PubMed Health⌕ Search

Biomedical subjects

Richard D Beger

Publications and source records attributed to Richard D Beger.

At least 19 recordsLinked to original sources
PubMed2006

Gene expression profiles in fathead minnow exposed to 2,4-DNT: correlation with toxicity in mammals.

Toxicogenomics, the genome-wide analysis of gene expression to study the effect of toxicants, has great potential for use in environmental toxicology. Applied to standard test organisms, it has possible applications in aquatic toxicology as a sensitive monitoring tool to detect the presence of contaminants while providing information on the mechanisms of action of these pollutants. We describe the use of a complementary DNA (cDNA) microarray of the fathead minnow (Pimephales promelas) a standard sentinel organism in aquatic toxicology, to better understand the mechanisms of toxicity of 2,4-dinitrotoluene (2,4-DNT) which is released in the environment through military and industrial use. We have constructed a fathead minnow microarray containing 5000 randomly picked anonymous cDNAs from a whole fish cDNA library. Expression profiles were analyzed in fish exposed to 2,4-DNT for 10 days at three concentrations (11, 22, and 44 microM, respectively) below the measured median lethal concentration (58 microM). Sequence analysis of cDNAs corresponding to differentially expressed genes affected by exposure revealed that lipid metabolism and oxygen transport genes were prominently affected in a dose-specific manner. We measured liver lipids and demonstrate that lipid metabolism is indeed perturbed following exposure. These observations correlate well with available toxicological data on 2,4-DNT. We present possible modes of action of 2,4-DNT toxicity and suggest that fathead minnow cDNA microarrays can be useful to identify mechanisms of toxicity in fish and as a predictive tool for toxicity in mammals.

Animals↗
PubMed2006

In vitro cytotoxicity of nonpolar constituents from different parts of kava plant (Piper methysticum).

Kava (Piper methysticum), a perennial shrub native to the South Pacific islands, has been used to relieve anxiety. Recently, several cases of severe hepatotoxicity have been reported from the consumption of dietary supplements containing kava. It is unclear whether the kava constituents, kavalactones, are responsible for the associated hepatotoxicity. To investigate the key components responsible for the liver toxicity, bioassay-guided fractionation was carried out in this study. Kava roots, leaves, and stem peelings were extracted with methanol, and the resulting residues were subjected to partition with a different polarity of solvents (hexane, ethyl acetate, n-butanol, and water) for evaluation of their cytotoxicity on HepG2 cells based on the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay and lactate dehydrogenase and aspartate aminotransferase enzyme leakage assays. Organic solvent fractions displayed a much stronger cytotoxicity than water fractions for all parts of kava. The hexane fraction of the root exhibited stronger cytotoxic effects than fractions of root extracted with other solvents or extracts from the other parts of kava. Further investigations using bioassay-directed isolation and analysis of the hexane fraction indicated that the compound responsible for the cytotoxicity was flavokavain B. The identity of the compound was confirmed by (1)H and (13) C NMR and MS techniques.

Aspartate Aminotransferases↗
PubMed2006

Computational modeling of biologically active molecules using NMR spectra.

The molecular structure and NMR chemical shift information of a compound can be combined to form powerful models of biological activity. NMR spectral data and structure information can be combined on a structural template analogous to 3D-QSAR methodology or orientation independently in spectral space. Surprisingly, quantitative spectrometric data-activity relationship (QSDAR) models built on structure templates are inferior to multi-dimensional QSDAR models built in spectral space. 3D-QSDAR modeling could be useful for estimating chemical toxicity, risk assessment of environmental contaminants and drug lead-compound identifications.

Anti-Bacterial Agents↗
PubMed2006

An integrated study of acute effects of valproic acid in the liver using metabonomics, proteomics, and transcriptomics platforms.

An integrated omics approach was undertaken in order to elucidate a systems biology level understanding of the acute hepatotoxcity of valproic acid (VPA). Metabonomics, proteomics and gene expression microarray platforms were employed in this systems biology study. CD-1 female pregnant mice were injected subcutaneously with 600 mg/kg VPA or vehicle control. Urine, serum, and liver tissue were collected at 6, 12, and 24 h after dosing. Principal component analysis (PCA) of the metabonomics data showed clustering of the dosed groups away from the controls for the urine samples. Looser clustering was seen in the other sample sets investigated. However, VPA administration resulted in altered glucose concentrations in urine samples at 12 and 24 h and in aqueous liver tissue extracts at 12 h after VPA administration. Proteomics studies identified two proteins, glycogen phosphorylase and amylo-1,6-glucosidase, which were increased in dosed animals relative to control. Both of these proteins are involved in converting glycogen to glucose. Examination of the expression of 20,000 liver genes did not reveal significantly altered expression at 6, 12, or 24 h after VPA exposure. The combined studies indicated a perturbation in the glycogenolysis pathway following administration of VPA.

