplasmic-nuclear shuttling and YAP transcriptional output. Treatment of the indicated CYT-YAP cell lines with vemurafenib re-localized YAP to the nucleus within 24-48 h, as shown by immunofluorescence and Western blots of nuclear and cytoplasmic lysates  and ERBB signalling (#10)  vary between BRAFelanomas –51. In telanomas . Analysitor Fig. E. YAP siHER3 (ERBB3), HER2 (ERBB2) and NRG1 mRNA levels. Silencing YAP reduced the expression of these genes in nuclear YAP cell lines, whereas in cytoplasmic YAP cell lines, expression of YAPS127A increased them  is associated with an increase of HER3 and HER2 in the cytoplasm, which is blocked with verteporfin, indicating that YAP1 activation governs this process Fig. F. Taken S127A expression rendered CYT-YAP cell lines comparatively resistant to the combination , which when applied to other contexts predict for YAP1-TEAD dependency [The pivotal role of YAP on the transcriptional regulation of genes involved in growth control, coupled to the diversity of inputs that regulate its cytoplasmic-nuclear shuttling, renders this pathway particularly vulnerable to disruptions in cancer . Howeverpendency .OFF) primarily corresponded to neuroendocrine-like tumors, such as retinoblastoma, small cell lung cancer and neuroendocrine prostate cancer. In these lineages, the YAP-TEAD complex functions as a tumor suppressor. This is not the case in the CYT-YAP thyroid cancer cell lines described here, since enforced YAPS127A expression consistently induced cell growth.Disruption of the activation of Hippo signaling by cell contact has been used as an approach to identify non-canonical pathways that promote YAP nuclear translocation , 21. We Although YAP-TEAD activation can promote resistance to a diverse set of cancer therapies, it has been dominantly implicated in driving insensitivity or acquired resistance to inhibition of drivers of the MAPK pathway. Interestingly, YAP activation signatures consistently overlap with MAPK and KRAS transcriptional outputs . In variV600E-driven thyroid cancers in vivo. The NRG1 pathway has been implicated in thyroid cancer pathogenesis at many levels. Genome-wide association studies identified a SNP within intron 1 of the NRG1 gene to be strongly associated with thyroid cancer (p < 10− 9) in European populations [The NRG1 pathway was the top IPA gene expression signature induced by Yap in Brafulations . This waulations . Moreoveulations .S127A, or conversely, of YAP targeted shRNAs, represent the opposite activation states of the YAP pathway. Since canonical HIPPO pathway mutations are infrequent in thyroid cancer, it is likely that in most tumors YAP pathway activity lies somewhere in between these extremes. Thus YAP/TEAD activity may serve as a rheostat controlling the magnitude of adaptive changes in RTK signaling following MAPK inhibition  Top: HIPPO pathway alterations in thyroid cancer cell lines derived from papillary thyroid carcinomas (PTC) or poorly differentiated or anaplastic thyroid cancers (PDTC/ATC). Bottom: Oncoprint of HIPPO pathway alterations in thyroid cancer cell lines, and in PTC, PDTC or ATC tissues. MUT: mutations; AMP: amplification; HOM del: homozygous deletion. B) Schematic design of transgenic lines used to investigate the role of Yap in transformation by HrasG12V in the context of Nf2 loss. C) IHC of Yap1 in thyroid sections of the indicated genotypes. D) Thyroid tumor volume by ultrasound at 10 and 20 weeks showing effects of Yap inactivation on tumor development in HrasG12V/Nf2flox mice. **p < 0.01; ***p < 0.001. Fig Sup 2. YAP Western blots of nuclear (N) and cytoplasmic (C) fractions of confluent cells. TATA-BS: nuclear fraction control. HK1: cytoplasmic fraction control. All cell lines were grown to > 90% confluency in 10% FBS. Two top panels: NU-YAP cell lines; Two lower panels: CYT-YAP cell lines. Fig Sup 3. A) Immunofluorescence for YAP, FLAG and DAPI in NUC-YAP and CYT-YAP cell lines in 1%FBS following 3 days of dox-induced expression of shYAP or YAPS127A, respectively. B) Western blots probed against the indicated antibodies. Cells treated with dox for 3 days in 1%FBS. C) Cell viability after expression of FLAG-YAPS94A in NUC-YAP and CYT-YAP cell lines. D) Representative images of mechanical scratch assays at baseline and 24 h after the lesion in NUC-YAP (red) and CYT-YAP (blue) cell lines. E) Top: Representative images of 24 h mechanical scratch assays in NUC-YAP and CYT-YAP with or without dox-inducible expression of YAP shRNA. Bottom: Quantification of effects of YAP shRNA on wound healing in NU-YAP and CYT-YAP lines. F) Top: Wound healing in CYT-YAP cell lines with or without dox-induced expression of YAPS127A. Bottom: quantification of effects of expression of YAPS127A on wound healing in CYT-YAP lines. Data in panels C, E and F represent 3 independent experiments with each line and condition. *p < 0.05 **p < 0.01 ***p < 0.001. Fig Sup 4. A) GSEA of RNA-seq of tumor cells sorted from YAPS127A-driven mouse thyroid tumors revealed a significant increase in the Cordenonsi_YAP_conserved gene expression signature compared with thyroid cells from WT mice. B) NES