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Article

Untargeted UHPLC-MS Metabolomics Reveals the Metabolic Perturbations of Helicoverpa armigera under the Stress of Novel Insect Growth Regulator ZQ-8

Key Laboratory of Oasis Agricultural Pest Management and Plant Protection Utilization, College of Agriculture, Shihezi University, Shihezi 832002, China
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(5), 1315; https://doi.org/10.3390/agronomy13051315
Submission received: 18 April 2023 / Revised: 6 May 2023 / Accepted: 6 May 2023 / Published: 8 May 2023
(This article belongs to the Special Issue Insecticide Resistance and Novel Insecticides)

Abstract

:
According to the previous research of our group, we found compound ZQ-8 ((1S,2R,4S)-1,3,3-trimethylbicyclo [2.2.1]heptan-2-yl-4-(tert-butyl)benzoate). This compound showed a strong growth inhibitory effect on Helicoverpa armigera by inhibiting chitinase 2 and endochitinase. To further understand the mechanism of ZQ-8 interfering with the growth and development of H. armigera, ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was utilized to analyze the metabolomics of the epidermis and viscera of H. armigera after ZQ-8 stress. The results revealed that the content of most metabolites was down-regulated after ZQ-8 treatment. Through the analysis of metabolic pathways, it was found that ZQ -8 mainly interfered with energy metabolism and amino acid biosynthesis pathways, which may be one of the important factors in which ZQ-8 caused the death of H. armigera larvae. Furthermore, ZQ-8 not only inhibits chitin degradation but also inhibits chitin synthesis in vivo. These findings provide new insights into a better understanding of the mechanism of action of ZQ-8.

1. Introduction

Helicoverpa armigera (Lepidoptera: Noctuidae) is one of the most catastrophic economic pests in the world [1,2]. As a polyphagous insect, H. armigera has various hosts and can consume more than 200 different plant species, including grain crops, cotton, oil crops, fruits, and vegetables. Moreover, H. armigera has gluttony after the third instar and its last instar damage can account for more than 86% of the overall damage to cotton [3]. The damage caused by H. armigera to crops exceeds USD$ 2 billion annually [4]. However, the high reliance on traditional chemical insecticides to control H. armigera has resulted in the development of resistance to many insecticides [5,6]. Therefore, it is urgent to discover and develop new insecticides to achieve green prevention and control of H. armigera.
Insect growth regulators (IGRs) have high selectivity to pests, a short residual period in the environment, and low toxicity to non-target organisms [7], which have become effective tools in integrated pest management (IPM) in agriculture. Chitin is a linear polymer of β-(1,4)-N-acetyl-d-glucosamine (GlcNAc), widely present in the cell walls of fungi, radulae and beaks of mollusks, and exoskeletons of arthropods [8]. In insects, chitin is an essential structural component of the epidermis, perinatal matrix (PM), and trachea [9]. Hence, its structure and metabolic pathways are ideal targets for new pest management strategies. Benzoylurea chitin synthesis inhibitors are the earliest-developed insect growth regulators. This kind of compound primarily inhibits the conversion of N-acetylglucosamine into chitin in insects, which prevents the formation of a new epidermis during molting and thus interferes with their growth and development. As a result, insects are unable to pupate appropriately and perish [10]. In recent decades, scientists have synthesized more than 10,000 benzoylurea derivatives, of which 15 benzoylurea chitin synthesis inhibitors have been commercialized. However, Lepidoptera pests have developed strong resistance to benzoylurea insect growth regulators [11]. Therefore, the creation of IGRs with novel structural features and distinctive modes of action is essential for the prevention and management of major pests.
Metabolomics, as a newly developed omics after genomics and proteomics, is widely used in the study of multivariate dynamic responses of pesticides to the metabolite levels of target and non-target organisms [12]. Li et al. studied the metabolic expression level and spatial distribution of Plutella xylostella treated with rotenone using spatial metabolomics and lipidomics. They found that rotenone inhibited purine and amino acid metabolism in P. xylostella, damaging its cell membrane [13]. Zhao et al. found that the tryptophan metabolic pathway is the most important pathway in the pathogenicity of the entomopathogenic nematode (EPN) Steinernema feltiae, and 3-hydroxyanthranilic acid (3-HAA) is the most likely potential marker for mediating EPN to kill host insects [14]. Chen et al. found that juglone could cause a physiological disorder of Aphis gossypii by affecting the hemolymph metabolomic, eventually leading to mortality [15].
In recent years, molecular design and structure optimization based on natural products has become one of the most significant approaches for the development of new pesticides. In the previous work, we designed and synthesized a terpenoid ester compound ZQ-8, which has a significant impact on the growth and development of H. armigera, with the active substance tschimganin in Ferula as the lead compound [16]. Through transcriptome, molecular docking, and protein interaction studies, it was found that ZQ-8 acts on the epidermis of H. armigera, and chitinase 2 and endochitinase are its potential targets [17].
In order to further validate the mechanism of ZQ-8 regulating the growth and development of H. armigera, herein, we used untargeted UHPLC-MS/MS metabolomics to evaluate the differences in metabolite profiles of the epidermis and viscera of H. armigera treated with ZQ-8. This work provides new insights into the underlying metabolic molecular mechanisms of H. armigera or other Lepidoptera pests under insecticide stress.

