Understanding Synergistic Toxicity of Terpenes as Insecticides: Contribution of Metabolic Detoxification in Musca domestica.

Essential oils, which are mixtures of terpenes, frequently show stronger insecticide activity, i.e., lower lethal dose 50 (LC50), than their most abundant terpenes. Synergy between terpenes provides a plausible explanation, but its demonstration has been elusive. In the present work, we look for an alternative explanation, by considering the influence of insect metabolic detoxification. Basically, we propose a model (metabolic model, MM) in which the LC50 of the major terpene in a mixture is expected to include a fraction that is detoxified by the insect, whereas a minor terpene would act unimpeded, showing a lower LC50 than when acting alone. In order to test this idea, we analyzed the effects of inhibiting the cytochrome P450 detoxification system with piperonyl butoxide (PBO), on the lethal concentration of terpenes as fumigants against Musca domestica. We found that, within a group of 10 terpenes [linalool, citronellal, (R)-α-pinene, 1,8-cineole, γ-terpinene, limonene, α-terpinene, (S)-ß-pinene, thymol and (R)-pulegone], seven showed the LC50PBO (the lethal concentration for PBO-treated flies) between 1.7 and 12.4 times lower than the corresponding LC50 when P450 was not inhibited. Only in one case, that of (R)-pulegone, was the LC50PBO greater than the LC50, while two terpenes [(S)-ß-pinene and thymol] showed no changes in toxicity. The increased activity of most terpenes (particularly linalool and citronellal) in PBO-treated flies supports our hypothesis that normally the LC50 includes a fraction of inactive compound, due to detoxification. Having previously determined that M. domestica preferentially oxidizes the most abundant terpene in a mixture, while terpenes in smaller proportions are poorly or not detoxified by the P450 system, we assessed whether the toxicity of minority terpenes in a mixture is similar to their activity under P450 inhibition. We chose suitable binary combinations in such a way that one terpene (in greater proportion) should be the target of P450 while the other (in smaller proportion) should intoxicate the fly with LC50PBO or similar. Combinations of 1,8-cineole-citronellal, 1,8-cineole-linalool, linalool-citronellal, (R)-pulegone-linalool, (R)-pulegone-1,8-cineole and (R)-pulegone-citronellal were assayed against M. domestica, and the LC50 of each mixture was determined and compared to values predicted by MM (considering the LC50PBO for minor component) or by the classical approach (LC50 for both components). The MM showed the best fit to the data, suggesting additive rather than synergistic effects, except for the combination of (R)-pulegone-citronellal that was clearly synergistic. Thus, the experimental data indicate that the insect preferentially oxidizes the major component in a mixture, while the terpene in lesser proportion acts as a toxicant, with higher toxicity than when it was assayed alone. These findings contribute to a deeper understanding of the higher toxicity of essential oils compared to their component terpenes and provide important information for the design of effective insecticides based on essential oils or terpenes.

UNIPred: Unbalance-Aware Network Integration and Prediction of Protein Functions.

The proper integration of multiple sources of data and the unbalance between annotated and unannotated proteins represent two of the main issues of the automated function prediction (AFP) problem. Most of supervised and semisupervised learning algorithms for AFP proposed in literature do not jointly consider these items, with a negative impact on both sensitivity and precision performances, due to the unbalance between annotated and unannotated proteins that characterize the majority of functional classes and to the specific and complementary information content embedded in each available source of data. We propose UNIPred (unbalance-aware network integration and prediction of protein functions), an algorithm that properly combines different biomolecular networks and predicts protein functions using parametric semisupervised neural models. The algorithm explicitly takes into account the unbalance between unannotated and annotated proteins both to construct the integrated network and to predict protein annotations for each functional class. Full-genome and ontology-wide experiments with three eukaryotic model organisms show that the proposed method compares favorably with state-of-the-art learning algorithms for AFP.

A neural network algorithm for semi-supervised node label learning from unbalanced data.

Given a weighted graph and a partial node labeling, the graph classification problem consists in predicting the labels of all the nodes. In several application domains, from gene to social network analysis, the labeling is unbalanced: for instance positive labels may be much less than negatives. In this paper we present COSNet (COst Sensitive neural Network), a neural algorithm for predicting node labels in graphs with unbalanced labels. COSNet is based on a 2-parameter family of Hopfield networks, and consists of two main steps: (1) the network parameters are learned through a cost-sensitive optimization procedure; (2) a suitable Hopfield network restricted to the unlabeled nodes is considered and simulated. The reached equilibrium point induces the classification of the unlabeled nodes. The restriction of the dynamics leads to a significant reduction in time complexity and allows the algorithm to nicely scale with large networks. An experimental analysis on real-world unbalanced data, in the context of the genome-wide prediction of gene functions, shows the effectiveness of the proposed approach.

