Abstract
Introduction
The coconut leaf beetle, Brontispa longissima, is a major pest of palm plants in China and is significantly affected by the parasitoid wasp Aseodes hispanarum, which targets the beetle’s larval stage.
Methods
Using single-molecule real-time (SMRT) sequencing, we constructed a high-quality transcriptome reference to assess gene expression associated with A. hispanarum parasitism in B. longissima. Differentially expressed genes (DEGs) were identified at 24, 48, 72, and 96 h post-parasitism compared with non-parasitized controls.
Results
Based on 12,196 reference transcripts, a total of 2,120, 467, 500, and 5,749 DEGs were identified at 24, 48, 72, and 96 h post-parasitism, respectively. Over 75% of DEGs were upregulated across all time points. Gene Ontology (GO) enrichment analysis revealed significant enrichment of immune-related processes alongside hemocyte differentiation. Validation by quantitative PCR (qPCR) confirmed the consistency and reliability of the RNA-seq results.
Discussion
The study identified immune-related genes differentially expressed in the fourth instar larvae of B. longissima. Overall, the results indicate that parasitism by A. hispanarum is associated with changes in the immune response of B. longissima larvae, offering preliminary insights into host-parasite interactions for potential biological control strategies.
Keywords: Asecodes hispinarum, Brontispa longissima, immune response, parasitism, transcriptome
1. Introduction
The coconut leaf beetle, Brontispa longissima Gestro (Coleoptera: Chrysomelidae), originally native to Indonesia and Papua New Guinea, has become a major invasive pest in China, particularly in the southern regions (1, 2). Both the larvae and adults of this beetle feed on the heart leaves of palm plants, causing significant damage such as leaf curling, wrinkling, and the wilting of tender shoots. In severe cases, this feeding can lead to the death of the entire plant (3). As a result, B. longissima has caused substantial economic losses to palm plantations in China (4, 5). Currently, both chemical and biological control methods are employed to manage this pest. The beetle typically feeds on the unexpanded heart leaves of its host, where the waxy coating on the leaves makes it difficult for pesticides to penetrate and effectively control the pest. Additionally, the height of some palm trees complicates pesticide application, further reducing the effectiveness of chemical control. Once introduced, the beetle is also difficult to eradicate (6, 7).
Biological control, particularly the use of parasitoids like Asecodes hispinarum Bouček (Hymenoptera: Eulophidae), has shown promise in managing B. longissima. A. hispinarum, a parasitic wasp native to Western Samoa and Papua New Guinea, targets the larvae of B. longissima, especially preferring the fourth instar larvae. It can also parasitize larvae and pupae of various instars when hosts are scarce (8–14). In March 2004, China introduced A. hispinarum from Vietnam for the biological control of B. longissima, and the results have been positive, as reported in several studies (7, 8, 11, 15).
Parasitic wasps like A. hispinarum regulate various physiological processes in their host insects, including immunity, metabolism, and development, ultimately leading to the host’s death. These wasps are vital natural control agents and play an effective role in pest management. While there is considerable knowledge on the effects of parasitism on B. longissima the specific impact on its immune system remains underexplored (2, 14, 16, 17). Third-generation transcriptome sequencing facilitates the acquisition of high-quality reference transcriptomes more easily than de novo reference-free transcriptome sequencing, making it highly valuable for studying gene expression changes (18–21). This study aims to use Illumina sequencing technology to obtain the transcriptome of B. longissima and analyze the differential expression of immune-related genes in both parasitized and non-parasitized larvae. The findings will offer valuable insights into how A. hispinarum parasitism affects the immune system of B. longissima and could contribute to the development of new pest control strategies targeting the pest’s immune response.
2. Materials and methods
2.1. Experimental insects
The B. longissima beetles and A. hispinarum wasps used in this experiment were sourced from the Environment and Plant Protection Institute at the Chinese Academy of Tropical Agricultural Sciences. A. hispinarum was reared on B. longissima larvae as hosts, and the adult wasps were provided with a 10% sucrose solution. The B. longissima larvae were fed leaves from Washingtonia filifera Linden.Wendland. Both species were maintained under controlled conditions at 25 ± 1 °C, with 70 ± 5% relative humidity and a 12-hour light/dark cycle.
The fourth instar larvae of B. longissima were placed in a petri dish with A. hispinarum to observe parasitism under a microscope. The larvae were then transferred to culture tubes with the appropriate amount of Washingtonia filifera palm leaves, and they were cultured under the same conditions. Samples were collected at 24, 48, 72, and 96 hours post-parasitism (PP), as well as for the control group (non-parasitized larvae, NP), to analyze gene expression at different time points. Samples were collected for each treatment and sent to Gene Denovo Biotechnology Co. (Guangzhou, China) for third-generation full-length transcriptome sequencing. Three independent biological replicates were included for each time point. Additionally, second-generation transcriptome sequencing was performed for comparison.