and Nom p-values for each YAP signature from the GSEA of RNA-seq of the indicated human thyroid cancer cell comparators  comparing NU-YAP vs CYT-YAP and CYT-YAP vs CYT-YAPS127A. Statistically significant (p < 0.05) up and downregulated genes are represented in light red and light blue, respectively, and YAP-Cluster 2 signature genes common to both groups are highlighted in the respective dark colors. There is an inverse relationship in expression of YAP-Cluster 2 genes in the two comparators: upregulated in NU-YAP vs CYT-YAP and downregulated in CYT-YAP vs CYT-YAPS127A. Fig Sup 5. Time course of vemurafenib (1000 nM) on expression and phosphorylation of the indicated proteins in NU-YAP cell lines (Hth104 and SW1736) after YAP silencing and CYT-YAP cell lines (MDAT32 and BHT101) after expression of YAPS127A in 10% FBS. Fig Sup 6. A) Baseline mRNA expression by real-time qRT-PCR of ERBB3 and ERBB2 in NU-YAP cell lines compared with CYT-YAP cell lines. B) Effect of dox-withdrawal on Erbb3 and Erbb2 mRNAs in Braf-YAPS127A mouse cell line (BY96s). C) ERBB3 and ERBB2 mRNA fold-change after vemurafenib relative to vehicle in NU-YAP vs CYT-YAP cell lines. D) Western blot showing the expression of the indicated proteins after a 48 h treatment with 1 μM vemurafenib and after silencing of ERBB2 or ERBB3 in a representative NU-YAP (8505C) cell line. The vertical lines represent the sites of a deleted lane. **p < 0.01 ***p < 0.001. Fig Sup 7. A) YAP immunofluorescence in Braf.pV600E-mutant melanoma (SK-MEL28 and MM96L) and colorectal  cancer cell lines in sparse and confluent conditions in the presence of 10% FBS. B) Cell viability after dox-induced expression of shYAP or FLAG-YAPS127A in CYT-YAP melanoma and colorectal lines for 6d. C) Dose-dependent effects of a 6-day incubation with vemurafenib on cell viability of CYT-YAP cells with or without dox-induced expression of YAPS127A. D) Effect of YAPS127A on ERBB3 and ERBB2 mRNA after 24 h of vemurafenib treatment in melanoma cell lines. E) Time course of vemurafenib (1000 nM) on expression and phosphorylation of HER2 and HER3 in SK-MEL28 cells in the presence or absence of YAPS127A in 10% FBS. F) Effect of dox-induced expression of YAPS127A in SK-MEL-28 cells by vemurafenib, lapatinib or their combination. Growth was measured at day 6 after addition of the indicated concentrations of the drugs in 1% FBS. Sup Table 1. Thyroid histology of mouse GEMM models and frequency of metastasis. Sup Table 2. Classification of thyroid cancer cell lines based on YAP localization. Supplementary Methods. Sup Methods Table 1. Primers used for genotyping. Sup Methods Table 2. Primers for qRT-PCR.


ATTO594) and NOP receptor function with N/OFQ stimulated ERK1/2 phosphorylation. As an in vitro sepsis mimic we variably incubated cells with 100ng/ml Lipopolysaccharide and Peptidoglycan G (LPS/PepG). HUVECs express NOP mRNA and this was reduced by ~80% (n = 49) after 24–48 hours treatment with LPS/PepG. Untreated cells do not express surface NOP receptors but when treated with LPS/PepG the reduced mRNA was translated into protein visualised by N/OFQATTO594 binding (n = 49). These NOP receptors in treated cells produced an N/OFQ (1μM) driven increase in ERK1/2 phosphorylation (n = 20). One (of 50) HUVEC lines expressed NOP mRNA and receptor protein in the absence of LPS/PepG treatment. In contrast, HVSMC expressed NOP mRNA and surface receptor protein (n = 10) independently of LPS/PepG treatment. These receptors were also coupled to ERK1/2 where N/OFQ (1μM) increased phosphorylation. Collectively these data show that an in vitro sepsis mimic (LPS/PepG) upregulates functional NOP expression in the vascular endothelium. Activation of these endothelial receptors as suggested from in vivo whole animal work may contribute to the hypotensive response seen in sepsis. Moreover, blockade of these receptors might be a useful adjunct in the treatment of sepsis.Sepsis is a dysregulated host response to infection that can cause widespread effects on other organs including cardiovascular depression, hypotension and organ failure. The receptor for Nociceptin/Orphanin FQ (N/OFQ), NOP is expressed on immune cells and these cells can release the peptide. Exogenous N/OFQ can dilate blood vessels and this peptide is increased in animal and human sepsis. We hypothesise that NOP receptors are present on vascular endothelial cells and therefore provide the target for released N/OFQ to cause vasodilation and hence hypotension. Using human umbilical vein endothelial cells (HUVEC) and human vascular smooth muscle cells (HVSMC) freshly prepared from umbilical cords and up to passage 4, we assessed NOP mRNA expression by Polymerase Chain Reaction (PCR), NOP surface receptor expression using a fluorescent NOP selective probe (N/OFQ Sepsis is a dysregulated host response to infection that can lead to life-threatening organ dysfunction and is responsible for up to 11 million deaths worldwide per year . In certi/o proteins leading to a decrease in cyclic Adenosine Monophosphate (cAMP) production, activation of inwardly-rectifying potassium channels, closure of calcium