Screening of Drug Metabolizing Enzymes for the Ginsenoside Compound K In Vitro: An Efficient Anti-Cancer Substance Originating from Panax Ginseng

Ginsenoside compound K (CK), a rare ginsenoside originating from Panax Ginseng, has been found to possess unique pharmacological activities specifically as anti-cancers. However, the role of cytochrome P450s (CYPs) in the metabolism of CK is unclear. In this study, we screened the CYPs for the metabolism of CK in vitro using human liver microsomes (HLMs) or human recombinant CYPs. The results showed that CK inhibited the enzyme activities of CYP2C9 and CYP3A4 in the HLMs. The K_m and V_max values of CK were 84.20±21.92 μM and 0.28±0.04 nmol/mg protein/min, respectively, for the HLMs; 34.63±10.48 μM and 0.45±0.05 nmol/nmol P450/min, respectively, for CYP2C9; and 27.03±5.04 μM and 0.68±0.04 nmol/nmol P450/min, respectively, for CYP3A4. The IC₅₀ values were 16.00 μM and 9.83 μM, and K_i values were 14.92 μM and 11.42μM for CYP2C9 and CYP3A4, respectively. Other human CYP isoforms, including CYP1A2, CYP2A6, CYP2D6, CYP2E1, and CYP2C19, showed minimal or no effect on CK metabolism. The results suggested that CK was a substrate and also inhibitors for both CYP2C9 and CYP3A4. Patients using CK in combination with therapeutic drugs that are substrates of CYP2C9 and CYP3A4 for different reasons should be careful, although the inhibiting potency of CK is much poorer than that of enzyme-specific inhibitors.     

Iron plaque formation and its effect on arsenic uptake by different genotypes of paddy rice

Background and aims

Iron plaque on roots has been hypothesized to be an effective restraint on the uptake of arsenic (As) by rice plants. Evaluating the formation of iron plaque and its effect on As uptake by various rice cultivars is valuable because selecting low As uptake rice cultivars results in reduced risks associated with rice consumption. This study examines iron plaque formation and its effect on As uptake by different genotypes of rice cultivars.

Methods

Hydroponic cultures were conducted in phytotron at day 25/night 20°C and the rice seedlings in fifth-leaf age were treated with Fe (II) at the levels of 0 and 100 mg L^?1 in the Kimura B nutrient solutions for 14 days. The amount of iron plaque formation of 28 rice cultivars was determined by using the DCB extractable Fe of roots. Four cultivars representing high and low iron plaque formation capability, from indica and japonica respectively, were selected out of the 28 cultivars and processed for Fe and As treatments. After Fe treatments for 4 days, the seedlings were fed with As (III) at levels of 0, 0.5, and 1 mg L^?1 for another 10 days. We were thus able to determine the amounts of iron plaque formation and the As content in iron plaque, roots, and shoots of the four tested cultivars.

Results

Iron plaque formation capability differed among tested twenty-eight rice cultivars. Feeding As to four tested cultivars enhanced iron plaque formation on roots; the As uptake by roots and shoots was decreased by the addition of Fe. Both the retention of As on iron plaque and the decrease of As uptake by the addition of Fe varied among tested cultivars and were not correlated with the iron plaque formation capability.

Conclusions

Iron plaque can sequestrate As on the roots and reduce rice’s As uptake. However, other factors also influence the As uptake, namely the differences in binding affinity of iron plaque to As, the existent As species in the rhizosphere, and the uptake capability of various As species by rice plants. These factors should also be considered when selecting low As uptake rice cultivars.     

