Metabolic and Behavioral Impacts of Gustatory Receptor NlGr23 Silencing in the Brown Planthopper

Abstract

The brown planthopper (BPH), Nilaparvata lugens, is the most destructive insect pest of rice. BPH infestations severely threaten rice yield worldwide. The gustatory receptor NlGr23 plays a critical role in mediating the repulsive reaction to oxalic acid of the BPH. We integrated transcriptomic and proteomic analyses to determine the metabolic and behavioral consequences of NlGr23 silencing. The RNAi-mediated knockdown of NlGr23 increased body weight and honeydew production, indicating enhanced feeding activity. The results of multiomics profiling revealed disrupted lipid homeostasis, identifying 187 differentially expressed genes and 150 differentially expressed proteins. These genes were enriched in pathways including glycerophospholipid metabolism, fatty acid biosynthesis, and AMPK signaling. The results of biochemical assays showed that NlGr23 silencing elevated triacylglycerol levels by 68.83%, and reduced glycerol and free fatty acid levels, suggesting impaired lipolysis. The NlGr23 loss-of-function mutation mechanistically activates the AMPK pathway, suppresses lipid breakdown, and promotes energy storage. This study established NlGr23 as a key regulator linking chemosensation to metabolic reprogramming, providing new insights into gustatory receptor-mediated energy homeostasis in the BPH.

1. Introduction

Rice is one of the most important staple crops in China, with over half of the population relying on rice as their primary food source [1]. The brown planthopper (BPH), Nilaparvata lugens, seriously threatens healthy rice plant growth, development, and reproduction [2]. Large BPH populations feed by piercing and sucking, which severely weakens plants and leads to considerable yield reductions. Additionally, BPHs act as vectors for various rice viruses, indirectly exacerbating crop damage. Therefore, effective measures for controlling this pest must be identified and implemented to safeguard rice production.

Gustatory receptors (GRs) are a family of seven transmembrane domain proteins that allow insects to appropriately respond to environmental cues [3,4]. Insects detect external chemical signals through GRs, which convert these signals into neural impulses and ultimately influence their behavioral patterns. This regulatory mechanism is crucial for insect survival and reproduction, affecting behaviors such as foraging, mate selection, oviposition site choice, and predator avoidance [5,6].

Advances in molecular biology and bioinformatics have led to the discovery of numerous GRs with sequenced genomes since the first insect GR was identified in Drosophila melanogaster [7]. A single receptor can respond to multiple ligands and a single ligand can activate multiple receptors in HEK293 cell lines and Xenopus oocyte expression systems. For example, D. melanogaster Gr32a and Gr33a detect strychnine [8] and nicotine [9], respectively, whereas Gr66a responds to strychnine, canavanine, and caffeine [10,11,12]. BmorGr53 and BmorGr16 detect caffeine and coumarin, whereas BmorGr18 responds to caffeine, pilocarpine, and coumarin in Bombyx mori [13]. Gr28 in Pieris rapae specifically recognizes sinigrin, which is a key stimulant in cruciferous plants [14]. These receptors influence feeding and ovipositioning through binding to specific ligands. However, downstream signaling mechanisms after ligand binding remain poorly understood.

Galactose binding in the BPH (Nilaparvata lugens) to NlGr11 activates the PI3K-AKT-PFK-ATP pathway via G proteins and insulin receptors, regulating ovipositioning [15]. In addition, the sugar receptor NlGr7 reduces vitellogenin (Vg) expression levels in female BPHs through interacting with ATP hydrolases [16]. Studies on GR signaling have primarily relied on calcium flux or electrophysiological recordings [9,17,18]; analyses of the downstream pathways have been limited [19].

Advances in RNA-seq and iTRAQ sequencing have enabled considerable progress in the multiomics analyses of insects. Examples include identifying the reproduction-related genes in the BPH [20], the Ca²⁺ signaling pathways linked to diapause in Tetranychus urticae [21], and the diapause mechanisms in Aphidius gifuensis [22]. Transcriptomic and proteomic analyses of Myzus persicae infected with cucumber mosaic virus were used to identify key regulators such as ribosomal proteins, cuticular proteins, and cytochrome P450 enzymes, the accumulation of which was altered after infection with the virus [23]. Integrated multiomics studies on Leptinotarsa decemlineata revealed 2840 proteins (409 upregulated and 200 downregulated) that were differentially expressed after exposure to cold proteins, highlighting their impact on selective metabolism and RNA-related processes [24]. These studies demonstrate how multiomics approaches have enhanced our understanding of post-transcriptional and translational regulation.

