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. 2021 Jan 27;11(2):98. doi: 10.1007/s13205-021-02651-9

Dynamic changes of transcriptome of fifth-instar spodoptera litura larvae in response to insecticide

Li Tian 1,2, Xueke Gao 3, Shulin Zhang 2, Yuanchen Zhang 2, Deying Ma 1,✉, Jinjie Cui 2,3,✉
PMCID: PMC7840824  PMID: 33520584

Abstract

Spodoptera litura is a major insect with a cosmopolitan distribution and strong resistance to multiple insecticides. Determining the molecular basis and key candidate genes of the insecticide resistance of S. litura may help in managing this insect. In this study, fifth-instar S. litura larvae were subjected to transcriptome analysis at 6, 12, 24, 48, and 72 h after feeding on an LC20 dose of avermectin. The result showed that genes responding to avermectin changed dynamically with different gene counts and resistance mechanisms at the fifth instar based on a metabolic pathway map. These responses included degrading the insecticide by a series of P450 and glutathione-S-transferase enzymes starting at the 12 h time point, with subsequent increases in the number of genes involved and shifts to TOLL and immune deficiency (IMD) pathways at 48 h after feeding the insecticide. Weighted correlation network analysis (WGCNA) determined a co-expression module related to the avermectin response at 12 and 24 h (r = 0.403, p = 0.0371; r = 0.436, p = 0.023), in which a hub gene (LOC111358940) related to metalloproteinase activity was identified. In addition, Analysis of the genes in the co-expression module further revealed that eight genes encoding UDP-glucuronosyltransferases were directly associated with insecticide response in S. litura. These results provide better understanding of the avermectin response mechanism of S. litura and may be useful in developing improved control strategies for this species.

Supplementary Information

The online version of this article (10.1007/s13205-021-02651-9) contains supplementary material, which is available to authorized users.

Keywords: Spodoptera litura, Transcriptome, Gene co-expression network, Insecticide, Resistance

Introduction

The common cutworm, Spodoptera litura Fabricius (Lepidoptera: Noctuidae), is a polyphagous insect that feeds on more than 100 species of crops and vegetables. The common cutworm is widely distributed in Asia, Africa, North America, and Oceania, and causes significant losses to many crops, including vegetables, corn, soybeans, cotton, tobacco, and groundnut (Tuan et al. 2014; Wang et al. 2019). Although various insecticides have been used to control S. litura, the common cutworm has evolved resistance to many of these, including organochlorines, organophosphates, carbamates, pyrethroids, abamectin, avermectins, and indoxacarb. Insecticide resistance has been observed in China (Sang et al. 2016; Wang et al. 2018, 2019), Puerto Rico and Mexico (Gutiérrez-Moreno et al. 2019), India (Gandhi et al. 2016), and Pakistan (Saleem et al. 2016). As a result, farmers often use more frequent application or higher amounts of insecticides for control, and this has led to serious environmental pollution (Jia et al. 2020). Therefore, understanding the insecticide-resistance mechanism of S. litura is important for the development of new and safer insecticides.

The understanding of insecticide resistance at the molecular level has advanced dramatically over the last three decades and is now known to predominantly involve either metabolic resistance or target-site resistance (Dia et al. 2018; Ffrench-Constant 2013; Hemingway 2000; Liu 2015). For metabolic resistance, three main enzyme families, esterases (ESTs) (Jia et al. 2016), glutathione S-transferases (GSTs) (Navarro-Roldan et al. 2020; Su et al. 2020), and cytochrome P450 monooxygenases (P450s) (David et al. 2013; Yunta et al. 2019) can decompose or bind to the insecticide. For target-site resistance, some conservative mutations confer varying degrees of insensitivity to insecticides (e.g., acetylcholinesterase for organophosphates/carbamates, the voltage-gated sodium channel for pyrethroids, and the GABA receptor for cyclodienes) (Fang et al. 2019; Itokawa et al. 2019) that render the insecticide-target protein less sensitive to the toxic effects of the pesticide via structural changes (mutations). In recent years, some of these insecticide-resistance candidate genes have been cloned and analysed based on the previous results of resistance mechanism research (Wang et al. 2017; Ji et al. 2019), but there were a few studies on insecticide-resistance mechanism of S. litura. Therefore, it is necessary to identify the major genes and gene networks to understand the evolution and mechanism of insecticide resistance in the common cutworm.

