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Article

The Discovery of Potential Repellent Compounds for Zeugodacus cucuribitae (Coquillett) from Six Non-Favored Hosts

by
Yu Fu
1,†,
Yupeng Chen
2,3,†,
Yani Wang
1,
Xinyi Fu
1,
Shunda Jin
4,5,
Chunyan Yi
6,
Xue Bai
7,
Youqing Lu
1,
Wang Miao
1,
Xingyu Geng
1,
Xianli Lu
1,
Rihui Yan
1,
Zhongshi Zhou
8,* and
Fengqin Cao
1,*
1
School of Tropical Agriculture and Forestry, Hainan University, Haikou 570100, China
2
Institute of Systems Medicine and Health Sciences, Hong Kong Baptist University, Hong Kong SAR 999077, China
3
School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR 999077, China
4
School of Life Sciences, Sanya Institute of Henan University, Sanya 572025, China
5
National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Henan Joint International Laboratory for Crop Multi-Omics Research, School of Life Sciences, Henan University, Kaifeng 475000, China
6
Key Laboratory of Integrated Pest Management of Southwest Crops, Institute of Plant Protection, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China
7
State Key Laboratory of Rice Biology and Breeding, Ministry of Agricultural and Rural Affairs Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Institute of Insect Sciences, Zhejiang University, Hangzhou 310058, China
8
National Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572019, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2025, 26(14), 6556; https://doi.org/10.3390/ijms26146556
Submission received: 16 May 2025 / Revised: 7 June 2025 / Accepted: 20 June 2025 / Published: 8 July 2025
(This article belongs to the Special Issue Molecular Research in Natural Products)
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Abstract

Zeugodacus cucuribitae (Coquillett) (Z. cucuribitae) is a global extremely invasive quarantine pest which has a wide host range of fruits and vegetables. At present, there are a few control measures for Z. cucuribitae, and deltamethrin and avermectin are commonly used. Among the hosts of Z. cucuribitae, Luffa acutangular, Luffa cylindrica, Sechium edule, Brassica oleracea var. botrytis, Musa nana, and Fragaria × ananassa are non-favored hosts. However, it is still not clear why these hosts are non-favored and whether there are any repellent components of Z. cucuribitae in these hosts. In this study, the components of these six hosts were collected from the literature, and the genes of odor and chemical sensation were determined from the genome of Z. cucuribitae. After the potential relationships between these components and genes were determined by molecular docking methods, the KEGG and GO enrichment analysis of these genes was conducted, and a complex network of genes vs. components vs. Kegg pathway vs. GO terms was constructed and used to select the key components for experiments. The results show that oleanolic acid (1 mg/mL, 0.1 mg/mL, and 0.01 mg/mL), rotenone (1 mg/mL, 0.1 mg/mL, and 0.01 mg/mL), and beta-caryophyllene oxide (1 mg/mL, 0.1 mg/mL, and 0.01 mg/mL) had a significant repellent effect on Z. cucuribitae, and three components, rotenone (1 mg/mL and 0.1 mg/mL), echinocystic acid (1 mg/mL, 0.1 mg/mL, and 0.01 mg/mL), and beta-caryophyllene oxide (1 mg/mL, and 0.1 mg/mL) had significant stomach toxicity in Z. cucuribitae. Furthermore, a complex signaling pathway was built and used to predict the effect of these components on Z. cucuribitae. These components probably play roles in the neuroactive ligand–receptor interaction (ko04080) and calcium signaling (ko04020) pathways. This study provides a reference for the prevention and control of Z. cucuribitae and a scientific reference for the rapid screening and development of new pest control drugs.

1. Introduction

Zeugodacus cucuribitae (Coquillett) (Z. cucuribitae) is a destructive agricultural pest and is distributed across a range of climatic regions, such as Central and East Asia and Oceania [1]. Due to its vast adaptability, high reproduction potential, and invasion capacity, it has been the subject of a worldwide pest management program [2]. It has widely invaded many crops and brought about serious losses to agricultural production. Z. cucuribitae can damage a wide variety of fruits and vegetables, including Luffa acutangular (L. acutangular), Luffa cylindrica (L. cylindrica), and Sechium edule (S. edule) [3,4]. At present, chemical insecticides, such as cypermethrin, fenvalerate, and abamectin, were commonly used in the field to reduce the damage caused to fruits and vegetables by Z. cucuribitae [5]. However, conventional methods in controlling Z. cucuribitae have some limitations, such as environmental pollution and a small number of physical trappings [5,6,7,8]. Therefore, there is an urgent need to find some effective and environmental protective components to control Z. cucuribitae.
As reported, many plants have evolved various methods to resist insects, such as secondary metabolites. Attacks by Bruchus pisorum would induce Pisum sativum to produce an isoflavone compound called pisatin, which is an insect resistance substance produced by plants and enables resistance against the invasion of B. pisorum [9,10]. The feeding of Spodoptera exigua would induce the production of momilactone A and momilactone B in Oryza sativa L. and could achieve a defense effect against Spodoptera exigua [11]. At the same time, natural compounds extracted from plants have been reported to have good application prospects in pest control. For example, volatile substances and secondary metabolites in some plant extracts have significant repellent effects on Bactrocera dorsalis (Hendel) (B. dorsalis) [12,13]. These metabolites usually have high safety and environmental protection and have little impact on non-target organisms. Secondly, plant-derived compounds have been proven not to easily cause pests to develop drug resistance and have effectively been used for pest control for a long time [14,15].
Plants of the Cucurbitaceae family are the main hosts of Z. cucuribitae. However, L. acutangular, L. cylindrica, and S. edule belong to Cucurbitaceae and are the relatively non-favored hosts of Z. cucuribitae. Compared with other Cucurbitaceae plants, the visit rate and oviposition rate of these species by Z. cucuribitae are lower [16,17,18]. In addition, some non-Cucurbitaceae plants, such as Brassica oleracea var. botrytis (B. oleracea), Musa nana (M. nana), and Fragaria × ananassa (F. ananassa), are relatively less attractive to Z. cucuribitae. The relative host performance and fecundity of Z. cucuribitae on these species is significantly lower than that on other species [16,19]. However, there are still no reports about whether there are some compounds that have a repellent effect on Z. cucuribitae. The main purpose of this study was to discover the compounds of host plants with repellent effects and a low damage rate against Z. cucuribitae.
Insects’ perception of chemical compounds is a diverse and complex process. According to previous reports, insects have some compound receptor proteins, including an olfactory receptor (OR), odor binding protein and general odorant-binding proteins (OBPGOBP), gustatory receptor (GR), pheromone-binding protein (PBP), ionotropic receptor (IR), and sensory neuron membrane protein (SNMP) [20]. For example, OR, a complex family of membrane protein receptors, was responsible for olfactory perception and communication in most insects [21,22]. These proteins were widely used in the screening of compounds to control pests [23,24].
The combination of network pharmacology and molecular docking has been widely used in the identification of effective drugs in the medical field [25,26,27] and is gradually being applied in the study of pest control [28,29,30]. These integrated approaches could accelerate the development of pesticides, disease-resistant breeding, and the screening of natural products. For example, molecular docking has been used to screen the attractive compounds of Z. cucuribitae and to screen the insecticidal metabolic compounds of Nephrolepis exaltata extract against Culicidae [31].
In this study, the potential repellent compounds of Z. cucuribitae in six non-favored hosts were screened out mainly based on network pharmacology, molecular docking technology, and behavior determinations. By screening the genes of Z. cucuribitae genome, a number of proteins related to the perception of chemical compounds of Z. cucuribitae were identified. These genes were combined with the compounds contained in the metabolic compounds of the six hosts, molecular virtual screening was carried out to obtain potential repellent compounds, and a genes vs. compounds vs. Kegg pathways vs. GO terms (CPPG) network was constructed. The effective repellent compounds were finally screened out with the degree value in this network. This study proposes an effective method in calculating and screening the compounds that have potential effects on Z. cucuribitae. Furthermore, oleanolic acid, rotenone, and beta-caryophyllene oxide were proven to have significant repellent effects on Z. cucuribitae with two-way selection tests. Rotenone and beta-caryophyllene oxide were proven to have the significant stomach toxicity of Z. cucuribitae with stomach toxicity tests. These results provide a new basis for the development of a novel insecticide to control Z. cucuribitae and provide a scientific reference in the prevention and management of pests.