2.2. RNA extraction and transcriptome sequencing
The third-generation transcriptome library was constructed using the SMRTbell® Prep Kit 3.0 and sequenced on the PacBio Sequel II platform. The SMRT Link V8.0.0 software was used to analyze the raw data. High-quality circular consensus sequences (CCS) were extracted, and poly(A) tails, primers, and barcodes were removed to obtain full-length non-chimeric (FLNC) reads. These reads were clustered using Minimap2, and the Quiver algorithm was applied to correct and retain high-quality transcripts. Redundant sequences were removed using cd-hit to obtain the final transcript sequence. Functional annotation of those high-quality transcripts were performed based on the NCBI non-redundant protein database, KEGG, InterPro, and Gene Ontology (GO) using DIAMOND BLASTX (E-value < 1e–10).
For second-generation sequencing, the Hieff NGS® Ultima Dual-mode mRNA Library Prep Kit (12309ES, Yeasen) was used to construct the Illumina cDNA library, with a total of 24 libraries created. Sequencing was performed on the Illumina Novaseq 6000 platform. Raw reads were quality-controlled using Fastp, and low-quality data were filtered. Clean reads were then used for transcript quantification, and gene expression levels were estimated by RSEM based on the high-quality mapped reads.
2.3. Differential expression gene analysis
Differentially expressed genes (DEGs) between the parasitized and non-parasitized B. longissima larvae were identified based on the raw read counts generated by RSEM. And significantly DEGs were defined using thresholds of FDR ≤ 0.05 and |log2FC| ≥ 1. Gene Ontology (GO) enrichment analysis was performed using clusterProfiler, with adjusted p-values ≤ 0.05 considered significantly enriched. In addition, KEGG pathway analysis was performed to identify the major biochemical and signal transduction pathways associated with the DEGs.
2.4. qRT-PCR validation
Based on the differentially expressed genes from the transcriptome data, immune-related genes were screened and analyzed. These genes were further explored to understand how A. hispinarum parasitism influences the immune response of B. longissima larvae.
To validate the RNA-seq results, 12 genes were selected for qRT-PCR analysis. RNA samples were collected, and cDNA was synthesized using the Takara PrimeScript™ FAST RT Reagent Kit. qPCR was conducted using specific primers (Supplementary Table 1), and gene expression levels were calculated using the 2-ΔΔCt method.
3. Results
3.1. Analysis of SMAT sequencing results for B. longissima
3.1.1. Length analysis of high quality transcripts in B. longissima
After conducting single-molecule real-time (SMRT) sequencing, we obtained a total of 8,033,690 subreads, with an average length of 3,068 base pairs (bps), resulting in 24 gigabases (GB) of raw sequence data. Through the processes of clustering, error correction, and redundancy removal, we successfully generated the full-length transcriptome for B. longissima, yielding a total of 12,196 full-length transcript sequences. The longest transcript was 10,299 bps in length, while the average length of all transcripts was 3,310 bps (Table 1; Figure 1).
Table 1.
Summary of single molecule real-time sequencing of B. longissima.
| Sequencing Parameters | Number |
|---|---|
| Total base(bp) | 24654271483 |
| subreads number | 8033690 |
| average length | 3068 |
| N50 | 3404 |
| Number of reads | 229228 |
| Number of CCS bases | 783398881 |
| CCS Read Length (mean) | 3417 |
| Number of Passes (mean) | 29 |
| Number of polished high-quality transcripts | 13470 |
| Number of polished low-quality transcripts | 145 |
| Total Number | 12196 |
| Total length (bp) | 40378425 |
| Maximum Length(bp) | 10299 |
| Minimum Length(bp) | 104 |
| Average Length(bp) | 3310.79 |
| N50 Length(bp)* | 3716 |
| GC content | 39.48% |
N50, Median length of all contigs or unigenes.
Figure 1.
Length distribution of transcript sequences in the full-length transcriptome of Brontispa longissima larvae.The x-axis represents the length of the transcripts. The left y-axis corresponds to the histogram, showing the number of transcripts within each length interval along the x-axis. The right y-axis represents the cumulative distribution, indicating the number of transcripts with lengths greater than or equal to a given value on the x-axis.
3.1.2. transcripts functional annotation and classification for B. longissima
To assign functional annotations to the transcript sequences, we performed BLASTx comparisons with several protein databases, including Nr, SwissProt, KEGG, and COG/KOG (with an e-value threshold of < e-5). The top-ranked protein from each BLAST result was used to define the coding sequence (CDS) for each transcript. In total, 12,196 CDS sequences were identified, of which 11,860 transcripts were successfully annotated in at least one of the protein databases. However, 336 transcript sequences remained unannotated. Specifically, 11,857 transcripts were annotated in the NR database (Figure 2).
Figure 2.
