Exploring the Midgut Transcriptome and Brush Border Membrane Vesicle Proteome of the Rice Stem Borer, Chilo suppressalis (Walker)

Abstract

The rice stem borer, Chilo suppressalis (Walker) (Lepidoptera: Pyralidae), is one of the most detrimental pests affecting rice crops. The use of Bacillus thuringiensis (Bt) toxins has been explored as a means to control this pest, but the potential for C. suppressalis to develop resistance to Bt toxins makes this approach problematic. Few C. suppressalis gene sequences are known, which makes in-depth study of gene function difficult. Herein, we sequenced the midgut transcriptome of the rice stem borer. In total, 37,040 contigs were obtained, with a mean size of 497 bp. As expected, the transcripts of C. suppressalis shared high similarity with arthropod genes. Gene ontology and KEGG analysis were used to classify the gene functions in C. suppressalis. Using the midgut transcriptome data, we conducted a proteome analysis to identify proteins expressed abundantly in the brush border membrane vesicles (BBMV). Of the 100 top abundant proteins that were excised and subjected to mass spectrometry analysis, 74 share high similarity with known proteins. Among these proteins, Western blot analysis showed that Aminopeptidase N and EH domain-containing protein have the binding activities with Bt-toxin Cry1Ac. These data provide invaluable information about the gene sequences of C. suppressalis and the proteins that bind with Cry1Ac.

De novo analysis of the Adelphocoris suturalis Jakovlev metathoracic scent glands transcriptome and expression patterns of pheromone biosynthesis-related genes

“…The domains related to immunoglobulin I-set, protein kinase, RNA recognition, reverse transcriptase, and trypsin were also abundant in these unigenes (Table 3, Table S3). These results are similar to a previous analysis of Pfam domains in C. suppressalis midgut (Ma et al, 2012), which may indicated that there are similar gene profiles in insects. In addition, we used Pfam to identify putative transposable elements (TEs), which play a fundamental role as drivers of genome evolution and can complicate de novo transcriptome assemblies (Kazazian, 2004;Sze et al, 2012), within the A. suturalis MTG sequences following Durand et al (2006).…”

“…3). Similar observations were reported in the midgut transcriptome of the Chilo suppressalis (Ma et al, 2012). To identify the active biological pathways in A. suturalis MTGs, we mapped the 26,744 annotated sequences in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (the reference canonical pathways) (Kanehisa et al, 2004).…”

Binding Site Concentration Explains the Differential Susceptibility of Chilo suppressalis and Sesamia inferens to Cry1A-Producing Rice

“…The pattern of Cry1Ac binding proteins on ligand blots with BBMV proteins from C. suppressalis in this study are similar to that in previous reports (40) and support the existence of multiple Cry1Ac binding sites in these BBMV. Proteomic studies identified APN isoforms (approximately 130 and 150 kDa) and an EH domain-containing protein 1 (approximately 80 kDa) as Cry1Ac binding proteins in C. suppressalis BBMV (21). In comparison, a 119-kDa APN and a 197-kDa cadherin were reported to bind Cry1Ab on ligand blots of C. suppressalis BBMV (20).…”

“…In the case of C. suppressalis, binding competition studies in larval brush border membrane vesicles (BBMV) revealed that Cry1A toxins share binding sites with Cry1Ba toxin (17) but they are not recognized by Cry2A, Cry9C, or Cry1C toxins (18,19). Diverse studies have suggested aminopeptidase-N and cadherin-like proteins as Cry1A binding sites in C. suppressalis BBMV (20,21). In contrast, no Cry toxin binding data are available for S. inferens.…”

Identification and Characterization of Receptors for Insecticidal Toxins from Bacillus thuringiensis

“…Some of the studies of differential gene expression in insects using massive sequencing technologies have been made using the 454 platform of Roche (Park et al 2009;Pauchet et al 2010;Oppert et al 2012); however, even when the reads are large in average size which facilitates the assembly and subsequent analysis, they are relatively few compared to those obtained from massive sequencing experiments using other platforms such as Illumina platforms Genome Analyzer GAIIx and HiSeq (He et al 2012;Ma et al 2012;Lei et al 2014). The sequence data obtained should be analyzed using a limited number of programs currently available on the Internet.…”