Prediction of cytochrome P450 3A4, 2D6, and 2C9 inhibitors and substrates by using support vector machines

Statistical learning methods have been used in developing filters for predicting inhibitors of two P450 isoenzymes, CYP3A4 and CYP2D6. This work explores the use of different statistical learning methods for predicting inhibitors of these enzymes and an additional P450 enzyme, CYP2C9, and the substrates of the three P450 isoenzymes. Two consensus support vector machine (CSVM) methods, "positive majority" (PM-CSVM) and "positive probability" (PP-CSVM), were used in this work. These methods were first tested for the prediction of inhibitors of CYP3A4 and CYP2D6 by using a significantly higher number of inhibitors and noninhibitors than that used in earlier studies. They were then applied to the prediction of inhibitors of CYP2C9 and substrates of the three enzymes. Both methods predict inhibitors of CYP3A4 and CYP2D6 at a similar level of accuracy as those of earlier studies. For classification of inhibitors of CYP2C9, the best CSVM method gives an accuracy of 88.9% for inhibitors and 96.3% for noninhibitors. The accuracies for classification of substrates and nonsubstrates of CYP3A4, CYP2D6, and CYP2C9 are 98.2 and 90.9%, 96.6 and 94.4%, and 85.7 and 98.8%, respectively. Both CSVM methods are potentially useful as filters for predicting inhibitors and substrates of P450 isoenzymes. These methods generally give better accuracies than single SVM classification systems, and the performance of the PP-CSVM method is slightly better than that of the PM-CSVM method.

Early intervention of recent onset mild persistent asthma in children aged under 11 yrs: the Steroid Treatment As Regular Therapy in early asthma (START) trial

Inhaled corticosteroids are known to be effective in persistent asthma, but their long-term effect in mild persistent disease of recent onset, which is particularly relevant in children, requires clarification. The objective of this study was to determine the long-term efficacy of regular inhaled low-dose budesonide in children aged <11 yrs with mild persistent asthma with onset within 2 yrs of enrollment. Children aged 5-10 yrs formed part of the population of the inhaled Steroid Treatment As Regular Therapy in early asthma (START) study, and they were randomized in a double-blind manner to treatment with once daily budesonide 200 microg or placebo via Turbuhaler in addition to usual clinical care and other asthma medication. The double-blind treatment phase continued for 3 yrs. Of the 1974 children, 1000 in the budesonide group and 974 in the placebo group, were analyzed for efficacy. Addition of once-daily budesonide to usual care was associated with a significant increase in the time to first severe asthma-related event (SARE) and significantly reduced risk of SARE over 3 yrs. The hazard ratio relative to usual care (placebo) was 0.60 (95% confidence interval: 0.40-0.90; p = 0.012), with a relative risk reduction of 40%. Children receiving budesonide also needed significantly less intervention with other inhaled corticosteroids (12.3% vs. 22.5% over 3 yrs; p < 0.01), with trends towards decreased usage of oral/systemic corticosteroids and inhaled short-acting beta2-agonists. Budesonide treatment also had a significant beneficial effect on lung function relative to placebo. In conclusion, early intervention adding once-daily budesonide to usual care in children with mild, persistent asthma of recent onset reduces the long-term risk and frequency of SAREs and improves lung function compared with usual care alone.

Prediction of the functional class of lipid binding proteins from sequence-derived properties irrespective of sequence similarity

Lipid binding proteins play important roles in signaling, regulation, membrane trafficking, immune response, lipid metabolism, and transport. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting lipid binding proteins irrespective of sequence similarity. This work explores the use of support vector machines (SVMs) as such a method. SVM prediction systems are developed using 14,776 lipid binding and 133,441 nonlipid binding proteins and are evaluated by an independent set of 6,768 lipid binding and 64,761 nonlipid binding proteins. The computed prediction accuracy is 78.9, 79.5, 82.2, 79.5, 84.4, 76.6, 90.6, 79.0, and 89.9% for lipid degradation, lipid metabolism, lipid synthesis, lipid transport, lipid binding, lipopolysaccharide biosynthesis, lipoprotein, lipoyl, and all lipid binding proteins, respectively. The accuracy for the nonmember proteins of each class is 99.9, 99.2, 99.6, 99.8, 99.9, 99.8, 98.5, 99.9, and 97.0%, respectively. Comparable accuracies are obtained when homologous proteins are considered as one, or by using a different SVM kernel function. Our method predicts 86.8% of the 76 lipid binding proteins nonhomologous to any protein in the Swiss-Prot database and 89.0% of the 73 known lipid binding domains as lipid binding. These findings suggest the usefulness of SVMs for facilitating the prediction of lipid binding proteins. Our software can be accessed at the SVMProt server (http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi).

Prediction of functional class of novel bacterial proteins without the use of sequence similarity by a statistical learning method

A substantial percentage of the putative protein-encoding open reading frames (ORFs) in bacterial genomes have no homolog of known function, and their function cannot be confidently assigned on the basis of sequence similarity. Methods not based on sequence similarity are needed and being developed. One method, SVMProt (http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi), predicts protein functional family irrespective of sequence similarity (Nucleic Acids Res. 2003;31:3692-3697). While it has been tested on a large number of proteins, its capability for non-homologous proteins has so far been evaluated for a relatively small number of proteins, and additional tests are needed to more fully assess SVMProt. In this work, 90 novel bacterial proteins (non-homologous to known proteins) are used to evaluate the capability of SVMProt. These proteins are such that none of their homologs are in the Swiss-Prot database, their functions not clearly described in the literature, and they themselves and their homologs are not included in the training sets of SVMProt. They represent proteins whose function cannot be confidently predicted by sequence similarity methods at present. The predicted functional class of 76.7% of each of these proteins shows various levels of consistency with the literature-described function, compared to the overall accuracy of 87% for the SVMProt functional class assignment of 34,582 proteins that have at least one homolog of known function. Our study suggests that SVMProt is capable of assigning functional class for novel bacterial proteins at a level not too much lower than that of sequence alignment methods for homologous proteins.

Prediction of functional class of the SARS coronavirus proteins by a statistical learning method

The complete genome of severe acute respiratory syndrome coronavirus (SARS-CoV) reveals the existence of putative proteins unique to SARS-CoV. Identification of their function facilitates a mechanistic understanding of SARS infection and drug development for its treatment. The sequence of the majority of these putative proteins has no significant similarity to those of known proteins, which complicates the task of using sequence analysis tools to probe their function. Support vector machines (SVM), useful for predicting the functional class of distantly related proteins, is employed to ascribe a possible functional class to SARS-CoV proteins. Testing results indicate that SVM is able to predict the functional class of 73% of the known SARS-CoV proteins with available sequences and 67% of 18 other novel viral proteins. A combination of the sequence comparison method BLAST and SVMProt can further improve the prediction accuracy of SMVProt such that the functional class of two additional SARS-CoV proteins is correctly predicted. Our study suggests that the SARS-CoV genome possibly contains a putative voltage-gated ion channel, structural proteins, a carbon-oxygen lyase, oxidoreductases acting on the CH-OH group of donors, and an ATP-binding cassette transporter. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi .

PEARLS: Program for Energetic Analysis of Receptor−Ligand System

Analysis of the energetics of small molecule ligand-protein, ligand-nucleic acid, and protein-nucleic acid interactions facilitates the quantitative understanding of molecular interactions that regulate the function and conformation of proteins. It has also been extensively used for ranking potential new ligands in virtual drug screening. We developed a Web-based software, PEARLS (Program for Energetic Analysis of Ligand-Receptor Systems), for computing interaction energies of ligand-protein, ligand-nucleic acid, protein-nucleic acid, and ligand-protein-nucleic acid complexes from their 3D structures. AMBER molecular force field, Morse potential, and empirical energy functions are used to compute the van der Waals, electrostatic, hydrogen bond, metal-ligand bonding, and water-mediated hydrogen bond energies between the binding molecules. The change in the solvation free energy of molecular binding is estimated by using an empirical solvation free energy model. Contribution from ligand conformational entropy change is also estimated by a simple model. The computed free energy for a number of PDB ligand-receptor complexes were studied and compared to experimental binding affinity. A substantial degree of correlation between the computed free energy and experimental binding affinity was found, which suggests that PEARLS may be useful in facilitating energetic analysis of ligand-protein, ligand-nucleic acid, and protein-nucleic acid interactions. PEARLS can be accessed at http://ang.cz3.nus.edu.sg/cgi-bin/prog/rune.pl.

Prediction of transporter family from protein sequence by support vector machine approach

Transporters play key roles in cellular transport and metabolic processes, and in facilitating drug delivery and excretion. These proteins are classified into families based on the transporter classification (TC) system. Determination of the TC family of transporters facilitates the study of their cellular and pharmacological functions. Methods for predicting TC family without sequence alignments or clustering are particularly useful for studying novel transporters whose function cannot be determined by sequence similarity. This work explores the use of a machine learning method, support vector machines (SVMs), for predicting the family of transporters from their sequence without the use of sequence similarity. A total of 10,636 transporters in 13 TC subclasses, 1914 transporters in eight TC families, and 168,341 nontransporter proteins are used to train and test the SVM prediction system. Testing results by using a separate set of 4351 transporters and 83,151 nontransporter proteins show that the overall accuracy for predicting members of these TC subclasses and families is 83.4% and 88.0%, respectively, and that of nonmembers is 99.3% and 96.6%, respectively. The accuracies for predicting members and nonmembers of individual TC subclasses are in the range of 70.7-96.1% and 97.6-99.9%, respectively, and those of individual TC families are in the range of 60.6-97.1% and 91.5-99.4%, respectively. A further test by using 26,139 transmembrane proteins outside each of the 13 TC subclasses shows that 90.4-99.6% of these are correctly predicted. Our study suggests that the SVM is potentially useful for facilitating functional study of transporters irrespective of sequence similarity.

Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods

Quantitative structure-pharmacokinetic relationships (QSPkR) have increasingly been used for the prediction of the pharmacokinetic properties of drug leads. Several QSPkR models have been developed to predict the total clearance (CL(tot)) of a compound. These models give good prediction accuracy but they are primarily based on a limited number of related compounds which are significantly lesser in number and diversity than the 503 compounds with known CL(tot) described in the literature. It is desirable to examine whether these and other statistical learning methods can be used for predicting the CL(tot) of a more diverse set of compounds. In this work, three statistical learning methods, general regression neural network (GRNN), support vector regression (SVR) and k-nearest neighbour (KNN) were explored for modeling the CL(tot) of all of the 503 known compounds. Six different sets of molecular descriptors, DS-MIXED, DS-3DMoRSE, DS-ATS, DS-GETAWAY, DS-RDF and DS-WHIM, were evaluated for their usefulness in the prediction of CL(tot). GRNN-, SVR- and KNN-developed models have average-fold errors in the range of 1.63 to 1.96, 1.66-1.95 and 1.90-2.23, respectively. For the best GRNN-, SVR- and KNN-developed models, the percentage of compounds with predicted CL(tot) within two-fold error of actual values are in the range of 61.9-74.3% and are comparable or slightly better than those of earlier studies. QSPkR models developed by using DS-MIXED, which is a collection of constitutional, geometrical, topological and electrotopological descriptors, generally give better prediction accuracies than those developed by using other descriptor sets. These results suggest that GRNN, SVR, and their consensus model are potentially useful for predicting QSPkR properties of drug leads.

Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents

Statistical-learning methods have been developed for facilitating the prediction of pharmacokinetic and toxicological properties of chemical agents. These methods employ a variety of molecular descriptors to characterize structural and physicochemical properties of molecules. Some of these descriptors are specifically designed for the study of a particular type of properties or agents, and their use for other properties or agents might generate noise and affect the prediction accuracy of a statistical learning system. This work examines to what extent the reduction of this noise can improve the prediction accuracy of a statistical learning system. A feature selection method, recursive feature elimination (RFE), is used to automatically select molecular descriptors for support vector machines (SVM) prediction of P-glycoprotein substrates (P-gp), human intestinal absorption of molecules (HIA), and agents that cause torsades de pointes (TdP), a rare but serious side effect. RFE significantly reduces the number of descriptors for each of these properties thereby increasing the computational speed for their classification. The SVM prediction accuracies of P-gp and HIA are substantially increased and that of TdP remains unchanged by RFE. These prediction accuracies are comparable to those of earlier studies derived from a selective set of descriptors. Our study suggests that molecular feature selection is useful for improving the speed and, in some cases, the accuracy of statistical learning methods for the prediction of pharmacokinetic and toxicological properties of chemical agents.

Purging of murine erythroblastic leukemia by ZnPcS2P2-based-photodynamic therapy

A key point for successful transplantation of autologous hematopoietic stem cells in the treatment of leukemia is the purging technique, of which photodynamic therapy (PDT) proved effective and promising. The aim of this study was to evaluate the purging effect of a novel amphipathic photosensitizer, di-sulfo-di-phthalimidomethyl phthalolcyanine zinc (ZnPcS2P2)-based PDT (ZnPcS2P2-PDT) on murine erythroblastic leukemic EL9611 cells. Bone marrow cells (BMC), harvested from normal BALB/c mice, were contaminated with variable EL9611 cells. Cell suspensions were incubated with 4 microg/ml ZnPcS2P2 for 5 h and then exposed to 2.1 J/cm2 irradiation by a semiconductor laser 670 nm. Lethally irradiated recipient BALB/c mice (7 Gy) received syngeneic bone marrow transplantation with purged or nonpurged cell mixtures of 10(7) BMC contaminated with variable numbers (10(2)-10(5)) of EL9611 cells. All of the irradiated controls died due to sepsis. All of the mice injected with nonpurged cell mixtures developed leukemia and died, whereas the mice transplanted with ZnPcS2P2-PDT-treated mixtures had a longer survival time, and the fewer leukemic cells there were in the cell mixtures, the higher the leukemia-free survival rate. We conclude that ZnPcS2P2-PDT could purge leukemic cells from bone marrow autografts but could retain sufficient progenitor cells for the hematopoietic activity.