channels and activation of mitogen-activated protein kinase pathways (MAPK) including ERK 1/2 N/OFQ binding [ATTO594 was incubated with the cells for 5 minutes at 4°C, following which unbound was cleared by perfusion with 4°C Krebs buffer. As we previously reported this low temperature eliminates receptor internalisation [ATTO594 and 488nm, for Hoechts live cell nuclear dye (blue). Specific binding of N/OFQATTO594 was determined through use of the NOP antagonist, SB-612111 (1μM) [ATTO594 [N/OFQreceptor , was add binding . Images lisation . Cells w[ATTO594 . In all [ATTO594  and usedFunctional NOP receptor expression was determined by measuring ERK1/2 phosphorylation in both HUVEC and HVSMC using Western blotting; this assay was selected as it requires relatively small amounts of tissue and produces an easily quantified readout. HUVEC or HVSMC were grown in 6-well plates until 70% confluent. Cells were then serum starved for 24hrs and variably treated with 100ng/ml LPS and 100ng/ml PepG for 24 or 48hours. Next cells were incubated in 37°C Krebs buffer for 30 minutes then challenged with 1μM N/OFQ for up to 30 minutes. Upon completion of the time course, the assay was terminated by removal of Krebs buffer, rinsing with ice-cold PBS and addition of ice-cold lysis buffer . Cells wFollowing denaturation, proteins were separated on 10% SDS-PAGE, transferred onto nitrocellulose paper by wet transfer and blocked in 10% milk TBS-T solution  for 2 hr at room temperature under gentle agitation. Membranes were subsequently incubated in primary phospho-ERK1/2 antibodies  and loading control antibody; vinculin , diluted in TBS-T overnight at 4°C. Primary antibody was removed and membranes washed 6 times, for 5 minutes per wash, in TBS-T. Membranes were incubated in HRP-conjugated secondary antibodies . Chemiluminescence detection, using the ChemiDoc™ MP imaging system  was used to visualise immune-reactive bands. Blots were analysed using the ImageLab software . All data is corrected for loading (Density of ERK/Density of Vinculin) and then measured as a ratio over basal activity .seeresults). Statistical analysis is described in the relevant Table and Figure legends and P-values ≤0.05 were considered statistically significant.All data are presented as mean±SEM from (n) experiments as noted in the results and Figure/Table legends. Confocal images are representative of (n) as noted. In HUVEC experiments PCR and confocal microscopy was performed in cell lines from 50 patients and 20 were analysed in Western blotting. In VSMC experiments PCR and confocal microscopy was performed in cell lines from 10 patients and 8 were analysed in Western blotting. The reduced number was selected as there was no LPS/PepG inducible signal  (Table 1). These changes at 24 and 48 hours equated to an approximate 80% decrease in NOP mRNA expression.Messenger RNA extracted from primary cultured HUVECs was used in qPCR to probe for NOP expression. All samples produced robust amplification of the housekeeper genes B2M and ELF2B1. In control conditions, NOP mRNA was present in all 50 cell lines tested demonstrating a mean ΔCATTO594, was used. As a positive control N/OFQATTO594 bound to the surface of HEKhNOP cells  and this was blocked by the NOP antagonist SB-612111  In 49 individual control HUVEC cell lines, 100nM N/OFQATTO594 failed to bind to untreated (100ng/ml of LPS/PepG) cells . In these 49 cell lines, treatment at both 24hrs  and 48hrs  with 100ng/ml LPS and 100ng/ml PepG treatment, led to binding of N/OFQATTO594. Binding of N/OFQATTO594 in these samples is blocked by the NOP-selective antagonist SB-612111 (1μM) . In PN5 cells, 100nM N/OFQATTO594 was able to bind to control samples  and could be blocked by SB-612111 (n = 5) . As noted above 100ng/ml of LPS/PepG treatment caused cell death in this line.Results from PCR demonstrated the presence of NOP mRNA in all 50 HUVEC cell lines, however the presence of mRNA does not necessarily equate to surface expression of a functional receptor. In order to determine the presence of NOP receptors the NOP-specific fluorescent ligand, N/OFQFig 3). In both instances, PN8  and PN5  demonstrated no change in their respective expression, with PN5 maintaining NOP expression from passage 2  through to passage 22 .Many studies using primary cultured HUVECs are with older passages. To determine whether long term culture changes NOP expression, PN5 (which expresses NOP in the absence of LPS/PepG) and PN8 (a cell line that required LPS/PepG treatment to induce NOP expression) were maintained over 20 passages with expression probed .There was no correlation between change in mRNA and N/OFQFig 4A). This is consistent with the lack of surface receptor protein demonstrated with N/OFQATTO594. When incubated for 24hr with 100ng/ml LPS/ PepG, N/OFQ-induced activation of ERK1/2 was seen after 10 minutes and continued to increase up to 30 minutes . In the HUVEC cell line, PN5, activation of ERK1/2 was seen in the untreated control sample, with activation beginning at 5 minutes and continuing through to 30 minutes post N/OFQ incubation (n = 5) . Measurement of ERK1/2 