Dimeric diarylheptanoids with anti-inflammatory activity from Zingiber officinale.

Two previously undescribed chain diarylheptanoid derivatives (2-3), five previously undescribed dimeric diarylheptanoids (4-8), together with one known cyclic diarylheptanoid (1) were isolated from Zingiber officinale. Their structures were elucidated by extensive spectroscopic analyses (HR-ESI-MS, IR, UV, 1D and 2D NMR) and ECD calculations. Biological evaluation of compounds 1-8 revealed that compounds 2, 3 and 4 could inhibit nitrite oxide and IL-6 production in lipopolysaccharide induced RAW264.7 cells in a dose-dependent manner.

Unusual acetylated flavonol glucuronides, oxyphyllvonides A-H with renoprotective activities from the fruits of Alpinae oxyphylla.

As a widely consumed spice and Traditional Chinese Medicine, Alpinae oxyphylla has been used to treat conditions such as diarrhea, ulcers, dementia, and enuresis. Fruits of A. oxyphylla were phytochemically studied and the bioactive constituents against renal fibrosis were identified. Eight previously undescribed acetylated flavonol glucuronides named oxyphyllvonides A-H (1-7 and 10), two known acetylated flavonol glucuronides (8 and 9), together with seven known flavone glycosides (11-17) were isolated from the fruits of A. oxyphylla. Among them, flavonol glucuronides were discovered in Zingiberaceae for the first time. The planar structures of 1-7 and 10 were determined using HRESIMS and extensive spectroscopic techniques (UV, IR, 1D-NMR, and 2D-NMR). The absolute configurations of the sugar moiety in these compounds were determined by using LC-MS analysis of acid-hydrolyzed derivatized monosaccharides. Biological evaluation showed that 7-10, 13, 14, 16 and 17 inhibit renal fibrosis in TGF-ß1-induced kidney proximal tubular cells. In addition, 7, 8 and 14 were superior to nootkatone in inhibiting Fibronectin expression. The finding has significant relevance to our ongoing research on the anti-renal fibrosis activity of A. oxyphylla.

Correction: Non-peptide compounds from Kronopolites svenhedini (Verhoeff) and their antitumor and iNOS inhibitory activities.

[This corrects the article DOI: 10.3762/bjoc.19.59.].

Succipenoids A-C: A dimeric abietane and nor-abietane diterpenoids from fossil Chinese medicinal succinum and their anti-inflammatory potential.

An unprecedented dimeric abietane, succipenoid A (1), and two previously undescribed nor-abietane diterpenoids featuring a rarely occurring naphthalene ring or with a large conjugated system, succipenoids B and C (2 and 3), along with seven known diterpenoids (4-10) were isolated from the CH2Cl2 extract of succinum. The structures of these compounds, including their absolute configurations, were elucidated using spectroscopic and computational techniques. Notably, compounds 1-4 and 6-10 were isolated from succinum for the first time. In order to evaluate their anti-inflammatory potential, in vitro tests were conducted. The results demonstrated that compounds 1, 2, 4, and 6-10 exhibite dose-dependent inhibition of iNOS expression in lipopolysaccharide-induced RAW 264.7 cells.

Nine pairs of undescribed enantiomers from the resin of Styrax tonkinensis (Pierre) Craib ex Hart with anti-inflammatory activity.

Nine pairs of undescribed enantiomers, (±)-styraxoids A-I (1-9), were isolated from the resin of Styrax tonkinensis, and their structures were assigned by spectroscopic and computational methods. Compounds (±)-1 are a pair of degraded lignans, and the remaining compounds (±)-(2-9) are phenylpropanoid skeletons. Compounds (±)-8 and (±)-9 feature a 1,3-dioxolane moiety. The biological evaluation showed that both enantiomers of 1 could inhibit LPS-induced INOS and COX-2 in RAW264.7 cells in a dose-dependent manner.

Five new sesquiterpenoids from agarwood of Aquilaria sinensis.