The transcriptome is the total expressed RNA of a specific tissue or cell at a given developmental stage or state (Zhao et al. 2018). Transcriptome analysis can reveal differences in the expression levels of the same gene in different states (Velculescu et al. 1997) and provide information concerning gene expression levels, functions, and interactions; therefore, transcriptomics is an effective means to study biological processes and molecular mechanisms under precise conditions (Nagalakshmi et al. 2008). In recent years, transcriptome has been widely used to explore the molecular mechanisms of the genes involved in the insecticide and toxin responses of S. litura and other insects. For example, Jia et al. (2020) identified fluralaner-responsive genes in S. litura through RNA-seq; Song et al. (2016) examined the transcriptional response of S. litura larvae to Vip3Aa toxin and implicated trypsin in Vip3Aa activation. Although many transcriptomes have been conducted to determine insecticide-resistance genes (Ji et al. 2019; Wang et al. 2017), the dynamic changes in the resistance mechanism and key genes involved in S. litura are poorly understood.

Gene expression data from transcriptome analysis can be used to construct gene co-expression networks (GCNs) to study genes with similar expression patterns or those participating in similar functions or pathways. GCNs analysis (HZorvath and Dong 2008; Zhang and Horvath 2005) is also especially suitable for the study of complex large-scale gene expression data for different developmental stages of the same tissue (Zhou et al. 2020), different organs or tissues (Singh et al. 2017), or responses at different time points after abiotic stress (Hopper et al. 2016) or pathogen infection (Li et al. 2011). A default threshold of Pearson’s correlation coefficient has been used to validate the use of GCNs. Meanwhile, Zhang and Horvath (2005) proposed a new ‘soft’ threshold framework (weighted correlation network) with a consistent, scale-free network distribution and increased biological significance for selecting a suitable threshold. Weighted correlation network analysis is performed after GCN and involves the use of a topological overlap measure (TOM) to calculate the correlations between genes and identify clusters (modules) with highly related genes (co-expressed gene networks) (Langfelder and Horvath 2008). The central nodes in the network (the nodes that are most connected to other nodes) are considered as hub genes. A gene expression profile in the module is represented by the module characteristic gene (ME). Module membership (MM) quantifies the distance between a gene and a module. The MM value of the hub gene in each module is often high. Biologically meaningful modules and genes are then found after the construction of the GCN. The correlation between a single gene and a biological trait is defined as gene significance (GS), and the average GS of genes in a module is the module significance (Langfelder and Horvath 2008). Therefore, a high significance of the GS value and module indicates the biological significance of genes and modules. Weighted GCN analysis (WGCNA) revealed a 34-gene network module that is highly correlated with anthocyanidin content in apples (r = 0.95, p = 9.0 × 10 –13) (El-Sharkawy et al. 2015). WGCNA was used to identify eight key genes that affect the biosynthesis and accumulation of three flavonoids during the development of tea flowers (Rothenberg et al. 2019).

The dynamic transcriptome of S. litura has not been used to study the response of common cutworm to insecticides or other toxins. Due to avermectins being broad-spectrum insecticides that are usually employed against S. litura in agriculture, we subjected fifth-instar S. litura larvae to transcriptome analysis at 6, 12, 24, 48, and 72 h after feeding on an LC20 dose of avermectin to study the dynamic transcriptomic changes and key genes involved in the response to insecticide exposure. Weighted correlation network analysis (WGCNA) was further conducted to investigate the gene network associated with the insecticide response and to determine candidate genes for insecticide resistance. This information will increase our knowledge of the mechanism of insecticide resistance at the gene transcriptional level in S. litura and may help guide research on S. litura management strategies.