We employed RNA interference (RNAi) to silence the NlGr23 gene in the BPH and conducted integrated transcriptomic and proteomic analyses to compare differences in the mRNA and protein expression levels between NlGr23-knockdown and control groups. Our goal was to identify the molecular pathways linked to NlGr23 function, validate their roles, and establish a foundation for understanding GR-mediated behavioral regulation in the BPH. This study provides insights for guiding the development of biocontrol strategies for the BPH.

2. Materials and Methods

2.1. Insects

BPH populations were collected from Zunyi, Guizhou, China. The insects were maintained in the laboratory at 26 ± 2 °C under a 16:8 h (light/dark) photoperiod at a relative humidity level of 80% (±10%) on susceptible rice seedlings (Huang Huazhan variety).

2.2. Preparing and Injecting dsRNA into the BPH

Template DNA was prepared, dsRNA was synthesized as previously described [25], and experiments were performed in October 2023. Briefly, first-strand complementary DNA (cDNA) was synthesized using 1 μg of total RNA and a PrimeScript™ RT reagent Kit with gDNA Eraser (Takara, Kyoto, Japan). The full-length cDNA sequences of NlGr23 were amplified using 2× plus Taq HiFi PCR mix with blue dye (Mikx, Shenzhen, China), following the manufacturer’s protocol. A T7 RiboMAX™ Express RNAi System (Promega, Madison, WI, USA) was then used to synthesize dsRNA from the purified PCR products. All primers used for PCR are listed in Table S1. The dsNlGr23 (239 bp) concentration was determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).

Approximately 250 ng of dsRNA was injected into newly eclosed female BPHs anesthetized with CO₂ for 20 s for RNAi experiments [25]. BPHs were reared on fresh rice plants after this injection. The gene silencing efficiency at different time points (24, 48, and 72 h after injection) was determined using quantitative reverse-transcription PCR (qRT-PCR).

2.3. RNA Extraction and qRT-PCR Analysis

The total RNA was prepared using a Magnetic Column RNA Extraction Kit (Mikx, Shenzhen, China) according to the instructions of the manufacturer. First-strand cDNA was synthesized as previously described. qRT-PCR was performed using a CFX Connect real-time PCR system (Bio-Rad, Hercules, CA, USA) with 2× PolarSignal qPCR Mix (MIKX, MKG800-10, Shenzhen, China) following the instructions of the manufacturer. Each reaction mixture included 10 μL of 2× PolarSignal qPCR Mix, 1 μL of each primer (10 μM), and 1 μL of cDNA template in a total volume of 20 µL. The experiment was repeated three times, and three reactions were performed for each biological replicate. Gene expression levels were normalized to the β-actin expression level of the BPH [25]. Amplification was conducted as follows: 94 °C for 20 s, followed by 40 cycles of 94 °C for 10 s and 60 °C for 20 s. The melting curve was analyzed after amplification to verify primer specificity.

2.4. Measuring Body Weight of and Honeydew Produced by BPHs

One-day-old female BPHs were microinjected with either dsNlGr23 or dsGFP and used for measurement. Their honeydew secretion was assessed through a protocol based on the method described in a previous study [12]. Parafilm membranes 5 cm long and 3 cm wide were preweighed using a sensitive balance. Subsequently, a single individual BPH was introduced into each sachet using fine forceps. The sachets were then affixed to the stems of the rice lines positioned 2 cm above the soil surface. Isolated BPHs were allowed to feed for 72 h. The weights of the bags in which both insects remained alive were measured again at the end of the feeding period. These live BPHs were individually weighed using a microbalance. The honeydew excreted from BPH nymphs was evaluated based on the difference in bag weight between before and after BPH feeding. Forty bag samples for each treatment were used to ensure that sufficient amounts of valid data were obtained for analyzing the differences (30 replicates). The entire experiment was repeated three times.