Animals↗
PubMed2006

Monitoring the health to disease continuum with global metabolic profiling and systems biology.

Global metabolic profiling, which includes both metabolomics and metabonomics studies, is the latest 'omics' research platform that is being applied to understand the health and disease continuum. Metabolic profiling analyses have been demonstrated for the investigation of inborn errors of metabolism, organ transplant rejection, drug toxicity, disease diagnosis and prognosis, drug efficacy and nutritional status. Combining information generated from a metabolic profiling platform with that obtained based on genetics, transcriptomics and proteomics research paradigms will pave the way for a better understanding of the mechanisms of disease and toxicity. Metabolomics and nutrition will lay the groundwork for the application of personalized medicine in the 21st century.

Animals↗
PubMed2004

Metabolic activation of the tumorigenic pyrrolizidine alkaloid, monocrotaline, leading to DNA adduct formation in vivo.

Monocrotaline is a representative naturally occurring genotoxic pyrrolizidine alkaloid. Metabolism of monocrotaline by liver microsomes of F344 female rats generated (+/-)6,7-dihydro-7-hydroxy-1-hydroxymethyl-5H-pyrrolizine (DHP) and monocrotaline-N-oxide as major metabolites. Metabolism in the presence of triacetyleandomycin, a P450 3A enzyme inhibitor, reduced the formation of DHP by 52% and monocrotaline N-oxide formation by 59%. Dexamethasone significantly induced microsomal monocrotaline metabolizing enzyme activities in rat liver and lung. Previously, we have identified a set of DHP-derived DNA adducts from DHP-modified calf thymus DNA by (32)P-post labeling/HPLC analysis. Metabolism of monocrotaline in the presence of calf thymus DNA resulted in a similar set of DHP-DNA adducts. These DHP-DNA adducts were also found in the liver DNA of rats treated with monocrotaline. The time course of the DHP-derived DNA adduct formation and removal in the liver of rats gavaged with a single dose (10mg/kg) of monocrotaline was similar to that of rats treated with riddelliine. The levels of DHP-DNA adducts in liver DNA of rats treated with monocrotaline were much lower than that of riddelliine-treated rats. Results from this study indicate that (i) DHP is a common reactive metabolite for retronecine-type of pyrrolizidine alkaloids, (ii) the formation of DHP-derived DNA adducts in the liver DNA of rats treated with monocrotaline suggests that monocrotaline-induced tumorigenicity is through a genotoxic mechanism.

Animals↗
PubMed2004

In vitro metabolism of hyperforin in rat liver microsomal systems.

Hyperforin is an important active component of St. John's wort (Hypericum perforatum) that has been suggested to be responsible for the St. John's wort antidepressive effects and herbal-drug interactions. In this study, the in vitro metabolism profile of hyperforin was investigated using liver microsomes from male and female Sprague-Dawley rats, with or without induction by phenobarbital or dexamethasone. Four major Phase I metabolites, named 19-hydroxyhyperforin, 24-hydroxyhyperforin, 29-hydroxyhyperforin, and 34-hydroxyhyperforin, were isolated by high performance liquid chromatography and identified by mass spectrometry and NMR. Results suggest that hydroxylation is a major biotransformation of the hyperforin pathway in rat liver and that inducible cytochrome P450 3A (CYP450 3A) and/or CYP2B may be the major cytochrome P450 isoforms catalyzing these hydroxylation reactions.

Animals↗
PubMed2004

Predicting toxic equivalence factors from 13C nuclear magnetic resonance spectra for dioxins, furans, and polychlorinated biphenyls using linear and nonlinear pattern recognition methods.

Two quantitative spectrometric data-activity relationships (QSDAR) models have been developed relating 29 dioxin or dioxin-like molecules to their toxic equivalence factors (TEFs). These models were based on patterns in simulated 13C nuclear magnetic resonance (NMR) data with the patterns defined by comparative spectral analysis (CoSA). Two versions of CoSA multiple linear regression (MLR) models using 7 or 10 spectral bins had, respectively, explained variances (r2) of 0.88 and 0.95, and leave-one-out (LOO) cross-validated variances (q2) of 0.78 and 0.88. A third, artificial neural network model--using a feed forward, back propagating, three-layer neural network--produced an r2 of 0.99, a LOO q2 of 0.82, and a leave-three-out q2 of 0.81. A postulated reason that the results of these QSDAR models are better than traditional quantitative structure-activity relationship (QSAR) models is based on the difference in descriptors rather than on any differences in pattern recognition approach. Results suggest that the 13C NMR spectral data contain molecular quantum mechanical information more reflective of each molecule's biochemical properties than do the calculated electrostatic potentials and molecular alignment assumptions used in developing QSAR models. The QSDAR models provide a rapid, simple way to model the toxicity of dioxin and dioxin-like compounds.

