Inferring novel genes related to colorectal cancer via random walk with restart algorithm

Colorectal cancer (CRC) is the third most common type of cancer. In recent decades, genomic analysis has played an increasingly important role in understanding the molecular mechanisms of CRC. However, its pathogenesis has not been fully uncovered. Identification of genes related to CRC as complete as possible is an important way to investigate its pathogenesis. Therefore, we proposed a new computational method for the identification of novel CRC-associated genes. The proposed method is based on existing proven CRC-associated genes, human protein-protein interaction networks, and random walk with restart algorithm. The utility of the method is indicated by comparing it to the methods based on Guilt-by-association or shortest path algorithm. Using the proposed method, we successfully identified 298 novel CRC-associated genes. Previous studies have validated the involvement of the majority of these 298 novel genes in CRC-associated biological processes, thus suggesting the efficacy and accuracy of our method.

Prediction of potential drivers connecting different dysfunctional levels in lung adenocarcinoma via a protein-protein interaction network

Lung cancer is a serious disease that threatens an affected individual's life. Its pathogenesis has not yet to be fully described, thereby impeding the development of effective treatments and preventive measures. "Cancer driver" theory considers that tumor initiation can be associated with a number of specific mutations in genes called cancer driver genes. Four omics levels, namely, (1) methylation, (2) microRNA, (3) mutation, and (4) mRNA levels, are utilized to cluster cancer driver genes. In this study, the known dysfunctional genes of these four levels were used to identify novel driver genes of lung adenocarcinoma, a subtype of lung cancer. These genes could contribute to the initiation and progression of lung adenocarcinoma in at least two levels. First, random walk with restart algorithm was performed on a protein-protein interaction (PPI) network constructed with PPI information in STRING by using known dysfunctional genes as seed nodes for each level, thereby yielding four groups of possible genes. Second, these genes were further evaluated in a test strategy to exclude false positives and select the most important ones. Finally, after conducting an intersection operation in any two groups of genes, we obtained several inferred driver genes that contributed to the initiation of lung adenocarcinoma in at least two omics levels. Several genes from these groups could be confirmed according to recently published studies. The inferred genes reported in this study were also different from those described in a previous study, suggesting that they can be used as essential supplementary data for investigations on the initiation of lung adenocarcinoma. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

HIV-1 protease cleavage site prediction based on two-stage feature selection method

Knowledge of the mechanism of HIV protease cleavage specificity is critical to the design of specific and effective HIV inhibitors. Searching for an accurate, robust, and rapid method to correctly predict the cleavage sites in proteins is crucial when searching for possible HIV inhibitors. In this article, HIV-1 protease specificity was studied using the correlation-based feature subset (CfsSubset) selection method combined with Genetic Algorithms method. Thirty important biochemical features were found based on a jackknife test from the original data set containing 4,248 features. By using the AdaBoost method with the thirty selected features the prediction model yields an accuracy of 96.7% for the jackknife test and 92.1% for an independent set test, with increased accuracy over the original dataset by 6.7% and 77.4%, respectively. Our feature selection scheme could be a useful technique for finding effective competitive inhibitors of HIV protease.

Virus-ECC-mPLoc: a multi-label predictor for predicting the subcellular localization of virus proteins with both single and multiple sites based on a general form of Chou's pseudo amino acid composition

Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of viral proteins in a host cell or virus-infected cell is important because it is closely related to their destructive tendencies and consequences. Prediction of viral protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods specialized for viral proteins are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, a new predictor, called Virus-ECC-mPLoc, has been developed that can be used to deal with the systems containing both singleplex and multiplex proteins by introducing a powerful multi-label learning approach which exploits correlations between subcellular locations and by hybridizing the gene ontology information with the dipeptide composition information. It can be utilized to identify viral proteins among the following six locations: (1) viral capsid, (2) host cell membrane, (3) host endoplasmic reticulum, (4) host cytoplasm, (5) host nucleus, and (6) secreted. Experimental results show that the overall success rates thus obtained by Virus-ECC-mPLoc are 86.9% for jackknife test and 87.2% for independent data set test, which are significantly higher than that by any of the existing predictors. As a user-friendly web-server, Virus-ECCmPLoc is freely accessible to the public at the web-site http://levis.tongji.edu.cn:8080/bioinfo/Virus-ECC-mPLoc/.

