Soybean Insect Research Articles

Identification of Insect Pests on Soybean Leaves Based on SP-YOLO

Soybean insect pests can seriously affect soybean yield, so efficient and accurate detection of soybean insect pests is crucial for soybean production. However, pest detection in complex environments suffers from the problems of small pest targets, large inter-class feature similarity, and background interference with feature extraction. To address the above problems, this study proposes the detection algorithm SP-YOLO for soybean pests based on YOLOv8n. The model utilizes FasterNet to replace the backbone of YOLOv8n, which reduces redundant features and improves the model’s ability to extract effective features. Second, we propose the PConvGLU architecture, which enhances the capture and representation of image details while reducing computation and memory requirements. In addition, this study proposes a lightweight shared detection header, which enables the model parameter amount computation to be reduced and the model accuracy to be further improved by shared convolution and GroupNorm. The improved model achieves 80.8% precision, 66.4% recall, and 73% average precision, which is 6%, 5.4%, and 5.2%, respectively, compared to YOLOv8n. The FPS reaches 256.4, and the final model size is only 6.2 M, while the number of computational quantities of covariates is basically comparable to that of the original model. The detection capability of SP-YOLO is significantly enhanced compared to that of the existing methods, which provides a good solution for soybean pest detection. SP-YOLO provides an effective technical support for soybean pest detection.

CRISPR/Cas9-mediated knockout of scarlet gene produces eye color mutants in the soybean looper, Chrysodeixis includens.

The CRISPR/Cas9 technology has greatly progressed research on non-model organisms, demonstrating successful applications in genome editing for various insects. However, its utilization in the case of the soybean looper, Chrysodeixis includens, a notable pest affecting soybean crops, has not been explored due to constraints such as limited genomic information and the embryonic microinjection technique. This study presents successful outcomes in generating heritable knockout mutants for a pigment transporter gene, scarlet, in C. includens through CRISPR/Cas9-mediated mutagenesis. The scarlet locus identified in the genome assembly of C. includens consists of 14 exons, with a coding sequence extending for 1,986 bp. Two single guide RNAs (sgRNAs) were designed to target the first exon of scarlet. Microinjection of these two sgRNAs along with the Cas9 protein into fresh embryos resulted in the successful production of variable phenotypes, particularly mutant eyes. The observed mutation rate accounted for about 16%. Genotype analysis revealed diverse indel mutations at the target site, presumably originating from double-strand breaks followed by the nonhomologous end joining repair, leading to a premature stop codon due to frame shift. Single-pair mating of the mutant moths produced G1 offspring, and the establishment of a homozygous mutant strain occurred in G2. The mutant moths exhibited lightly greenish or yellowish compound eyes in both sexes, confirming the involvement of scarlet in pigmentation in C. includens. Notably, the CRISPR/Cas9-mediated genome editing technique serves as a visible phenotypic marker, demonstrating its proof-of-concept applicability in C. includens, as other pigment transporter genes have been utilized as visible markers to establish genetic control for various insects. These results provide the first successful case that the CRISPR/Cas9 method effectively induces mutations in C. includes, an economically important soybean insect pest.

Overexpression of cry1c* Enhances Resistance against to Soybean Pod Borer (Leguminivora glycinivorella) in Soybean.

Soybean [Glycine max (L.) Merr.], an essential staple food and oil crop worldwide, boasts abundant vegetable proteins and fats beneficial for both human and animal consumption. However, the soybean pod borer (Leguminivora glycinivorella) (SPB) stands as the most destructive soybean insect pest in northeast China and other northeastern Asian regions, leading to significant annual losses in soybean yield and economic burden. Therefore, this study aims to investigate the introduction of a previously tested codon-optimized cry1c gene, cry1c*, into the soybean genome and assess its effect on the SPB infestation by generating and characterizing stable transgenic soybeans overexpressing cry1c*. The transgenic soybean lines that constitutively overexpressed cry1c* exhibited a significant reduction in the percentage of damaged seeds, reaching as low as 5% in plants under field conditions. Additionally, feeding transgenic leaves to the larvae of S. exigua, S. litura, and M. separta resulted in inhibited larval growth, decreased larval body weight, and lower survival rates compared to larvae fed on wild-type leaves. These findings showed that the transgenic lines maintained their resistance to SPB and other lepidopteran pests, especially the transgenic line KC1. Southern blotting and genome-wide resequencing analysis revealed that T-DNA integration occurred as a single copy between loci 50,868,122 and 50,868,123 of chromosome 10 in the transgenic line KC1. Therefore, the transgenic line KC1, overexpressing high levels of cry1c* in leaves and seeds, holds strong potential for commercial use in the integrated management of SPB and other lepidopteran pests.