Assessment of approximate string matching in a biomedical text retrieval problem

Text-based search is widely used for biomedical data mining and knowledge discovery. Character errors in literatures affect the accuracy of data mining. Methods for solving this problem are being explored. This work tests the usefulness of the Smith-Waterman algorithm with affine gap penalty as a method for biomedical literature retrieval. Names of medicinal herbs collected from herbal medicine literatures are matched with those from medicinal chemistry literatures by using this algorithm at different string identity levels (80-100%). The optimum performance is at string identity of 88%, at which the recall and precision are 96.9% and 97.3%, respectively. Our study suggests that the Smith-Waterman algorithm is useful for improving the success rate of biomedical text retrieval.

Prediction of functional class of novel plant proteins by a statistical learning method

In plant genomes, the function of a substantial percentage of the putative protein-coding open reading frames (ORFs) is unknown. These ORFs have no significant sequence similarity to known proteins, which complicates the task of functional study of these proteins. Efforts are being made to explore methods that are complementary to, or may be used in combination with, sequence alignment and clustering methods. A web-based protein functional class prediction software, SVMProt, has shown some capability for predicting functional class of distantly related proteins. Here the usefulness of SVMProt for functional study of novel plant proteins is evaluated. To test SVMProt, 49 plant proteins (without a sequence homolog in the Swiss-Prot protein database, not in the SVMProt training set, and with functional indications provided in the literature) were selected from a comprehensive search of MEDLINE abstracts and Swiss-Prot databases in 1999-2004. These represent unique proteins the function of which, at present, cannot be confidently predicted by sequence alignment and clustering methods. The predicted functional class of 31 proteins was consistent, and that of four other proteins was weakly consistent, with published functions. Overall, the functional class of 71.4% of these proteins was consistent, or weakly consistent, with functional indications described in the literature. SVMProt shows a certain level of ability to provide useful hints about the functions of novel plant proteins with no similarity to known proteins.

Rapid non-genomic inhibitory effects of glucocorticoids on human neutrophil degranulation

BACKGROUND

Glucocorticoids acting as anti-inflammatory or immunosuppressive drugs have been shown to exert most of their effects genomically. Recent findings suggest that non-genomic activity might be relatively more important in mediating the therapeutic effects of high-dose pulsed glucocorticoid. However, few non-genomic anti-inflammatory effects were reported, much less non-genomic mechanisms.

OBJECTIVE

This study was performed to investigate the nongenomic effects of glucocorticoids on human neutrophil degranulation.

METHODS

Purified human neutrophils were pretreated with 6 alpha-methylprednisolone or hydrocortisone for 5 min, and then primed with N-formyl-methionyl-leucyl-phenylalanine (fMLP) (10(-6) M) or phorbol myristate acetate (PMA) (50 ng/ml) in the presence of cytochalasin B. The release of two markers of neutrophil granules, lactoferrin and myeloperoxidase, was measured by ELISA and enzymology methods respectively.

RESULTS

Both 6 alpha-methylprednisolone (10(-5)-10(-4) M) and hydrocortisone (10(-4) M) showed significant inhibitory effects on neutrophil degranulation within 5 min after fMLP administration. For PMA stimulated degranulation, 6 alpha-methylprednisolone (10(-4) M) showed significant inhibitory effects (p < 0.01), while hydrocortisone (10(-4) M) only showed an inhibitory tendency (P > 0.05). Neither RU486 (10(-5) M) nor cycloheximide (10(-4) M) could alter the inhibitory effects of glucocorticoids.

CONCLUSION

Our results demonstrate that megadoses of glucocorticoids exert rapid inhibitory effects on human neutrophil degranulation at the cellular level via a new mechanism that is independent of corticosteroid type II receptor occupation or protein synthesis. We infer that these effects may be very important when glucocorticoids act as anti-inflammatory drugs during pulse therapy.

Plasma propofol concentrations during orthotopic liver transplantation

OBJECTIVE

The aim of this study was to investigate the changes in plasma concentrations of propofol in three phases (the paleohepatic, anhepatic, and neohepatic phases) during orthotopic liver transplantation (OLT) using target-controlled infusion (TCI).

METHODS

Ten patients undergoing OLT without venovenous bypass were studied (age 29-53 years, weight 56-79 kg). After intubation, a non-hypnotic target concentration of propofol 0.5 microg ml(-1) using a Diprifusor pump (Zeneca Pharmaceuticals, Macclesfield, UK) was administered as a supplement anesthesia throughout the procedure. Plasma samples were obtained in each phase for propofol assay, respectively. Performance parameters for the Diprifusor system in each phase, the percentage median performance error (MDPE), the percentage median absolute performance error (MDAPE), and the percentage median absolute constancy error (MDACE) were evaluated.

RESULTS

In all patients, measured plasma propofol concentrations were several times higher than Diprifusor values in each phase during the procedure. In nine patients, propofol concentrations in the anhepatic phase were higher than those in the paleohepatic or neohepatic phase (P < 0.05). There were no significant differences between the paleohepatic and neohepatic phases. Interindividual variation of the plasma propofol concentrations was significant (P < 0.05). Percentage median performance error of Diprifusor in each phase, as well as MDAPE, was large (>300%) and was significantly higher in the anhepatic phase (P < 0.01), whereas MDACE was relatively small and there was no significant difference between phases.

CONCLUSIONS

Models used by Diprifusor are not suitable for liver transplantation patients. A further study should be performed in order to determine all pharmacokinetic parameters of propofol in these patients.

Prediction of Genotoxicity of Chemical Compounds by Statistical Learning Methods

Various toxicological profiles, such as genotoxic potential, need to be studied in drug discovery processes and submitted to the drug regulatory authorities for drug safety evaluation. As part of the effort for developing low cost and efficient adverse drug reaction testing tools, several statistical learning methods have been used for developing genotoxicity prediction systems with an accuracy of up to 73.8% for genotoxic (GT+) and 92.8% for nongenotoxic (GT-) agents. These systems have been developed and tested by using less than 400 known GT+ and GT- agents, which is significantly less in number and diversity than the 860 GT+ and GT- agents known at present. There is a need to examine if a similar level of accuracy can be achieved for the more diverse set of molecules and to evaluate other statistical learning methods not yet applied to genotoxicity prediction. This work is intended for testing several statistical learning methods by using 860 GT+ and GT- agents, which include support vector machines (SVM), probabilistic neural network (PNN), k-nearest neighbor (k-NN), and C4.5 decision tree (DT). A feature selection method, recursive feature elimination, is used for selecting molecular descriptors relevant to genotoxicity study. The overall accuracies of SVM, k-NN, and PNN are comparable to and those of DT lower than the results from earlier studies, with SVM giving the highest accuracies of 77.8% for GT+ and 92.7% for GT- agents. Our study suggests that statistical learning methods, particularly SVM, k-NN, and PNN, are useful for facilitating the prediction of genotoxic potential of a diverse set of molecules.

