AbstractStriped stem-borer (SSB) infestation is one of the most serious sources of damage to rice growth. A rapid and non-destructive method of early SSB detection is essential for rice-growth protection. In this study, hyperspectral imaging combined with chemometrics was used to detect early SSB infestation in rice and identify the degree of infestation (DI). Visible/near-infrared hyperspectral images (in the spectral range of 380 nm to 1030 nm) were taken of the healthy rice plants and infested rice plants by SSB for 2, 4, 6, 8 and 10 days. A total of 17 characteristic wavelengths were selected from the spectral data extracted from the hyperspectral images by the successive projection algorithm (SPA). Principal component analysis (PCA) was applied to the hyperspectral images, and 16 textural features based on the gray-level co-occurrence matrix (GLCM) were extracted from the first two principal component (PC) images. A back-propagation neural network (BPNN) was used to establish infestation degree evaluation models based on full spectra, characteristic wavelengths, textural features and features fusion, respectively. BPNN models based on a fusion of characteristic wavelengths and textural features achieved the best performance, with classification accuracy of calibration and prediction sets over 95%. The accuracy of each infestation degree was satisfactory, and the accuracy of rice samples infested for 2 days was slightly low. In all, this study indicated the feasibility of hyperspectral imaging techniques to detect early SSB infestation and identify degrees of infestation. View Full-Text
Keywords: rice; striped stem-borer; hyperspectral imaging; texture feature; data fusionrice; striped stem-borer; hyperspectral imaging; texture feature; data fusion►▼ Figures
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Fan, Y.; Wang, T.; Qiu, Z.; Peng, J.; Zhang, C.; He, Y. Fast Detection of Striped Stem-Borer (Chilo suppressalis Walker) Infested Rice Seedling Based on Visible/Near-Infrared Hyperspectral Imaging System. Sensors2017, 17, 2470.
Fan Y, Wang T, Qiu Z, Peng J, Zhang C, He Y. Fast Detection of Striped Stem-Borer (Chilo suppressalis Walker) Infested Rice Seedling Based on Visible/Near-Infrared Hyperspectral Imaging System. Sensors. 2017; 17(11):2470.Chicago/Turabian Style
Fan, Yangyang; Wang, Tao; Qiu, Zhengjun; Peng, Jiyu; Zhang, Chu; He, Yong. 2017. "Fast Detection of Striped Stem-Borer (Chilo suppressalis Walker) Infested Rice Seedling Based on Visible/Near-Infrared Hyperspectral Imaging System." Sensors 17, no. 11: 2470.
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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.
Citation: Ma W, Zhang Z, Peng C, Wang X, Li F, Lin Y (2012) Exploring the Midgut Transcriptome and Brush Border Membrane Vesicle Proteome of the Rice Stem Borer, Chilo suppressalis (Walker). PLoS ONE 7(5): e38151. https://doi.org/10.1371/journal.pone.0038151
Editor: Alfredo Herrera-Estrella, Cinvestav, Mexico
Received: August 26, 2011; Accepted: April 30, 2012; Published: May 29, 2012
Copyright: © 2012 Ma et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was surpported by the National Special Transgenic Project from the Chinese Ministry of Agriculture (grant No. 2008ZX08001-001; 2009ZX08011-007B; 2011ZX08012-004, http://www.agri.gov.cn/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding received for this study.
Competing interests: The authors have declared that no competing interests exist.
The rice stem borer, Chilo suppressalis (Walker) (Lepidoptera: Pyralidae), is one of the most important rice pests. Various methods have been used to try to control C. suppressalis, including cultural practices, pheromone traps, planting resistant rice varieties, and insecticides. Among these methods, farmers still prefer to use insecticides because of their high efficiency . However, widespread insecticide use poses potential threats to the environment and food safety. Thus, an alternative strategy for controlling the rice stem borer is needed. One promising possibility is use of the toxic protein produced by the bacterium Bacillus thuringiensis (Bt). Bt toxins are considered to be safe and have been widely used in transgenic plants to control lepidopteran pests such as Helicoverpa armigera–. Although this tactic is successful in controlling rice stem borer at present, the ability of target pests to develop resistance poses a challenge. Better understanding of the interactions between Bt toxins and the receptors in the midgut of pests is very important for monitoring resistance and for pest management –.