2. Materials and Methods

2.1. Chemicals and Reagents

The ZQ-8 (98%, synthesized in the laboratory), N,N-dimethylformamide (DMF), and Triton X-100 were purchased from Tianjin Guangfu Technology Development Co., Ltd. (Tianjin, China). Phosphate buffer solution (PBS) was purchased from Beijing Biotuoda Technology Co., Ltd. (Beijing, China). Methanol, formic acid, and ammonium acetate (ThermoFisher, Waltham, MA, USA) were HPLC grade.

2.2. Test Insects

H. armigera larvae were purchased from Henan Jiyuan Baiyun Industrial Co., Ltd. (Jiyuan, China). The H. armigera pupae purchased were soaked in 0.2% sodium hypochlorite solution disinfectant for 5 min and then rinsed. The male and female pupae were kept separately in beakers for 5 to 7 days before eclosion. The emergening males were put into the female’s beaker for mating, and the appropriate amount of cotton soaked in honey water was added to the beaker. The beaker was covered with clean gauze. After emergence, adults were left to lay eggs on the gauze, and the gauze was changed daily. The gauze with eggs was soaked in 5% formaldehyde solution for 15–25 min the day before the eggs hatched (blackhead stage). Then they were washed with sterile water 3 times, hung on sterilized plastic sticks, dried naturally in a sterile room, put in a zip-lock bag, and then transferred to an incubator. Let them hatch. The newly hatched H. armigera larvae were placed in a sterilized plastic box with feed. The amount of feed per box was 70–80 g. The larvae were fed artificial feed in a light incubator to ensure consistent development of the test insects. Rearing conditions: temperature 26 ± 1 °C, relative humidity 70 ± 10%, photoperiod 14L:10D. The larvae were treated after reaching the third instar.

2.3. Sample Preparation for the Epidermis and Viscera of H. armigera

ZQ-8 was dissolved in DMF and then diluted to 200 mg/L with 0.01% Triton X-100 aqueous solution. H. armigera was treated by immersion method. The 3rd instar larvae of H. armigera were treated at 200 mg/L ZQ-8 medicine and 0.01% Triton X-100 aqueous solution (control group) for 5 s [17], and then transferred to a light incubator and fed with artificial feed for 48 h. The treated H. armigera were placed on ice for 2 min. As their activity waned, the larvae were pinned to a wax plate, the epidermis removed with tweezers and scissors, with the remaining organs wrapped with aluminum foil, and quickly frozen with liquid nitrogen. The epidermis was quickly rinsed in pre-cooled PBS solution to remove blood and dirt, then absorbed with absorbent paper. Finally, the epidermis was divided into freezer tubes and transferred to the refrigerator at −80 °C for storage. In this study, CB is the epidermis of the control group, DB is the epidermis of the ZQ-8 treatment group, CN is the viscera of the control group, and DN is the viscera of the ZQ-8 treatment group. Each treatment and control group consisted of six replicates, and each replicate consisted of 30 larvae (Table S1).