Five new eudesmane-type sesquiterpenoids (aquisinenoids F-J (1-5)) and five known compounds (6-10) were isolated from the agarwood of Aquilaria sinensis. Their structures, including absolute configurations, were identified by comprehensive spectroscopic analyses and computational methods. Inspired by our previous study on the same kinds of skeletons, we speculated that the new compounds have anticancer and anti-inflammatory activities. The results did not show any activity, but they revealed the structure-activity relationships (SAR).

Loss of ANCO1 Expression Regulates Chromatin Accessibility and Drives Progression of Early-Stage Triple-Negative Breast Cancer.

Mutations in the gene ankyrin repeat domain containing 11 (ANKRD11/ANCO1) play a role in neurodegenerative disorders, and its loss of heterozygosity and low expression are seen in some cancers. Here, we show that low ANCO1 mRNA and protein expression levels are prognostic markers for poor clinical outcomes in breast cancer and that loss of nuclear ANCO1 protein expression predicts lower overall survival of patients with triple-negative breast cancer (TNBC). Knockdown of ANCO1 in early-stage TNBC cells led to aneuploidy, cellular senescence, and enhanced invasion in a 3D matrix. The presence of a subpopulation of ANCO1-depleted cells enabled invasion of the overall cell population in vitro and they converted more rapidly to invasive lesions in a xenograft mouse model. In ANCO1-depleted cells, ChIP-seq analysis showed a global increase in H3K27Ac signals that were enriched for AP-1, TEAD, STAT3, and NFκB motifs. ANCO1-regulated H3K27Ac peaks had a significantly higher overlap with known breast cancer enhancers compared to ANCO1-independent ones. H3K27Ac engagement was associated with transcriptional activation of genes in the PI3K-AKT, epithelial-mesenchymal transition (EMT), and senescence pathways. In conclusion, ANCO1 has hallmarks of a tumor suppressor whose loss of expression activates breast-cancer-specific enhancers and oncogenic pathways that can accelerate the early-stage progression of breast cancer.

Non-peptide compounds from Kronopolites svenhedini (Verhoeff) and their antitumor and iNOS inhibitory activities.

Six new compounds, including a tetralone 1, two xanthones 2 and 3, a flavan derivative 4, and two nor-diterpenoids 7 and 8, accompanied by two known flavan derivatives 5 and 6 and a known olefine acid (9) were isolated from whole bodies of Kronopolites svenhedini (Verhoeff). The structures of the new compounds were determined by 1D and 2D nuclear magnetic resonance (NMR) and other spectroscopic methods, as well as computational methods. Selected compounds were evaluated for their biological properties against a mouse pancreatic cancer cell line and inhibitory effects on iNOS and COX-2 in RAW264.7 cells.

Eight new 2-(2-phenylethyl)chromone derivatives from agarwood of Aquilaria sinensis with anti-inflammatory activity.

Eight previously unknown 2-(2-phenylethyl)chromone derivatives, called aquichromones A - E (1-3, 5 and 6) and 8-epi-aquichromone C (4), including two pairs of enantiomers [(±)-1 and (±)-2] were isolated from the agarwood of Aquilaria sinensis. The structures and absolute stereochemistry of these natural products were elucidated by using spectroscopic and computational methods. The result of biological assay showed that two members of this group, 4 and 5, have significant dose-dependent anti-inflammatory activity.

Six new diterpenoids from the resins of Populus euphratica and their anti-inflammatory activities.

Six new diterpenoids, identified as two abietane derivatives, euphraticanoids J and K (1 and 2), two pimarane derivatives, euphraticanoids L and M (3 and 4), and two 9,10-seco-abietane derivatives, euphraticanoids N and O (5 and 6) were isolated from Populus euphratica resins. Their structures including absolute configurations were characterized using spectroscopic, quantum chemical NMR, and ECD calculation methods. The anti-inflammatory activity of the compounds was tested and the results revealed that compounds 4 and 6 inhibited the production of iNOS and COX-2 in a dose-dependent manner in lipopolysaccharide (LPS)-induced RAW 264.7 cells.

Design and modeling for drug combination experiments with order effects.

