Next Article in Journal
Protein Unfolding—Thermodynamic Perspectives and Unfolding Models
Previous Article in Journal
IFN-Inducible SerpinA5 Triggers Antiviral Immunity by Regulating STAT1 Phosphorylation and Nuclear Translocation
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 24
Issue 6
10.3390/ijms24065461
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Transcriptomic Analysis Reveals the Detoxification Mechanism of Chilo suppressalis in Response to the Novel Pesticide Cyproflanilide

by
Jungang Zhou
1,
Lin Qiu
1,2,
Qiyao Liang
1,
Yi Zhou
1,
Jinjun Sun
1,
Qiao Gao
1,
Hualiang He
1,2,
Wenbing Ding
1,3,4,
Jin Xue
1,2 and
Youzhi Li
1,3,4,*
1
College of Plant Protection, Hunan Agricultural University, Changsha 410128, China
2
Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests, Hunan Agricultural University, Changsha 410128, China
3
Hunan Provincial Engineering & Technology Research Center for Biopesticide and Formulation Processing, Hunan Agricultural University, Changsha 410128, China
4
National Research Center of Engineering & Technology for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(6), 5461; https://doi.org/10.3390/ijms24065461
Submission received: 10 February 2023 / Revised: 7 March 2023 / Accepted: 8 March 2023 / Published: 13 March 2023
(This article belongs to the Section Molecular Toxicology)
Browse Figures
Review Reports Versions Notes

Abstract

:
Chilo suppressalis is one of the most damaging rice pests in China’s rice-growing regions. Chemical pesticides are the primary method for pest control; the excessive use of insecticides has resulted in pesticide resistance. C. suppressalis is highly susceptible to cyproflanilide, a novel pesticide with high efficacy. However, the acute toxicity and detoxification mechanisms remain unclear. We carried out a bioassay experiment with C. suppressalis larvae and found that the LD10, LD30 and LD50 of cyproflanilide for 3rd instar larvae was 1.7 ng/per larvae, 6.62 ng/per larvae and 16.92 ng/per larvae, respectively. Moreover, our field trial results showed that cyproflanilide had a 91.24% control efficiency against C. suppressalis. We investigated the effect of cyproflanilide (LD30) treatment on the transcriptome profiles of C. suppressalis larvae and found that 483 genes were up-regulated and 305 genes were down-regulated in response to cyproflanilide exposure, with significantly higher CYP4G90 and CYP4AU10 expression in the treatment group. The RNA interference knockdown of CYP4G90 and CYP4AU10 increased mortality by 20% and 18%, respectively, compared to the control. Our results indicate that cyproflanilide has effective insecticidal toxicological activity, and that the CYP4G90 and CYP4AU10 genes are involved in detoxification metabolism. These findings provide an insight into the toxicological basis of cyproflanilide and the means to develop efficient resistance management tools for C. suppressalis.

1. Introduction

Chilo suppressalis (Walker) (Lepidoptera: Crambidae) is one of China’s most damaging rice pests, with climate change, cropping systems and cultivar changes all contributing to increasingly frequent outbreaks [1,2,3]. Additionally, extensive pesticide use and improper application practices have been significant contributors to C. suppressalis resistance, resulting in severe economic losses due to outbreaks in rice-growing areas [4,5]. There is growing evidence that C. suppressalis has developed resistance to most pesticides, including nereistoxin insecticides (monosultap), organophosphorus insecticides (triazophos, chlorpyrifos), microbial pesticides (avermectin) and bisamide (cyantraniliprole, flubendiamide and chlorantraniliprole) [6,7]. Furthermore, recent studies have revealed that C. suppressalis has developed moderate resistance to cyantraniliprole (RR:77.6 folds), flubendiamide (RR:42.6 folds), triazophos (RR:64.5-461.3 folds) and chlorpyrifos (RR:10.1-125 folds) [4,7,8]. Hence, the development of new pesticides is crucial in the fight against C. suppressalis.
Cyproflanilide is a novel meta-diamide insecticide with toxicological activity against lepidopteran, coleoptera and thysanoptera pest species. It was classified into Group 30 “GABA-Gated Chloride Channel Allosteric Modulators” by the Insecticide Resistance Action Committee (IRAC). Several studies have identified metabolic and target resistance in C. suppressalis as resistance mechanisms to diamide insecticides [1,7,9,10,11,12]. Huang et al. (2020) reported that resistance to diamide insecticides was associated with a mutation of the ryanodine receptor (RyRs) at the I4578M, Y4667D/C, G4915E and Y4891F sites in C. suppressalis [8]. Furthermore, the overexpression of CYP genes (CYP6CV5, CYP9A68, CYP321F3, CYP324A12, UGT40AL1 and UGT33AG3) were found to be involved in the metabolic resistance of C. suppressalis to chlorantraniliprole. [1,10] Cyproflanilide has been identified as playing an important role in pest resistance management because it binds to different insect receptors and induces toxicity via different modes of action than other diamide insecticides. However, the underlying mechanisms of C. suppressalis resistance remain unknown.
The ability of insects to metabolize and detoxify pesticides is thought to play a significant role in pesticide resistance. The detoxification enzyme genes involved in xenobiotic metabolism, degradation and detoxification belong to three distinct pathways: phase I (hydrolysis and oxidation-reduction), phase II (conjugation) and phase III (transport) [13,14]. Several important enzymes are involved in the metabolism of xenobiotics, including cytochrome P450 monooxygenase (P450s), esterase (EST, including carboxylesterase and phosphoesterase), glutathione S-transferase (GST), UDP-glycosyltransferase (UGT) and ATP-binding cassette transporter (ABC) [15,16]. P450 belongs to the heme family of proteins involved in the metabolism of endogenous and exogenous compounds and the regulation of insect growth and development [15,17]. It is worth noting that the CYP gene plays a central role in insect xenobiotic metabolism, hydrolysis and oxidation-reduction and regulates a variety of endogenous signaling molecules [16]. However, we do not know whether CYP genes are involved in the metabolism of cyproflanilide.
To evaluate the control efficiency of cyproflanilide on C. suppressalis, we carried out a laboratory bioassay and field trial. Our results revealed that cyproflanilide has effective toxicological insecticidal activity against C. suppressalis. Furthermore, in order to find the detoxification metabolism genes that were mainly involved in cyproflanilide, a combined transcriptome RNA-seq and RNA interference approach identified two P450 genes, CYP4G90 and CYP4AU10, that are involved in cyproflanilide metabolism in C. suppressalis. Our findings contribute to a better understanding of cyproflanilide metabolism in C. suppressalis and provide insight into the development of pest and resistance management strategies for this species.