Evaluation of selected foliar insecticides for control of soybean insects, 2023

Evaluation of selected foliar insecticides for control of soybean insects, 2023

Detection of Soybean Insect Pest and a Forecasting Platform Using Deep Learning with Unmanned Ground Vehicles

Soybeans (Glycine max (L.) Merr.), a popular food resource worldwide, have various uses throughout the industry, from everyday foods and health functional foods to cosmetics. Soybeans are vulnerable to pests such as stink bugs, beetles, mites, and moths, which reduce yields. Riptortus pedestris (R. pedestris) has been reported to cause damage to pods and leaves throughout the soybean growing season. In this study, an experiment was conducted to detect R. pedestris according to three different environmental conditions (pod filling stage, maturity stage, artificial cage) by developing a surveillance platform based on an unmanned ground vehicle (UGV) GoPro CAM. Deep learning technology (MRCNN, YOLOv3, Detectron2)-based models used in this experiment can be quickly challenged (i.e., built with lightweight parameter) immediately through a web application. The image dataset was distributed by random selection for training, validation, and testing and then preprocessed by labeling the image for annotation. The deep learning model localized and classified the R. pedestris individuals through a bounding box and masking in the image data. The model achieved high performances, at 0.952, 0.716, and 0.873, respectively, represented through the calculated means of average precision (mAP) value. The manufactured model will enable the identification of R. pedestris in the field and can be an effective tool for insect forecasting in the early stage of pest outbreaks in crop production.

Effects of feeding injury from Popillia japonica (Coleoptera: Scarabaeidae) on soybean spectral reflectance and yield.

Remote sensing has been shown to be a promising technology for the detection and monitoring of plant stresses including insect feeding. Popillia japonica Newman, is an invasive insect species in the United States, and a pest of concern to soybean, Glycine max (L.) Merr., in the upper Midwest. To investigate the effects of P. japonica feeding injury (i.e., defoliation) on soybean canopy spectral reflectance and yield, field trials with plots of caged soybean plants were established during the summers of 2020 and 2021. In each year, field-collected P. japonica adults were released into some of the caged plots, creating a gradient of infestation levels and resulting injury. Estimates of injury caused by P. japonica, ground-based hyperspectral readings, total yield, and yield components were obtained from the caged plots. Injury was greatest in the upper canopy of soybean in plots infested with P. japonica. Overall mean canopy injury (i.e., across lower, middle, and upper canopy) ranged from 0.23 to 6.26%, which is representative of injury levels observed in soybean fields in the Midwest United States. Feeding injury from P. japonica tended to reduce measures of soybean canopy reflectance in near infra-red wavelengths (~700 to 1000 nm). These results indicate that remote sensing has potential for detection of injury from P. japonica and could facilitate scouting for this pest. Effects of P. japonica injury on total yield were not observed, but a reduction in seed size was detected in one of the two years. The threat to soybean yield posed by P. japonica alone appears minimal, but this pest adds to the guild of other defoliating insects in soybean whose combined effects could threaten yield. The results of this research will guide refinement of management recommendations for this pest in soybean and hold relevance for other cropping systems.

Genome-Wide Association Studies Reveal Novel Loci for Herbivore Resistance in Wild Soybean (Glycine soja).

