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1.  Apoptosis induction associated with the ER stress response through up-regulation of JNK in HeLa cells by gambogic acid 
Gambogic acid (GA) was extracted from the dried yellow resin of gamboge (Garcinia hanburyi) which is traditionally used as a coloring material for painting and cloth dying. Gamboge has been also used as a folk medicine for an internal purgative and externally infected wound. We focused on the mechanisms of apoptosis induction by GA through the unfold protein response (ER stress) in HeLa cells.
The cytotoxic effect of GA against HeLa cells was determined by trypan blue exclusion assay. Markers of ER stress such as XBP-1, GRP78, CHOP, GADD34 and ERdj4 were analyzed by RT-PCR and Real-time RT-PCR. Cell morphological changes and apoptotic proteins were performed by Hoechst33342 staining and Western blotting technique.
Our results indicated a time- and dose-dependent decrease of cell viability by GA. The ER stress induction is determined by the up-regulation of spliced XBP1 mRNA and activated GRP78, CHOP, GADD34 and ERdj4 expression. GA also induced cell morphological changes such as nuclear condensation, membrane blebbing and apoptotic body in Hela cells. Apoptosis cell death detected by increased DR5, caspase-8, −9, and −3 expression as well as increased cleaved-PARP, while decreased Bcl-2 upon GA treatment. In addition, phosphorylated JNK was up-regulated but phosphorylated ERK was down-regulated after exposure to GA.
These results suggest that GA induce apoptosis associated with the ER stress response through up-regulation of p-JNK and down-regulation of p-ERK in HeLa cells.
PMCID: PMC4340837  PMID: 25887496
Gambogic acid; Anticancer; ER stress; Apoptosis; HeLa cells
2.  SMK-17, a MEK1/2-specific inhibitor, selectively induces apoptosis in β-catenin-mutated tumors 
Scientific Reports  2015;5:8155.
Although clinical studies have evaluated several MEK1/2 inhibitors, it is unlikely that MEK1/2 inhibitors will be studied clinically. BRAF mutations have been proposed as a responder marker of MEK1/2 inhibitors in a preclinical study. However, current clinical approaches focusing on BRAF mutations have shown only moderate sensitivity of MEK1/2 inhibitors. This has led to insufficient support for their promoted clinical adoption. Further characterization of tumors sensitive to MEK inhibitors holds great promise for optimizing drug therapy for patients with these tumors. Here, we report that β-catenin mutations accelerate apoptosis induced by MEK1/2 inhibitor. SMK-17, a selective MEK1/2 inhibitor, induced apoptosis in tumor cell lines harboring β-catenin mutations at its effective concentration. To confirm that β-catenin mutations and mutant β-catenin-mediated TCF7L2 (also known as TCF4) transcriptional activity is a predictive marker of MEK inhibitors, we evaluated the effects of dominant-negative TCF7L2 and of active, mutated β-catenin on apoptosis induced by MEK inhibitor. Indeed, dominant-negative TCF7L2 reduced apoptosis induced by MEK inhibitor, whereas active, mutated β-catenin accelerated it. Our findings show that β-catenin mutations are an important responder biomarker for MEK1/2 inhibitors.
PMCID: PMC4313120  PMID: 25640451
3.  Xanthohumol suppresses oestrogen-signalling in breast cancer through the inhibition of BIG3-PHB2 interactions 
Scientific Reports  2014;4:7355.
Xanthohumol (XN) is a natural anticancer compound that inhibits the proliferation of oestrogen receptor-α (ERα)-positive breast cancer cells. However, the precise mechanism of the antitumour effects of XN on oestrogen (E2)-dependent cell growth, and especially its direct target molecule(s), remain(s) largely unknown. Here, we focus on whether XN directly binds to the tumour suppressor protein prohibitin 2 (PHB2), forming a novel natural antitumour compound targeting the BIG3-PHB2 complex and acting as a pivotal modulator of E2/ERα signalling in breast cancer cells. XN treatment effectively prevented the BIG3-PHB2 interaction, thereby releasing PHB2 to directly bind to both nuclear- and cytoplasmic ERα. This event led to the complete suppression of the E2-signalling pathways and ERα-positive breast cancer cell growth both in vitro and in vivo, but did not suppress the growth of normal mammary epithelial cells. Our findings suggest that XN may be a promising natural compound to suppress the growth of luminal-type breast cancer.
PMCID: PMC4258681  PMID: 25483453
4.  Identification of Licopyranocoumarin and Glycyrurol from Herbal Medicines as Neuroprotective Compounds for Parkinson's Disease 
PLoS ONE  2014;9(6):e100395.