2. Results

2.1. Identification and Enrichment of Olfactory Sensory Genes in Z. cucuribitae

2.1.1. Phylogenetic Analysis

The phylogenetic trees of Z. cucurbitae, including PBP, SNMP, OBP/GOBP, GR, OR, and IR genes, were constructed with R software (Version 4.3.1) after the identification of these genes from its genome collected from the NCBI database, an acknowledged database (Figure S1). The genes of Z. cucurbitae were clustered together with homologous genes from other Diptera insects, such as B. dorsalis and Musca domestica (M. domestica), demonstrating the accuracy of the odor-sensing genes of Z. cucurbitae. For example, in the phylogenetic analysis of IR genes (Figure 1), genes BcucIR25a and BcucIR21a from Z. cucurbitae and genes BdorIR25a and BdorIR21a from B. dorsalis formed a cluster with a short branching length, indicating high homology with the defined IR genes of B. dorsalis.

2.1.2. KEGG and GO Enrichment

In the KEGG and GO enrichment analysis of the olfactory sensory genes of Z. cucurbitae, 16 pathways and 20 GO terms were identified (Figure 2A,B). The results show that the odor recognition gene LOC105211704 was enriched in 16 Kegg pathways, including Systemic Lupus Erythematosus [Br: ko05322], Nicotine Addiction [Br: ko05033], Long-term Potentiation [Br: ko04720], Ion Channels [Br: ko04750], and Glutamatergic Synapse [Br: ko04724]. Previous studies have shown that Ion Channels [Br: ko04750] could affect the odor perception of the pea aphid Acyrthosiphon pisum to the host [32], and Glutamatergic Synapse [Br: ko04724] was related to neuronal development in Drosophila melanogaster and nerve sensation [33]. In addition, a total of 120 genes were enriched into 20 GO terms, including the sensory perception of chemical stimuli [GO:0007606] and the sensory perception of smell [GO:0007608]. According to studies, the sensory perception of smell [GO:0007608] has been shown to be closely related to OR and IR in Locusta migratoria [34].

2.1.3. Metabolite Analysis of L. acutangular, L. cylindrica, S. edule, B. oleracea, M. nana, and F. ananassa

A total of 165 compounds of the six non-favored hosts, L. acutangular, L. cylindrica, S. edule, B. oleracea, M. nana, and F. ananassa, were collected from the literature. Among these 165 compounds, 55, 46, 10, 27, 57, and 24 compounds were from L. acutangular, L. cylindrical, S. edule, B. oleracea, M. nana, and F. ananassa, respectively (Table 1).

2.2. Molecular Docking

To identify whether the compounds of six non-favored hosts could effectively bind to the odorant proteins of Z. cucurbitae, molecular docking was performed on 166 compounds, and the proteins were translated by 196 olfactory sensory genes. A total of 239,475 relationships were obtained from the dockings.
The molecular docking information was evaluated with binding affinity, and the effective binding affinity ranged from −5.00 kcal/mol to −18.00 kcal/mol. The results show that A total of 160 compounds had affinity values equal to or lower than −5.00 kcal/mol, indicating that these compounds could successfully combine with the proteins of olfactory sensory genes of Z. cucurbitae. A total of 154 compounds had affinity values equal to or lower than −6.00 kcal/mol, and 40 compounds had affinity values equal to or lower than −10.00 kcal/mol. These 40 compounds had particularly strong binding capabilities and can be considered for further use.
Among the 196 genes encoding the proteins of olfactory sensory genes (Figure 3A), the average affinities of all genes were equal to or lower than −5.00 kcal/mol (Figure 3B), indicating that these compounds had strong affinity with the proteins of olfactory sensory genes of Z. cucurbitae.
Furthermore, according to the docking results, the affinity values of maslinic acid 3-O-β-D-glucoside, lucyoside H, and lucyoside G were the lowest, indicating that among all collected compounds, these three compounds had the strongest binding capabilities with the odor-sensing genes. Additionally, the genes LOC105218953, LOC105209256, and LOC105215418 had the lowest affinity in all docking results of olfactory sensory genes, suggesting that these genes might be more sensitive to the compounds of the six hosts. Z. cucurbitae was likely to recognize harmful metabolites produced by the hosts through these genes and produce repellent or stomach toxicity effects.
The most effective combinations in each host are shown in Figure 3C–H. The maslinic acid 3-O-b-D-glucoside existing in L. acutangular could effectively bind with the gene LOC105217972 with a binding energy of −9.28 kcal/mol (Figure 3C). The echinocystic acid existing in L. cylindrica could effectively bind with the gene LOC105218953 with a binding energy of −11.71 kcal/mol (Figure 3D). The isorhoifolin existing in S. edule could effectively bind with the gene LOC105215418 with a binding energy of −11.16 kcal/mol (Figure 3E). The kaempferol existing in M. nana could effectively bind with the gene LOC105209256 with a binding energy of −11.80 kcal/mol (Figure 3F). The rutin existing in B. oleracea could effectively bind with the gene LOC105209182 with a binding energy of −6.98 kcal/mol (Figure 3G). The damascenone existing in F. ananassa could effectively bind with the gene LOC105217972 with a binding energy of −7.73 kcal/mol (Figure 3H).
According to reports, maslinic acid 3-O-b-D-glucoside was transformed by maslinic acid, which had a potential effect on pest control [71]. Echinocystic acid was isolated from Eclipta prostrate and played an important role in insect control by inhibiting the activation of NF-κB [72]. Isorhoifolin is a flavonoid, which played an important role in insect prevention and control by affecting the OR and GR of insects [73]. Rutin significantly reduced the feeding behavior and reproductive ability of Bemisia tabaci by regulating the defense mechanism of plants [74]. Rutin and quercetin could also significantly change the feeding behavior of aphids and reduce the damage on plants [75]. Damascenone, a pheromone, was released through modified cellulose nanocrystals to attract and control Sogatella furcifera [76].

