QSPR correlation and predictions of GC retention indexes for methyl-branched hydrocarbons produced by insects

Effects of genotype and host environment on the cuticular hydrocarbon profiles of Lysiphlebus parasitoids and aggression by aphid-tending ants

Parasitoids in the genus Lysiphlebus specialize on ant-tended aphids and have previously been reported to mimic the cuticular hydrocarbon (CHC) profiles of their aphid hosts to avoid detection by ants. However, the precise mechanisms that mediate reduced ant aggression toward Lysiphlebus spp. are not known, nor is it clear whether such mechanisms are broadly effective or specialized on particular aphid hosts. Here we explore the effects of wasp genotype and host environment on Lysiphlebus CHC profiles and ant aggression. Rearing asexual Lysiphlebus lines in different host aphid environments revealed effects of both wasp line and aphid host on wasp CHCs. However, variation in genotype and host affected different features of the CHC profile, with wasp genotype explaining most variation in linear and long-chain methyl alkanes, while aphid host environment primarily influenced short-chain methyl alkanes. Subsequent behavioural experiments revealed no effects of host environment on ant aggression, but strong evidence for genotypic effects. The influence of genotypic variation on experienced ant aggression and relevant chemical traits is particularly relevant in light of recent evidence for genetic divergence among Lysiphlebus parasitoids collected from different aphid hosts.

Aphids harbouring different endosymbionts exhibit differences in cuticular hydrocarbon profiles that can be recognized by ant mutualists

Cuticular hydrocarbons (CHCs) have important communicative functions for ants, which use CHC profiles to recognize mutualistic aphid partners. Aphid endosymbionts can influence the quality of their hosts as ant mutualists, via effects on honeydew composition, and might also affect CHC profiles, suggesting that ants could potentially use CHC cues to discriminate among aphid lines harbouring different endosymbionts. We explored how several strains of Hamiltonella defensa and Regiella insecticola influence the CHC profiles of host aphids (Aphis fabae) and the ability of aphid-tending ants (Lasius niger) to distinguish the profiles of aphids hosting different endosymbionts. We found significant compositional differences between the CHCs of aphids with different infections. Some endosymbionts changed the proportions of odd-chain linear alkanes, while others changed primarily methyl-branched compounds, which may be particularly important for communication. Behavioural assays, in which we trained ants to associate CHC profiles of endosymbiont infected or uninfected aphids with food rewards, revealed that ants readily learned to distinguish differences in aphid CHC profiles associated with variation in endosymbiont strains. While previous work has documented endosymbiont effects on aphid interactions with antagonists, the current findings support the hypothesis that endosymbionts also alter traits that influence communicative interactions with ant mutualists.

Cuticular Chemistry of the Queensland Fruit Fly Bactrocera tryoni (Froggatt)

The cuticular layer of the insect exoskeleton contains diverse compounds that serve important biological functions, including the maintenance of homeostasis by protecting against water loss, protection from injury, pathogens and insecticides, and communication. Bactrocera tryoni (Froggatt) is the most destructive pest of fruit production in Australia, yet there are no published accounts of this species' cuticular chemistry. We here provide a comprehensive description of B. tryoni cuticular chemistry. We used gas chromatography-mass spectrometry to identify and characterize compounds in hexane extracts of B. tryoni adults reared from larvae in naturally infested fruits. The compounds found included spiroacetals, aliphatic amides, saturated/unsaturated and methyl branched C12 to C20 chain esters and C29 to C33 normal and methyl-branched alkanes. The spiroacetals and esters were found to be specific to mature females, while the amides were found in both sexes. Normal and methyl-branched alkanes were qualitatively the same in all age and sex groups but some of the alkanes differed in amounts (as estimated from internal standard-normalized peak areas) between mature males and females, as well as between mature and immature flies. This study provides essential foundations for studies investigating the functions of cuticular chemistry in this economically important species.