Prostaglandin reductase-3 negatively modulates adipogenesis through regulation of PPARγ activity

Adipocyte differentiation is a multistep program under regulation by several factors. Peroxisome proliferator-activated receptor γ (PPARγ) serves as a master regulator of adipogenesis. However, the endogenous ligand for PPARγ remained elusive until 15-keto-PGE₂ was identified recently as an endogenous PPARγ ligand. In this study, we demonstrate that zinc-containing alcohol dehydrogenase 2 (ZADH2; here termed prostaglandin reductase-3, PTGR-3) is a new member of prostaglandin reductase family that converts 15-keto-PGE₂ to 13,14-dihydro-15-keto-PGE₂. Adipogenesis is accelerated when endogenous PTGR-3 is silenced in 3T3-L1 preadipocytes, whereas forced expression of PTGR-3 significantly decreases adipogenesis. PTGR-3 expression decreased during adipocyte differentiation, accompanied by an increased level of 15-keto-PGE₂. 15-keto-PGE₂ exerts a potent proadipogenic effect by enhancing PPARγ activity, whereas overexpression of PTGR-3 in 3T3-L1 preadipocytes markedly suppressed the proadipogenic effect of 15-keto-PGE₂ by repressing PPARγ activity. Taken together, these findings demonstrate for the first time that PTGR-3 is a novel 15-oxoprostaglandin-Δ¹³-reductase and plays a critical role in modulation of normal adipocyte differentiation via regulation of PPARγ activity. Thus, modulation of PTGR-3 might provide a novel avenue for treating obesity and related metabolic disorders.     

Genetic Variation in the NOC Gene Is Associated with Body Mass Index in Chinese Subjects

Circadian clock genes are critical regulators of energy homeostasis and metabolism. However, whether variation in the circadian genes is associated with metabolic phenotypes in humans remains to be explored. In this study, we systemically genotyped 20 tag single nucleotide polymorphisms (SNPs) in 8 candidate genes involved in circadian clock, including CLOCK, BMAL1(ARNTL), PER1, PER2, CRY1, CRY2, CSNK1E,, and NOC(CCRN4L) in 1,510 non-diabetic Chinese subjects in Taipei and Yunlin populations in Taiwan. Their associations with metabolic phenotypes were analyzed. We found that genetic variation in the NOC gene, rs9684900 was associated with body mass index (BMI) (P = 0.0016, Bonferroni corrected P = 0.032). Another variant, rs135764 in the CSNK1E gene was associated with fasting glucose (P = 0.0023, Bonferroni corrected P = 0.046). These associations were consistent in both Taipei and Yunlin populations. Significant epistatic and joint effects between SNPs on BMI and related phenotypes were observed. Furthermore, NOC mRNA levels in human abdominal adipose tissue were significantly increased in obese subjects compared to non-obese controls.

Conclusion

Genetic variation in the NOC gene is associated with BMI in Chinese subjects.     

A Hybrid Approach for CpG Island Detection in the Human Genome

Background

CpG islands have been demonstrated to influence local chromatin structures and simplify the regulation of gene activity. However, the accurate and rapid determination of CpG islands for whole DNA sequences remains experimentally and computationally challenging.

Methodology/Principal Findings

A novel procedure is proposed to detect CpG islands by combining clustering technology with the sliding-window method (PSO-based). Clustering technology is used to detect the locations of all possible CpG islands and process the data, thus effectively obviating the need for the extensive and unnecessary processing of DNA fragments, and thus improving the efficiency of sliding-window based particle swarm optimization (PSO) search. This proposed approach, named ClusterPSO, provides versatile and highly-sensitive detection of CpG islands in the human genome. In addition, the detection efficiency of ClusterPSO is compared with eight CpG island detection methods in the human genome. Comparison of the detection efficiency for the CpG islands in human genome, including sensitivity, specificity, accuracy, performance coefficient (PC), and correlation coefficient (CC), ClusterPSO revealed superior detection ability among all of the test methods. Moreover, the combination of clustering technology and PSO method can successfully overcome their respective drawbacks while maintaining their advantages. Thus, clustering technology could be hybridized with the optimization algorithm method to optimize CpG island detection.

Conclusion/Significance

The prediction accuracy of ClusterPSO was quite high, indicating the combination of CpGcluster and PSO has several advantages over CpGcluster and PSO alone. In addition, ClusterPSO significantly reduced implementation time.     