Animals↗
PubMed2003

Riddelliine N-oxide is a phytochemical and mammalian metabolite with genotoxic activity that is comparable to the parent pyrrolizidine alkaloid riddelliine.

Pyrrolizidine alkaloids (PAs) and their N-oxide derivatives are naturally-formed genotoxic phytochemicals that are widely distributed throughout the world. Although, the quantities of PAs and PA N-oxides in plants are nearly equal, the biological and genotoxic activities of PA N-oxides have not been studied extensively. PA N-oxides are major metabolites of PAs and are generally regarded as detoxification products. However, in this study, we report that rat liver microsomes converted riddelliine N-oxide to the genotoxic 6,7-dihydro-7-hydroxy-1-hydroxymethyl-5H-pyrrolizine (DHP) metabolite. Metabolism of riddelliine N-oxide by rat liver microsomes under hypoxic conditions (argon) generated predominantly the parent PA, riddelliine. The reduction of riddelliine N-oxide to riddelliine was diminished, when the metabolism of riddelliine N-oxide with rat liver microsomes was conducted aerobically. Rat liver microsomal incubations of riddelliine N-oxide in the presence of calf thymus DNA produced a set of DHP-derived DNA adducts as detected and quantified by 32P-postlabeling/HPLC. The same DHP-derived DNA adducts were also found in liver DNA of F344 rats fed riddelliine N-oxide or riddelliine. When rats received doses of 1.0 mg/kg riddelliine N-oxide for three consecutive days, the level of DNA adducts was 39.9 +/- 0.6 adducts/10(7) nucleotides, which was 2.6-fold less than that measured in rats treated with riddelliine at the same dose. We have previously shown that these DHP-derived DNA adducts are produced by chronic feeding of riddelliine and that the adduct levels correlated with liver tumor formation. Results presented in this paper indicate that riddelliine N-oxide, through its conversion to riddelliine, is also a potential genotoxic hepatocarcinogen.

Animals↗
PubMed2003

Variations in metabolism of the soy isoflavonoid daidzein by human intestinal microfloras from different individuals.

Isoflavonoids found in legumes, such as soybeans, are converted by intestinal bacteria to metabolites that might have increased or decreased estrogenic activity. Variation in the effects of dietary isoflavonoids among individuals has been attributed to differences in their metabolism by intestinal bacteria. To investigate this variation, the metabolism of the isoflavonoid daidzein by bacteria from ten fecal samples, provided at different times by six individuals on soy-containing diets, was compared. After anaerobic incubation of bacteria with daidzein for 2 weeks, four samples had metabolized daidzein and six samples had not. Three of the positive samples were from individuals whose microflora had not metabolized daidzein in previous samples. Dihydrodaidzein was observed in one sample, dihydrodaidzein and equol in another sample, and equol and O-desmethylangolensin in two other samples. These results corroborate the hypothesis that the microflora of the gastrointestinal tract of an individual influences the particular isoflavone metabolites produced following consumption.

Anaerobiosis↗
PubMed2003

The use of carbon thirteen nuclear magnetic resonance spectra to predict dioxin and furan binding affinities to the aryl hydrocarbon receptor.

Four spectroscopic data-activity relationship (SDAR) models for polychlorinated dibenzofurans (PCDFs) and dibenzodioxins (PCDDs) binding to the aryl hydrocarbon receptor (AhR) have been developed based on simulated 13C nuclear magnetic resonance (NMR) data. Models were developed using discriminant function analysis of the compounds' spectral data. An SDAR model with two classifications for 26 PCDF compounds had a leave-one-out (LOO) cross-validation accuracy of 89%. A two-classification SDAR model for 14 PCDD compounds had LOO cross-validation accuracy of 95%. A two-classification SDAR model combining 14 PCDD and 26 PCDF compounds had LOO cross-validation accuracy of 88%, while a four-classification SDAR model based on the same 14 PCDD and 26 PCDF compounds had LOO cross-validation accuracy of 92%. We used each appropriate SDAR model to classify 41 PCDD and/or 121 PCDF compounds with unknown binding affinities to the AhR. The SDAR models provide a rapid, simple, and valid way to model the PCDF and PCDD binding activity in relation to the AhR.