The production of soybean [Glycine max (L.) Merr.] is seriously threatened by various leaf-feeding insects, and wild soybean [Glycine soja Sieb. & Zucc.] has a greater resistance capacity and genetic diversity. In this study, a natural population consisting of 121 wild soybean accessions was used for detecting insect resistance genes. The larval weight (LW) of the common cutworm (CCW), the resistance level (RL) and the index of damaged leaf (IDL) were evaluated as resistance indicators to herbivores. An association synonymous SNP AX-94083016 located in the coding region of the respiratory burst oxidase gene GsRbohA1 was identified by genome-wide association study (GWAS) analyses. The overexpression of GsRbohA1 in soybean hairy roots enhanced resistance to CCW. One SNP in the promoter region cosegregated with AX-94083016 contributing to soybean resistance to CCW by altering GsRbohA1 gene expression and reactive oxygen species (ROS) accumulation. Two major haplotypes, GsRbohA1A and GsRbohA1G, were identified based on the SNP. The resistant haplotype GsRbohA1A predominates in wild soybeans, although it has been gradually lost in landraces and cultivars. The nucleotide diversity around GsRbohA1 is much lower in landraces and cultivars than in its ancestors. In conclusion, a new resistant haplotype, GsRbohA1A, was identified in wild soybean, which will be a valuable gene resource for soybean insect resistance breeding through introducing into improvement lines, and it offers a strategy for exploring resistance gene resources from its wild relatives.

CRISPR/Cas9-Mediated Targeted Mutagenesis of GmUGT Enhanced Soybean Resistance Against Leaf-Chewing Insects Through Flavonoids Biosynthesis.

Leaf-chewing insects are important pests that cause yield loss and reduce seed quality in soybeans (Glycine max). Breeding soybean varieties that are resistant to leaf-chewing insects can minimize the need for insecticide use and reduce yield loss. The marker gene for QTL-M, Glyma.07g110300 (LOC100775351) that encodes a UDP-glycosyltransferase (UGT) is the major determinant of resistance against leaf-chewing insects in soybean; it exhibits a loss of function in insect-resistant soybean germplasms. In this study, Agrobacterium-mediated transformation introduced the CRISPR/Cas9 expression vector into the soybean cultivar Tianlong No. 1 to generate Glyma.07g110300-gene mutants. We obtained two novel types of mutations, a 33-bp deletion and a single-bp insertion in the GmUGT coding region, which resulted in an enhanced resistance to Helicoverpa armigera and Spodoptera litura. Additionally, overexpressing GmUGT produced soybean varieties that were more sensitive to H. armigera and S. litura. Both mutant and overexpressing lines exhibited no obvious phenotypic changes. The difference in metabolites and gene expression suggested that GmUGT is involved in imparting resistance to leaf-chewing insects by altering the flavonoid content and expression patterns of genes related to flavonoid biosynthesis and defense. Furthermore, ectopic expression of the GmUGT gene in the ugt72b1 mutant of Arabidopsis substantially rescued the phenotype of H. armigera resistance in the atugt72b1 mutant. Our study presents a strategy for increasing resistance against leaf-chewing insects in soybean through CRISPR/Cas9-mediated targeted mutagenesis of the UGT genes.