A mathematical model for the validation of gene selection methods.

Gene selection methods aim at determining biologically relevant subsets of genes in DNA microarray experiments. However, their assessment and validation represent a major difficulty since the subset of biologically relevant genes is usually unknown. To solve this problem a novel procedure for generating biologically plausible synthetic gene expression data is proposed. It is based on a proper mathematical model representing gene expression signatures and expression profiles through Boolean threshold functions. The results show that the proposed procedure can be successfully adopted to analyze the quality of statistical and machine learning-based gene selection algorithms.

Insecticidal activity of essential oils from native medicinal plants of Central Argentina against the house fly, Musca domestica (L.).

The insecticidal activity of nine essential oils (EOs) against the house fly (Musca domestica) was evaluated by placing flies in a screw-cap glass jar holding a piece of EO-treated cotton yarn. The dose necessary to kill 50% of flies (LC(50)) in 30 min was determined at 26 +/- 1 degrees C. The EOs showed LC(50) values ranging from 0.5 to 46.9 mg/dm(3). The EO from Minthostachys verticillata was the most potent insecticide (LC(50) = 0.5 mg/dm(3)) followed by EOs from Hedeoma multiflora (LC(50) = 1.3 mg/dm(3)) and Artemisia annua (LC(50) = 6.5 mg/dm(3)). The compositions of the nine EOs, obtained by hydrodistillation of medicinal herbs, were analyzed by gas chromatography/mass spectroscopy. These analyses showed that (4R)(+)-pulegone (69.70%), menthone (12.17%), and limonene (2.75%) were the principal components of M. verticillata EO. (4R)(+)-pulegone was also the main constituent (52.80%) of H. multiflora, while artemisia ketone (22.36%) and 1,8-cineole (16.67%) were the major constituents of A. annua EO. The terpene (4R)(+)-pulegone showed a lower toxicity (LC(50) = 1.7 mg/dm(3)) than M. verticillata or H. multiflora EOs. Dimethyl 2,2-dichlorovinyl phosphate, selected as a positive control, showed an LC(50) of 0.5 mg/dm(3). EOs from M. verticillata and H. multiflora show promise as natural insecticides against houseflies.

Efficacy of essential oils from edible plants as insecticides against the house fly, Musca domestica L.

The compositions of 12 essential oils (EOs) obtained by hydrodistillation of edible fruits and herbs were analyzed by gas chromatography/mass spectroscopy (GC/MS). The insecticidal activity of each oil against the house fly Musca domestica was evaluated by placing flies in a glass jar with a screw cap that held a piece of EO-treated cotton yarn. The dose necessary to kill 50% of flies (LC(50)) in 30 min was determined at 26 +/- 1 degrees C. Twelve EOs and 17 individual terpenes were assayed against M. domestica, showing LC(50) values ranging from 3.9 to 85.2 and from 3.3 to >100 mg/dm(3), respectively. EO from Citrus sinensis was the most potent insecticide (LC(50 )= 3.9 mg/dm(3)), followed by EOs from C. aurantium (LC(50 )= 4.8 mg/dm(3)) and Eucalyptus cinerea (LC(50 )= 5.5 mg/dm(3)). According to GC/MS analysis, limonene (92.47%), linalool (1.43%), and b-myrcene (0.88%) were the principal components of C. sinensis EO. Limonene was also the principal constituent (94.07%) of C. aurantium, while 1,8-cineole (56.86%) was the major constituent of E. cinerea EO. 1,8-Cineole was most active against M. domestica (LC(50 )= 3.3 mg/dm(3)), while (4R)(+)-limonene, was moderately active (LC(50 )= 6.2 mg/dm(3)). Dimethyl 2,2-dichlorovinyl phosphate (DDVP) selected as a positive control, showed an LC(50) of 0.5 mg/dm(3). EOs from C. sinensis, C. aurantium, and E. cinerea show promise as natural insecticides against houseflies.

Discovering multi-level structures in bio-molecular data through the Bernstein inequality.

BACKGROUND: The unsupervised discovery of structures (i.e. clusterings) underlying data is a central issue in several branches of bioinformatics. Methods based on the concept of stability have been recently proposed to assess the reliability of a clustering procedure and to estimate the "optimal" number of clusters in bio-molecular data. A major problem with stability-based methods is the detection of multi-level structures (e.g. hierarchical functional classes of genes), and the assessment of their statistical significance. In this context, a chi-square based statistical test of hypothesis has been proposed; however, to assure the correctness of this technique some assumptions about the distribution of the data are needed. RESULTS: To assess the statistical significance and to discover multi-level structures in bio-molecular data, a new method based on Bernstein's inequality is proposed. This approach makes no assumptions about the distribution of the data, thus assuring a reliable application to a large range of bioinformatics problems. Results with synthetic and DNA microarray data show the effectiveness of the proposed method. CONCLUSIONS: The Bernstein test, due to its loose assumptions, is more sensitive than the chi-square test to the detection of multiple structures simultaneously present in the data. Nevertheless it is less selective, that is subject to more false positives, but adding independence assumptions, a more selective variant of the Bernstein inequality-based test is also presented. The proposed methods can be applied to discover multiple structures and to assess their significance in different types of bio-molecular data.