2.4. Extraction of Metabolites

Samples (100 mg) were individually grounded with liquid nitrogen and the homogenate was resuspended with prechilled 80% methanol by well vortex. The samples were incubated on ice for 5 min and then centrifuged at 15,000 rpm at 4 °C for 20 min. Some of the supernatant was diluted to a final concentration containing 53% methanol by LC-MS grade water. The samples were subsequently transferred to a fresh Eppendorf tube and then were centrifuged at 15,000 rpm at 4 °C for 20 min. Finally, the supernatant was injected into the UHPLC-MS/MS system analysis [18]. The test samples were mixed in equal volumes to prepare quality control samples (QC). The volume of each QC is the same as that of the sample. QC samples are used to balance the chromatography-mass spectrometry system and monitor the instrument status, and evaluate the system stability throughout the experiment.

2.5. UHPLC-MS/MS Conditions

The Vanquish UHPLC system (ThermoFisher, Karlsruhe, Germany) coupled with Orbitrap Q ExactiveTM HF mass spectrometer (ThermoFisher, Karlsruhe, Germany) were used for UHPLC-MS/MS analysis. Samples were injected into Hypesil Goldcolumn (100 × 2.1 mm, 1.9 μm) with a flow rate of 0.2 mL/min and a linear gradient of 17 min. The eluents for the positive polarity mode were eluent A (0.1% FA in water) and eluent B (methanol). The eluents for the negative polarity mode were eluent A (5 mM ammonium acetate, pH 9.0) and eluent B (methanol). The solvent gradient was set as follows: 2% B 1.5 min, 2–100% B 3 min, 100% B 10 min, 100–2% B 10.1 min, 2% B 12 min. Q ExactiveTM HF mass spectrometer was operated in positive/negative polarity mode with spray voltage of 3.5 kV, capillary temperature of 320 °C, sheath gas flow rate of 35 psi, aux gas flow rate of 10 L/min, S-lens RF level of 60, Aux gas heater temperature of 350 °C, and the scanning range is m/z 100–1500.

2.6. Data Preprocessing and Metabolite Identification

The raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.1 (CD3.1, ThermoFisher) to perform peak alignment, peak picking, and quantitation for each metabolite. The main parameters were set as follows: retention time tolerance, 0.2 min; actual mass tolerance, 5 ppm; signal intensity tolerance, 30%; signal/noise ratio, 3, and minimum intensity, etc. After that, peak intensities were normalized to the total spectral intensity. The normalized data were used to predict the molecular formula based on additive ions, molecular ion peaks, and fragment ions. Then, peaks were matched with the mzCloud (https://www.mzcloud.org/ (accessed on 14 January 2022)), mzVault, and MassList database to obtain the accurate qualitative and relative quantitative results. Statistical analyses were performed using the statistical software R (R version R-3.4.3), Python (Python 2.7.6 version), and CentOS (CentOS release 6.6). When data were not normally distributed, normal transformations were attempted using the area normalization method.

2.7. Data Analysis

These metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https://www.genome.jp/kegg/pathway.html (accessed on 14 January 2022)), HMDB database (https://hmdb.ca/metabolites (accessed on 14 January 2022)), and LIPIDMaps database (http://www.lipidmaps.org/ (accessed on 14 January 2022)). In the part of multivariate statistical analysis, principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed at metaX (a flexible and comprehensive software for processing metabolomics data) [19], and then obtained the VIP value of each metabolite. In the univariate analysis part, the statistical significance (p-value) and the fold change (FC value) of each metabolite between the two groups was calculated based on the t-test. The metabolites with VIP > 1 and p-value < 0.05 and FC ≥ 2 or FC ≤ 0.5 were considered to be differential metabolites. Volcano plots were used to filter metabolites of interest based on log2(FC) and −log10(p-value) of metabolites by ggplot2 in R language. For clustering heat maps, the data were normalized using z-scores of the intensity areas of differential metabolites and were plotted by Pheatmap package in R language. The correlation between differential metabolites were analyzed by cor () in R language (method = Pearson). Statistically significant correlation between differential metabolites were calculated by cor.mtest () in R language; p-value < 0.05 was considered as statistically significant and correlation plots were plotted by corrplot package in R language. The functions of these metabolites and metabolic pathways were studied using the KEGG database. The metabolic pathway enrichment of differential metabolites was performed, when the ratio was satisfied by x/n > y/N. The metabolic pathways were considered as enrichment, and when the p-value of metabolic pathway < 0.05, metabolic pathways were considered as statistically significant enrichment.