Methylome and transcriptome analyses of soybean response to bean pyralid larvae

BackgroundBean pyralid is one of the major leaf-feeding insects that affect soybean crops. DNA methylation can control the networks of gene expressions, and it plays an important role in responses to biotic stress. However, at present the genome-wide DNA methylation profile of the soybean resistance to bean pyralid has not been reported so far.ResultsUsing whole-genome bisulfite sequencing (WGBS) and RNA-sequencing (RNA-seq), we analyzed the highly resistant material (Gantai-2-2, HRK) and highly susceptible material (Wan82–178, HSK), under bean pyralid larvae feeding 0 h and 48 h, to clarify the molecular mechanism of the soybean resistance and explore its insect-resistant genes. We identified 2194, 6872, 39,704 and 40,018 differentially methylated regions (DMRs), as well as 497, 1594, 9596 and 9554 differentially methylated genes (DMGs) in the HRK0/HRK48, HSK0/HSK48, HSK0/HRK0 and HSK48/HRK48 comparisons, respectively. Through the analysis of global methylation and transcription, 265 differentially expressed genes (DEGs) were negatively correlated with DMGs, there were 34, 49, 141 and 116 negatively correlated genes in the HRK0/HRK48, HSK0/HSK48, HSK0/HRK0 and HSK48/HRK48, respectively. The MapMan cluster analysis showed that 114 negatively correlated genes were clustered in 24 pathways, such as protein biosynthesis and modification; primary metabolism; secondary metabolism; cell cycle, cell structure and component; RNA biosynthesis and processing, and so on. Moreover, CRK40; CRK62; STK; MAPK9; L-type lectin-domain containing receptor kinase VIII.2; CesA; CSI1; fimbrin-1; KIN-14B; KIN-14 N; KIN-4A; cytochrome P450 81E8; BEE1; ERF; bHLH25; bHLH79; GATA26, were likely regulatory genes involved in the soybean responses to bean pyralid larvae. Finally, 5 DMRs were further validated that the genome-wide DNA data were reliable through PS-PCR and 5 DEGs were confirmed the relationship between DNA methylation and gene expression by qRT-PCR. The results showed an excellent agreement with deep sequencing.ConclusionsGenome-wide DNA methylation profile of soybean response to bean pyralid was obtained for the first time. Several specific DMGs which participated in protein kinase, cell and organelle, flavonoid biosynthesis and transcription factor were further identified to be likely associated with soybean response to bean pyralid. Our data will provide better understanding of DNA methylation alteration and their potential role in soybean insect resistance.

Resistance of Soybean Genotypes to Anticarsia gemmatalis (Lepidoptera: Erebidae): Antixenosis and Antibiosis Characterization.

Injury by herbivores is a major biotic stress that limits soybean [Glycine max (L.) Merrill] crop production. Among the main soybean insect pests, Anticarsia gemmatalis Hübner is responsible for causing significant economic damage in soybean. The primary management strategy for this insect is chemical control and use of Bt transgenic soybean. Alternative strategies, such as host plant resistance, are considered an efficient and less-aggressive method, especially in association with other strategies as part of an integrated pest management (IPM) approach. In this study, we evaluated 30 soybean genotypes to verify antixenosis expression through oviposition, attractiveness, and food consumption tests. From this, we selected 13 promising genotypes to verify the possible presence of antibiosis. Our results suggest that antixenosis was found in genotypes ‘TMG 133’ RR, ‘TMG 1179’ RR, ‘IAC 19’, ‘IAC 17’, ‘IAC 100’, D75-10169, and IAC 78-2318. By influence on behavior and negative impact on larval viability, antixenosis and antibiosis were indicated for the genotypes IAC 74-2832, ‘IAC 19’, ‘IAC 17’, ‘IAC 100’, and PI 274454. ‘TMG 7062’ IPRO was found to provide antibiosis resistance by negatively affecting larval development and viability. Because of reduced food consumption by larvae, antixenosis was indicated for ‘IAC 24’. These genotypes should be considered in soybean breeding programs focusing on soybean resistance to A. gemmatalis.

Bacillus thuringiensis chimeric proteins Cry1A.2 and Cry1B.2 to control soybean lepidopteran pests: New domain combinations enhance insecticidal spectrum of activity and novel receptor contributions.