Despite the economic importance of the damage caused by C. suppressalis, genetic information about this species is scarce. Few C. suppressalis gene sequences are known: Currently, only 192 annotated genes and 147 protein sequences are available in the NCBI database. The lack of gene sequences hinders in-depth study of gene function in C. suppressalis. The midgut of insect pests is an important organ because it participates in digestion, detoxification, and nutrient intake. It is also the place where Bt toxins interact with insect receptors. Therefore, exploring the midgut transcriptome and proteome of C. suppressalis will provide invaluable information about its gene sequences and promote investigations of plant-insect interactions.
To gain insight into the complexity of the midgut transcriptome and proteome of C. suppressalis and to identify genes and proteins related to digestion and detoxification, we conducted high-throughput Illumina sequencing of the midgut transcriptome of C. suppressalis and two-dimensional protein electrophoresis in combination with mass spectrometry of the brush border membrane vesicle (BBMV) proteome. This analysis dramatically increases the number of known genes and proteins of midgut and enhances the understanding of the C. suppressalis midgut.
Results and Discussion
The C. suppressalis midgut cDNA sample was prepared and sequenced using the Illumina sequencing platform. After cleaning and quality checks, we obtained 39 million 90-bp-long reads,which were submitted to the SRA database with accession number SRA050703.2. After they were assembled using Trinity  software, a total of 37,040 contigs were obtained (Table 1). The mean contig size was 497 bp, with lengths ranging from 201 to 9,744 bp. The size distribution of these contigs is shown in Figure S1.
Annotation of predicted proteins
Among the midgut transcripts, 15,446 (41%) showed significant similarity (E-value <1e−5) to known proteins in the NCBI database (Table 2). The majority of the transcripts with similar sequences in the database (35%) matched to arthropod proteins. The remaining midgut transcripts were similar to proteins of non-insect eukaryotes (3%) and bacteria (3%). A total of 132 sequences were similar to viral proteins, and one midgut sequence matched to an Archaea protein (Table 2).
Comparison of the derived C. suppressalis midgut transcripts with protein sequences in the draft genomes of Drosophila melanogaster, Anopheles gambiae, and Tribolium casteneum conducted using the BLASTX algorithm program revealed high sequence similarity (38%, 13,968 out of 37,040) to the T. casteneum genome (Figure 1). The analysis also showed 33% and 34% similarity to the genomes of D. melanogaster and A. gambiae, respectively. A total of 11,419 sequences were shared among all four insect species. About 60% of sequences (22,320 out of 37,040) did not show BLASTX similarity, implying that they represented un-translated regions, non-conserved regions, or proteins novel to C. suppressalis (Figure 1).
Figure 1. Summary of the comparisons analysis.
Chilo suppressalis transcriptomic sequences were used to compare with protein sequences from the draft genomes of Anopheles gambiae, Drosophila melanogaster, and Tribolium casteneum. a=228; b=e; c=11419; d=138; e=1564; f=435; g=550; h=22320.
To further evaluate the completeness of our transcriptome library and the effectiveness of our annotation process, we searched the annotated sequences for the genes involved in COG classification. In total, out of 15,446 hits, 5,824 sequences have a COG classification (Figure 2). Among the 25 COG categories, the cluster for “general function prediction” represents the largest group (1,646, 28%), followed by “replication, recombination and repair” (704, 12%) and “translation, ribosomal structure and biogenesis” (584, 10%). The categories of “extracellular structures” (1, 0.01717%) and “nuclear structure” (2, 0.03434%) represent the smallest groups (Figure 2). Similar observations were reported by Wang et al.  for the transcriptome of the whitefly Bemisia tabaci. To identify the biological pathways that are active in the C. suppressalis midgut, we mapped the 15,446 annotated sequences to the reference canonical pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) . In total, we assigned 12,358 sequences to 218 KEGG pathways (Table S1). Among these sequences, 1,252 belong to metabolic pathways, followed by 373 sequences in spliceosome pathway and 246 sequences in purine metabolism.