3. Results

3.1. UHPLC-MS/MS Identification Results and Data Quality Control

The mass spectrum peaks and ion responses of H. armigera treated with ZQ-8 in both positive and negative ion modes were investigated through UHPLC-MS/MS analysis. In total, 1021 metabolites were found by UHPLC-MS/MS in the epidermis and 906 metabolites in the viscera. The retention time and response intensity of chromatographic peaks of quality control (QC) samples overlapped (Figure S1), indicating that the variation caused by instrumental error was very minor. The Pearson correlation between the QC samples was high (R2 was close to 1, Figure S2), and the distribution on PCA was clustered together (Figure S3), indicating that the whole test process was stable and repeatable, and the obtained data were reliable and could be used for further analysis.

3.2. Multivariate Statistical Analysis

To further evaluate and identify differential metabolites, multivariate statistical analysis was performed on the UHPLC-MS/MS metabolomics data. The PCA score plots between the control and treatment groups revealed extremely strong separation in the viscera and epidermis (Figure S4), demonstrating that ZQ-8 treatment had an impact on the metabolic profiles of the two groups. However, the detailed differences between each cluster remain unknown. Following this, PLS-DA was used to maximize the differences between the metabolic profiles of the control and treatment groups and to identify differential metabolites. PLS-DA plots revealed that metabolites were clearly separated (Figure 1 and Figure S5). Model parameters indicated that the model was capable of modeling and prediction. The parameters of PLS-DA model were as follows, R2Y = 1.00, Q2Y = 0.94 in positive ion mode, R2Y = 1.00, Q2Y = 0.94 in negative ion mode (CB vs. DB); R2Y = 1.00, Q2Y = 0.96, positive ion mode, R2Y = 1.00, Q2Y = 0.9, negative ion mode (CN vs. DN). A ranking test (n = 200) was performed to validate the model. After re-modeling and making predictions following a random disruption of the group marking in each sample, it was found that the R2 value was larger than the Q2 value and the intercept of the Q2 regression line was negative (Figure 1 and Figure S5). The results indicated that there was no overfitting phenomenon in the model, which can accurately describe the samples and can be used for subsequent analysis.

3.3. Effect of ZQ-8 on Metabolite Levels in Epidermis and Viscera of H. armigera

A total of 1021 metabolites (635 in positive ion mode and 386 in negative ion mode) were identified in the epidermis of H. armigera treated with ZQ-8, including 129 lipids and lipid-like molecules, 126 organic acids and derivatives, 72 kinds of organoheterocyclic compounds, 44 kinds of benzenoids, etc. (Figure 2A). A total of 449 differential metabolites were examined (VIP > 1.0, FC > 1.5 or FC < 0.667, and p-value < 0.05), in which 167 metabolites were significantly up-regulated in the DB group. The top 5 differential up-regulated metabolites are stercobilin, sedoheptulose-1,7-bisphosphate, N1-(2,3-dihydro-1,4-benzodioxin-2-ylmethyl)-2,2-dimethylpropanamide, L-aspartic acid, and taurochenodeoxycholic acid. There were 282 metabolites significantly down-regulated in the DB group, and the top 5 differential metabolites down-regulated were sphingomyelin (SM) (d23:0/18:1), sphingomyelin (SM) (d19:0/22:1), N1, N1-diethyl-4-[5-(4-nitrophenyl)-1,3-oxazolan-2-yl]aniline, DL-o-tyrosine, and DL-metanephrine (Figure 3A,B). In the viscera of H. armigera treated with ZQ-8, there were 906 metabolites (409 in positive ion mode and 497 in negative ion mode) identified, including 116 lipids and lipid-like molecules, 99 organic acids and derivatives, 55 organoheterocyclic compounds, 43 organic oxygen compounds, etc. (Figure 2B). There were 416 differential metabolites screened, 128 of which were significantly up-regulated in the DN group, and the top 5 differential metabolites up-regulated were phosphopyruvic acid, lysophosphatidylserine (LPS) (16:0), 7-Methylxanthine, 6,7-dihydroxycoumarin, and branched fatty acid esters of hydroxy fatty acids (FAHFA) (20:1/22:3). There were 288 metabolites significantly down-regulated in the DN group (Figure 3C,D), and the top 5 differential metabolites down-regulated were epinephrine, 4-pyridoxic acid, riboflavin, N -acetylneuraminic acid, and piperolein A.
Comparing the control group with the treatment group, it was found that there were 130 differential metabolites common to the epidermis and viscera of H. armigera treated with ZQ-8, in which 27 were commonly up-regulated, mainly lipid compounds, including lysophosphatidylserine (LPS), lysophosphatidylcholine (LPC), and phosphatidylcholine (PC). There were 73 differential metabolites commonly down-regulated, mainly organic acids and lipid compounds (Figure 4). A clustering heat map of the different metabolites in the viscera and epidermis was shown in Figure S6. The figure revealed that ZQ-8 had a substantial effect on the secretion level of metabolites in the epidermis and viscera of H. armigera. Metabolite levels changed similarly in the viscera and the epidermis. Amino acids and fatty acids showed the most significant modifications. Therefore, it is crucial to find out the mechanism of action of ZQ-8 on H. armigera by analyzing the metabolic pathways of differential metabolites.