2.3. Construction of CPPG Networks and Screening of Core Functional Compounds

The CPPG network was constructed based on the results of molecular docking and the KEGG and GO enrichment of olfactory sensory genes. The CPPG network contained 356 nodes, including 160 compounds, 196 genes, 16 pathways, and 20 GO terms (Figure 4). To more clearly illustrate the interactions between nodes, the top 30 compound nodes, top 30 gene nodes by degree, KEGG pathway nodes, and GO term nodes were selected to construct the CPPG network (Figure 4, Supplementary Table S1). Through an analysis of the CPPG network, the ranking of the 165 compounds from the host plants after docking with the odor recognition genes of Z. cucurbitae could be obtained. Futhermore, five compounds were randomly selected from the top 20 compounds which had the lowest degrees in the network and were used for further experiments of the two-way selections, and the gastric toxicity of oleanolic acid (C90), rotenone (C10), echinocystic acid (C91), diosmin (C72), and beta-caryophyllene oxide (C165) was finally determined. These compounds might play the most important role in Z. cucurbitae. Additionally, the nodes representing genes LOC105218013, LOC105214272, and LOC105219487 had the highest degree, indicating the greatest interaction with each compound.

2.4. Behavior Determination of Z. cucuribitae

2.4.1. Results of Two-Way Selection Experiments

The results of the two-way experiments show that oleanolic acid, rotenone, and beta-caryophyllene oxide had significant repellent effects on Z. cucurbitae at concentrations of 0.01 mg/mL, 0.1 mg/mL, and 1 mg/mL (Figure 5A,C,D). However, different concentrations of echinocystic acid and diosmin had no significant effect (Figure 5B,F). The repellent rate of 0.1 mg/mL rotenone could reach 86.67%, representing the most remarkable repellent effect on Z. cucurbitae.

2.4.2. Results of Gastric Toxicity Experiments

The results of the gastric toxicity experiments show that the mortality of Z. cucurbitae in the beta-caryophyllene oxide treatment reached 63.33% and 53.33% at concentrations of 1 mg/mL and 0.1 mg/mL, respectively (Figure 5F), which were significantly higher than the control (21.67%). The mortality rate of Z. cucurbitae in the rotenone treatment reached 63.33% and 50.00% at concentrations of 1 mg/mL and 0.1 mg/mL, respectively (Figure 5H), indicating a significant toxic effect on Z. cucurbitae. Echinocystic acid had significant insecticidal activity at concentrations of 1 mg/mL, 0.1 mg/mL, and 0.01 mg/mL, with mortality rates of 75.33%, 55.33%, and 55.00%, respectively (Figure 5J). Among these, the highest mortality was observed at a concentration of 1 mg/mL, and echinocystic acid also had a significant repellent effect at all concentrations. However, different concentrations of diosmin did not show significant effects in the experiment (Figure 5G,I).
These compounds also had certain control effects on other insects. According to reports, beta-caryophyllene oxide isolated from plants had obvious repellent activity against Tribolium castaneum and sitophilus granarius L. [77,78] and exhibited considerable fumigation toxicity and repellent characteristics toward Callosobruchus chinensis [79]. In addition, oleanolic acid had significant larvicidal activity against Aedes aegypti L. [80] and the significant antifeedant activity against spodoptera litura F. [81]. Rotenone is a broad-spectrum insecticidal active ingredient, which has a cytotoxic effect on SL-1 cells and significantly inhibits the larval growth of Spodoptera litura [82,83]. Furthermore, rotenone could disrupt the energy metabolism and mitochondrial dysfunction of Bombus terrestis glial cells and damage intestinal peristalsis [84].

2.5. Predicted Results of Molecular Mechanisms

The results of the two-way selection experiments and gastric toxicity experiments verified the effective effects of oleanolic acid, rotenone, and beta-caryophyllene oxide on Z. cucurbitae. The control effects of these compounds on Z. cucurbitae were achieved through the joint action of genes and pathways. According to the complex network, the targets of the successful actions of these compounds could be identified. Through the constructed CGGP network, we identified the gene LOC105217288. After the KEGG enrichment of LOC105217288, it was found that these compounds were mainly enriched in two pathways, the neuroactive ligand–receptor interaction (ko04080) and calcium signaling (ko04020) pathways. By marking the activated genes in these two pathways, we determined a molecular mechanism of the influence of the compounds apigenin, testosterone propionate, and biochanin A on the behavior of Z. cucurbitae (Figure 6). These compounds further activate proteins located on the cell membrane by activating the gene LOC105217288, including the glutamate receptor ionotropic NMDA1 (GRIN), glutamate receptor 1 (GRI), and nicotinic acetylcholine receptor alpha-7 (nAChRα7). These targets further affect downstream pathways by activating calcium ions and calmodulin (CALM), such as the MAPK signaling pathway (ko04010), apoptosis (ko04210), and long-term depression (ko04730). Eventually, Z. cucurbitae exhibits avoidance behavior and even death. Reports have shown that neonicotinoids, such as imidacloprid, could activate neuroactive ligand–receptor interaction (ko04080), leading to the dysfunction of insect nervous system and subsequently causing oxidative stress, mitochondrial dysfunction, inflammatory reaction, and apoptosis [85]. By combining RNA-Seq with isobaric ectopic tags for relative and absolute quantitative (iTRAQ) analysis, it was found that the calcium signaling pathway (ko04020) plays an important role in the adaptation and toxic reaction of Bursaphelenchus mucronatus to the host [86].