The use of fast molecular descriptors and artificial neural networks approach in organochlorine compounds electron ionization mass spectra classification

Developing of theoretical tools can be very helpful for supporting new pollutant detection. Nowadays, a combination of mass spectrometry and chromatographic techniques are the most basic environmental monitoring methods. In this paper, two organochlorine compound mass spectra classification systems were proposed. The classification models were developed within the framework of artificial neural networks (ANNs) and fast 1D and 2D molecular descriptor calculations. Based on the intensities of two characteristic MS peaks, namely, [M] and [M-35], two classification criterions were proposed. According to criterion I, class 1 comprises [M] signals with the intensity higher than 800 NIST units, while class 2 consists of signals with the intensity lower or equal than 800. According to criterion II, class 1 consists of [M-35] signals with the intensity higher than 100, while signals with the intensity lower or equal than 100 belong to class 2. As a result of ANNs learning stage, five models for both classification criterions were generated. The external model validation showed that all ANNs are characterized by high predicting power; however, criterion I-based ANNs are much more accurate and therefore are more suitable for analytical purposes. In order to obtain another confirmation, selected ANNs were tested against additional dataset comprising popular sunscreen agents disinfection by-products reported in previous works.

Chemical structure and correlation analysis of HIV-1 NNRT and NRT inhibitors and database-curated, published inhibition constants with chemical structure in diverse datasets

Human immunodeficiency virus (HIV-1) reverse transcriptase is a major target for designing anti-HIV drugs. Developed inhibitors are divided into non-nucleoside analog reverse-transcriptase inhibitors (NNRTIs) and nucleoside analog reverse-transcriptase inhibitors (NRTIs) depending on their mechanism. Given that many inhibitors have been studied and for many of them binding affinity constants have been calculated, it is beneficial to analyze the chemical landscape of these families of inhibitors and correlate these inhibition constants with molecular structure descriptors. For this, the HIV-1 RT data was retrieved from the ChEMBL database, carefully curated, and original literature verified, grouped into NRTIs and NNRTIs, analyzed using a hierarchical scaffold classification method and modelled with best multi-linear regression approach. Analysis of the HIV-1 NNRTIs subset results in ten different common structural parent types of oxazepanone, piperazinone, pyrazine, oxazinanone, diazinanone, pyridine, pyrrole, diazepanone, thiazole, and triazine. The same analysis for HIV-1 NRTIs groups structures into four different parent types of uracil, pyrimide, pyrimidione, and imidazole. Each scaffold tree corresponding to the parent types has been carefully analyzed and examined, and changes in chemical structure favorable to potency and stability are highlighted. For both subsets, descriptive and predictive QSAR models are derived, discussed and externally validated, revealing general trends in relationships between molecular structure and binding affinity constants in structurally diverse datasets. Data and QSAR models are available at the QsarDB repository (http://dx.doi.org/10.15152/QDB.202).

Facultative slave-making ants Formica sanguinea label their slaves with own recognition cues instead of employing the strategy of chemical mimicry

Slave-making ant species use the host workforce to ensure normal colony functioning. Slaves are robbed as pupae from their natal nest and after eclosion, assume the parasite colony as their own. A possible factor promoting the successful integration of slaves into a foreign colony is congruence with the slave-makers in terms of cuticular hydrocarbons, which are known to play the role of recognition cues in social insects. Such an adaptation is observed in the obligate slave-making ant species, which are chemically adjusted to their slaves. To date, however, no reports have been available on facultative slave-making species, which represent an earlier stage of the evolution of slavery. Such an example is Formica sanguinea, which exploit F. fusca colonies as their main source of a slave workforce. Our results show that F. sanguinea ants have a distinct cuticular hydrocarbon profile, which contains compounds not present in free-living F. fusca ants from potential target nests. Moreover, enslaved F. fusca ants acquire hydrocarbons from their slave-making nestmates to such an extent that they become chemically differentiated from free-living, conspecific ants. Our study shows that F. sanguinea ants promote their own recognition cues in their slaves, rather than employing the strategy of chemical mimicry. Possible reasons why F. sanguinea is not chemically well adjusted to its main host species are discussed in this paper.