Materials and methods

Sample collection

S. litura was originally purchased from Henan Jiyuan Baiyun Industry Company (Jiyuan Henan). Insects were maintained on an artificial diet in the laboratory at 26 °C ± 1 °C with 70% relative humidity and a 16:8 h (L:D) photoperiod without insecticide exposure. Fifth-instar larvae were selected for the experiment and were starved for 12 h before being subjected to the following feeding method: larvae in the control group were fed only with artificial food, and those in the treatment group were fed with an artificial diet containing an LC20 concentration of avermectin that was determined through a sensitive concentration experiment. The guts from 10 larvae were sampled from each group at 6, 12, 24, 48, and 72 h after feeding, placed in 1.5 mL Eppendorf tubes, frozen in liquid nitrogen, and stored at − 80 °C before use. The experiment was conducted with three replicates. A total of 15 treatment tubes and 15 control tubes were obtained for RNA-seq analysis.

Total RNA extraction, cDNA library construction and sequencing

The total RNA of the gut in each tube was extracted separately using TRIzol™ Reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s instructions. The concentration and integrity of total RNA were measured with a NanoDrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA) and an RNA Nano 6000 Assay Kit on an Agilent Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA), respectively. RNA sequencing libraries were constructed in accordance with the standard Illumina protocol and sequenced using the Illumina HiSeq™ 2000 platform to generate 2 × 100-nucleotide paired-end reads. Low-quality reads that contained more than 50% low-quality (q value ≤ 20) bases were removed. Reads that were mapped to the ribosome RNA database using Bowtie2 were removed to obtain the final clean reads, which were then used for assembly and transcriptome analysis.

The sequence data obtained in this study have been deposited in the National Centre for Biotechnology Information Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra) under the BioProject number PRJNA640483.

Read mapping and expression analysis

The cleaned reads from each sample were mapped to the S. litura reference genome (GCF_002706865.1, https://www.ncbi.nlm.nih.gov/genome/?term=Prodenia litura Fabricius) using the Tophat2 (v2.09; http://ccb.jhu.edu/software/tophat/index.shtml) tool. Then, the aligned reads were subjected to transcript assembly using Cufflinks software (version 2.1.0; http://cole-trapnell-lab.github.io/cufflinks). Finally, gene expression levels were calculated in terms of fragments per kilobase of transcript per million fragments mapped (FPKM). The DESeq2 package was used to identify differentially expressed genes (DEGs) through pairwise comparisons of the control and treatment groups at 6, 12, 24, 48, and 72 h after insecticide treatment. Only genes with |log2FC|≥ 1 and false discovery rate (FDR) < 0.05 in a comparison were deemed to be significantly differentially expressed.

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses

GO enrichment and KEGG pathway analysis were conducted to investigate DEG function. For GO enrichment analysis, a hypergeometric p value was calculated and adjusted in the form of a q value, wherein the background was set to be the genes in the whole genome. GO terms with q < 0.05 were considered to be significantly enriched, and GO enrichment analysis was conducted to elucidate the biological functions of the DEGs. Significantly enriched KEGG pathways were identified using the same method as that used for the KEGG enrichment analysis.

Co-expression network analysis and hub gene identification

After removing the genes with FPKM values of less than 1 for every sample and removing duplicated genes, 9832 genes that were found in the control and treatment groups at all five time points were used for GCN analysis through WGCNA (ver. 1.49) (Langfelder and Horvath 2008). Network construction and consensus module detection were performed by applying TOM and Dynamic Tree Cut functions. After exploring the soft thresholds, the power β was set as 6, and the minimum module size was set at 150. The correlation between genes was quantified through Pearson’s correlation analysis, and co-expressed gene sets (modules) were detected using the hierarchical clustering method. The Pearson correlation between the time point of insecticide response and gene expression data was calculated as the GS value. A visual network was constructed using Cytoscape (Shannon et al. 2003).