3. Discussions

Z. cucurbitae is a destructive pest which can damage a wide variety of fruits and vegetables, including L. acutangular, L. cylindrica, and S. edule. Currently, chemical pesticides are the main method in controlling Z. cucurbitae. However, this method has obvious limitations, such as the limited effectiveness of deltamethrin and abamectin [3,4,7]. Previous studies have shown that plant metabolites had good control effects and economic value in pest control. For instance, azadirachtin isolated from Azadirachta indica was proven to effectively control pests [87,88]. Host plants also contain insect-resistant metabolites to defend against insect damage [89,90,91]. Long-term observations indicate that although Z. cucurbitae can harm L. acutangular, L. cylindrica, S. edule, B. oleracea, M. nana, and F. ananassa, the damage is not severe [16,92]. It has been speculated that both cucurbitaceous hosts, such as L. acutangular, L. cylindrica, and S. edule, and non-cucurbitaceous hosts, such as B. oleracea, M. nana, and F. ananassa, contain metabolites that adversely affect Z. cucurbitae, thereby reducing the damage caused by this pest. These potential metabolites are worthy of further exploration and study for the prevention and control of Z. cucurbitae.
The compounds of L. acutangular, L. cylindrica, S. edule, B. oleracea, M. nana, and F. ananassa were collected from the literature. Meanwhile, the olfactory sensory genes of Z. cucurbitae were gathered based on its genome. Molecular docking was then conducted between these compounds and the olfactory sensory genes. Moreover, the genes were enriched via the KEGG pathway and GO term analyses, and a CPPG network was constructed. Through these steps, important compounds that may have potential effects on Z. cucurbitae were identified.
In the two-way selection experiment, oleanolic acid (0.01 mg/mL, 0.1 mg/mL, and 1 mg/mL), rotenone (0.01 mg/mL, 0.1 mg/mL, and 1 mg/mL), and beta-caryophyllene oxide (1 mg/mL and 0.1 mg/mL) were successfully screened. In the two-way selection experiment, echinocystic acid (0.01 mg/mL, 0.1 mg/mL, and 1 mg/mL), rotenone (0.1 mg/mL and 1 mg/mL), and beta-caryophyllene oxide (1 mg/mL and 0.1 mg/mL) have significant repellent effects against Z. cucurbitae. In the gastric toxicity experiment, echinocystic acid (0.01 mg/mL, 0.1 mg/mL, and 1 mg/mL), rotenone (0.1 mg/mL and 1 mg/mL), and beta-caryophyllene oxide (0.1 mg/mL and 1 mg/mL) had significant gastric toxicity against Z. cucurbitae. Among these compounds, rotenone has been reported as a widely used botanical insecticide, which has various insecticidal activities, including neurotoxicity against Spodoptera litura and Bombus terrestis [82,83]. Beta-caryophyllene oxide has been widely reported to have significant toxicity against Sitophilus Granarius L. and Callosobruchus chinensis [77,78,79]. Oleanolic acid has been found to have had significant antifeedant activity against Aedes aegypti L. and spodoptera litura F. [80,81]. However, there is still a lack of studies of echinocystic acid in controlling pests, indicating that this compound is worth being further studied.
After verifying the effects of oleanolic acid, rotenone, and beta-caryophyllene oxide on Z. cucurbitae, the gene LOC105217288 was identified through the KEGG enrichment of the gene set successfully docked with these compounds. Mechanism prediction was carried out after constructing a composite pathway, and it was found that LOC105217288 is mainly involved in the neuroactive ligand–receptor interaction (ko04080) and calcium signaling (ko04020) pathways. In these pathways, the proteins glutamate receptor ionotropic NMDA1 (GRIN), glutamate receptor 1 (GRI), and nicotinic acetylcholine receptor alpha-7 (nAChRα7) were activated, affecting downstream proteins and pathways. In the composite pathway, calmodulin (CALM) was activated, which influenced pathways such as the MAPK signaling pathway (ko04010), apoptosis (ko04210), and long-term depression (ko04730). Neuroactive ligand–receptor interaction (ko04080) and calcium signaling (ko04020) pathways have been widely reported in insect research and are closely related to insect avoidance and death [85,86]. Additionally, the activation of these pathways could indirectly affect downstream pathways, such as the MAPK signaling pathway (ko04010), apoptosis (ko04210), and long-term depression (ko04730). Transcriptome analysis showed that the expression of the MAPK signaling pathway (ko04010) changed after Bombyx mori was parasitized by Exorista japonica and played an important role in the response of insects to parasitic stress [93]. Transcriptome analysis and PCR-RFLP analysis showed that the differential genes of Aphis gossypii and Aedes aegypti, after being treated with pyrethroid insecticides, were closely related to the genes in the apoptosis pathway (ko04210) [94,95].

4. Materials and Methods

4.1. Insect Rearing

Z. cucuribitae was reared in the Invasive Pest Laboratory, Hainan University (Haikou, China). The larvae of the Z. cucuribitae colony were provided with an artificial larval diet mixture of 50 g of yeast extract, 250 g of wheat bran powder, 50 g of sugar, 1 g of sodium benzoate, 50 g of paper, and 400 mL of water. Adults were fed artificial diets of a 3:1 ratio of sucrose/yeast extract. All experimental adults were maintained in cages (60 × 60 × 60 cm) at 27 ± 1 °C under a 16 h/8 h light/dark cycle at a relative humidity of 70 ± 5% [96].

4.2. Identification and Functional Enrichment of Odor-Sensing Genes from Z. cucuribitae

Odor-sensing genes sets, including the OBP, PBP, OR, IR, GR, and SNMP genes of Z. cucuribitae, were collected based on the genome data (ID: GCF028554725.1) which was obtained from the NCBI database (https://www.ncbi.nlm.nih.gov/, accessed on 16 January 2025). In this process, phylogenetic analysis of the odor-sensing genes of Z. cucurbitae was conducted to verify the reliability of the gene function [20]. A phylogenetic tree was constructed and visually displayed using the APE package and the ggTree package of R software (Version 4.3.1) (R Foundation for Statistical Computing, Vienna, Austria) [97].
Among the genetic data, the GR data set contains 29 sequences from several species, such as Z. cucuribitae, Drosophila melanogaster (D. melanogaster), and B. dorsalis. The IR data set contains 8 sequences from several species, such as Z. cucuribitae, D. melanogaster, and M. domestica. The OBPGOBP data set contains 49 sequences from several species, such as Z. cucuribitae, Apis Cerana (A. Cerana), and Drosophila Simulans. The OR data set contains 95 sequences from several species, such as Z. cucuribitae, D. melanogaster, and A. cerana. The PBP data set contains 7 sequences from several species, such as Z. cucuribitae, B. dorsalis, and M. domestica. The SNMP data set contains 8 sequences from several species, such as Z. cucuribitae, B. dorsalis, and M. domestica. KEGG enrichment and GO enrichment were performed on odor-related protein targets (p < 0.05). The Cluster Profiler package of R software (Version 4.3.1) (R Foundation for Statistical Computing, Vienna, Austria) was used for enrichment analysis, and the ggplot2 package was used for visualization [98].

4.3. Acquisition of Compound Models of L. acutangular, L. cylindrica, S. edule, B. oleracea, M. nana, and F. ananassa and Protein Models of Z. cucuribitae

The compounds from six host plants were collected from published studies and integrated into the public database PubChem (https://pubchem.ncbi.nlm.nih.gov, accessed on 20 February 2025). Three-dimensional structures of compounds were collected from the PubChem database in the sdf format and converted into the mol2 format using OpenBABEL software (Version 3.1.1) (Open Babel development team, Cambridge, MA, USA) for molecular docking.
Three-dimensional protein structural models corresponding to all odor-sensing genes were predicted based on the d SWISS-MODEL (https://swissmodel.expasy.org/interactive, accessed on 5 March 2025) and saved in pdb format. Kegg pathways and GO terms related to odor-sensing genes were collected using KEGG and GO enrichment. The Cluster Profiler package of R software (Version 4.3.1) (R Foundation for Statistical Computing, Vienna, Austria) was used for enrichment analysis, and the ggplot2 package was used for visualization.

4.4. CPPG Networks and Screening of Core Functional Compounds

4.4.1. Molecular Docking of Olfactory Sensory Genes and Metabolic Compounds

All docking relationships between compounds of hosts and odor-sensing genes of Z. cucuribitae were obtained by molecular docking with AutoDock-VINA (Version 1.1.2) (The Scripps Research Institute, La Jolla, CA, USA). In order to evaluate the results of molecular docking, the affinity value from molecular docking was used to evaluate the effectiveness of protein binding with chemical compounds. A lower affinity value represents better binding energy. When the affinity value was equal to or less than −5 kcal/mol and greater than or equal to −18 kcal/mol, it was considered that the protein can effectively bind with chemical compounds [99,100]. Finally, PyMOL (Version 2.5.4) (Schrödinger, Inc., New York, NY, USA) software was used for visualization.
Data analysis was conducted using Poisson distribution for a non-normal distribution in a generalized linear model. Then, analysis of variance (ANOVA) and multiple comparisons were used for significance analysis. Finally, a histogram was created using the ggplot2 package. The data were analyzed using the R software (version 4.3.1) (R Foundation for Statistical Computing, Vienna, Austria) and multcomp, glm, emmeans, lsmeans, ggsignif, and ggplot2 packages.