Prediction of protein-protein interactions based on feature selection and data balancing

Computational approaches are able to analyze protein-protein interactions (PPIs) from a different angle of view by complementing the experimental ones. And they are very efficient in determining whether two proteins can interact with each other. In this paper, KNNs (K-nearest neighbors) is applied to predict the PPIs by coding each protein with the physical and chemical properties of its residues, predicted secondary structures and amino acid compositions. mRMR (minimum-redundancy maximum-relevance) feature selection is adopted to select a compact feature set, features of which are considered to be important for the determination of PPI-nesses. Because the size of the negative dataset (containing non-interactive protein pairs) is much larger than that of the positive dataset (containing interactive protein pairs), the negative dataset is divided into 5 portions and each portion is combined with the positive dataset for one prediction. Thus 5 predictions are performed and the final results are obtained through voting. As a result, the prediction achieves an overall accuracy of 0.8369 with sensitivity of 0.7356. The predictor, developed by this research for the prediction of the fruit fly PPI-nesses, is available for public use at http://chemdata.shu.edu.cn/ppip.

Predicting the metabolic pathways of small molecules based on their physicochemical properties

How to correctly and efficiently map small molecule to its possible metabolic pathway is a meaningful topic to metabonomics research. In this work, a novel approach to address this problem was introduced to encode physicochemical properties of small molecules. Based on this encoding method, a two stage feature selection method called mRMR-FFSAdaBoost was adopted to map small molecules to their corresponding metabolic pathways possible. As a result, the accuracies of 10-folds cross-validation test and independent set test for predicting the metabolic pathways of small molecules reached 83.88% and 85.23%, respectively. An online server for predicting metabolic pathways of unknown small molecules as described in this paper is accessible at http://chemdata.shu.edu.cn:8080/PathwayPrediction/.

Prediction of the functional roles of small molecules in lipid metabolism based on ensemble learning

As many diseases like high cholesterol are referred to lipid metabolism, studying the lipid metabolic pathway has a positive effect on finding the knowledge about interactions between different elements within high complex living systems. Here, we employed a typical ensemble learning method, Bagging learner, to study and predict the possible sub lipid metabolic pathway of small molecules based on physical and chemical features of the compounds. As a result, jackknife cross validation test and independent set test on the model reached 89.85% and 91.46%, respectively. Therefore, our predictor may be used for finding the new compounds which participate in lipid metabolic procedures.

Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties

Background

With the huge amount of uncharacterized protein sequences generated in the post-genomic age, it is highly desirable to develop effective computational methods for quickly and accurately predicting their functions. The information thus obtained would be very useful for both basic research and drug development in a timely manner.

Methodology/Principal Findings

Although many efforts have been made in this regard, most of them were based on either sequence similarity or protein-protein interaction (PPI) information. However, the former often fails to work if a query protein has no or very little sequence similarity to any function-known proteins, while the latter had similar problem if the relevant PPI information is not available. In view of this, a new approach is proposed by hybridizing the PPI information and the biochemical/physicochemical features of protein sequences. The overall first-order success rates by the new predictor for the functions of mouse proteins on training set and test set were 69.1% and 70.2%, respectively, and the success rate covered by the results of the top-4 order from a total of 24 orders was 65.2%.

Conclusions/Significance

The results indicate that the new approach is quite promising that may open a new avenue or direction for addressing the difficult and complicated problem.