2. Results

2.1. Bioassay Results

Bioassay experiments showed that cyproflanilide exhibited potent pesticide activity against C. suppressalis. The probit analysis showed that LD10, LD30 and LD50 were 1.421, 5.261 and 13.022 ng/per larva at 24 h (Table 1). The third instar larvae were then treated with LD10, LD30 and LD50 cyproflanilide to test the expression of detoxification genes.

2.2. Field Experiment Results

After 21 days of spraying, compared to 200 g/L chlorantraniliprole (SC), the most effective pesticide for controlling C. suppressalis was 20% cyproflanilide (SC). The control efficiency on C. suppressalis of 16.2, 32.4, 48.6 and 64.8 g/hectare of 20% cyproflanilide (SC) were 77.38%, 84.60%, 87.26% and 91.24%, respectively (Table 2). There was a significant difference in control efficiency among low, medium and high doses (F = 74.941, p = 0.0001). However, there was no significant difference in control efficiency between 48.6 and 64.8 g/hectare of 20% cyproflanilide (SC) (Table 2). The dead heart rate was significantly reduced when rice was treated with different doses of 20% cyproflanilide (SC) for 21 days compared to 200 g/L chlorantraniliprole (SC).

2.3. Transcriptome Analysis

2.3.1. Sample Quality Control and Correlation Analysis of 6 Samples

The six individual cDNA libraries constructed from the contrast and cyproflanilide treatments were sequenced on Illumina Nova Seq 6000. Clean reads for subsequent analysis were obtained after filtration of the original data, a sequence error rate check and a GC content distribution check. A total of 36.58 GB of clean data was obtained from the RNA sequences of six samples. The GC content of the sequence data ranged from 46.29% to 46.83%, and the Q20 and Q30 ratios were > 97% and 93%, respectively, indicating good quality (Table S1).
Pearson’s correlation coefficient was used to analyze the relationship between the control and treatment groups. The square of coefficient was > 0.8 (Figure S1), indicating that the within-group biological replication was good. Gene expression levels (FPKM values) of each sample were used to calculate the within and between group correlation coefficients which were then plotted into a heat map (Figure 1a).

2.3.2. GO and KEGG Analysis of DEGs

GO pathway enrichment analysis was performed to investigate the function of DEGs in C. suppressalis third instar larvae exposed to cyproflanilide. Regarding the 19 significant GO terms, 393 DEGs were enriched, of which 339 were up-regulated and 54 were down-regulated. The 19 significantly enriched GO terms were divided into three categories: molecular functions, biological processes and cellular components. The most abundant genes in the molecular function terms category were “structural molecular activity” and “structural constituent of cuticle”. For the biological processes category, the most abundant of genes were “carbohydrate derivative metabolic process” and “drug metabolic process”. The “extracellular region” had the most genes in the cellular component category. Down-regulated genes were enriched in DNA replication. Additionally, “drug metabolic process”, “serine hydrolase activity” and “oxidoreductase activity” may also be related to cyproflanilide exposure (Figure 1b and Figure S4).
The results of the enrichment analysis revealed that “DNA replication” was significantly enriched in 101 KEGG pathways (Figure S2). The abundant DEGs found in the KEGG database mainly included categories such as “pentose and glucuronate interconversions”, “ubiquinone and other terpenoid-quinone biosynthesis”, “drug metabolism-other enzymes”, “fatty acid biosynthesis” and “base excision repair”. It is worth noting that “drug metabolism-other enzymes”, “drug metabolism-cytochrome P450” and “metabolism of xenobiotics by cytochrome P450” may also be involved in the metabolism of cyproflanilide. We found that the detoxification metabolism pathway was significantly enriched, indicating that these pathway genes were involved in detoxification metabolism to cyproflanilide.

2.3.3. Transcriptome Profiling Reveals Detoxification Genes Associated with Cyproflanilide

Using a log2 fold change cutoff of ≥ 1 and ≤ − 1, and corrected p value of < 0.05, a total of 788 genes were identified as differentially expressed between the two treatments, of which 483 genes were up-regulated and 305 down-regulated (Figure S3). Finally, the analysis of DEGs identified 28 detoxification metabolism genes, including 3 glutathione S-transferase, 4 glucosyltransferase, 11 cytochrome oxidoreductase, 5 carboxylesterase and 5 ATP-binding cassette transporters. qRT-PCR was performed on the 12 metabolic detoxification genes to check if there were any differences in the transcriptome data. The qRT-PCR results show a correlation between the metabolic detoxification gene expression level and transcriptome data.

2.4. Analysis Detoxification Gene Expression Level by qRT-PCR

We used qRT-PCR to see if the 12 detoxifications genes differed with different pesticide concentrations, and over time. At 24 h, UGT33AG3, EST5 and CYP6AB49 were significantly different under the stress of three sublethal doses (Figure 2a,c and Figure 3a). ABCC3 and EST17 had significantly different levels under LD10 stress, while EST17 was also significantly different under LD30 stress (Figure 2b,c). ABCC6 was significantly different under LD10 and LD50 stress (Figure 2b). CYP6AB48 and CYP367A9 were significantly different under LD50 and LD30 stress (Figure 3a). At 48 h, ABCC3, CYP6AB49 and CYP6AB48 had significantly different levels only under LD50 stress (Figure 2e and Figure 3b). CYP321F1 was significantly different under the stress of three sublethal doses (Figure 3b). The expressions of EST17 and CYP367A9 were inhibited under the stress of three sublethal doses (Figure 2f and Figure 3b). UGT33AG1 was significantly different only under LD30 stress. UGT33AG3, ABCC6 and EST5 had significantly different levels under LD10 stress, while ABCC6 was also significantly different under LD30 stress (Figure 2d–f). Interestingly, the results of the gene expression level showed that CYP4G90 and CYP4AU10 have stable up-regulation expression levels at different concentrations and across time (Figure 3a,b).