activity in PN5 HUVEC treated with 100ng/ml LPS/ PepG was not possible due to cell death. Full blots are provided in the Activation of HUVEC NOP receptors was measured by determining activation of the ERK1/2 pathway. When measuring activity in untreated control samples, 20 cell lines showed no activation over a period of 30 minutes when incubated with 1μM N/OFQ (t of 6.94 (Table 1). All 10 cell lines were treated with 100ng/ml of LPS/PepG for 24 hour and there was no change in NOP mRNA; ΔCt 6.75. Due to the lack of an inducible signal we limited the number of cell lines to 10.Messenger RNA extracted from primary cultured HVSMC was used in qPCR to probe for NOP expression. All cell lines produced robust amplification of the housekeeper genes B2M and ELF1. In control untreated samples, NOP mRNA was present in all 10 cell lines tested with a mean ΔCFig 6A) were subsequently tested for N/OFQATTO594 binding in confocal microscopy. In untreated samples N/OFQATTO594 bound to the cell surface of VSMC , with binding unaffected by 24hr incubation with 100ng/ml LPS/PepG . Binding of N/OFQATTO594 was blocked by the NOP-selective antagonist SB-612111 (1μM) .All ten cell lines . This series of experiments were performed on 8 cell lines. Full blots are provided in the Activation of HVSMC NOP receptors was measured by determining activation of the ERK1/2 pathway. N/OFQ (1μM) increased ERK1/2 phosphorylation after 2.5 minutes, peaking between 7.5–10 minutes, before returning to basal levels at 20 minutes  with neither the probe (large peptide) nor the antagonist being able to cross the plasma membrane. Moreover, as the experiments were performed at 4°C agonist driven NOP internalisation will not occur; we have confirmed this in high expressing recombinant systems [While the presence of NOP mRNA was detected in all cell lines, the presence of surface NOP receptor was found in only one (PN5) of the 50 untreated HUVEC cell lines. However, following incubation with LPS/PepG for both 24 and 48 hours NOP receptor was detected on the surface of these treated HUVEC via binding of the fluorescent probe N/OFQ systems . InteresIn 49 HUVEC cell lines, phosphorylation of ERK1/2 was used to detect functional activity following LPS/PepG induced NOP surface expression. In these samples, 1μM N/OFQ produced phosphorylation of ERK1/2 after 10 minutes, with ERK1/2 activity remaining elevated for the remainder of the 30 minute assay. In the untreated PN5 sample, N/OFQ produced an initial response after 5 minutes. This increased, throughout the course of the 30 minute assay.ATTO594, binding of the ligand to the cell surface was detected; this was totally blocked by the NOP antagonist SB-612111. These cell surface receptors were also coupled to ERK1/2 but the response was different to that seen in HUVECs. The very early response in muscle might suggest a G-protein driven event while the later response in the endothelium might suggest an arrestin driven event [In freshly isolated VSMC, NOP mRNA was detected in all cell lines tested, this was unaffected by LPS/PepG treatment for 24 hours) and there was no adverse consequences in terms of survival. When untreated VSMC were probed with N/OFQen event ; this wi hours anS2 Fig) we have confirmed that PN5 is HUVEC in origin; it was possible that this line was muscle that we know expresses NOP independent of LPS/PepG treatment. Our incidence of 1 in 50 compared favourably with incidence of post-partum infection at 3.9% [In this study a single cell line was shown to express NOP without the need for LPS/PepG stimulation. This cell line (termed PN5) displayed several distinct features not seen in the remainder of the HUVECs tested. Activation of ERK1/2 occurred earlier but was still different to that seen in muscle. The most striking difference was the sensitivity (cell death) to LPS/PepG challenge. Our ethics approval does not allow detailed collection of patients’ clinical or personal information so it is not possible to look back at pre-existing pathology, clinical course or concurrent medications for this subject. In a series of immunohistochemical analyses  there was no correlation between message and receptor. Based on N/OFQATTO594 binding (inset) expression peaked after 24 hours .HUVEC NOP mRNA and NOP receptor protein was measured by qPCR and N/OFQ(DOCX)Click here for additional data file.S2 FigImmunofluorescence Analysis of HUVEC cell Line PN5. In order to confirm the cell type identity of line PN5, immunofluorescence for the endothelial marker von-Willebrand Factor (vWF), the smooth muscle cell marker α-smooth muscle actin (α-SMA) and the fibroblast marker TE-7 with smooth muscle cell  and fibroblast (MRC5) positive controls. A secondary antibody-only control was included for all cell types. Green fluorescence indicates positive staining for the indicated marker. Nuclei were counterstained with Hoechst 33342 (blue). Images were obtained using with an Operetta CLS high-content imaging system (Perkin Elmer) using a 10x objective lens. Scale bar = 200μm.(DOCX)Click here for additional data file.S1 Raw data(PDF)Click here for additional data file.