Improving prediction accuracy of drug activities by utilising unlabelled instances with feature selection

Molecular activities can be predicted by Quantitative Structure Activity Relationship (QSAR). Because of the high cost of experiments, the number of drug molecules with known activity is much less than that of unknown, to predict molecular activities utilising unlabelled instances will be an interesting issue. Here, Semi-Supervised Learning (SSL) is introduced and a SSL method, Co-Training is investigated on predicting drug activities utilising unlabelled instances. At the same time, a novel algorithm called FESCOT is proposed, which applies feature selection to remove redundant and irrelevant features for Co-Training. Numerical experimental results show that Co-Training and feature selection helps to improve the prediction ability of Co-Training.

Prediction of small molecules' metabolic pathways based on functional group composition

How to correctly and efficiently determine small molecules' biological function is a challenge and has a positive effect on further metabonomics analysis. Here, we introduce a computational approach to address this problem. The new approach is based on AdaBoost method and featured by function group composition to the metabolic pathway analysis, which can fast and automatically map the small chemical molecules back to the possible metabolic pathway that they belong to. As a result, jackknife cross validation test and independent set test on the model reached 73.7% and 73.8%, respectively. It can be concluded that the current approach is very promising for mapping some unknown molecules' possible metabolic pathway. An online predictor developed by this research is available at http://chemdata.shu.edu.cn/pathway.

Prediction of interactiveness of proteins and nucleic acids based on feature selections

It is important to identify which proteins can interact with nucleic acids for the purpose of protein annotation, since interactions between nucleic acids and proteins involve in numerous cellular processes such as replication, transcription, splicing, and DNA repair. This research tries to identify proteins that can interact with DNA, RNA, and rRNA, respectively. mRMR (Minimum redundancy and maximum relevance), with its elegant mathematical formulation, has been applied widely in processing biological data and feature analysis since its introduction in 2005. mRMR plus incremental feature selection (IFS) is known to be very efficient in feature selection and analysis, and able to improve both effectiveness and efficiency of a prediction model. IFS is applied to decide how many features should be selected from feature list provided by mRMR. In the end, the selected features of mRMR and IFS are further refined by a conventional feature selection method--forward feature wrapper (FFW), by reordering the features. Each protein is coded by 132 features including amino acid compositions and physicochemical properties. After the feature selection, k-Nearest Neighbor algorithm, the adopted prediction model, is trained and tested. As a result, the optimized prediction accuracies for the DNA, RNA, and rRNA are 82.0, 83.4, and 92.3%, respectively. Furthermore, the most important features that contribute to the prediction are identified and analyzed biologically. The predictor, developed for this research, is available for public access at http://chemdata.shu.edu.cn/protein_na_mrmr/.

Molecular characterization and expression of three heat shock protein70 genes from the carmine spider mite, Tetranychus cinnabarinus (Boisduval)

Three heat shock protein 70 (Hsp70) cDNAs were isolated from the carmine spider mite, Tetranychus cinnabarinus. They were tentatively named as TCHsp70-1, TCHsp70-2 and TCHsp70-3. Structural analyses showed that all of the three TCHsp70 cDNAs held the full open reading frame (ORF). Putative protein sequences and a phylogenetic tree suggested that TCHsp70-1 and TCHsp70-3 were cytoplasm HSP70 and TCHsp70-2 was endoplasmic reticulum HSP70. Comparison of deduced amino acid sequences of TCHsp70-1 and TCHsp70-3 showed 84.78% identity, TCHsp70-1 and TCHsp70-2 showed 57.33% identity, TCHsp70-2 and TCHsp70-3 showed 58.26% identity. Real-time comparative quantitative PCR revealed that the relative expression of TCHsp70-2 was lower than TCHsp70-1 and TCHsp70-3 at each temperature tested. TCHsp70-1 and TCHsp70-3 shared a similar expression pattern after cold and heat shock compared with their expression at normal temperature (26 degrees C), but the mRNA expression of TCHsp70-1 was significantly higher and lower than that of TCHsp70-3 at cold and heat shock temperatures (except for 34 degrees C), respectively. This result possibly indicated the expression patterns of TCHsp70 were affected by their location in different cellular compartments. The results also indicated that three TCHsp70s, especially TCHsp70-1 and TCHsp70-3, may play an important role in mediating tolerance to cold, thermal stress for Tetranychus cinnabarinus.