2.5. Sensitivity to Pesticides after Gene Silencing

The high expressions levels of CYP4G90 and CYP4AU10 at different cyproflanilide concentrations and exposure times suggest their likely involvement in cyproflanilide metabolism. Hence, RNAi was used to verify the functions of CYP4G90 and CYP4AU10 in C. suppressalis. After 24 h and 48 h of feeding on a diet containing dsRNA specific for CYP4G90 and CYP4AU10, the mRNA level decreased by 65%, 46% and 44%, 42% (Figure 4a,b), respectively. When larvae were treated with LD30 cyproflanilide for 12 h, the knockdown of CYP4G90 and CYP4AU10 significantly increased the mortality rates of third instar larvae compared to the control groups (fed dsEGFP, Figure 4c). After 24 h, the knockdown of CYP4G90 during LD30 cyproflanilide treatment resulted in a significant increase in the mortality of third instar larvae compared to the control groups (Figure 4d). In contrast, the knockdown of CYP4AU10 had no significant effect on the larvae mortality of LD30 cyproflanilide treatment in 24 h (Figure 4d). Collectively, these results demonstrate that a decrease in CYP4G90 and CYP4AU10 mRNA levels can result in an increase in the mortality of larvae exposed to cyproflanilide, further confirming the involvement of CYP4G90 and CYP4AU10 in the metabolism of cyproflanilide.

3. Discussion

Due to their high efficiency, chemical pesticides such as monosultap, triazophos, avermectin and chlorantraniliprole are currently the most effective method of controlling large-scale outbreaks of C. suppressalis [6,7]. Chlorantraniliprole sales in China have increased significantly due to the increasing number of C. suppressalis outbreaks, resulting in a significant increase in resistance level [18,19]. The new insecticide cyproflanilide is highly effective against C. suppressalis larvae. Over time, lethal doses of the insecticide in the field will gradually reduce to sublethal doses, which will continuously threaten larval growth and development.
Sublethal doses of insecticides have been reported to affect development duration, pupal weight and reproduction of pests. For example, chlorpyrifos, etofenprox and phosmet prolonged larval development time, and chlorantraniliprole and flubendiamide increased larval and pre-pupal development times and decreased larval weight [20,21]. During physiological activity, carbohydrate and lipid metabolism, and energy metabolism are the primary source of energy and substrate for organisms [22]. In this study, 7 out of 12 DEGs involved in carbohydrate metabolism were down-regulated, indicating that it may influence C. suppressalis development. Interestingly, under chlorantranilamide treatment, these pathway genes were also altered which affected the development time of C. suppressalis larvae [22,23]. Lipids play an important role in insect reproduction [24,25,26], and studies have demonstrated that changes in lipid content affect silkworm reproduction [27]. In our study, 31 DEGs were associated with glycerolipid metabolism, sphingolipid metabolism, steroid biosynthesis, fatty acid elongation, glycerophosholipid metabolism, fatty acid degradation, fatty acid biosynthesis, fatty acid metabolism and unsaturated fatty acid biosynthesis.
In addition to the effect of exogenous substances on insect reproduction and development, the detoxification and metabolism of exogenous substances by pests have been extensively studied [28,29,30,31]. In this study, 28 DEGs were primarily enriched in the “drug metabolism- other enzymes”, “glutathione metabolism” and “Metabolism of xenobiotics by cytochrome P450” pathways. P450 genes, in particular, have received increasing attention due to their broad range of functions [32]. For example, the overexpression of CYP4G19 in beta-cypermethrin-resistant strains of Blattella germanica was positively correlated with a high level of cuticular hydrocarbons, while the knockdown of CYP4G19 expression resulted in a decrease in cuticular hydrocarbons and reduced insecticide tolerance in resistant strains [33]. Furthermore, previous studies have reported a link between the function of the CYP4 family of genes and the insect cuticle [17,33,34,35]. In this study, we demonstrated that CYP4G90 and CYP4AU10 are involved in cyproflanilide metabolism, and we found a number of DEGs related to the insect cuticle. Therefore, C. suppressalis may develop cuticular resistance to cyproflanilide. Transcriptome analysis of C. suppressalis treated with chlorantraniliprole revealed that CYP4G90 is also a DEG. However, there was no evidence to suggest that CYP4G90 was involved in chlorantraniliprole metabolism [22]. A significant increase in UGT33AG3 expression was found in C. suppressalis strains resistant to chlorantraniliprole [10]. Our results showed a significant difference in UGT33AG3 expression levels following 24 h of cyproflanilide treatment. It is evident that the development of pest resistance is not dependent on a single gene [36,37,38]. Previous research using RNAi demonstrated that CYP6CV5, CYP9A68, CYP321F3 and CYP324A12 are involved in metabolic resistance to chlorantraniliprole [1].
In this study, CYP4G90 and CYP4AU10 had higher expression levels compared with other detoxifying metabolic genes under the cyproflanilide treatment of C. suppressalis third instar larvae. Meanwhile, we found that knockdown of these two genes increased the mortality of C. suppressalis larvae exposed to cyproflanilide. In the reports of the resistant populations of insects to pesticides, the overexpression of P450 genes were related to the metabolic resistance of pesticides. For example, CYP321A6 and CYP332A1 had mediate chlopyrifos resistance in Spodoptera exigua, CYP6BG1 was involved in the resistance of imidacloprid to whiteflies [39,40] and CYP6CM1 and CYP4C62 were proven to have participated resistance to chlorantraniliproe and chlorpyrifos in Plutella xylostella and Nilaparvata lugens, respectively, by RNAi [41,42]. In future, with the wider use of cyproflanilide, the C.suppressalis may develop resistance to it. Therefore, these two genes are the potential target resistance genes of C. suppressalis to cyproflanilide.
In conclusion, the novel insecticide cyproflanilide is highly effective against C. suppressalis, with a control efficiency of 64.8 g/hectare reaching 91.24% efficacy. We also demonstrated the role of CYP4G90 and CYP4AU10 in the metabolic detoxification of cyproflanilide. Our findings show that the novel pesticide cyproflanilide is highly effective against C. suppressalis and we identified potential targets for further pesticide resistance research into cyproflanilide.