3.4. Effect of ZQ-8 on Metabolic Pathways of Epidermis and Viscera of H. armigera

KEGG pathway analysis is a powerful tool for in vivo metabolic analysis and metabolic network research. Pathway enrichment can identify the most essential biochemical metabolic processes and signal transduction pathways involved in differential metabolites. In positive ion mode, 58 differential metabolites in the epidermis of H. armigera were enriched in 28 metabolic pathways, and 34 differential metabolites in the viscera were enriched in 22 metabolic pathways after ZQ-8 treatment (Table S2). In negative ion mode, 45 differential metabolites in the epidermis of H. armigera were enriched in 43 metabolic pathways, and 54 differential metabolites in the viscera were enriched in 26 metabolic pathways (Table S3). A metabolite often participates in multiple metabolic pathways and each pathway involves multiple metabolites. To make the results easier to grasp, we selected the top 20 metabolic pathways with the highest significance according to the p-value, and the results of the enrichment analysis were displayed in the form of a bubble diagram, as shown in Figure 5. In the epidermis of H. armigera, the metabolic pathways with more enriched metabolites or significantly enriched degrees mainly included oxidative phosphorylation, valine, leucine and isoleucine biosynthesis, caffeine metabolism, cysteine and methionine metabolism, pantothenate and CoA biosynthesis, carbon metabolism, biosynthesis of amino acids, etc. In the viscera of H. armigera, the metabolic pathways with more enriched metabolites or significant enrichment mainly included purine metabolism, fatty acid biosynthesis, nicotinate and nicotinamide metabolism, lysine degradation, etc. (Figure 5).