GalNAc-transferase specificity prediction based on feature selection method

GalNAc-transferase can catalyze the biosynthesis of O-linked oligosaccharides. The specificity of GalNAc-transferase is composed of nine amino acid residues denoted by R4, R3, R2, R1, R0, R1', R2', R3', R4'. To predict whether the reducing monosaccharide will be covalently linked to the central residue R0(Ser or Thr), a new method based on feature selection has been proposed in our work. 277 nonapeptides from reference [Chou KC. A sequence-coupled vector-projection model for predicting the specificity of GalNAc-transferase. Protein Sci 1995;4:1365-83] are chosen for training set. Each nonapeptide is represented by hundreds of amino acid properties collected by Amino Acid Index database (http://www.genome.jp/aaindex) and transformed into a numeric vector with 4554 features. The Maximum Relevance Minimum Redundancy (mRMR) method combining with Incremental Feature Selection (IFS) and Feature Forward Selection (FFS) are then applied for feature selection. Nearest Neighbor Algorithm (NNA) is used to build prediction models. The optimal model contains 54 features and its correct rate tested by Jackknife cross-validation test reaches 91.34%. Final feature analysis indicates that amino acid residues at position R3' play the most important role in the recognition of GalNAc-transferase specificity, which were confirmed by the experiments [Elhammer AP, Poorman RA, Brown E, Maggiora LL, Hoogerheide JG, Kezdy FJ. The specificity of UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferase as inferred from a database of in vivo substrates and from the in vitro glycosylation of proteins and peptides. J Biol Chem 1993;268:10029-38; O'Connell BC, Hagen FK, Tabak LA. The influence of flanking sequence on the O-glycosylation of threonine in vitro. J Biol Chem 1992;267:25010-8; Yoshida A, Suzuki M, Ikenaga H, Takeuchi M. Discovery of the shortest sequence motif for high level mucin-type O-glycosylation. J Biol Chem 1997;272:16884-8]. Our method can be used as a tool for predicting O-glycosylation sites and for investigating the GalNAc-transferase specificity, which is useful for designing competitive inhibitors of GalNAc-transferase. The predicting software is available upon the request.

HIV-1 protease cleavage site prediction based on amino acid property

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired is useful for designing specific and efficient HIV protease inhibitors. Recently, several works have approached the HIV-1 protease specificity problem by applying a number of classifier creation and combination methods. The pace in searching for the proper inhibitors of HIV protease will be greatly expedited if one can find an accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this article, we selected HIV-1 protease as the subject of the study. 299 oligopeptides were chosen for the training set, while the other 63 oligopeptides were taken as a test set. The peptides are represented by features constructed by AAIndex (Kawashima et al., Nucleic Acids Res 1999, 27, 368; Kawashima and Kanehisa, Nucleic Acids Res 2000, 28, 374). The mRMR method (Maximum Relevance, Minimum Redundancy; Ding and Peng, Proc Second IEEE Comput Syst Bioinformatics Conf 2003, 523; Peng et al., IEEE Trans Pattern Anal Mach Intell 2005, 27, 1226) combining with incremental feature selection (IFS) and feature forward search (FFS) are applied to find the two important cleavage sites and to select 364 important biochemistry features by jackknife test. Using KNN (K-nearest neighbors) to combine the selected features, the prediction model obtains high accuracy rate of 91.3% for Jackknife cross-validation test and 87.3% for independent-set test. It is expected that our feature selection scheme can be referred to as a useful assistant technique for finding effective inhibitors of HIV protease, especially for the scientists in this field.