Trends in exploration of therapeutic targets

Lead discovery against a preselected therapeutic target is a key component in modern drug development. Continuous effort and increasing interest has been directed at the search for new targets, which has led to the identification of a growing number of them. Data from the therapeutic target database, at http://bidd.nus.edu.sg/group/cjttd/ttd.asp, show that, as of July 2004, the number of documented targets of marketed and investigational drugs has reached 1,174 distinct proteins (including subtypes) and 27 nucleic acids, 239 of which are targets of the marketed drugs. Analysis of these targets, particularly those of recently approved drugs and patented investigational agents, provide useful hints about general trends of target exploration and current focus in drug discovery for the treatment of high impact diseases needing effective or more treatment options.

Quantitative Structure-Pharmacokinetic Relationships for Drug Distribution Properties by Using General Regression Neural Network

Quantitative Structure-Pharmacokinetic Relationships (QSPkR) have increasingly been used for developing models for the prediction of the pharmacokinetic properties of drug leads. QSPkR models are primarily developed by means of statistical methods such as multiple linear regression (MLR). These methods often explore a linear relationship between the pharmacokinetic property of interest and the structural and physicochemical properties of the studied compounds, which are not applicable to those agents with nonlinear relationships. Hence, statistical methods capable of modeling nonlinear relationships need to be developed. In this work, a relatively new kind of nonlinear method, general regression neural network (GRNN), was explored for modeling three drug distribution properties based on diverse sets of drugs. The three properties are blood-brain barrier penetration, binding to human serum albumin, and milk-plasma distribution. The prediction capability of GRNN-developed models was compared to those developed using MLR and a nonlinear multilayer feedforward neural network (MLFN) method. For blood-brain barrier penetration, the computed r(2) and MSE values of the GRNN-, MLR-, and MLFN-developed models are 0.701 and 0.130, 0.649 and 0.154, and 0.662 and 0.147, respectively, by using an independent validation set. The corresponding values for human serum albumin binding are 0.851 and 0.041, 0.770 and 0.079, and 0.749 and 0.089, respectively, and that for milk-plasma distribution are 0.677 and 0.206, 0.224 and 0.647, and 0.201 and 0.587, respectively. These suggest that GRNN is potentially useful for predicting QSPkR properties of chemical agents.

Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach

The function of a protein that has no sequence homolog of known function is difficult to assign on the basis of sequence similarity. The same problem may arise for homologous proteins of different functions if one is newly discovered and the other is the only known protein of similar sequence. It is desirable to explore methods that are not based on sequence similarity. One approach is to assign functional family of a protein to provide useful hint about its function. Several groups have employed a statistical learning method, support vector machines (SVMs), for predicting protein functional family directly from sequence irrespective of sequence similarity. These studies showed that SVM prediction accuracy is at a level useful for functional family assignment. But its capability for assignment of distantly related proteins and homologous proteins of different functions has not been critically and adequately assessed. Here SVM is tested for functional family assignment of two groups of enzymes. One consists of 50 enzymes that have no homolog of known function from PSI-BLAST search of protein databases. The other contains eight pairs of homologous enzymes of different families. SVM correctly assigns 72% of the enzymes in the first group and 62% of the enzyme pairs in the second group, suggesting that it is potentially useful for facilitating functional study of novel proteins. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

Current understanding and therapy of asthma workshop summary

The prevalence of asthma has increased globally in the past 2 decades. To address this critical issue, a workshop on "Current Understanding and Therapy of Asthma" was recently held in Beijing, as a part of the 10th International Conference of the Society of Chinese Bioscientists in America (SCBA). Several pertinent topics were addressed by leading experts from China, Taiwan, Japan and the US, which include epidemiology, the molecular genetic mechanism, pathogenesis, treatment and prevention of asthma. This article highlights the issues presented and discussed in this ground-breaking symposium emphasizing this important public health problem in the Chinese population.

MoViES: molecular vibrations evaluation server for analysis of fluctuational dynamics of proteins and nucleic acids

Analysis of vibrational motions and thermal fluctuational dynamics is a widely used approach for studying structural, dynamic and functional properties of proteins and nucleic acids. Development of a freely accessible web server for computation of vibrational and thermal fluctuational dynamics of biomolecules is thus useful for facilitating the relevant studies. We have developed a computer program for computing vibrational normal modes and thermal fluctuational properties of proteins and nucleic acids and applied it in several studies. In our program, vibrational normal modes are computed by using modified AMBER molecular mechanics force fields, and thermal fluctuational properties are computed by means of a self-consistent harmonic approximation method. A web version of our program, MoViES (Molecular Vibrations Evaluation Server), was set up to facilitate the use of our program to study vibrational dynamics of proteins and nucleic acids. This software was tested on selected proteins, which show that the computed normal modes and thermal fluctuational bond disruption probabilities are consistent with experimental findings and other normal mode computations. MoViES can be accessed at http://ang.cz3.nus.edu.sg/cgi-bin/prog/norm.pl.

APP induces neuronal apoptosis through APP-BP1-mediated downregulation of β-catenin

Alzheimer's disease (AD) is a neurodegenerative disease associated with progressive dementia. This mini-review focuses on how the amyloid precursor protein (APP) plays a central role in AD and Down syndrome as the regulator of the APP-BP1/hUba3 activated neddylation pathway. It is argued that the physiological function of APP is to downregulate the level of beta-catenin. However, this APP function is abnormally amplified in patients with familial AD (FAD) mutations in APP and presenilins, resulting in the hyperactivation of neddylation and the decrease of beta-catenin below a threshold level. Evidence in the literature is summarized to show that dysfunction of APP in downregulating beta-catenin may underlie the mechanism of neuronal death in AD and Down syndrome.

Prediction of P-Glycoprotein Substrates by a Support Vector Machine Approach

P-glycoproteins (P-gp) actively transport a wide variety of chemicals out of cells and function as drug efflux pumps that mediate multidrug resistance and limit the efficacy of many drugs. Methods for facilitating early elimination of potential P-gp substrates are useful for facilitating new drug discovery. A computational ensemble pharmacophore model has recently been used for the prediction of P-gp substrates with a promising accuracy of 63%. It is desirable to extend the prediction range beyond compounds covered by the known pharmacophore models. For such a purpose, a machine learning method, support vector machine (SVM), was explored for the prediction of P-gp substrates. A set of 201 chemical compounds, including 116 substrates and 85 nonsubstrates of P-gp, was used to train and test a SVM classification system. This SVM system gave a prediction accuracy of at least 81.2% for P-gp substrates based on two different evaluation methods, which is substantially improved against that obtained from the multiple-pharmacophore model. The prediction accuracy for nonsubstrates of P-gp is 79.2% using 5-fold cross-validation. These accuracies are slightly better than those obtained from other statistical classification methods, including k-nearest neighbor (k-NN), probabilistic neural networks (PNN), and C4.5 decision tree, that use the same sets of data and molecular descriptors. Our study indicates the potential of SVM in facilitating the prediction of P-gp substrates.

