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Hong Fang

Publications and source records attributed to Hong Fang.

51 records · Page 3Linked to original sources
PubMed2003

ArrayTrack--supporting toxicogenomic research at the U.S. Food and Drug Administration National Center for Toxicological Research.

The mapping of the human genome and the determination of corresponding gene functions, pathways, and biological mechanisms are driving the emergence of the new research fields of toxicogenomics and systems toxicology. Many technological advances such as microarrays are enabling this paradigm shift that indicates an unprecedented advancement in the methods of understanding the expression of toxicity at the molecular level. At the National Center for Toxicological Research (NCTR) of the U.S. Food and Drug Administration, core facilities for genomic, proteomic, and metabonomic technologies have been established that use standardized experimental procedures to support centerwide toxicogenomic research. Collectively, these facilities are continuously producing an unprecedented volume of data. NCTR plans to develop a toxicoinformatics integrated system (TIS) for the purpose of fully integrating genomic, proteomic, and metabonomic data with the data in public repositories as well as conventional (Italic)in vitro(/Italic) and (Italic)in vivo(/Italic) toxicology data. The TIS will enable data curation in accordance with standard ontology and provide or interface a rich collection of tools for data analysis and knowledge mining. In this article the design, practical issues, and functions of the TIS are discussed through presenting its prototype version, ArrayTrack, for the management and analysis of DNA microarray data. ArrayTrack is logically constructed of three linked components: a) a library (LIB) that mirrors critical data in public databases; b) a database (MicroarrayDB) that stores microarray experiment information that is Minimal Information About a Microarray Experiment (MIAME) compliant; and c) tools (TOOL) that operate on experimental and public data for knowledge discovery. Using ArrayTrack, we can select an analysis method from the TOOL and apply the method to selected microarray data stored in the MicroarrayDB; the analysis results can be linked directly to gene information in the LIB.

Databases, Factual↗
PubMed2003

Quantitative structure-activity relationship methods: perspectives on drug discovery and toxicology.

Quantitative structure-activity relationships (QSARs) attempt to correlate chemical structure with activity using statistical approaches. The QSAR models are useful for various purposes including the prediction of activities of untested chemicals. Quantitative structure-activity relationships and other related approaches have attracted broad scientific interest, particularly in the pharmaceutical industry for drug discovery and in toxicology and environmental science for risk assessment. An assortment of new QSAR methods have been developed during the past decade, most of them focused on drug discovery. Besides advancing our fundamental knowledge of QSARs, these scientific efforts have stimulated their application in a wider range of disciplines, such as toxicology, where QSARs have not yet gained full appreciation. In this review, we attempt to summarize the status of QSAR with emphasis on illuminating the utility and limitations of QSAR technology. We will first review two-dimensional (2D) QSAR with a discussion of the availability and appropriate selection of molecular descriptors. We will then proceed to describe three-dimensional (3D) QSAR and key issues associated with this technology, then compare the relative suitability of 2D and 3D QSAR for different applications. Given the recent technological advances in biological research for rapid identification of drug targets, we mention several examples in which QSAR approaches are employed in conjunction with improved knowledge of the structure and function of the target receptor. The review will conclude by discussing statistical validation of QSAR models, a topic that has received sparse attention in recent years despite its critical importance.

Drug Design↗
PubMed2003

Structure-activity relationship approaches and applications.

New techniques and software have enabled ubiquitous use of structure-activity relationships (SARs) in the pharmaceutical industry and toxicological sciences. We review the status of SAR technology by using examples to underscore the advances as well as the unique technical challenges. Applying SAR involves two steps: Characterization of the chemicals under investigation, and application of chemometric approaches to explore data patterns or to establish the relationships between structure and activity. We describe generally but not exhaustively the SAR methodologies popular use in toxicology, including representation of chemical structure, and chemometric techniques where models are both unsupervised and supervised. The utility of SAR technology is most evident when supervised methods are used to predict toxicity of untested chemicals based only on chemical structure. Such models can predict on both an ordinal scale (e.g., active vs inactive) or a continuouis scale (e.g., median lethal dose [LD50] dose). The reader is also referred to a companion paper in this issue that discusses quantitative structure-activity relationship (QSAR) methods that have advanced markedly over the past decade.

Forecasting↗
PubMed2002

Factors governing nonoverlapping substrate specificity by mitochondrial inner membrane peptidase.

At least three peptidases are involved in cleaving presequences from imported mitochondrial proteins. One of the peptidase, the inner membrane peptidase, has two catalytic subunits, Imp1p and Imp2p, which are structurally related but functionally distinct in the yeast Saccharomyces cerevisiae. Whereas both subunits are members of the type I signal peptidase family, they exhibit nonoverlapping substrate specificities. A clue to the substrate specificity mechanism has come from our discovery of the importance not only of the -1 and -3 residues in the signal peptides cleaved by Imp1p and Imp2p but also the +1 cargo residues attached to the signal peptides. We specifically find that Imp1p prefers substrates having a negatively charged residue (Asp or Glu) at the +1 position, whereas Imp2p prefers substrates having the Met residue at the +1 position. We further suggest that the conformation of the cargo is important for substrate recognition by Imp2p. A role for the cargo in presequence recognition distinguishes Imp1p and Imp2p from other type I signal peptidases.

