One-step purification of mammalian deoxyribonucleases I and differences among pancreas, parotid, and pancreas-parotid (mixed) types based on species-and organ-specific N-linked glycosylation

Mammalian deoxyribonucleases I (DNase I) are classified into three types, namely, pancreas, parotid, and pancreas-parotid (mixed), based on differences in their tissue concentrations. In this study, DNase I purification by concanavalin A-wheat germ agglutinin mixture-agarose column from rat (parotid type), rabbit (mixed type), and pig (pancreas type) is described. This method permits a relatively easy one-step purification of DNase I from rat and rabbit parotid glands, the rat submaxillary gland, and porcine pancreas. To elucidate differences among the three types, these DNases I were subjected to enzymatic deglycosylation either by peptide N-glycosidase F (PNGase F) or endoglycosidase H (Endo H). Following deglycosylation, digests were separated on DNA-casting polyacrylamide gel electrophoresis. PNGase F produced a single lower mobility product in all samples. Endo H produced a double band in rat and rabbit parotid glands and porcine pancreas, and a single band in the rabbit pancreas corresponding with the PNGase F product. DNase I activity of the porcine pancreas was completely extinguished by deglycosylation, while that of the parotid glands and rabbit pancreas was unaffected. Our results suggest that the distinct properties of DNase I exhibited by the three types may be attributed to differences in the extent of post-translational N-linked glycosylation of the enzyme.

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.

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.

Stabilization analysis and modified Korteweg-de Vries equation in a cooperative driving system

Two lattice traffic models are proposed by incorporating a cooperative driving system. The lattice versions of the hydrodynamic model of traffic flow are described by the differential-difference equation and difference-difference equation, respectively. The stability conditions for the two models are obtained using the linear stability theory. The results show that considering more than one site ahead in vehicle motion leads to the stabilization of the system. The modified Korteweg-de Vries equation (the mKdV equation, for short) near the critical point is derived by using the reductive perturbation method to show the traffic jam which is proved to be described by kink-anti-kink soliton solutions obtained from the mKdV equations.

OsAGAP, an ARF-GAP from rice, regulates root development mediated by auxin in Arabidopsis

Arf (ADP-ribosylation factor) proteins, which mediate vesicular transport, have little or no intrinsic GTPase activity. They rely on the action of GTPase-activating proteins (GAPs) and guanine nucleotide exchange factors (GEFs) for their function. In the present study the OsAGAP gene in rice, which encoded a protein with predicted structure similar to ArfGAP, was identified. The purified OsAGAP-GST fusion protein was able to stimulate the GTPase activity of rice Arf. Furthermore, OsAGAP can rescue the defect of vesicular transport in the yeast gcs1 delta glo3 delta double-mutant cells. Transgenic Arabidopsis with OsAGAP constitutively expression showed reduced apical dominance, shorter primary roots, increasing number of longer adventitious roots. Many of the phenotypes can be phenocopied by treatment of exogenous indoleacetic acid level (IAA) in wild-type plants. Determination of whole-plant IAA level showed that there is a sharp increase of free IAA in OsAGAP transgenic Arabidopsis seedlings. In addition, removal of the 4-day-old shoot apex could inhibit the adventitious root formation in the transgenic seedlings. These results suggest OsAGAP, an ARF-GAP of rice, maybe involved in the mediation of plant root development by regulating auxin level.

Stabilization effect of traffic flow in an extended car-following model based on an intelligent transportation system application

An extended car following model is proposed by incorporating an intelligent transportation system in traffic. The stability condition of this model is obtained by using the linear stability theory. The results show that anticipating the behavior of more vehicles ahead leads to the stabilization of traffic systems. The modified Korteweg-de Vries equation (the mKdV equation, for short) near the critical point is derived by applying the reductive perturbation method. The traffic jam could be thus described by the kink-antikink soliton solution for the mKdV equation. From the simulation of space-time evolution of the vehicle headway, it is shown that the traffic jam is suppressed efficiently with taking into account the information about the motion of more vehicles in front, and the analytical result is consonant with the simulation one.

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.