Prediction of Torsade-Causing Potential of Drugs by Support Vector Machine Approach No funding was used to assist in conducting the study and the authors do not have any conflicts of interest directly relevant to the contents of the manuscript

In an effort to facilitate drug discovery, computational methods for facilitating the prediction of various adverse drug reactions (ADRs) have been developed. So far, attention has not been sufficiently paid to the development of methods for the prediction of serious ADRs that occur less frequently. Some of these ADRs, such as torsade de pointes (TdP), are important issues in the approval of drugs for certain diseases. Thus there is a need to develop tools for facilitating the prediction of these ADRs. This work explores the use of a statistical learning method, support vector machine (SVM), for TdP prediction. TdP involves multiple mechanisms and SVM is a method suitable for such a problem. Our SVM classification system used a set of linear solvation energy relationship (LSER) descriptors and was optimized by leave-one-out cross validation procedure. Its prediction accuracy was evaluated by using an independent set of agents and by comparison with results obtained from other commonly used classification methods using the same dataset and optimization procedure. The accuracies for the SVM prediction of TdP-causing agents and non-TdP-causing agents are 97.4 and 84.6% respectively; one is substantially improved against and the other is comparable to the results obtained by other classification methods useful for multiple-mechanism prediction problems. This indicates the potential of SVM in facilitating the prediction of TdP-causing risk of small molecules and perhaps other ADRs that involve multiple mechanisms.

Enzyme family classification by support vector machines

One approach for facilitating protein function prediction is to classify proteins into functional families. Recent studies on the classification of G-protein coupled receptors and other proteins suggest that a statistical learning method, Support vector machines (SVM), may be potentially useful for protein classification into functional families. In this work, SVM is applied and tested on the classification of enzymes into functional families defined by the Enzyme Nomenclature Committee of IUBMB. SVM classification system for each family is trained from representative enzymes of that family and seed proteins of Pfam curated protein families. The classification accuracy for enzymes from 46 families and for non-enzymes is in the range of 50.0% to 95.7% and 79.0% to 100% respectively. The corresponding Matthews correlation coefficient is in the range of 54.1% to 96.1%. Moreover, 80.3% of the 8,291 correctly classified enzymes are uniquely classified into a specific enzyme family by using a scoring function, indicating that SVM may have certain level of unique prediction capability. Testing results also suggest that SVM in some cases is capable of classification of distantly related enzymes and homologous enzymes of different functions. Effort is being made to use a more comprehensive set of enzymes as training sets and to incorporate multi-class SVM classification systems to further enhance the unique prediction accuracy. Our results suggest the potential of SVM for enzyme family classification and for facilitating protein function prediction. Our software is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

TRMP: a database of therapeutically relevant multiple pathways

Disease processes often involve crosstalks between proteins in different pathways. Different proteins have been used as separate therapeutic targets for the same disease. Synergetic targeting of multiple targets has been explored in combination therapy of a number of diseases. Potential harmful interactions of multiple targeting have also been closely studied. To facilitate mechanistic study of drug actions and a more comprehensive understanding the relationship between different targets of the same disease, it is useful to develop a database of known therapeutically relevant multiple pathways (TRMPs). Information about non-target proteins and natural small molecules involved in these pathways also provides useful hint for searching new therapeutic targets and facilitate the understanding of how therapeutic targets interact with other molecules in performing specific tasks. The TRMPs database is designed to provide information about such multiple pathways along with related therapeutic targets, corresponding drugs/ligands, targeted disease conditions, constituent individual pathways, structural and functional information about each protein in the pathways. Cross links to other databases are also introduced to facilitate the access of information about individual pathways and proteins.

AVAILABILITY

This database can be accessed at http://bidd.nus.edu.sg/group/trmp/trmp.asp and it currently contains 11 entries of multiple pathways, 97 entries of individual pathways, 120 targets covering 72 disease conditions together with 120 sets of drugs directed at each of these targets. Each entry can be retrieved through multiple methods including multiple pathway name, individual pathway name and disease name.

SUPPLEMENTARY INFORMATION

http://bidd.nus.edu.sg/group/trmp/sm.pdf

Protein function classification via support vector machine approach

Support vector machine (SVM) is introduced as a method for the classification of proteins into functionally distinguished classes. Studies are conducted on a number of protein classes including RNA-binding proteins; protein homodimers, proteins responsible for drug absorption, proteins involved in drug distribution and excretion, and drug metabolizing enzymes. Testing accuracy for the classification of these protein classes is found to be in the range of 84-96%. This suggests the usefulness of SVM in the classification of protein functional classes and its potential application in protein function prediction.

[Occurrence of Pagumogonimus veocularis in Fujian Province]

OBJECTIVE

To prove that Fujian Province is also a natural focus of Pagumogonimus veocularis(Pv).

METHODS

The adult worms were obtained from a cat fed with Pv metacercariae.

RESULTS

Pv were found in Jianou, Fujian Province. All 1,873 Semisulcospira libertina showed negative. The positive rate of Tricula fujianensis and Erhaia jianouenesis were 0.10% (1/695) and 0.25% (5/2,038), respectively. The main crab host was S. fujianensis. Ps alone and mixed infection with Pv were found in the Sinopotamon, the infection rates were 36.8% (43/117) and 20.5% (24/117), respectively. The numbers of the metacercariae were 806 and 40, respectively. A cat was infected with 12 metacercriae of Pv, eggs were found in the stool 56 days after infection, and 6 worms were found in the lungs 68 days after infection.

CONCLUSION

Fujian is one of the natural focus of Pv, cat is the adequate host. The fluke was identified as Pv according to the characteristics of the metacercariae.

SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence

Prediction of protein function is of significance in studying biological processes. One approach for function prediction is to classify a protein into functional family. Support vector machine (SVM) is a useful method for such classification, which may involve proteins with diverse sequence distribution. We have developed a web-based software, SVMProt, for SVM classification of a protein into functional family from its primary sequence. SVMProt classification system is trained from representative proteins of a number of functional families and seed proteins of Pfam curated protein families. It currently covers 54 functional families and additional families will be added in the near future. The computed accuracy for protein family classification is found to be in the range of 69.1-99.6%. SVMProt shows a certain degree of capability for the classification of distantly related proteins and homologous proteins of different function and thus may be used as a protein function prediction tool that complements sequence alignment methods. SVMProt can be accessed at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

Construction of a dense comparative map between human chromosome 1p36-->p35 and swine chromosome 6 by using human sequence-tagged sites

Construction of a comprehensive comparative map between swine and human chromosomes is a prerequisite, in order to select candidate swine genes for traits from the human genome database as well as to understand the evolutionary process of the two species. The present study attempted to use 910 sequence-tagged sites (STSs) localized in human chromosome (HSA) 1p36-->p35 (35 Mbp) for radiation hybrid (RH) mapping to swine chromosomes (SSCs). Out of the 910 STSs subjected to amplification of swine orthologues, primer pairs for 13 STSs were found to amplify the respective orthologues and the STSs were assigned to SSCs. Eleven STSs were assigned to SSC6 in the same order as that in HSA1: SSC6cen-(SHGC-150)-(A006H31)-(X82877)-(A007E03)-(IB404)-(stGDB:371372)-(stSG31658)-(A009Q18)-(stSG14201/A009C01)-(H08335)-qter. One of the remaining two STSs, WI-20819, was assigned to SSCX, and the other, R91D18R, was not linked to any first-generation markers of the IMpRH map with a lod score greater than 3.

