In silico prediction of mosquito repellents for clothing application.

Use of protective clothing is a simple and efficient way to reduce the contacts with mosquitoes and consequently the probability of transmission of diseases spread by them. This mechanical barrier can be enhanced by the application of repellents. Unfortunately the number of available repellents is limited. As a result, there is a crucial need to find new active and safer molecules repelling mosquitoes. In this context, a structure-activity relationship (SAR) model was proposed for the design of repellents active on clothing. It was computed from a dataset of 2027 chemicals for which repellent activity on clothing was measured against Aedes aegypti. Molecules were described by means of 20 molecular descriptors encoding physicochemical properties, topological information and structural features. A three-layer perceptron was used as statistical tool. An accuracy of 87% was obtained for both the training and test sets. Most of the wrong predictions can be explained. Avenues for increasing the performances of the model have been proposed.

Elevation of neuronal expression of NAIP reduces ischemic damage in the rat hippocampus.

We show here that transient forebrain ischemia selectively elevates levels of neuronal apoptosis inhibitory protein (NAIP) in rat neurons that are resistant to the injurious effects of this treatment. This observation suggests that increasing NAIP levels may confer protection against ischemic cell death. Consistent with this proposal, we demonstrate that two other treatments that increase neuronal NAIP levels, systemic administration of the bacterial alkaloid K252a and intracerebral injection of an adenovirus vector capable of overexpressing NAIP in vivo, reduce ischemic damage in the rat hippocampus. Taken together, these findings suggest that NAIP may play a key role in conferring resistance to ischemic damage and that treatments that elevate neuronal levels of this antiapoptotic protein may have utility in the treatment of stroke.

Topological QSAR Modelling of Carboxamides Repellent Activity to Aedes Aegypti.

Aedes aegypti vector control is of paramount importance owing to the damages induced by the various severe diseases that these insects may transmit, and the increasing risk of important outbreaks of these pathologies. Search for new chemicals efficient against Aedes aegypti, and devoid of side-effects, which have been associated to the currently most used active substance i. e. N,N-diethyl-m-toluamide (DEET), is therefore an important issue. In this context, we developed various Quantitative Structure Activity Relationship (QSAR) models to predict the repellent activity against Aedes aegypti of 43 carboxamides, by using Multiple Linear Regression (MLR) and various machine learning approaches. The easy computation of the four topological descriptors selected in this study, compared to the CODESSA descriptors used in the literature, and the predictive ability of the here proposed MLR and machine learning models developed using the software QSARINS and R, make the here proposed QSARs attractive. As demonstrated in this study, these models can be applied at the screening level, to guide the design of new alternatives to DEET.

Decision trees versus support vector machine for classification of androgen receptor ligands.

With the current concern of limiting experimental assays, increased interest now focuses on in silico models able to predict toxicity of chemicals. Endocrine disruptors cover a large number of environmental and industrial chemicals which may affect the functions of natural hormones in humans and wildlife. Structure-activity models are now increasingly used for predicting the endocrine disruption potential of chemicals. In this study, a large set of about 200 chemicals covering a broad range of structural classes was considered in order to categorize their relative binding affinity (RBA) to the androgen receptor. Classification of chemicals into four activity groups, with respect to their log RBA value, was carried out in a cascade of recursive partitioning trees, with descriptors calculated from CODESSA software and encoding topological, geometrical and quantum chemical properties. The hydrophobicity parameter (log P), Balaban index, and descriptors relying on charge distribution (maximum partial charge, nucleophilic index on oxygen atoms, charged surface area, etc.) appear to play a major role in the chemical partitioning. Separation of strongly active compounds was rather straightforward. Similarly, about 90% of the inactive compounds were identified. More intricate was the separation of active compounds into subsets of moderate and weak binders, the task requiring a more complex tree. A comparison was made with support vector machine yielding similar results.

QSAR models for predicting the toxicity of piperidine derivatives against Aedes aegypti.

