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Ants are among the most abundant terrestrial invertebrate predators on Earth. To overwhelm their prey, they employ several remarkable behavioral, physiological, and biochemical innovations, including an effective paralytic venom. Ant venoms are thus cocktails of toxins finely tuned to disrupt the physiological systems of insect prey. They have received little attention yet hold great promise for the discovery of novel insecticidal molecules. To identify insect-neurotoxins from ant venoms, we screened the paralytic activity on blowflies of nine synthetic peptides previously characterized in the venom of Tetramorium bicarinatum. We selected peptide U11, a 34-amino acid peptide, for further insecticidal, structural, and pharmacological experiments. Insecticidal assays revealed that U11 is one of the most paralytic peptides ever reported from ant venoms against blowflies and is also capable of paralyzing honeybees. An NMR spectroscopy of U11 uncovered a unique scaffold, featuring a compact triangular ring helix structure stabilized by a single disulfide bond. Pharmacological assays using Drosophila S2 cells demonstrated that U11 is not cytotoxic, but suggest that it may modulate potassium conductance, which structural data seem to corroborate and will be confirmed in a future extended pharmacological investigation. The results described in this paper demonstrate that ant venom is a promising reservoir for the discovery of neuroactive insecticidal peptides. [ABSTRACT FROM AUTHOR]

Aphids cause severe damage to agricultural crops, resulting in significant economic losses, and an increased use of pesticides with decreased efficiency. Monitoring aphid infestations through regular field surveys is time-consuming and does not always provide an accurate spatiotemporal representation of the distribution of pests. Therefore, an automated, non-destructive method to detect and evaluate aphid infestation would be beneficial for targeted treatments. In this study, we present a machine learning model to identify and quantify aphids, localizing their spatial distribution over leaves, using a One-Class Support Vector Machine and Laplacian of Gaussians blob detection. To train this model, we built the first large database of aphids' hyperspectral images, which were captured in a controlled laboratory environment. This database contains more than 160 images of three aphid lines, distinctive in color, shape, and developmental stages, and are displayed laying on leaves or neutral backgrounds. This system exhibits high-quality validation scores, with a P r e c i s i o n of 0.97, a R e c a l l of 0.91, an F 1 score of 0.94, and an A U P R score of 0.98. Moreover, when assessing this method on new and challenging images, we did not observe any false negatives (and only a few false positives). Our results suggest that a machine learning model of this caliber could be a promising tool to detect aphids for targeted treatments in the field. [ABSTRACT FROM AUTHOR]

Self-expressiveness is a mathematical property that aims at characterizing the relationship between instances in a dataset. This property has been applied widely and successfully in computer-vision tasks, time-series analysis, and to infer underlying network structures in domains including protein signaling interactions and social-networks activity. Nevertheless, despite its potential, self-expressiveness has not been explicitly used to infer gene networks. In this article, we present Generalizable Gene Self-Expressive Networks, a new, interpretable, and generalization-aware formalism to model gene networks, and we propose two methods: GXN•EN and GXN•OMP, based respectively on E l a s t i c N e t and O M P (Orthogonal Matching Pursuit), to infer and assess Generalizable Gene Self-Expressive Networks. We evaluate these methods on four Microarray datasets from the DREAM5 benchmark, using both internal and external metrics. The results obtained by both methods are comparable to those obtained by state-of-the-art tools, but are fast to train and exhibit high levels of sparsity, which make them easier to interpret. Moreover we applied these methods to three complex datasets containing RNA-seq informations from different mammalian tissues/cell-types. Lastly, we applied our methodology to compare a normal vs. a disease condition (Alzheimer), which allowed us to detect differential expression of genes' sub-networks between these two biological conditions. Globally, the gene networks obtained exhibit a sparse and modular structure, with inner communities of genes presenting statistically significant over/under-expression on specific cell types, as well as significant enrichment for some anatomical GO terms, suggesting that such communities may also drive important functional roles. [ABSTRACT FROM AUTHOR]

Context: Inferring gene regulatory networks (GRN) from high-throughput gene expression data is a challenging task for which different strategies have been developed. Nevertheless, no ever-winning method exists, and each method has its advantages, intrinsic biases, and application domains. Thus, in order to analyze a dataset, users should be able to test different techniques and choose the most appropriate one. This step can be particularly difficult and time consuming, since most methods' implementations are made available independently, possibly in different programming languages. The implementation of an open-source library containing different inference methods within a common framework is expected to be a valuable toolkit for the systems biology community. Results: In this work, we introduce GReNaDIne (Gene Regulatory Network Data-driven Inference), a Python package that implements 18 machine learning data-driven gene regulatory network inference methods. It also includes eight generalist preprocessing techniques, suitable for both RNA-seq and microarray dataset analysis, as well as four normalization techniques dedicated to RNA-seq. In addition, this package implements the possibility to combine the results of different inference tools to form robust and efficient ensembles. This package has been successfully assessed under the DREAM5 challenge benchmark dataset. The open-source GReNaDIne Python package is made freely available in a dedicated GitLab repository, as well as in the official third-party software repository PyPI Python Package Index. The latest documentation on the GReNaDIne library is also available at Read the Docs, an open-source software documentation hosting platform. Contribution: The GReNaDIne tool represents a technological contribution to the field of systems biology. This package can be used to infer gene regulatory networks from high-throughput gene expression data using different algorithms within the same framework. In order to analyze their datasets, users can apply a battery of preprocessing and postprocessing tools and choose the most adapted inference method from the GReNaDIne library and even combine the output of different methods to obtain more robust results. The results format provided by GReNaDIne is compatible with well-known complementary refinement tools such as PYSCENIC. [ABSTRACT FROM AUTHOR]