Variation in spatial scale of competing polydomous twig-nesting ants in coffee agroecosystems

Arboreal ants are both highly diverse and ecologically dominant in the tropics. This ecologically important group is particularly useful in ongoing efforts to understand processes that regulate species diversity and coexistence. Our study addresses how polydomy can influence patterns of nest occupation in competing arboreal ants. We examined the spatial structure of nest occupation (nest distance, abundance and density) in three polydomous co-occurring twig-nesting ant species (Pseudomyrmex simplex, P. ejectus and P. PSW-53) by mapping twigs occupied by ants from each species within plots in our study site. We then used two colony structure estimators (intraspecific aggression and cuticular hydrocarbon variation) to determine the relative degree of polydomy for each species. All work was conducted in coffee agroforests in Chiapas, Mexico. Our results revealed that the two species with highest abundance and nest density were also highly polydomous, where both species had either single or multiple non-aggressive colonies occupying nests on a large spatial scale (greater than the hectare level). Our results also indicate that the species with the lowest abundance and density is less polydomous, occupying several overlapping and territorial colonies at the hectare level in which multiple colonies never co-occur on the same host plant. These results contribute evidence that successful coexistence and highly polydomous colony structure may allow ants, through reduced intraspecific aggression, to successfully occupy more nests more densely than ant species that have multiple territorial colonies. Furthermore our study highlights the importance of considering intraspecific interactions when examining community assembly of ants.

Structural revision of aristol: a fresh look at the oxidative coupling of thymol under iodination conditions

Aristol represents a complex mixture of products of oxidative coupling of iodothymols and monomeric iodinated thymol derivatives.

Design of novel leads: ligand based computational modeling studies on non-nucleoside reverse transcriptase inhibitors (NNRTIs) of HIV-1

Researchers are on the constant lookout for new antiviral agents for the treatment of AIDS. In the present work, ligand based modeling studies are performed on analogues of substituted phenyl-thio-thymines, which act as non-nucleoside reverse transcriptase inhibitors (NNRTIs) and novel leads are extracted. Using alignment-dependent descriptors, based on group center overlap (SALL, HDALL, HAALL and RALL), an alignment-independent descriptor (S log P), a topological descriptor (Balaban index (J)) and a 3D descriptor dipole moment (μ) and shape based descriptors (Kappa 2 index ((2)κ)), a correlation is derived with inhibitory activity. Linear and non-linear techniques have been used to achieve the goal. Support Vector Machine (SVM, R = 0.929, R(2) = 0.863) and Back Propagation Neural Network (BPNN, R = 0.928, R(2) = 0.861) methods yielded near similar results and outperformed Multiple Linear Regression (MLR, R = 0.915, R(2) = 0.837). The predictive ability of the models are cross-validated using a test dataset (SVM: R = 0.846, R(2) = 0.716, BPNN: R = 0.841, R(2) = 0.707 and MLR: R = 0.833, R(2) = 0.694). It is concluded that the hydrophobicity (S log P) and the polarity (μ) of a ligand and the presence of hydrogen donor (HDALL) moieties are the deciding factors in improving antiviral activity and pharmaco-therapeutic properties. Based on the above findings, a virtual dataset is created to extract probable leads with reasonable antiviral activity as well as better pharmacophoric properties.

Recognition in a social symbiosis: chemical phenotypes and nestmate recognition behaviors of neotropical parabiotic ants

Social organisms rank among the most abundant and ecologically dominant species on Earth, in part due to exclusive recognition systems that allow cooperators to be distinguished from exploiters. Exploiters, such as social parasites, manipulate their hosts' recognition systems, whereas cooperators are expected to minimize interference with their partner's recognition abilities. Despite our wealth of knowledge about recognition in single-species social nests, less is known of the recognition systems in multi-species nests, particularly involving cooperators. One uncommon type of nesting symbiosis, called parabiosis, involves two species of ants sharing a nest and foraging trails in ostensible cooperation. Here, we investigated recognition cues (cuticular hydrocarbons) and recognition behaviors in the parabiotic mixed-species ant nests of Camponotus femoratus and Crematogaster levior in North-Eastern Amazonia. We found two sympatric, cryptic Cr. levior chemotypes in the population, with one type in each parabiotic colony. Although they share a nest, very few hydrocarbons were shared between Ca. femoratus and either Cr. levior chemotype. The Ca. femoratus hydrocarbons were also unusually long-chained branched alkenes and dienes, compounds not commonly found amongst ants. Despite minimal overlap in hydrocarbon profile, there was evidence of potential interspecific nestmate recognition -Cr. levior ants were more aggressive toward Ca. femoratus non-nestmates than Ca. femoratus nestmates. In contrast to the prediction that sharing a nest could weaken conspecific recognition, each parabiotic species also maintains its own aggressive recognition behaviors to exclude conspecific non-nestmates. This suggests that, despite cohabitation, parabiotic ants maintain their own species-specific colony odors and recognition mechanisms. It is possible that such social symbioses are enabled by the two species each using their own separate recognition cues, and that interspecific nestmate recognition may enable this multi-species cooperative nesting.

