Proteomics and Interspecies Interaction Analysis Revealed Abscisic Acid Signalling to Be the Primary Driver for Oil Palm's Response against Red Palm Weevil Infestation

Molecular Insights into Red Palm Weevil Resistance Mechanisms of Coconut (Cocos nucifera) Leaves

Red palm weevil (RPW) (Rhynchophorus ferrugineus) threatens most palm species worldwide. This study investigated the molecular responses of coconut (Cocos nucifera) leaves to RPW infestation through metabolomics and transcriptomics analysis. An RPW insect attack model was developed by placing different RPW larval densitiesin coconut plants and measuring the relative chlorophyll content of different leaf positions and physiological indicators of dysfunction after RPW infestation. The metabolomic changes were detected in the leaves of 10, 20, 30, 40, and 50 days after infestation (DAI) using GC-MS. Certain metabolites (glycine, D-pinitol, lauric acid, allylmalonic acid, D-glucaro-1, 4-lactone, protocatechuic acid, alpha, and alpha-trehalose) were found to be possible indicators for distinct stages of infestation using metabolomics analysis. The influence on ABC transporters, glutathione, galactose, and glycolipid metabolism was emphasized by pathway analysis. Differentially expressed genes (DEGs) were identified at 5, 10, 15, and 20 DAI through transcriptomics analysis of infested coconut leaves, with altered expression levels under RPW infestation. The KEGG pathway and GO analysis revealed enrichment in pathways related to metabolism, stress response, and plant-pathogen interactions, shedding light on the intricate mechanisms underlying coconut-RPW interactions. The identified genes may serve as potential markers for tracking RPW infestation progression and could inform strategies for pest control and management.

Taxonomy, Biology, Symbionts, Omics, and Management of Rhynchophorus Palm Weevils (Coleoptera: Curculionidae: Dryophthorinae)

Palm weevils, Rhynchophorus spp., are destructive pests of native, ornamental, and agricultural palm species. Of the 10 recognized species, two of the most injurious species, Rhynchophorus ferrugineus and Rhynchophorus palmarum, both of which have spread beyond their native range, are the best studied. Due to its greater global spread and damage to edible date industries in the Middle East, R. ferrugineus has received more research interest. Integrated pest management programs utilize traps baited with aggregation pheromone, removal of infested palms, and insecticides. However, weevil control is costly, development of resistance to insecticides is problematic, and program efficacy can be impaired because early detection of infestations is difficult. The genome of R. ferrugineus has been sequenced, and omics research is providing insight into pheromone communication and changes in volatile and metabolism profiles of weevil-infested palms. We outline how such developments could lead to new control strategies and early detection tools.

Transcriptome sequencing of Cocos nucifera leaves in response to Rhynchophorus ferrugineus infestation

Red palm weevil (RPW, Rhynchophorus ferrugineus) is an invasive pest of palms. In China, coconut (Cocos nucifera) production is being significantly affected by the RPW attack. To develop a long-term RPW control strategy, host-plant resistance is the most sustainable option. In this regard, the availability of transcriptome sequencing data from RPW-infected coconut plants can be highly useful. Therefore, the present study assessed coconut leaf physiological responses and transcriptional changes after different days of RPW attack i.e., 5, 10, 15, 20, and 25 days after infestation (DAI). A comparison of physiological data indicated that populations with the higher number of RPW insects i.e., population C (15 males +21 females) and D (20 males +28 females) triggered higher antioxidant enzyme activities. We used this data to study the transcriptomic responses on 5 and 20 DAI. Of the 38,432 detected transcripts, 3,984, 1,981, 3,925, and 2,257 were differentially expressed in CK (control/no RPW)_vs._C (5 DAI), CK_vs._D (5 DAI), CK_vs._C (20 DAI), and CK_vs._D (20 DAI), respectively. These transcripts were enriched in plant-pathogen interaction, phenylpropanoid/flavonoid biosynthesis, amino sugar and nucleotide sugar metabolism, plant hormone signal transduction, mitogen-activated protein kinase, and reactive oxygen scavenging pathway. We discuss these results and present several candidate genes to be manipulated for developing a sustainable strategy to control RPW attack regarding host-plant resistance. Furthermore, these findings provide a basis for developing effective early and late RPW attack detection strategies.

Proteome-wide analysis of lysine 2-hydroxyisobutyrylation in Frankliniella occidentalis

BACKGROUND

Lysine 2-hydroxyisobutyrylation (Khib) is a novel and conserved post-translational modification (PTM). Frankliniella occidentalis are economically important agricultural pests globally and also notorious for vectoring destructive plant viruses. To better study the disease transmission mechanism of F. occidentalis, it is necessary to conduct in-depth analysis of it. So far, no Khib modification of insects has been reported.

RESULTS

In this study, a proteome-wide analysis of Khib modifications in F. occidentalis was analyzed for the first time through the combination of high performance liquid chromatography fractionation technology and 2-hydroxyisobutyrylated peptide enrichment and other advanced technologies, 4093 Khib sites were identified on 1125 modified proteins. Bioinformatics and functional enrichment analyses showed that Khib-modified proteins were significantly enriched in many cell compartments and pathways, especially related to various cellular components and biological processes, and were more concentrated in ribosomes and proteasome subunits, involved in energy metabolism, protein synthesis and degradation, compared to the other nine species including Japonica rice, Homo sapiens, P. patens, Botrytis, Ustilaginoidea virens, Saccharomyces cerevisiae, T. gondii, C. albicans, and F. oxysporum. And Khib sites on virus-interacting insect proteins were discovered for the first time, such as cyclophilin and endoCP-GN.

CONCLUSIONS

After three repeated experiments, we found a total of 4093 Khib sites on 1125 proteins. These modified proteins are mainly concentrated in ribosomes and proteasome subunits, and are widely involved in a variety of critical biological activities and metabolic processes of F. occidentalis. In addition, for the first time, Khib modification sites are found on the proteome of F. occidentalis, and these sites could be acted as for the virus interaction, including cyclophilin and endoCP-GN. The global map of 2-hydroxyisobutyrylation in thrips is an invaluable resource to better understand the biological processes of thrips and provide new means for disease control and mitigation of pest damage to crops.

A Deep-Learning Model for Real-Time Red Palm Weevil Detection and Localization

Background and motivation: Over the last two decades, particularly in the Middle East, Red Palm Weevils (RPW, Rhynchophorus ferruginous) have proved to be the most destructive pest of palm trees across the globe. Problem: The RPW has caused considerable damage to various palm species. The early identification of the RPW is a challenging task for good date production since the identification will prevent palm trees from being affected by the RPW. This is one of the reasons why the use of advanced technology will help in the prevention of the spread of the RPW on palm trees. Many researchers have worked on finding an accurate technique for the identification, localization and classification of the RPW pest. This study aimed to develop a model that can use a deep-learning approach to identify and discriminate between the RPW and other insects living in palm tree habitats using a deep-learning technique. Researchers had not applied deep learning to the classification of red palm weevils previously. Methods: In this study, a region-based convolutional neural network (R-CNN) algorithm was used to detect the location of the RPW in an image by building bounding boxes around the image. A CNN algorithm was applied in order to extract the features to enclose with the bounding boxes—the selection target. In addition, these features were passed through the classification and regression layers to determine the presence of the RPW with a high degree of accuracy and to locate its coordinates. Results: As a result of the developed model, the RPW can be quickly detected with a high accuracy of 100% in infested palm trees at an early stage. In the Al-Qassim region, which has thousands of farms, the model sets the path for deploying an efficient, low-cost RPW detection and classification technology for palm trees.