4. Discussion

The molting of insects is an important biological process in their growth and development. Insects must undergo periodic molting to adapt to growth and overcome the constraints of the rigid cuticle that cannot be expanded. As an emerging omics technology, metabolomics has received widespread attention in insect drug resistance [20] and diapause [21] because its detection results are closer to the phenotype and can more directly and accurately reflect the physiological state of organisms. Based on the advantages of a wide range of detection substances and simple pretreatment of LC-MS/MS, it is more and more widely used in untargeted metabolomics research [22]. The epidermis and viscera of H. armigera in the ZQ-8 treatment group and control group were detected and analyzed using the UHPLC-MS/MS method in order to acquire the overall metabolic characteristics after treatment. This was performed to investigate the mechanism by which ZQ-8 interferes with the growth and development of H. armigera.
In the epidermis of H. armigera after ZQ-8 treatment for 48 h, the content of glucose metabolism-related substances mainly decreased, including fructose, D-fructose-6-phosphate, α-D-glucose-1,6-bisphosphate, pentose phosphate pathway intermediate D-erythrose 4-phosphate, and D-sedoheptulose-7-phosphate. However, the content of α-ketoglutarate and fumarate, which are intermediate products of the tricarboxylic acid (TCA) cycle, increased during this time. The content of fatty acid substances decreased, including 12-hydroxydodecanoic acid, 16-hydroxyhexadecanoic acid, malonic acid, 2-oxobutyric acid, succinic acid, and a variety of unsaturated fatty acids, including (9Z)-hexadecenoic acid, glutaconic acid, cis-7-hexadecenoic acid, and (2E)-dodecenedioic acid. The content of free amino acids mainly decreased, including tyrosine, arginine, threonine, cystine, norvaline, and ergothioneine, while the contents of aspartic acid and homocysteine increased after treatment (Table S4). After ZQ-8 treatment, the number of down-regulated differential metabolites was greater than the number of up-regulated ones in the epidermis of H. armigera. This is mutually supportive of our earlier transcriptomics study, which discovered that there were more down-regulated differentially expressed genes (DEGs) than up-regulated DEGs [17].
Energy metabolism is mainly the process of anaerobic glycolysis and aerobic respiration (tricarboxylic acid cycle, TCA) to produce the oxidative respiration product ATP. It also includes the decomposition of amino acids, cholesterol, and other substances to produce substrates that enter the TCA cycle to participate in the metabolism process. It was evident that the oxidative phosphorylation pathway was enriched in the epidermis of H. armigera (Figure 5). Metabolites α-ketoglutarate and fumarate were up-regulated in DB compared with CB, with log2 fold changes of 2.18 and 1.80, respectively (Table S4). α-ketoglutarate and fumarate are metabolites related to the TCA cycle. Mutual enhancement of TCA cycle intermediates may lead to increased ATP production, which also corresponded to increased ADP content in the oxidative phosphorylation pathway (Tables S2 and S4). This indicated an increased energy requirement after exposure to pesticides, and a similar phenomenon has been observed in other insects [23]. Furthermore, compared with the control group, substances related to glucose metabolism in the epidermis of H. armigera were down-regulated after ZQ-8 treatment (Figure 6), indicating that ZQ-8 reduced the glycolytic pathway. This was consistent with the fact that camptothecin inhibited the growth of Spodoptera litura larvae through decreased glycolysis and increased TCA cycle reported by Deng et al. [24].
In addition to energy-related metabolites, the valine, leucine, and isoleucine biosynthesis pathway was also significantly affected in the ZQ-8 treatment group. 2-ketobutyric acid is catalyzed by various enzymes to generate isoleucine, and 3-methyl-2-oxobutyric acid is catalyzed under the action of branched-chain amino acid aminotransferase to generate valine or under the action of 2-isopropylmalate synthase to generate 2-isopropylmalate; 2-isopropylmalate participates in the biosynthesis of leucine in the follow-up [25]. Leucine, isoleucine, and valine are important branched-chain amino acids, which are also glycosylated amino acids. Additionally, they have specific biological and physiological purposes and can serve as raw materials for protein production [26]. Furthermore, the biosynthesis of the amino acids pathway was enriched to the most differential metabolites (Table S2), which mainly showed a decrease in content. Phosphoserine is the precursor of serine and cysteine, saccharopine is involved in the synthesis of lysine, N-acetylornithine is an intermediate in the synthesis of ornithine and arginine, and anthranilic acid is an intermediate of tryptophan (Figure 6). This fact was also consistent with the decrease in free amino acid content. Therefore, we speculated that the decreased glycolysis, increased TCA cycle, and decreased amino acid synthesis in H. armigera when treated with ZQ-8 might be the main reason why ZQ-8 caused the growth inhibition and death of H. armigera larvae. This also further verified the conclusion that ZQ-8 may act on carbohydrate metabolism and protein amino acid synthesis pathways obtained from transcriptomics [17].
In the viscera of H. armigera, the enriched products of the purine metabolism pathway were the most and most significantly enriched (p < 0.05) (Table S3), where guanosine diphosphate (GDP), guanosine monophosphate (GMP), inosine monophosphate (IMP), adenosine 5′-monophosphate (AMP), inosine 5′-diphosphate (IDP), adenosine, 2′-deoxyadenosine 5′-diphosphate (dADP), N6-(1,2-dicarboxyethyl)-AMP, deoxyguanosine, xanthosine, allantoate, and urate content decreased, while inosine, hypoxanthine, and deoxyinosine content increased (Table S5). Urate is the main product of purine metabolism, which plays multiple roles as nitrogenous waste, nitrogen storage, pigment, antioxidant, and possibly signaling molecule [27]. Reactive oxygen species (ROS) can be formed after the application of pesticides causing oxidative stress [28]. Urate inhibits the generation of ROS by chelating metals and scavenges the generated oxygen free radicals [29]. We speculated that after ZQ-8 treatment for 48 h, the purine metabolism pathway of H. armigera was significantly disturbed, resulting in severe oxidative stress (Figure 7). However, we have no more direct evidence to support this view.
In addition, we found that the levels of metabolites involved in the synthesis and degradation of chitin in H. armigera had significant changes. Compared with CN, the contents of fructose-6-phosphate and glucose-6-phosphate in DN group increased significantly, while the contents of glucosamine 6-phosphate and UDP-N-acetylglucosamine decreased. During chitin synthesis, glutamine fructose-6-phosphate amidotransferase (GFAT) catalyzes the conversion of fructose-6-phosphate (F6P) to glucosamine-6-phosphate (G6P) [30], and then catalyzes by a series of enzymes to produce UDP-N-acetylglucosamine (UDP-GlcNAc), which is the key precursor for building chitin [31]. Finally, UDP-GlcNAc is polymerized into chitin under the action of chitin synthase [32]. There are two key enzymes involved in the degradation of chitin in insects, namely, chitinase and β-N-acetylhexosaminidase. Chitinase can randomly hydrolyze the long chain of chitin into chitin oligosaccharides, and then degrade chitin oligosaccharides from the non-reducing end to monomer N-acetylglucosamine under the action of β-N-acetylhexosaminidase. Finally, UDP-GlcNAc is synthesized under the catalysis of enzymes such as UDP-N-acetylglucosamine pyrophosphorylase and phosphoacetylglucosamine mutase to complete the cycle of chitin in insects [33]. During the molting stage of insects, several function-specific chitinases are highly expressed and involved in the degradation of the old epidermis. The actual synthesis of the new epidermis and the degradation of the old epidermis occur simultaneously [34]. Metabolomics revealed that after ZQ-8 treatment, the synthesis and degradation of chitin in the body of degradation may be inhibited contemporaneously, finally leading to its death (Figure 8). Notably, a recent study has shown that inhibiting chitinase gene expression of Spodoptera frugiperda not only affected the degradation of chitin in the old epidermis but also affected the synthesis of chitin, resulting in molting and developmental difficulties [35].

