An efficient computer-aided structural elucidation strategy for mixtures using an iterative dynamic programming algorithm.

The identification of chemical structures in natural product mixtures is an important task in drug discovery but is still a challenging problem, as structural elucidation is a time-consuming process and is limited by the available mass spectra of known natural products. Computer-aided structure elucidation (CASE) strategies seek to automatically propose a list of possible chemical structures in mixtures by utilizing chromatographic and spectroscopic methods. However, current CASE tools still cannot automatically solve structures for experienced natural product chemists. Here, we formulated the structural elucidation of natural products in a mixture as a computational problem by extending a list of scaffolds using a weighted side chain list after analyzing a collection of 243,130 natural products and designed an efficient algorithm to precisely identify the chemical structures. The complexity of such a problem is NP-complete. A dynamic programming (DP) algorithm can solve this NP-complete problem in pseudo-polynomial time after converting floating point molecular weights into integers. However, the running time of the DP algorithm degrades exponentially as the precision of the mass spectrometry experiment grows. To ideally solve in polynomial time, we proposed a novel iterative DP algorithm that can quickly recognize the chemical structures of natural products. By utilizing this algorithm to elucidate the structures of four natural products that were experimentally and structurally determined, the algorithm can search the exact solutions, and the time performance was shown to be in polynomial time for average cases. The proposed method improved the speed of the structural elucidation of natural products and helped broaden the spectrum of available compounds that could be applied as new drug candidates. A web service built for structural elucidation studies is freely accessible via the following link ( http://csccp.cmdm.tw/ ).

NP-StructurePredictor: Prediction of Unknown Natural Products in Plant Mixtures.

Identification of the individual chemical constituents of a mixture, especially solutions extracted from medicinal plants, is a time-consuming task. The identification results are often limited by challenges such as the development of separation methods and the availability of known reference standards. A novel structure elucidation system, NP-StructurePredictor, is presented and used to accelerate the process of identifying chemical structures in a mixture based on a branch and bound algorithm combined with a large collection of natural product databases. NP-StructurePredictor requires only targeted molecular weights calculated from a list of m/z values from liquid chromatography-mass spectrometry (LC-MS) experiments as input information to predict the chemical structures of individual components matching the weights in a mixture. NP-StructurePredictor also provides the predicted structures with statistically calculated probabilities so that the most likely chemical structures of the natural products and their analogs can be proposed accordingly. Four data sets consisting of different Chinese herbs with mixtures containing known compounds were selected for validation studies, and all their components were correctly identified and highly predicted using NP-StructurePredictor. NP-StructurePredictor demonstrated its applicability for predicting the chemical structures of novel compounds by returning highly accurate results from four different validation case studies.

Rule-based classification models of molecular autofluorescence.

Fluorescence-based detection has been commonly used in high-throughput screening (HTS) assays. Autofluorescent compounds, which can emit light in the absence of artificial fluorescent markers, often interfere with the detection of fluorophores and result in false positive signals in these assays. This interference presents a major issue in fluorescence-based screening techniques. In an effort to reduce the time and cost that will be spent on prescreening of autofluorescent compounds, in silico autofluorescence prediction models were developed for selected fluorescence-based assays in this study. Five prediction models were developed based on the respective fluorophores used in these HTS assays, which absorb and emit light at specific wavelengths (excitation/emission): Alexa Fluor 350 (A350) (340 nm/450 nm), 7-amino-4-trifluoromethyl-coumarin (AFC) (405 nm/520 nm), Alexa Fluor 488 (A488) (480 nm/540 nm), Rhodamine (547 nm/598 nm), and Texas Red (547 nm/618 nm). The C5.0 rule-based classification algorithm and PubChem 2D chemical structure fingerprints were used to develop prediction models. To optimize the accuracies of these prediction models despite the highly imbalanced ratio of fluorescent versus nonfluorescent compounds presented in the collected data sets, oversampling and undersampling strategies were applied. The average final accuracy achieved for the training set was 97%, and that for the testing set was 92%. In addition, five external data sets were used to further validate the models. Ultimately, 14 representative structural features (or rules) were determined to efficiently predict autofluorescence in data sets containing both fluorescent and nonfluorescent compounds. Several cases were illustrated in this study to demonstrate the applicability of these rules.

Metabolomic Dynamic Analysis of Hypoxia in MDA-MB-231 and the Comparison with Inferred Metabolites from Transcriptomics Data.

Hypoxia affects the tumor microenvironment and is considered important to metastasis progression and therapy resistance. Thus far, the majority of global analyses of tumor hypoxia responses have been limited to just a single omics level. Combining multiple omics data can broaden our understanding of tumor hypoxia. Here, we investigate the temporal change of the metabolite composition with gene expression data from literature to provide a more comprehensive insight into the system level in response to hypoxia. Nuclear magnetic resonance spectroscopy was used to perform metabolomic profiling on the MDA-MB-231 breast cancer cell line under hypoxic conditions. Multivariate statistical analysis revealed that the metabolic difference between hypoxia and normoxia was similar over 24 h, but became distinct over 48 h. Time dependent microarray data from the same cell line in the literature displayed different gene expressions under hypoxic and normoxic conditions mostly at 12 h or earlier. The direct metabolomic profiles show a large overlap with theoretical metabolic profiles deduced from previous transcriptomic studies. Consistent pathways are glycolysis/gluconeogenesis, pyruvate, purine and arginine and proline metabolism. Ten metabolic pathways revealed by metabolomics were not covered by the downstream of the known transcriptomic profiles, suggesting new metabolic phenotypes. These results confirm previous transcriptomics understanding and expand the knowledge from existing models on correlation and co-regulation between transcriptomic and metabolomics profiles, which demonstrates the power of integrated omics analysis.

