Evolutionary novelty in the apoptotic pathway of aphids.

Apoptosis, a conserved form of programmed cell death, shows interspecies differences that may reflect evolutionary diversification and adaptation, a notion that remains largely untested. Among insects, the most speciose animal group, the apoptotic pathway has only been fully characterized in Drosophila melanogaster, and apoptosis-related proteins have been studied in a few other dipteran and lepidopteran species. Here, we studied the apoptotic pathway in the aphid Acyrthosiphon pisum, an insect pest belonging to the Hemiptera, an earlier-diverging and distantly related order. We combined phylogenetic analyses and conserved domain identification to annotate the apoptotic pathway in A. pisum and found low caspase diversity and a large expansion of its inhibitory part, with 28 inhibitors of apoptosis (IAPs). We analyzed the spatiotemporal expression of a selected set of pea aphid IAPs and showed that they are differentially expressed in different life stages and tissues, suggesting functional diversification. Five IAPs are specifically induced in bacteriocytes, the specialized cells housing symbiotic bacteria, during their cell death. We demonstrated the antiapoptotic role of these five IAPs using heterologous expression in a tractable in vivo model, the Drosophila melanogaster developing eye. Interestingly, IAPs with the strongest antiapoptotic potential contain two BIR and two RING domains, a domain association that has not been observed in any other species. We finally analyzed all available aphid genomes and found that they all show large IAP expansion, with new combinations of protein domains, suggestive of evolutionarily novel aphid-specific functions.

Spatial-Spectral Analysis of Hyperspectral Images Reveals Early Detection of Downy Mildew on Grapevine Leaves.

Downy mildew is a highly destructive disease of grapevine. Currently, monitoring for its symptoms is time-consuming and requires specialist staff. Therefore, an automated non-destructive method to detect the pathogen before the visible symptoms appear would be beneficial for early targeted treatments. The aim of this study was to detect the disease early in a controlled environment, and to monitor the disease severity evolution in time and space. We used a hyperspectral image database following the development from 0 to 9 days post inoculation (dpi) of three strains of Plasmopara viticola inoculated on grapevine leaves and developed an automatic detection tool based on a Support Vector Machine (SVM) classifier. The SVM obtained promising validation average accuracy scores of 0.96, a test accuracy score of 0.99, and it did not output false positives on the control leaves and detected downy mildew at 2 dpi, 2 days before the clear onset of visual symptoms at 4 dpi. Moreover, the disease area detected over time was higher than that when visually assessed, providing a better evaluation of disease severity. To our knowledge, this is the first study using hyperspectral imaging to automatically detect and show the spatial distribution of downy mildew on grapevine leaves early over time.

Gene Self-Expressive Networks as a Generalization-Aware Tool to Model Gene Regulatory Networks.

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 ElasticNet and OMP (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.

GReNaDIne: A Data-Driven Python Library to Infer Gene Regulatory Networks from Gene Expression Data.

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.

A comparative study revealed hyperspectral imaging as a potential standardized tool for the analysis of cuticle tanning over insect development.

Cereal-feeding beetles are a major risk for cereal crop maintenance. Cereal weevils such as Sitophilus oryzae have symbiotic intracellular bacteria that provide essential aromatic amino acid to the host for the biosynthesis of their cuticle building blocks. Their cuticle is an important protective barrier against biotic and abiotic stresses, providing high resistance from insecticides. Quantitative optical methods specialized in insect cuticle analysis exist, but their scope of use and the repeatability of the results remain limited. Here, we investigated the potential of Hyperspectral Imaging (HSI) as a standardized cuticle analysis tool. Based on HSI, we acquired time series of average reflectance profiles from 400 to 1000 nm from symbiotic (with bacteria) and aposymbiotic (without bacteria) cereal weevils S. oryzae exposed to different nutritional stresses. We assessed the phenotypic changes of weevils under different diets throughout their development and demonstrated the agreement of the results between the HSI method and the classically used Red-Green-Blue analysis. Then, we compared the use of both technologies in laboratory conditions and highlighted the assets of HSI to develop a simple, automated, and standardized analysis tool. This is the first study showing the reliability and feasibility of HSI for a standardized analysis of insect cuticle changes.

Weevil Carbohydrate Intake Triggers Endosymbiont Proliferation: A Trade-Off between Host Benefit and Endosymbiont Burden.

Nutritional symbioses between insects and intracellular bacteria (endosymbionts) are a major force of adaptation, allowing animals to colonize nutrient-poor ecological niches. Many beetles feeding on tyrosine-poor substrates rely on a surplus of aromatic amino acids produced by bacterial endosymbionts. This surplus of aromatic amino acids is crucial for the biosynthesis of a thick exoskeleton, the cuticle, which is made of a matrix of chitin with proteins and pigments built from tyrosine-derived molecules, providing an important defensive barrier against biotic and abiotic stress. Other endosymbiont-related advantages for beetles include faster development and improved fecundity. The association between Sitophilus oryzae and the Sodalis pierantonius endosymbiont represents a unique case study among beetles: endosymbionts undergo an exponential proliferation in young adults concomitant with the cuticle tanning, and then they are fully eliminated. While endosymbiont clearance, as well as total endosymbiont titer, are host-controlled processes, the mechanism triggering endosymbiont exponential proliferation remains poorly understood. Here, we show that endosymbiont exponential proliferation relies on host carbohydrate intake, unlike the total endosymbiont titer or the endosymbiont clearance, which are under host genetic control. Remarkably, insect fecundity was preserved, and the cuticle tanning was achieved, even when endosymbiont exponential proliferation was experimentally blocked, except in the context of a severely unbalanced diet. Moreover, a high endosymbiont titer coupled with nutrient shortage dramatically impacted host survival, revealing possible environment-dependent disadvantages for the host, likely due to the high energy cost of exponentially proliferating endosymbionts. IMPORTANCE Beetles thriving on tyrosine-poor diet sources often develop mutualistic associations with endosymbionts able to synthesize aromatic amino acids. This surplus of aromatic amino acids is used to reinforce the insect's protective cuticle. An exceptional feature of the Sitophilus oryzae/Sodalis pierantonius interaction is the exponential increase in endosymbiotic titer observed in young adult insects, in concomitance with cuticle biosynthesis. Here, we show that host carbohydrate intake triggers endosymbiont exponential proliferation, even in conditions that lead to the detriment of the host survival. In addition, when hosts thrive on a balanced diet, endosymbiont proliferation is dispensable for several host fitness traits. The endosymbiont exponential proliferation is therefore dependent on the nutritional status of the host, and its consequences on host cuticle biosynthesis and survival depend on food quality and availability.