2.5. RNA-Seq Analysis

Total RNA was extracted from whole insects 48 h after dsRNA injection (Section 2.2). Three biological replicates per treatment (5 insects each) were used for RNA-seq. The mRNA was enriched with a poly (A) tail using magnetic beads containing oligo (dT), and the mRNA was interrupted with a buffer. To synthesize first-strand cDNA, we used fragmented mRNA as a template and random hexamer primers with an M-MuLV reverse-transcriptase system. Subsequently, the RNA strand was degraded using RNase H, and the second-strand cDNA was synthesized using dNTPs as the raw material in the DNA polymerase I system. Purified double-stranded cDNA was subjected to end repair, A-tailed addition, and sequencing. Library fragments were purified using an AMPure XP system (Beckman Coulter, Beverly, MA, USA) to select cDNA fragments 370–420 bp long. PCR was performed using Phusion High-Fidelity DNA polymerase (NEB, Beijing, China), Universal PCR primers (Beijing Balb, Beijing, China), and Index (X) Primer (Beijing Balb, Beijing, China). The cDNA libraries were sequenced on an Illumina NovaSeq 6000 platform, and 150 bp paired-end reads were generated.

Raw data were preprocessed using SOAPnuke (v1.6.5) with parameters “-n 0.01 -l 15 -q 0.4 -G”, and clean reads were aligned to the reference genome (NCBI accession No. GCA_014356525.1) with HISAT2 (v2.2.1). Gene expression levels were quantified using RSEM (v1.3.1). DESeq2 (v1.22.1) was used to analyze differences in gene expression between groups. Genes with |log2foldchang| ≥ 1 and a false discovery rate (FDR) < 0.05 were identified as significantly differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed for DEGs using Phyper (Q-value ≤ 0.05).

2.6. Protein Extraction and Trypsin Digestion

For proteomic analysis, whole insects were harvested 48 h post dsRNA injection (Section 2.2) and processed for protein extraction. Each treatment included three biological replicates with 10 insects pooled per replicate. The total protein was extracted from each group using a plant protein isolation kit (Qiagen, Hilden, Germany), following the protocol of the manufacturer. The purity and concentration of these isolated proteins were assessed using the Bradford protein assay [26]. 0.1 mg of protein sample was digested using mass spectrometry-grade trypsin (1:50 w/w, trypsin/protein) for 4 h at 37 °C. Peptides were desalted after digestion using a Strata X column to remove urea, and dried via vacuum centrifugation.

2.7. Proteomic Analysis

DIA data were corrected for retention time using indexed retention time (iRT) peptides. False positives were then controlled with an FDR ≤ 0.01 based on the target–decoy model applied using SWATH-MS to obtain significant quantitative results. The bioinformatics of the proteomic data were analyzed with MSstats (version 3.14.1) [27]. The thresholds of a log2 fold change (FC) ≥ 1 and an adjusted p-value < 0.05 were used to identify differentially expressed proteins (DEPs). All identified proteins were functionally annotated using GO (http://geneontology.org/) (accessed on 15 March 2024) and KEGG pathway analyses (http://www.genome.jp/kegg/) (accessed on 15 March 2024). DEPs were further subjected to GO and KEGG enrichment analyses.

2.8. Triacylglycerol, Glycerol, and Fatty Acid Assays

Samples were collected to determine the triacylglycerol (TAG) and glycerol contents in BPHs as follows. RNAi was quantified on one-day-old female BPHs that were used in follow-up experiments. Samples were collected 72 h after injection. Collected samples were stored at −80 °C after liquid nitrogen quick-freezing until further analysis. The TAG and glycerol contents were quantified as previously described [28]. The free fatty acid content was determined using copper soap colorimetry, according to the instructions of the manufacturer (Sangon Biotech, Shanghai, China). Each sample contained tissue extracts from five adult female BPHs, with three biological replicates per sample.

2.9. Statistical Analysis

Data were analyzed and visualized using GraphPad Prism software (version 8.0). Normality was confirmed with the Shapiro–Wilk test (p > 0.05). Two-group comparisons employed the Student’s t-test.