Expression of Nur77 induced by an n-butylidenephthalide derivative promotes apoptosis and inhibits cell growth in oral squamous cell carcinoma.

In spite of numerous advances, the 5-year survival rate for head and neck squamous cell cancer has remained largely stagnant and few new anti-tumor drugs have been developed. PCH4, a derivative of n-butylidenephthalide, has been investigated for its anti-tumor effects on oral squamous cell carcinoma (OSCC). The aim of this study was to investigate the anti-tumor mechanism of a potential target gene, Nur77, in OSCC cells, which can be induced by PCH4 treatment. Data show that PCH4 promoted Nur77 translocation from the nucleus to the cytoplasm and induced cell apoptosis in OSCC cells. When Nur77 translocation was blocked, the degree of tumor apoptosis caused by PCH4 was significantly inhibited (p < 0.05). Within the MAPK pathway, PCH4 only induced JNK phosphorylation. Furthermore, treatment with a JNK inhibitor significantly reduced PCH4-induced apoptosis (p < 0.05) and decreased PCH4-induced Nur77 expression (p < 0.05). In a xenograft animal model, administration of PCH4 also showed anti-tumor effects. We have demonstrated that OSCC cells are sensitive to PCH4 and that Nur77 protein translocation from the nucleus to the cytoplasm might be associated with the induction of apoptosis by PCH4. These results indicate that PCH4 may serve as a potential anti-tumor drug for OSCC therapy.

Butylidenephthalide suppresses human telomerase reverse transcriptase (TERT) in human glioblastomas.

BACKGROUND: Telomerase is widely expressed in most human cancers, but is almost undetectable in normal somatic cells and is therefore a potential drug target. Using the human telomerase promoter platform, the naturally occurring compound butylidenephthalide (BP) was selected for subsequent investigation of antitumor activity in vitro and in vivo. METHODS: We treated human glioblastoma cells with BP and found a dose-dependent decrease in human telomerase reverse transcriptase (hTERT) mRNA expression and a concomitant increase in p16 and p21 expression. Because c-Myc and Sp1 are involved in transcriptional regulation of hTERT, the effect of BP on c-Myc and Sp1 expression was examined. RESULTS: Using electrophoretic mobility shift assays and western blotting, we showed that BP represses hTERT transcriptional activity via downregulation of Sp1 expression. Using the telomerase repeat amplification protocol, an association between BP concentration and suppression of telomerase activity, induction of human glioblastoma senescence, and inhibition of cellular proliferation was identified. This was supported by a mouse xenograft model, in which BP repressed telomerase and inhibited tumor proliferation, resulting in tumor senescence. Overexpression of hTERT restored telomerase activity in human glioblastoma cells and overcame replicative senescence. CONCLUSIONS: These findings suggest that BP inhibits proliferation and induces senescence in human glioblastomas by downregulating hTERT expression and consequently telomerase activity. This is the first study to describe regulation of telomerase activity by BP in human glioblastomas.

Overexpression of the orphan receptor Nur77 and its translocation induced by PCH4 may inhibit malignant glioma cell growth and induce cell apoptosis.

BACKGROUND: In previous study, n-butylidenephthalide (BP), a natural compound from Angelica sinensis, has anti-glioblastoma multiform (GBM) cell effects. In this study, we modified BP structure to increase anti-GBM cell effects. The anti-GBM cell effects of one derivative of BP, (Z)-N-(2-(dimethylamino)ethyl)-2-(3-((3-oxoisobenzofuran-1(3H)-ylidene)methyl)phenoxy)acetamide (PCH4) were tested in vitro and in vivo. METHODS: MTT assay and PI/Annexin V assay were performed to evaluate the anti-GBM effects of PCH4. The Nur77 expression and translocation were assayed by RT-PCR and Western blot. The Nur77 siRNA was used to downregulate the Nur77 expression. The JNK inhibitor (SP600125) was used to block the JNK pathway. RESULTS: The anti-GBM effect of PCH4 is four times more than BP. The IC(50) of PCH4 on DBTRG-05MG cells was 50 µg/ml. Nur77 expression and translocation from the nucleus to the cytoplasm were important in PCH4-induced apoptosis. Furthermore, the downregulation of PCH4-induced Nur77 expression by Nur77 siRNA reduced PCH4-induced apoptosis. In addition, PCH4-induced apoptosis was associated with the JNK pathway. The JNK inhibitor, SP600125, inhibited Nur77 mRNA expression and reduced PCH4-induced apoptosis. CONCLUSIONS: In conclusion, PCH4, a derivative of BP, induced Nur77-mediated apoptosis via the JNK pathway and this mechanism, which is different from that of BP, may explain the increase in the anti-tumor effects on GBM.