G-quadruplex (G4) structures are critical epigenetic regulatory elements, which usually form in guanine-rich regions in DNA. However, predicting the formation of G4 structures within living cells remains a challenge. Here, we present an ultra-robust machine learning method, G4Beacon, which utilizes the Gradient-Boosting Decision Tree (GBDT) algorithm, coupled with the ATAC-seq data and the surrounding sequences of in vitro G4s, to accurately predict the formation ability of these in vitro G4s in different cell types. As a result, our model achieved excellent performance even when the test set was extremely skewed. Besides this, G4Beacon can also identify the in vivo G4s of other cell lines precisely with the model built on a special cell line, regardless of the experimental techniques or platforms. Altogether, G4Beacon is an accurate, reliable, and easy-to-use method for the prediction of in vivo G4s of various cell lines. A G-quadruplex G4) is a non-canonical structure that forms in guanine-rich sequences . The bas is a nonThe G4-detection techniques can be classified into two categories: experimental methods and computational methods. Early experimental techniques of G4-detection, such as NMR  and X-raExcept for the experimental methods discussed above, there are several computational methods  designed to predict G4 sequences. Most of these algorithms are used to detect the potential in vitro G4 sites without considering the specific cellular environment . The fir9 genome . The fur9 genome , using mtructure . The G-structure . After ttructure  and G4detructure , were prFollowing the development of in vivo G4 detection experiments, an individual computational method for predicting active G4 regions in cells, DeepG4, was presented . DeepG4 In this study, we proposed a cell-type-specific G4 prediction tool called G4Beacon, which implemented the Gradient-Boosting Decision Tree (GBDT) algorithm to predict if a G4-seq entry can fold into the quadruplex structure in a given cell type . The maiTo build and evaluate G4Beacon, the ATAC-seq data and in vivo G4 sequencing data were required for feature construction and dataset division. The critical problem was that there were few in vivo G4 sequencing data due to the high cost and experimental difficulty of in vivo G4 sequencing. We required cell lines with both high-quality in vivo G4 sequencing and ATAC-seq data, and, finally, three human cell lines—K562 (human chronic myelogenous leukemia cell line), HepG2 (human hepatoblastoma cell line), and MCF7 (human breast adenocarcinoma cell line)—were collected and used in this research.The human G4-seq data were downloaded from the Gene Expression Omnibus (GEO) database with the accession number of GSE110582 . We usedWe classified G4-seq entries into two parts, namely active G4 entries (positive samples) and inactive G4 entries (negative samples), for each cell line according to whether these G4-seq entries were supported by in vivo G4 experiments . We usedDue to the imbalance of positive and negative samples, which may affect the performance of traditional machine learning algorithms , traininG4Beacon took both the chromatin accessibility data (ATAC-seq) and the sequence data to construct features for each input G4-seq entry in the candidate dataset . To acquThe chromatin environment can influence the folding capability of in vivo G4s, as they were found to appear together with ATAC-seq peaks . The ATAMost of the existing G4 prediction models merely use the sequences from G4 sites to learn the sequence patterns of G4 structures ,22. Howehttps://github.com/microsoft/LightGBM, accessed on 2 November 2022) to implement our model workflow as it can provide categorical feature support and high training efficiency [We compared the performance of several machine learning methods, including Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting Decision Tree (GBDT) , and finficiency .As mentioned in To explore whether different hyperparameters will have a significant impact on the model performance and to select the best model configurations, we utilized the grid-search method to optimize the hyperparameters for LightGBM using a 2-fold cross-validation experiment on the HepG2 cell line dataset  was applied to the training set before it was taken as the input of the GBDT classifier for training. Finally, the performance of the trained model was tested on the test set. We applied this evaluation workflow to all the cell line data (K562/HepG2/MCF7) that we collected. The sizes of each training set and test set were displayed . We compThe cross-cell-line experiment was applied for further evaluating the robustness of G4Beacon. One of the basic hypotheses of our study is that the active G4s in different cell lines exhibit similar chromatin accessibility and sequence patterns, and therefore the performance of our model should persist when the target samples come from different cell lines. In the cross-cell-line experiment, one cell line dataset was used as the training set and the remaining cell line datasets were used as the test sets. Similar to the one-cell-line experiment, the dataset used for training was preprocessed with over-sampling on positive samples and the datasets for the test remained unchanged. In this study, the HepG2 cell line data were utilized as the training set and the performance was tested on K562 and MCF7 cell line data.Accuracy, precision, recall, and F1-score were used as the basic criteria of prediction performance. Moreover, ROC and PRC, as well as AUROC and the average precision (AP), were also used to provide a more complete picture of the model’s performance. In both one-cell-line and cross-cell-line experiments, we repeated the process five times with different random seeds and obtained the mean ± standard error for each criterion.Besides the machine learning evaluation method, we also performed chromatin state analysis to further evaluate our method. Furthermore, we also compared the prediction results of G4Beacon with a recent in vivo G4 prediction tool, DeepG4 .https://github.com/raphaelmourad/DeepG4, accessed on 2 November 2022) and directly used for further comparative analysis [Consistent with the cross-cell-line experiment, we used the full HepG2 dataset balanced with the over-sampling method to train our G4Beacon model. The trained DeepG4 model was obtained from GitHub  when discriminating between the positive and negative samples .In the G4 ChIP-seq group experiments, we employed data from the K562 cell line and HepG2 cell line. For the K562 cell line, we found that models constructed with different feature combinations exhibited diverse characteristics . The seqThe HepG2 experiment had similar performance to that of the K562 experiment . The modThe performance of G4Beacon in K562/HepG2 experiments exhibited diverse characteristics. First, the sequence-only feature failed to provide enough information for in vivo G4 prediction, which means that the composition of sequences was not sufficient to characterize