Benzofurans↗
PubMed2002

Structure and dynamics of thioguanine-modified duplex DNA.

Mercaptopurine and thioguanine, two of the most widely used antileukemic agents, exert their cytotoxic, therapeutic effects by being incorporated into DNA as deoxy-6-thioguanosine. However, the molecular mechanism(s) by which incorporation of these thiopurines into DNA translates into cytotoxicity is unknown. The solution structure of thioguanine-modified duplex DNA presented here shows that the effects of the modification on DNA structure were subtle and localized to the modified base pair. Specifically, thioguanine existed in the keto form, formed weakened Watson-Crick hydrogen bonds with cytosine and caused a modest approximately 10 degrees opening of the modified base pair toward the major groove. In contrast, thioguanine significantly altered base pair dynamics, causing an approximately 80-fold decrease in the base pair lifetime with cytosine compared with normal guanine. This perturbation was consistent with the approximately 6 degrees C decrease in DNA melting temperature of the modified oligonucleotide, the 1.13 ppm upfield shift of the thioguanine imino proton resonance, and the large increase in the exchange rate of the thioguanine imino proton with water. Our studies provide new mechanistic insight into the effects of thioguanine incorporation into DNA at the level of DNA structure and dynamics, provide explanations for the effects of thioguanine incorporation on the activity of DNA-processing enzymes, and provide a molecular basis for the specific recognition of thioguanine-substituted sites by proteins. These combined effects likely cooperate to produce the cellular responses that underlie the therapeutic effects of thiopurines.

Antimetabolites, Antineoplastic↗
PubMed2002

Isolation of an anaerobic intestinal bacterium capable of cleaving the C-ring of the isoflavonoid daidzein.

Colonic bacteria were screened for bacteria involved in the conversion of phytoestrogens. A gram-positive anaerobic bacterium, strain HGH 136, capable of conversion of the isoflavonoid daidzein, was isolated and identified as a Clostridium sp. The bacterium cleaved the C-ring of daidzein to produce O-demethylangolensin ( O-Dma). This compound was identified by comparison of the HPLC retention time and UV spectrum of the metabolite with chemically synthesized O-Dma. The identity of the metabolite was confirmed by liquid chromatography-mass spectrometry and NMR using synthetic O-Dma as a standard. The bacterium incubated with synthetic dihydrodaidzein also produced O-Dma. After 3 days of incubation, 28% of added daidzein and 12% of added dihydrodaidzein were converted to O-Dma. This is the first study in which an anaerobic bacterium involved in the ring cleavage of daidzein to produce O-Dma has been identified.

Bacteria, Anaerobic↗
PubMed2002

Combining NMR spectral and structural data to form models of polychlorinated dibenzodioxins, dibenzofurans, and biphenyls binding to the AhR.

A three-dimensional quantitative spectrometric data-activity relationship (3D-QSDAR) modeling technique which uses NMR spectral and structural information that is combined in a 3D-connectivity matrix has been developed. A 3D-connectivity matrix was built by displaying all possible assigned carbon NMR chemical shifts, carbon-to-carbon connections, and distances between the carbons. Two-dimensional 13C-13C COSY and 2D slices from the distance dimension of the 3D-connectivity matrix were used to produce a relationship among the 2D spectral patterns for polychlorinated dibenzofurans, dibenzodioxins, and biphenyls (PCDFs, PCDDs, and PCBs respectively) binding to the aryl hydrocarbon receptor (AhR). We refer to this technique as comparative structural connectivity spectral analysis (CoSCoSA) modeling. All CoSCoSA models were developed using forward multiple linear regression analysis of the predicted 13C NMR structure-connectivity spectral bins. A CoSCoSA model for 26 PCDFs had an explained variance (r2) of 0.93 and an average leave-four-out cross-validated variance (q(2)4) of 0.89. A CoSCoSA model for 14 PCDDs produced an r2 of 0.90 and an average leave-two-out cross-validated variance (q(2)2) of 0.79. One CoSCoSA model for 12 PCBs gave an r2 of 0.91 and an average q(2)2 of 0.80. Another CoSCoSA model for all 52 compounds had an r2 of 0.85 and an average q(2)2 of 0.52. Major benefits of CoSCoSA modeling include ease of development since the technique does not use molecular docking routines.

Algorithms↗
PubMedDate not supplied

Comparative structural connectivity spectra analysis (CoSCoSA) models of steroids binding to the aromatase enzyme.