In the course of screening for the anti-Parkinsonian drugs from a library of traditional herbal medicines, we found that the extracts of choi-joki-to and daio-kanzo-to protected cells from MPP+-induced cell death. Because choi-joki-to and daio-kanzo-to commonly contain the genus Glycyrrhiza, we isolated licopyranocoumarin (LPC) and glycyrurol (GCR) as potent neuroprotective principals from Glycyrrhiza. LPC and GCR markedly blocked MPP+-induced neuronal PC12D cell death and disappearance of mitochondrial membrane potential, which were mediated by JNK. LPC and GCR inhibited MPP+-induced JNK activation through the suppression of reactive oxygen species (ROS) generation, thereby inhibiting MPP+-induced neuronal PC12D cell death. These results indicated that LPC and GCR derived from choi-joki-to and daio-kanzo-to would be promising drug leads for PD treatment in the future.
PMCID: PMC4069009  PMID: 24960051
5.  Chemical Genomic-Based Pathway Analyses for Epidermal Growth Factor-Mediated Signaling in Migrating Cancer Cells 
PLoS ONE  2014;9(5):e96776.
To explore the diversity and consistency of the signaling pathways that regulate tumor cell migration, we chose three human tumor cell lines that migrated after treatment with EGF. We then quantified the effect of fifteen inhibitors on the levels of expression or the phosphorylation levels of nine proteins that were induced by EGF stimulation in each of these cell lines. Based on the data obtained in this study and chemical-biological assumptions, we deduced cell migration pathways in each tumor cell line, and then compared them. As a result, we found that both the MEK/ERK and JNK/c-Jun pathways were activated in all three migrating cell lines. Moreover, GSK-3 and p38 were found to regulate PI3K/Akt pathway in only EC109 cells, and JNK was found to crosstalk with p38 and Fos related pathway in only TT cells. Taken together, our analytical system could easily distinguish between the common and cell type-specific pathways responsible for tumor cell migration.
PMCID: PMC4018296  PMID: 24820097
6.  HCV NS3 protease enhances liver fibrosis via binding to and activating TGF-β type I receptor 
Scientific Reports  2013;3:3243.
Viruses sometimes mimic host proteins and hijack the host cell machinery. Hepatitis C virus (HCV) causes liver fibrosis, a process largely mediated by the overexpression of transforming growth factor (TGF)-β and collagen, although the precise underlying mechanism is unknown. Here, we report that HCV non-structural protein 3 (NS3) protease affects the antigenicity and bioactivity of TGF-β2 in (CAGA)9-Luc CCL64 cells and in human hepatic cell lines via binding to TGF-β type I receptor (TβRI). Tumor necrosis factor (TNF)-α facilitates this mechanism by increasing the colocalization of TβRI with NS3 protease on the surface of HCV-infected cells. An anti-NS3 antibody against computationally predicted binding sites for TβRI blocked the TGF-β mimetic activities of NS3 in vitro and attenuated liver fibrosis in HCV-infected chimeric mice. These data suggest that HCV NS3 protease mimics TGF-β2 and functions, at least in part, via directly binding to and activating TβRI, thereby enhancing liver fibrosis.
PMCID: PMC3837337  PMID: 24263861
7.  A chemical genomic study identifying diversity in cell migration signaling in cancer cells 
Scientific Reports  2012;2:823.
The aim of this study was to analyze the diversity and consistency of regulatory signaling in cancer cell migration, using a chemical genomic approach. The effects of 34 small molecular compounds were assessed quantitatively by wound healing assay in ten types of migrating cells. Hierarchical clustering was performed on the subsequent migration inhibition profile of the compounds and cancer cell types. The result was that hierarchical clustering accurately classified the compounds according to their targets. Furthermore, the cancer cells tested in this study were classified into three clusters, and the compounds were grouped into four clusters. An inhibitor of JNK suppressed all types of cell migration; however, inhibitors of ROCK, GSK-3 and p38MAPK only inhibited the migration of a subset of cell lines. Thus, our analytical system could easily distinguish between the common and cell type-specific signals responsible for cell migration.
PMCID: PMC3492869  PMID: 23139868
8.  Comprehensive predictions of target proteins based on protein-chemical interaction using virtual screening and experimental verifications 
BMC Chemical Biology  2012;12:2.
Identification of the target proteins of bioactive compounds is critical for elucidating the mode of action; however, target identification has been difficult in general, mostly due to the low sensitivity of detection using affinity chromatography followed by CBB staining and MS/MS analysis.
We applied our protocol of predicting target proteins combining in silico screening and experimental verification for incednine, which inhibits the anti-apoptotic function of Bcl-xL by an unknown mechanism. One hundred eighty-two target protein candidates were computationally predicted to bind to incednine by the statistical prediction method, and the predictions were verified by in vitro binding of incednine to seven proteins, whose expression can be confirmed in our cell system.
As a result, 40% accuracy of the computational predictions was achieved successfully, and we newly found 3 incednine-binding proteins.
