Dynamics of the Interaction between Cotton Bollworm Helicoverpa armigera and Nucleopolyhedrovirus as Revealed by Integrated Transcriptomic and Proteomic Analyses

Abstract

Over the past decades, Helicoverpa armigera nucleopolyhedrovirus (HearNPV) has been widely used for biocontrol of cotton bollworm, which is one of the most destructive pest insects in agriculture worldwide. However, the molecular mechanism underlying the interaction between HearNPV and host insects remains poorly understood. In this study, high-throughput RNA-sequencing was integrated with label-free quantitative proteomics analysis to examine the dynamics of gene expression in the fat body of H. armigera larvae in response to challenge with HearNPV. RNA sequencing-based transcriptomic analysis indicated that host gene expression was substantially altered, yielding 3,850 differentially expressed genes (DEGs), whereas no global transcriptional shut-off effects were observed in the fat body. Among the DEGs, 60 immunity-related genes were down-regulated after baculovirus infection, a finding that was consistent with the results of quantitative real-time RT-PCR. Gene ontology and functional classification demonstrated that the majority of down-regulated genes were enriched in gene cohorts involved in energy, carbohydrate, and amino acid metabolic pathways. Proteomics analysis identified differentially expressed proteins in the fat body, among which 76 were up-regulated, whereas 373 were significantly down-regulated upon infection. The down-regulated proteins are involved in metabolic pathways such as energy metabolism, carbohydrate metabolism (CM), and amino acid metabolism, in agreement with the RNA-sequence data. Furthermore, correlation analysis suggested a strong association between the mRNA level and protein abundance in the H. armigera fat body. More importantly, the predicted gene interaction network indicated that a large subset of metabolic networks was significantly negatively regulated by viral infection, including CM-related enzymes such as aldolase, enolase, malate dehydrogenase, and triose-phosphate isomerase. Taken together, transcriptomic data combined with proteomic data elucidated that baculovirus established systemic infection of host larvae and manipulated the host mainly by suppressing the host immune response and down-regulating metabolism to allow viral self-replication and proliferation. Therefore, this study provided important insights into the mechanism of host-baculovirus interaction.

Fig. 1.

Effect of H. armigera nucleopolyhedrovirus infection on cotton bollworm larvae and transcription pattern of viral genes during the infection process. A, weight changes of larvae in the control and infected groups throughout the infection process. The data were presented as means ± S.E. (mg). **, p ≤ 0.01; ***, p ≤ 0.001. Notably, the average weight of the larvae in the infected group was significantly lower than that in the control group. B, mRNA abundance of HearNPV over the infection time course. Each column represents the reads as a percentage of the total reads (Illumina), and the percentage of mRNA derived from baculovirus accounted for is >15% of the total mRNA content at 72 hpi. C, heatmap analysis showing the baculoviral gene expression patterns during the infection process. Each gene at each of the four time points post-infection was represented as a horizontal short line. Most of the genes showed a similar trend in expression level, continuously increasing over time. D, relative expression levels of two representative viral genes, virus-encoded DNA polymerase and lef-6, were measured at 0, 24, 48, and 72 hpi using qRT-PCR, and the results were consistent with those of RNA-seq.

Fig. 2.

Comparison of fat body transcriptomes between control and infected larvae. A, hierarchical clustering analysis of fat body transcripts that were differentially expressed for any comparison between control and infected samples at the four time points. RPKM values for H. armigera transcripts were calculated using RSEM software. Each gene at each time point is shown as a line color-coded according to expression level. Noticeably, most of the genes were significantly down-regulated in the infected group compared with the mock-infected group. B, Venn diagram analysis of commonly and uniquely regulated genes at three time points post-infection (24, 48, and 72 hpi, respectively). The on-line interactive tool Draw Venn Diagram was used to generate a Venn diagram. C, gene ontology enrichment analysis revealed the biological processes and molecular functions most associated with the DEGs. The plots of second-level GO terms corresponding to the up- and down-regulated enriched genes are shown; herein, enrichment analysis was conducted using the functional annotation tool DAVID. Statistical significance was determined at a p value ≤0.05. *, P_adj ≤0.05; **, P_adj ≤0.01; ***, P_adj ≤0.001. The symbol n.s. indicates no significance for the corresponding GO term. D, KEGG functional classification of baculovirus-regulated transcripts. To explore the effect of viral infection on the mRNA abundance of fat body genes, the DEGs induced and repressed upon viral infection were classified functionally. Functional group abbreviations: OS, organismal systems; ST, signal transduction; R/R, replication and repair; F/S/D, folding, sorting and degradation; Tlat, translation; Tran, transcription; IMM, immunity-related; XBM, xenobiotics biodegradation and metabolism; EM, energy metabolism; AAM, amino acid metabolism; NM, nucleotide metabolism; LM, lipid metabolism; CM, carbohydrate metabolism.