4.4.2. Construction of CPPG Networks and Screening of Core Functional Compounds

Network visualization was carried out using Cytoscape (Version 3.9.1) (Cytoscape Consortium, Seattle, WA, USA). Based on the results of molecular docking, a CPPG network was constructed with metabolites from L. acutangular, L. cylindrica, S. edule, B. oleracea, M. nana, and F. ananassa, olfactory sensory genes of Z. cucurbitae, KEGG pathways, and GO terms as nodes. To clarify the relationships between different nodes, the degree of each node was used as an index to judge the importance of nodes. The degree was calculated as the product of the number of successful docking results and the average binding energy for that node. The higher the degree was, the more important the node was in the network, and the node played a more significant role in the comprehensive effect relationships among genes, compounds, KEGG pathways, and GO terms [29,30,101].

4.5. Effect of Core Compounds on Behavior of Z. cucuribitae

4.5.1. Two-Way Selection Experiment

A two-way selection experiment was conducted in an independent and ventilated laboratory. The behavior measurement laboratory was equipped with an exhaust fan for ventilation, maintained in a dark environment with a temperature of 25 ± 2 °C, a relative humidity of 70 ± 5%, and a photoperiod of 16 h:8 h. Each compound to be verified was dissolved in 5 µL of dimethyl sulfoxide (DMSO, RT, 99%), and distilled water was added. The concentration of DMSO used in the cell experiments needed to be less than 0.1% [29]. All Z. cucurbitae were starved for 24 h. Each compound was prepared into solutions of 0.01 mg/mL, 0.1 mg/mL, and 1 mg/mL, respectively. Screen cages (60 cm × 60 cm × 60 cm) were placed in the behavior measurement laboratory, and two thick slices of Cucurbita pepo (C. pepo) (radius ≈ 5 cm, thickness ≈ 2 cm) were placed in each cage. One slice was evenly coated with the treatment group liquid on the surface, and the other was coated with a DMSO solution of the same concentration as the control. Fifty adult Z. cucurbitae (male/female = 1:1) were placed in each cage, and the numbers on the C. pepo slices regarding the treatment and control were determined after 10 min. To reduce the possibility of interference between treatments as much as possible, the avoidance behavior test was carried out in an independent room, and each treatment was repeated 3 times.
r e p e l l e n t   r a t e = N u m b e r   o f   C o n t r o l − N u m b e r   o f   T r e a t m e n t N u m b e r   o f   C o n t r o l + N u m b e r   o f   T r e a t m e n t × 100 %

4.5.2. Gastric Toxicity Experiment

Based on the methods of the two-way selection experiment described above, the compounds to be verified were prepared into solutions of 0.01 mg/mL, 0.1 mg/mL, and 1 mg/mL. The experiment was also conducted in an independent laboratory. An amount of 10 mL of each treatment group liquid of the solution was poured into a 250 mL conical flask (DaLong Experimental Instrument, Beijing, China), shaken well, and then slowly rotated for 1 min to form a uniform film on the flask wall. When the flask was dry, 30 adult Z. cucurbitae (male/female = 1:1) were placed in a conical flask coated with the drug film for 24 h and then transferred to a clean 250 mL conical flask (DaLong Experimental Instrument, Beijing, China) for observation. Each treatment was repeated three times.
m o r t a l i t y   r a t e = N u m b e r   o f   d e a d   i n s e c t s N u m b e r   o f   t e s t   i n s e c t s × 100 %

4.6. Predicted Mechanism of Core Compounds

After verification using the two-way selection experiment and gastric toxicity experiment, genes of verified core components were collected in CPPG network. Furthermore, the KEGG enrichment results linked to genes were also found and collected. The maps of these pathways were obtained from the KEGG database, and the positions of these genes in the pathways were accurately marked. Combined with the genes and products of the upstream and downstream pathways of these genes, the action mechanism of composite pathways was predicted. Furthermore, in combination with the positions of marked genes, the influence of metabolic reactions or signal transduction events on the operation of the entire pathway was discussed.

5. Conclusions

In this study, the combination of network pharmacology and molecular docking technology was used to screen the components that have a repellent effect on Z. cucurbitae. This method comprehensively analyzes the relationship compounds of six non-favored hosts (L. acutangular, L. cylindrica, S. edule, B. oleracea, M. nana, and F. ananassa) and odor-sensing genes of Z. cucurbitae by constructing a CPPG network. Through this method, the compounds rotenone, beta-caryophyllene oxide, and echinocystic acid were successfully identified to exhibit significant repellent effects. These compounds were first found to have significant insecticidal activities against Z. cucurbitae. This study lays a solid foundation for further studies on the repellent mechanisms of Z. cucurbitae and the development of new and efficient control strategies. Future research will further explore the mechanisms, optimization, and applications of these metabolites to provide a scientific reference for the effective prevention and control of Z. cucurbitae.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ijms26146556/s1.

Author Contributions

F.C., Y.C., Z.Z. and S.J. created the concept and designed the study. Y.F. and Y.C. conducted the analyses and wrote the manuscript. Y.W. participated in data analysis, figure design, and revision. X.F., C.Y., X.B., Y.L. and W.M. participated in data analysis. X.G., X.L. and R.Y. contributed to the revision and proofreading of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financially supported by the project of the Regional Collaborative Prevention and Control of Major Invasive Species: Technical System Design and Eco-Economic Evaluation (project leader: Fengqin Cao; Grant No. 2022YFC2601405).

Institutional Review Board Statement

Z. cucuribitae is not included in the “List of Endangered and Protected Animals in China” because it is a major pest in tropical and sub-tropical countries. All experiments were performed in compliance with the general ethical guidelines in order to minimize pain and discomfort to the insects.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
A. CeranaApis cerana
AcerApis cerana
Z. cucuribitaeZeugodacus cucuribitae (Coquillett)
B. dorsalisBactrocera dorsalis
B. oleraceaBrassica oleracea var. botrytis
BdorBactrocera dorsalis
BoleBactrocera oleae
C.pepoCucurbita pepo
CPPGGenes vs. components vs. Kegg pathways vs. GO terms
D. melanogasterDrosophila melanogaster
DanaDrosophila ananassae
DmelDrosophila melanogaster
DsimDrosophila simulans
F. ananassaFragaria × ananassa
GROlfactory receptor
IRIonospheric receptor
L. cylindricaLuffa cylindrica
L. acutangularLuffa acutangular
M. domesticaMusca domestica
M. nanaMusa nana
MdomMusca domestica
OBPGOBPOdor-binding protein and general odorant-binding proteins
OROlfactory receptor
PBPPheromone-binding protein
S. eduleSechium edule
SNMPSensory neuron membrane protein
BcucZeugodacus cucuribitae (Coquillett)

