Odorant-Binding Proteins and Chemosensory Proteins in Spodoptera frugiperda: From Genome-Wide Identification and Developmental Stage-Related Expression Analysis to the Perception of Host Plant Odors, Sex Pheromones, and Insecticides

Jia, Chen; Mohamed, Amr A.; Cattaneo, Alberto Maria; Huang, Xiaohua; Keyhani, Nemat O.; Gu, Maiqun; Zang, Lian‐Sheng; Zhang, Wei

doi:10.3390/ijms24065595

Cited by 16 publications

(15 citation statements)

References 103 publications

(121 reference statements)

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“…We found that the odorant-binding protein (OBP) genes were under strong selection. The OBP is involved in the regulation of insect host recognition, foraging, courtship, and other behaviors [ 41 ].…”

Section: Resultsmentioning

confidence: 99%

Population genomics of Agrotis segetum provide insights into the local adaptive evolution of agricultural pests

Wang,

Jin,

et al. 2024

BMC Biol

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Background The adaptive mechanisms of agricultural pests are the key to understanding the evolution of the pests and to developing new control strategies. However, there are few studies on the genetic basis of adaptations of agricultural pests. The turnip moth, Agrotis segetum (Lepidoptera: Noctuidae) is an important underground pest that affects a wide range of host plants and has a strong capacity to adapt to new environments. It is thus a good model for studying the adaptive evolution of pest species. Results We assembled a high-quality reference genome of A. segetum using PacBio reads. Then, we constructed a variation map of A. segetum by resequencing 98 individuals collected from six natural populations in China. The analysis of the population structure showed that all individuals were divided into four well-differentiated populations, corresponding to their geographical distribution. Selective sweep analysis and environmental association studies showed that candidate genes associated with local adaptation were functionally correlated with detoxification metabolism and glucose metabolism. Conclusions Our study of A. segetum has provided insights into the genetic mechanisms of local adaptation and evolution; it has also produced genetic resources for developing new pest management strategies.

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Section: Resultsmentioning

confidence: 99%

Population genomics of Agrotis segetum provide insights into the local adaptive evolution of agricultural pests

Wang,

Jin,

et al. 2024

BMC Biol

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“…OBPs can transport chemicals to odorant receptors (ORs), which comprise the first step in insect olfactory molecule recognition [ 44 ]. As demonstrated here and in prior publications, OBP functions can be delineated through a combination of fluorescence competition binding, EAG assays, and RNA interference (RNAi) [ 45 ]. For example, Bradysia odoriphaga shows strong EAG responses to the host plant volatiles methyl allyl disulfide and diallyl disulfide.…”

Section: Discussionmentioning

confidence: 99%

Molecular Characterization of Plant Volatile Compound Interactions with Cnaphalocrocis medinalis Odorant-Binding Proteins

Qian,

Guo,

et al. 2024

Plants

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Odorant-binding proteins (OBPs) play important roles in the insect olfactory system since they bind external odor molecules to trigger insect olfactory responses. Previous studies have identified some plant-derived volatiles that attract the pervasive insect pest Cnaphalocrocis medinalis (Lepidoptera: Pyralidae), such as phenylacetaldehyde, benzyl acetate, 1-heptanol, and hexanal. To characterize the roles of CmedOBPs in the recognition of these four volatiles, we analyzed the binding abilities of selected CmedOBPs to each of the four compounds, as well as the expression patterns of CmedOBPs in different developmental stages of C. medinalis adult. Antennaes of C. medinalis adults were sensitive to the studied plant volatile combinations. Expression levels of multiple CmedOBPs were significantly increased in the antennae of 2-day-old adults after exposure to volatiles. CmedOBP1, CmedOBP6, CmedPBP1, CmedPBP2, and CmedGOBP2 were significantly up-regulated in the antennae of volatile-stimulated female and male adults when compared to untreated controls. Fluorescence competition assays confirmed that CmedOBP1 could strongly bind 1-heptanol, hexanal, and phenylacetaldehyde; CmedOBP15 strongly bound benzyl acetate and phenylacetaldehyde; and CmedOBP26 could weakly bind 1-heptanol. This study lays a theoretical foundation for further analysis of the mechanisms by which plant volatiles can attract C. medinalis. It also provides a technical basis for the future development of efficient plant volatile attractants of C. medinalis.

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“…Research on bactericidal and insect properties heavily relies on the ability to identify chemicals that smell green. Prospective green odorants could be applied to forest preservation and agricultural production as fungicidal repellents [ 20 ]. The identification of more green odorant-molecules will greatly advance the study of insects, as green odorants have the potential to function as insect pheromones.…”

Section: Introductionmentioning

confidence: 99%

Mlp4green: A Binary Classification Approach Specifically for Green Odor

Yang,

Qian,

et al. 2024

IJMS

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Fresh green leaves give off a smell known as “green odor.” It has antibacterial qualities and can be used to attract or repel insects. However, a common method for evaluating green odor molecules has never existed. Machine learning techniques are widely used in research to forecast molecular attributes for binary classification. In this work, the green odor molecules were first trained and learned using machine learning methods, and then clustering analysis and molecular docking were performed to further explore their molecular characteristics and mechanisms of action. For comparison, four algorithmic models were employed, MLP performed the best in all metrics, including Accuracy, Precision, Average Precision, Matthews coefficient, and Area under curve. We determined by difference analysis that, in comparison to non-green odor molecules, green odor molecules have a lower molecular mass and fewer electrons. Based on the MLP algorithm, we constructed a binary classification prediction website for green odors. The first application of deep learning techniques to the study of green odor molecules can be seen as a signal of a new era in which green odor research has advanced into intelligence and standardization.

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Odorant-Binding Proteins and Chemosensory Proteins in Spodoptera frugiperda: From Genome-Wide Identification and Developmental Stage-Related Expression Analysis to the Perception of Host Plant Odors, Sex Pheromones, and Insecticides

Cited by 16 publications

References 103 publications

Population genomics of Agrotis segetum provide insights into the local adaptive evolution of agricultural pests

Population genomics of Agrotis segetum provide insights into the local adaptive evolution of agricultural pests

Molecular Characterization of Plant Volatile Compound Interactions with Cnaphalocrocis medinalis Odorant-Binding Proteins

Mlp4green: A Binary Classification Approach Specifically for Green Odor

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