Two new chimeric Bacillus thuringiensis (Bt) proteins, Cry1A.2 and Cry1B.2, were constructed using specific domains, which provide insecticidal activity against key lepidopteran soybean pests while minimizing receptor overlaps between themselves, current, and soon to be commercialized plant incorporated protectants (PIP’s) in soybean. Results from insect diet bioassays demonstrate that the recombinant Cry1A.2 and Cry1B.2 are toxic to soybean looper (SBL) Chrysodeixis includens Walker, velvetbean caterpillar (VBC) Anticarsia gemmatalis Hubner, southern armyworm (SAW) Spodoptera eridania, and black armyworm (BLAW) Spodoptera cosmioides with LC50 values < 3,448 ng/cm2. Cry1B.2 is of moderate activity with significant mortality and stunting at > 3,448 ng/cm2, while Cry1A.2 lacks toxicity against old-world bollworm (OWB) Helicoverpa armigera. Results from disabled insecticidal protein (DIP) bioassays suggest that receptor utilization of Cry1A.2 and Cry1B.2 proteins are distinct from each other and from current, and yet to be commercially available, Bt proteins in soy such as Cry1Ac, Cry1A.105, Cry1F.842, Cry2Ab2 and Vip3A. However, as Cry1A.2 contains a domain common to at least one commercial soybean Bt protein, resistance to this common domain in a current commercial soybean Bt protein could possibly confer at least partial cross resistance to Cry1A2. Therefore, Cry1A.2 and Cry1B.2 should provide two new tools for controlling many of the major soybean insect pests described above.

Efficacy of Selected Insecticides for Control of Soybean Insect Pest, 2016

Efficacy of Selected Insecticides for Control of Soybean Insect Pest, 2016

Development of web-based system for soybean insect pests management

The paper describes the development of a web-based system for identification of soybean insects and their management. It presents the system design, development methodology, functionality and utility of the system. The user interface of the system is developed using ASP.NET and backend using SQL. It is designed using responsive web design so that the users can easily use the system on different devices with variable screen sizes-Desktops, Laptops, iPads, Tablets and Mobile phones. The system is accessible from the institute website https://iisrindore.icar.gov.in. It is very useful for farmers in taking right decision at right time in their fields. The users could identify the insects correctly with the image-based identification interface. The information in Hindi language will help farmers to easily understand the contents. The systemwas evaluated on eight user interface design features on 1-10 point scale. The overall average rating was more than 8 points indicating that most ofthe evaluators were satisfied with the user interface. It served as an effective IT tool for farmers to take appropriate and timely measures to minimize field losses due to insect attack.

Comparative effect of different insecticides on the growth and yield of soybeans

The yield of soybeans, an economically important crop worldwide, is substantially reduced by different abiotic and biotic factors, including insect pests. Different insecticides are applied to control soybean insect pests. The application of insecticides may also affect the plants along with the pests. The effects of four insecticides (fenitrothion, etofenprox, thiamethoxam, and lambda-cyhalothrin-cum-thiamethoxam; LT) on the growth and yield of two soybean cultivars over two years were investigated. The plant height (PH), pod number, shoot dry matter without seed (SDWS), total shoot dry matter, seed yield per plant (SYP), harvest index (HI), and hundred-seed weight significantly varied with the insecticides. However, the primary branch number was not significantly affected by the insecticides. Significant interactions between the year and insecticide, except for the SDWS and HI, indicated that the growing environment also affected the influence of the insecticides. The PH was significantly tall in the thiamethoxam (50.07 cm) and short for the LT (46.66 cm) application. The SYP was significantly high for the LT (20.51 g) and low for the fenitrothion (11.51 g). This study showed that the type of insecticide could significantly affect the plant growth and yield of the soybean.

A Deep-Learning Approach for Automatic Counting of Soybean Insect Pests

The occurrence of insect pest attacks in soybean fields has worried farmers around the world. Early and automatic diagnosis of insect pests number could assess the infestation level of each plantation area to optimize the applications of pesticides in the crop and, consequently, reduce production costs and environmental impact. Recent research on insect count has adopted deep neural networks. However, researches have employed models trained to count only one species of insect, using images captured in a controlled environment, quite different from a real scenario. In order to obtain high accuracy, we evaluated three models of convolutional neural networks (CNNs) with three different training strategies: 100% fine-tuning with the weights obtained from ImageNet, a complete network with the weights initialized randomly and transfer learning with the weights obtained from ImageNet. Data augmentation and dropout were used during network training to reduce overfitting and increase generalization of the model. Our approach consists in segmenting an image from the plantation with the simple linear iterative clustering (SLIC) method and classifying each superpixel segment into a pest insect class using the CNN-trained classification model. The pest insect count is obtained by adding the insects of each superpixel class identified by our computer vision system. The results indicate that the deep-learning models can be used successfully to support specialists and farmers in the insect pest management in soybean fields.