4. Materials and Methods

4.1. Insect and Insecticide

The C. suppressalis strain used in this study was collected from a field population in Yongan, Hunan province, China, during 2021. All larvae were maintained at 28 ± 1 °C under 60–70% relative humidity (RH) and a 16 h: 8 h (light/dark) photoperiod. Larvae were fed on Zizania latifolia (Griseb). Cyproflanilide (98.91% active ingredient) and cyproflanilide SC (20% active ingredient) were provided by Nantong Taihe Chemical Co., Ltd. (Nantong, Jiangsu, China). Chlorantraniliprole was purchased from Fumeishi China Investment limited (Shanghai, China).

4.2. Bioassay

The third instar larvae were used for the bioassay in this research. Each treatment was repeated thrice, with 30 larvae per replicate. Cyproflanilide was dissolved in acetone to obtain concentrations of 40 mg/L, 20 mg/L, 10 mg/L, 5 mg/L, 2.5 mg/L, 1.25 mg/L and 0.625 mg/L, with the acetone-treated group as a control. C. suppressalis larvae were treated with 0.5 µL of the insecticide solution drop on the pronotum using a hand micro applicator (Burkard Manufacturing Co Ltd., Rickmansworth, Hertfordshire, England), and mortality was recorded at 24 h. The sublethal dose of cyproflanilide to the C. suppressalis was determined via a log-probit analysis of bioassay data using SPSS 22. Larvae treated with LD10, LD30 and LD50 were collected at 24 h and 48 h, and were immediately frozen in liquid nitrogen and then stored at −80 °C for RNA extraction.

4.3. Evaluting and Verifying Field Efficacy

The field-plot trials were located in Xidu Town (Hengyang County, Hunan Province, China). When the treatment rice plant was applied, most of the pests were second and third instar larvae. The field experiments were conducted to evaluate the control efficacies of cyproflanilide and chlorantraniliprole against C. suppresalis larvae on rice plants. All of the experiments were designed as randomized complete blocks with four replicates of each treatment. The 7.5 × 4 m plots were used for each treatment application. The following four treatments of 20% cyproflanilide (SC) and one treatment of 200 g/L chlorantraniliprole (SC) were evaluated: 16.2 g/hectare, 32.4 g/hectare, 48.6 g/hectare 64.8 g/hectare and 30 g/hectare, with water as a control. After 21 days of pesticide application, we counted the number of rice dead hearts caused by C. suppressalis. The number of rice dead hearts was used to calculate the insecticide control effect. No other pesticides were applied during the experimental period. The average total number of rice plants in the plot was obtained according to the number of rows and columns of rice in the plot and the average tiller number of 50 clusters. The control efficacies of each insecticide-treated group C. suppressalis larvae were calculated using the following Equations (1) and (2):
Dead heart rate (%) = (The number of dead rice hearts / Total number of rice plants in the experimental plot) × 100.
Control efficiency (%) = (Number of dead rice hearts of control plants − Dead heart rate of pesticide-treated plants/ Dead heart rate of control) × 100.

4.4. RNA Sequencing and Annotation of Unigenes

Total RNA was used as the input material for the RNA sample preparations. Briefly, mRNA was purified from total RNA using poly-T oligo-attached beads. Fragmentation was carried out using divalent cations under an elevated temperature in the First Stand Synthesis Reacting Buffer (5X). The first strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase; we then used RNaseH to degrade the RNA. Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and dNTP. The remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of the 3′ ends of DNA fragments, Adaptor with a hairpin loop structure was ligated to prepare for hybridization. To select cDNA fragments of preferentially 370~420 bp in length, the library fragments were purified with the AMPure XP system (Beckman Coulter, Beverly, USA). After PCR amplification, the PCR product was purified using AMPure XP beads, and the final library was obtained.
The library was initially quantified using a Qubit2.0. Fluorometer, then diluted to 1.5 ng/µL. The insert size of the library was detected using an Agilent 2100 bioanalyzer. After the insert size met expectations, qRT-PCR was used to accurately quantify the effective concentration of the library (the effective concentration of the library is higher than that of 2 nM) to confirm the quality of the library. The different libraries were then pooled according to the effective concentration and the target amount of data of the machine before being sequenced using the Illumina NovaSeq 6000. The reference genome and gene model annotation files were downloaded from a genome website directly: http://v2.Insect-genome.com/api/Download/.-01_data-01_speciesChilo-suppressalis-Chilo_suppressalis.genome.fa (accessed on 11 August 2021). The index of the reference genome was built using Hisat2 (v2.0.5) and paired-end clean reads were aligned to the reference genome using Hisat2 (v2.0.5). Transcripts with an adjusted p-value < 0.05 found by DESeq were assigned as differentially expressed.

4.5. Quantitative Real Time PCR

First-stand cDNA templates were synthesized using the PrimeScriptTM RT reagent kit with gDNA Eraser (Taraka, Dalian, China). The specific primers for qRT-PCR were designed using online website NCBI: https://www.ncbi.nlm.nih.gov/tools/primer-blast/ (accessed on 27 December 2021) with the EF-1 house-keeping gene used as the internal gene (Table S2) [43]. The qRT-PCR reaction was performed using Hieff® qPCR SYBR Green Master Mix (Yeasen, Shanghai, China) following the manufacturer’s instructions. The 10 µL PCR reaction volume contained 5 µL SYBR Green Master Mix, 2.2 µL diethylpyrocarbonate-treated water (DEPC), 2 µL diluted cDNA template with a concentration of 100 ng/µL, and 0.4 µL of each primer. The qRT-PCR program was as follows: 95 °C for 30 s, 40 cycles of 95 °C for 5 s and 60 °C for 30 s. The relative gene expression levels were represented using the 2−ΔΔCT method.

