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Functional characterizations of chemosensory proteins of the alfalfa plant bug Adelphocoris lineolatus indicate their involvement in host recognition.

ABSTRACT: Insect chemosensory proteins (CSPs) have been proposed to capture and transport hydrophobic chemicals from air to olfactory receptors in the lymph of antennal chemosensilla. They may represent a new class of soluble carrier protein involved in insect chemoreception. However, their specific functional roles in insect chemoreception have not been fully elucidated. In this study, we report for the first time three novel CSP genes (AlinCSP1-3) of the alfalfa plant bug Adelphocoris lineolatus (Goeze) by screening the antennal cDNA library. The qRT-PCR examinations of the transcript levels revealed that all three genes (AlinCSP1-3) are mainly expressed in the antennae. Interestingly, these CSP genes AlinCSP1-3 are also highly expressed in the 5(th) instar nymphs, suggesting a proposed function of these CSP proteins (AlinCSP1-3) in the olfactory reception and in maintaining particular life activities into the adult stage. Using bacterial expression system, the three CSP proteins were expressed and purified. For the first time we characterized the types of sensilla in the antennae of the plant bug using scanning electron microscopy (SEM). Immunocytochemistry analysis indicated that the CSP proteins were expressed in the pheromone-sensitive sensilla trichodea and general odorant-sensitive sensilla basiconica, providing further evidence of their involvement in chemoreception. The antennal activity of 55 host-related semiochemicals and sex pheromone compounds in the host location and mate selection behavior of A. lineolatus was investigated using electroantennogram (EAG), and the binding affinities of these chemicals to the three CSPs (AlinCSP1-3) were measured using fluorescent binding assays. The results showed several host-related semiochemicals, (Z)-3-hexen-1-ol, (E)-2-hexen-1-al and valeraldehyde, have a high binding affinity with AlinCSP1-3 and can elicit significant high EAG responses of A. lineolatus antennae. Our studies indicate the three antennae-biased CSPs may mediate host recognition in the alfalfa plant bug A. lineolatus.

SUBMITTER: Gu SH

PROVIDER: S-EPMC3416781 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Functional characterizations of chemosensory proteins of the alfalfa plant bug Adelphocoris lineolatus indicate their involvement in host recognition.

Gu Shao-Hua SH Wang Song-Ying SY Zhang Xue-Ying XY Ji Ping P Liu Jing-Tao JT Wang Gui-Rong GR Wu Kong-Ming KM Guo Yu-Yuan YY Zhou Jing-Jiang JJ Zhang Yong-Jun YJ

PloS one 20120810 8

Insect chemosensory proteins (CSPs) have been proposed to capture and transport hydrophobic chemicals from air to olfactory receptors in the lymph of antennal chemosensilla. They may represent a new class of soluble carrier protein involved in insect chemoreception. However, their specific functional roles in insect chemoreception have not been fully elucidated. In this study, we report for the first time three novel CSP genes (AlinCSP1-3) of the alfalfa plant bug Adelphocoris lineolatus (Goeze) ...[more]

PMID: 22900060

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Project description:The chemosensory system is essential for insects to detect exogenous compounds, and odorant binding proteins (OBPs) play crucial roles in odorant binding and transduction. In the alfalfa plant bug Adelphocoris lineolatus, an important pest of multiple crops, our understanding of the physiological roles of antenna-biased OBPs has increased dramatically, whereas OBPs related to gustation have remained mostly unexplored. In this study, we employed RNA sequencing and RACE PCR methods to identify putative OBPs from the adult forelegs of both sexes. Eight candidate OBPs were identified, and three OBPs (AlinOBP15, 16, and 17) were novel. Full-length sequence alignment and phylogenetic analyses suggested that these three candidate OBPs had characteristics typical of the insect OBP family. AlinOBP16 and 17 displayed six highly conserved cysteines, placing them in the classic OBP subfamily, whereas AlinOBP15 resembled AlinOBP14 and clustered with the Plus-C clade. Quantitative real-time PCR (qRT-PCR) revealed distinct and significant tissue- and sex-biased expression patterns. AlinOBP15 was highly expressed in female heads, and AlinOBP16 and 17 were strongly expressed in female antennae. In particular, AlinOBP11, the most abundant OBP gene in our foreleg transcriptome dataset, was predominately expressed in adult legs. Furthermore, four types of sensilla hairs were observed on the forelegs of adult A. lineolatus, including sensilla trichodea, setae, and two types of sensilla chaetica (Sch1 and Sch2). Anti-AlinOBP11 antiserum strongly labeled the outer sensillum lymph of Sch2, implying that it has important gustatory functions in A. lineolatus. Our current findings provide evidence that OBPs can be functionally expressed in the tarsal gustatory sensilla of hemipteran mirid species, broadening our understanding of OBP chemosensory function in insects and facilitating the discovery of new functional targets for the regulation of insect host-searching behaviors.

Project description:Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.

Dataset Information

Functional characterizations of chemosensory proteins of the alfalfa plant bug Adelphocoris lineolatus indicate their involvement in host recognition.

Publications

Functional characterizations of chemosensory proteins of the alfalfa plant bug Adelphocoris lineolatus indicate their involvement in host recognition.

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