Soybean insect pests: A review of Ukrainian and world data

Soya – is the main albuminous and oily crop of the world agriculture. It takes an important place in the structure of crops, grain, fodder and food balance. This is a strategic crop in solving the global food problem that is why it is grown on all continents in the main agricultural regions of our planet. In recent years soya has no competitors by the rates of crops growth and production volumes. Though Ukraine takes the first place in Europe by area of soya cultivation, its yield still does not correspond to potential possibilities. One of the factors which limit potential productivity is pests. Soybean protection from them is an important and relevant component for getting stably high yields and raising the quality of seeds simultaneously. Especially effective it has been monitoring in recent years as a result of an increase of sown areas and yield due to implementation of intensive technologies for soya cultivation. However, it should be taken into account that many species of harmful insects damage soya at all stages of ontogenesis, at the same time the loss of yield can make 30–40%. Harmful insect species from the following series have been discovered on soya crops in the Eastern Forest-Steppe of Ukraine: Orthoptera, Homoptera, Hemiptera, Thysanoptera, Coleoptera and Lepidoptera.

Efficacy of afidopyropen against soybean aphid (Hemiptera: Aphididae) and toxicity to natural enemies.

Soybean aphid, Aphis glycines (Hemiptera: Aphididae), remains the most significant soybean insect pest in the North Central Region of the USA. The sustainability of reliance on only a few insecticide groups for this pest is questionable. We evaluate afidopyropen, a novel pyropene insecticide (Group 9D), for efficacy against A. glycines in field and greenhouse experiments and toxicity to common natural enemies in laboratory experiments. Across 4 site-years of field experiments and a greenhouse experiment, afidopyropen reduced A. glycines populations similar to commonly used broad-spectrum [i.e. lambda-cyhalothrin (Group 3A) and chlorpyrifos (Group 1B)] insecticides and potential selective insecticides [i.e. sulfoxaflor (Group 4C) and flupyradifurone (Group 4D)]. In the greenhouse, however, A. glycines mortality was delayed slightly for afidopyropen compared to the other insecticides. In laboratory experiments with natural enemies of A. glycines, afidopyropen was not toxic to adult or third instar Hippodamia convergens (Coleoptera: Coccinellidae) or adult Orius insidiosus (Hemiptera: Anthocoridae), and was only moderately toxic to Aphelinus certus (Hymenoptera: Aphelinidae). Afidopyropen is effective against A. glycines and relatively non-toxic to natural enemies, and appears to be an effective option for integrated pest management and insecticide resistance management programs for A. glycines. © 2019 Society of Chemical Industry.

Status of Stink Bug (Hemiptera: Pentatomidae) Egg Parasitoids in Soybeans in Louisiana.

Stink bugs are important insect pests of soybean, Glycine max (L.), in Louisiana. Scouting, economic thresholds, and insecticide applications are the main strategies used in the integrated pest management of soybean insect pests. However, biological control of stink bug eggs by parasitoids has the potential to reduce populations of these pests. A survey of stink bug egg parasitoids was conducted at different sites in Louisiana from 2008 to 2010. Similarly, a study on the incidence of stink bug egg parasitoids within soybean vertical strata and plant structures was conducted in 2009 to 2011. Species of stink bug eggs collected during the studies included the redbanded stink bug, Piezodorus guildinii (Westwood), the brown stink bug species complex, Euschistus spp., the southern green stink bug, Nezara viridula L., the spined soldier bug, Podisus maculiventris (Say), and the green stink bug, Chinavia hilaris (Say). The percentage of stink bug eggs parasitized in the central location of Louisiana was higher compared with the northwest location during the 2008 to 2010 growing seasons. There were no differences in percentage of stink bug egg parasitism within soybean plant strata between 2009 and 2011. Telenomus podisi Ashmead (Hymenoptera: Platygastridae) was the most abundant parasitoid, and it showed preference for P. guildinii. Other platygastrid species that emerged included Trissolcus euschisti Ashmead (Hymenoptera: Platygastridae), Gryon obesum Masner (Hymenoptera: Platygastridae), and Telenomus longicornis Johnson (Hymenoptera: Platygastridae). This study is the first to report the parasitization of P. guildinii eggs on soybeans in the United States.