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Figure 1. Construction of phylogenetic analysis of IR genes based on gene sequences of Z. cucuribitae and several Diptera insects. Genes from Z. cucuribitae are marked in blue. Notes: Acer: Apis Cerana, Zcuc: Zeugodacus Cucurbitae, Bdor: Bactrocera Dorsalis, Bole: Bactrocera Oleae, Dmel: Drosophila Melanogaster, Mdom: Musca Domestica, Dana: Drosophila Ananassae, Dsim: Drosophila Simulans.
Figure 1. Construction of phylogenetic analysis of IR genes based on gene sequences of Z. cucuribitae and several Diptera insects. Genes from Z. cucuribitae are marked in blue. Notes: Acer: Apis Cerana, Zcuc: Zeugodacus Cucurbitae, Bdor: Bactrocera Dorsalis, Bole: Bactrocera Oleae, Dmel: Drosophila Melanogaster, Mdom: Musca Domestica, Dana: Drosophila Ananassae, Dsim: Drosophila Simulans.
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Figure 2. KEGG and GO enrichment. (A) KEGG enrichment; (B) GO enrichment.
Figure 2. KEGG and GO enrichment. (A) KEGG enrichment; (B) GO enrichment.
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Figure 3. A visualization of the docking of olfactory sensory genes with the compounds of hosts. (A) The numbers of compounds with different affinities between −5 kcal/mol and 18 kcal/mol were counted. (B) The numbers of genes with different affinities between −5 kcal/mol and −18 kcal/mol were counted. (C) The prediction results of molecular docking between maslinic acid 3-O-b-D-glucoside and the LOC105217972 gene of Luffa cylindrica. (D) The prediction of the combination of the metabolic compound echinocystic acid and the gene LOC105218953 of Luffa cylindrica. (E) The prediction of the combination of isorhoifolin and the gene LOC105215418. (F) The prediction of the combination of kaempferol, a metabolic compound of banana, and the gene LOC105209256. (G) The prediction of the combination of rutin, a metabolic compound of cauliflower, and the gene LOC105209182. (H) The prediction of the combination of damascenone, a metabolic compound of strawberry, and the gene LOC105217972. Notes: The molecular docking of Ligand X with Protein Receptor Y is shown. The figure illustrates the binding mode of Ligand X (shown in stick representation, colored by atom type, with carbon shown in blue and oxygen shown in red) within the active site of Protein Receptor Y (shown in cartoon representation, colored by secondary structure). Ligand X (stick representation) is shown interacting with key residues (labeled) of the receptor (cartoon representation). Hydrogen bonds are depicted as dashed lines.
Figure 3. A visualization of the docking of olfactory sensory genes with the compounds of hosts. (A) The numbers of compounds with different affinities between −5 kcal/mol and 18 kcal/mol were counted. (B) The numbers of genes with different affinities between −5 kcal/mol and −18 kcal/mol were counted. (C) The prediction results of molecular docking between maslinic acid 3-O-b-D-glucoside and the LOC105217972 gene of Luffa cylindrica. (D) The prediction of the combination of the metabolic compound echinocystic acid and the gene LOC105218953 of Luffa cylindrica. (E) The prediction of the combination of isorhoifolin and the gene LOC105215418. (F) The prediction of the combination of kaempferol, a metabolic compound of banana, and the gene LOC105209256. (G) The prediction of the combination of rutin, a metabolic compound of cauliflower, and the gene LOC105209182. (H) The prediction of the combination of damascenone, a metabolic compound of strawberry, and the gene LOC105217972. Notes: The molecular docking of Ligand X with Protein Receptor Y is shown. The figure illustrates the binding mode of Ligand X (shown in stick representation, colored by atom type, with carbon shown in blue and oxygen shown in red) within the active site of Protein Receptor Y (shown in cartoon representation, colored by secondary structure). Ligand X (stick representation) is shown interacting with key residues (labeled) of the receptor (cartoon representation). Hydrogen bonds are depicted as dashed lines.
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Figure 4. A complex network node diagram of the compounds of L. acutangular, L. cylindrica, S. edule, B. oleracea, M. nana, and F. ananassa vs. Z. cucuribitae olfactory sensory genes vs. Kegg pathway vs. GO term. The core compounds are presented in blue, the Z. cucuribitae olfactory sensory genes are presented in yellow, the kegg pathway is presented in green, and the GO term is presented in red. The metabolites selected for the two-way selection test and gastric toxicity test are shown in dark blue.
Figure 4. A complex network node diagram of the compounds of L. acutangular, L. cylindrica, S. edule, B. oleracea, M. nana, and F. ananassa vs. Z. cucuribitae olfactory sensory genes vs. Kegg pathway vs. GO term. The core compounds are presented in blue, the Z. cucuribitae olfactory sensory genes are presented in yellow, the kegg pathway is presented in green, and the GO term is presented in red. The metabolites selected for the two-way selection test and gastric toxicity test are shown in dark blue.
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Figure 5. Avoidance rate and mortality rate of Z. cucuribitae under different concentrations of five metabolic compounds. (A) Avoidance rate of rotenone. (B) Avoidance rate of diosmin. (C) Avoidance rate of beta-caryophyllene oxide. (D) Avoidance rate of oleanolic acid. (E) Avoidance rate of echinocystic acid. (F) Mortality rate of rotenone. (G) Mortality rate of diosmin. (H) Mortality rate of beta-caryophyllene oxide. (I) Mortality rate of oleanolic acid. (J) Mortality rate of echinocystic acid. Note: ck means control check; ns means no significance between two compared groups. * represents significant difference at p < 0.05; ** represents significant difference at p < 0.01; *** represents significant difference at p < 0.001 compared to model group.
Figure 5. Avoidance rate and mortality rate of Z. cucuribitae under different concentrations of five metabolic compounds. (A) Avoidance rate of rotenone. (B) Avoidance rate of diosmin. (C) Avoidance rate of beta-caryophyllene oxide. (D) Avoidance rate of oleanolic acid. (E) Avoidance rate of echinocystic acid. (F) Mortality rate of rotenone. (G) Mortality rate of diosmin. (H) Mortality rate of beta-caryophyllene oxide. (I) Mortality rate of oleanolic acid. (J) Mortality rate of echinocystic acid. Note: ck means control check; ns means no significance between two compared groups. * represents significant difference at p < 0.05; ** represents significant difference at p < 0.01; *** represents significant difference at p < 0.001 compared to model group.
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Figure 6. Mechanism deduction of effect of effective compounds on odor recognition of Z. cucurbitae. Red nodes represent activated genes. Notes: GRIN: glutamate receptor ionotropic; GRI: glutamate receptor 1; ROC: nicotinic acetylcholine receptor alpha-7; CALN: calmodulin; TFEB: transcription factor EB; NFAT: nuclear factor of activated T-cells, cytoplasmic 1; NOS: nitric-oxide synthase, brain; FAK2: focal adhesion kinase 2; IP3 3K: 1D-myo-inositol-triphosphate 3-kinase; PKC: classical protein kinase C alpha type.
Figure 6. Mechanism deduction of effect of effective compounds on odor recognition of Z. cucurbitae. Red nodes represent activated genes. Notes: GRIN: glutamate receptor ionotropic; GRI: glutamate receptor 1; ROC: nicotinic acetylcholine receptor alpha-7; CALN: calmodulin; TFEB: transcription factor EB; NFAT: nuclear factor of activated T-cells, cytoplasmic 1; NOS: nitric-oxide synthase, brain; FAK2: focal adhesion kinase 2; IP3 3K: 1D-myo-inositol-triphosphate 3-kinase; PKC: classical protein kinase C alpha type.
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Table 1. Analysis of L. acutangular, L. cylindrica, S. edule, B. oleracea, M. nana, and F. ananassa.
Table 1. Analysis of L. acutangular, L. cylindrica, S. edule, B. oleracea, M. nana, and F. ananassa.
NameIDCASHostReferences
cinncassiol EC1 L. acutangular[35]
hymenoxonC257377-32-9L. acutangular[35]