Prediction of compounds' biological function (metabolic pathways) based on functional group composition

Efficient in silico screening approaches may provide valuable hints on biological functions of the compound-candidates, which could help to screen functional compounds either in basic researches on metabolic pathways or drug discovery. Here, we introduce a machine learning method (Nearest Neighbor Algorithm) based on functional group composition of compounds to the analysis of metabolic pathways. This method can quickly map small chemical molecules to the metabolic pathway that they likely belong to. A set of 2,764 compounds from 11 major classes of metabolic pathways were selected for study. The overall prediction rate reached 73.3%, indicating that functional group composition of compounds was really related to their biological metabolic functions.

Predicting protein structural class with AdaBoost Learner

The structural class is an important feature in characterizing the overall topological folding type of a protein or the domains therein. Prediction of protein structural classification has attracted the attention and efforts from many investigators. In this paper a novel predictor, the AdaBoost Learner, was introduced to deal with this problem. The essence of the AdaBoost Learner is that a combination of many 'weak' learning algorithms, each performing just slightly better than a random guessing algorithm, will generate a 'strong' learning algorithm. Demonstration thru jackknife cross-validation on two working datasets constructed by previous investigators indicated that AdaBoost outperformed other predictors such as SVM (support vector machine), a powerful algorithm widely used in biological literatures. It has not escaped our notice that AdaBoost may hold a high potential for improving the quality in predicting the other protein features as well, such as subcellular location and receptor type, among many others. Or at the very least, it will play a complementary role to many of the existing algorithms in this regard.

Oxidation Pattern of Small Silicon Oxide Clusters: Structures and Stability of Si6On (n = 1−12)

We have performed systematic ab initio calculations to study the structures and stability of Si(6)O(n)() clusters (n = 1-12) in order to understand the oxidation process in silicon systems. Our calculation results show that oxidation pattern of the small silicon cluster, with continuous addition of O atoms, extends from one side to the entire Si cluster. Si atoms are found to be separated from the pure Si cluster one-by-one by insertion of oxygen into the Si-O bonds. From fragmentation energy analyses, it is found that the Si-rich clusters usually dissociate into a smaller pure Si clusters (Si(5), Si(4), Si(3), or Si(2)), plus oxide fragments such as SiO, Si(2)O(2), Si(3)O(3), Si(3)O(4), and Si(4)O(5). We have also studied the structures of the ionic Si(6)O(n)(+/-) (n = 1-12) clusters and found that most of ionic clusters have different lowest-energy structures in comparison with the neutral clusters. Our calculation results suggest that transformation Si(6)O(n)+(a) + O --> Si(6)O(n+1)+(a) should be easier.

Molecule intrinsic minimal basis sets. II. Bonding analyses for Si4H6 and Si2 to Si10

The method, introduced in the preceding paper, for recasting molecular self-consistent field (SCF) or density functional theory (DFT) orbitals in terms of intrinsic minimal bases of quasiatomic orbitals, which differ only little from the optimal free-atom minimal-basis orbitals, is used to elucidate the bonding in several silicon clusters. The applications show that the quasiatomic orbitals deviate from the minimal-basis SCF orbitals of the free atoms by only very small deformations and that the latter arise mainly from bonded neighbor atoms. The Mulliken population analysis in terms of the quasiatomic minimal-basis orbitals leads to a quantum mechanical interpretation of small-ring strain in terms of antibonding encroachments of localized molecular-orbitals and identifies the origin of the bond-stretch isomerization in Si4H6. In the virtual SCF/DFT orbital space, the method places the qualitative notion of virtual valence orbitals on a firm basis and provides an unambiguous ab initio identification of the frontier orbitals.

Molecule intrinsic minimal basis sets. I. Exact resolution of ab initio optimized molecular orbitals in terms of deformed atomic minimal-basis orbitals

A method is presented for expressing the occupied self-consistent-field (SCF) orbitals of a molecule exactly in terms of chemically deformed atomic minimal-basis-set orbitals that deviate as little as possible from free-atom SCF minimal-basis orbitals. The molecular orbitals referred to are the exact SCF orbitals, the free-atom orbitals referred to are the exact atomic SCF orbitals, and the formulation of the deformed "quasiatomic minimal-basis-sets" is independent of the calculational atomic orbital basis used. The resulting resolution of molecular orbitals in terms of quasiatomic minimal basis set orbitals is therefore intrinsic to the exact molecular wave functions. The deformations are analyzed in terms of interatomic contributions. The Mulliken population analysis is formulated in terms of the quasiatomic minimal-basis orbitals. In the virtual SCF orbital space the method leads to a quantitative ab initio formulation of the qualitative model of virtual valence orbitals, which are useful for calculating electron correlation and the interpretation of reactions. The method is applicable to Kohn-Sham density functional theory orbitals and is easily generalized to valence MCSCF orbitals.