Aphids (Hemiptera: Aphidoidea) are among the most detrimental insects for agricultural plants, and their management is a great challenge in agronomical research. A new class of proteins, called Bacteriocyte-specific Cysteine-Rich (BCR) peptides, provides an alternative to chemical insecticides for pest control. BCRs were initially identified in the pea aphid Acyrthosiphon pisum. They are small disulfide bond-rich proteins expressed exclusively in aphid bacteriocytes, the insect cells that host intracellular symbiotic bacteria. Here, we show that one of the A. pisum BCRs, BCR4, displays prominent insecticidal activity against the pea aphid, impairing insect survival and nymphal growth, providing evidence for its potential use as a new biopesticide. Our comparative genomics and phylogenetic analyses indicate that BCRs are restricted to the aphid lineage. The 3D structure of BCR4 reveals that this peptide belongs to an as-yet-unknown structural class of peptides and defines a new superfamily of defensins. [ABSTRACT FROM AUTHOR]

Dependence on multiple nutritional bacterial symbionts forming a metabolic unit has repeatedly evolved in many insect species that feed on nutritionally unbalanced diets such as plant sap. This is the case for aphids of the subfamilies Lachninae and Chaitophorinae, which have evolved di-symbiotic systems in which the ancient obligate nutritional symbiont Buchnera aphidicola is metabolically complemented by an additional nutritional symbiont acquired more recently. Deciphering how different symbionts integrate both metabolically and anatomically in such systems is crucial to understanding how complex nutritional symbiotic systems function and evolve. In this study, we sequenced and analyzed the genomes of the symbionts B. aphidicola and Serratia symbiotica associated with the Chaitophorinae aphids Sipha maydis and Periphyllus lyropictus. Our results show that, in these two species, B. aphidicola and S. symbiotica complement each other metabolically (and their hosts) for the biosynthesis of essential amino acids and vitamins, but with distinct metabolic reactions supported by each symbiont depending on the host species. Furthermore, the S. symbiotica symbiont associated with S. maydis appears to be strictly compartmentalized into the specialized host cells housing symbionts in aphids, the bacteriocytes, whereas the S. symbiotica symbiont associated with P. lyropictus exhibits a highly invasive phenotype, presumably because it is capable of expressing a larger set of virulence factors, including a complete flagellum for bacterial motility. Such contrasting levels of metabolic and anatomical integration for two S. symbiotica symbionts that were recently acquired as nutritional co-obligate partners reflect distinct coevolutionary processes specific to each association. [ABSTRACT FROM AUTHOR]

Obesity is a chronic disease affecting millions of people worldwide. The fruit fly (Drosophila melanogaster) is an interesting research model to study metabolic and transcriptomic responses to obesogenic diets. However, the sex-specific differences in these responses are still understudied and perhaps underestimated. In this study, we exposed adult male and female Dahomey fruit flies to a standard diet supplemented with sugar, fat, or a combination of both. The exposure to a diet supplemented with 10% sugar and 10% fat efficiently induced an increase in the lipid content in flies, a hallmark for obesity. This increase in lipid content was more prominent in males, while females displayed significant changes in glycogen content. A strong effect of the diets on the ovarian size and number of ma-ture oocytes was also present in females exposed to diets supplemented with fat and a combina-tion of fat and sugar. In both males and females, fat body morphology changed and was associ-ated with an increase in lipid content of fat cells in response to the diets. The expression of me-tabolism-related genes also displayed a strong sexually dimorphic response under normal condi-tions and in response to sugar and/or fat-supplemented diets. Here, we show that the exposure of adult fruit flies to an obesogenic diet containing both sugar and fat allowed studying sexual dimorphism in metabolism and the expression of genes regulating metabolism. [ABSTRACT FROM AUTHOR]

Obesity is a chronic disease affecting millions of people worldwide. The fruit fly ( Drosophila melanogaster ) is an interesting research model to study metabolic and transcriptomic responses to obesogenic diets. However, the sex-specific differences in these responses are still understudied and perhaps underestimated. In this study, we exposed adult male and female Dahomey fruit flies to a standard diet supplemented with sugar, fat, or a combination of both. The exposure to a diet supplemented with 10% sugar and 10% fat efficiently induced an increase in the lipid content in flies, a hallmark for obesity. This increase in lipid content was more prominent in males, while females displayed significant changes in glycogen content. A strong effect of the diets on the ovarian size and number of ma-ture oocytes was also present in females exposed to diets supplemented with fat and a combina-tion of fat and sugar. In both males and females, fat body morphology changed and was associ-ated with an increase in lipid content of fat cells in response to the diets. The expression of me-tabolism-related genes also displayed a strong sexually dimorphic response under normal condi-tions and in response to sugar and/or fat-supplemented diets. Here, we show that the exposure of adult fruit flies to an obesogenic diet containing both sugar and fat allowed studying sexual dimorphism in metabolism and the expression of genes regulating metabolism.