1-(alpha-methyl-4-(2-methylpropyl)benzeneacetate)-beta-D-Glucopyranuronic acidC3115075-59-7L. acutangular[35]
cinncassiol C3C4 L. acutangular[35]
clerodinC5464-71-1L. acutangular[35]
nigakilactone EC628360-79-4L. acutangular[35]
shogaolC7555-66-8L. acutangular[35]
maritimetinC8576-02-3L. acutangular[35]
gartaninC933390-42-0L. acutangular[35]
rotenoneC1083-79-4L. acutangular[35]
apiinC1126544-34-3L. acutangular[35]
galanginC12548-83-4L. acutangular[35]
hispidulinC131447-88-7L. acutangular[35]
berbamunineC14485-18-7L. acutangular[35]
daphnolineC15479-36-7L. acutangular[35]
ellagic acidC16476-66-4L. acutangular, L. cylindrica[36,37]
chlorogenic acidC17202650-88-2L. acutangular, L. cylindrica, M. nana, B. oleracea[36,38,39,40]
sinapaldehydeC184206-58-0L. acutangular[36]
mandelic acidC1990-64-2L. acutangular[36]
scopoletinC2092-61-5L. acutangular[36]
salicylic acidC2169-72-7L. acutangular, L. cylindrica, M. nana, B. oleracea[36,38,39,41]
gallic acidC22149-91-7L. acutangular, L. cylindrica, M. nana, B. oleracea[36,38,39,40]
4-hydroxycoumarinC231076-38-6L. acutangular[36]
phenylacetic acidC24103-82-2L. acutangular[36]
guaiacolC2532994L. acutangular[36]
3,4-dihydroxyphenylacetic acidC26102-32-9L. acutangular[36]
hydroxycaffeic acidC27 L. acutangular, L. cylindrica, B. oleracea, F. ananassa[36,40,42,43]
2,5-dihydroxybenzoic acidC28 L. acutangular, L. cylindrica, M. nana[36,38,39]
4-ethylguaiacol-d5C29 L. acutangular[36]
3-(4-hydroxy-3-methoxyphenyl)propionic acidC301135-23-5L. acutangular[36]
caffeic acidC31501-16-6L. acutangular, M. nana[36,39]
pinocembrinC32480-39-7L. acutangular[36]
benzoic acidC3365-85-0L. acutangular[36]
cirsimaritinC346601-62-3L. acutangular[36]
eugenolC3597-53-0L. acutangular[36]
p-coumaric acidC36501-98-4L. acutangular, L. cylindrica, M. nana, B. oleracea, F. ananassa[36,38,39,40,43]
sakuranetinC372957-21-3L. acutangular[36]
gardenin BC382798-20-1L. acutangular[36]
chrysinC39480-40-0L. acutangular, M. nana[36,39]
scutellareinC40529-53-3L. acutangular[36]
tetramethylscutellareinC411168-42-9L. acutangular[36]
melleinC421200-93-7L. acutangular[36]
geraldoneC4321583-32-4L. acutangular[36]
umbelliferoneC44202-240-3L. acutangular[44]
lucyoside AC45 L. acutangular[44]
lucyoside JC46100156-31-8L. acutangular[44]
luteolinC47491-70-3L. acutangular, L. cylindrica, S. edule, M. nana[39,44,45,46]
apigeninC48520-36-5L. acutangular, M. nana[39,44]
diosmetinC49520-34-3L. acutangular, S. edule[44]
lucyoside GC50 L. acutangular[44]
13-trihydroxy-octadecenoic acidC51 L. acutangular[44]
lucyoside HC52 L. acutangular[44]
lucyoside IC5399543-11-0L. acutangular[44]
Maslinic acid 3-O-b-D-glucosideC54163634-06-8L. acutangular[44]
lucyin AC55152845-76-6L. acutangular[44]
4-hydroxybenzoic acidC5699-96-7L. cylindrica[38]
catecholC57120-80-9L. cylindrica, B. oleracea[38,40]
vanillic acidC58121-34-6L. cylindrica, M. nana, B. oleracea[38,39,40]
rutinC59153-18-4L. cylindrica, M. nana, B. oleracea[38,39,40]
ferulic acidC60537-98-4L. cylindrica, M. nana, B. oleracea, F. ananassa[38,39,40,43]
naringeninC61480-41-1L. cylindrica, M. nana, B. oleracea[38,39,40]
2-hydroxy-4-methylbenzaldehydeC62698-27-1L. cylindrica[47]
4-acetoxy-2-azetidinoneC6328562-53-0L. cylindrica[47]
mahalebosideC64 L. cylindrica[47]
crotanecineC655096-50-4L. cylindrica[47]
perlolyrineC6629700-20-7L. cylindrica[47]
dihydrocapsaicinC6719408-84-5L. cylindrica[47]
morindoneC68478-29-5L. cylindrica[47]
4-aminosalicylic acidC6965-49-6L. cylindrica[38]
apigenin 7-glucuronideC7029741-09-1L. cylindrica[48]
kaempferideC71491-54-3L. cylindrica, [49]
diosminC72520-27-4L. cylindrica
S. edule		[50,51]
5-hydroxy-2-(3-hydroxy-4-methoxyphenyl)-4-oxo-4H-1-benzopyran-7-yl 2-O-(6-deoxy-alpha-L-mannopyranosyl)-beta-D-glucopyranosideC7338665-01-9L. cylindrica[52]
eriodictyol-7-O-glucosideC7438965-51-4L. cylindrica[52]
quercetinC75117-39-5L. cylindrica, M. nana, B. oleracea[39,40,53]
myricetinC76529-44-2L. cylindrica[54]
cianidanolC777295-85-4L. cylindrica, M. nana, B. oleracea[39,40,55]
hyperosideC78482-36-0L. cylindrica[56]
lespedinC79482-38-2L. cylindrica[57]
quercitrinC80522-12-3L. cylindrica, M. nana[39,58]
tilirosideC8120316-62-5L. cylindrica[59]
acacetinC82480-44-4L. cylindrica, M. nana[39,60]
saponarinC8320310-89-8L. cylindrica[60]
datiscinC84 L. cylindrica[61]
fortunellinC8520633-93-6L. cylindrica[61]
linarinC86480-36-4L. cylindrica[62]
vitexinC873681-93-4L. cylindrica, S. edule[51,63]
vitexin 2″-O-rhamnosideC8864820-99-1L. cylindrica[64]
2-hydroxycinnamic acid,(2E)-C89614-60-8L. cylindrica, M. nana[39,64]
oleanolic acidC90508-02-1L. cylindrica[65]
echinocystic acidC91510-30-5L. cylindrica[66]
gypsogeninC92639-14-5L. cylindrica[67]
3-O-[beta-D-glucopyranosyl]-28-O-[alpha-L-rhamnopyranosyl-(1->2)-beta-D-glucopyranosyl]maslinic acidC931268696-94-1L. cylindrica[68]
luteolin 7-rutinosideC9420633-84-5S. edule[51]
luteolin 7-O-glucosideC951268798S. edule,
M. nana		[39,51]
isorhoifolinC96552-57-8S. edule[51]
leucosideC97 S. edule[46]
myricitrinC9817912-87-7S. edule[46]
vicenin 2C9923666-13-9S. edule[46]
all-trans-vaucheriaxanthinC100 M. nana[69]
neochromeC101 M. nana[69]
neoxanthinC10214660-91-4M. nana[69]
5,8:5′,8′-diepoxy-5,8,5′,8′-tetrahydro-beta,beta-carotene-3,3′-diolC103 M. nana[69]
violaxanthinC104126-29-4M. nana[69]
lutein 5,6-epoxideC10528368-08-3M. nana[69]
luteinC106127-40-2M. nana[69]
fumaric acidC107110-17-8M. nana[39]
cis-aconitic acidC108585-84-2M. nana[39]
epigallocatechinC109970-74-1M. nana[39]
3,4-dihydroxybenzoic acidC11099-50-3M. nana, B. oleracea[39,40]
protocatechualdehydeC111139-85-5M. nana[39]
epigallocatechin gallateC112989-51-5M. nana[39]
1,5-dicaffeoylquinic acidC11330964-13-7M. nana[39]
4-carboxyphenylglycineC1147292-81-1M. nana[39]
syringic acidC115530-57-4M. nana, B. oleracea, F. ananassa[39,40,43]
vanillinC116121-33-5M. nana[39]
syringaldehydeC117134-96-3M. nana[39]
daidzinC118552-66-9M. nana[39]
(-)-epicatechingallateC11925615-05-8M. nana[39]
piceidC12027208-80-6M. nana[39]
sinapinic acidC1217362-37-0M. nana, B. oleracea, F. ananassa[39,40,43]
coumarinC12291-64-5M. nana[39]
quercituroneC12322688-79-5M. nana[39]
isoquercetinC124482-35-9M. nana[39]
hesperidinC125520-26-3M. nana[39]
genistinC126529-59-9M. nana[39]
rosmarinic acidC12720283-92-5M. nana, B. oleracea[39,40]
apigetrinC128578-74-5M. nana[39]
astragalinC129480-10-4M. nana[39]
kaempferol-3-O-rutinosideC13017650-84-9M. nana, B. oleracea[39,40]
fisetinC131528-48-3M. nana[39]
daidzeinC132486-66-8M. nana[39]
quercetin 3′-isobutyrateC133 M. nana[39]
hesperetinC134520-33-2M. nana[39]
genisteinC135446-72-0M. nana[39]
kaempferolC136520-18-3M. nana[39]
amentoflavoneC1371617-53-4M. nana[39]
cinnamic acidC138140-10-3B. oleracea, F. ananassa[40,43]
lactoyl isovanillic acidC139 B. oleracea[40]
isorhamnetinC140480-19-3B. oleracea[40]
3-hydroxyflavoneC141577-85-5B. oleracea[40]
pyrogallolC14287-66-1B. oleracea[40]
pyruvic acidC143127-17-3B. oleracea[40]
lactic acidC14450-21-5B. oleracea[40]
valineC14572-18-4B. oleracea[40]
alanineC14656-41-7B. oleracea[40]
glycolic acidC14779-14-1B. oleracea[40]
linalool, (+/−)-C14878-70-6F. ananassa[70]
beta-FarneseneC14918794-84-8F. ananassa[70]
alpha-TerpineolC15098-55-5F. ananassa[70]
damascenoneC15123726-93-4F. ananassa[70]
trans-NerolidolC15235944-21-9F. ananassa[70]
2,5-dimethyl-4-methoxy-3(2H)-furanoneC1534077-47-8F. ananassa[70]
furaneolC1543658-77-3F. ananassa[70]
(E)-5-(3-hexenyl)dihydrofuran-2(3H)-oneC15597416-87-0F. ananassa[70]
gamma-OctalactoneC156104-50-7F. ananassa[70]
gamma-DecalactoneC157706-14-9F. ananassa[70]
gamma-DodecalactoneC158148051F. ananassa[70]
acetophenoneC159202-708-7F. ananassa[70]
3,6-Octadienal,3,7-dimethyl-C1601754-00-3F. ananassa[43]
(Z)-3,7-dimethylocta-3,6-dienalC16172203-97-5F. ananassa[43]
citralC162C10H16OF. ananassa[43]
nerylacetoneC1633879-26-3F. ananassa[43]
nizatidineC16476963-41-2F. ananassa[43]
beta-caryophyllene oxideC1651139-30-6F. ananassa[43]
Note: “CAS” is the unique number assigned to each chemical substance by the Chemical Abstracts Service. It is used to unambiguously identify chemical compounds in the scientific literature and databases.
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MDPI and ACS Style