KDBI: Kinetic Data of Bio-molecular Interactions database

Understanding of cellular processes and underlying molecular events requires knowledge about different aspects of molecular interactions, networks of molecules and pathways in addition to the sequence, structure and function of individual molecules involved. Databases of interacting molecules, pathways and related chemical reaction equations have been developed. The kinetic data for these interactions, which is important for mechanistic investigation, quantitative study and simulation of cellular processes and events, is not provided in the existing databases. We introduce a new database of Kinetic Data of Bio-molecular Interactions (KDBI) aimed at providing experimentally determined kinetic data of protein-protein, protein-RNA, protein-DNA, protein-ligand, RNA-ligand, DNA-ligand binding or reaction events described in the literature. KDBI contains information about binding or reaction event, participating molecules (name, synonyms, molecular formula, classification, SWISS-PROT AC or CAS number), binding or reaction equation, kinetic data and related references. The kinetic data is in terms of one or a combination of the following quantities as given in the literature of a particular event: association/dissociation or on/off rate constant, first/second/third/. order rate constant, equilibrium rate constant, catalytic rate constant, equilibrium association/dissociation constant, inhibition constant and binding affinity constant. Each entry can be retrieved through protein or nucleic acid or ligand name, SWISS-PROT AC number, ligand CAS number and full-text search of a binding or reaction event. KDBI currently contains 8273 entries of biomolecular binding or reaction events involving 1380 proteins, 143 nucleic acids and 1395 small molecules. Hyperlinks are provided for accessing references in Medline and available 3D structures in PDB and NDB. This database can be accessed at http://xin.cz3.nus.edu.sg/group/kdbi/kdbi.asp.

Correlation between normal modes in the 20-200 cm-1 frequency range and localized torsion motions related to certain collective motions in proteins

In certain biologically relevant collective motions, such as protein domain motions and sub-domain motions, large amplitude movements are localized in one or a few flexible regions consisting of a small number of residues. This paper explores the possible use of normal mode analysis in probing localized vibrational torsion motions in these flexible regions that may be related to certain collective motions. The normal modes of 10 structures of five proteins in different conformation (TRP repressor, calmodulin, calbindin D(9k), HIV-1 protease and troponin C), known to have shear or hinge domain or sub-domain motion, respectively, are analyzed. Our study identifies, for each structure, unique normal modes in the 20-200 cm-1 frequency range, whose corresponding motions are primarily concentrated in the region where large amplitude torsion movements of a known domain or sub-domain motion occur. This suggests possible correlation between normal modes at 20-200 cm-1 frequency range and initial fluctuational motions leading to localized collective motions in proteins, and thus the potential application of normal mode analysis in facilitating the study of biologically important localized motions in biomolecules.

Computer automated prediction of potential therapeutic and toxicity protein targets of bioactive compounds from Chinese medicinal plants

Understanding the molecular mechanism and pharmacology of bioactive compounds from Chinese medicinal plants (CMP) is important in facilitating scientific evaluation of novel therapeutic approaches in traditional Chinese medicine. It is also of significance in new drug development based on the mechanism of Chinese medicine. A key step towards this task is the determination of the therapeutic and toxicity protein targets of CMP compounds. In this work, newly developed computer software INVDOCK is used for automated identification of potential therapeutic and toxicity targets of several bioactive compounds isolated from Chinese medicinal plants. This software searches a protein database to find proteins to which a CMP compound can bind or weakly bind. INVDOCK results on three CMP compounds (allicin, catechin and camptotecin) show that 60% of computer-identified potential therapeutic protein targets and 27% of computer-identified potential toxicity targets have been implicated or confirmed by experiments. This software may potentially be used as a relatively fast-speed and low-cost tool for facilitating the study of molecular mechanism and pharmacology of bioactive compounds from Chinese medicinal plants and natural products from other sources.

ADME-AP: a database of ADME associated proteins

Drug absorption, distribution, metabolism and excretion (ADME) often involve interaction of a drug with specific proteins. Knowledge about these ADME-associated proteins is important in facilitating the study of the molecular mechanism of disposition and individual response as well as therapeutic action of drugs. It is also useful in the development and testing of pharmacokinetics prediction tools. Several databases describing specific classes of ADME-associated proteins have appeared. A new database, ADME-associated proteins (ADME-AP), is introduced to provide comprehensive information about all classes of ADME-associated proteins described in the literature including physiological function of each protein, pharmacokinetic effect, ADME classification, direction and driving force of disposition, location and tissue distribution, substrates, synonyms, gene name and protein availability in other species. Cross-links to other databases are also provided to facilitate the access of information about the sequence, 3D structure, function, polymorphisms, genetic disorders, nomenclature, ligand binding properties and related literatures of each protein. ADME-AP currently contains entries for 321 proteins and 964 substrates.

CLiBE: a database of computed ligand binding energy for ligand-receptor complexes

Consideration of binding competitiveness of a drug candidate against natural ligands and other drugs that bind to the same receptor site may facilitate the rational development of a candidate into a potent drug. A strategy that can be applied to computer-aided drug design is to evaluate ligand-receptor interaction energy or other scoring functions of a designed drug with that of the relevant ligands known to bind to the same binding site. As a tool to facilitate such a strategy, a database of ligand-receptor interaction energy is developed from known ligand-receptor 3D structural entries in the Protein Databank (PDB). The Energy is computed based on a molecular mechanics force field that has been used in the prediction of therapeutic and toxicity targets of drugs. This database also contains information about ligand function and other properties and it can be accessed at http://xin.cz3.nus.edu.sg/group/CLiBE.asp. The computed energy components may facilitate the probing of the mode of action and other profiles of binding. A number of computed energies of some PDB ligand-receptor complexes in this database are studied and compared to experimental binding affinity. A certain degree of correlation between the computed energy and experimental binding affinity is found, which suggests that the computed energy may be useful in facilitating a qualitative analysis of drug binding competitiveness.

Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand-protein inverse docking approach

Determination of potential drug toxicity and side effect in early stages of drug development is important in reducing the cost and time of drug discovery. In this work, we explore a computer method for predicting potential toxicity and side effect protein targets of a small molecule. A ligand-protein inverse docking approach is used for computer-automated search of a protein cavity database to identify protein targets. This database is developed from protein 3D structures in the protein data bank (PDB). Docking is conducted by a procedure involving multiple conformer shape-matching alignment of a molecule to a cavity followed by molecular-mechanics torsion optimization and energy minimization on both the molecule and the protein residues at the binding region. Potential protein targets are selected by evaluation of molecular mechanics energy and, while applicable, further analysis of its binding competitiveness against other ligands that bind to the same receptor site in at least one PDB entry. Our results on several drugs show that 83% of the experimentally known toxicity and side effect targets for these drugs are predicted. The computer search successfully predicted 38 and missed five experimentally confirmed or implicated protein targets with available structure and in which binding involves no covalent bond. There are additional 30 predicted targets yet to be validated experimentally. Application of this computer approach can potentially facilitate the prediction of toxicity and side effect of a drug or drug lead.