Validation of mRNA-seq data using quantitative real-time PCR (qRT-PCR)

Seven DEGs related to the Toll and Imd signalling pathways and eight UDP-glucuronosyltransferase genes were selected for qRT-PCR analysis. The analysis was performed using SYBR® Premix Ex Taq™ (Takara) and the ROCHE Real-time PCR System (Applied Biosystems) based on the procedures of Zhang et al. (2019) to verify the RNA-seq results. The housekeeping gene EF-1α was used as an internal standard to quantify the relative expression level of target genes (Huang et al. 2010). Three technical replications per biological replicate were conducted, and the relative mRNA expression levels of genes were analysed using the 2−ΔΔCt method (Livak and Schmittgen 2001). The gene-specific primers used in this study are listed in Table S1.

Results

Overview of RNA-seq datasets

To obtain global transcriptome maps of the avermectin response of fifth-instar S. litura, we performed transcriptome analysis on the samples of control and treatment groups at 6, 12, 24, 48, and 72 h time points. The analysis generated approximately 170 million high-quality reads, and approximately 90% of the reads were uniquely mapped to the S. litura genome (Table S2). The reads mapped to the S. litura genome varied at different time points (Fig. S1). These results indicated that the RNA-seq data were reliable for further analysis, and also showed that the transcriptome dynamically changed during insecticide response.

The gene expression levels at each time point for the control and treatment larvae were classified into five levels on the basis of their FPKM values (Fig. 1a–j, Table S3) to study the temporal and dynamic gene expression patterns of S. litura larvae. Approximately 900 genes were expressed at a high level (FPKM > 100), while approximately 4200 genes were expressed at the lowest level (0 < FPKM ≤ 1) (Fig. 1). Only genes with FPKM values ≥ 1 were considered as expressed genes and were used to study the differences between the control and treatment groups and to reduce the effect of transcription noise. In total, 9832 expressed genes were found in the control and treatment groups at all five time points after the removal of duplicated genes (Fig. 1l). The overall number of specifically expressed genes between the control and treatment conditions was 411. These included 162, 31, and 35 genes in the control group that were specifically expressed at 72, 12, and 6 h, respectively, and 127 and 56 genes in the treatment group that were expressed at 72 and 48 h, respectively (Fig. 1k).

Fig. 1.

Fig. 1

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Distribution of the genes expressed in fifth-instar S. litura larvae under normal and treatment conditions. a–j Distribution of transcripts at five expression levels based on fragments per kilobase of transcript per million fragments mapped (FPKM) values. The pie charts show the numbers and percentages of transcripts at different expression levels under normal conditions a–e and under treatment conditions (f–j). k Upset plot showing the numbers of expressed genes in the control and treatment groups at different time points. l Numbers of expressed genes detected in the control and treatment groups at different time points

Identification and functional analysis of DEGs between control and treatment groups

Pairwise comparisons between the control and treatment groups at each time point were performed to study the gene expression patterns of S. litura larvae. A total of 1692 DEGs between control and treatment conditions were identified based on |log2FC|≥ 1 and FDR ≤ 0.05. The statistical information and a Venn diagram of the DEGs identified by pairwise comparisons are shown in Fig. 2. The number of up-regulated DEGs at each time point was higher than the number of down-regulated DEGs (Fig. 2a), and the highest numbers of DEGs were identified at 6 and 48 h, with 541 and 518 specific DEGs, respectively (Fig. 2b). These results suggest that the 6 and 48 h time points are important for the development or insecticide resistance in fifth-instar S. litura.

Fig. 2.