Combinations of drugs are now ubiquitous in treating complex diseases such as cancer and HIV due to their potential for enhanced efficacy and reduced side effects. The traditional combination experiments of drugs focus primarily on the dose effects of the constituent drugs. However, with the doses of drugs remaining unchanged, different sequences of drug administration may also affect the efficacy endpoint. Such drug effects shall be called as order effects. The common order-effect linear models are usually inadequate for analyzing combination experiments due to the nonlinear relationships and complex interactions among drugs. In this article, we propose a random field model for order-effect modeling. This model is flexible, allowing nonlinearities, and interaction effects to be incorporated with a small number of model parameters. Moreover, we propose a subtle experimental design that will collect good quality data for modeling the order effects of drugs with a reasonable run size. A real-data analysis and simulation studies are given to demonstrate that the proposed design and model are effective in predicting the optimal drug sequences in administration.

Does Chronic Use of High Dose Proton Pump Inhibitors Increase Risk for Pancreatic Cancer?

OBJECTIVES: To analyze whether use of proton pump inhibitors increase the risk for pancreatic cancer in a mouse model and human clinical cohorts. METHODS: p48-Cre/LSL-KrasG12D mice that develop precancerous pancreatic intraepithelial neoplasia (PanINs) were treated with low- or high-dose proton pump inhibitors (PPIs) orally for 1 and 4 months. The mechanism for the cholecystokinin receptor 2 (CCK-2R) activation was investigated in vitro. Two resources were employed to analyze the risk of pancreatic cancer in human subjects with PPI use. RESULTS: Serum gastrin levels were increased 8-fold (P < 0.0001) in mice treated with chronic high-dose PPIs, and this change correlated with an increase (P = 0.02) in PanIN grade and the development of microinvasive cancer. The CCK-2R expression was regulated by microRNA-148a in the p48-Cre/LSL-KrasG12D mice pancreas and in human pancreatic cancer cells in vitro. Proton pump inhibitor consumption in human subjects was correlated with pancreatic cancer risk (odds ratio, 1.54). A validation analysis conducted using the large-scale United Kingdom Biobank database confirmed the correlation (odds ratio, 1.9; P = 0.00761) of pancreatic cancer risk with PPI exposure. CONCLUSIONS: This investigation revealed in both murine models and human subjects, PPI use is correlated with a risk for development of pancreatic cancer.

Diarylheptanoid glycosides from Zingiber officinale peel and their anti-apoptotic activity.

Four new diarylheptanoid glycosides (1-4), (1S,3R,5S)-2-(4-hydroxy-3- methoxyphenyl)-6-[2-(4-hydroxyphenyl)ethyl]-tetrahydropyran-4-ol-4'-O-ß-D-glucopyranoside (1), (1S,3R,5S)-2-(4,5-dihydroxy-3-methoxyphenyl)-6-[2-(4-hydroxyphenyl) ethyl]-tetrahydropyran-4-ol-4'-O-ß-D-glucopyranoside (2), (1S,3R,5S)-2-(4-hydroxy- 3,5-dimethoxyphenyl)-6-[2-(4-hydroxy-3-methoxyphenyl)ethyl]-tetrahydropyran-4-ol-4'-O-ß-D-glucopyranoside (3), and (1R,3R,5R)-2-(4-hydroxy-3,5-dimethoxyphenyl)- 6-[2-(4-hydroxy-3-methoxyphenyl)ethyl]-tetrahydropyran-4-ol-3-O-ß-D-glucopyranoside (4) were isolated from the 50% ethanol extract of Zingiber officinale peel. The structures of the isolated compounds were determined by HR-ESI-MS and extensive spectroscopic techniques (UV, IR, 1D-NMR, and 2D-NMR). Compounds 1-4 significantly increased the survival rate of human normal lung bronchial epithelial cells (BEAS-2B) induced by lipopolysaccharide (LPS) at the concentration of 10 µM.

Integrative analysis with a system of semiparametric projection non-linear regression models.