Can an optimization/scoring procedure in ligand-protein docking be employed to probe drug-resistant mutations in proteins?

A simple ligand-protein structural optimization and binding evaluation procedure has been routinely used in high-speed ligand-protein docking studies. In this work, we examine whether such an optimization/scoring procedure is useful in indicating possible drug-resistant mutations in proteins. Crystal structures of three wild-type enzymes (HIV-1 protease, HIV-1 reverse transcriptase, and Mycobacterium tuberculosis H37Rv enoyl-ACP reductase) complexed to a variety of inhibitors are studied. Mutations are introduced into these structures by using the molecular modeling software, SYBYL. Structural optimization and scoring of a mutant complex is conducted by a procedure similar to that used in a recent docking study (Wang et al., 1999). The computed results are compared with observed drug resistance data and the profile of nonresistant mutations. Most mutations studied show an energy change in the same direction as those indicated by observed resistance data. 50% of the polar to polar or nonpolar to nonpolar mutations are found to correlate qualitatively with observed drug resistance data. Van der Waals interactions account for most of these changes, which is in agreement with conclusions from structural studies. Substantially larger deviations are found between computed results and observed data for most polar to nonpolar or nonpolar to polar mutations, which result from deficiency in modelling and scoring ligand-protein interactions in our procedure. Our results suggest that an optimization/docking scoring procedure is useful for qualitatively probing polar to polar or nonpolar to nonpolar resistant mutations in addition to its application in screening active compounds. More accurate description of ligand-protein interactions and the use of methods such as free energy perturbation and Poisson-Boltzmann may be needed to further improve the quality of prediction.

Individual allergens as risk factors for asthma and bronchial hyperresponsiveness in Chinese children

The role of allergen sensitization in the development of asthma in the Chinese is not clear. This study aims to determine the relationship of sensitization to individual allergens, and the development of asthma and bronchial hyperresponsiveness (BHR) in schoolchildren from three Chinese cities: Hong Kong, Beijing and Guangzhou. Community-based random samples of 10-yr-old schoolchildren from three Chinese cities were recruited for study using the International Study of Asthma and Allergies in Childhood (ISAAC) Phase II protocol. Subjects were studied by parental questionnaires (n=10,902), skin-prick tests (n=3,479), and methacholine challenge tests (n=608). The prevalence rates of wheeze in the past 12 months (Hong Kong, 5.8%; Beijing, 3.8%; Guangzhou, 3.4%) and atopy (Hong Kong, 41.2%; Beijing, 23.9%; Guangzhou, 30.8%) were highest in schoolchildren from Hong Kong. Multivariate logistic regression analyses revealed that sensitization to Dermatophagoides pteronyssinus (odds ratio (OR)=4.48; 95% confidence interval (CI): 3.02-6.66), cat (2.59; 1.67-4.03), Dermatophagoides farinae (2.41; 1.65-3.51) and mixed grass pollen (2.85; 1.24-6.50) were significantly associated with current wheeze. Atopy, defined as having > or = 1 positive skin-prick tests, was not an independent risk factor for current wheeze in children from any of the three cities. Furthermore, atopy (OR=2.53; 95% CI: 1.07-5.97), sensitization to cat (3.01; 1.39-6.52) and D. farinae (3.67; 1.93-6.97) were significantly associated with BHR. The authors confirmed that sensitization to house dust mite and cat was significantly associated with current wheeze and bronchial hyperresponsiveness in Chinese schoolchildren. However, the difference in the prevalence rate of atopic sensitization cannot explain the higher prevalence of childhood asthma in Hong Kong, when compared with those children from Beijing and Guangzhou.

TTD: Therapeutic Target Database

A number of proteins and nucleic acids have been explored as therapeutic targets. These targets are subjects of interest in different areas of biomedical and pharmaceutical research and in the development and evaluation of bioinformatics, molecular modeling, computer-aided drug design and analytical tools. A publicly accessible database that provides comprehensive information about these targets is therefore helpful to the relevant communities. The Therapeutic Target Database (TTD) is designed to provide information about the known therapeutic protein and nucleic acid targets described in the literature, the targeted disease conditions, the pathway information and the corresponding drugs/ligands directed at each of these targets. Cross-links to other databases are also introduced to facilitate the access of information about the sequence, 3D structure, function, nomenclature, drug/ligand binding properties, drug usage and effects, and related literature for each target. This database can be accessed at http://xin.cz3.nus.edu.sg/group/ttd/ttd.asp and it currently contains entries for 433 targets covering 125 disease conditions along with 809 drugs/ligands directed at each of these targets. Each entry can be retrieved through multiple methods including target name, disease name, drug/ligand name, drug/ligand function and drug therapeutic classification.

Inhibition of epidermal growth factor receptor tyrosine kinase by chalcone derivatives

In our previous study, butein, a chalcone derivative, was found to be an inhibitor of tyrosine kinases and the inhibition was ATP-competitive. In this work, chalcone and seven chalcone derivatives were used to analyse the relationship between the structure of these compounds and their inhibition of tyrosine kinase activity. Three of chalcone derivatives, including butein, marein and phloretin, were found to have an ability to inhibit the tyrosine kinase activity of epidermal growth factor receptor (EGFR) in vitro. IC(50) was 8 microM for butein, 19 microM for marein and 25 microM for phloretin. The structural characterisations of these inhibitors suggest that the hydroxylations at C4 and C4' of these molecules may be required for them to act as EGFR tyrosine kinase inhibitors. The inhibition of EGF-induced EGFR tyrosine phosphorylation by butein was also observed in human hepatocellular carcinoma HepG2 cells, while marein and phloretin were inactive at the doses tested. Molecular modelling suggests that butein, marein and phloretin can be docked into the ATP binding pocket of EGFR. Hydrogen bonds and hydrophobic interaction appear to be important in the binding of these inhibitors to EGFR.

Possible genomic consequence of nongenomic action of glucocorticoids in neural cells

The nongenomic, rapid effects of glucocorticoid activate multiple intracellular transduction pathways. This review proposes a possible genomic consequence of the nongenomic action of steroids. The genomic actions of hormonal steroids may be twofold: classic genomic and nongenomically induced genomic.

Sesquiterpene polyol esters from Euonymus phellomana Loes

Three new (1-3) and six known (4-9) beta-dihydroagarofuran sesquiterpene polyol esters were isolated from a methanol extract of the seed oil of Euonymus phellomana Loes. and their structures were established on the basis of spectral analysis, including 2D-NMR spectroscopy.