the formation ability of in vivo G4s. Second, the models using chromatin accessibility features provided excellent prediction accuracy in in vivo G4 prediction. Even on the test set with a skewed positive/negative ratio, G4Beacon could balance the performance in both precision and recall. Finally, adding a sequence feature to our approach improved the performance compared with the chromatin-accessibility-feature-only models. Although the sequence-only models performed poorly, integrating sequence information with chromatin accessibility information did improve the performance in our experiments.In the G4 CUT&Tag data experiment, we used MCF7 cell line data to construct the training set and the test set. In the MCF7 cell line experiment, G4Beacon with different feature selection approaches performed similarly as on K562 and HepG2 cell line data . The seqIn summary, we validated and confirmed that our model was powerful enough to predict in vivo G4s within both the training set and test set derived from the same cell line.In this study, we aim to propose a robust in vivo G4 predictor for different human cell lines. Therefore, it is indispensable to test the performance in the situation of a training set and test set coming from different cell lines. We designed a cross-cell-line train–test workflow to evaluate the performance of our model—that is, training on one cell line and testing on other cell lines. Specifically, we conducted a cross-cell-line experiment in which the model was trained on the HepG2 dataset and tested on the other two cell lines, i.e., the K562 cell line and MCF7 cell line. The entire data of each cell line were used in the above experiment. The data for model training were balanced using over-sampling methods, as for the training set in the one-cell-line experiment. As we had confirmed in the former experiments that the combined feature model exhibited the best performance, we then used ATAC and sequence-combined features in the cross-cell-line experiments.We found that the prediction model trained on HepG2 G4 ChIP-seq data (HepG2 model) in the cross-cell-line experiment had robust performance in predicting in vivo G4s in other cell lines . The HepOur G4Beacon approach has been proven to work robustly in the cross-cell-line experiment. Predictions on the K562 dataset, which was divided with the same in vivo G4 detection method data (G4 ChIP-seq), showed prominent precision as well as recall on the whole-genome scale test set. Moreover, G4Beacon can also perform robustly on a dataset (MCF7) constructed with different sequencing techniques. These results demonstrate that G4Beacon is equipped to learn the general ATAC-seq and surrounding sequence patterns of in vivo G4s detected by G4 ChIP-seq/G4 CUT&Tag experiments.Previous studies have shown that active G4s are preferentially located in active chromatin regions and are related to some histone modifications ,12,16. IWe characterized the histone modification around in vivo G4s predicted by G4Beacon and DeepG4, which could potentially justify the G4 prediction results as in vivo G4s are preferentially formed in active genomic regions ,16. We uAdditionally, we used a more stringent threshold  for the identification of active G4s and visualized the histone modification states around them, focusing on the most confident results of G4Beacon and DeepG4 . As a reIn summary, we described the histone modification states around the active G4s derived from G4Beacon and DeepG4. The results showed that the G4s predicted by G4Beacon were significantly enriched in open chromatin histone modification regions rather than closed modifications, which implied the characteristics of active G4s.The development of G4 detection methods, including G4-seq, G4 ChIP-seq, G4P-ChIP-seq, and G4 CUT&Tag ,10,13,15In this research, we focused on implementing the in vivo G4 predictions through a data-driven method. We developed an effective method called G4Beacon, which contained two core modules, namely the complete feature-construct module and the predictor module. In the feature-construct module, we used chromatin accessibility data, i.e., ATAC-seq data and the sequence data, as input to describe a candidate G4 sample. The predictor module was built based on a strong ensemble learning method, GBDT, and we used one of the widespread implements: LightGBM .Different from most of the existing G4 prediction tools that took only sequence data as input and predict non-cell-type-specific G4s, our model utilized the chromatin accessibility profiles, which reflect the intracellular environments of cells within G4 folding regions, as well as the surrounding sequences, which contain information about regulatory elements related to G4s, e.g., transcription factor binding sites. Due to the importance of in vivo G4 prediction, another deep learning algorithm has recently been proposed to characterize and predict in vivo G4s based on chromatin accessibility and sequence patterns, namely DeepG4 . AlthougTo verify the performance of G4Beacon, we profiled both machine learning evaluation experiments and bioinformatics analysis of histone modifications around the predicted active G4s. The machine learning evaluation experiments, including one-cell-line and cross-cell-line experiments, showed that G4Beacon not only performed well on data from one cell line but was also robust in cross-cell-line predictions . MoreoveHowever, the features that we considered in constructing the G4Beacon model are still inadequate to completely describe the cellular environment involved in G4 folding, which is a limitation of this model. Although the sequence patterns can be different between active and inactive G4s, the main differences should arise from the cell line environment where they are harbored—that is, the chromatin accessibility feature used in our research. With the developments in researching the environmental factors that can influence the formation of in vivo G4s, more useful information can be taken as input features. Even so, the framework of our G4Beacon can be generalized to support any type of sequential environment data. It is conceivable that G4Beacon can easily be employed to train on new expressive features, and thereby we can acquire predictors with better performance.In conclusion, G4Beacon is a very accurate, stable, and concise tool that can be used for the prediction of in vivo G4s.In this research, we proposed G4Beacon, a machine-learning-based in vivo G4 prediction method that utilized both the sequence and chromatin accessibility information to depict and identify the cell-type-specific active G4s. We tested G4Beacon in a one-cell-line experiment and a cross-cell-line experiment, which showed that our model was considerably accurate and robust in whole-genome in vivo G4 prediction. In summary, our method can quickly and extensively describe the landscape of in vivo G4s in existing cell lines and can facilitate genome-wide G4 studies and the discovery of their potential new biological functions.