3. Results

3.1. Effect of NlGr23 Knockdown on BPH Weight and Honeydew Production

NlGr23 mediates the repulsive responses of BPHs to oxalic acid in rice plants and artificial diets [25]. qRT-PCR analysis showed NlGr23 transcript knockdown at 24 h, 48 h, and 72 h post-injection. No significant difference in efficiency was found between time points (Figure S1). Honeydew production and weight gain rates were measured following the injection of dsNlGr23 to investigate the effects of NlGr23 on BPH feeding. The body weight of and honeydew produced by BPHs substantially increased after the injection (Figure 1).

3.2. Transcriptome Analysis of BPHs After Injecting dsNlGr23

We analyzed the RNA-seq of three biological replicate samples 48 h after injecting dsNlGr23 to identify the underlying mechanisms. The total raw and clean data for each sample ranged from 42,091,806 to 46,022,766 bp and 41,481,222 to 45,202,956 bp, respectively (Table S2). More than 92.91% of the sequenced bases had a quality score of Q30 or higher. The total mapping rate of all samples to the reference genome ranged between 74.66% and 83.12% (Table S2), indicating that these data were usable for our subsequent analyses. A total of 11,247 genes were expressed in both the dsNlGr23 and dsGFP groups, and 530 and 454 genes were identified in the dsNlGr23 and dsGFP groups, respectively (Figure 2A). The Pearson correlation heatmap demonstrated low intergroup correlations, but high intragroup correlations, indicating the effects of NlGr23 knockdown on gene expression patterns (Figure 2B). Clustering heat maps visually confirmed differences in the expression profiles between the groups (Figure 2C). These two groups contained 187 DEGs that met the |log2FC| ≥ 0.5 and p  <  0.05 thresholds, of which 156 genes were upregulated and 31 were downregulated (Figure 2C). The results of KEGG metabolic pathway enrichment analysis showed that these DEGs were primarily involved in the glycerophospholipid, cysteine, and methionine metabolism pathways (Figure 2D). These results suggested that NlGr23 knockdown substantially affected multiple biological processes at the transcriptional level in BPHs.

3.3. Proteomics Analysis of BPHs After Injecting dsNlGr23

Proteomics reflect the effects of post-transcriptional regulation. Therefore, we used next-generation label-free quantitative proteomics technology to obtain the protein profile and identified 4496 proteins (Table S3). The length of most peptides in the proteomic data varied between 8 and 20 bp, indicating that the sample preparation met the standard (Figure 3A). InterProScan was used to analyze the gene family and structure of all detected proteins using Pfam, PRINTS, ProDom, SMART, ProSite, and PANTHER. A total of 1610 proteins were annotated using GO, the KEGG, Clusters of Orthologous Groups (COG), and Interpro (IPR) (Figure 3B). Distinct protein expression clusters separated the dsGFP and dsNlGr23 groups along the principal component analysis (PCA) axis (principal component 1 (PC1) = 48.28% and PC2 = 21.95%; Figure 3C). A total of 150 significant DEPs were identified, of which 79 were upregulated and 71 were downregulated (Figure 3D). The results of GO and KEGG analyses revealed that disrupting NlGr23 in BPHs substantially altered their protein expression profiles (Figure 4). The GO analysis showed that biological processes were predominantly enriched in cholesterol metabolism and mono-organic substance transport. The enrichment was highest for the extracellular region and intercellular space, whereas molecular functions were strongly associated with copper ion-dependent enzymatic and peptidase activities (Figure 4A). The KEGG pathway analysis further demonstrated the significant enrichment of DEPs in critical metabolic and signaling networks, including glycerophospholipid, tyrosine, and fatty acid metabolism, as well as the AMPK signaling pathway (Figure 4B). These findings suggest that NlGr23 disruption impairs cellular membrane function through disturbing lipid metabolic homeostasis, reducing energy supply efficiency through altered energy metabolism networks, and regulating cell survival via the activation of stress-responsive pathways such as the AMPK pathway.