In-depth Proteomic Analysis of Six Types of Exudative Pleural Effusions for Nonsmall Cell Lung Cancer Biomarker Discovery

Pleural effusion (PE), a tumor-proximal body fluid, may be a promising source for biomarker discovery in human cancers. Because a variety of pathological conditions can lead to PE, characterization of the relative PE proteomic profiles from different types of PEs would accelerate discovery of potential PE biomarkers specifically used to diagnose pulmonary disorders. Using quantitative proteomic approaches, we identified 772 nonredundant proteins from six types of exudative PEs, including three malignant PEs (MPE, from lung, breast, and gastric cancers), one lung cancer paramalignant PE, and two benign diseases (tuberculosis and pneumonia). Spectral counting was utilized to semiquantify PE protein levels. Principal component analysis, hierarchical clustering, and Gene Ontology of cellular process analyses revealed differential levels and functional profiling of proteins in each type of PE. We identified 30 candidate proteins with twofold higher levels (q<0.05) in lung cancer MPEs than in the two benign PEs. Three potential markers, MET, DPP4, and PTPRF, were further verified by ELISA using 345 PE samples. The protein levels of these potential biomarkers were significantly higher in lung cancer MPE than in benign diseases or lung cancer paramalignant PE. The area under the receiver-operator characteristic curve for three combined biomarkers in discriminating lung cancer MPE from benign diseases was 0.903. We also observed that the PE protein levels were more clearly discriminated in effusions in which the cytological examination was positive and that they would be useful in rescuing the false negative of cytological examination in diagnosis of nonsmall cell lung cancer-MPE. Western blotting analysis further demonstrated that MET overexpression in lung cancer cells would contribute to the elevation of soluble MET in MPE. Our results collectively demonstrate the utility of label-free quantitative proteomic approaches in establishing differential PE proteomes and provide a new database of proteins that can be used to facilitate identification of pulmonary disorder-related biomarkers.The lungs are covered by parietal and visceral pleural membranes, including a small amount of fluid (10–20 ml) in the pleural cavity that helps the lungs expand and contract smoothly. Pleural effusions (PE)¹, an accumulation of pleural fluid, contain proteins originating from the plasma filtrate and are released by inflammatory or epithelial cells. PE is triggered by a variety of etiologies, including malignancies and benign diseases such as pneumonia (PN), tuberculosis (TB), pulmonary embolism, heart failure, renal dysfunction, and autoimmune disease (1). Based on their biochemical characteristics, PEs are classified as transudative or exudative; determination of the PE type is a crucial step in the differential diagnosis and management of PEs. Transudative effusions, generally caused by systemic diseases, can be effectively distinguished from exudative PEs using the established modified Light''s criteria (2, 3). However, further discrimination among different exudate types such as malignant and nonmalignant effusions (e.g. paramalignancies or acute and chronic inflammatory diseases) is sometimes diagnostically challenging because of similar biochemical and/or cellular profiles. For example, neutrophil-rich fluid is generally observed in patients with bacterial PN whereas lymphocytic effusions are generally observed in cancer or chronic inflammatory diseases such as TB (4).PEs caused by cancer are generally divided into two categories, malignant (MPE) and paramalignant (PMPE). MPEs result when cancer cells metastasize to the pleural cavity (stage IV), wherein exfoliated malignant cells are observed in pleural fluid by cytological examination or detected in percutaneous pleural biopsy, thoracoscopy, thoracotomy, or at autopsy (5). PMPE occurs in cancer patients with no evidence of tumor invasion in the pleural space and may be caused by airway obstruction with lung collapse, lymphatic obstruction, or the systemic effects of cancer treatment (5). A high percentage of MPEs (>75%) arise from lung, breast, and ovarian cancer or lymphoma/leukemia. Lung cancer is a major etiology underlying MPE (6); however, only ∼40–87% patients with MPE can be accurately diagnosed upon initial examination (7). Inaccurate diagnosis of MPE and PMPE underestimates or overestimates the disease stage and leads to inappropriate therapy. Thus, it is important to identify a specific and powerful biomarker to distinguish MPE from benign diseases and PMPE.Notably, tumor-proximal body fluids are promising sources for biomarker discovery because they represent a reservoir of in vivo tumor-secreted proteins without a large dynamic range or complexity of plasma or serum (8). Tumor-proximal fluids include PEs, nipple aspirate, stool, saliva, lavage, and ascites fluid. Previously, we utilized the powerful analytical capability of high-abundance protein depletion followed by one-dimensional SDS-PAGE combined with nano-LC-MS/MS (GeLC-MS/MS) for biomarker discovery to generate a comprehensive MPE proteome data set from 13 pooled nonsmall cell lung cancer (NSCLC) patients (9). Because a variety of pathological conditions can lead to exudative effusions, generating different PE proteomic profiles would accelerate discovery of potential PE biomarkers that can be used to discriminate between malignant and nonmalignant pulmonary disorders. The aim of this study is to establish differential PE proteomes from six types of exudative PEs, including three MPEs (from NSCLC, breast, and gastric cancers), one PMPE from NSCLC, and two benign diseases (TB and PN), using a label-free semiquantitative proteomics approach. Our results were verified by clinical validation of three potential biomarkers using an enzyme-linked immunosorbent assay (ELISA; Fig. 1).Open in a separate windowFig. 1.Biomarker discovery strategy for identifying differentially expressed proteins from six pleural effusion (PE) types. The strategy comprised prefractionation by removal of high-abundance proteins, GeLC-MS/MS, comparative analysis of the six PE proteomes based on spectral counts, proteome clustering, functional classification of differentially expressed proteins, and selection and validation of biomarker candidates by ELISA.     