A large scale test dataset to determine optimal retention index threshold based on three mass spectral similarity measures

Retention index (RI) is useful for metabolite identification. However, when RI is integrated with mass spectral similarity for metabolite identification, many controversial RI threshold setup are reported in literatures. In this study, a large scale test dataset of 5844 compounds with both mass spectra and RI information were created from National Institute of Standards and Technology (NIST) repetitive mass spectra (MS) and RI library. Three MS similarity measures: NIST composite measure, the real part of Discrete Fourier Transform (DFT.R) and the detail of Discrete Wavelet Transform (DWT.D) were used to investigate the accuracy of compound identification using the test dataset. To imitate real identification experiments, NIST MS main library was employed as reference library and the test dataset was used as search data. Our study shows that the optimal RI thresholds are 22, 15, and 15 i.u. for the NIST composite, DFT.R and DWT.D measures, respectively, when the RI and mass spectral similarity are integrated for compound identification. Compared to the mass spectrum matching, using both RI and mass spectral matching can improve the identification accuracy by 1.7%, 3.5%, and 3.5% for the three mass spectral similarity measures, respectively. It is concluded that the improvement of RI matching for compound identification heavily depends on the method of MS spectral similarity measure and the accuracy of RI data.

A quantitative structure- property relationship of gas chromatographic/mass spectrometric retention data of 85 volatile organic compounds as air pollutant materials by multivariate methods

A quantitative structure-property relationship (QSPR) study is suggested for the prediction of retention times of volatile organic compounds. Various kinds of molecular descriptors were calculated to represent the molecular structure of compounds. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and artificial neural network (ANN). The stepwise regression was used for the selection of the variables which gives the best-fitted models. After variable selection ANN, MLR methods were used with leave-one-out cross validation for building the regression models. The prediction results are in very good agreement with the experimental values. MLR as the linear regression method shows good ability in the prediction of the retention times of the prediction set. This provided a new and effective method for predicting the chromatography retention index for the volatile organic compounds.

Volatile organic compounds produced by the phytopathogenic bacterium Xanthomonas campestris pv. vesicatoria 85-10

Xanthomonas campestris is a phytopathogenic bacterium and causes many diseases of agricultural relevance. Volatiles were shown to be important in inter- and intraorganismic attraction and defense reactions. Recently it became apparent that also bacteria emit a plethora of volatiles, which influence other organisms such as invertebrates, plants and fungi. As a first step to study volatile-based bacterial-plant interactions, the emission profile of Xanthomonas c. pv. vesicatoria 85-10 was determined by using GC/MS and PTR-MS techniques. More than 50 compounds were emitted by this species, the majority comprising ketones and methylketones. The structure of the dominant compound, 10-methylundecan-2-one, was assigned on the basis of its analytical data, obtained by GC/MS and verified by comparison of these data with those of a synthetic reference sample. Application of commercially available decan-2-one, undecan-2-one, dodecan-2-one, and the newly synthesized 10-methylundecan-2-one in bi-partite Petri dish bioassays revealed growth promotions in low quantities (0.01 to 10 μmol), whereas decan-2-one at 100 μmol caused growth inhibitions of the fungus Rhizoctonia solani. Volatile emission profiles of the bacteria were different for growth on media (nutrient broth) with or without glucose.