3.4. Proteomics and Transcriptome Coexpression Analyses

The correlation of the relationship between proteomic and transcriptomic alterations during protein synthesis was analyzed through quantifying DEPs and DEGs (Figure 5). The Pearson correlation coefficient of transcriptomic and proteomic data was −0.293, suggesting that some genes were significantly expressed at the mRNA level, but not at the protein level (Figure 5B). We identified 12 corresponding DEGs and DEPs (co-DEGs-DEPs) in the dsNlGr23 group compared with the dsGFP group. Among the 12 coexpressed DEGs and DEPs, the enriched pathways included metabolic pathways, amino acid biosynthesis, fatty acid metabolism, tyrosine metabolism, fatty acid biosynthesis, and glycine, serine, and threonine metabolism. This finding indicated that NlGr23 interference disrupted nutrient metabolism and synthesis, particularly fatty acid biosynthesis and metabolism, in BPHs.

3.5. Effect of NlGr23 Silencing on Lipid Mobilization

We hypothesized that increases in body weight and honeydew production in BPHs after NlGr23 silencing resulted from disordered lipid metabolism and subsequent fat accumulation based on transcriptomic and proteomic results. We measured triglyceride levels in the NlGr23-silenced BPHs to test this hypothesis. The results showed NlGr23 silencing substantially increased TAG levels by 68.83% (Figure 6A). In contrast, glycerol and free fatty acid contents were considerably lower in BPHs after NlGr23 silencing compared with those of the wild type (Figure 6B,C).

4. Discussion

Insect GRs play pivotal roles in mediating feeding decisions and metabolic homeostasis. We found that silencing NlGr23 in BPHs considerably increased feeding behavior by increasing honeydew excretion and body weight while disrupting lipid metabolism (Figure 1). The results of integrated transcriptomic and proteomic analyses revealed the molecular pathways underlying these phenotypic changes, highlighting the critical role of GRs in bridging chemosensation and metabolic regulation.

NlGr23 is a receptor that mediates repulsion with oxalic acid, a key deterrent in rice plants [25]. Our results showed that honeydew production and weight gain increased after NlGr23 knockdown, indicating reduced aversion to host plant defenses. This aligns with the established roles of bitter-tuned GRs, such as Drosophila Gr66a, in recognizing toxins such as strychnine [29] and Pieris rapae Gr28 in detecting sinigrin [14]. The loss of aversive signaling likely explains the increased feeding, which is analogous to glucose-averse cockroaches, in which shifts in GR expression alter feeding preferences [30].

The multiomics predictions agreed with the findings of disrupted lipid homeostasis, which was experimentally confirmed using biochemical assays. NlGr23 silencing increased triglyceride levels by 68.83% while depleting glycerol and free fatty acids (Figure 6), a phenotype signifying impaired lipolysis. We attributed this finding to AMPK activation, which suppressed lipases to promote lipid storage during energy deficit. Consequently, the limited free fatty acid availability restricted ATP synthesis, exacerbated metabolic inefficiency, and reduced insect fitness.

Our study identified a function of GRs in regulating systemic energy. GRs typically mediate neuronal activation [31]; NlGr23 disruption directly reprograms the AMPK and lipid pathways. This disruption expands in parallel to that of sugar receptors, such as NlGr7, which suppress vitellogenin [16], or Bombyx mori Gr6/Gr9 in response to both sugars and plant stimulants [32]. We propose that NlGr23 modulates neuroendocrine signaling or directly influences enterocyte metabolism. This study shows NlGr23 silencing causes transcriptomic/proteomic changes in BPHs, but we cannot determine if these result directly from receptor loss or indirectly from increased oxalate intake.

Exploiting the GR-mediated metabolic pathways is an avenue for managing this pest. Compounds blocking NlGr23 could enhance feeding in crops treated with low-toxicity deterrents such as oxalic acid, leveraging the behavioral and metabolic roles of the receptor. Alternatively, the activation of the AMPK or lipid storage pathways could induce energy deficiency without using conventional insecticides. These approaches align with emerging strategies for developing behavioral modulators that target GR networks.

5. Conclusions

We found that NlGr23 silencing in the BPH disrupts lipid homeostasis via transcriptomic and proteomic reprogramming, culminating in the AMPK-mediated inhibition of lipolysis and TAG accumulation. We established GRs as key integrators of chemosensation and metabolism, providing a mechanistic basis for pest control strategies targeting GR-associated pathways. Future studies should explore the crosstalk between GRs and the neuroendocrine system and produce in vivo metabolic interventions.