Using LogitBoost classifier to predict protein structural classes

Prediction of protein classification is an important topic in molecular biology. This is because it is able to not only provide useful information from the viewpoint of structure itself, but also greatly stimulate the characterization of many other features of proteins that may be closely correlated with their biological functions. In this paper, the LogitBoost, one of the boosting algorithms developed recently, is introduced for predicting protein structural classes. It performs classification using a regression scheme as the base learner, which can handle multi-class problems and is particularly superior in coping with noisy data. It was demonstrated that the LogitBoost outperformed the support vector machines in predicting the structural classes for a given dataset, indicating that the new classifier is very promising. It is anticipated that the power in predicting protein structural classes as well as many other bio-macromolecular attributes will be further strengthened if the LogitBoost and some other existing algorithms can be effectively complemented with each other.

Structure and stability of oxygen adsorption on Si(n) (n = 5-10) clusters

The structures, binding energies, and electronic properties of one oxygen atom (O) and two oxygen atoms (2O) adsorption on silicon clusters Si(n) with n ranging from 5 to 10 are studied systematically by ab initio calculations. Twelve stable structures are obtained, two of which are in agreement with those reported in previous literature and the others are new structures that have not been proposed before. Further investigations on the fragmentations of Si(n)O and Si(n)O2 (n = 5-10) clusters indicate that the pathways Si(n)O --> Si(n-1) + SiO and Si(n)O2 --> Si(n-2) + Si2O2 are most favorable from thermodynamic viewpoint. Among the studied silicon oxide clusters, Si8O, Si9O, Si5O2 and Si8O2 correspond to large adsorption energies of silicon clusters with respect to O or 2O, while Si8O, with the smallest dissociation energy, has a tendency to separate into Si7 + SiO. Using the recently developed quasi-atomic minimal-basis-orbital method, we have also calculated the unsaturated valences of the neutral Si(n) clusters. Our calculation results show that the Si atoms which have the largest unsaturated valences are more attractive to O atom. Placing O atom right around the Si atoms with the largest unsaturated valences usually leads to stable structures of the silicon oxide clusters.

Potential Energy Surfaces of SimOn Cluster Formation and Isomerization

The reaction paths for formation and isomerization of a set of silica SimOn (m = 2,3, n = 1-5) nanoclusters have been investigated using second-order perturbation theory (MP2) with the 6-31G(d) basis set. The MP2/6-31G(d) calculations have predicted singlet ground states for all clusters excluding Si3O2. The total energies of the most important points on the potential energy surfaces (PES) have been determined using the completely renormalized (CR) singles and doubles coupled cluster method including perturbative triples, CR-CCSD(T) with the cc-pVTZ basis set. Although transition states have been located for many isomerization reactions, only for Si3O3 and Si3O4 have some transition states been found for the formation of a cluster from the separated reactants. In all other cases, the process of formation of SimOn clusters appears to proceed without potential energy barriers.

Semiempirical Quantum Chemical Method and Artificial Neural Networks Applied for λmax Computation of Some Azo Dyes

The maximum absorption wavelengths of 31 azo dyes have been calculated by two comprehensive methods using the semiempirical quantum chemical method, PM3, and the weight decay based artificial neural network (WD-ANN) or the early stopping based artificial neural network (ES-ANN). The average absolute errors of WD-ANN and that of ES-ANN are 10.07 nm and 12.40 nm, respectively. These results are much better than the results using ZINDO/S with the default value (0.585) only.