QSAR models are proposed for predicting the toxicity of 33 piperidine derivatives against Aedes aegypti. From 2D topological descriptors, calculated with the PaDEL software, ordinary least squares multilinear regression (OLS-MLR) treatment from the QSARINS software and machine learning and related approaches including linear and radial support vector machine (SVM), projection pursuit regression (PPR), radial basis function neural network (RBFNN), general regression neural network (GRNN) and k-nearest neighbours (k-NN), led to four-variable models. Their robustness and predictive ability were evaluated through both internal and external validation. Determination coefficients (r2) greater than 0.85 on the training sets and 0.8 on the test sets were obtained with OLS-MLR and linear SVM. They slightly outperform PPR, radial SVM and RBFNN, whereas GRNN and k-NN showed lower performance. The easy availability of the involved structural descriptors and the simplicity of the MLR model make the corresponding model attractive at an exploratory level for proposing, from this limited dataset, guidelines in the design of new potentially active molecules.

Dopamine-receptor stimulation: biobehavioral and biochemical consequences.

The MPTP monkey is a well-characterized animal model of parkinsonism and provides an exceptional tool for the study of dyskinesias induced by dopamine-like agents. Several such agents have been tested during the past 15 years, and it has been found that the duration of action of these compounds is the most reliable variable with which to predict their dyskinesiogenic profile. It is proposed that L-dopa-induced dyskinesias represent a form of pathological learning caused by chronic pulsatile (nonphysiological) stimulation of dopamine receptors, which activates a cascade of molecular and biochemical events. These events include defective regulation of Fos proteins that belong to the deltaFosB family, increased expression of neuropeptides, and defective GABA- and glutamate-mediated neurotransmission in the output structures of the basal ganglia.

MolDIA: XML based system of molecular diversity analysis towards virtual screening and QSPR.

In this paper a new chemoinformatics tool for Molecular Diversity Analysis (MolDIA) is introduced. The objective of this system is the analysis of molecular similarity and diversity through the treatment of structural and physicochemical information. Current needs for chemical databases include the analysis, the management and the retrieval of chemical information. The implementation of eXtended Markup Languages (XML) is proposed as a basis for representing and structuring the chemical information contained in data structures and databases. The adequate descriptor vector and related physicochemical properties have been defined and constructed. The benefits of XML in chemoinformatics are discussed, as well as, the applications of this system in a virtual screening environment.

Quantitative structure-toxicity relationships (QSTRs): a comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics to Tetrahymena pyriformis.

Prediction of toxicity of 203 nitro- and cyano-aromatic chemicals to Tetrahymena pyriformis was carried out by radial basis function neural network, general regression neural network and support vector machine, in non-linear response surface methodology. Toxicity was predicted from hydrophobicity parameter (log Kow) and maximum superdelocalizability (Amax). Special attention was drawn to prediction ability and robustness of the models, investigated both in a leave-one-out and 10-fold cross validation (CV) processes. The influence that the corresponding changes in the learning sets during these CV processes could have on a common external test set including 41 compounds was also examined. This allowed us to establish the stability of the models. The non linear results slightly outperform (as expected) multilinear relationships (MLR) and also favourably compete with various other non linear approaches recently proposed by Ren (J. Chem. Inf. Comput. Sci., 43 1679 (2003)).

Investigation of the influence of protein corona composition on gold nanoparticle bioactivity using machine learning approaches.

The understanding of the mechanisms and interactions that occur when nanomaterials enter biological systems is important to improve their future use. The adsorption of proteins from biological fluids in a physiological environment to form a corona on the surface of nanoparticles represents a key step that influences nanoparticle behaviour. In this study, the quantitative description of the composition of the protein corona was used to study the effect on cell association induced by 84 surface-modified gold nanoparticles of different sizes. Quantitative relationships between the protein corona and the activity of the gold nanoparticles were modelled by using several machine learning-based linear and non-linear approaches. Models based on a selection of only six serum proteins had robust and predictive results. The Projection Pursuit Regression method had the best performances (r(2) = 0.91; Q(2)loo = 0.81; r(2)ext = 0.79). The present study confirmed the utility of protein corona composition to predict the bioactivity of gold nanoparticles and identified the main proteins that act as promoters or inhibitors of cell association. In addition, the comparison of several techniques showed which strategies offer the best results in prediction and could be used to support new toxicological studies on gold-based nanomaterials.