Comparison of quantitative trait loci controlling seedling characteristics at two seedling stages using rice recombinant inbred lines

A better understanding of the genetics of seedling characteristics in rice could be helpful in improving rice varieties. Zhenshan 97 and Minghui 63, the parents of Shanyou 63, an elite hybrid developed during the last decade in China, vary greatly with respect to their physiological and morphological traits at the seedling growth stage. In this study, we used a population of 240 recombinant inbred lines derived from a cross between Zhenshan 97 and Minghui 63 to identify quantitative trait loci (QTL) for seedling characteristics. All plant material was grown in hydroponic culture. Data for the following characters were collected at 30 days and 40 days post-sowing: plant height, shoot dry matter weight (SDW), maximum root length, root dry weight (RDW), total dry weight, and root-shoot ratio (the ratio of SDW to RDW). Analysis using composite interval mapping detected 16 QTL for the six traits in 30-day-old seedlings. Of these 16 QTL, Minghui 63 alleles increased trait values at only two of them. The QTL in the vicinity of R3166 on chromosome 5 simultaneously influenced PH, SDW, MRL, RDW, and TDW in the same direction. Twenty QTL were detected for the same traits in the 40-day-old seedlings. However, at this stage Minghui 63 alleles increased trait values at eight QTL. The QTL linked to R3166 also affected PH, SDW, MRL, RDW, and TDW. Only four QTL were common to the two stages. These results clearly indicate that different genes (QTL) control the same traits during different time intervals. Zhenshan 97 alleles had positive effects during the first 30 days of seedling growth, but thereafter the positive effects of Minghui 63 alleles on seedling growth gradually became more pronounced.

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.

Rat intralaminar thalamic nuclei projections to the globus pallidus: A biotinylated dextran amine anterograde tracing study

The topographical organization and ultrastructural features of the intralaminar thalamic nuclei (ITN) projections to the globus pallidus (GP) were studied using the biotinylated dextran amine (BDA) anterograde tracing method in the rat. To assess the functional association of BDA injection sites in the ITN, the known topographical organization of the ITN-neostriatal (Str) projections and calcium binding protein (CaBP) immunostaining patterns of the Str and GP were used. BDA injection in the lateral part of the lateral parafascicular nucleus and the caudal part of the central lateral nucleus labeled fibers and boutons mainly in the dorsolateral sensorimotor territory of the Str and the middle territories of the GP. BDA injection in the medial part of the lateral parafascicular nucleus and the central lateral nucleus labeled mainly the middle association territory of the Str and the border and the caudomedial territories of the GP. BDA injection in the medial parafascicular nucleus and the central medial nucleus labeled mainly the medial limbic territory of the Str. The medial parafascicular nucleus projected to the medial-most region of the GP, while the central medial nucleus projection to the GP was very sparse. Electron microscopic observations indicated that BDA-labeled boutons form asymmetric synapses mainly on 0.5-2.0 microm diameter dendritic shafts in the GP. The boutons were small but had a relatively long active zone. The present observations together with the known topographical organization of striatopallidal projections indicated that the ITN-GP projections were topographically organized in parallel to the ITN-Str projections. Thus, each part of the ITN projecting to the sensorimotor, the association, and the limbic territories of the Str also projects to the corresponding functional territories of the GP.

Expressional profiling of genes related to pollination and fertilization in rice

Pollination and fertilization are key steps leading to seed and fruit formation. To obtain genes involved in pollination and fertilization in rice, an RNA fingerprinting technique, cDNA-AFLP (amplified fragment length polymorphism), was used to generate transcript profiles related to pollination. Of 15,000 cDNA fragments inspected, 2,100 showed altered expression in the pollinated pistil, of which about 1/5 were up-regulated (URP) and the rest down-regulated (DRP), suggesting that gene repression is a predominant mode of gene regulation in the pollinated pistil. Over 200 URP genes were sequenced and databank searches revealed that 70% of them represented previously unnoticed rice genes. DNA blot analysis of 20 URP genes detected no restriction fragment length polymorphisms (RFLP) between two relatively distant rice varieties, suggesting that the URP genes are highly conserved and likely play important roles in pollination and fertilization. Furthermore, two genes, URP47 and URP63, probably encoding an ADP-ribosylation factor and a membrane transporter, respectively, in relation to pollination were discussed.

Expression and deletion analysis of EcoRII endonuclease and methylase gene

OBJECTIVE

To clone complete EcoRII restriction endonuclease gene (ecoRIIR) and methyltransferase gene (ecoRIIM) in one vector and to analyze the coordinating expression of this whole R-M system.

METHODS

Unidirectional deletion subclones were constructed with ExoIII. ecoRIIR/ M genes were preliminarily located in the cloned fragment according to the enzyme activities of subclones. Exact deletion sites were determined by sequencing, and transcriptional start sites were determined by S1 mapping.

RESULTS

The DNA fragment which was cloned into pBluescript SK + contained intact ecoRIIR gene and ecoRIIM gene, and two transcriptional start sites of ecoRIIR gene were determined. 132bp to 458bp from 3'end of ecoRIIR gene are indispensable to enzyme activities and deletion of 202bp from 3' end of ecoRIIM gene made enzyme lose the capability in DNA protection to resist specific cut with EcoRII endonuclease (EcoRII. R). Deletion of the coding and flanking sequences of one gene did not affect the expression of the other gene, and the recombinants only containing ecoRIIR gene appeared to be lethal to dcm+ host.