Higher LRRFIP1 expression in glioblastoma multiforme is associated with better response to teniposide,a type II topoisomerase inhibitor

Previous studies from this laboratory indicated that microRNA-21 (miR-21) contributes to chemoresistance of glioblastoma multiforme (GBM) cells to teniposide, a type II topoisomerase inhibitor. We also showed that LRRFIP1 is a target of miR-21. In this study, we found that higher baseline LRRFIP1 expression in human GBM tissue (n = 60) is associated with better prognosis upon later treatment with teniposide. Experiments in cultured U373MG cells showed enhanced toxicity of teniposide against U373MG cells transfected with a vector that resulted in LRRFIP1 overexpression (vs. cells transfected with control vector). Experiments in nude mice demonstrated better response of LRRFIP1 overexpressing xenografts to teniposide. These findings indicate that high baseline LRRFIP1 expression in GBM is associated with better response to teniposide, and encourage exploring LRRFIP1 as a target for GBM treatment.     

GNMT expression increases hepatic folate contents and folate-dependent methionine synthase-mediated homocysteine remethylation

Glycine N-methyltransferase (GNMT) is a major hepatic enzyme that converts S-adenosylmethionine to S-adenosylhomocysteine while generating sarcosine from glycine, hence it can regulate mediating methyl group availability in mammalian cells. GNMT is also a major hepatic folate binding protein that binds to, and, subsequently, may be inhibited by 5-methyltetrafolate. GNMT is commonly diminished in human hepatoma; yet its role in cellular folate metabolism, in tumorigenesis and antifolate therapies, is not understood completely. In the present study, we investigated the impacts of GNMT expression on cell growth, folate status, methylfolate-dependent reactions and antifolate cytotoxicity. GNMT-diminished hepatoma cell lines transfected with GNMT were cultured under folate abundance or restriction. Folate-dependent homocysteine remethylation fluxes were investigated using stable isotopic tracers and gas chromatography/mass spectrometry. Folate status was compared between wild-type (WT), GNMT transgenic (GNMT(tg)) and GNMT knockout (GNMT(ko)) mice. In the cell model, GNMT expression increased folate concentration, induced folate-dependent homocysteine remethylation, and reduced antifolate methotrexate cytotoxicity. In the mouse models, GNMT(tg) had increased hepatic folate significantly, whereas GNMT(ko) had reduced folate. Liver folate levels correlated well with GNMT expressions (r = 0.53, P = 0.002); and methionine synthase expression was reduced significantly in GNMT(ko), demonstrating impaired methylfolate-dependent metabolism by GNMT deletion. In conclusion, we demonstrated novel findings that restoring GNMT assists methylfolate-dependent reactions and ameliorates the consequences of folate depletion. GNMT expression in vivo improves folate retention and bioavailability in the liver. Studies on how GNMT expression impacts the distribution of different folate cofactors and the regulation of specific folate dependent reactions are underway.