QSRR Models for Kováts' Retention Indices of a Variety of Volatile Organic Compounds on Polar and Apolar GC Stationary Phases Using Molecular Connectivity Indexes

Quantitative structure-retention relationship (QSRR) approaches, based on molecular connectivity indices are useful to predict the gas chromatography of Kováts relative retention indices (GC-RRIs) of 132 volatile organic compounds (VOCs) on different 12 (4 apolar and 8 polar) stationary phases (C₆₇, C₁₀₃, C₇₈, C_∞, POH, TTF, MTF, PCL, PBR, TMO, PSH and PCN) at 130 °C. Full geometry optimization based on Austin model 1 semi-empirical molecular orbital method was carried out. The sets of 30 molecular descriptors were derived directly from the topological structures of the compounds from DRAGON program. By means of the final variable selection method, which is elimination selection stepwise regression algorithms, three optimal descriptors were selected to develop a QSRR model to predict the RRI of organic compounds on each stationary phase with a correlation coefficient between 0.9378 and 0.9673 and a leave-one-out cross-validation correlation coefficient between 0.9325 and 0.9653. The root mean squares errors over different 12 phases were within the range of 0.0333–0.0458. Furthermore, the accuracy of all developed models was confirmed using procedures of Y-randomization, external validation through an odd–even number and division of the entire dataset into training and test sets. A successful interpretation of the complex relationship between GC RRIs of VOCs and the chemical structures was achieved by QSRR. The three connectivity indexes in the models are also rationally interpreted, which indicated that all organic compounds’ RRI was precisely represented by molecular connectivity indexes.

Quantitative structure-property relationship study on the determination of binding constant by fluorescence quenching

Models to predict binding constant (logK) to bovine serum albumin (BSA) should be very useful in the pharmaceutical industry to help speed up the design of new compounds, especially as far as pharmacokinetics is concerned. We present here an extensive list of logK binding constants for thirty-five compounds to BSA determined by florescence quenching from the literature. These data have allowed us the derivation of a quantitative structure-property relationship (QSPR) model to predict binding constants to BSA of compounds on the basis of their structure. A stepwise multiple linear regression (MLR) was performed to build the model. The statistical parameter provided by the MLR model (R = 0.9200, RMS = 0.3305) indicated satisfactory stability and predictive ability for the model. Using florescence quenching spectroscopy, we also experimentally determined the binding constants to BSA for two bioactive components in traditional Chinese medicines. Using the proposed model it was possible to predict the binding constants for each, which were in good agreement with the experimental results. This QSPR approach can contribute to a better understanding of structural factors of the compounds responsible for drug-protein interactions, and be useful in predicting the binding constants of other compounds.

Quantitative structure-property relationship study for estimation of quantitative calibration factors of some organic compounds in gas chromatography

Quantitative structure-property relationship (QSPR) models have been used to predict and explain gas chromatographic data of quantitative calibration factors (f(M)). This method allows for the prediction of quantitative calibration factors in a variety of organic compounds based on their structures alone. Stepwise multiple linear regression (MLR) and non-linear radial basis function neural network (RBFNN) were performed to build the models. The statistical characteristics provided by multiple linear model (R2=0.927, RMS=0.073; AARD=6.34% for test set) indicated satisfactory stability and predictive ability, while the predictive ability of RBFNN model is somewhat superior (R2=0.959; RMS=0.0648; AARD=4.85% for test set). This QSPR approach can contribute to a better understanding of structural factors of the compounds responsible for quantitative analysis by gas chromatography, and can be useful in predicting the quantitative calibration factors of other compounds.

Theoretical prediction of the Kovat's retention index for oxygen-containing organic compounds using novel topological indices

For the retention index of polar compounds, polar groups in molecules would participate in polar interactions between eluents and stationary phases and thus would be expected to make large and separate contributions to the total retention index (RI). The characterization of the structural feature will help to elucidate the quantitative structure-retention relationship (QSRR). In this paper, on the basis of the PEI index previously developed by Cao, two novel molecular polarizability effect index, modified molecular polarizability index (MPEI(m)) and modified inner molecular polarizability index (IMPEI(m)) were proposed to predict the GC retention of a variety of oxygen-containing organic compounds with diverse chemical structures on OV-1 and SE-54 stationary phases. The sets of molecular descriptors were derived directly from the structure of the compounds based on graph theory. Simple linear regression equations between the RI and the topological indices were established for each stationary phase separately (R>0.99). Statistical analysis showed that the QSRR models have high internal stability and good predictive ability for external groups. The molecular properties known to be relevant for GC retention data, such as molecular size, branching and polar functional groups were well covered by the generated descriptors. The models with topological indices were compared with those based on quantum-chemical descriptors. It is observed that topological indices produce better correlations with Kovat's retention index. The results indicate the efficiency of presented indices in the structure-retention index correlations of complex compounds with polar multi-functional groups.