Functional annotation for the full-length transcriptome of B. longissima larvae. The diagram shows the overlap of transcripts annotated against four public databases: KEGG (blue), KOG (green), Nr (orange), and Swiss-Prot (pink). The numbers in each region represent the number of transcripts annotated in the corresponding database(s), with shared regions indicating transcripts annotated in multiple databases.
The functional annotation revealed that the species with the closest homology to B. longissima transcript sequences was Anoplophora glabripennis Motschulsky, another Coleopteran insect. In total, 5,411 transcripts of A. glabripennis were identified as closely related to B. longissima transcripts (Figure 3).
Figure 3.
Nr-annotated species distribution statistics of the full-length transcriptome of (B) longissima larvae (Top 10). The X-axis represents the top 10 annotated species, while the Y-axis indicates the number of unigenes. Anoplophora glabripennis(5411 unigenes) showed the highest homology, followed by Leptinotarsa decemlineata(1858 unigenes) and Gonioctena quinquepunctata(1648 unigenes).
Gene Ontology (GO) analysis was performed to predict the potential functions of the inferred proteins. In total, 11,857 transcripts were successfully annotated with GO terms and classified into three main categories: Biological Process, Cellular Component, and Molecular Function. Within the Biological Process category, the largest proportion of genes was associated with cellular processes (9,475 genes) and metabolic processes (7,826 genes). In the Cellular Component category, most genes were annotated to cellular anatomical entities (8,846 genes), followed by protein-containing complexes (4,171 genes). For the Molecular Function category, the majority of genes were related to binding activity (8,611 genes), while a substantial number were involved in catalytic activity (6,130 genes) (Figure 4).
Figure 4.
GO functional classification of the full-length transcriptome of (B) longissima larvae. The distribution of annotated unigenes is categorized into three main groups: Biological Process (red), Cellular Component (green), and Molecular Function (blue). The X-axis lists the specific Level 2 GO terms, and the Y-axis represents the number of genes associated with each term.
To further characterize the functional roles of the predicted proteins, KOG analysis was also conducted. The annotated transcripts were grouped into 25 KOG functional categories. Among these, signal transduction mechanisms represented the most abundant category, comprising 2,183 transcripts. transcripts with unknown functions formed the second largest group, whereas nuclear structure was the least represented category, with only 67 transcripts (Figure 5).
Figure 5.
KOG functional classification of the full-length transcriptome of (B) longissima larvae. The X-axis represents the 24 KOG categories (abbreviated from A to S), while the Y-axis indicates the number of annotated unigenes. Different colors correspond to various functional categories, with the specific class represented by each color detailed in the legend on the right. Notably, the categories “General function prediction only” (R, grey bar) and “Signal transduction mechanisms” (T, pink bar) contain the highest number of unigenes, with 2082 and 2183 respectively.
3.2. Next-generation sequencing in B. longissima
3.2.1. Transcriptome profiling of differentially expressed genes
Based on the second-generation (NGS) sequencing data, transcript abundance was analyzed to identify genes that were differentially expressed between non-parasitized (NP) and parasitized (PP) larvae at different time points. Across the comparisons np24 vs. pp24, np48 vs. pp48, np72 vs. pp72, and np96 vs. pp96, a total of 6,672 differentially expressed genes (DEGs) were identified, of which 152 genes were shared among all four comparison groups. The numbers of DEGs uniquely expressed at 24 h, 48 h, 72 h, and 96 h post-parasitism were 730, 48, 42, and 4,288, respectively (Figure 6).
Figure 6.
Venn diagram of DEGs identified at 24 h, 48 h, 72 h, and 96 h post parasitization of (B) longissimalarvae by (A) hispinarum.The diagram illustrates the overlap of differentially expressed genes (DEGs) among the four time points. Each circle represents the unique and shared DEGs for a specific comparison group: np24-vs-pp24 (yellow), np48-vs-pp48 (light blue), np72-vs-pp72 (red), and np96-vs-pp96 (green). The numbers within each section indicate the count of DEGs specific to that group or shared between groups.
Volcano plots were used to visualize the overall distribution of DEGs in each comparison group. In these plots, significantly upregulated genes are shown as red dots, significantly downregulated genes as blue dots, and genes without significant expression changes as grey dots. The volcano plots for all comparisons are presented in Figure 7. Specifically, the np24 vs. pp24 comparison revealed 2,120 DEGs, including 1,624 upregulated and 496 downregulated genes. In the np48 vs. pp48 comparison, 467 DEGs were detected, with 286 genes upregulated and 181 downregulated. The np72 vs. pp72 comparison identified 500 DEGs, comprising 341 upregulated and 159 downregulated genes. The largest number of DEGs was observed in the np96 vs. pp96 comparison, with 5,749 DEGs, of which 3,332 were upregulated and 2,417 were downregulated. Overall, across all four time points, the majority of differentially expressed genes showed an upregulation trend following parasitism (Figure 7).
Figure 7.