A Pfam domain search yielded 11,895 domains within the 37,040 contigs that have significant hits in the NCBI nr database (Table 3). Among the identified Pfam domains, zinc finger C2H2 type (Znf–C2H2), which is the most common DNA-binding motif , was the most abundant, existing in 542 transcripts (5%) from the C. suppressalis midgut. The WD G-beta repeat was the second most abundant domain in the C. suppressalis midgut sequences (Table 3). The domains related to reverse transcriptase, protein kinase, sugar transporters, carboxyl esterase, and trypsin also were abundant in these contigs (Table 3, Table S2). These results are consistent with a previous analysis of Pfam domains in Agrilus planipennis midgut , which may indicate the existence of similar gene profiles in the midgut of insects.
Genes of interest
In this study, we were interested in identifying genes involved in detoxification and the antioxidant response, Bt Cry1A toxin binding, digestion, immunity and defense, metabolism, remodeling and peritrophic membrane biosynthesis, (Table 4). Of particular interest are proteins that bind the Bt-toxin Cry1A, which are known to be cadherin-like proteins – and glycosylphosphatidylinositol (GPI)-anchored proteins (aminopeptidase N, APN, or alkaline phosphatase, ALP) –. We identified 16 cadherin-like proteins, 27 APNs, and 11 ALP proteins in the C. suppressalis midgut.
Exploring the BBMV proteome of C. suppressalis
The most abundant proteins in the BBMV of C. suppressalis were also investigated in this study. Figure 3 shows a representative two-dimensional (2D) electrophoresis gel revealing the profile of BBMV proteins from C. suppressalis. Among the detected protein spots, 100 abundant proteins that were present on replicated gels were excised and subjected to mass spectrometry analysis. The spectra obtained from MALDI-TOF/TOF were searched for in a local database (translated transcriptome data) and the NCBInr database, and a total of 74 selected proteins had significant results (Table S3). The abundant proteins identified by this proteome analysis are consistent with the transcriptome data. The RNA transcripts encoding these proteins are also abundant in the transcriptome of the C. suppressalis midgut.
Figure 3. Representative example of 2D-gel of Chilo suppressalis larval midgut BBMV.
BBMV were separated by 2D-gel electrophoresis followed by silver staining. Positions of molecular size markers (kDa) are indicated on the side of gel. Abundant protein spots were excised (numbers 1–100), digested, and subjected to mass spectrometry analysis for identification. Spot numbers correspond to the proteins listed in Table S3.
The annotations of the identified proteins from the UniProt knowledge base (http://www.expasy.org/sprot/) were used to categorize the proteins based on their molecular function in terms of information obtained from the GO database (http://www.geneontology.org/) (Table S3). The identified proteins were functionally categorized as follows: binding proteins, structural maintenance, metabolism and electron transport processes, translation factors, molecular transducers, transcription factors, antioxidant and redox processes, and digestion (Table S3). The proteins identified in this study are in good agreement with other proteomic analyses of the lepidopteran midgut –, which indicates that a common protein complex is shared among lepidopteran insects.
To verify the accuracy of our transcriptome and proteome sequences, we chose the Bt-toxin Cry1Ac binding protein and actin as the molecule markers. Western blot analysis was used to detect the proteins that bind Bt toxins in the midgut of C. suppressalis. As expected, the biotin-labeled Cry1Ac showed significant binding activity with spots 3, 48, 73, and 74 (Figures 3 and 4, Table S3). Using the MALDI-TOF/TOF technique, spot 3 and 48 were identified as two isoforms of APN. Spot 74 was identified as EH domain-containing protein 1, but spot 73 had no significant match (Table S3). These results confirmed that APN binds with the Cry1Ac toxin . To the best of our knowledge, this is the first report that EH domain-containing protein 1 interacts with Bt toxins, and this result should be confirmed by further study. Unexpectedly, cadherin was not identified as a potential binding protein for Cry1Ac, which was reported previously . We reasoned that the absence of cadherin in our proteome might be due to the limitation of 2D electrophoresis that is not suitable for separating proteins larger than ∼200 kDa. Actin is found in almost all eukaryotic cells and is widely used as a molecule marker in many kinds of molecular experiments. Spot 82 was identified as actin and showed strong binding activity to the mouse anti-beta-actin monoclonal antibody (Sigma) (Figures 3 and 5, Table S3).