Electrophoretic studies of the protein constituents of pig spleen lymphocyte plasma membrane and of a non-ionic detergent extract of intact cells.

This report describes a study of the protein constituents of pig spleen lymphocyte plasma membrane, separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis and analyzed under reducing and non-reducing conditions. The electrophoretic patterns of purified plasma membranes, of the endoplasmic reticulum fraction and of a non-ionic detergent (Nonidet P-40) extract of intact lymphocytes are compared. The lymphocytes were ruptured by the nitrogen cavitation method and the plasma membranes were purified by differential centrifugation in sucrose gradients. Plasma membranes were enriched in marker enzymes and appeared, in electron micrographs, as vesicles of various sizes and as fragmented membranes. The protein constituents were resolved into more than 60 bands which appeared, except in the endoplasmic reticulum fraction, as discrete Coomassie-positive bands. Characteristic bands were present in each of the three fractions when samples were not reduced. However, the majority of Coomassie-stained proteins migrated with the same relative mobility in all three fractions. Interestingly, all Coomassie-positive bands stained (some weakly) for glycoproteins. When samples were reduced and alkylated prior to electrophoresis, the electrophoretogram of each fraction differed from the non-reduced samples. In general, we observed that the fastest migrating portion of each gel was more intensively stained, when compared to non-reduced extracts. In addition, some bands characteristic of each fraction were evident, although most bands were common to each fraction. Coomassie-positive bands also stained with a glycoprotein staining reagent. Activity of alkaline phosphatase was demonstrated in the electrophoretograms of the non-reduced plasma membrane extracts. The results suggest that each of the three fractions analyzed under the conditions described here possesses a characteristic glycoprotein content. Furthermore, the data show that a Nonidet P-40 extract of lymphocytes is effective in solubilizing the glycoproteins from pig lymphocyte plasma membranes. Comparison of electrophoretic patterns with those reported for lymphocytes of other lymphoid organs of the pig suggests close similarities.

Separation and localization on sodium dodecyl sulfate polyacrylamide gels of pig spleen lymphocyte plasma membrane proteins which bind 125I-labelled phytohemagglutinin.

The protein constituents of splenic pig lymphocytes derived from the plasma membranes, the endoplasmic reticulum or from a non-ionic (Nonidet P-40) detergent of whole cells have been separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis, under reducing and non-reducing conditions. The binding of Phaseolus vulgaris (red kidney bean) phytohemagglutinin to these constituents has been studied by incubation of the labelled lectin with the undried electrophoretograms. Results show characteristic phytohemagglutinin-reactive components in each cell fraction, although the majority of the lectin-binding bands are common to the three fractions. Comparison of binding patterns for the reduced and non-reduced electrophoretograms does not reveal significant differences between the binding profiles. The components which bind the lectin in the largest amounts are located in the upper halves of the gels, which correspond to a molecular range of (75-250) . 10(3) daltons. Our data suggest that pig spleen lymphocyte plasma membranes contain at least 25-30 glycoproteins which can bind phytohemagglutinin. Iodine-labelled phytohemagglutinin binds to the vesicles prepared from lymphocyte plasma membranes and the Scatchard plot shows a non-linear upward concave curve.

Assembly of a high-resolution map of the Acadian Usher syndrome region and localization of the nuclear EF-hand acidic gene.