This study revealed that our proposed protocol of predicting target protein combining in silico screening and experimental verification is useful, and provides new insight into a strategy for identifying target proteins of small molecules.
PMCID: PMC3471015  PMID: 22480302
9.  Caffeine induces apoptosis by enhancement of autophagy via PI3K/Akt/mTOR/p70S6K inhibition 
Autophagy  2011;7(2):176-187.
Caffeine is one of the most frequently ingested neuroactive compounds. All known mechanisms of apoptosis induced by caffeine act through cell cycle modulation or p53 induction. It is currently unknown whether caffeine-induced apoptosis is associated with other cell death mechanisms, such as autophagy. Herein we show that caffeine increases both the levels of microtubule-associated protein 1 light chain 3-II and the number of autophagosomes, through the use of western blotting, electron microscopy and immunocytochemistry techniques. Phosphorylated p70 ribosomal protein S6 kinase (Thr389), S6 ribosomal protein (Ser235/236), 4E-BP1 (Thr37/46) and Akt (Ser473) were significantly decreased by caffeine. In contrast, ERK1/2 (Thr202/204) was increased by caffeine, suggesting an inhibition of the Akt/mTOR/p70S6K pathway and activation of the ERK1/2 pathway. Although insulin treatment phosphorylated Akt (Ser473) and led to autophagy suppression, the effect of insulin treatment was completely abolished by caffeine addition. Caffeine-induced autophagy was not completely blocked by inhibition of ERK1/2 by U0126. Caffeine induced reduction of mitochondrial membrane potentials and apoptosis in a dose-dependent manner, which was further attenuated by the inhibition of autophagy with 3-methyladenine or Atg7 siRNA knockdown. Furthermore, there was a reduced number of early apoptotic cells (annexin V positive, propidium iodide negative) among autophagy-deficient mouse embryonic fibroblasts treated with caffeine than in their wild-type counterparts. These results support previous studies on the use of caffeine in the treatment of human tumors and indicate a potential new target in the regulation of apoptosis.
PMCID: PMC3039768  PMID: 21081844
apoptosis; autophagy; PI3K/Akt/mTOR/p70S6K; ERK1/2; caffeine
10.  Integrating Statistical Predictions and Experimental Verifications for Enhancing Protein-Chemical Interaction Predictions in Virtual Screening 
PLoS Computational Biology  2009;5(6):e1000397.
Predictions of interactions between target proteins and potential leads are of great benefit in the drug discovery process. We present a comprehensively applicable statistical prediction method for interactions between any proteins and chemical compounds, which requires only protein sequence data and chemical structure data and utilizes the statistical learning method of support vector machines. In order to realize reasonable comprehensive predictions which can involve many false positives, we propose two approaches for reduction of false positives: (i) efficient use of multiple statistical prediction models in the framework of two-layer SVM and (ii) reasonable design of the negative data to construct statistical prediction models. In two-layer SVM, outputs produced by the first-layer SVM models, which are constructed with different negative samples and reflect different aspects of classifications, are utilized as inputs to the second-layer SVM. In order to design negative data which produce fewer false positive predictions, we iteratively construct SVM models or classification boundaries from positive and tentative negative samples and select additional negative sample candidates according to pre-determined rules. Moreover, in order to fully utilize the advantages of statistical learning methods, we propose a strategy to effectively feedback experimental results to computational predictions with consideration of biological effects of interest. We show the usefulness of our approach in predicting potential ligands binding to human androgen receptors from more than 19 million chemical compounds and verifying these predictions by in vitro binding. Moreover, we utilize this experimental validation as feedback to enhance subsequent computational predictions, and experimentally validate these predictions again. This efficient procedure of the iteration of the in silico prediction and in vitro or in vivo experimental verifications with the sufficient feedback enabled us to identify novel ligand candidates which were distant from known ligands in the chemical space.
Author Summary
This work describes a statistical method that identifies chemical compounds binding to a target protein given the sequence of the target or distinguishes proteins to which a small molecule binds given the chemical structure of the molecule. As our method can be utilized for virtual screening that seeks for lead compounds in drug discovery, we showed the usefulness of our method in its application to the comprehensive prediction of ligands binding to human androgen receptors and in vitro experimental verification of its predictions. In contrast to most previous virtual screening studies which predict chemical compounds of interest mainly with 3D structure-based methods and experimentally verify them, we proposed a strategy to effectively feedback experimental results for subsequent predictions and applied the strategy to the second predictions followed by the second experimental verification. This feedback strategy makes full use of statistical learning methods and, in practical terms, gave a ligand candidate of interest that structurally differs from known drugs. We hope that this paper will encourage reevaluation of statistical learning methods in virtual screening and that the utilization of statistical methods with efficient feedback strategies will contribute to the acceleration of drug discovery.
PMCID: PMC2685987  PMID: 19503826

Results 1-10 (10)