Fig. 2

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Statistics for differentially expressed genes (DEGs) identified through pairwise comparisons of the control and treatment groups of fifth-instar S. litura. a Statistical information of DEGs. b Venn diagram of the DEGs for specific DEGs

KEGG enrichment analysis was performed on DEGs that were up-regulated and down-regulated at different time points. The top 10 KEGG pathway enrichment terms for the pairwise comparisons (up- and down-regulated) between the control and treatment groups are shown in Table S4. Based on the enrichment results, we constructed a preliminary rough transcriptomic dynamic pathway map for the insecticide response of fifth-instar S. litura (Fig. 3). The dynamic transcriptome pathway map showed that fifth-instar larvae began to respond to avermectin at 12 h based on many genes involving drug metabolism, with P450s as one of the main insecticide metabolism mechanisms. Subsequently, the number of genes involved in the insecticide metabolism mechanism gradually increased. However, beginning at 48 h, the metabolic pathways began to switch to the Toll and immune deficiency (Imd) pathways. In addition, by analysing the maximum difference in DEGs detected at 6 and 48 h time points, we found that the number of genes involved in the ribosome was the highest at 6 h (Fig. S2A). This pathway might be related to the normal growth of S. litura. By contrast, multiple metabolic pathways were found at 48 h (Fig. S2b). Among these, the Toll and Imd signalling pathways were possibly related to the insecticide response.

Fig. 3.

Fig. 3

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Preliminary rough transcriptomic dynamic pathway map for the insecticide response of fifth-instar S. litura based on Enriched differentially regulated Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. The red arrow indicates up-regulated DEGs, and the blue arrow indicates down-regulated DEGs

Verification of the DEGs involved in the Toll and Imd signalling pathway at 48 h via qRT-PCR

The Toll and Imd signalling pathways regulate the expression of antimicrobial peptide genes and improve insecticide resistance, both of which are important mechanisms in insect defence against insecticides (Zhao et al. 2020). The KEGG analysis results for the DEGs at 48 h revealed that seven DEGs were involved in the Toll and Imd signalling pathways. We profiled the expression of seven DEGs, including coding Modsp, GNP1, Spz, Toll, and Ankrin genes that are key factors in metabolism (Fig. 4a). The expression patterns of these seven DEGs were also determined through qRT-PCR and were similar to the RNA-seq patterns (fig. 4b-h).

Fig. 4.

Fig. 4

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Quantitative real-time PCR (qRT-PCR) and RNA-Seq analyses of the expression patterns of differentially expressed genes (DEGs) involved in the Toll and Imd signalling pathways. a Expression map of DEGs in the Toll and Imd signalling pathways. b–h Verification of the expression patterns of DEGs via qRT-PCR and RNA-Seq. The scale on the left corresponding to the bar indicates gene expression level based on qRT-PCR results. The scale on the right corresponding to the red line indicates gene expression level based on RNA-Seq results. The x-axis indicates the time point

Co-expression gene networks and their correlations with insecticide response

We performed WGCNA to identify co-expressed groups using the expressed genes to study the gene interaction network of fifth-instar larvae in response to avermectin exposure. Modules that were related to specific time points were identified based on the correlations between MEs and the samples. Four modules were identified in the analysis and marked with different colours. Each module contained at least 150 genes (Fig. 5a). Among these modules, the turquoise modules contained 4497 genes that showed significant (p ≤ 0.05) positive correlations with the 72 h time point, and blue modules with 1109 genes specifically accumulated (p ≤ 0.05) at 12 and 24 h after insecticide treatment. Given that GS is defined as the correlation between gene expression and traits, a high absolute GS value represents a high correlation between gene expression patterns and traits (Li et al. 2018). Therefore, the blue module with the highest average absolute GS value that was significantly correlated with the 12 and 24 h time points (r = 0.403, p = 0.0371 and r = 0.436, p = 0.023) was identified as the most sensitive to insecticide response (Fig. 5b). Subsequently, the KEGG enrichment analysis of the genes in the modules revealed that the pathways shown in Fig. 5 and Table S5 were significantly enriched (p < 0.01). Among these pathways, the ‘metabolism of xenobiotics by cytochrome P450′, ‘drug metabolism-cytochrome P450′, ‘chemical carcinogenesis’, and ‘drug metabolism-other enzymes’ belonged to drug metabolism. This result indicated that the blue module was the most relevant to insecticide response and further supported the above dynamic pathway map.

Fig. 5.