Effect of chain connectivity on the structure of Lennard-Jones liquid and its implicationon statistical potentials for protein folding

Statistical contact potentials and bead-spring models have been widely used for computational studies of protein folding. However, there has been speculation that systematic error may arise in the contact energy calculations when the statistical potentials are deduced under the assumption that the chain connectivity in proteins can be ignored. To address this issue, we have performed molecular-dynamics simulations to study the structure and dynamics of a simple liquid system in which the beads are either connected or unconnected with springs. Results from the present study provide useful information for assessing the accuracy of the statistical potentials for protein structure simulations.

Hyper-polyhedron model applied to molecular screening of guanidines as Na/H exchange inhibitors

AIM

To investigate structure-activity relationships of N-(3-Oxo-3,4-dihydro-2H-benzo[1,4]oxazine-6-carbonyl) guanidines in Na/H exchange inhibitory activities and probe into a new method of the computer-aided molecular screening.

METHODS

The hyper-polyhedron model (HPM) was proposed in our lab.

RESULTS

The samples with probably higher activities could be determined in such a way that their representing points should be in the hyper-polyhedron region where all known samples with high activities were distributed. And the predictive ability of different methods available was tested by the cross-validation experiment.

CONCLUSION

The accurate rate of molecular screening of N-(3-Oxo-3,4-dihydro-2H-benzo[1,4]oxazine-6-carbonyl) guanidines by HPM was much higher than that obtained by PCA (principal component analysis) and Fisher methods for the data set available here. Therefore, HPM could be used as a powerful tool for screening new compounds with probably higher activities.

Trans-activation of heparanase promoter by ETS transcription factors

The remodeling of extracellular matrix (ECM) is an important process required for cancer cells to turn into invasive and metastatic cancer cells. To dissolve the protein components of ECM, matrix metalloproteinases are some of the essential enzymes. Another ECM remodeling enzyme is the heparanase (Hpa) that digests the heparin sulfate component of the matrix. In metastatic cancer cells the Hpa gene is upregulated. To investigate the mechanism of why Hpa was upregulated in metastatic cancer cells, the regulatory sequence of heparanase gene was isolated and its function analysed in metastatic breast cancer cells. We found there are four ETS transcription factor binding sites. Two of them flanking the transcription initiation of the Hpa gene are nonfunctional, whereas two others are highly functional and responded to exogenously added ETS transcription factors. Mutation of these two ETS binding sites abolished the transcriptional activation of Hpa promoter by ETS transcription factors. Among four transcription factors tested (ETS1, ETS2, PEA3, and ER81), ETS1 and ETS2 are more potent in transactivating the human Hpa gene. Furthermore, dominant-negative ETS transcription factors failed to transactivate Hpa promoter and could abrogate the function of wild-type transcription factor in transactivation activity of ETS transcription factors on the Hpa promoter. These results suggest that ETS transcription factors play an important role in tumor invasion and metastasis by modulating the remodeling of ECM.

Structural organization of the human neuronal nitric oxide synthase gene (NOS1)

Neuronal nitric oxide (NO) synthase, localized to human chromosome 12, uniquely participates in diverse biologic processes; neurotransmission, the regulation of body fluid homeostasis, neuroendocrine physiology, control of smooth muscle motility, sexual function, and myocyte/myoblast biology, among others. Restriction enzyme mapping, subcloning, and DNA sequence analysis of bacteriophage- and yeast artificial chromosome-derived human genomic DNA indicated that the mRNA for neuronal NO synthase is dispersed over a minimum of 160 kilobases of human genomic DNA. Analysis of intron-exon splice junctions predicted that the open reading frame is encoded by 28 exons, with translation initiation and termination in exon 2 and exon 29, respectively. Determination of transcription initiation sites in brain poly(A) RNA with primer extension analysis and RNase protection revealed a major start site 28 nucleotides downstream from a TATA box. Sequence inspection of 5'-flanking regions revealed potential cis-acting DNA elements: AP-2, TEF-1/MCBF, CREB/ATF/c-Fos, NRF-1, Ets, NF-1, and NF-kappa B-like sequences. Diversity appears to represent a major theme apparent upon analysis of human neuronal NO synthase mRNA transcripts. A microsatellite of the dinucleotide variety was detected within the 3'-untranslated region of exon 29. Multiple alleles were evident in normal individuals indicating the existence of allelic mRNA sequence variation. Characterization of variant human neuronal NO synthase cDNAs indicated the existence of casette exon 9/10 and exon 10 deletions as examples of structural mRNA diversity due to alternative splicing. The latter deletion of a 175-nucleotide exon introduces a frame-shift and premature stop codon indicating the potential existence of a novel NH2 terminus protein. In summary, analysis of the human neuronal NO synthase locus reveals a complex genomic organization and mRNA diversity that is both allelic and structural.