In integrative analysis parametric or nonparametric methods are often used. The former is easier for interpretation but not robust, while the latter is robust but not easy to interpret the relationships among the different types of variables. To combine the advantages of both methods and for flexibility, here a system of semiparametric projection non-linear regression models is proposed for the integrative analysis, to model the innate coordinate structure of these different types of data, and a diagnostic tool is constructed to classify new subjects to the case or control group. Simulation studies are conducted to evaluate the performance of the proposed method, and shows promising results. Then the method is applied to analyze a real omics data from The Cancer Genome Atlas study, compared the results with those from the similarity network fusion, another integrative analysis method, and results from our method are more reasonable.

Linear mixed models for association analysis of quantitative traits with next-generation sequencing data.

We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F -statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F -distributed statistics provide a good control of the type I error rate. The F -test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The F -statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05 . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.

Prevention of Lipid Peroxidation-derived Cyclic DNA Adduct and Mutation in High-Fat Diet-induced Hepatocarcinogenesis by Theaphenon E.

Obesity is associated with cancer risk and its link with liver cancer is particularly strong. Obesity causes non-alcoholic fatty liver disease (NAFLD) that could progress to hepatocellular carcinoma (HCC). Chronic inflammation likely plays a key role. We carried out a bioassay in the high-fat diet (HFD)-fed C57BL/6J mice to provide insight into the mechanisms of obesity-related HCC by studying γ-OHPdG, a mutagenic DNA adduct derived from lipid peroxidation. In an 80-week bioassay, mice received a low-fat diet (LFD), high-fat diet (HFD), and HFD with 2% Theaphenon E (TE) (HFD+TE). HFD mice developed a 42% incidence of HCC and LFD mice a 16%. Remarkably, TE, a standardized green tea extract formulation, completely blocked HCC in HFD mice with a 0% incidence. γ-OHPdG measured in the hepatic DNA of mice fed HFD and HFD+TE showed its levels increased during the early stages of NAFLD in HFD mice and the increases were significantly suppressed by TE, correlating with the tumor data. Whole-exome sequencing showed an increased mutation load in the liver tumors of HFD mice with G>A and G>T as the predominant mutations, consistent with the report that γ-OHPdG induces G>A and G>T. Furthermore, the mutation loads were significantly reduced in HFD+TE mice, particularly G>T, the most common mutation in human HCC. These results demonstrate in a relevant model of obesity-induced HCC that γ-OHPdG formation during fatty liver disease may be an initiating event for accumulated mutations that leads to HCC and this process can be effectively inhibited by TE. Cancer Prev Res; 11(10); 665-76. ©2018 AACR.

An epigenetic classifier for early stage lung cancer.

Background: Methylated genes detected in sputum are promise biomarkers for lung cancer. Yet the current PCR technologies for quantification of DNA methylation and diagnostic value of the sputum biomarkers are not sufficient to be used for lung cancer early detection. The emerging droplet digital PCR (ddPCR) is a straightforward means for precise, direct, and absolute quantification of nucleic acids. Here, we investigate whether ddPCR can sensitively and robustly quantify DNA methylation in sputum for more precise diagnosis of lung cancer. Results: First, the analytic performance of methylation-specific ddPCR (ddMSP) and quantitative methylation-specific PCR (qMSP) is determined in methylated and unmethylated DNA samples. Second, 29 genes, previously proposed as potential sputum biomarkers for lung cancer, are analyzed by using ddMSP in a training set of 127 lung cancer patients and 159 controls. ddMSP has higher sensitivity, precision, and reproducibility for quantification of methylation compared with qMSP (all p < 0.05). A classifier comprising four sputum methylation biomarkers for lung cancer is developed by using ddMSP, producing 86.6% sensitivity and 90.6% specificity, independent of stage and histology of lung cancer (all p > 0.05). The classifier has higher accuracy compared with sputum cytology (88.8 vs. 70.6%, p < 0.01). The diagnostic performance is confirmed in a testing set of 89 cases and 107 controls. Conclusions: ddMSP is a robust tool for reliable quantification of DNA methylation in sputum, and the epigenetic classifier could help diagnose lung cancer at the early stage.