CONCLUSION

ecoRIIM gene linking closely to ecoRIIR gene is very important for the existence of the R-M system in process of evolution, but the key to control EcoRII R-M order may not exist in transcriptional level .

Generation of three selenium-containing catalytic antibodies with high catalytic efficiency using a novel hapten design method

A novel strategy for design of haptens that were used to produce catalytic antibodies was developed and three monoclonal antibodies, 3G5, 2F3, and 5C9, were generated using this strategy. These monoclonal antibodies were converted into selenium-containing abzymes by chemically modifying the hydroxyl group of serines followed by sodium hydrogen selenide displacement. These selenium-containing abzymes exhibited remarkable glutathione peroxidase activity, which surpasses the activity of some native glutathione peroxidases. The activities of the selenium-containing abzymes Se-3G5, Se-2F3, and Se-5C9 which catalyzed reduction of hydroperoxides by glutathione were 2.23, 4.20, and 3.79 times that of rabbit liver glutathione peroxidase, respectively. Detailed steady-state kinetics study on Se-2F3 was carried out and the value of k(cat)/K(m) (H(2)O(2)) was found to be 2.11 x 10(7) M(-1) min(-1) which was supposed to be one of the highest among the known catalytic antibodies. The data of association constants and glutathione peroxidase activities of these catalytic antibodies and the steady-state kinetics of Se-2F3 showed that the method might be a remarkably efficient one for generating catalytic antibodies with glutathione peroxidase activity.

A novel dicyclodextrinyl ditelluride compound with antioxidant activity

Reactive oxygen species (ROS) primarily arise from products of normal metabolic activities and are thought to be the etiology of many diseases. A novel dicyclodextrinyl ditelluride (2-TeCD) compound was designed to be a functional mimic of the glutathione peroxidase that normally removes ROS. 2-TeCD exhibited highly catalytic efficiency and good water solubility. Antioxidant activity was studied by using ferrous sulfate/ascorbate-induced mitochondria damage model system. 2-TeCD protected the mitochondria against oxidative damage in a dose-dependent manner and exhibited also great antioxidant ability in comparison with 2-phenyl-1,2-benziososelenazol-3(2H)-one. The mimic may result in better clinical therapies for the treatment of ROS-mediated diseases.

[Detection of -5/5q- chromosome abnormality in myelodysplastic syndromes by interphase fluorescence in situ hybridization]

OBJECTIVE

To evaluate the value of interphase fluorescence in situ hybridization(FISH) in the detection of acquired complete and partial deletion of chromosome 5(-5/5q-) in myelodysplastic syndromes (MDS).

METHODS

Marrow cells from 48 MDS patients and 10 normal controls were studied by interphase FISH using SpectrumRed directly labelled DNA specific probe for 5q31, 200 to 300 cells were scored for each case and cells with a red hybridization signal > 6% were defined as -5/5q- positive.

RESULTS

In 48 MDS patients, 13 were positive for interphase FISH, of whom, 7 were positive and 6 were negative for conventional cytogenetics (CC). The difference in the percentage of positive cells detected by the two methods was statistically significant (P < 0.05).

CONCLUSION

Interphase FISH is more sensitive than CC for the detection of -5/5q- in MDS.

[Preliminary study on postremission therapy courses in acute myeloid leukemia]

OBJECTIVE

To explore the optimum postremission therapy courses in acute myeloid leukemia (AML).

METHODS

Data from medical records of AML patients in our hospital in 7 year were analyzed with SPSS 8.0 system software.

RESULTS

One hundred and ninety one untreated de novo AML patients received different induction chemotherapy regimens: HA, DA, AA and HAD. The complete remission (CR) rate was 81.4%, 89.9% for one to two courses. The median disease free survival (DFS) in 144 CR patients whose survival time could be analyzed was 9.6 months. The probability of survival was 21.6% at 3 year and 12.9% at 5 year. For the patients received less than 6 courses of post-remission therapy, the median DFS was 7.1 months and the probability of survival was 11.4% at 3 year and 6.3% at 5 years, Whereas for patients received 6 or more courses of post-remission therapy, they were 35.3 months, 43.2% and 27.0%, respectively. The differences between the two groups were statistically significant. For patients received 8 or more courses of post-remission therapy, the median DFS was 48.8 months and the probability of survival was 57.9% at 3 year and 31.6% at 5 year.

CONCLUSION

AML patients should at least receive 6 courses of post-remission therapy, and 8 courses therapy seems better.