4.6. RNAi Experiment

Primer for RNA interference (RNAi) was designed using online website siDirect: http://sidirect2.rnai.jp/ (accessed on 27 March 2022) and Vazyme: https://crm.vazyme.com/cetool/en-us/singlefra-gment.html (accessed on 27 March 2022). The templates of double-stranded RNA (dsRNA) synthesis were obtained by the real-time polymerase chain reaction (RT-PCR) using specific primers (Table S3). These two genes, a 528 bp fragment of CYP4G90 and a 553 bp fragment of CYP4AU10, were amplified and subcloned into the pET-2p expression vector using the ClonExpress II One Step Cloning Kit (Vazyme, Nanjing, China). The recombinant vectors of CYP4G90 and CYP4AU10 were transformed into HT115-competent cells (Shanghai Weidi Biotechnology Co., Ltd., Shanghai, China) for dsRNA expression. Individual colonies were inoculated and grown until the cultures reached an OD600 of 0.8. Isopropyl-β-D-Thiogalactoside (IPTG) (Beijing coolaber Technology Co., Ltd., Beijing, China) was added to the cultures to produce a final concentration of 0.1 mM; the culture was then incubated at 37 °C for approximately 4 h. The expression of dsRNA was verified by 1% agarose gel. The induced cultures were centrifuged at 8000× g for 5 min before being resuspended in one-tenth of the original culture volume of 0.05 M phosphate-buffered saline (PBS). The resuspended bacterial solution was then used for oral RNAi. The third instar larvae were starved for 2 h, while Zizania latifolia (Griseb) was soaked in the resuspended bacterial solution and then dried for 30 min. After feeding on dsRNA for 24 h, larvae were treated with of 0.5 µL of the LD30 cyproflanilide solution. Each treatment was replicated five times with 30 third instar larvae per replication and the mortality was recorded every 12 h until the end of the 24 h period.

4.7. Data Processing and Statistical Analysis

We used GraphPad Prism8 and IBM SPSS Statistics Version 22 software for data analyses. T tests (nonparametric tests) were used to compare means between treatments and their respective controls. The results are given as means ± standard error. p < 0.05 was considered a significant difference, and p < 0.01 was considered a highly significant difference.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms24065461/s1.