Intracellular Membranes↗
PubMed2002

Selection of cefoxitin-resistant bacteroides thetaiotaomicron mutants and mechanisms involved in beta-lactam resistance.

The beta-lactam antibiotics are the most widely used of all the groups of antimicrobials, but beta-lactam resistance is increasingly common among members of the Bacteroides fragilis group. Three major mechanisms are involved in beta-lactam resistance, and they act together in certain instances. In the present study, 2 resistant mutants (238m and 1186m) of Bacteroides thetaiotaomicron, obtained from clinical isolates (238 and 1186) by selection with increasing concentrations of cefoxitin, showed decreased susceptibilities to cefoxitin and other beta-lactam antibiotics. Alterations in both penicillin-binding proteins (PBPs) and outer-membrane proteins (OMPs) were observed in the mutants in comparison with their parent strains. The similar alteration in OMPs was also observed in clinical isolates. In conclusion, the beta-lactam-resistant mutants of B. thetaiotaomicron with deficiency in both PBPs and OMPs can be selected for by exposure to cefoxitin, and several mechanisms are involved in the beta-lactam resistance in the strains investigated.

Bacterial Outer Membrane Proteins↗
PubMed2002

Structural chemistry of arachno-nonaboranes.

Single-crystal conventional-tube and synchrotron X-ray diffraction studies of the anions in [NMe(4)][arachno-B(9)H(12)-4,8-Br(2)] 1 and K[arachno-B(9)H(14)] 2, and also of the series of adducts [arachno-B(9)H(13)-4-L], where L is P(CCH)(3) (3), NHEt(2) (4), NC(5)H(5) (5), or NH(2)CH(2)Ph (6), are reported. Structural studies of 1-6, determined at low temperatures, located all atoms, including bridging and endo-terminal hydrogen atoms. The basic boron-hydride clusters of these, and of all the other known species with the arachno nine-vertex i-nonanborane geometry reported in the literature, are isostructural and feature three bridging and two endo-terminal hydrogen atoms on the open face. This arrangement is different from that previously reported for Cs[arachno-B(9)H(14)] 7 and for [arachno-B(9)H(13)-4-(NCMe)] 9. However, a new X-ray diffraction data set and refinement experimentally confirm the [3 x mu-H, 2 x endo] arrangement for 9 also. The experimental results for 1-6 support recently reported calculations for [B(9)H(14)](-), which predict both the structures and the (11)B NMR chemical shifts. These conclusions are also supported by calculations for 3, 4, and 9 and also for the [arachno-B(9)H(13)-4-(NCS)](-) anion in [NMe(4)][B(9)H(13)(NCS)] 8.

Journal Article↗
PubMed2002

New findings in beta-lactam and metronidazole resistant Bacteroides fragilis group.

Beta-lactam antibiotics and 5-nitroimidazoles have been extensively used against anaerobic bacteria. However, antibiotic resistance is increasingly common among anaerobic Gram-negative bacilli. The classical mechanisms of resistance to beta-lactams are, (1) production of beta-lactamases; (2) alteration of penicillin-binding proteins (PBPs); and (3) changes in outer membrane permeability to beta-lactams. The 5-nitroimidazole molecule is a prodrug whose activation depends upon reduction of the nitro group in the absence of oxygen. Decreased uptake and altered reduction are believed to be responsible for metronidazole resistance. Five nim genes (A, B, C, D and E) have been identified in Bacteroides fragilis group spp. that confer resistance to 5-nitroimidazole antibiotics. Knowledge of the status and the mechanisms of resistance is critical for both the selection of antimicrobial therapy and the design of new antimicrobial agents. The purpose of this article is to review the mechanisms for and the prevalence of beta-lactam and metronidazole resistance in strains belonging to the B. fragilis group.

Anti-Bacterial Agents↗
PubMed2002

Phytoestrogens and mycoestrogens bind to the rat uterine estrogen receptor.

Consumption of phytoestrogens and mycoestrogens in food products or as dietary supplements is of interest because of both the potential beneficial and adverse effects of these compounds in estrogen-responsive target tissues. Although the hazards of exposure to potent estrogens such as diethylstilbestrol in developing male and female reproductive tracts are well characterized, less is known about the effects of weaker estrogens including phytoestrogens. With some exceptions, ligand binding to the estrogen receptor (ER) predicts uterotrophic activity. Using a well-established and rigorously validated ER-ligand binding assay, we assessed the relative binding affinity (RBA) for 46 chemicals from several chemical structure classes of potential phytoestrogens and mycoestrogens. Although none of the test compounds bound to ER with the affinity of the standard, 17beta-estradiol (E(2)), ER binding was found among all classes of chemical structures (flavones, isoflavones, flavanones, coumarins, chalcones and mycoestrogens). Estrogen receptor relative binding affinities were distributed across a wide range (from approximately 43 to 0.00008; E(2) = 100). These data can be utilized before animal testing to rank order estimates of the potential for in vivo estrogenic activity of a wide range of untested plant chemicals (as well as other chemicals) based on ER binding.