Volcano plots of DEGs during (B) longissima larval parasitization by (A) hispinarumat different time points. (A) Volcano plot of DEGs at 24 h post-parasitization (np24-vs-pp24). (B) Volcano plot of DEGs at 48 h post-parasitization (np48-vs-pp48). (C) Volcano plot of DEGs at 72 h post-parasitization (np72-vs-pp72). (D) Volcano plot of DEGs at 96 h post-parasitization (np96-vs-pp96). In each volcano plot, the x-axis represents the log2(fold change) (log2FC) of gene expression, and the y-axis represents the -log10(false discovery rate) (-log10FDR). Red dots indicate significantly upregulated DEGs, blue dots indicate significantly downregulated DEGs, and gray dots indicate non-significant DEGs (nosig). The vertical dashed lines denote the threshold for |log2FC|, and the horizontal dashed line denotes the threshold for -log10(FDR) (typically corresponding to an FDR < 0.05).
3.2.2. Functional annotation and enrichment analysis of differentially expressed genes
To further clarify the biological functions of the differentially expressed genes (DEGs), Gene Ontology (GO) enrichment analysis was conducted for each comparison between parasitized and non-parasitized larvae at different time points. The top 20 GO terms enriched by DEGs, particularly those associated with immune responses to parasitism, are presented in the corresponding figures.
GO enrichment analysis revealed dynamic and stage-specific functional responses following parasitism. At 24 h (np24 vs. pp24), DEGs were predominantly enriched in binding-related functions, including fatty acid binding, organic acid binding, and heme binding, along with cellular component terms such as extracellular region and endocytic vesicle. At 48 h (np48 vs. pp48), enrichment shifted toward hydrolase-related activities, including catalase, peroxidase, and peptidase activities, as well as processes associated with cell adhesion and structural remodeling, such as cell junction disassembly and actin filament severing. By 72 h (np72 vs. pp72), DEGs were mainly involved in immune and metabolic processes, including hemolymph coagulation, peptidase activity, glucosamine-containing compound metabolism, and continued enrichment in binding functions and extracellular components. At 96 h (np96 vs. pp96), enrichment was strongly associated with immune regulation and hemocyte differentiation, including pathways related to the regulation of lamellocyte and plasmatocyte differentiation, semaphorin–plexin signaling, intercellular bridge organization, and transcription coactivator activity, indicating a pronounced late-stage immune response (Figure 8).
Figure 8.
Top 20 GO pathway enrichments of DEGs at different parasitism time points in (B) longissima larvae parasitized by (A) hispinarum(related to parasite immunity) The bubble plot illustrates the enrichment of differentially expressed genes (DEGs) in the top 20 Gene Ontology (GO) pathways associated with parasite immunity. The x-axis represents the gene ratio (proportion of DEGs in a pathway), the y-axis lists the GO terms (sorted by significance), and the size of each bubble corresponds to the number of DEGs in the pathway (key: 7, 97, 1344 genes). The color gradient (from purple to yellow) indicates the -log₁₀(Q-value) (significance level, with higher values indicating stronger enrichment). Different colors represent different time points: np24-vs-pp24 (cyan), np48-vs-pp48 (pink), np72-vs-pp72 (yellow), and np96-vs-pp96 (purple).
Overall, these results indicate that parasitism by A. hispinarum induces dynamic and time-dependent changes in gene expression in B. longissima, with early responses dominated by binding and metabolic functions and later responses strongly associated with immune cell differentiation and regulation.
Through KEGG pathway enrichment analysis, we investigated the functional significance of differentially expressed genes (DEGs) at various time points following parasitism. At 24 hours post-parasitism, DEGs were mapped to 315 KEGG pathways, of which 25 were significantly enriched (Q value < 0.05). Key enriched pathways included protein digestion and absorption, the PI3K-Akt signaling pathway, and ECM–receptor interaction, among others (Table 2).
Table 2.
The KEGG significant enrichment pathway of 24 h in B. longissima larva following parasitization by A. hispinarum.