Usher syndrome type 1C (USH1C) occurs in a small population of Acadian descendants from southwestern Louisiana. Linkage and linkage disequilibrium analyses localize USH1C to chromosome 11p between markers D11S1397 and D11S1888, an interval of less than 680 kb. Here, we refine the USH1C linkage to a region less than 400 kb, between genetic markers D11S1397 and D11S1890. Using 17 genetic markers from this interval, we have isolated a contiguous set of 60 bacterial artificial chromosomes (BACs) that span the USH1C critical region. Exon trapping of BAC clones from this region resulted in the recovery of an exon of the nuclear EF-hand acidic (NEFA) gene. However, DNA sequence analysis of the NEFA cDNA from lymphocytes of affected individuals provided no evidence of mutation, making structural mutations in the NEFA protein unlikely as the cellular cause of Acadian Usher syndrome.

Epidermal growth factor induction of cellular proliferation and protooncogene expression in growth-arrested rat H4IIE hepatoma cells: role of cyclic adenosine monophosphate.

The precise molecular events involved in growth factor-mediated cell proliferation in eukaryotes have not been entirely elucidated. Identification and characterization of the itnracellular molecular signaling systems linking growth factor function with nuclear events would provide insight into the regulatory mechanisms governing eukaryotic cell growth. In this report, we demonstrate that serum-deprived rat H4IIE hepatoma cells enter a quiescent state and remain viable in the absence of serum for up to 7 days. These cells can be stimulated to transverse the cell cycle and proliferate in response to epidermal growth factor (EGF) after a 24-h lag phase. We were able to completely mimic the mitogenic effects of EGF with 8-p-chlorophenylthio-cAMP (8-CPT-cAMP) but only partially with N6-(Bu)2-cAMP. EGF and 8-CPT-cAMP together induce a synergistic increase in H4IIE hepatoma cell proliferation. The calcium ionophore A23187 and the phorbol ester, 4 beta-phorbol 12-myristate 13-acetate had little effect on H4IIE cell proliferation. EGF treatment led to a rapid and transient increase of intracellular cAMP concentration. Both 8-CPT-cAMP and EGF were also equally effective in causing a rapid and transient induction of c-fos and c-myc protooncogene mRNA levels when added to growth-arrested H4IIE cells while A23187, N-(Bu)2-cAMP, and 4 beta-phorbol 12-myristate 13-acetate were significantly less effective. Both EGF and 8-CPT-cAMP affect protooncogene expression in growth-arrested rat H4IIE hepatoma cells primarily at the transcriptional level. Localization and semi-quantification of nuclear pp55c-fos and 63 (kilodalton)-myc protooncoproteins by immunocolloidal gold electron microscopy revealed that EGF and/or 8-CPT-cAMP treatment of quiescent H4IIE hepatoma cells led to a marked and rapid nuclear accumulation of these proteins in discrete nuclear substructures. Cummulatively, these results suggest that cAMP participates in the intracellular signaling system mediating the mitogenic and protooncogene inducing effects of EGF on growth-arrested rat H4IIE hepatoma cells.

Induction of macrophage-like differentiation of HL-60 leukemia cells by tumor necrosis factor-alpha: potential role of fos expression.

Tumor necrosis factor-alpha (TNF-alpha) is a macrophage-derived cytokine elicited during cellular responses to various microbial infections. TNF-alpha exerts direct cytotoxicity toward some tumor cells in vitro and produces hemorrhagic tumor necrosis in vivo. In human promyelocytic HL-60 leukemia cells, human recombinant TNF-alpha (rTNF-alpha) exhibits a small early proliferative effect (within 48 h), followed by marked cytostatic activity at 96 h after the addition of rTNF-alpha. Cytostasis is contiguous with an induction of cell differentiation along the monocyte/macrophage lineage. The cell proliferation effects and the induction of the differentiated phenotype are preceded by an approximate 5-fold increase in c-fos mRNA levels within 90 min after rTNF-alpha treatment of log phase HL-60 cells. Nuclear in vitro transcription assays indicate that the effect of rTNF-alpha on c-fos mRNA abundance is controlled at the transcriptional level. We have also used a postembedding immunocolloidal gold electron microscopy technique to localize and semiquantitate pp55c-fos proto-oncoprotein levels in the nucleus of both control and rTNF-alpha-treated HL-60 leukemia cells. In response to rTNF-alpha, we have observed a rapid and transient accumulation of pp55c-fos in discrete nuclear substructures within 2 h after treatment. C-fos staining appears in clusters, which are preferentially localized over semi-condensed chromatin and interchromatin granules. These results suggest that pp55c-fos is involved in the signal transduction system initiated by rTNF-alpha during the induction of HL-60 differentiation.