Restoration of decreased N-methyl-d-asparate receptor activity by brain-derived neurotrophic factor in the cultured hippocampal neurons: involvement of cAMP

Brain-derived neurotrophic factor (BDNF) may play an important role in the modulation of N-methyl-d-asparate (NMDA) receptor function. To elucidate the underlying mechanisms, whole-cell patch-clamp recording was used to assess the effect of BDNF on the responses of cultured hippocampal neurons to the glutamate receptor agonist NMDA. We found that peak amplitude of NMDA-evoked currents in cultured hippocampal pyramidal neurons at Day 18 in vitro decreased significantly compared to that of NMDA currents at Day 10 or 14. Interestingly, NMDA-evoked currents were greatly enhanced by BDNF (50 ng/ml) in cultured neurons at Day 18, but not at Day 10 or 14. Treatment with Rp-cAMP abolished the potentiating effects of BDNF on NMDA current. Elevating the amount of cytosolic cAMP by preincubation with forskolin or Sp-cAMP also enhanced NMDA currents as effectively as BDNF in 18-day-old hippocampal neurons. Measurement of the cellular content of cAMP by RIA indicated that cultured hippocampal neurons showed decreased basal cAMP levels at the time NMDA currents were decreased and BDNF increased the decreased cAMP levels. Taken together, these results suggest that BDNF may restore decreased NMDA receptor activity in cultured hippocampal neurons by the cAMP pathway.

Sharing international experiences in disasters: summary and action plan

INTRODUCTION

The discussions in this theme provided an opportunity to share specific experiences with disasters that occurred outside of the Asia-Pacific Rim.

METHODS

Details of the methods used are provided in the preceding paper. The chairs moderated all presentations and produced a summary that was presented to an assembly of all of the delegates. Since the findings from the Theme 7 and Theme 3 groups were similar, the chairs of both groups presided over one workshop that resulted in the generation of a set of action plans that then were reported to the collective group of all delegates.

RESULTS

The main points developed during the presentations and discussion included: (1) disaster response planning, (2) predetermined command and organizational structure, (3) rapid response capability, (4) mitigation, and (5) communications and alternatives.

DISCUSSION

The action plans presented are in common with those presented by Theme 3, and include: (1) plan disaster responses including the different types, identification of hazards, training based on experiences, and provision of public education; (2) improving coordination and control; (3) maintaining communications assuming infrastructure breakdown; (4) maximizing mitigation through standardized evaluations, creation of a legal framework, and recognition of advocacy and public participation; and (5) providing resources and knowledge through access to existing therapies, using the media, and increasing decentralization of hospital inventories.

CONCLUSIONS

Most of the problems that occurred outside the Asia-Pacific rim relative to disaster management are similar to those experienced within it. They should be addressed in common with the rest of the world.

Expression of estrogen receptor (ER)-alpha and -beta immunoreactivity in hippocampal cell cultures with special attention to GABAergic neurons

This study investigated the expression patterns of estrogen receptor-alpha (ER alpha) and -beta (ER beta) in the cultured hippocampal cells of neonatal rats by combined application of cell culture and immunocytochemistry. The results revealed that the expression difference between ER alpha and ER beta seemed to be not obvious in the cultured hippocampal cells of neonatal rats. Moreover, immunoreactivity for either ER alpha or ER beta was observed to be localized in the majority of not only neurons but also astrocytes. The coexpression of both ER alpha and ER beta in the same individual cell was also demonstrated by the double-label immunocytochemistry. Western blot analysis showed that immunoreactivity for ER alpha in the neonatal hippocampal tissues was much higher than in the adult (became rather weak), although there was not such a great difference of immunoreactivity for ER beta. The data also provide direct evidence for the expression of ER subtypes within GABAergic neurons in hippocampal cell cultures and suggest that estrogen's effect on the hippocampus may be mediated at least in part by its ER-containing GABAergic neurons.

Genomic structure and mutational analysis of the human KIF1B gene which is homozygously deleted in neuroblastoma at chromosome 1p36.2

In order to clone candidate tumor suppressor genes whose loss contributes to the pathogenesis of neuroblastoma (NB), we performed polymerase chain reaction (PCR) screening using a high-density sequence tagged site-content map within a commonly deleted region (chromosome band 1p36) in 24 NB cell lines. We found a approximately 480 kb homozygously deleted region at chromosome band 1p36.2 in one of the 24 NB cell lines, NB-1, and cloned the human homologue (KIF1B-beta) of the mouseKif1B-beta gene in this region. The KIF1B-beta gene had at least 47 exons, all of which had a classic exon-intron boundary structure. Mouse Kif1B is a microtubule-based putative anterograde motor protein for the transport of mitochondria in neural cells. We performed mutational analysis of the KIF1B-beta gene in 23 cell lines using 46 sets of primers and also an allelic imbalance (AI) analysis of KIF1B-beta in 50 fresh NB samples. A missense mutation at codon 1554, GTG (Gly) to ATG (Met), silent mutations at codon 409 (ACG to ACA) and codon 1721 (ACC to ACT), and polymorphisms at codon 170, GAT (Asp) to GAA (Glu), and at codon 1087, TAT (Tyr), to TGT (Cys), were all identified, although their functional significances remain to be determined. The AI for KIF1B-beta was slightly higher (38%) than those for the other two markers (D1S244, D1S1350) (35 and 32%) within the commonly deleted region (1p36). Reverse transcriptase-PCR analysis of the KIF1B-beta gene revealed obvious expression in all NB cell lines except NB-1, although decreased expression of the KIF1B-beta gene was found in a subset of early- and advanced-stage NBs. These results suggest that the KIF1B-beta gene may not be a candidate for tumor suppressor gene of NB.

Prevalence of respiratory and atopic disorders in Chinese schoolchildren

BACKGROUND

Epidemiological surveys have shown that the prevalence of asthma in the Asian population is relatively low. Within the Chinese population, schoolchildren from Hong Kong were found to have the highest rate of asthma.

OBJECTIVE

To compare the prevalence of respiratory and atopic disorders, and to assess the role of atopy in the development of asthma, in Chinese schoolchildren from Hong Kong, Beijing and Guangzhou.

METHODS

Community-based random samples of schoolchildren aged 9-11 years from three Chinese cities (Hong Kong, Beijing and Guangzhou) were recruited for study using the International Study of Asthma and Allergies in Childhood (ISAAC) Phase II protocol. Subjects were studied by parental questionnaires (n = 10902), skin-prick tests (n = 3479) and skin examination (n = 3479).

RESULTS

The prevalence rates of current wheeze, speech limiting wheeze, rhinoconjunctivitis and flexural dermatitis were significantly more common in Hong Kong than in Beijing or Guangzhou. The atopy rate was also higher in Hong Kong (41.2%) than in Beijing (23.9%) or Guangzhou (30.8%). Atopy was strongly correlated with current wheeze (OR 7.74; 95% CI = 5.70-10.51). Subgroup analyses of children from Hong Kong revealed that children born in mainland China who had subsequently migrated to Hong Kong had a significantly lower rate of allergic symptoms and atopy than those children born in Hong Kong.

CONCLUSION

Using a standardized written questionnaire along with a skin prick test and skin examination, we confirmed that the prevalence of asthma, allergic diseases and atopy was highest in schoolchildren from Hong Kong. Atopic sensitization is an important factor associated with asthma in Chinese children.