| Pathway | np24-vs-pp24 (799) | Pvalue | Pathway ID |
|---|---|---|---|
| ECM-receptor interaction | 71 | 2.48E-22 | ko04512 |
| Amoebiasis | 70 | 1.05E-19 | ko05146 |
| AGE-RAGE signaling pathway in diabetic complications | 47 | 9.74E-19 | ko04933 |
| Protein digestion and absorption | 46 | 9.45E-17 | ko04974 |
| Amino sugar and nucleotide sugar metabolism | 47 | 2.71E-16 | ko00520 |
| Relaxin signaling pathway | 46 | 1.50E-13 | ko04926 |
| Small cell lung cancer | 50 | 4.44E-13 | ko05222 |
| PI3K-Akt signaling pathway | 83 | 7.54E-11 | ko04151 |
| Human papillomavirus infection | 102 | 5.29E-08 | ko05165 |
| Inflammatory bowel disease | 6 | 1.22E-05 | ko05321 |
| Focal adhesion | 77 | 1.76E-05 | ko04510 |
| Pathways in cancer | 84 | 1.91E-05 | ko05200 |
| Antigen processing and presentation | 29 | 2.81E-05 | ko04612 |
| IL-17 signaling pathway | 17 | 2.97E-05 | ko04657 |
| Cytosolic DNA-sensing pathway | 8 | 2.99E-05 | ko04623 |
| Complement and coagulation cascades | 10 | 8.61E-05 | ko04610 |
| Protein export | 11 | 0.00013317 | ko03060 |
| DNA replication | 10 | 0.000271738 | ko03030 |
| Lipid and atherosclerosis | 37 | 0.000483485 | ko05417 |
| Prostate cancer | 23 | 0.000506222 | ko05215 |
| Cell cycle | 26 | 0.001280773 | ko04110 |
| Arrhythmogenic right ventricular cardiomyopathy | 20 | 0.002468712 | ko05412 |
| Acute myeloid leukemia | 13 | 0.002855724 | ko05221 |
| B cell receptor signaling pathway | 11 | 0.003213767 | ko04662 |
| Glycosaminoglycan biosynthesis - heparan sulfate/heparin | 7 | 0.003867367 | ko00534 |
At 48 hours post-parasitism, DEGs were associated with 252 KEGG pathways, with 22 pathways showing significant enrichment. These included apoptosis, protein processing in the endoplasmic reticulum, and lysosome-related pathways (Table 3).
Table 3.
The KEGG significant enrichment pathway of 48 h in B. longissima larva following parasitization by A. hispinarum.
| Pathway | np48-vs-pp48 (217) | Pvalue | Pathway ID |
|---|---|---|---|
| Antigen processing and presentation | 23 | 4.992722e-13 | ko04612 |
| Fatty acid elongation | 10 | 8.461927e-09 | ko00062 |
| Protein processing in endoplasmic reticulum | 28 | 4.216645e-07 | ko04141 |
| Lysosome | 29 | 1.232821e-05 | ko04142 |
| Apoptosis | 19 | 2.678867e-05 | ko04210 |
| Spliceosome | 16 | 0.000144102 | ko03040 |
| Estrogen signaling pathway | 13 | 0.000209839 | ko04915 |
| Rheumatoid arthritis | 11 | 0.000221608 | ko05323 |
| Biosynthesis of unsaturated fatty acids | 7 | 0.000260149 | ko01040 |
| Longevity regulating pathway - worm | 11 | 0.000441022 | ko04212 |
| Legionellosis | 10 | 0.000531867 | ko05134 |
| Lipid and atherosclerosis | 15 | 0.000694034 | ko05417 |
| Fatty acid metabolism | 18 | 0.000726952 | ko01212 |
| Necroptosis | 10 | 0.000833622 | ko04217 |
| IL-17 signaling pathway | 7 | 0.001087392 | ko04657 |
| NOD-like receptor signaling pathway | 10 | 0.001142346 | ko04621 |
| Autophagy - animal | 16 | 0.001833368 | ko04140 |
| Fluid shear stress and atherosclerosis | 12 | 0.002094725 | ko05418 |
| Nucleocytoplasmic transport | 14 | 0.003001381 | ko03013 |
| Prostate cancer | 9 | 0.003483324 | ko05215 |
| Progesterone-mediated oocyte maturation | 8 | 0.004077558 | ko04914 |
| Th17 cell differentiation | 6 | 0.004235618 | ko04659 |
By 72 hours post-parasitism, 199 KEGG pathways were enriched, 23 of which were significant. The primary pathways involved the PI3K-Akt signaling pathway, ECM–receptor interaction, and DNA replication (Table 4).
Table 4.
The KEGG significant enrichment pathway of 72 h in B. longissima larva following parasitization by A. hispinarum.