[Six semi-sold media methods for detecting motility of gram negative bacilli]

Motility is recognized as a significant biological character of certain bacteria, and is used as a fundamental basis of classification in many taxonomic systems. We compared wet mount method, semi-solid medium method and flagella stain method to evaluate the motility activity of 538 Enterobacteriaceae and 300 glucose non-fermentative gram negative bacilli. The results showed a sensitivity of 100% in flagella stain, Gilardi medium, Mueller Hinton semisolid medium and sulfide-indole-motility (SIM) medium (Difco); 99.6% in SIM (Kyokuto) and SIM (BBL); 99.3% in motility test medium (BBL) and 97% in wet mount. Motile Enterobacteriaceae grow well, and turbidity changes clearly and is easy to interpret. Motile glucose nonfermentative gram negative bacilli grow so lightly and rapidly diffuse throughout the medium and are hardly to interpret. It is better to use colorless Gilardi medium and Mueller Hinton semi-solid medium and to read within 4-8 hours or 24 hours. The advantages of the semi-solid medium method are particularly evident in teaching schedules and routine testing, because the results are cumulative, macroscopic, highly sensitive, and easy to manipulate.

[The relation of vasoactive intestinal peptide and acute hypoxia]

In order to observe the effect of acute hypoxia on release of vasoactive intestinal peptide (VIP), the plasma VIP content was determined in anesthetized dogs by a specific radioimmunoassay technique during acute hypoxia. Blood gases and hemodynamics were monitored simultaneously. After inhalation of 10% oxygen. the plasma VIP levels elevated along with decrease in PaO2 and increase in pulmonary artery pressure. The plasma concentration of VIP in the portal vein increased significantly from 106 +/- 21 pg/ml before hypoxia to 173 +/- 36 pg/ml 15 minutes after the onset of hypoxia (P less than 0.01). The difference of arterio-venous VIP content increased from -3 +/- 6 pg/ml before hypoxia to +9 +/- 7 pg/ml after inhalation of 10% oxygen for 30 minutes. The results suggested that VIP was released from the gastrointestinal tract as well as from the lung in case of hypoxia and pulmonary hypertension. It is considered that the release of VIP may be an adaptive and compensatory response, promoting vasodilation and perfusion in vital organs.

Specificity of adhesion between murine tumor cells and capillary endothelium: an in vitro correlate of preferential metastasis in vivo

We have compared the rate and extent of adhesion of various types of mouse tumor cells to endothelial cells derived from different organ sources. Our panel of tumors has included sarcoma, bladder carcinoma, glioma, teratoma, hepatoma, endothelioma, mammary adenocarcinoma, and lymphoma cells. Endothelial cell monolayers have included murine microvascular endothelial cells from ovary, brain, lung, and liver as well as large vessel endothelium from thoracic duct and dorsal aorta. Tumor cells differ both in the adhesive propensity and adhesive preference for different endothelial cells. Some, but not all, of the adhesive preferences correlate with the known in vivo metastatic behavior of these tumors. Our results support the hypothesis that endothelial cell surface-associated specificities may play a significant role in determining the pattern of metastasis.