Meanwhile, cell line U-87 was the most strongly sensitive to DOX when treated with 2 µM betulinic acid (change of EC50 = −45.1% ± 4.5%) in 2D culture. The combination of apple extract (E3) and DOX reduced the viability of HT-29 spheroids the most . Our study in 2D and 3D cultures showed that combining apple extract’s triterpenic complexes or individual triterpenic acids with DOX may sensitize chemotherapeutic drugs and increase the cytotoxicity effects in HT-29 and U-87 cell lines.Cancer cells’ resistance to anticancer drugs represents a major clinical problem and the most important failure of treatment. Combination chemotherapy is more effective than monotherapy due to additive or synergistic effects. The aim of our research was to assess the effects of the combinations of apple extract’s triterpenic compounds, individual triterpenic acids, and doxorubicin (DOX) on human colon adenocarcinoma (HT-29) and human glioblastoma (U-87) cell lines in 2D and 3D cultures. The effect of the combination of apple extracts, the triterpenic standards, and DOX against HT-29 and U-87 cell viability was tested by the MTT and spheroid growth assays. Cell line HT-29 was more sensitive to DOX when incubated with all tested apple extracts than DOX alone. Cell line HT-29 was the most strongly sensitive to DOX when it was treated with 5 µM oleanolic acid (change of EC Cancer is one of the prevailing reasons for morbidity and mortality worldwide. Based on Global Cancer Statistics data, in 2020, 18.1 million new cancer cases were established, with colon cancer representing 10.7% of all cases, and brain and central nervous system cancers 1.7% of all cases . A majorDoxorubicin is one of the most effective chemotherapeutic agents used routinely in the treatment of colon cancer and other solid tumors ,8,9. TheChemotherapy, regardless of its many adverse effects, is still the most acceptable way of curing cancer . Cancer Combination administration applying standard anticancer drugs and natural biological compounds extracted from the plant, or entire plant extracts, may be one of the most valuable strategies to defeat drug resistance. According to the scientific literature, most studies assess the effect of the combination of phenolic compounds and chemotherapy drugs on cancer cells. Shakibaei et al. showed that curcumin enhanced the effect of 5-fluorouracil on the colorectal HCT116 and HCT116 + ch3 cancer cell lines . StaedleThe aim of our research was to assess the effects of the combinations of apple extracts, triterpenic acids, and doxorubicin on HT-29 and U-87 cell lines in 2D and 3D cultures.This research involved the apple cultivars ‘Auksis’, ‘Kostele’, ‘Ligol’, and ‘Rubin’, which were cultivated in the orchard of the Institute of Horticulture, Lithuanian Research Centre for Agriculture and Forestry, Babtai, Lithuania . The apple trees were cultivated in 2020–2021.2 at 37 °C. Cell cultures were grown to 70% confluency, then trypsinized with 0.125% TrypLE Express solution before passage, and used until passage 20.The human colon adenocarcinoma cell line HT-29 and human glioblastoma cell line U-87 were purchased from the American Type Culture Collection . Human fibroblasts were provided by prof. Helder A. Santos . Cells were cultured in DMEM Glutamax medium supplemented with 10% fetal bovine serum (FBS), 10,000 U/mL penicillin, and 10 mg/mL streptomycin solution, in a humidified atmosphere containing 5% COAll solvents, reagents, and standards used were of analytical grade. Standards used in HPLC analysis: ursolic acid (UA), oleanolic acid (OA), corosolic acid (CA), and betulinic acid (BA), were purchased from Sigma-Aldrich GmbH . Solvents used in the study: 99.9% acetonitrile and 99.9% acetone, were acquired from Sigma-Aldrich GmbH , and 99.5% dimethyl sulfoxide (DMSO) was purchased from Scharlab S.L. . In this study, we used deionized water prepared by a Milli-Q  water purification system. Doxorubicin (DOX) hydrochloride was bought from Abcam , and 3--2,5-diphenyl tetrazolium bromide  was purchased from Buchs GmbH, Switzerland. The plasticware for cell cultures was obtained from Techno Plastic Products , Corning , and Thermo Fisher Scientific . Dulbecco’s modified Eagle high-glucose medium (DMEM Glutamax), TrypLETM Express reagent, fetal bovine serum (FBS), penicillin/streptomycin solution (100×), and phosphate-buffered saline (PBS) were obtained from Gibco . NanoShuttle magnetic nanoparticles were purchased from Nano3D Biosciences Inc. .Apple samples and dry triterpenic extracts were prepared using the method described by Butkeviciute et al. . In thisThe triterpenic compounds were identified and quantified by the HPLC-PDA method in the same manner reported in previous research by Butkeviciute et al. .50) values were calculated using the Hill equation. The EC50 value represents the concentration of a compound causing a 50% reduction of cancer cell metabolic activity.The effect of the combination of apple extracts, the triterpenic standards, and DOX against human colon adenocarcinoma HT-29 and glioblastoma U-87 cell viability was tested by the MTT assay, as reported by Grigalius and Petrikaite . The con50 (expressed in %) was established, which indicates how much DOX toxicity changed when the apple extracts and individual triterpenic acids solution were added to the medium. The experiments were repeated three times independently, and the results are presented as the means ± SD.The change of EC2 at 37 °C, until spheroids formed. After 48 h of incubation, the tested compounds and extracts were added. The DOX concentration used in spheroids was 20 nM, and the concentrations of tested material were as follows: 2 µM of triterpenic acids, and 30 µg/mL of extracts. In addition, control groups were included: the spheroids not treated with any substances or only with tested triterpenic acids and extracts (without doxorubicin).Spheroids were formed by a magnetic 3D bioprinting method, as described elsewhere . BrieflyPhotos of spheroids were taken every 48 h, simultaneously changing the medium with tested materials. The effect of the combination of apple extract’s triterpenic compounds, individual triterpenic acids, and DOX in 3D glioblastoma and colorectal carcinoma cells was analyzed by measuring the diameter change of spheroids using ImageJ software  and by the change of cell viability in spheroids by the MTT assay. The experiments were repeated three times independently, and the results