Perdeuterated n-alkanes for improved data processing in thermal desorption gas chromatography/mass spectrometry

The identification of organic compounds by GC/MS is useful in various areas such as fuel, indoor and outdoor air and flavour and fragrance applications. Multi-compound mixtures often contain isomeric compounds which have similar mass spectra and sometimes cannot be unambiguously identified by library search alone. Retention indices can help with confirmation of identification if they are reproducible. Using perdeuterated n-alkanes as a reference series for calculation of retention indices in GC/MS has a clear benefit because of the distinctive ion trace of m/z 34. Thermal desorption is useful for analysis of volatile organic compounds (VOCs) in air after sampling on appropriate sorbent cartridges. Comparison of indices between three systems, consisting of a thermal desorption unit, a gas chromatograph and a mass spectrometer, showed good agreement for compounds with well-defined peaks, whereas retention times varied.

Quantitative structure-(chromatographic) retention relationships

Since the pioneering works of Kaliszan (R. Kaliszan, Quantitative Structure-Chromatographic Retention Relationships, Wiley, New York, 1987; and R. Kaliszan, Structure and Retention in Chromatography. A Chemometric Approach, Harwood Academic, Amsterdam, 1997) no comprehensive summary is available in the field. Present review covers the period of 1996-August 2006. The sources are grouped according to the special properties of kinds of chromatography: Quantitative structure-retention relationship in gas chromatography, in planar chromatography, in column liquid chromatography, in micellar liquid chromatography, affinity chromatography and quantitative structure enantioselective retention relationships. General tendencies, misleading practice and conclusions, validation of the models, suggestions for future works are summarized for each sub-field. Some straightforward applications are emphasized but standard ones. The sources and the model compounds, descriptors, predicted retention data, modeling methods and indicators of their performance, validation of models, and stationary phases are collected in the tables. Some important conclusions are: Not all physicochemical descriptors correlate with the retention data strongly; the heat of formation is not related to the chromatographic retention. It is not appropriate to give the errors of Kovats indices in percentages. The apparently low values (1-3%) can disorient the reviewers and readers. Contemporary mean interlaboratory reproducibility of Kovats indices are about 5-10 i.u. for standard non polar phases and 10-25 i.u. for standard polar phases. The predictive performance of QSRR models deteriorates as the polarity of GC stationary phase increases. The correlation coefficient alone is not a particularly good indicator for the model performance. Residuals are more useful than plots of measured and calculated values. There is no need to give the retention data in a form of an equation if the numbers of compounds are small. The domain of model applicability of models should be given in all cases.

Analysis of Insect Cuticular Hydrocarbons Using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry

Insect cuticular hydrocarbons (CHCs) were probed by matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry with a lithium 2,5-dihydroxybenzoate matrix. CHC profiles were obtained for 12 species of diverse insect taxa (termites, ants, a cockroach, and a flesh fly). MALDI spectra revealed the presence of high molecular weight CHCs on the insect cuticle. Hydrocarbons with more than 70 carbon atoms, both saturated and unsaturated, were detected. When compared with gas chromatography/mass spectrometry (GC/MS), MALDI-TOF covered a wider range of CHCs and enabled CHCs of considerably higher molecular weight to be detected. Good congruity between GC/MS and MALDI-TOF was observed in the overlapping region of molecular weights. Moreover, a number of previously undiscovered hydrocarbons were detected in the high mass range beyond the analytical capabilities of current GC/MS instruments. MALDI was shown to hold potential to become an alternative analytical method for insect CHC analyses. The ability of MALDI to discriminate among species varying in the degree of their relatedness was found to be similar to GC/MS. However, neither MALDI-MS nor GC/MS data were able to describe the phylogenetic relationships.