Author Contributions

Conceptualization, J.Z.; Data curation, J.Z.; Formal analysis, J.Z. and H.H.; Funding acquisition, L.Q. and Y.L.; Investigation, J.Z., L.Q., Q.L., Y.Z., J.S., Q.G., H.H., W.D. and J.X.; Methodology, J.Z.; Project administration, Y.L.; Resources, J.S. and Y.L.; Software, J.Z. and Q.G.; Supervision, L.Q., W.D. and Y.L.; Validation, J.Z., Q.L. and Y.Z.; Writing—original draft, J.Z.; Writing—review and editing, L.Q. and Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Science and Technology Innovation Program of Hunan Province (2022RC1025), the National Natural Science Foundation of China (Grant No. 31801736) and the Double first-class construction project of Hunan Agricultural University (Grant No. SYL2019029).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We thank CAC Nantong Chemical Co. Ltd. for providing the novel pesticide cyproflanilide.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Xu, L.; Zhao, J.; Sun, Y.; Xu, D.; Xu, G.; Xu, X.; Zhang, Y.; Huang, S.; Han, Z.; Gu, Z. Constitutive overexpression of cytochrome P450 monooxygenase genes contributes to chlorantraniliprole resistance in Chilo suppressalis (Walker). Pest Manag. Sci. 2019, 75, 718–725. [Google Scholar] [CrossRef] [PubMed]
  2. Yang, L.; Wang, S.; Wang, R.; Zheng, Q.; Ma, Q.; Huang, S.; Chen, J.; Zhang, Z. Floating chitosan-alginate microspheres loaded with chlorantraniliprole effectively control Chilo suppressalis (Walker) and Sesamia inferens (Walker) in rice fields. Sci. Total. Environ. 2021, 783, 147088. [Google Scholar] [CrossRef]
  3. Lu, Y.; Wang, G.; Zhong, L.; Zhang, F.; Bai, Q.; Zheng, X.; Lu, Z. Resistance monitoring of Chilo suppressalis (Walker) (Lepidoptera: Crambidae) to chlorantraniliprole in eight field populations from east and central China. Crop. Prot. 2017, 100, 196–202. [Google Scholar] [CrossRef]
  4. Mao, K.; Li, W.; Liao, X.; Liu, C.; Qin, Y.; Ren, Z.; Qin, X.; Wan, H.; Sheng, F.; Li, J. Dynamics of Insecticide Resistance in Different Geographical Populations of Chilo suppressalis (Lepidoptera: Crambidae) in China 2016–2018. J. Econ. Èntomol. 2019, 112, 1866–1874. [Google Scholar] [CrossRef]
  5. Savary, S.; Willocquet, L.; Pethybridge, S.J.; Esker, P.; McRoberts, N.; Nelson, A. The global burden of pathogens and pests on major food crops. Nat. Ecol. Evol. 2019, 3, 430–439. [Google Scholar] [CrossRef] [PubMed]
  6. Zhao, X.; Xu, X.; Wang, X.-G.; Yin, Y.; Li, M.-Y.; Wu, Y.-Q.; Liu, Y.-H.; Cheng, Q.-H.; Gong, C.-W.; Shen, L.-T. Mechanisms for multiple resistances in field populations of rice stem borer, Chilo suppressalis (Lepidoptera: Crambidae) from Sichuan Province, China. Pestic. Biochem. Physiol. 2021, 171, 104720. [Google Scholar] [CrossRef]
  7. Yao, R.; Zhao, D.-D.; Zhang, S.; Zhou, L.-Q.; Wang, X.; Gao, C.-F.; Wu, S.-F. Monitoring and mechanisms of insecticide resistance in Chilo suppressalis(Lepidoptera: Crambidae), with special reference to diamides. Pest Manag. Sci. 2017, 73, 1169–1178. [Google Scholar] [CrossRef] [PubMed]
  8. Huang, J.; Sun, H.; He, L.; Liu, C.; Ge, W.; Ni, H.; Gao, C.; Wu, S. Double ryanodine receptor mutations confer higher diamide resistance in rice stem borer, Chilo suppressalis. Pest Manag. Sci. 2021, 77, 4971–4979. [Google Scholar] [CrossRef] [PubMed]
  9. Huang, J.-M.; Rao, C.; Wan, S.; He, L.-F.; Zhao, S.-Q.; Zho, L.-Q.; Zhao, Y.-X.; Yang, F.-X.; Gao, C.-F.; Wu, S.-F. Multiple target-site mutations occurring in lepidopterans confer resistance to diamide insecticides. Insect Biochem. Mol. Biol. 2020, 121, 103367. [Google Scholar] [CrossRef]
  10. Zhao, J.; Xu, L.; Sun, Y.; Song, P.; Han, Z. UDP-Glycosyltransferase Genes in the Striped Rice Stem Borer, Chilo suppressalis (Walker), and Their Contribution to Chlorantraniliprole Resistance. Int. J. Mol. Sci. 2019, 20, 1064. [Google Scholar] [CrossRef] [Green Version]
  11. Chang, C.; Cheng, X.; Huang, X.-Y.; Dai, S.-M. Amino acid substitutions of acetylcholinesterase associated with carbofuran resistance in Chilo suppressalis. Pest Manag. Sci. 2014, 70, 1930–1935. [Google Scholar] [CrossRef] [PubMed]
  12. Guan, D.; Yang, X.; Jiang, H.; Zhang, N.; Wu, Z.; Jiang, C.; Shen, Q.; Qian, K.; Wang, J.; Meng, X. Identification and Validation of ATP-Binding Cassette Transporters Involved in the Detoxification of Abamectin in Rice Stem Borer, Chilo suppressalis. J. Agric. Food Chem. 2022, 70, 4611–4619. [Google Scholar] [CrossRef]
  13. Li, X.; Deng, Z.; Chen, X. Regulation of insect P450s in response to phytochemicals. Curr. Opin. Insect Sci. 2021, 43, 108–116. [Google Scholar] [CrossRef] [PubMed]
  14. Zhang, Y.; Yu, R.; Tang, J.; Du, L.; Wang, Y.; Wang, J.; Liu, L.; Gao, S.; Li, B. Three cytochrome P450 CYP4 family genes regulated by the CncC signaling pathway mediate phytochemical susceptibility in the red flour beetle, Tribolium castaneum. Pest Manag. Sci. 2022, 78, 3508–3518. [Google Scholar] [CrossRef]
  15. Zhang, Y.-C.; Gao, S.-S.; Xue, S.; An, S.-H.; Zhang, K.-P. Disruption of the cytochrome P450 CYP6BQ7 gene reduces tolerance to plant toxicants in the red flour beetle, Tribolium castaneum. Int. J. Biol. Macromol. 2021, 172, 263–269. [Google Scholar] [CrossRef] [PubMed]
  16. Shan, J.; Sun, X.; Li, R.; Zhu, B.; Liang, P.; Gao, X. Identification of ABCG transporter genes associated with chlorantraniliprole resistance in Plutella xylostella (L.). Pest Manag. Sci. 2021, 77, 3491–3499. [Google Scholar] [CrossRef]
  17. Feyereisen, R. Origin and evolution of the CYP4G subfamily in insects, cytochrome P450 enzymes involved in cuticular hydrocarbon synthesis. Mol. Phylogenetics Evol. 2020, 143, 106695. [Google Scholar] [CrossRef]
  18. Shuijin, H.; Qiong, C.; Wenjing, Q.; Yang, S.; Houguo, Q. Resistance Monitoring of Four Insecticides and a Description of an Artificial Diet Incorporation Method for Chilo suppressalis (Lepidoptera: Crambidae). J. Econ. Èntomol. 2017, 110, 2554–2561. [Google Scholar] [CrossRef]
  19. Wei, Y.; Yan, R.; Zhou, Q.; Qiao, L.; Zhu, G.; Chen, M. Monitoring and Mechanisms of Chlorantraniliprole Resistance in Chilo suppressalis (Lepidoptera: Crambidae) in China. J. Econ. Èntomol. 2019, 112, 1348–1353. [Google Scholar] [CrossRef]