Figure 4. Blot analysis of Cry1Ac binding proteins in Chilo suppressalis midgut.
Proteins were separated by 2D-gel electrophoresis. Arrows denote positions of the Cry1Ac binding proteins. To detect Cry1Ac binding proteins, filters were probed with biotin-Cry1Ac. Positions of molecular size markers (kDa) are indicated on the side of gel.
Figure 5. Blot detection of actin in Chilo suppressalis midgut.
Proteins were separated by 2D-gel electrophoresis. Arrows denote positions of the anti-actin antibody binding protein. The filters were first probed with mouse anti-beta-actin monoclonal antibody (Sigma) and then visualized with goat anti-mouse IgG conjugated with alkaline phosphatase (Sigma) and the BCIP/NBT system (Sigma). Positions of molecular size markers (kDa) are indicated on the side of gel.
Materials and Methods
Insect rearing and midgut sample preparation
The C. suppressalis strain was collected from the farm at the Huazhong Agricultural University, Wuhan, Hubei, China in 2010. This strain was reared in plastic containers (15 cm diameter ×8 cm high) on an artificial diet at 28±1°C under a 16 h photoperiod and >80% relative humidity. For total RNA and protein preparation, midguts were dissected from fifth instar larvae and cleaned in ice-cold 0.7% NaCl solution, immediately frozen in liquid nitrogen, and then stored at −80°C until use. The midgut content was removed before processing.
RNA isolation and library preparation for transcriptome analysis
Total RNA was isolated using the SV total RNA isolation system (Promega) according to the manufacturer's protocol. To obtain complete gene expression information, the RNA sample from the midgut was used for transcriptome analysis. Poly(A) + RNA was purified using oligo(dT) magnetic beads and fragmented into short sequences at 94°C for 5 min. The cleaved poly(A) + RNA was transcribed, followed by synthesis of the second-strand cDNA. After the end repair and ligation of adaptors, the products were amplified by PCR and purified using the QIAquick PCR Purification Kit.
The cDNA library was sequenced on the Illumina sequencing platform (GAII). The raw reads from the images were generated using Solexa GA pipeline 1.6.
Bioinformatics data analysis
The high quality reads were assembled into contigs using Trinity software . All contigs were used as the queries to search a local protein database containing all of the protein sequences of the nr database with the BLASTX algorithm. COG classification was analyzed using annotated contigs to search the COG database with the cutoff 10–3. For KEGG pathway analysis, all contigs was used to search the KEGG database with default parameters. For comparative genomics analysis, the annotated contigs were used to BLASTP against known genes in three insect species: Drosophila melanogaster, Anopheles gambiae, and Tribolium casteneum.
BBMV extraction and 2D SDS-PAGE Gel Analysis
The BBMV were prepared according to a published method . Briefly, midguts were homogenized in ice-cold Tris/Mannitol buffer (300 mM mannitol, 5 mM EGTA, 17 mM Tris-HCl, pH 7.5) containing a cocktail of protease inhibitors (Roche) and 24 mM MgCl2. The homogenate was centrifuged at 2500 g at 4°C for 15 min, and the supernatant was centrifuged at 30,000 g at 4°C for 30 min. The pellet was resuspended in Tris/CHAPS buffer (150 mM NaCl, 5 mM EGTA, 1 mM PMSF, 20 mM Tris-HCl, 1% CHAPS) containing a cocktail of protease inhibitors (Roche).