| Pathway | np72-vs-pp72 (172) | Pvalue | Pathway ID |
|---|---|---|---|
| Complement and coagulation cascades | 18 | 7.747894e-28 | ko04610 |
| Coronavirus disease - COVID-19 | 20 | 5.617284e-13 | ko05171 |
| Neutrophil extracellular trap formation | 18 | 8.956681e-12 | ko04613 |
| ECM-receptor interaction | 23 | 1.679352e-10 | ko04512 |
| Antigen processing and presentation | 18 | 3.132207e-10 | ko04612 |
| PI3K-Akt signaling pathway | 30 | 3.020534e-09 | ko04151 |
| Platelet activation | 20 | 3.037193e-08 | ko04611 |
| DNA replication | 8 | 1.01616e-07 | ko03030 |
| Cell cycle | 14 | 1.71371e-06 | ko04110 |
| Progesterone-mediated oocyte maturation | 9 | 0.000181895 | ko04914 |
| Protein processing in endoplasmic reticulum | 19 | 0.000279846 | ko04141 |
| Human papillomavirus infection | 27 | 0.000292203 | ko05165 |
| Focal adhesion | 23 | 0.000303153 | ko04510 |
| Estrogen signaling pathway | 11 | 0.000381329 | ko04915 |
| Legionellosis | 9 | 0.000410498 | ko05134 |
| Spliceosome | 13 | 0.000492249 | ko03040 |
| Lipid and atherosclerosis | 13 | 0.000694815 | ko05417 |
| Nucleocytoplasmic transport | 13 | 0.001026759 | ko03013 |
| Rheumatoid arthritis | 8 | 0.002926639 | ko05323 |
| Fluid shear stress and atherosclerosis | 10 | 0.003457077 | ko05418 |
| Riboflavin metabolism | 3 | 0.003498871 | ko00740 |
| Amyotrophic lateral sclerosis | 18 | 0.004531599 | ko05014 |
| Longevity regulating pathway - worm | 8 | 0.004725637 | ko04212 |
At 96 hours post-parasitism, DEGs were enriched in 338 KEGG pathways, with 20 significantly enriched. Notable pathways included the MAPK signaling pathway, Wnt signaling pathway, cell cycle regulation, and lysosome-related processes (Table 5; Figure 9).
Table 5.
The KEGG significant enrichment pathway of 96 h in B. longissima larva following parasitization by A. hispinarum.
| Pathway | np96-vs-pp96 (2326) | Pvalue | Pathway ID |
|---|---|---|---|
| MAPK signaling pathway | 139 | 1.138187e-09 | ko04010 |
| Proteoglycans in cancer | 165 | 2.832244e-09 | ko05205 |
| Salmonella infection | 156 | 4.40654e-09 | ko05132 |
| Other glycan degradation | 48 | 3.708782e-08 | ko00511 |
| Lysosome | 175 | 3.538883e-07 | ko04142 |
| Prostate cancer | 54 | 9.396751e-07 | ko05215 |
| Antigen processing and presentation | 61 | 2.833277e-06 | ko04612 |
| Cell cycle | 63 | 6.26788e-06 | ko04110 |
| Complement and coagulation cascades | 17 | 9.922283e-06 | ko04610 |
| FoxO signaling pathway | 71 | 1.251356e-05 | ko04068 |
| Apoptosis | 97 | 1.844616e-05 | ko04210 |
| Adherens junction | 83 | 2.419683e-05 | ko04520 |
| Parathyroid hormone synthesis, secretion and action | 52 | 2.93376e-05 | ko04928 |
| Glutathione metabolism | 42 | 4.68079e-05 | ko00480 |
| Renin-angiotensin system | 29 | 0.000129787 | ko04614 |
| Breast cancer | 50 | 0.000481758 | ko05224 |
| Wnt signaling pathway | 79 | 0.000997078 | ko04310 |
| Morphine addiction | 26 | 0.001225022 | ko05032 |
| Notch signaling pathway | 31 | 0.001281882 | ko04330 |
| Longevity regulating pathway - multiple species | 42 | 0.001767213 | ko04213 |
| Bladder cancer | 24 | 0.003172305 | ko05219 |
| Cellular senescence | 54 | 0.003593351 | ko04218 |
| Longevity regulating pathway | 46 | 0.004363872 | ko04211 |
| MicroRNAs in cancer | 113 | 0.004491494 | ko05206 |
Figure 9.
Top 20 KEGG pathway enrichments of DEGs at different parasitism time points in (B) longissima larvae parasitized by (A) hispinarum(related to parasite immunity) The bubble plot illustrates the enrichment of differentially expressed genes (DEGs) in the top 20 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with parasite immunity. The x-axis represents the gene ratio (proportion of DEGs in a pathway), the y-axis lists the KEGG terms (sorted by significance), and the size of each bubble corresponds to the number of DEGs in the pathway (key: 7, 35, 174 genes). The color gradient (from purple to yellow) indicates the -log₁₀(Q-value) (significance level, with higher values indicating stronger enrichment). Different colors represent different time points: np24-vs-pp24 (pink), np48-vs-pp48 (purple), np72-vs-pp72 (cyan), and np96-vs-pp96 (yellow).
These findings highlight key signaling and metabolic pathways that are dynamically regulated in B. longissima following A. hispinarum parasitism. Understanding these enriched pathways provides valuable insights into the molecular mechanisms by which parasitism suppresses the immune response of B. longissima larvae.
3.2.3. Impact of parasitism on host immune-related gene expression
Sequencing analysis suggested that parasitism by Asecodes hispinarum affects the immune system of Brontispa longissima, prompting a detailed examination of immune-related genes (Supplementary Table 2). In insects, the first step of the immune response is recognition, mediated by pattern recognition receptors (PRRs). In this study, PRRs such as beta-1,3-glucan recognition proteins (βGBP) and Down syndrome cell adhesion molecules (Dscam) were upregulated at 24 h and 48 h post-parasitism. Additionally, βGBP expression remained elevated at 96 h.