5. Conclusions

In summary, UHPLC-MS untargeted metabolomics provides a systematic and reliable method to study the changes of metabolite levels in the epidermis and viscera of H. armigera treated with ZQ-8 for 48 h. Decreased glycolysis, increased TCA cycle, and decreased amino acid synthesis were the main causes of growth inhibition and death of H. armigera larvae. In addition, the synthesis and degradation of chitin in H. armigera may be disturbed contemporaneously after treatment with ZQ-8. These results provide a new perspective to elucidate the mechanism of ZQ-8 against H. armigera.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13051315/s1, Table S1: Metabolic sample information; Table S2: Results of KEGG enrichment in the epidermis; Table S3: Results of KEGG enrichment in the viscera; Table S4: Differential metabolites annotated to the KEGG pathway in the epidermis; Table S5: Differential metabolites annotated to the KEGG pathway in the viscera; Figure S1: UHPLC-MS total ion chromatogram (TICs) of QC samples; Figure S2: Correlation analysis of QC samples of the epidermis and viscera; Figure S3: PCA analysis of total samples of the epidermis and viscera; Figure S4: Epidermis and viscera principal component analysis (PCA); Figure S5: PLS-DA diagram and sequencing verification diagram of the viscera; Figure S6: Clustering heat maps of differential metabolites in the epidermis and viscera.