Structure-activity relationship (SAR) modelling of mosquito larvicides.

An attempt was made to derive structure-activity models allowing the prediction of the larvicidal activity of structurally diverse chemicals against mosquitoes. A database of 188 chemicals with their activity on Aedes aegypti larvae was constituted from analysis of original publications. The activity values were expressed in log 1/IC50 (concentration required to produce 50% inhibition of larval development, mmol). All the chemicals were encoded by means of CODESSA and autocorrelation descriptors. Partial least squares analysis, classification and regression tree, random forest and boosting regression tree analyses, Kohonen self-organizing maps, linear artificial neural networks, three-layer perceptrons, radial basis function artificial neural networks and support vector machines with linear, polynomial, radial basis function and sigmoid kernels were tested as statistical tools. Because quantitative models did not give good results, a two-class model was designed. The three-layer perceptron significantly outperformed the other statistical approaches regardless of the threshold value used to split the data into active and inactive compounds. The most interesting configuration included eight autocorrelation descriptors as input neurons and four neurons in the hidden layer. This led to more than 96% of good predictions on both the training set and external test set of 88 and 100 chemicals, respectively. From the overall simulation results, new candidate molecules were proposed which will be shortly synthesized and tested.

Linear and non-linear modelling of the cytotoxicity of TiO2 and ZnO nanoparticles by empirical descriptors.

Titanium oxide (TiO2) and zinc oxide (ZnO) nanoparticles are among the most widely used in different applications in daily life. In this study, local regression and classification models were developed for a set of ZnO and TiO2 nanoparticles tested at different concentrations for their ability to disrupt the lipid membrane in cells. Different regression techniques were applied and compared by checking the robustness of the models and their external predictive ability. Additionally, a simple classification model was developed, which predicts the potential for disruption of the studied nanoparticles with good accuracy (overall accuracy, specificity, and sensitivity >80%) on the basis of two empirical descriptors. The present study demonstrates that empirical descriptors, such as experimentally determined size and tested concentrations, are relevant to modelling the activity of nanoparticles. This information may be useful to screen the potential for harmful effect of nanoparticles in different experimental conditions and to optimize the design of toxicological tests. Results from the present study are useful to support and refine the future application of in silico tools to nanoparticles, for research and regulatory purposes.

Excitable membranes, lipid messengers, and immediate-early genes. Alteration of signal transduction in neuromodulation and neurotrauma.

The physical nature of neuronal cells, particularly in the functional and morphological segregation of synapse, soma, and dendrites, imparts special importance on the integrity of their cell membranes for the localization of function, generation of intrinsic second messengers, and plasticity required for adaptation and repair. The component phospholipids of neural membranes are important sources of bioactive mediators that participate in such diverse phenomena as memory formation and cellular damage following trauma. A common role for PAF in these processes is established through the suppressive effects of its antagonists. Furthermore, being both an extracellular and intracellular agonist of phospholipase activation, in addition to being a product of phospholipase activity, PAF assumes a centralized role in the cellular metabolism following neural stimulation. The linkage of PAF to neural immediate-early gene expression, both in vitro and in vivo, suggests that its effects are initiating to long-term formative and reparative processes. Such a common link between destructive and plastic responses provides an important view of cellular and tissue maintenance in the nervous system.

Detection of low molecular mass GTP-binding proteins in chromaffin granules and other subcellular fractions of chromaffin cells.