A method that combines NMR spectral and structural information into a constructed three-dimensional (3D)-connectivity matrix is developed for modeling biological binding activity of small molecules. The 3D-connectivity matrix for a molecule is defined by associating the distances between all possible carbon-to-carbon connections with their assigned carbon NMR chemical shifts. In this project we selected from the total 3D-connectivity matrix a subset, the two-dimensional (2D) (13)C-(13)C COSY and a theoretical long range 2D (13)C-(13)C distance connectivity spectral plane. Patterns of (13)C chemical shifts observed at these two relative distances for 50 steroids were used to produce a mathematical relationship for the steroids' relative binding affinity (pK(i)) to the aromatase enzyme. We call this technique comparative structural connectivity spectra analysis (CoSCoSA) modeling. Using combinations of the 2D COSY and 2D long-range distance spectra as modeling parameters, we built four CoSCoSA models. One model was made from the 2D COSY spectra alone and another was developed using only the 2D long-range distance spectra. Then the COSY and long-distance spectra were combined in two different ways: starting with the combined principal components (PCs) from the separately calculated COSY and distance spectra or using the combined raw spectra (3D). The best CoSCoSA model was based on the combined PCs from COSY and distance spectra. This model had an r(2) of 0.96 and a leave-one-out cross-validation (q(2)) of 0.92. In general CoSCoSA modeling combines the quantum mechanical information inherent in NMR chemical shifts with internal molecular atom-to-atom distances to give a reliable and straightforward basis for predictive modeling. The technique has the flexibility and accuracy to outperform not only the cross-validated variance q(2) of previously published quantitative structure-activity relationships (QSAR) but also those obtained by related quantitative spectral data-activity relationships (QSDARs) lacking connectivity dimensions.

Aromatase↗
PubMedDate not supplied

Comparative structural connectivity spectra analysis (CoSCoSA) models of steroid binding to the corticosteroid binding globulin.

A three-dimensional quantitative spectrometric data-activity relationship (3D-QSDAR) model was developed that is built by combining NMR spectral information with structural information in a 3D-connectivity matrix. The 3D-connectivity matrix is built by displaying all possible carbon-to-carbon connections with their assigned carbon NMR chemical shifts and distances between the carbons. Selected 2D (13)C-(13)C COrrelation SpectroscopY (COSY) (through-bond nearest neighbors) and selected theoretical 2D (13)C-(13)C distance connectivity spectral slices from the 3D-connectivity matrix to produce a relationship among the spectral patterns for 30 steroids binding to corticosteroid binding globulin. We call this technique a comparative structural connectivity spectra analysis (CoSCoSA) modeling. A CoSCoSA principal component linear regression model based on the combination of (13)C-(13)C COSY and (13)C-(13)C distance spectra principal components (PCs) had an r(2) of 0.96 and a leave-one-out (LOO) cross-validation q(2) of 0.92. A CoSCoSA parallel distributed artificial neural network (PD-ANN) model based on the combination of (13)C-(13)C COSY and (13)C-(13)C distance spectra had an r(2) of 0.96, a leave-three-out q(3)(2) of 0.78, and a leave-ten-out q(10)(2) of 0.73. CoSCoSA modeling attempts to uniquely combine the quantum mechanics information from the NMR chemical shifts with internal molecular atom-to-atom distances into an accurate modeling technique. The CoSCoSA modeling technique has the flexibility and accuracy to outperform the cross-validated variance q(2) of previously published quantitative structure-activity relationship (QSAR), quantitative spectral data-activity relationship (QSDAR), self-organizing map (SOM), and electrotopological state (E-state) models.

Binding Sites↗
PubMedDate not supplied

Discriminant function analyses of liver-specific carcinogens.

The ability to predict organ-specific carcinogenicity would aid FDA reviewers in evaluating new chemical applications. A NCTR liver cancer database (NCTRlcdb) containing 999 compounds has been developed with three sets of descriptors. The NCTRlcdb has Cerius2, Molconn-Z, and (13)C NMR descriptors for each compound. Each compound in the database was assigned a liver cancer or a nonliver cancer classification. Compounds within the NCTRlcdb were evaluated for liver-specific carcinogenicity using partial least squares principal component discriminant function (PLS-DF) modeling. PLS-DF models based on estimated a priori classification probabilities of 0.29 for liver cancer and 0.71 for noncancer yielded an overall predictability of 70.6% which was comprised of a liver cancer sensitivity of 18.8% and a noncancer specificity of 90.8%. PLS-DF models based on equal a priori classification probabilities, 0.50 for liver cancer and 0.5 for noncancer, yielded an overall predictability of 61.0% which was comprised of a liver cancer sensitivity of 50.5% and a noncancer specificity of 65.3%.

Carcinogens↗