Animals↗
PubMed2002

Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts.

A number of environmental chemicals, by mimicking natural hormones, can disrupt endocrine function in experimental animals, wildlife, and humans. These chemicals, called "endocrine-disrupting chemicals" (EDCs), are such a scientific and public concern that screening and testing 58,000 chemicals for EDC activities is now statutorily mandated. Computational chemistry tools are important to biologists because they identify chemicals most important for in vitro and in vivo studies. Here we used a computational approach with integration of two rejection filters, a tree-based model, and three structural alerts to predict and prioritize estrogen receptor (ER) ligands. The models were developed using data for 232 structurally diverse chemicals (training set) with a 10(6) range of relative binding affinities (RBAs); we then validated the models by predicting ER RBAs for 463 chemicals that had ER activity data (testing set). The integrated model gave a lower false negative rate than any single component for both training and testing sets. When the integrated model was applied to approximately 58,000 potential EDCs, 80% (approximately 46,000 chemicals) were predicted to have negligible potential (log RBA < -4.5, with log RBA = 2.0 for estradiol) to bind ER. The ability to process large numbers of chemicals to predict inactivity for ER binding and to categorically prioritize the remainder provides one biologic measure to prioritize chemicals for entry into more expensive assays (most chemicals have no biologic data of any kind). The general approach for predicting ER binding reported here may be applied to other receptors and/or reversible binding mechanisms involved in endocrine disruption.

Animals↗
PubMed2002

[Detection report on noxious elements in Radix astragli].

OBJECTIVE: The contents and dissolution rates of As, Hg, Cd and Pb including 44 batches of Astragali samples from major habitats were analysed. METHOD: As was determined by hydride generation-AFS. Hg was determined by cold vapour-ASS. Cd and Pb were determined by ICP-ES. RESULTS: The contents of As, Hg, Cd, Pb were 0.018-0.617 microgram/g, 0.004-0.181 microgram/g, 0.029-0.168 microgram/g, and 0.290-5.501 micrograms/g respectively. The As, Hg, Cd, Pb dissolution rates were 30.2%-54.2%, 4.9%-9.8%, 50.%-87.9%, and 12.6%-27.8% respectively. CONCLUSION: The contents of As, Hg and Cd were very low and below the limitation of national food and hygiene standard, but that of Pb was high in Astragali and its extracts.

Arsenic↗
PubMed2002

[Utilization of sugar cane bagasse hydrolysates for xylitol production by yeast].

The effects of the concentration of sulfuric acid and the ratio of liquid to solid on xylose yield from sugar cane bagasse in its hemicellulose hydrolysis process were studied with the Quadratic Rotary Combination Design. Regression analysis showed that there was a marked regression relationship between the two factors and xylose yield. As the result of optimizing the hydrolysis conditions by regression equation, xylose yield of 24 g/100 g sugar cane bagasse was obtained when sulfuric acid concentration was 2.4 g/L and liquid to solid ratio was 6.2 under the conditions of stream pressure of 2.5 x 10(4) Pa and hydrolysis time of 2.5 h. The macroporous resin adsorption was proved to be a good method to reduce the concentration of yeast cell growth inhibitor in sugar cane bagasse hemicellulose hydrolysate and to enhance the hydrolysate fermentability. The hydrolysate treated with macroporous resin adsorption under pH2 was used as the substrate for xylitol production by a xylitol-producting yeast, Candida tropicalis AS2.1776. At an initial xylose concentration of 200 g/L, all xylose was consumed within 110 h with a xylitol production rate of 1.15 g/L.h, and a xylitol yield of 0.64 g/g xylose.

Candida tropicalis↗
PubMedDate not supplied

Decision forest: combining the predictions of multiple independent decision tree models.

The techniques of combining the results of multiple classification models to produce a single prediction have been investigated for many years. In earlier applications, the multiple models to be combined were developed by altering the training set. The use of these so-called resampling techniques, however, poses the risk of reducing predictivity of the individual models to be combined and/or over fitting the noise in the data, which might result in poorer prediction of the composite model than the individual models. In this paper, we suggest a novel approach, named Decision Forest, that combines multiple Decision Tree models. Each Decision Tree model is developed using a unique set of descriptors. When models of similar predictive quality are combined using the Decision Forest method, quality compared to the individual models is consistently and significantly improved in both training and testing steps. An example will be presented for prediction of binding affinity of 232 chemicals to the estrogen receptor.

Algorithms↗
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↗