A homogenate of purified chromaffin cells was fractionated, after removal of the nuclear fraction, by sucrose density gradient ultracentrifugation. The presence and subcellular localization of low molecular mass GTP-binding proteins was explored by incubation of blots of proteins from different subcellular fractions with [alpha-32P]GTP in the presence of Mg2+. The fractions enriched in intact chromaffin granule markers, i.e. catecholamines, chromogranin A, chromogranin B and cytochrome b-561 were also enriched in labelled GTP-binding proteins. Two major labelled components of 23 and 29 kDa were rapidly detected by autoradiography. Traces of 26 and 27 kDa components were also present. These components were detectable in both plasma and granule membranes. In addition to these components, the cytosolic fraction contained another GTP-binding protein of about 20 kDa. Binding of [alpha-32P]GTP was specific and dependent on Mg2+. By analogy to the findings reported in non-mammalian systems, the observations described here suggest the involvement of low molecular mass GTP-binding proteins in the chromaffin cell secretory process.

Phosphorylation and dephosphorylation of chromaffin cell proteins in response to stimulation.

Stimulation of bovine chromaffin cell in culture changed (increased or decreased) the phosphorylation state of several proteins as examined by 32P incorporation. Enhanced phosphorylation of 22 protein bands as well as increased dephosphorylation of a 20.4 kilodaltons protein band was observed when extracts of cultured chromaffin cells stimulated by either acetylcholine or high K+ were subjected to mono-dimensional gel electrophoresis. For several protein bands, the degree of phosphorylation was larger in cells stimulated by acetylcholine than in those challenged by a depolarizing concentration of K+. The most affected phosphoproteins have apparent molecular weights of 14,800, 29,000, 33,000, 57,000 (tubulin subunit), 63,000 (tyrosine hydroxylase subunit) and 94,000. The presence of a low extracellular calcium concentration (0.5 mM Ca2+ plus 15 mM Mg2+) in the incubation medium inhibited (38-100%) the acetylcholine-evoked increases in protein phosphorylation observed previously for 18 protein bands. Trifluoperazine at the concentration required for 50% inhibition of acetylcholine-induced catecholamine release decreases (33-100%) the stimulation-induced phosphorylation in all polypeptides, with the exception of the 14.8 kilodaltons and the dephosphorylated 20.4 kilodaltons components which were not affected. Two-dimensional gel electrophoresis analysis revealed that exposure of chromaffin cells to acetylcholine produced two types of effect on protein phosphorylation: activation of protein kinase activities affecting about 30 polypeptides; activation of protein phosphatase activities resulting in the dephosphorylation of about 40 polypeptides, most of them appearing as minor phosphoproteins, with the exception of the alpha-subunit of pyruvate dehydrogenase and the 20.4 kilodaltons polypeptide. On the basis of their molecular properties (molecular weight and pI) and their abundance in chromaffin cells, the 80 kilodaltons phosphoprotein which focused at pI 4.8 and the 117.5 kilodaltons phosphoprotein which focused at pI 5.0 were identified as chromogranins A and B, respectively. The relationship between acetylcholine-induced protein phosphorylation (or dephosphorylation) and catecholamine secretion was also investigated. The time course of protein phosphorylation (or dephosphorylation) paralleled or preceded [3H]noradrenaline release for 16 phosphoproteins.(ABSTRACT TRUNCATED AT 400 WORDS)

Application of topological descriptors in QSAR and drug design: history and new trends.

Powerful methodologies for drug design and drug database screening and selection are presently available. Studies relating the structure of molecules to a property or a biological activity by means of statistical tools (QSPR and QSAR studies, respectively) are particularly relevant. An important point for this methodology is the use of good structural descriptors that are representative of the molecular features responsible for the relevant activity. Topological indices (TIs) are two-dimensional descriptors which take into account the internal atomic arrangement of compounds, and which encode in numerical form information about molecular size, shape, branching, presence of heteroatoms and multiple bonds. The usefulness of TIs in QSPR and QSAR studies has been extensively demonstrated, and they have also been used as a measure of structural similarity or diversity by their application to databases virtually generated by computer. In this article we will briefly review the history of TIs, their advantages and limitations with respect to other descriptors, and their possibilities in drug design and database selection. These applications rely on new computational techniques such as virtual combinatorial synthesis, virtual computational screening or inverse QSAR.