Regulatory genes, which modulate immune responses, also showed dynamic changes. Serine protease persephone and serine protease inhibitor dipetalogastin were downregulated following parasitism, while serine protease inhibitor 28Dc-like was upregulated during the early stages of parasitism.
Key components of the melanization pathway were differentially expressed: prophenoloxidase activating factor (PPAF) was upregulated at 24 h, phenoloxidase expression was downregulated at 72 h and 96 h, and PPAF was again upregulated at 96 h. These results suggest that A. hispinarum suppresses the immune response of B. longissima larvae by inhibiting the conversion of prophenoloxidase into active phenoloxidase.
Furthermore, major insect immune signaling pathways, including Toll, IMD (immune deficiency), JAK/STAT (Janus kinase/signal transducer and activator of transcription), and MAPK (mitogen-activated protein kinase), were affected by parasitism. The expression of genes associated with these pathways was modulated over the course of parasitism, indicating that A. hispinarum manipulates multiple immune signaling mechanisms to suppress host defense responses.
These findings highlight the complex and time-dependent modulation of the host immune system by parasitic wasps, providing valuable insights into host–parasite interactions at the molecular level.
3.2.4. qRT-PCR validation of transcriptomic data
To confirm the reliability of the Illumina-based transcriptomic analysis, twelve genes were randomly selected for validation using quantitative real-time PCR (qRT-PCR). The results showed that ten of the twelve genes exhibited expression patterns consistent with the RNA-seq data, reflecting similar trends of upregulation or downregulation in response to parasitism. However, two genes displayed discrepancies between qRT-PCR and Illumina sequencing results (Figure 10).
Figure 10.
qRT - PCR validation of 12 selected genes in (B) longissima larvae with differential expression after parasitization by (A) hispinarum (based on Illumina sequencing analysis). The relative expression levels of these unigenes were transformed into the log2(ratio) of parasitized (P) to non - parasitized (NP). In the bar graph, the red bars represent the RNA - seq data, and the blue bars represent the RT - qPCR data. Different colors at the bottom (red, green, purple, cyan) correspond to different time - point comparisons: np24 - vs - pp24, np48 - vs - pp48, np72 - vs - pp72, and np96 - vs - pp96, respectively. The error bars indicate the standard deviation (or standard error) of the mean values from independent biological replicates.
Overall, these findings support the accuracy and reproducibility of the transcriptomic analysis, while also highlighting the importance of experimental validation for individual genes.
4. Discussion
Parasitic wasps are a diverse and ecologically significant group within the order Hymenoptera. These wasps are capable of manipulating multiple physiological processes in their host insects, including immunity, metabolism, and development, often leading to the host’s death (22–24). As such, they serve as critical natural biocontrol agents and are widely recognized as effective tools for biological pest management.
Insects, through long evolutionary histories, have developed a robust innate immune system consisting of both humoral and cellular components. Humoral immunity is primarily mediated through three key signaling pathways: Toll, IMD (immune deficiency), and JAK/STAT (Janus kinase/signal transducer and activator of transcription). These pathways regulate the expression of immune-related genes via signal transduction cascades, ultimately inducing the production of antimicrobial peptides and other effector molecules (25, 26). Cellular immunity, mediated by hemocytes, is responsible for pathogen encapsulation, phagocytosis, and aggregation (27, 28).
To comprehensively understand immune modulation by parasitic wasps, it is essential to establish an extensive immune gene database for insects, which would allow for more accurate classification of immune-related genes and proteins. However, current databases such as KEGG, GO, and GOG do not provide a dedicated classification for insect immune genes (29). Research on the immune system of Brontispa longissima remains limited. To address this gap, we performed extensive annotation and screening of immune-related genes in B. longissima larvae following parasitism by Asecodes hispinarum, using homologous sequence alignment and KEGG database information.
Our analysis revealed significant changes in pathogen recognition, signal transduction, regulatory processes, and the expression of effector genes in B. longissima larvae after parasitism. These results indicate that A. hispinarum parasitism can indeed activate and modulate the immune response of B. longissima.
Recognition is the first step in the insect immune response. Pathogen invasion triggers the activation of pattern recognition receptors (PRRs) and downstream signaling pathways such as Toll and IMD (30, 31). PRRs serve multiple roles: they act as pathogen sensors, opsonins that promote phagocytosis, and initiators of immune signaling cascades (32). In our study, parasitism induced transcriptional regulation of PRRs in B. longissima larvae. For example, beta-1,3-glucan-binding protein-like (βGBP) was upregulated at 24 and 48 h, while the Down syndrome cell adhesion molecule-like protein Dscam2 was upregulated at 24 and 96 h. Similar observations have been reported in other host–parasitoid systems. For instance, Trichopria drosophilae parasitism alters the expression of PRRs in Drosophila melanogaster, with PGRP-SA upregulated and PGRP-SB2 and CG9673 downregulated (33). In Ostrinia furnacalis larvae parasitized by Macrocentrus cingulum, PRRs such as C-type lectins, βGBP, and integrins were significantly upregulated (34).