  20. Santos, K.F.A.; Zanardi, O.Z.; de Morais, M.R.; Jacob, C.R.O.; de Oliveira, M.B.; Yamamoto, P.T. The impact of six insecticides commonly used in control of agricultural pests on the generalist predator Hippodamia convergens (Coleoptera: Coccinellidae). Chemosphere 2017, 186, 218–226. [Google Scholar] [CrossRef]
  21. Barros, L.S.; Yamamoto, P.T.; Merten, P.; Naranjo, S.E. Sublethal Effects of Diamide Insecticides on Development and Flight Performance of Chloridea virescens (Lepidoptera: Noctuidae): Implications for Bt Soybean Refuge Area Management. Insects 2020, 11, 269. [Google Scholar] [CrossRef] [PubMed]
  22. Meng, X.; Dong, F.; Qian, K.; Miao, L.; Yang, X.; Ge, H.; Wu, Z.; Wang, J. Transcriptome analysis reveals global gene expression changes of Chilo suppressalis in response to sublethal dose of chlorantraniliprole. Chemosphere 2019, 234, 648–657. [Google Scholar] [CrossRef] [PubMed]
  23. Huang, L.; Lu, M.; Han, G.; Du, Y.; Wang, J. Sublethal effects of chlorantraniliprole on development, reproduction and vitellogenin gene (CsVg) expression in the rice stem borer, Chilo suppressalis. Pest Manag. Sci. 2016, 72, 2280–2286. [Google Scholar] [CrossRef] [PubMed]
  24. Lu, K.; Wang, Y.; Chen, X.; Zhang, X.; Li, W.; Cheng, Y.; Li, Y.; Zhou, J.; You, K.; Song, Y.; et al. Adipokinetic Hormone Receptor Mediates Trehalose Homeostasis to Promote Vitellogenin Uptake by Oocytes in Nilaparvata lugens. Front. Physiol. 2019, 9, 1904. [Google Scholar] [CrossRef] [Green Version]
  25. Hansen, M.; Flatt, T.; Aguilaniu, H. Reproduction, Fat Metabolism, and Life Span: What Is the Connection? Cell Metab. 2014, 17, 10–19. [Google Scholar] [CrossRef] [Green Version]
  26. Lu, K.; Chen, X.; Li, Y.; Li, W.; Zhou, Q. Lipophorin receptor regulates Nilaparvata lugens fecundity by promoting lipid accumulation and vitellogenin biosynthesis. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 2018, 219–220, 28–37. [Google Scholar] [CrossRef]
  27. He, P.; Wei, E.; Wang, R.; Wang, Q.; Zhang, Y.; Tang, X.; Zhu, F.; Shen, Z. The spirotetramat inhibits growth and reproduction of silkworm by interfering with the fatty acid metabolism. Pestic. Biochem. Physiol. 2022, 188, 105282. [Google Scholar] [CrossRef]
  28. Yu, X.; Killiny, N. RNA interference of two glutathione S-transferase genes, Diaphorina citri DcGSTe2 and DcGSTd1, increases the susceptibility of Asian citrus psyllid (Hemiptera: Liviidae) to the pesticides fenpropathrin and thiamethoxam. Pest Manag. Sci. 2018, 74, 638–647. [Google Scholar] [CrossRef]
  29. Ma, M.; Zhai, X.-D.; Xu, H.-Q.; Guo, P.-Y.; Wang, J.-J.; Wei, D. Genome-wide screening and expression of glutathione S-transferase genes reveal that GSTe4 contributes to sensitivity against β-cypermethrin in Zeugodacus cucurbitae. Int. J. Biol. Macromol. 2022, 227, 915–924. [Google Scholar] [CrossRef]
  30. Bastarache, P.; Bouafoura, R.; Omakele, E.; Moffat, C.E.; Vickruck, J.L.; Morin, P.J. Spinosad-associated modulation of select cytochrome P450s and glutathione S-transferases in the Colorado potato beetle, Leptinotarsa decemlineata. Arch. Insect Biochem. Physiol. 2022, 112, e21993. [Google Scholar] [CrossRef]
  31. Pei, Y.; Hao, H.; Zuo, Y.; Xue, Y.; Aioub, A.A.A.; Hu, Z. Functional validation of CYP304A1 associated with haedoxan A detoxification in Aedes albopictus by RNAi and transgenic drosophila. Pest Manag. Sci. 2023, 79, 447–453. [Google Scholar] [CrossRef]
  32. Wang, B.; Shahzad, M.F.; Zhang, Z.; Sun, H.; Han, P.; Li, F.; Han, Z. Genome-wide analysis reveals the expansion of Cytochrome P450 genes associated with xenobiotic metabolism in rice striped stem borer, Chilo suppressalis. Biochem. Biophys. Res. Commun. 2014, 443, 756–760. [Google Scholar] [CrossRef] [PubMed]
  33. Chen, N.; Pei, X.; Li, S.; Fan, Y.; Liu, T. Involvement of integument-rich CYP4G19 in hydrocarbon biosynthesis and cuticular penetration resistance in Blattella germanica (L.). Pest Manag. Sci. 2020, 76, 215–226. [Google Scholar] [CrossRef] [PubMed]
  34. Jing, T.; Yuan, C.; Meng, L.; Hou, Q.; Liu, X.; Dou, W.; Yuan, G.; Wang, J. CYP4G100 contributes to desiccation resistance by mediating cuticular hydrocarbon synthesis in Bactrocera dorsalis. Insect Mol. Biol. 2022, 31, 772–781. [Google Scholar] [CrossRef]
  35. Calla, B.; MacLean, M.; Liao, L.; Dhanjal, I.; Tittiger, C.; Blomquist, G.J.; Berenbaum, M.R. Functional characterization of CYP4G11—A highly conserved enzyme in the western honey bee Apis mellifera. Insect Mol. Biol. 2018, 27, 661–674. [Google Scholar] [CrossRef] [PubMed]
  36. Ullah, F.; Gul, H.; Tariq, K.; Desneux, N.; Gao, X.; Song, D. Functional analysis of cytochrome P450 genes linked with acetamiprid resistance in melon aphid, Aphis gossypii. Pestic. Biochem. Physiol. 2020, 170, 104687. [Google Scholar] [CrossRef] [PubMed]
  37. Jin, Y.; Gao, Y.; Zhang, H.; Wang, L.; Yang, K.; Dong, H. Detoxification enzymes associated with butene-fipronil resistance in Epacromius coerulipes. Pest Manag. Sci. 2020, 76, 227–235. [Google Scholar] [CrossRef]
  38. Yan, Z.-W.; He, Z.-B.; Yan, Z.-T.; Si, F.-L.; Zhou, Y.; Chen, B. Genome-wide and expression-profiling analyses suggest the main cytochrome P450 genes related to pyrethroid resistance in the malaria vector, Anopheles sinensis (Diptera Culicidae). Pest Manag. Sci. 2018, 74, 1810–1820. [Google Scholar] [CrossRef]
  39. Bo, H.; Miaomiao, R.; Jianfeng, F.; Sufang, H.; Xia, W.; Elzaki, M.E.A.; Chris, B.; Palli, S.R.; Jianya, S. Xenobiotic transcription factors CncC and maf regulate expression of CYP321A16 and CYP332A1 that mediate chlorpyrifos resistance in Spodoptera exigua. J. Hazard. Mater. 2020, 398, 122971. [Google Scholar] [CrossRef]
  40. Li, X.; Shan, C.; Li, F.; Liang, P.; Smagghe, G.; Gao, X. Transcription factor FTZ-F1 and cis -acting elements mediate expression of CYP6BG1 conferring resistance to chlorantraniliprole in Plutella xylostella. Pest Manag. Sci. 2018, 75, 1172–1180. [Google Scholar] [CrossRef]
  41. Yang, X.; Deng, S.; Wei, X.; Yang, J.; Zhao, Q.; Yin, C.; Du, T.; Guo, Z.; Xia, J.; Yang, Z.; et al. MAPK-directed activation of the whitefly transcription factor CREB leads to P450-mediated imidacloprid resistance. Proc. Natl. Acad. Sci. USA 2020, 117, 10246–10253. [Google Scholar] [CrossRef] [PubMed]