Fig. 5

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Correlation between gene co-expression network (GCN) and insecticide response at different time points during the fifth instar. a Correlations between gene co-expression modules and different time points during the fifth instar. The corresponding correlation coefficient (top) and p value (bottom) are displayed in each cell. The cells are colour-coded using the correlation coefficient (r) as shown by the colour key on the right. Red represents a positive correlation, whereas blue represents a negative correlation. b Bar plot representing the average gene significance for each detected module

Based on the MM value (Table S6), we identified the hub genes in the blue modules. Among the hub genes, LOC111358940, which is involved in metalloendopeptidase activity, showed the highest MM value (MM = 0.99149) in this module. A co-expression network of LOC111358940 was constructed and showed that 27 exogenous genes were involved in ‘metabolism of xenobiotics by cytochrome P450 pathway’; 26 genes were involved in ‘chemical carcinogenesis pathway’; 24 genes were involved in ‘drug metabolism-cytochrome P450′; and 24 genes were involved in ‘drug metabolism- other enzymes’ (Fig. 6). These results showed that the hub gene LOC111358940 is important in co-expression networks for insecticide response.

Fig. 6.

Fig. 6

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Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of genes involved in the co-expression network of LOC111358940 in the blue module. FDR false discovery rate

Identification of candidate genes related to insecticide resistance at 12 and 24 h time points

The WGCNA results showed that the blue module related to the 12 and 24 h time points contained 1109 genes. Based on a comparative analysis, DEGs with GS values greater than 0.2667 (average value of all GSs in Fig. 5b) and the difference between the control and the treatment groups at 12 and 24 h time points (|log2 Foldchange|> 2, FDR < 0.05) were selected. A total of 48 genes related to insecticide resistance were determined from the candidate genes in this module (Table S7), and the KEGG enrichment analysis showed that the 48 genes were significantly distributed in 11 pathways (Table S8). It was interesting that eight pathways in these pathways related to Carbohydrate metabolism, Lipid metabolism, Metabolism of cofactors and vitamins, and Xenobiotics biodegradation and metabolism replicative all contained the eight genes of UDP-glucuronosyltransferase (i.e., LOC111364771, LOC111364777, LOC111364512, LOC111364812, LOC111364507, LOC111355824, LOC111355676, and LOC111355746), indicating that these UDP-glucuronosyltransferase genes were related to insecticide resistance. To further validate the results from the bioinformatics analysis, the expression patterns of the UDP-glucuronosyltransferase genes were determined using qRT-PCR with gene-specific primers (Fig. 7). The results further confirmed that these UDP-glucuronosyltransferase genes were involved in insecticide resistance.

Fig. 7.

Fig. 7

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Expression analysis of eight genes of UDP-glucuronosyltransferase under control and insecticide treatment according to a qRT-PCR assay. Three biological replicates were used to calculate error bars based on standard errors (**means p < 0.01)

Discussion

Spodoptera litura is a polyphagous and widely distributed agricultural insect that has developed resistance to a broad range of insecticides (Jia et al. 2020). A few studies have analysed the genome-wide transcriptional response of S. litura to insecticides, but dynamic transcriptome studies on the insecticide response of this insect are limited. In this study, we performed dynamic transcriptome analysis based on high-throughput RNA sequencing and assembled the transcriptomes of S. litura in the presence and absence of insecticide exposure. A total of 17,426 genes (Table S3) were found. This number exceeded the number of genes previously obtained with de novo or genome assembly transcriptomes (Gong et al. 2015; Jia et al. 2020), likely because of the differences in sampling methods, sequencing depth, and sequencing platform used. These results may help identify the gene regulatory networks and metabolic processes involved in insecticide responses.

Gene expression pattern analysis is effective for revealing key genes and complexity at different time points during insecticide response. We performed dynamic gene expression pattern identification and comparative transcriptome analyses. A total of 1962 DEGs were found in the control and treatment groups at all time points. However, the numbers of DEGs varied at different time points (Fig. 2). These results confirmed that the insecticide response of fifth-instar S. litura is a dynamic process involving gene expression and related regulation. Therefore, dynamic transcriptomic analysis must be conducted to fully understand the insecticide resistance of S. litura. Pairwise comparisons showed that at each time point, the number of up-regulated DEGs was greater than the number of down-regulated DEGs (Fig. 2a). This result suggests that after insecticide exposure, S. litura generated a response that was regulated by gene expression. This conclusion is supported by the dynamic transcriptomic map of the DEGs at every time point. This map was based on KEGG pathway enrichment analysis. For example, some DEGs that were upregulated at 6 h were enriched in the ribosome pathway that regulates protein synthesis and animal development (Byrne 2009), and some DEGs that were expressed at 12 and 24 h were involved in the drug metabolism pathway. The MAPK signalling pathway that is involved in mitosis was expressed after 48 h (Hwang et al. 2018) to regulate development (Fig. 3 and Table S4).

Co-expressed genes (proteins) are usually members of the same pathway or protein complex and are functionally related to, or controlled by, the same transcriptional regulatory process. The use of multiple RNA sequencing samples to study complex co-expression networks is now routine via high-throughput sequencing technology. However, traditional pairwise comparisons do not show the overall dynamic characteristics of all samples effectively (Yang et al. 2019). In WGCNA, a large amount of transcriptomic data was used to effectively divide the entire genome into gene co-expression modules and to study the correlation between co-expression modules and target traits. This method is especially suitable for the study of multiple samples at different developmental stages or under different treatments (Greenham et al. 2017; Hollender et al. 2014). Hub genes that are highly interconnected with nodes in the same module are generally considered to have important functions (Zhou et al. 2018). We identified a blue module (Fig. 4) that was highly associated with the insecticide response of S. litura at 12 and 24 h. The top hub gene in this module was LOC111358940 encoding a metallopeptidase with varied activities. Previous research reported that the relative expression of metallopeptidase genes in some insects was significantly increased under application of the insecticide imidacloprid (Ewere et al. 2020). The metallopeptidase genes thus could be considered as important genes in the co-expression networks for insecticide response (Fig. 6). Co-expressed genes are also involved in the same pathway and the related functions (Liao et al. 2011). The co-expression network of LOC111358940 showed that the exogenous genes were mainly involved in ‘metabolism of xenobiotics by cytochrome P450 pathway’, ‘chemical carcinogenesis pathway’, ‘drug metabolism-cytochrome P450′, and ‘drug metabolism- other enzymes’ (Fig. 6, Fig. S3), suggesting roles of these pathways in insecticide resistance. These results further indicated that the hub gene LOC111358940 is an important gene involved in insecticide resistance.

GS values based on the correlation between a gene expression profile and a sample trait calculated by WGCNA were efficiently used to identify key candidate genes. The GS of a node is defined as the correlation between a node and a phenotypic trait (Li et al. 2018). A total of 48 candidate genes associated with insecticide resistance were identified by combining genes in the modules with GS values greater than 0.2667 (the mean value of all GS) and with significant positive correlations with insecticide response and |log2 Fold change| greater than 2 (FDR < 0.05) and comparing the treatment group with the control group at 12 and 24 h. Among these genes, eight UDP-glucuronosyltransferase genes and one GST gene were responsible for insect metabolic resistance to insecticides (Su et al. 2020; Wang et al. 2020). The newly identified candidate genes will not only increase our knowledge of the molecular basis of insecticide resistance in S. litura but will promote discovery of new insecticide targets, improve current insect resistance testing (monitoring) technology, and deepen our understanding of insecticide-resistance evolution.

Conclusion

Dynamic transcriptome pathway analysis showed that fifth-instar larvae began to respond to avermectin at 12 h via the P450 insecticide metabolism mechanism. The number of genes involved in this mechanism gradually increased with time, but the insecticide metabolism mechanism began to change to the Toll and Imd metabolism pathways at 48 h. A co-expression module related to insecticide response at 12 and 24 h (r = 0.403, p = 0.0371; r = 0.436, p = 0.023) was determined by weighted correlation network analysis (WGCNA), and a hub gene (LOC111358940) related to metalloproteinase activity was identified. Analysing the genes in the co-expression module determined that eight candidate genes encoding UDP-glucuronosyltransferases were associated with the insecticide response in S. litura.

Accession Numbers

SRA number in NCBI: PRJNA640483.

Electronic supplementary material

Below is the link to the electronic supplementary material.

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Table S1 Quantitative real-time PCR (qRT-PCR) primers of seven genes involved in the Toll and Toll and Imd signalling pathways.

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Table S2 Statistical analysis of the sequenced and mapped reads of the control and treatment groups.

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Table S3 Information on the expressed genes in the control and treatment groups.

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Table S4 Pathway enrichment terms of pairwise differentially expressed genes (DEGs).

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Table S5 Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the differentially expressed genes (DEGs) in the blue module based on weighted gene co-expression network analysis (WGCNA).

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Table S6 Hub genes in the blue module ranked based on module membership (MM) value.

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Table S7 Forty-eight candidate genes related to insecticide resistance of fifth-instar S. litura expressed at 12 and 24 h.

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Table S8 Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for Forty eight candidate genes related to insecticide resistance of fifthinstar S. litura expressed at 12 and 24 h.

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Fig. S1 Read coverages of the control and treatment groups at different time points.

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Fig. S2 Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of pairwise differentially expressed genes (DEGs) at 6 and 24 h. (A) KEGG enrichment analysis of pairwise DEGs at 6 h. (B) KEGG enrichment analysis of pairwise DEGs at 48 h.

Acknowledgments

Authors extend their sincere appreciation to the experimental assistance from Molecular&Cell Genetics Laboratory at Anyang Institute of Technology, Anyang 455000, China.

Authors’ contributions

LT and SZ analyzed the data and wrote the manuscript. LT performed the experiments. XG, performed sampling and handing. YZ check data and revised the manuscript. JC and DM conceived and designed the whole project. All authors read and approved the final manuscript.

Funding

This research was funded by the Fund of Agro-Ecological Risk Monitoring and Control Technology (2016ZX08012-004), Open Fund of State Key Laboratory of Cotton Biology (CB2019A17,CB2019A19), and Foundation of Henan Educational Committee (19B180002).

Availability of data and materials

Not applicable.

Compliance with ethical standards

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflicts of interest

The authors declare that they have no conflict of interest in the publication.

Contributor Information

Deying Ma, Email: mdyxnd@163.com.

Jinjie Cui, Email: aycuijinjie@163.com.

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

13205_2021_2651_MOESM1_ESM.xlsx (9.4KB, xlsx)

Table S1 Quantitative real-time PCR (qRT-PCR) primers of seven genes involved in the Toll and Toll and Imd signalling pathways.

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Table S2 Statistical analysis of the sequenced and mapped reads of the control and treatment groups.

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Table S3 Information on the expressed genes in the control and treatment groups.

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Table S4 Pathway enrichment terms of pairwise differentially expressed genes (DEGs).

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Table S5 Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the differentially expressed genes (DEGs) in the blue module based on weighted gene co-expression network analysis (WGCNA).

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Table S6 Hub genes in the blue module ranked based on module membership (MM) value.

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Table S7 Forty-eight candidate genes related to insecticide resistance of fifth-instar S. litura expressed at 12 and 24 h.

13205_2021_2651_MOESM8_ESM.xls (9.7KB, xls)

Table S8 Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for Forty eight candidate genes related to insecticide resistance of fifthinstar S. litura expressed at 12 and 24 h.

13205_2021_2651_MOESM9_ESM.pdf (414.4KB, pdf)

Fig. S1 Read coverages of the control and treatment groups at different time points.

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Fig. S2 Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of pairwise differentially expressed genes (DEGs) at 6 and 24 h. (A) KEGG enrichment analysis of pairwise DEGs at 6 h. (B) KEGG enrichment analysis of pairwise DEGs at 48 h.

Data Availability Statement

Not applicable.

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