Experimental design for multi-drug combination studies using signaling networks.

Combinations of multiple drugs are an important approach to maximize the chance for therapeutic success by inhibiting multiple pathways/targets. Analytic methods for studying drug combinations have received increasing attention because major advances in biomedical research have made available large number of potential agents for testing. The preclinical experiment on multi-drug combinations plays a key role in (especially cancer) drug development because of the complex nature of the disease, the need to reduce development time and costs. Despite recent progresses in statistical methods for assessing drug interaction, there is an acute lack of methods for designing experiments on multi-drug combinations. The number of combinations grows exponentially with the number of drugs and dose-levels and it quickly precludes laboratory testing. Utilizing experimental dose-response data of single drugs and a few combinations along with pathway/network information to obtain an estimate of the functional structure of the dose-response relationship in silico, we propose an optimal design that allows exploration of the dose-effect surface with the smallest possible sample size in this article. The simulation studies show our proposed methods perform well.

The repurposed anthelmintic mebendazole in combination with trametinib suppresses refractory NRASQ61K melanoma.

Structure-based drug repositioning in addition to random chemical screening is now a viable route to rapid drug development. Proteochemometric computational methods coupled with kinase assays showed that mebendazole (MBZ) binds and inhibits kinases important in cancer, especially both BRAFWT and BRAFV600E. We find that MBZ synergizes with the MEK inhibitor trametinib to inhibit growth of BRAFWT-NRASQ61K melanoma cells in culture and in xenografts, and markedly decreased MEK and ERK phosphorylation. Reverse Phase Protein Array (RPPA) and immunoblot analyses show that both trametinib and MBZ inhibit the MAPK pathway, and cluster analysis revealed a protein cluster showing strong MBZ+trametinib - inhibited phosphorylation of MEK and ERK within 10 minutes, and its direct and indirect downstream targets related to stress response and translation, including ElK1 and RSKs within 30 minutes. Downstream ERK targets for cell cycle, including cMYC, were down-regulated, consistent with S- phase suppression by MBZ+trametinib, while apoptosis markers, including cleaved caspase-3, cleaved PARP and a sub-G1 population, were all increased with time. These data suggest that MBZ, a well-tolerated off-patent approved drug, should be considered as a therapeutic option in combination with trametinib, for patients with NRASQ61mut or other non-V600E BRAF mutant melanomas.

Experimental design and statistical analysis for three-drug combination studies.

Drug combination is a critically important therapeutic approach for complex diseases such as cancer and HIV due to its potential for efficacy at lower, less toxic doses and the need to move new therapies rapidly into clinical trials. One of the key issues is to identify which combinations are additive, synergistic, or antagonistic. While the value of multidrug combinations has been well recognized in the cancer research community, to our best knowledge, all existing experimental studies rely on fixing the dose of one drug to reduce the dimensionality, e.g. looking at pairwise two-drug combinations, a suboptimal design. Hence, there is an urgent need to develop experimental design and analysis methods for studying multidrug combinations directly. Because the complexity of the problem increases exponentially with the number of constituent drugs, there has been little progress in the development of methods for the design and analysis of high-dimensional drug combinations. In fact, contrary to common mathematical reasoning, the case of three-drug combinations is fundamentally more difficult than two-drug combinations. Apparently, finding doses of the combination, number of combinations, and replicates needed to detect departures from additivity depends on dose-response shapes of individual constituent drugs. Thus, different classes of drugs of different dose-response shapes need to be treated as a separate case. Our application and case studies develop dose finding and sample size method for detecting departures from additivity with several common (linear and log-linear) classes of single dose-response curves. Furthermore, utilizing the geometric features of the interaction index, we propose a nonparametric model to estimate the interaction index surface by B-spine approximation and derive its asymptotic properties. Utilizing the method, we designed and analyzed a combination study of three anticancer drugs, PD184, HA14-1, and CEP3891 inhibiting myeloma H929 cell line. To our best knowledge, this is the first ever three drug combinations study performed based on the original 4D dose-response surface formed by dose ranges of three drugs.