Fu, Y.; Chen, Y.; Wang, Y.; Fu, X.; Jin, S.; Yi, C.; Bai, X.; Lu, Y.; Miao, W.; Geng, X.; et al. The Discovery of Potential Repellent Compounds for Zeugodacus cucuribitae (Coquillett) from Six Non-Favored Hosts. Int. J. Mol. Sci. 2025, 26, 6556. https://doi.org/10.3390/ijms26146556

AMA Style

Fu Y, Chen Y, Wang Y, Fu X, Jin S, Yi C, Bai X, Lu Y, Miao W, Geng X, et al. The Discovery of Potential Repellent Compounds for Zeugodacus cucuribitae (Coquillett) from Six Non-Favored Hosts. International Journal of Molecular Sciences. 2025; 26(14):6556. https://doi.org/10.3390/ijms26146556

Chicago/Turabian Style

Fu, Yu, Yupeng Chen, Yani Wang, Xinyi Fu, Shunda Jin, Chunyan Yi, Xue Bai, Youqing Lu, Wang Miao, Xingyu Geng, and et al. 2025. "The Discovery of Potential Repellent Compounds for Zeugodacus cucuribitae (Coquillett) from Six Non-Favored Hosts" International Journal of Molecular Sciences 26, no. 14: 6556. https://doi.org/10.3390/ijms26146556

APA Style

Fu, Y., Chen, Y., Wang, Y., Fu, X., Jin, S., Yi, C., Bai, X., Lu, Y., Miao, W., Geng, X., Lu, X., Yan, R., Zhou, Z., & Cao, F. (2025). The Discovery of Potential Repellent Compounds for Zeugodacus cucuribitae (Coquillett) from Six Non-Favored Hosts. International Journal of Molecular Sciences, 26(14), 6556. https://doi.org/10.3390/ijms26146556

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