are presented as the means ± SD.p < 0.05.The statistical analysis of the study data was performed using Microsoft Office Excel 2013  and SPSS 25.0  computer software. All the experiments were performed in at least triplicate independent measurements and the obtained values were reported as mean ± standard deviation. Tukey’s multiple comparison test was used. The differences were held as statistically significant at 50 varied from −43.2% ± 2.2% to −53.0% ± 1.4%), and there were no statistically significant differences between them. Cell line U-87 became even less sensitive to DOX when treated with all apple extracts (change of EC50 varied from 7.2% ± 0.4% to 34.2% ± 1.7%)  a.50 varied from −64.6% ± 4.4% to −43.9% ± 10.7%) (50 = −64.6% ± 4.4%) and with 5 µM ursolic acid (change of EC50 = −61.9% ± 8.8%) (50 = −43.9% ± 10.7%) or with 2 µM betulinic acid (change of EC50 = −49.3% ± 9.0%), though the effect was still synergistic (50 varied from −45.1% ± 4.5% to −16.0% ± 2.4%) (50 = −45.1% ± 4.5%). We found that cell line U-87 was not sensitized to DOX when treated with 5 µM ursolic acid  b. Cell l ± 8.8%) b. Cell lergistic b. Cell l ± 2.4%) b. Cell llic acid b.Miscellaneous fruits, including apples, can be held as possible sources of a chemosensitizer . For somMost researchers have invoked 2D culture models, which have shown restricted opportunities in the translational to clinical practices for their deficiencies in simulating the complexity of the tumor microenvironment . The appAn analysis of 3D cell cultures showed that most combinations of apple peel extracts and DOX  reduced HT-29 spheroids’ size, compared to spheroids treated with DOX alone . HT-29 sUnlike the previously described research results in 2D cultures, the effect of the combinations of apple extract′s triterpenic complexes and DOX on the size of U-87 spheroids was not revealed b. In ourDespite small differences in spheroid size, the MTT assay in spheroids showed more expressed differences between groups in terms of cell viability . After cOf all tested combinations, the betulinic acid and DOX combination most efficiently reduced the viability of HT-29 spheroids  a. U-87 sSilybum marianum (L.) Gaertn. Seeds’ standardized extract with DOX or paclitaxel [Solanum nigrum leaf extract and cisplatin, DOX, docetaxel, or 5-fluorouracil [Artemisia princeps var. orientalis leaf extract and DOX [Some authors reported that combinations of plant extracts and chemotherapeutic agents might act additively or synergistically. In recent decades, many studies have been conducted evaluating the effects of the combination of DOX and plant extracts on cancer cell lines. Previous studies have described more synergistic interactions between plant extracts and anticancer drugs, such as clitaxel , Solanumrouracil , and Art and DOX .In previous studies, we evaluated the effect of apple extract’s triterpenic and phenolic compounds on the viability of human colon adenocarcinoma HT-29 and human glioblastoma U-87 cell lines . During In our study, we found the different effects of apple extracts and individual triterpenic acids in 2D and 3D human colon adenocarcinoma and human glioblastoma spheroids’ cultures. As expected, the compounds were more active in cell monolayers than in spheroids. We determined that the human colon adenocarcinoma HT-29 cell line cultivated in a monolayer and spheroids was more sensitive to DOX when incubated with all apple extracts  and individual triterpenic acids  than DOX alone. Meanwhile, cell line U-87 in 2D cultures became even less sensitive to DOX when treated with all apple extracts and ursolic acid. The combination of apple extracts and DOX had no significant effect on the viability of U-87 cells in 3D cultures, compared to spheroids treated with DOX alone. After comparing the composition of the tested apple extracts, we found that the highest amounts of individual triterpenic compounds  were found in the apple extract, marked E1 . HoweverSome authors described the effects of the combinations of individual triterpenic compounds and chemotherapeutic agents in cancer cell lines in 2D cultures. Bao et al. determined the synergistic interaction between oleanolic acid, its derivatives, and paclitaxel in breast MDA-MB-231 cancer cell lines . Villar We hypothesize that possibly, different apple extract concentrations of individual triterpenic acid induce different mechanisms of action in HT-29 and U-87 cell lines. The success of the combinations of triterpenic compounds and chemotherapeutic agents on cancer cells may be accompanied by various mechanisms of action and depend on the selected cancer cell types ,41. Our Triterpenic compounds not only demonstrated a relevant apoptotic effect on cancer cells but also sensitized them to chemotherapeutic drugs . Blanco In some incidences, it is possible to bypass gained chemoresistance by combination therapy of anticancer agents with chemosensitizers such as triterpenic compounds. The published scientific results confirm that the combinations of individual triterpenic compounds with a chemotherapeutic drug can increase its sensitivity to cancer cells and increase cytotoxic activity. As a result, the desired effect can be achieved with a lower dose of the drug, and combined chemotherapy can reduce the consequences of side effects.We evaluated that human colon adenocarcinoma HT-29 and human glioblastoma U-87 cells were sensitive to DOX when treated with apple extracts and individual triterpenic acids in 2D and 3D cultures. We determined that the HT-29 cell line was most strongly sensitive to DOX when treated with all tested apple extracts and triterpenic acids, compared to U-87 cells. HT-29 cells were most strongly sensitive to DOX when treated with 5 µM oleanolic acid. On the contrary, U-87 cells were most strongly sensitive to DOX when treated with 2 µM betulinic acid. The 3D cell cultures study showed that a combination of betulinic acid and DOX most efficiently reduced the viability of HT-29 and U-87 spheroids, compared to spheroids treated with DOX alone. Our study in 2D and 3D cultures showed that combining natural-origin apple extracts or individual triterpenic acids with DOX may sensitize it and increase the cytotoxicity effects in HT-29 and U-87 cell lines. The obtained results are important in combination chemotherapy and may help to decrease the effective dose of anticancer agents, overcome the resistance, and diminish possible side effects.