GO pathway enrichment analysis indicated that hemocyte differentiation-related pathways were enriched at 96 h post-parasitism, including regulation of lamellocyte and plasmatocyte differentiation and negative regulation of hemocyte differentiation. In insects, hemocyte quantity and function can be affected by foreign invaders. For example, in Helicoverpa armigera, total hemocyte counts remain unchanged after Nomuraea rileyi infection, yet phagocytosis, nodulation, and encapsulation are impaired (35). A similar phenomenon was observed in B. longissima, where the proportion of plasmatocytes remained largely unchanged, and encapsulation responses were absent. Plasmatocytes are the primary hemocyte type responsible for cellular immunity, pathogen engulfment, and encapsulation (36, 37). In Bombyx mori, plasmatocytes can differentiate into oenocytoids, which contain prophenoloxidase (PPO) precursors and participate in melanization, humoral immunity, and encapsulation. Lamellocytes, on the other hand, specialize in encapsulating parasitoids and large foreign objects (38).
In our study, the transcription of prophenoloxidase activating factor (PPAF) and phenoloxidase (PO) was upregulated at 24 h post-parasitism, but PO expression was suppressed at 72 and 96 h. This may relate to the negative regulation of plasmatocyte differentiation. Typically, insect immune responses involve melanization alongside encapsulation (39–41). Melanization is a serine protease (SP)-mediated cascade, where inactive PPO is converted to active PO by terminal PPAFs (24). Here, serine protease persephone was downregulated at 24, 48, and 72 h, which aligns with its role in Toll pathway activation in Drosophila. Serine protease inhibitor dipetalogastin was downregulated at 24 and 48 h, consistent with its antibacterial and immune-regulatory roles in Galleria mellonella (42). Activation of the PO cascade not only drives melanization but also supports coagulation, nodule formation, and encapsulation (43). Parasitic wasps are known to disrupt this cascade: for example, LbSPNy in Leptopilina boulardi venom inhibits PO activation in D. melanogaster (44), and Scleroderma guani suppresses melanization in Tenebrio molitor (45). Comparable effects have been reported in Octodonta nipae and O. nipae pupae parasitized by Tetrastichus brontispae (40).
Overall, A. hispinarum parasitism induces widespread modulation of both humoral and cellular immunity in B. longissima larvae. While these findings reveal critical immune responses, the underlying mechanisms remain incompletely understood and warrant further investigation.
5. Conclusions
This study presents the first comprehensive transcriptome of B. longissima larvae and provides a pioneering analysis of how A. hispinarum parasitism regulates immune-related gene expression. Comparative transcriptomic analysis revealed that parasitism modulates genes involved in pathogen recognition, signal transduction, regulatory processes, and melanization, thereby disrupting both cellular and humoral immunity. These immune-related genes provide insight into the molecular mechanisms by which parasitic wasps suppress host defenses. Furthermore, they offer candidate targets for developing pest control strategies based on manipulation of host immunity and contribute to a better understanding of host–parasitoid interactions at the molecular level.
Acknowledgments
All of the authors sincerely thank Zhengqiang Peng (the Environment and Plant Protection Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China) for providing samples of A. hispinarum for the experiment.
Funding Statement
The author(s) declared that financial support was received for this work and/or its publication. This research was funded by the Natural Science Foundation of Fujian Province (2021J01448, 2025J01547), the National Key Research and Development Program of China (2022YFC2601400) and the Science and Technology Innovation Special Foundation of Fujian Agriculture and Forestry University (KFB25087A).
Footnotes
Edited by: Ioannis Eleftherianos, Health and Life Sciences, Queen’s University Belfast, United Kingdom
Reviewed by: Fabio Gomes, Federal University of Rio de Janeiro, Brazil
Muhammad Hafeez, University of Nevada, Reno, United States
Data availability statement
The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA041259, CRA041261) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa/browse/CRA041261, https://ngdc.cncb.ac.cn/gsa/browse/CRA041259.
Author contributions
ZC: Investigation, Project administration, Formal analysis, Methodology, Validation, Writing – review & editing, Funding acquisition, Conceptualization, Resources, Writing – original draft. HYZ: Writing – review & editing, Writing – original draft. JL: Writing – review & editing, Writing – original draft. TF: Writing – original draft, Writing – review & editing. BC: Writing – original draft, Writing – review & editing. YL: Writing – review & editing, Writing – original draft. HHZ: Writing – review & editing, Writing – original draft. BT: Funding acquisition, Writing – original draft, Writing – review & editing. YH: Writing – original draft, Funding acquisition, Writing – review & editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2026.1823349/full#supplementary-material
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA041259, CRA041261) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa/browse/CRA041261, https://ngdc.cncb.ac.cn/gsa/browse/CRA041259.