Wooded field margins increase potential for cultural and biological control of soybean pests

Crop field margins provide benefits to growers by inhibiting pest dispersal through cultural control and provisioning resources for predators through conservation biological control. The purpose of this study was to elucidate potential soybean insect pest control measures by determining the relationships between pests and spiders, common generalist predators in agroecosystems, and field margin type and distance from the field (0–3 and 3–6 m). Both margin type and distance were significantly correlated with in-field pest abundance (both pooled and taxa-specific). Generally, pest abundance was negatively correlated with the presence of wooded field margins at both distances and could be due to cultural control as hedgerows provide similar ecosystem services in other cropping systems. Spider abundance and spider-pest spatial associations (an indication of potential predation events) within fields were positively correlated with wooded margins from 3 to 6 m, indicating that conservation biological control in addition to cultural control might be contributing to diminished pest abundance in fields with these margins. The lack of wooded margin influence from 0 to 3 m might indicate a need for a minimum wooded area before margins can become a source for within-field spider populations. Furthermore, the number of spider-pest spatial associations was greatest early in the season when pest abundance was lowest and pests were more likely to be spatially aggregated. These data indicate that growers might be able to maintain their field margins, particularly wooded areas, to maximize both potential cultural control and conservation biological control resources, particularly early in the growing season.

Comparative transcriptome analysis of soybean response to bean pyralid larvae

BackgroundSoybean is one of most important oilseed crop worldwide, however, its production is often limited by many insect pests. Bean pyralid is one of the major soybean leaf-feeding insects in China. To explore the defense mechanisms of soybean resistance to bean pyralid, the comparative transcriptome sequencing was completed between the leaves infested with bean pyralid larvae and no worm of soybean (Gantai-2-2 and Wan82–178) on the Illumina HiSeq™ 2000 platform.ResultsIn total, we identified 1744 differentially expressed genes (DEGs) in the leaves of Gantai-2-2 (1064) and Wan82–178 (680) fed by bean pyralid for 48 h, compared to 0 h. Interestingly, 315 DEGs were shared by Gantai-2-2 and Wan82–178, while 749 and 365 DEGs specifically identified in Gantai-2-2 and Wan82–178, respectively. When comparing Gantai-2-2 with Wan82–178, 605 DEGs were identified at 0 h feeding, and 468 DEGs were identified at 48 h feeding. Gene Ontology (GO) annotation analysis revealed that the DEGs were mainly involved in the metabolic process, single-organism process, cellular process, responses to stimulus, catalytic activities and binding. Pathway analysis showed that most of the DEGs were associated with the plant-pathogen interaction, phenylpropanoid biosynthesis, phenylalanine metabolism, flavonoid biosynthesis, peroxisome, plant hormone signal transduction, terpenoid backbone biosynthesis, and so on. Finally, we used qRT-PCR to validate the expression patterns of several genes and the results showed an excellent agreement with deep sequencing.ConclusionsAccording to the comparative transcriptome analysis results and related literature reports, we concluded that the response to bean pyralid feeding might be related to the disturbed functions and metabolism pathways of some key DEGs, such as DEGs involved in the ROS removal system, plant hormone metabolism, intracellular signal transduction pathways, secondary metabolism, transcription factors, biotic and abiotic stresses. We speculated that these genes may have played an important role in synthesizing substances to resist insect attacks in soybean. Our results provide a valuable resource of soybean defense genes that will benefit other studies in this field.