Fig. 3.

Expression profiling of immunity-related genes in the larval fat body after baculovirus infection. A, hierarchical clustering analysis of immunity-related genes that were differentially expressed following viral infection. The immune genes were divided into three major groups according to their respective functions: recognition molecules, signaling modulators, and immune effectors. The RPKM value of each gene is shown as a rectangle coded in blue (lower expression level) or red (higher expression level). B, Venn diagram showing the unique and common immune genes that were significantly regulated at three time points post-infection. Two genes were commonly down-regulated at three time points. The number of down-regulated genes was apparently more than that of up-regulated ones. C, qRT-PCR analysis of selected immunity-related genes. Pairwise Student's t tests were used for statistical analysis. **, p ≤ 0.01; ***, p ≤ 0.001. The results showed that the recognition molecules C-type lectin 7 (CTL7), lysozyme 1, antimicrobial peptides attacin, moricin 4, and gloverin 1, and the key member of melanization cascade reactions, PPO2, was substantially suppressed at the transcript level at 72 hpi.

Fig. 4.

Expression profiling of CM-related genes during the infection process in the normal and infected groups. A, hierarchical clustering of CM-related genes during the infection process. CM genes were divided into three categories: glycolysis, TCA cycle, and pentose-phosphate pathway. B, relative expression levels of selected CM genes at 0 and 72 hpi were validated by qPCR analysis. *, p ≤ 0.05; **, p ≤ 0.01; ***, p ≤ 0.001. The mRNA abundances of aldolase, phosphofructokinase (PFK), enolase, fumarase, fructose-1,6-bisphosphatase (FBP), and glucose-6-phosphate isomerase (GPI) were decreased at 72 hpi, consistent with the RNA-seq data results.

Fig. 5.

DEPs in the fat body after baculovirus infection. A, proteins identified by LC-MS/MS analysis that were unique and common between the control and infected groups. The red and green arrowheads denote up- and down-regulation at the protein level, respectively. B, KEGG functional classification of proteins that were significantly differentially expressed during proteomics analysis. Functional group abbreviations: ST, signal transduction; F/S/D, folding, sorting, and degradation; MOC, metabolism of cofactors and vitamins; XBM, xenobiotics biodegradation and metabolism; NM, nucleotide metabolism; GOM, global and overview maps; LM, lipid metabolism; EM, energy metabolism; AAM, amino acid metabolism; CM, carbohydrate metabolism.

Fig. 6.

Correlation analysis between the mRNA and protein abundances and their changes in the fat body. The normalized peak area of proteins to the normalized RPKM of transcripts in the control and infected groups was plotted in the log₂ scale, with the y and x axis indicating the relative protein and mRNA levels, respectively. A, correlations between the mRNA and protein levels in fat body samples in the control and infected groups. The upper panel denotes correlations between the mRNA and protein levels for all of the proteins identified in the fat body, and the lower panel represents correlations for the DEPs. B, correlations of the changes between the mRNA and protein levels. The upper panel represents all of the proteins identified in the fat body, and the lower panel represents the genes that show a similar trend at both mRNA and protein levels.

Fig. 7.

Predicted gene interaction networks regulated by baculovirus infection. The gene interaction networks were retrieved from the STRING protein-protein interaction database. The interaction networks were exported to Cytoscape for visualization. Gray nodes indicate their presence in the corresponding gene sets. Three types of interaction modes were included in the interaction network: the red edge indicates correlated expression; the black edge denotes predicted protein-protein interactions; and the purple edge represents known protein-protein interactions. The false discovery rate for each enriched term is indicated in the figure. A, interaction network for the genes up-regulated by baculovirus infection. The interaction network indicated that anatomical structure formation was up-regulated by baculovirus infection. B, interaction network for the genes down-regulated by immune challenge with baculovirus. Among the down-regulated genes, more interaction networks related to translation and metabolic pathways such as CM, fatty acid metabolism, energy metabolism, and purine metabolism were significantly enriched, suggesting substantial modulation of host physiological functions.