Author Contributions

X.H. and C.L. conceived and designed the experiments, C.L., L.Y. (Lin Yang), F.J., Y.Y., Z.X. and L.Y. (Longfei Yang) performed the experiments and analyzed the data, C.L. and X.H. wrote the paper, S.Z., G.Z. and D.Y. revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (32260684) and Innovative Top Talent Project of Shihezi University (CXBJ202007).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. PLS-DA diagrams (A,C) and permutation test diagrams (B,D) of the epidermis of ZQ-8 treatment group and the control group (CB vs. DB); (A,B) is positive, (C,D) is negative.
Figure 1. PLS-DA diagrams (A,C) and permutation test diagrams (B,D) of the epidermis of ZQ-8 treatment group and the control group (CB vs. DB); (A,B) is positive, (C,D) is negative.
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Figure 2. HMDB classification notes of ZQ-8 treatment group and control group. (A) is the epidermis, (B) is the viscera.
Figure 2. HMDB classification notes of ZQ-8 treatment group and control group. (A) is the epidermis, (B) is the viscera.
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Figure 3. Volcano plots of differential metabolites identified from the epidermis (DB vs. CB) and the viscera (DN vs. CN) of H. armigera; (A,B) is positive, (C,D) is negative.
Figure 3. Volcano plots of differential metabolites identified from the epidermis (DB vs. CB) and the viscera (DN vs. CN) of H. armigera; (A,B) is positive, (C,D) is negative.
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Figure 4. Venn diagrams showing overlapping and specific differential metabolites in the epidermis and viscera of H. armigera after ZQ-8 treatment: (A) is the total differential metabolites in the epidermis and viscera. (B) is the up-regulated differential metabolites in the epidermis and viscera. (C) is the down-regulated differential metabolites in the epidermis and viscera.
Figure 4. Venn diagrams showing overlapping and specific differential metabolites in the epidermis and viscera of H. armigera after ZQ-8 treatment: (A) is the total differential metabolites in the epidermis and viscera. (B) is the up-regulated differential metabolites in the epidermis and viscera. (C) is the down-regulated differential metabolites in the epidermis and viscera.
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Figure 5. KEGG enrichment bubble map of epidermis and viscera.
Figure 5. KEGG enrichment bubble map of epidermis and viscera.
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Figure 6. Schematic diagram of the toxicity mechanism of the metabolic pathway disorder in the epidermis of H. armigera after ZQ-8 treatment for 48 h. Red for up-regulated, Blue for down-regulated.
Figure 6. Schematic diagram of the toxicity mechanism of the metabolic pathway disorder in the epidermis of H. armigera after ZQ-8 treatment for 48 h. Red for up-regulated, Blue for down-regulated.
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Figure 7. Schematic diagram of purine metabolism pathway in the viscera of H. armigera after ZQ-8 treatment for 48 h. Red for up-regulated, Blue for down-regulated.
Figure 7. Schematic diagram of purine metabolism pathway in the viscera of H. armigera after ZQ-8 treatment for 48 h. Red for up-regulated, Blue for down-regulated.
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Figure 8. Schematic diagram of metabolite perturbation of chitin synthesis and degradation in H. armigera after ZQ-8 treatment. Red for up-regulated, Blue for down-regulated.
Figure 8. Schematic diagram of metabolite perturbation of chitin synthesis and degradation in H. armigera after ZQ-8 treatment. Red for up-regulated, Blue for down-regulated.
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Liu, C.; Yang, L.; Jin, F.; Yin, Y.; Xie, Z.; Yang, L.; Zhao, S.; Zhang, G.; Yang, D.; Han, X. Untargeted UHPLC-MS Metabolomics Reveals the Metabolic Perturbations of Helicoverpa armigera under the Stress of Novel Insect Growth Regulator ZQ-8. Agronomy 2023, 13, 1315. https://doi.org/10.3390/agronomy13051315

AMA Style

Liu C, Yang L, Jin F, Yin Y, Xie Z, Yang L, Zhao S, Zhang G, Yang D, Han X. Untargeted UHPLC-MS Metabolomics Reveals the Metabolic Perturbations of Helicoverpa armigera under the Stress of Novel Insect Growth Regulator ZQ-8. Agronomy. 2023; 13(5):1315. https://doi.org/10.3390/agronomy13051315

Chicago/Turabian Style

Liu, Caiyue, Lin Yang, Fuqiang Jin, Yuelan Yin, Zizheng Xie, Longfei Yang, Sifeng Zhao, Guoqiang Zhang, Desong Yang, and Xiaoqiang Han. 2023. "Untargeted UHPLC-MS Metabolomics Reveals the Metabolic Perturbations of Helicoverpa armigera under the Stress of Novel Insect Growth Regulator ZQ-8" Agronomy 13, no. 5: 1315. https://doi.org/10.3390/agronomy13051315

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