Model order selection for bio-molecular data clustering.

BACKGROUND: Cluster analysis has been widely applied for investigating structure in bio-molecular data. A drawback of most clustering algorithms is that they cannot automatically detect the "natural" number of clusters underlying the data, and in many cases we have no enough "a priori" biological knowledge to evaluate both the number of clusters as well as their validity. Recently several methods based on the concept of stability have been proposed to estimate the "optimal" number of clusters, but despite their successful application to the analysis of complex bio-molecular data, the assessment of the statistical significance of the discovered clustering solutions and the detection of multiple structures simultaneously present in high-dimensional bio-molecular data are still major problems. RESULTS: We propose a stability method based on randomized maps that exploits the high-dimensionality and relatively low cardinality that characterize bio-molecular data, by selecting subsets of randomized linear combinations of the input variables, and by using stability indices based on the overall distribution of similarity measures between multiple pairs of clusterings performed on the randomly projected data. A chi2-based statistical test is proposed to assess the significance of the clustering solutions and to detect significant and if possible multi-level structures simultaneously present in the data (e.g. hierarchical structures). CONCLUSION: The experimental results show that our model order selection methods are competitive with other state-of-the-art stability based algorithms and are able to detect multiple levels of structure underlying both synthetic and gene expression data.

Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses.

OBJECTIVE: Clustering algorithms may be applied to the analysis of DNA microarray data to identify novel subgroups that may lead to new taxonomies of diseases defined at bio-molecular level. A major problem related to the identification of biologically meaningful clusters is the assessment of their reliability, since clustering algorithms may find clusters even if no structure is present. METHODOLOGY: Recently, methods based on random "perturbations" of the data, such as bootstrapping, noise injections techniques and random subspace methods have been applied to the problem of cluster validity estimation. In this framework, we propose stability measures that exploits the high dimensionality of DNA microarray data and the redundancy of information stored in microarray chips. To this end we randomly project the original gene expression data into lower dimensional subspaces, approximately preserving the distance between the examples according to the Johnson-Lindenstrauss (JL) theory. The stability of the clusters discovered in the original high dimensional space is estimated by comparing them with the clusters discovered in randomly projected lower dimensional subspaces. The proposed cluster-stability measures may be applied to validate and to quantitatively assess the reliability of the clusters obtained by a large class of clustering algorithms. RESULTS AND CONCLUSION: We tested the effectiveness of our approach with high dimensional synthetic data, whose distribution is a priori known, showing that the stability measures based on randomized maps correctly predict the number of clusters and the reliability of each individual cluster. Then we showed how to apply the proposed measures to the analysis of DNA microarray data, whose underlying distribution is unknown. We evaluated the validity of clusters discovered by hierarchical clustering algorithms in diffuse large B-cell lymphoma (DLBCL) and malignant melanoma patients, showing that the proposed reliability measures can support bio-medical researchers in the identification of stable clusters of patients and in the discovery of new subtypes of diseases characterized at bio-molecular level.

Enzymatic and immunochemical evaluation of phospholipid hydroperoxide glutathione peroxidase (PHGPx) in testes and epididymal spermatozoa of rats of different ages.

Selenium (Se) and selenoproteins such as glutathione peroxidases are necessary for the proper development and fertilizing capacity of sperm cells. Phospholipid hydroperoxide glutathione peroxidase (PHGPx, E.C. 1.11.1.12) is a monomeric seleno-enzyme present in different mammalian tissues in soluble and bound form. Its function, like the other glutathione peroxidases, was originally viewed as a protective role against hydroperoxides, but direct and indirect evidence indicates that it has additional regulatory roles. PHGPx is present in testis cells and sperm cells, and its appearance is hormone regulated. We present here biochemical data, which clearly indicate that the enzyme specific activity in rat is age-dependent during the life-span monitored (from 36 to 365 days), with a maximum at 3 months of age in the testis germ cells and at 6 months of age in the isolated epididymal sperm cells. Western blotting and immunocytochemical analysis by means of anti-PHGPx antibodies show the different distribution and the strong binding of PHGPx in the testes and sperm cell subcellular compartments (nucleus, acrosome, mitochondria and residual bodies) of rats of different age. The presence of the protein exhibits in the testis cells a pattern different from that of the catalytic activity, with a maximum at 6 months of age. The subcellular distribution of PHGPx is qualitatively, but not quantitatively, unchanged during ageing. These different behaviours are compared and discussed.