BBMV proteins were processed using the 2D-Clean-up Kit (Bio-Rad) following the manufacturer's instructions. These proteins were resuspended in a solubilization buffer containing 2 M thiourea, 7 M urea, 4% CHAPS, 65 mM DTT, and 0.2% Bio-lyte ampholytes (pH 3–10, Bio-Rad). Final BBMV samples were centrifuged at 12,000 g, 4°C for 5 min to remove any insoluble material. Isoelectric focusing (IEF) was performed on a Protean IEF Cell (Bio-Rad). Midgut BBMV proteins were applied to 11 cm IPG strips at pH 3–10 (Bio-Rad) for separation. Equilibrated strips were overlaid onto a 10% SDS-polyacrylamide gel for the second dimension separation. Gels were visualized by silver staining .
Mass spectrometric analysis and database searching
Stained protein spots were excised from the gel. Before MS/MS analyses, mixtures of proteolytic peptides were desalted using C18 ziptips. Samples were analyzed in the positive ion mode using a 4800 Plus MALDI-TOF/TOF TM Analyzer (Applied Biosystems), and MS/MS of the 10 most intense ions (with S/N >50 per digest) was analyzed using automated data acquisition. Spectra were processed and batch analyzed in the “Combined (MS + MS/MS)’’ mode using the Applied Biosystems GPS Explorer software.
The data were then used to search our local database of C. suppressalis midgut transcriptome sequences. MALDI-TOF/TOF data searches were also performed in the nr database (translated transcriptome sequences) using the MASCOT program. The search parameters used were as follows: “metazoa” was used for “taxonomy” and “trypsin” was used for “enzyme of specificity strict”; fixed modifications of carbamidomethyl (C); peptide tolerance of 100 ppm; fragment mass tolerance of 0.8 Da; peptide charge of 1+; and monoisotopic. Only significant hits (p<0.05) were accepted. Manual de novo sequence searching against the NCBI-BLASTP database, e-values, and frequency of matches to a specific protein were also the primary criteria for these determinations.
Western and ligand blotting
Separated BBMV were transferred onto NC membranes at 15 V constant voltage for 1 h using the Trans-Blot® SD Semi-Dry Electrophoretic Transfer Cell system (Bio-Rad). Membranes were blocked with 5% skimmed milk in PBST (137 mM NaCl, 2 mM KCl, 10 mM Na2HPO4, 2 mM KH2PO4, pH 7.4, 0.05% Tween-20) for 2 h. Following this incubation, the membranes were washed with three changes of PBST for10 min each.
For detection of Cry1Ac binding proteins, the pure activated Cry1Ac toxins (EnviroLogix Inc.) were first marked with biotin (Roche). The blocked membranes were incubated with biotinylated Cry1Ac (1∶5000) for 1 h at room temperature. After washing, membranes were incubated for 1 h with a 1∶5000 dilution of streptavidin-alkaline phosphatase conjugate (Sigma). Binding proteins were visualized using the BCIP®/NBT Kit (Sigma) following the manufacturer's instructions.
For detection of actin, blocked filters were probed with a 1∶1000 dilution of mouse anti-beta-actin monoclonal antibody (Sigma). Goat anti-mouse IgG conjugated with alkaline phosphatase (Sigma) was used as the secondary antibody. Membranes were then developed using the BCIP®/NBT Kit (Sigma) following the manufacturer's instructions. No endogenous alkaline phosphatase activity was detected when probing blots of BBMV proteins with streptavidin-AP or under direct exposure to NBT-BCIP. All blots were repeated in triplicate to ensure reproducibility of results.
We thank the technical assistance of Chunyan Guo. Lanzhi Han, Institute of Plant Protection, China Academy of Agricultural Sciences, assisted with insects indoor rearing and dissection of C. suppressalis larvae. We are grateful to Zhihui Zhu for comments on the manuscript. Our thanks also go to the two anonymous reviewers for their thoughtful comments that improved the paper.
Conceived and designed the experiments: WM FL YL. Performed the experiments: WM. Analyzed the data: WM ZZ FL YL. Contributed reagents/materials/analysis tools: CP XW ZZ. Wrote the paper: WM FL.
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