  42. Zhang, Y.X.; Yang, B.J.; Yang, Z.M.; Kai, L.; Liu, Z.W. Alternative Splicing and Expression Reduction of P450 Genes Mediating the Oxidation of Chlorpyrifos Revealed a Novel Resistance Mechanism in Nilaparvata lugens. J. Agric. Food Chem. 2023, 71, 4036–4042. [Google Scholar] [CrossRef] [PubMed]
  43. Tang, Y.; He, H.; Qu, X.; Cai, Y.; Ding, W.; Qiu, L.; Li, Y. RNA interference-mediated knockdown of the transcription factor Krüppel homologue 1 suppresses vitellogenesis in Chilo suppressalis. Insect Mol. Biol. 2020, 29, 183–192. [Google Scholar] [CrossRef] [PubMed]
Ijms 24 05461 g001 550
Figure 1. Heat map and GO classification of DEGs in the treatment (a): Cluster heat map of differentially expressed genes. The abscissa in the figure refers to the sample names and the ordinate is the normalized value of the differential gene FPKM. The higher the expression level, the redder the color, and the lower the expression level, the greener the color (b): The number of all differential genes in the transcriptome in the GO classification. Red indicates up-regulated genes and green indicates down-regulated genes. The x-axis displays the names of the pathways; the y-axis represents the number of genes enriched in the respective pathways.
Figure 1. Heat map and GO classification of DEGs in the treatment (a): Cluster heat map of differentially expressed genes. The abscissa in the figure refers to the sample names and the ordinate is the normalized value of the differential gene FPKM. The higher the expression level, the redder the color, and the lower the expression level, the greener the color (b): The number of all differential genes in the transcriptome in the GO classification. Red indicates up-regulated genes and green indicates down-regulated genes. The x-axis displays the names of the pathways; the y-axis represents the number of genes enriched in the respective pathways.
Ijms 24 05461 g001
Ijms 24 05461 g002 550
Figure 2. Expression levels of UGT, ABC and EST genes after cyproflanilide-treated larvae C. suppressalis. Relative expression levels following treatment durations of 24 h (a–c) and 48 h (d–f). Grey represents acetone treatment, the other colors represent different concentrations of cyproflanilide. * p < 0.05, ** p < 0.01, *** p < 0.001, ns = not significant.
Figure 2. Expression levels of UGT, ABC and EST genes after cyproflanilide-treated larvae C. suppressalis. Relative expression levels following treatment durations of 24 h (a–c) and 48 h (d–f). Grey represents acetone treatment, the other colors represent different concentrations of cyproflanilide. * p < 0.05, ** p < 0.01, *** p < 0.001, ns = not significant.
Ijms 24 05461 g002
Ijms 24 05461 g003 550
Figure 3. Expression level of CYP genes after cyproflanilide-treated larvae C. suppressalis. Relative expression level after 24 h (a) and 48 h (b) treatment duration. Grey represents acetone treatment, the other colors represent different concentrations of cyproflanilide. * p < 0.05, ** p < 0.01, *** p < 0.001, ns = not significant.
Figure 3. Expression level of CYP genes after cyproflanilide-treated larvae C. suppressalis. Relative expression level after 24 h (a) and 48 h (b) treatment duration. Grey represents acetone treatment, the other colors represent different concentrations of cyproflanilide. * p < 0.05, ** p < 0.01, *** p < 0.001, ns = not significant.
Ijms 24 05461 g003
Ijms 24 05461 g004 550
Figure 4. RNA interference efficiency and mortality. (a,b): RNA interference efficiency. The number of deaths at 12 h (c) and 24 h (d) post-LD30 treatment following RNAi. *p < 0.05, ** p < 0.01, *** p < 0.001, ns = not significant.
Figure 4. RNA interference efficiency and mortality. (a,b): RNA interference efficiency. The number of deaths at 12 h (c) and 24 h (d) post-LD30 treatment following RNAi. *p < 0.05, ** p < 0.01, *** p < 0.001, ns = not significant.
Ijms 24 05461 g004
Table
Table 1. Toxicity of cyproflanilide against the third instar larvae of C. suppressalis at 24 h after treatment.
Table 1. Toxicity of cyproflanilide against the third instar larvae of C. suppressalis at 24 h after treatment.
InsecticideDegree of
Freedom		χ2LD10 Value
(95% Confidence Interval)		LD30 Value
(95% Confidence Interval)		LD50 value
(95% Confidence Interval)
Cyproflanilide solution55.421.7 ng/per larva (1.034–1.8306)5.2612 ng/per larva (4.3203–6.4241)13.02 ng/per larva (10.3713–17.1871)
Table
Table 2. The control efficiency of cyproflanilide (SC) difference in active ingredients against the field strain C. suppressalis.
Table 2. The control efficiency of cyproflanilide (SC) difference in active ingredients against the field strain C. suppressalis.
Treatment GroupsActive Ingredient
(g/hectare)		Control Effect after 21 Days (%)Significance of Difference
(5%)
20% Cyproflanilide SC 16.277.38 ± 2.54b
32.484.6 ± 5.09ab
48.687.26 ± 3.58a
64.891.24 ± 2.72a
200 g/L Chlorantraniliprole®30.035.17 ± 1.36c
Note: Control efficacy significant differefnces based on Duncan’s multiple comparison test at p < 0.05 level. All data are represented means ± standard error (S.E.)
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhou, J.; Qiu, L.; Liang, Q.; Zhou, Y.; Sun, J.; Gao, Q.; He, H.; Ding, W.; Xue, J.; Li, Y. Transcriptomic Analysis Reveals the Detoxification Mechanism of Chilo suppressalis in Response to the Novel Pesticide Cyproflanilide. Int. J. Mol. Sci. 2023, 24, 5461. https://doi.org/10.3390/ijms24065461

AMA Style

Zhou J, Qiu L, Liang Q, Zhou Y, Sun J, Gao Q, He H, Ding W, Xue J, Li Y. Transcriptomic Analysis Reveals the Detoxification Mechanism of Chilo suppressalis in Response to the Novel Pesticide Cyproflanilide. International Journal of Molecular Sciences. 2023; 24(6):5461. https://doi.org/10.3390/ijms24065461

Chicago/Turabian Style

Zhou, Jungang, Lin Qiu, Qiyao Liang, Yi Zhou, Jinjun Sun, Qiao Gao, Hualiang He, Wenbing Ding, Jin Xue, and Youzhi Li. 2023. "Transcriptomic Analysis Reveals the Detoxification Mechanism of Chilo suppressalis in Response to the Novel Pesticide Cyproflanilide" International Journal of Molecular Sciences 24, no. 6: 5461. https://doi.org/10.3390/ijms24065461

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop